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\begin{document}

\title{Regulation of Host Gene Expression by Gastrointestinal Tract Microbiota
in Chinook Salmon (Oncorhynchus tshawytscha)}



\author[1]{Javad Sadeghi}%
\author[2]{Subba Rao Chaganti}%
\author[1]{Daniel Heath}%
\affil[1]{University of Windsor}%
\affil[2]{University of Michigan}%


\vspace{-1em}



  
  \date{May 27, 2024}


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\selectlanguage{english}
\begin{abstract}
Differences in gut microbiome composition are linked with health,
disease and ultimately host fitness; however, the molecular mechanisms
underlying that relationship are not well characterized. Here, we
modified the fish gut microbiota using antibiotic and probiotic feed
treatments to address the effect of host microbiome on gene expression
patterns. Chinook salmon (Oncorhynchus tshawytscha) gut gene expression
was evaluated using whole transcriptome sequencing (RNA-Seq) on hindgut
mucosa samples from individuals treated with antibiotic, probiotic and
control diets to determine differentially expressed (DE) host genes.
Fifty DE host genes were selected for further characterization using
nanofluidic qPCR chips. We used 16S rRNA gene metabarcoding to
characterize the rearing water and host gut microbiome bacterial
communities. Daily administration of antibiotics and probiotics resulted
in significant changes in fish gut and aquatic microbiota as well as
more than 100 DE genes in the antibiotic and probiotic treatment fish,
relative to healthy controls. Normal microbiota depletion by antibiotics
mostly led to downregulation of different aspects of immunity and
upregulation of apoptotic process. In the probiotic treatment, genes
related to post-translation modification and inflammatory responses were
up-regulated relative to controls. Our qPCR results revealed significant
effects of treatment (antibiotic and probiotic) on rabep2, aifm3, manf,
prmt3 gene transcription. Moreover, we found significant associations
between members of Lactobacillaceae and Aeromonadaceae with host gene
expression patterns. Overall, our analysis showed that the microbiota
had significant impacts on many host signaling pathways, specifically
targeting immune, developmental, and metabolic processes. Our
characterization of some of the molecular mechanisms involved in
microbiome-host interactions will help develop new strategies for
preventing/ treating microbiome disruption-related diseases.%
\end{abstract}%



\sloppy


\textbf{Introduction}

Nearly all animals examined to date show complex interactions with their
associated microbial communities. It is evident that there are
bidirectional interactions between the gut microbiome and the host in
humans (Davison et al., 2017; Dayama et al., 2020; Meisel et al., 2018)
and animals (Fuess et al., 2021; Muehlbauer et al., 2021; Naya-Catala et
al., 2021). These interactions affect a wide range of host phenotypes
including metabolism, immunity, and physiology (McFall-Ngai et al.,
2013). Recent studies have shown that host genetics can also shape their
gut microbiome (Lopera-Maya et al., 2022; Piazzon et al., 2020). The
evidence for benefits provided by the gut microbiota is growing for
example, gut microbiota can improve nutrition absorption
(Krajmalnik-Brown et al., 2012), facilitate resistance against pathogens
(Ducarmon et al., 2019), train the immune system and even modify
behaviour and mental state (Surana and Kasper, 2017). (4)Moreover, the
gut microbiota gain substantial benefits from their host (e.g.,
available nutrients and suitable habitat) resulting in a mutualistic
relationship with the host. This provides the context for a unique
coevolved process in which host and their gut microbiome interact in a
mutualistic adaptive scenario (Escalas et al., 2021; Groussin et al.,
2020). Coevolution is defined as the reciprocal adaptation process
experienced by two organisms as the result of their reciprocal selection
pressures; it is possible for the microbiome to evolve at the individual
species level, as well as a community response to host-mediated
selection (Koskella and Bergelson, 2020).

Many studies have shown the importance of the gut microbiome in healthy
and diseased host states, which ultimately affects host fitness (Bozzi
et al., 2021; Manor et al., 2020; Yao et al., 2018). The gut microbiome
has been shown to alter host gene expression (Davison et al., 2017;
Nichols and Davenport, 2021), perhaps a mechanism for the effect of the
microbiome on the host. However, the mechanisms and direction of these
effects is still not clear since the evidence is largely correlational.
Does a change in microbiome composition cause changes in host gene
expression, and if so, which genes will be most impacted? It is clearly
important to characterize the mechanisms through which the microbiome
can cause changes in host gene expression.

Fish live in diverse aquatic environments, but they all harbour complex
and diverse microbiomes, and those microbial communities start
developing when the eggs are laid (Llewellyn et al., 2014). The
bidirectional interaction between the host gut and its associated
microbes may arguably be better established in fish, relative to
terrestrial animals, as fish are in constant direct contact with the
aquatic environmental microbiome through their gut, gills, and skin.
Moreover, given the long evolutionary history of fish as a group,
studying host--microbe co-evolution in fish may provide unique insights
into the host--microbe relationships in general (Montalban-Arques et
al., 2015). Characterizing the mechanisms of how the gut microbiota and
gene expression processes of the host interact in a symbiotic manner
will help explain the physiological processes that maintain the balance
among these intricate cross-kingdom interactions and ultimately, help
attempts to prevent dysbiosis (Nichols and Davenport, 2021).

Most studies on host- microbiome interactions are correlative or
associative analyses without clearly defined cause and effect (Surana
and Kasper, 2017). To move beyond such studies, we must more directly
address causation through perturbation experimental analyses (Xia and
Sun, 2017). Using probiotics and antibiotics to alter gut microbiome in
healthy hosts can provide valuable experimental insight into the
mechanisms of host-microbiome interactions. Antibiotics can be used for
antibiotic-induced microbiome depletion (AIMD), this leads to changes in
the structure and function of the gut microbial communities (Ferrer et
al., 2017). Furthermore, probiotics can also be used to alter the gut
microbiome in a controlled manner, as well as stimulate the host
intestinal immune system (Lee and Bak, 2011). Experimental perturbations
of the gut microbial community with probiotic strains in human and
animal disease treatment is well documented (Azad et al., 2018).
However, the effect of probiotics in healthy individuals is not as well
characterized.

The direction and nature of host-gut microbiome interactions is still an
open question in the study of the microbiome, although it is likely
bidirectional and experimental analyses of the mechanisms behind these
interactions are needed. Here, our goal was to explore a broad range of
host gut tissue responses induced by the experimental manipulation of
the gut microbiome. We chose Chinook salmon (\emph{Oncorhynchus
tshawytscha} ) as our study organism as they are reared for commercial
and conservation purposes and provide logistical advantages for a study
such as ours. Specifically, we used antibiotic, probiotic and control
diet treatments to manipulate the gut microbiome in families of Chinook
salmon. We used 16S rRNA metabarcoding of the gut bacterial community
(BC), coupled with host gut tissue transcriptomics to; (i) quantify
treatment effects on the host gut and the fish rearing water BC
compositions, (ii) determine the response of the host gut tissue
transcriptome to the treatments, and (iii) use gene transcriptional
profiling Taqman\textsuperscript{TM} qPCR to characterize the host
response to the treatment-altered gut microbiome. Given the long
evolutionary history of the relationship between fish and their
microbiomes,we expect strong bidirectional effects, but predicted that
the effects of the microbiome on the host are more pronounced. We
specifically hypothesized that the host transcriptional responses to
each treatment could be attributed to the abundance of specific
bacterial taxa. The results obtained provide insight into the co-evolved
symbiotic relationship between host and its associated microbiome that
may inform future studies exploring host-microbiome interactions and
evolution. Additionally, our work will help in better using microbiome
manipulation (probiotics, antibiotics) to improve health in fishes and
potentially in other animals, including humans.



\textbf{Materials and methods}

\textbf{Study design}

We domesticated Chinook salmon from Yellow Island Aquaculture Ltd, an
organic salmon farm on Quadra Island, BC, Canada to create a nested
breeding design with two sires crossed with one dam (2\selectlanguage{ngerman}×1) replicated six
times. Eggs were fertilized in October 2019 and the eggs were incubated
in replicate cells of vertical stack incubation trays. When the
offspring reached the first feeding (March 2020) offspring from
replicated incubation tray cells were transferred to 200\selectlanguage{english} L tanks with
well water flow of 2 L per minute with continuous aeration with a 16:8\selectlanguage{english} h
light-dark cycle. Fish were fed \textasciitilde{}3\% of their body
weight three times per day until October 24\textsuperscript{th}, 2020.
At that time, 5 fish per family were moved to new 200 L tanks for a
total of 72 tanks (12 (families)*2 (replicates)* 3 (treatments -- see
below)).

\textbf{Microbiome manipulation}

We manipulated the gut microbiome of the fish in the tanks using control
(untreated) feed, antibiotic treated feed and probiotic treated feed:

\emph{Antibiotic treatment} : Oxytetracycline (OTC), and Chloramphenicol
(CAP), two broad spectrum antibiotics, were selected for the trial.
Twenty-four tanks (for 12 (families)*2 (replicates)) were labelled as
antibiotic and were treated with OTC (83 mg/kg/day concentration) (Kokou
et al., 2020; Rosado et al., 2019) for six days. After six days the fish
were switched to a combination of Chloramphenicol (CAP) (42 mg/kg/day)
(Bilandzic et al., 2012) plus the OTC for four more days, for a total of
10 days of antibiotic treatment. Fish were fed three times a day at
approximately 3\% of their body weight.

\emph{Probiotic treatment} : Twenty-four tanks (for 12 (families)*2
(replicates)) were labelled as probiotic treatment and fed commercially
available Jamieson Probiotic Complex with 60 billion colony forming
units (CFU) (Jamieson Laboratories, Canada; Supplementary Table S1).
Specifically, the probiotic-treated feed (3 capsules per 100 gram of
feed) was coated with 10 mL of sodium alginate (1\%) and 10 mL of 0.5 \%
calcium chloride prior to mixing with the probiotic powder. Fish were
fed three times a day at approximately 3\% of their body weight.

\emph{Control} : Twenty-four tanks (for 12 (families)*2 (replicates))
were labelled as control group and fish were fed with regular feed
without probiotic or antibiotic for ten days. Fish were fed three times
a day at approximately 3\% of their body weight (Fig. 1).

\textbf{Sampling}

All fish were terminally sampled after the ten-day trial (November 3,
2020). The fish were not fed on the day of sampling. The final mean mass
of the fish was 23.3 g (+-7.2 SE) across all families and treatments (no
treatment effect on fish body weight was detected). Three fish were dip
netted from each tank and humanely euthanized immediately in an overdose
solution of clove oil (Toews et al., 2019). Of the 72 tanks, four tanks
(control) had 100 \% mortality and those replicates were excluded from
the study, bringing the total number of samples to 204 fish (72
probiotic treated fish, 72 antibiotic treated fish, 60 control fish) and
68 water samples (one per tank)). The sampled fish were immediately
weighed and dissected, with the entire GI tract placed in a 50 mL tube
with 35 mL of a highly concentrated salt buffer (ammonium sulfate, 1 M
sodium citrate, 0.5 M EDTA, H\textsubscript{2}SO\textsubscript{4} to
bring the pH to 5.2) for preservation for later RNA and DNA extraction.
Additionally, 500 mL water samples were collected from each of the tanks
(N=68) before sampling the fish and filtered immediately using
0.22-micron pore size, 47 mm diameter polycarbonate filters (Isopore\selectlanguage{english},
Millipore, MA). All samples (tissue in preservative and the filters)
were stored at --20 degC, until used for DNA or RNA extraction.

\textbf{Lab Analyses}

The lab analyses consisted of three related but separate protocols
(Figure 1). The first was to assess the bacterial composition of the
fish gut and rearing water microbiomes using 16S rRNA metabarcoding. The
second was to determine the whole transcriptome response to treatment by
RNA-Seq of gut tissue from offspring from a single family. The third
analysis was designed to better characterize the transcriptional profile
response to the treatments using nanofluidic array qPCR analysis of 50
gene loci selected using the RNA-Seq analysis.

\textbf{Bacterial DNA extraction and 16S rRNA gene library preparation}

DNA was extracted from fish hindgut content using a sucrose lysis buffer
solution method previously described (Shahraki et al., 2019) and
extracted DNA was subsequently stored at --20 degC, until further
analysis. Additionally, the PCR conditions and 16S rRNA primer sets (1st
and second PCR) were the same as those used in previously described
methods (Sadeghi et al., 2021). Briefly, 16S rRNA variable regions of
V5-V6 were amplified with a PCR cycle program of 95 degC for 3 min
followed by 28 cycles of 95 degC for 30 s, 55 degC for 30 s, and 72 degC for 1
m, and a final step at 72 degC for 7 m. A second short-cycle PCR (7
cycles) using purified first PCR products ligated the adaptor and
barcode (10 -12 bp) sequences to the amplicons as required for sample
identification and sequencing. During the first and second PCR, nine
samples failed amplification and 263 samples (195 gut samples, and 68
water samples) remained for the gel extraction. For each 96 well PCR
plate, one negative control consisting of PCR mix (of first and second
PCR) with ultra-pure water instead of DNA template was included. The
pooled purified PCR amplicon mix (i.e., sequencing library) was
sequenced on an ION S5 sequencing system.

\textbf{16S Metabarcode Sequence Data Processing}

The resulting FASTQ file was analyzed using the Quantitative Insights
Into Microbial Ecology (QIIME2-2020.11) platform (Bolyen et al., 2019)
(details of the QIIME analysis is presented in the supplementary file).
A total of 8,820,568 sequences with 19,776 ASVs were obtained for the
267 samples (195 gut samples, 68 water samples, and 4 negative
controls). The four negative controls had 1 to 7 reads and were excluded
from the rest of the study. Using a taxon filter-table, ASVs related to
eukaryotes, mitochondria, chloroplasts (combined \textasciitilde{} 1\%),
and unassigned (1\%), were removed, resulting in a total of 8,655,659
(98\%) sequences remaining. Furthermore, samples with low sequence depth
(less than 3000 reads), low abundance taxa (less than 10 ASVs) and ASVs
that showed up in only one sample were removed. This decreased the total
number of samples to 255 samples (189 gut samples, 66 water samples)
with 8,217,478 sequences and 2888 ASVs. The 8 deleted samples were not
related to specific treatment type or family (antibiotic treatment (one
water sample), probiotic treatment (4 gut samples, and one water
sample), control (two gut samples)). Alpha diversity indices (Chao1 and
Faith's phylogenetic diversity (PD)) of BCs were calculated using the
QIIME2 alpha diversity plugin. The ASV table was rarefied to 3000 reads
per sample for the alpha diversity estimation (rarefaction curves
plateaued at 3000 reads). A Bray-Curtis dissimilarity matrix was
calculated to estimate \selectlanguage{greek}β-\selectlanguage{english}diversity.

\textbf{RNA extraction}

RNA was extracted from host hindgut tissue using TRIzol\selectlanguage{ngerman}® reagent (Life
Technologies, Mississauga, ON, CAT=15596018) following the
manufacturer's protocol. RNA was dissolved in sterile water and treated
with TURBO\selectlanguage{english} DNase (Life Technologies, Mississauga, ON) to remove genomic
DNA contamination and preserved at --80degC until RNA sequencing or cDNA
synthesis and qPCR were performed (see below).

\textbf{RNA sequencing and transcriptome assembly}

A total of 18 gut tissue samples from one family, but from all 3
treatments (6 fish per treatment), were used for transcriptome analyses
by RNAseq. Fish from one family were used to minimize differences due to
genetic variability among individuals. RNA quality was assessed using
the Eukaryotic RNA 6000 Nano assay on a 2100 Bioanalyzer (Agilent,
Mississauga, ON). All samples had an RIN \textgreater{} 7 and a 28S:18S
rRNA ratio \textgreater{}1.0. RNAseq libraries were prepared and
sequenced at the McGill University and Genome Quebec Innovation Centre
using the Illumina NovaSeq 6000 S4 PE100 protocol and 100-bp paired-end
sequencing. To remove potentially contaminating rRNA sequences, raw
sequences were filtered against eight default rRNA databases using
SortMeRNA v2.1 (Kopylova et al., 2012). The sequences were then
quality-filtered using Trimmomatic v0.38 (Bolger et al., 2014). The
non-rRNA sequences were aligned to the Chinook salmon
(GCF\_002872995.1\_Otsh\_v1.0;
https://www.ncbi.nlm.nih.gov/assembly/GCF\_002872995.1/) reference
genome using the splicing aligner HISAT2 (Kim et al., 2015).
FeatureCounts (Liao et al., 2014), was used to calculate the number of
transcript sequence fragments assigned to each gene.

\textbf{Differential expression gene analysis}

The output from FeatureCounts was imported into DESeq2 (version
`1.32.0') (Love et al., 2014) in R (R version 4.1.1) (Team, 2013) for
normalization and differentially expressed genes analysis.

\textbf{qPCR Primer/probe optimization and cDNA synthesis}

\emph{Primer and probe optimization} : Fifty transcripts (genes) that
were significantly DE between antibiotic and probiotic treatments versus
the control treatment in the DESeq2 analysis were selected for printing
on OpenArray Taqman qPCR chips (Supplementary Table S2). Four endogenous
control genes (\selectlanguage{greek}β-\selectlanguage{english}2-microglobulin, \selectlanguage{greek}β-\selectlanguage{english}Actin, ribosomal protein L13, and
glyceraldehyde-3-phosphate dehydrogenase (GAPDH)) were selected from
previous studies (Geffroy et al., 2021; Limbu et al., 2018; Toews et
al., 2019) to normalise the transcription profiles of the candidate
transcripts Primers for the candidate transcripts were designed using
Geneious Software v7.1.5 (http://www.geneious.com) and optimized on DNA
from Chinook salmon fry. After PCR optimization, the primers were tested
on a subset of our cDNA samples with SyBr\selectlanguage{ngerman}® Green Dye I (Thermo Fisher
Scientific) following the manufacturer's protocol on the QuantStudio 12K
Flex Real\selectlanguage{english}-Time PCR System (Thermo Fisher Scientific). After testing
positive for amplification of the expected sized fragment using SyBr(r)
Green assays, new qPCR primers and Taqman(r) probes were developed using
Primer Express(r) Software v3.0.1 (Thermo Fisher Scientific) for all 54
genes (50 candidate and 4 control genes; Supplementary Table 1). The
qPCR primers spanned intron\selectlanguage{english}-exon boundaries with a short amplicon size
(50--100 bp). The Taqman(r) probe was designed for a melting temperature
between 57 and 60 degC.

\emph{cDNA synthesis} : RNA was quality tested on a random subset of the
samples both on a 2100 Bioanalyzer and on 2\% agarose gels. RNA
Integrity Number (RIN) values were consistent among samples, ranging
between 7 and 9.8, while gel images showed the expected rRNA bands. The
RNA concentration for each sample was estimated by Spark(r) multimode
microplate reader and NanoQuant Plate\selectlanguage{english} (Tecan, Morrisville, NC, USA).
All total RNA preparations had purity values of 1.8 -- 2.1 (A260/A280)
with concentrations ranging from 2,000 to 5,000 ng/\selectlanguage{greek}μ\selectlanguage{english}L. TURBO DNA-free
Kits (Thermo Fisher Scientific, cat. no. AM1907) were used to remove
genomic DNA contamination. Total RNA was converted to cDNA using High
Capacity cDNA Kits (Applied Biosystems, Ontario, Canada), following the
manufacturer's protocol. Reverse transcriptase reactions contained 10 \selectlanguage{greek}µ\selectlanguage{english}L
of total RNA at a concentration of 200 ng/\selectlanguage{greek}μ\selectlanguage{english}L, 2 \selectlanguage{greek}µ\selectlanguage{english}L of 10X RT random
primers (Applied Biosystems), 0.8 \selectlanguage{greek}µ\selectlanguage{english}L of dNTP (100mM), 50 U of
MultiScribe RT (Applied Biosystems) and 40 U of RNase Inhibitor (Applied
Biosystems) in a 2 \selectlanguage{greek}µ\selectlanguage{english}L of 10X RT buffer at a final volume of 20 \selectlanguage{greek}µ\selectlanguage{english}L. RT
reactions were incubated at 25\selectlanguage{ngerman}°C for 10 min followed by 37°C for 120 min
and were stopped by incubating at 85°C for 5 min. cDNA samples were
stored at --20°C until further analysis.

\textbf{OpenArray high-throughput qPCR}

TaqMan® OpenArray® chips from Applied Biosystems (Burlington, ON,
Canada) were used to quantify transcription at the 54 genes (50
candidate and 4 endogenous control genes) on a QuantStudio 12K Flex
Real\selectlanguage{english}-Time PCR System following the manufacturer's protocol. Forty-eight
cDNA samples were run (two chips for 48 samples) for each of the 54
genes on each chip. A 5 \selectlanguage{greek}μ\selectlanguage{english}L reaction volume which includes 1.2 \selectlanguage{greek}μ\selectlanguage{english}L of cDNA
(100ng/\selectlanguage{greek}µ\selectlanguage{english}L/per sample), 1.3 \selectlanguage{greek}μ\selectlanguage{english}L of ddH\textsubscript{2}O and 2.5 \selectlanguage{greek}μ\selectlanguage{english}L of
TaqMan\selectlanguage{ngerman}® OpenArray® Real\selectlanguage{english}-Time PCR Master Mix (Applied Biosystems,
Burlington, ON, Canada) was used, aliquoted across a 384\selectlanguage{english}-well plate and
then loaded onto the TaqMan(r) OpenArray(r) chips using the OpenArray(r)
AccuFill System. A total of 10 chips were used for 213 cDNA samples. The
samples were randomly distributed among the chips. ExpressionSuite
Software (Applied Biosystems, Thermo Fisher Scientific, Carlsbad, CA,
USA) was used to analyse the endogenous control genes. Of four
endogenous control genes, \selectlanguage{greek}β-\selectlanguage{english}Actin was selected for normalization due to
lower among-sample variation compared to the three other endogenous
control genes. Subsequently, all 10 chips were normalized with the
selected endogenous control gene (\selectlanguage{greek}β-\selectlanguage{english}Actin) together in ExpressionSuite
Software v1.0.3 (Applied Biosystems, Burlington, Ontario, Canada).
Moreover, ExpressionSuite Software was used to calculate raw critical
threshold (C\textsubscript{T}) values and the relative critical
threshold values (\selectlanguage{greek}Δ\selectlanguage{english}C\textsubscript{T}). Values produced by this platform
are already corrected for the efficiency of the amplification
(Molina-Lopez et al., 2020). We tested for replicate effect using Paired
sample T test in SPSS (IBM SPSS Statistics for Windows, Version 27.0.
Armonk, NY: IBM Corp). As we found no evidence for a replicate effect (P
value \textgreater{} 0.05), C\textsubscript{T} and
\selectlanguage{greek}Δ\selectlanguage{english}C\textsubscript{T}values were averaged between the replicate and only
one C\textsubscript{T} or \selectlanguage{greek}Δ\selectlanguage{english}C\textsubscript{T} value was used for each
gene.

\textbf{Statistical analysis}

\textbf{Treatment effects on BC composition}

\emph{Aquatic BC composition:} To test for the effect of treatment on
the BC composition in the hold tank water, taxonomical compositions of
the BCs were visualized using stacked barplots and Pie charts of the
relative abundance of the bacteria at the phylum and family level using
the online tool MicrobiomeAnalyst (Chong et al., 2020). Moreover,
differences in alpha diversity indices (Chao1 and PD) among the
treatments (antibiotic, probiotic, control) for the tank water bacterial
communities (BCs) were tested using a Kruskal-Wallis (KW) rank test. In
the case of a significant association, a \emph{post hoc} Dunn tests with
Bonferroni corrected P values was done. To visualize among-treatment
divergence in the tank water BCs, a Principal-coordinate analysis (PCoA)
using the Bray Curtis distance matrix was used. Subsequently, the
significance of the observed clusters was assessed using permutational
multivariate analysis of variance (PERMANOVA) analysis permutations in
Primer 6 (v6.1.15). Pairwise comparisons were performed in cases of
significant PERMANOVA among treatment groups.

\emph{Fish gut bacterial community composition:} The effect of treatment
on taxonomic composition of the gut sample BCs was visualized using Pie
charts and stacked barplots of the relative abundance of the bacterial
taxa at the family and phylum level (Chong et al., 2020). To identify
treatment and parental effects on gut microbial community, alpha (Chao1
and PD) diversity indices for gut samples were compared using the KW
rank test in SPSS (IBM SPSS Statistics for Windows, Version 27.0.
Armonk, NY: IBM Corp). To visualize treatment effects on BC structure, a
PCoA using the Bray-Curtis distance matrix was used to generate
scatterplot of the first two PCoA axes in R (version 4.1.1). Moreover,
PERMANOVA analyses were performed in R (version 4.1.1) to test for
treatment and parental (dams, sires) effects on BC composition. Pairwise
comparisons were performed when significant differences among the
treatment groups were detected to identify specific treatment effects.

\emph{Comparison between fish gut and aquatic bacterial community
composition:} Fish gut BC composition was compared against the rearing
water BC at both the alpha and beta diversity level. Alpha diversity
measures (Chao1 and PD) of gut and water samples were compared using
Mann-Whitney U test in SPSS (IBM SPSS Statistics for Windows, Version
27.0. Armonk, NY: IBM Corp). PCoA first and second axes were used to
visualize clustering of the samples based on sample type (gut or water).
Subsequently, PERMANOVA analysis were performed in R (version 4.1.1) to
test sample type effect on BC composition.

\textbf{Gut transcriptome response to treatment}

The DESeq2 (version `1.32.0') package in R (version 4.1.1) was used to
identify differentially expressed transcripts in the host gut
transcriptome between any of the treatment groups in three pairwise
comparisons (antibiotic vs control, probiotic vs control, antibiotic vs
probiotic). The package uses a Wald test to test the significance of
gene transcription differences. To identify differentially expressed
transcripts, Benjamini--Hochberg corrections for multiple testing was
used (false discovery rate (FDA) \textless{} 0.05). We identified
differentially expressed transcripts as those genes with thresholds of
FDR \textless{} 0.05 and \textbar{}log\textsubscript{2} FC\textbar{}
\textgreater{} 1. Volcano plots of differentially expressed genes
between the treatments were generated by using the FC and the log-scaled
adjusted \emph{p} value using the EnhancedVolcano package (Blighe et
al., 2021) in R.

\textbf{Transcriptional profile (qPCR) response to treatment}

The 50 selected candidate transcripts (hereafter ``genes'') were tested
to determine which genes showed a transcription response to either of
the treatments. Two genes (\emph{cfap58} , \emph{ubr4} ) were dropped
from the analysis due to failure of PCR amplification for most of the
samples, thus 48 candidate genes were included for the rest of the
study. To reduce the number of independent variables and to avoid over
fitting the models, we used Principal Component Analyses (PCA) on the
qPCR data for the 48 selected genes using ``prcomp'' (which is a part of
the R statistical analysis package) and factoextra package (1.0.7)
(Kassambara and Mundt, 2017) in R (version 4.1.1). Based on a threshold
of Eigenvalue \textgreater{} 1, and \% variance explained \textgreater{}
2\%, the first nine PC axes were selected. We used Linear mixed models
(LMM) (lmerTest package (v3.1.3)) (Kuznetsova et al., 2017) in R with
the selected PC axes to test for the effect of treatment (fixed effect),
and the random effects of dam, sire, fish body weight, tank ID and chip
effect, with all interaction terms for fixed and random factors on gene
transcription patterns. Chip ID, body weight, dam, treatment\selectlanguage{ngerman}×dam,
treatment×sire effects were nonsignificant before FDR correction and
were removed from the model. When any of the nine PCs were found to
exhibit significant effects with any of the independent variables
(treatments, dam, sire, body weight, tank ID, or chip effect), we
examined the individual gene transcription loading values. We used
\emph{fviz\_contrib} within the factoextra package (1.0.7) to identify
genes with contributions to the PC greater than expected (Kassambara and
Mundt, 2017). The identified genes were included in a second analysis
that used LMM with the \selectlanguage{greek}Δ\selectlanguage{english}C\textsubscript{T} values for the selected genes
and the same independent variables (treatment, dam, sire, body weight,
tank ID and chip effect), including all interaction terms for fixed and
random factors. Nonsignificant factors (Chip ID, body weight, dam, and
all interactions) were removed from the model and the analysis was
re-run. Lastly, a sequential Bonferroni \emph{P} value correction was
applied for multiple testing correction (Rice, 1989).

\textbf{Correlation between gut bacterial community and host
transcriptional profile}

To investigate the direct effect of variation in the gut microbiome BC
composition on host gene expression patterns, regression analyses were
performed using the function \emph{lm} in R (R version 4.1.1). We
selected common bacterial taxa (bacteria families with more than 5\%
contribution to total sequence reads counts within each treatment; (7
taxonomic families) and individual genes with evidence for possible
treatment effects (\emph{P} value \textless{}0.1 (9 genes)) from the
gene-level analysis described above. We included treatment and family ID
as covariates to account for specific treatment and family effects on
the relationship between host gut microbiome BC composition and gene
transcription. Moreover, a sequential Bonferroni \emph{P} value
correction was applied for multiple testing correction (Rice, 1989). We
visualized the pattern of correlation across all genes and bacterial
taxa using a heatmap generated in the pheatmap function in R.



\textbf{Results}

\textbf{Impact of antibiotics and probiotics on aquatic and fish
microbiome.}

\textbf{Microbial community associated with water} : We characterized
the rearing tank water BC at two taxonomic levels; the phylum and
family. Tank BC diversity diverged among the treatments, with the top 10
most abundant families making up the majority of reads. Proteobacteria
were the most common phylum among all treatments (control (70\%),
antibiotic (68\%), probiotic (51\%)). Bacteroidota (13 \%), and
Actinobacteriota (17\%) were also common phylum in the control treatment
water. Moreover, in the antibiotic treated water, Firmicutes (24\%) and
Bacteroidota (12\%) were common phyla after Proteobacteria. On the other
hand, in the probiotic treated water, Bacteroidota (12\%) and Firmicutes
(8\%) were the common phyla after Proteobacteria (Supplementary Figure
S1). At the family level, the most common aquatic associated bacterial
taxa were members of \emph{Comamonadaceae,} a family of the
Betaproteobacteria accounting for 30\%, 28\%, and 35\% bacterial taxa in
control, antibiotic, and probiotic waters, respectively.
\emph{Mycoplasmataceae}were found in all samples, but at relatively
higher abundance in antibiotic challenge water compared to probiotic and
control waters. Members of \emph{Oxalobacteraceae} were also found in
all sampled tanks but at higher abundance in the probiotic and control
tanks relative to the antibiotic tanks. Other notable
freshwater-associated bacterial taxa at the family level were
\emph{Flavobacteriaceae} ,\emph{Pseudomonadaceae} ,
\emph{Sporichthyaceae} and\emph{Aeromonadaceae} (Figure 2A).

To quantify treatment effects on the aquatic BCs, alpha and beta
diversity indices for water samples were compared for the three
treatment groups (antibiotic, probiotic, control). Alpha diversity
analysis (Chao1, PD) showed no significant differences among the groups
(Chao1: KW 5, \emph{P} \textgreater{} 0.05; PD: KW 3,
\emph{P}\textgreater{} 0.05). However, our PCoA plot showed clear
separation between the water samples based on treatments (Figure 2B).
PERMANOVA results confirmed that the overall community structures were
significantly different among the three groups (F-value 8.9; R-squared:
0.22; p-value \textless{} 0.001). Pairwise comparison also showed that
the three groups are different from each other, but with the probiotic
treatment group compared to antibiotic treatment group showing the
highest dissimilarity (probiotic- control F: 2.17, P\textless{}0.001;
probiotic- antibiotic F: 2.86, P\textless{} 0.001; control-antibiotic F:
2.77, P \textless{} 0.001). Moreover, the average dissimilarity within
treatments was higher for the control tanks (73.2\%) compared to our
probiotic (65.4\%) and antibiotic treatment tanks (61.8\%).

\textbf{Microbial community associated with gut:} Firmicutes were the
most common phylum for the control and probiotic group fish (46\%, and
49\%, respectively). On the other hand, members of Desulfobacterota were
the most common bacteria in the antibiotic treated fish gut microbiomes
(Supplementary Figure S2A). We also compared members of Firmicutes
phylum among the treatments at the family level. Within the Firmicutes
phylum, \emph{Mycoplasmataceae} was the most common gut associated
bacterial taxa across all treatments, in addition to other important
taxa (Supplementary Figure S2B). For example, control and probiotic
treated fish had \emph{Mycoplasmataceae} (control (65\%), probiotic
(50\%)), \emph{Streptococcaceae} (control (30\%), probiotic (28\%)),
and\emph{Lactobacillaceae} (control (2\%), probiotic (17\%)) present.
However, in the antibiotic group, different families were present within
Firmicutes phylum (\emph{Mycoplasmataceae}
(68\%),\emph{Streptococcaceae} (14\%), and \emph{Leuconostocaceae} (5\%)
(Supplementary Figure S4.2B). At the family level, the most common gut
associated bacterial taxa across all treatment groups were members
of\emph{Desulfovibrionaceae} (related to Desulfobacterota phylum)
and\emph{Mycoplasmataceae} (Figure 3A). While \emph{Streptococcaceaehad}
high relative abundances in control group, samples \emph{in}probiotic
groups had high relative abundances \emph{Lactobacillaceae.}Moreover,
members of \emph{Pseudomonadaceae}had high relative abundances in
antibiotic group (Figure 3A). Unlike in the tank water
microbiome,\emph{Mycoplasmataceae} was higher in the control and
probiotic groups compared to the antibiotic group. We also found two
important fish associated pathogens, \emph{Enterovibrio} and
\emph{Photobacterium,} in the fish gut microbiome, but at low abundance.

To identify the treatment and parental (dams and sires) effects on the
gut microbial BC, alpha diversity indices for gut samples were compared.
Alpha diversity analysis (Chao1, PD) for the gut microbiome BC showed no
significant differences among the treatments (Chao1: KW 2.8,
\emph{P}\textgreater{} 0.05; PD: KW 3.2, \emph{P} \textgreater{} 0.05),
sires (Chao1: KW 6.9, \emph{P} \textgreater{} 0.05; PD: KW 6.8,
\emph{P}\textgreater{} 0.05), and dams (Chao1: KW 5.3, \emph{P}
\textgreater{} 0.05; PD: KW 8.9, \emph{P} \textgreater{} 0.05). Beta
diversity variation was also explored using Bray Curtis distance
matrices and a PCoA plot. The PCoA plot showed clear separation among
the samples based on treatments (Figure 3B). PERMANOVA results confirmed
that the overall BC structures were significantly different among the
treatments (Table 1). Treatment alone had the highest influence on the
gut microbial community (Pseudo-F:6.1, \emph{P} value \textless{} 0.05).
Pairwise comparisons also showed that the three treatment groups exhibit
significant difference in beta-diversity, with the probiotic versus
control treatment samples showing the highest dissimilarity (probiotic-
control F: 3.01, \emph{P} \textless{}0.001; probiotic- antibiotic F:
2.85, \emph{P} \textless{} 0.001; control-antibiotic F: 1.52,
\emph{P}\textless{} 0.05). Moreover, the average within treatment group
BC dissimilarity was higher for the control (82.2\%) than the probiotic
(77\%) and antibiotic treatments (80.5\%), indicating that the control
group had higher diversity than the other two groups in the fish
hindgut. Dams alone did not have significant effects. However, sires had
marginal significant effect effects on BC structures (Table 1).

\textbf{Association between gut and aquatic microbial community:} We
evaluated the relationship between the tank water microbiome BC and the
fish gut microbiome BC. Chao1 and PD (diversity measures) showed
significant differences in the species richness of the two sample types;
overall, diversity was significantly higher in the water samples than
gut samples (P \textless{}0.001, Mann-Whitney U test: 2191.5). The PCoA
plot (Figure 4) showed clear separation between the gut and water
samples. Moreover, PERMANOVA test also revealed that the clusters showed
in PCoA plot were significantly different (Pseudo-F: 39.6,
\emph{P}value\textless{} 0.05).

\textbf{Treatment effects on the host gut transcriptome.}

To determine if antibiotic and probiotic-induced changes in the
microbiome led changes in the host gut transcriptome, RNA-Seq was used
to determine host transcript levels in the hindgut. Pairwise treatment
comparisons resulted in 96 (control vs antibiotic; 35 control
upregulated and 61 control downregulated), 105 (control vs probiotic; 61
control upregulated, and 44 control downregulated), 120 (antibiotic vs
probiotic; 84 antibiotic upregulated, and 36 antibiotic downregulated)
transcripts that were differentially expressed among treatments
(Benjamini-Hochberg false-discovery rate (BH FDR) 0.1,
\textbar{}log\textsubscript{2} FC\textbar{} \textgreater{} 0.25).
However, for selecting candidate genes for the OpenArray high-throughput
qPCR analyses, we took a conservative approach and we only selected
genes with transcripts that were significantly expressed at
\textbar{}log\textsubscript{2} FC\textbar{} \textgreater{} 1 and FDR P
value \textless{} 0.05 (Supplementary Figure S3). This decreased the
differentially expressed transcripts to 29 (control vs antibiotic), 29
(control vs probiotic), and 27 transcripts (antibiotic vs probiotic)
(Table 2). For the control versus antibiotic group comparisons, the
selected genes related to cellular process (e.g., cell activation, cell
communication, cell cycle, and cell death) were upregulated and genes
related to metabolism and response to stimuli and stress were
downregulated in antibiotic group (Table 2). While in the control versus
probiotic group comparisons, genes related to regulation of a variety of
functions~(regulation of meiosis, intracellular protein transport,
angiogenesis, transmembrane transporter, cell adhesion, negative
regulation of apoptotic process) were downregulated and genes related to
post-translation modifications were over expressed in the probiotic
treated fish (Table 2). Moreover, when we compared antibiotic against
probiotic group transcription, genes related to cellular process (mostly
apoptotic process) were over expressed in antibiotic group while genes
related to cell adhesion, regulation of transcription were over
expressed in probiotic group (Table 2).

\textbf{OpenArray high-throughput} qRT-PCR

The LMM analysis showed PCs 4, 5, 6, 7 and 9 were significantly affected
by treatment (Table 3). We identified only those genes whose
contributions to the significantly affected principal component axes
were important (Supplementary Figure S4) and selected them for analyses.
In our analysis we also included tank, body weight, and OpenArray chip
ID as random effects to correct for possible technical, environmental,
and body size effects. Chip and body weight were not significant for any
of the genes and were dropped from our analyses. Sire effects (nested
within dam) were not significant after FDR correction. Moreover, a
significant tank effect was observed for only one gene (\emph{anxa1} , p
\textless{} 0.05) before FDR correction. We found no significant effects
for dam-by-treatment or sire-by-treatment interactions. After including
FDR correction into our model, \emph{aifm3} , \emph{manf} , and
\emph{prmt3} still showed a significant treatment effect (Table 4).

\textbf{Correlation between gut bacterial community and host
transcriptional profile}

A multiple regression analysis was carried out to evaluate the potential
link between bacterial taxon abundance (at the family level) for taxa
common to the gut and differentially transcribed genes, while
controlling for treatment and family effect. The abundance
of\emph{Lactobacillaceae, Aeromonadaceae} were positively and negatively
correlated with several gene transcription levels (Figure 5).

\textbf{Discussion}

Interactions between fish hosts and their microbiomes have been an
under-studied area of research, perhaps due to the complexity of the
host-microbiome relationship making the detection of specific microbial
features that impact the host phenotype challenging. We approached this
problem by manipulating gut microbiomes and measured the impact on key
candidate gene regulation -- such effects are likely mechanisms for
microbes to affect host phenotype and health. We found that our
treatments resulted in changes in host gene expression patterns, and
those changes were mostly related to immune function and cell
motility/integrity. By correcting for the direct effects of the
treatment, as well as the quantitative genetic effects of family, we
showed that changes in microbial communities do lead to changes in host
physiology. Given the putative function of the responding genes, our
work indicates a likely effect on host fitness as well. Indeed, many
recent studies have shown that microbial symbionts are critical
biological components for host traits closely associated with fitness,
such as immune system development and function (Fuess et al., 2021;
Langlois et al., 2021; Rosshart et al., 2017).

This is the first study to consider and compare the impact of probiotics
and antibiotics administered to captive fish on the rearing water
microbial communities and we found that the aquatic microbial
communities in the rearing tanks were significantly influenced by the
feed treatments. This was not expected as the fish food treatment itself
represented a small proportion of the tank volume, especially given the
low flow through water effect. One possible factor is that up 90\% of
administered antibiotics are excreted in the urine and faeces of the
fish, still in the active form (Polianciuc et al., 2020). The common
bacterial phyla we report in the tank water were also reported in other
studies that showed Proteobacteria, Bacteroidota, Firmicutes are the
dominant taxa in water where fish are held (Chiarello et al., 2015; He
et al., 2018; Stevick et al., 2019; Uren Webster et al., 2018; Zhang et
al., 2019). Nevertheless, we observed significant treatment effects on
the rearing water BCs, one possible explanation would be
antibiotic-associated diarrhea leading to more fish gut-associated
microbial excretion. Another reason could be antibiotic-susceptible taxa
being replaced by taxa resistant to antimicrobial agents
(e.g.,\emph{Mycoplasmataceae} (Firmicutes) (antibiotic (15\%), control
(1\%), probiotic (3\%)). Since the aquatic microbiome itself plays a
role in maintaining fish health (Blancheton et al., 2013) quantifying
the unexpected effects of feed-based treatment on the rearing water is
unexpected and important as it may contribute to dysbiosis and poor
health outcomes in the fish. Although the negative effects of
antibiotics on healthy fish have been reported before, few studies have
considered the effect of antibiotic treatment on the rearing water
microbiome. Furthermore, our study showed that probiotic feed treatment
also affected the water microbiome. Previous studies showed that that
treating water with probiotics can improve water quality (Elsabagh et
al., 2018; Tabassum et al., 2021).

The microbial communities present in fish rearing water are thought to
affect the initial colonization of the fish microbiota during
development (Llewellyn et al., 2014; Talwar et al., 2018). However,
similar to other studies (Uren Webster et al., 2018; Wu et al., 2018),
our fish gut microbiomes were distinct from the water sample
microbiomes. This indicates that the fish host gut microbiome is likely
largely independent of the water microbial community and that other
factors such as diet and host genome may be contributing
disproportionally (Talwar et al., 2018).

Our principal goal was to use probiotic and antibiotic treatments to
alter the Chinook salmon gut microbiome to determine the potential role
of gut microbiota composition variation in host-microbiome interactions.
However, we also assessed how the gut microbial community reacted to the
treatments. We found that, while fish gut BC alpha diversity was not
affected by the treatments, beta diversity was significantly different
among all three treatments. Similar results were reported in other
studies, indicating community richness (alpha diversity) did not respond
to treatment with probiotics and antibiotics, but beta diversity did
(Hernandez-Perez et al., 2022; Kokou et al., 2020; Laursen et al.,
2017). One possible reason for this is that using antibiotics does not
necessarily mean a reduced diversity of bacterial taxa. Indeed, a review
showed that individuals with dysbiosis (potentially caused by treatment)
can have even more diverse microbial community than healthy individuals
(Berg et al., 2020). For example, Rosado et al (2019) showed that
treatment of farmed seabass (\emph{Dicentrarchus labrax} ) with OTC
caused a decrease in core BC diversity in the gill and an increase in
the skin. One reason that our probiotic treatment did not change BC
alpha diversity may be we treated healthy fish. Previous studies in
human have shown that probiotics in healthy patients (healthy state)
does not greatly impact the resident microbial populations (Eloe-Fadrosh
et al., 2015; Lahti et al., 2013). In general, external stimuli that
affect the intestinal environment can drive a hierarchical series of
microbiome responses; resistance, resilience, redundancy or finally
dysbiosis-depending on if the disturbance overcomes the intestinal
microbial ecosystem (Lozupone et al., 2012; Moya and Ferrer, 2016;
Sommer et al., 2017). It appears that the microbial responses to
probiotics in our study is either resistance or resilience, as previous
studies have shown that the BCs tended to be more resilient to external
stimuli. On the other hand, treatment with antibiotics tends to result
in either of resilience, redundancy or dysbiosis.

We predicted that the gut microbial community would respond to the
treatments through an increase in beneficial gut bacteria (probiotic
treatment) or through a decrease in the beneficial microbes with a
related increase in the number of potential pathogens (antibiotic
treatment). This was based on the expectation that antibiotics can cause
dysbiosis in the gut, resulting in elevated levels of opportunistic
pathogens (Dethlefsen and Relman, 2011; Francino, 2015), while
prebiotics and probiotics are expected to increase the frequency of gut
barrier-protecting bacteria such as \emph{Lactobacillaceae}
and\emph{Bifidobacteriaceae} (Xiao et al., 2014). In this study,
bacteria with potential probiotic properties (\emph{Lactobacillaceae}
,\emph{Bifidobacteriaceae} , \emph{Streptococcaceae} ) were higher in
the probiotic group compared to other treatment groups, as expected. On
the other hand, \emph{Pseudomonadaceae} and \emph{Aeromonadaceae} had
higher relative abundances in the antibiotic treated fish. Similar
patterns of response to probiotics and antibiotics in BC structure and
composition have been reported by others (Falcinelli et al., 2016; Kokou
et al., 2020; Navarrete et al., 2008; Rutten et al., 2015). For example,
Kokou\emph{et al} (2020) showed that after seven days of of antibiotic
treatment, the European seabass (\emph{Dicentrarchus labrax} )
microbiome increased in \emph{Staphylococcus} , \emph{Pseudomonas}
genera (Proteobacteria). OTC treatment was reported to reduce gut
microbial diversity in Atlantic salmon, while enhancing possible
opportunistic pathogens belonging to \emph{Aeromonas} spp. likely due to
eliminating competing microorganisms (Navarrete et al., 2008). Moreover,
Falcinelli\emph{et al} (2016) showed that Firmicutes,
specifically\emph{Lactobacillus} genus, were significantly higher in
probiotic treated Zebrafish (\emph{Danio rerio} ) larvae relative to
controls.

Studies in humans (Qin et al., 2010) and fishes (Boutin et al., 2014)
have reported that the gut microbiome varies substantially at the
individual and population level, and the transcriptome of the fish gut
appears to correlate with this variation (Franzosa et al., 2014; Qin et
al., 2010). Moreover, Thaiss \emph{et al} (2016) showed that treatment
with antibiotics will change the mouse gut microbiome, and that the
microbiome in turn regulates fluctuations in the host transcriptome and
epigenome. In our study, we showed that our treatment altered the gut
microbiota, then we tested if these changes were associated with changes
in host gene expression. Specifically, we showed that several genes
related to cellular processes such as cell activation, cell
communication, and cell death were upregulated after treatment with
antibiotics in the feed. Although previous studies have shown a direct
effect of antibiotic treatment on gene transcription in humans (Ryu et
al., 2017), antibiotic treatment had a limited effect on gene expression
in germ-free mice (Morgun et al., 2015, Ruiz et al\emph{.,} 2017),
providing evidence that the microbiome mediates the effects of orally
administered antibiotics on the host. In this study we found that our
antibiotic treatment resulted in the upregulation of genes related to
cell death. Moreover, bacteria from the Firmicutes and Bacteroidetes
phyla were reduced while members of the Proteobacteria phylum increased.
Zarrinpar \emph{et al} (2018) showed a similar shift the BC in the mouse
cecal; however, a cecal transcriptome analysis showed that the changes
in the BC resulted in changes in the expression of genes related to
cellular growth and proliferation, as well as cell death and survival
pathways. This suggests that colonic remodeling after treatment with
antibiotics is directly driving changes in the host transcriptome.
Additionally, in our antibiotic treatment group, we showed increased
transcription of the \emph{mrp7} (multidrug resistance-associated
protein 7-like) gene. Moreover, our qPCR analyses showed upregulation of
\emph{aifm3} gene in antibiotic group. A study by Stoddard \emph{et al}
(2019) in zebrafish showed that after introducing antibiotics to fish,
inflammatory gene transcription was downregulated and apoptotic genes
such as \emph{aifm3} were upregulated within 24 hours.

Antibiotics are designed to pass the gut barrier and become systemic;
however, probiotics are live microorganisms that are not able to pass
the lumen barrier. Probiotics can directly modulate host physiology by
interacting with host cells (mostly immune cells), and through indirect
changes in microbiome composition (Langlois et al., 2021). We showed
that genes related to post-translation modifications were over-expressed
in the probiotic treatment group, relative to the control and antibiotic
treatment groups. Previous studies showed that probiotic diet
supplements elicit a proinflammatory response in fish (Nayak, 2010) and
honeybees (Daisley et al., 2020) which promotes more effective pathogen
clearance and improved disease resistance. In this study we found that
our treatment with probiotics indeed changes the BC composition with
increased numbers of potential probiotics taxa (\emph{Lactobacillaceae}
and \emph{Bifidobacteriaceae} ). Moreover, our treatment with probiotics
showed fewer genes related to apoptosis process responding, relative to
the antibiotics group. However, this was not the case for the control
treatment, which was expected as the fish in control group were healthy.
Finally, we noticed that our probiotic treatment did change the
expression of several genes related to immune function as reported in
other studies (Petrof et al., 2004; Tomosada et al., 2013). For example,
Tomosada \emph{et al} (2013), showed that Bifidobacteria strains can
have immunoregulatory effect in the intestinal epithelial cells by
modulation the ubiquitin-editing enzyme. Moreover, similar to this
study, Willms et al. (2022) also showed that beneficial bacteria can
promote intestinal angiogenesis in Zebrafish. The precise mechanism of
action of probiotics remains to be elucidated, especially in heathy
states.

One approach to characterize the bidirectional interactions between the
host and the microbiome BC is to perturb the gut and measure the
response of the host (such as in AIMD studies). In this study, we used
antibiotics and probiotics to modify the microbial communities within
the gut and measured host gene transcription responses to those
modifications. We explored this effect using correlation between
multiple common bacterial taxa and host gene transcription. The results
of that analysis were consistent with a microbiome-mediated effect on
the host. We found that specific microbial taxa are affecting the
regulation of several host genes, for example, the abundance
of\emph{Lactobacillaceae} was positively and negatively associated with
the transcription of the \emph{rabep2} and \emph{manf} host genes,
respectively. Previous work has shown that a single-nucleotide
polymorphism (SNP) in the\emph{rabep2} gene in humans is associated with
ulcerative colitis, consistent with a strong association between
\emph{rabep2} gene and gut bacterial taxa (Jostins et al., 2012).
Furthermore, \emph{rabep2} gene is involved in ciliogenesis (Airik et
al., 2016) and is important for the function of ciliated epithelial
cells (Look et al., 2001) which is in close contact with bacteria.
Moreover, upregulation of \emph{manf} gene can active innate immune
cells and repairing damaged tissue (Neves et al., 2016; Sereno et al.,
2017). However, further studies will be required to determine the
specific association of \emph{Lactobacillaceae} with \emph{manf} host
gene.

The direction of interaction between fish gut and microbiome is not
clear, yet it is the basis of the co-evolution of the host with it's
associated microbiomes. In this study we experimentally modified the
fish gut microbiome and evaluated host gut tissue responses to those
perturbation using transcriptome analysis and transcriptional profiling
coupled with a controlled breeding design to control for host genome
variation. Short term (10 days) perturbation of the juvenile Chinook
salmon gut microbiome with antibiotics and probiotics affected the
microbiome BC composition and host gene expression patterns. This study
achieved a number of important goals: (1) characterized the effects of
antibiotics and probiotics on the aquatic BC (2) characterized juvenile
Chinook salmon gut microbiome BC response to antibiotic and probiotic
treatment (3) characterized the host gut tissue transcriptional response
to antibiotic and probiotic treatments. We showed that our treatments
with antibiotics and probiotics not only changed the Chinook salmon
microbiome BC (composition), but we also observed significant changes at
the gene expression level in the gut tissue of the fish. This study
provides insight into a long-standing co-evolved symbiotic relationship
between fish gut tissue and its associated microbiome. Moreover,
understanding factors influencing the fish gut microbiome and its
influence on host health and fitness will help in better sustainable
growth for the aquaculture.

\textbf{Data Accessibility Statement}

The raw 16S rRNA gene sequencing data are available at the Sequence Read
Archive (SRA) of NCBI with PRJNA872508 BioProject accession number. The
RNAseq data have been deposited in NCBI's Gene Expression Omnibus (Edgar
et al., 2002) and are accessible through GEO Series accession number
GSE211372
(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE211372).

\textbf{Acknowledgments}

We thank the staff at Yellow Island Aquaculture
(http://yellowislandaquaculture.ca/), especially Dr. John Heath, Dr. Ann
Heath, Jane Drown and Earl Heath for their help during the breeding and
sampling. We would like to especially thank Shelby Mackie and Jonathon
Leblanc (Environmental Genomics Facility, GLIER) for helping with DNA
robotic extraction, high throughput metbarcoding library sequencing, and
Open Array nanofluidic chip analyses. We thank Zahra S. Taboun and Keta
Patel for their help with RNA sequencing analysis. This research
received financial support from Canada's Natural Sciences and
Engineering Research Council (NSERC) to DDH and JS received an Ontario
Trillium Scholarship (OTS).

\textbf{Conflict of Interest}

The authors declare no conflicts of interest.

\textbf{Authors' contributions}

J.S, S.R.C, D.D.H. conceived and planned the experiments. J.S carried
out field work. J.S. carried out the wet laboratory sample preparations
and experiments. All authors contributed to selecting the models and the
computational framework for the data analysis. J.S., S.R.C and D.D.H.
contributed to the interpretation of the results. J.S., took the lead in
performing the research, analyzing data and writing the manuscript.
S.R.C, and D.D.H., provided critical feedback and helped shape the
research, analysis, and manuscript.

\textbf{Reference}

Airik R, Schueler M, Airik M, Cho J, Ulanowicz KA, Porath JD, et al.
SDCCAG8 Interacts with RAB Effector Proteins RABEP2 and ERC1 and Is
Required for Hedgehog Signaling. PLoS One 2016; 11: e0156081.

Azad M, Sarker M, Li T, Yin J. Probiotic Species in the Modulation of
Gut Microbiota: An Overview. Biomed Res Int 2018; 2018: 9478630.

Berg G, Rybakova D, Fischer D, Cernava T, Verges MC, Charles T, et al.
Microbiome definition re-visited: old concepts and new challenges.
Microbiome 2020; 8: 103.

Bilandzic N, Tankovic S, Varenina I, Kolanovic BS, Smajlovic M.
Chloramphenicol residues in muscle of rainbow trout following two
different dose treatments. Bull Environ Contam Toxicol 2012; 89: 461-6.

Blancheton J, Attramadal K, Michaud L, d'Orbcastel ER, Vadstein O.
Insight into bacterial population in aquaculture systems and its
implication. Aquacultural engineering 2013; 53: 30-39.

Blighe K, Rana S, Lewis M.. EnhancedVolcano: Publication-ready volcano
plots with enhanced colouring and labeling. . In: 1.12.0 Rpv, editor,
2021.

Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for
Illumina sequence data. Bioinformatics 2014; 30: 2114-20.

Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA,
et al. Reproducible, interactive, scalable and extensible microbiome
data science using QIIME 2. Nat Biotechnol 2019; 37: 852-857.

Boutin S, Sauvage C, Bernatchez L, Audet C, Derome N. Inter individual
variations of the fish skin microbiota: host genetics basis of
mutualism? PLoS One 2014; 9: e102649.

Bozzi D, Rasmussen JA, Caroe C, Sveier H, Nordoy K, Gilbert MTP, et al.
Salmon gut microbiota correlates with disease infection status:
potential for monitoring health in farmed animals. Anim Microbiome 2021;
3: 30.

Chiarello M, Villeger S, Bouvier C, Bettarel Y, Bouvier T. High
diversity of skin-associated bacterial communities of marine fishes is
promoted by their high variability among body parts, individuals and
species. FEMS Microbiol Ecol 2015; 91.

Chong J, Liu P, Zhou G, Xia J. Using MicrobiomeAnalyst for comprehensive
statistical, functional, and meta-analysis of microbiome data. Nat
Protoc 2020; 15: 799-821.

Daisley BA, Pitek AP, Chmiel JA, Gibbons S, Chernyshova AM, Al KF, et
al. Lactobacillus spp. attenuate antibiotic-induced immune and
microbiota dysregulation in honey bees. Commun Biol 2020; 3: 534.

Davison JM, Lickwar CR, Song L, Breton G, Crawford GE, Rawls JF.
Microbiota regulate intestinal epithelial gene expression by suppressing
the transcription factor Hepatocyte nuclear factor 4 alpha. Genome Res
2017; 27: 1195-1206.

Dayama G, Priya S, Niccum DE, Khoruts A, Blekhman R. Interactions
between the gut microbiome and host gene regulation in cystic fibrosis.
Genome Med 2020; 12: 12.

Dethlefsen L, Relman DA. Incomplete recovery and individualized
responses of

the human distal gut microbiota to repeated antibiotic perturbation.
Proc Natl Acad Sci U S A 2011; 108 Suppl 1: 4554-61.

Ducarmon QR, Zwittink RD, Hornung BVH, van Schaik W, Young VB, Kuijper
EJ. Gut Microbiota and Colonization Resistance against Bacterial Enteric
Infection. Microbiol Mol Biol Rev 2019; 83.

Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene
expression and hybridization array data repository. Nucleic Acids Res
2002 ;30(1):207-10.

Eloe-Fadrosh EA, Brady A, Crabtree J, Drabek EF, Ma B, Mahurkar A, et
al. Functional dynamics of the gut microbiome in elderly people during
probiotic consumption. mBio 2015; 6.

Elsabagh M, Mohamed R, Moustafa EM, Hamza A, Farrag F, Decamp O, et al.
Assessing the impact of Bacillus strains mixture probiotic on water
quality, growth performance, blood profile and intestinal morphology of
Nile tilapia, Oreochromis niloticus. Aquaculture nutrition 2018; 24:
1613-1622.

Escalas A, Auguet J-C, Avouac A, Seguin R, Gradel A, Borrossi L, et al.
Ecological Specialization Within a Carnivorous Fish Family Is Supported
by a Herbivorous Microbiome Shaped by a Combination of Gut Traits and
Specific Diet. Frontiers in Marine Science 2021; 8: 91.

Falcinelli S, Rodiles A, Unniappan S, Picchietti S, Gioacchini G,
Merrifield DL, et al. Probiotic treatment reduces appetite and glucose
level in the zebrafish model. Sci Rep 2016; 6: 18061.

Ferrer M, Mendez-Garcia C, Rojo D, Barbas C, Moya A. Antibiotic use and
microbiome function. Biochem Pharmacol 2017; 134: 114-126.

Franzosa EA, Morgan XC, Segata N, Waldron L, Reyes J, Earl AM, et al.
Relating the metatranscriptome and metagenome of the human gut. Proc
Natl Acad Sci U S A 2014; 111: E2329-38.

Fuess LE, den Haan S, Ling F, Weber JN, Steinel NC, Bolnick DI. Immune
Gene Expression Covaries with Gut Microbiome Composition in Stickleback.
mBio 2021; 12.

Geffroy B, Gesto M, Clota F, Aerts J, Darias MJ, Blanc MO, et al.
Parental selection for growth and early-life low stocking density
increase the female-to-male ratio in European sea bass. Sci Rep 2021;
11: 13620.

Groussin M, Mazel F, Alm EJ. Co-evolution and Co-speciation of Host-Gut
Bacteria Systems. Cell Host Microbe 2020; 28: 12-22.

He X, Chaganti SR, Heath DD. Population-Specific Responses to
Interspecific Competition in the Gut Microbiota of Two Atlantic Salmon
(Salmo salar) Populations. Microb Ecol 2018; 75: 140-151.

Hernandez-Perez A, Zamora-Briseno JA, Soderhall K, Soderhall I. Gut
microbiome alterations in the crustacean Pacifastacus leniusculus
exposed to environmental concentrations of antibiotics and effects on
susceptibility to bacteria challenges. Dev Comp Immunol 2022; 126:
104181.

Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al.
Host-microbe interactions have shaped the genetic architecture of
inflammatory bowel disease. Nature 2012; 491: 119-24.

Kassambara A, Mundt F. Factoextra: extract and visualize the results of
multivariate data analyses. R package version 2017; 1: 337-354.

Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low
memory requirements. Nat Methods 2015; 12: 357-60.

Kokou F, Sasson G, Mizrahi I, Cnaani A. Antibiotic effect and microbiome
persistence vary along the European seabass gut. Sci Rep 2020; 10:
10003.

Kopylova E, Noe L, Touzet H. SortMeRNA: fast and accurate filtering of
ribosomal RNAs in metatranscriptomic data. Bioinformatics 2012; 28:
3211-7.

Koskella B, Bergelson J. The study of host-microbiome (co)evolution
across levels of selection. Philos Trans R Soc Lond B Biol Sci 2020;
375: 20190604.

Krajmalnik-Brown R, Ilhan ZE, Kang DW, DiBaise JK. Effects of gut
microbes on nutrient absorption and energy regulation. Nutr Clin Pract
2012; 27: 201-14.

Kuznetsova A, Brockhoff PB, Christensen RH. lmerTest package: tests in
linear mixed effects models. Journal of statistical software 2017; 82:
1-26.

Lahti L, Salonen A, Kekkonen RA, Salojarvi J, Jalanka-Tuovinen J, Palva
A, et al. Associations between the human intestinal microbiota,
Lactobacillus rhamnosus GG and serum lipids indicated by integrated
analysis of high-throughput profiling data. PeerJ 2013; 1: e32.

Langlois L, Akhtar N, Tam KC, Dixon B, Reid G. Fishing for the right
probiotic: host-microbe interactions at the interface of effective
aquaculture strategies. FEMS Microbiol Rev 2021; 45.

Laursen MF, Laursen RP, Larnkjaer A, Michaelsen KF, Bahl MI, Licht TR.
Administration of two probiotic strains during early childhood does not
affect the endogenous gut microbiota composition despite probiotic
proliferation. BMC Microbiol 2017; 17: 175.

Lee BJ, Bak YT. Irritable bowel syndrome, gut microbiota and probiotics.
J Neurogastroenterol Motil 2011; 17: 252-66.

Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose
program for assigning sequence reads to genomic features. Bioinformatics
2014; 30: 923-30.

Limbu SM, Zhou L, Sun SX, Zhang ML, Du ZY. Chronic exposure to low
environmental concentrations and legal aquaculture doses of antibiotics
cause systemic adverse effects in Nile tilapia and provoke differential
human health risk. Environ Int 2018; 115: 205-219.

Llewellyn MS, Boutin S, Hoseinifar SH, Derome N. Teleost microbiomes:
the state of the art in their characterization, manipulation and
importance in aquaculture and fisheries. Front Microbiol 2014; 5: 207.

Look DC, Walter MJ, Williamson MR, Pang L, You Y, Sreshta JN, et al.
Effects of paramyxoviral infection on airway epithelial cell Foxj1
expression, ciliogenesis, and mucociliary function. The American journal
of pathology 2001; 159: 2055-2069.

Lopera-Maya EA, Kurilshikov A, van der Graaf A, Hu S, Andreu-Sanchez S,
Chen L, et al. Effect of host genetics on the gut microbiome in 7,738
participants of the Dutch Microbiome Project. Nat Genet 2022; 54:
143-151.

Love MI, Huber W, Anders S. Moderated estimation of fold change and
dispersion for RNA-seq data with DESeq2. Genome Biol 2014; 15: 550.

Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity,
stability and resilience of the human gut microbiota. Nature 2012; 489:
220-30.

Manor O, Dai CL, Kornilov SA, Smith B, Price ND, Lovejoy JC, et al.
Health and disease markers correlate with gut microbiome composition
across thousands of people. Nat Commun 2020; 11: 5206.

McFall-Ngai M, Hadfield MG, Bosch TC, Carey HV, Domazet-Loso T, Douglas
AE, et al. Animals in a bacterial world, a new imperative for the life
sciences. Proc Natl Acad Sci U S A 2013; 110: 3229-36.

Meisel JS, Sfyroera G, Bartow-McKenney C, Gimblet C, Bugayev J,
Horwinski J, et al. Commensal microbiota modulate gene expression in the
skin. Microbiome 2018; 6: 20.

Molina-Lopez J, Ricalde MAQ, Hernandez BV, Planells A, Otero R, Planells
E. Effect of 8-week of dietary micronutrient supplementation on gene
expression in elite handball athletes. PLoS One 2020; 15: e0232237.

Montalban-Arques A, De Schryver P, Bossier P, Gorkiewicz G, Mulero V,
Gatlin DM, 3rd, et al. Selective Manipulation of the Gut Microbiota
Improves Immune Status in Vertebrates. Front Immunol 2015; 6: 512.

Morgun A, Dzutsev A, Dong X, Greer RL, Sexton DJ, Ravel J, et al.
Uncovering effects of antibiotics on the host and microbiota using
transkingdom gene networks. Gut 2015; 64: 1732-43.

Moya A, Ferrer M. Functional Redundancy-Induced Stability of Gut
Microbiota Subjected to Disturbance. Trends Microbiol 2016; 24: 402-413.

Muehlbauer AL, Richards AL, Alazizi A, Burns MB, Gomez A, Clayton JB, et
al. Interspecies variation in hominid gut microbiota controls host gene
regulation. Cell Rep 2021; 37: 110057.

Navarrete P, Mardones P, Opazo R, Espejo R, Romero J. Oxytetracycline
treatment reduces bacterial diversity of intestinal microbiota of
Atlantic salmon. J Aquat Anim Health 2008; 20: 177-83.

Naya-Catala F, do Vale Pereira G, Piazzon MC, Fernandes AM,
Calduch-Giner JA, Sitja-Bobadilla A, et al. Cross-Talk Between
Intestinal Microbiota and Host Gene Expression in Gilthead Sea Bream
(Sparus aurata) Juveniles: Insights in Fish Feeds for Increased
Circularity and Resource Utilization. Front Physiol 2021; 12: 748265.

Nayak SK. Probiotics and immunity: a fish perspective. Fish Shellfish
Immunol 2010; 29: 2-14.

Neves J, Zhu J, Sousa-Victor P, Konjikusic M, Riley R, Chew S, et al.
Immune modulation by MANF promotes tissue repair and regenerative
success in the retina. Science 2016; 353: aaf3646.

Nichols RG, Davenport ER. The relationship between the gut microbiome
and host gene expression: a review. Hum Genet 2021; 140: 747-760.

Petrof EO, Kojima K, Ropeleski MJ, Musch MW, Tao Y, De Simone C, et al.
Probiotics inhibit nuclear factor-kappaB and induce heat shock proteins
in colonic epithelial cells through proteasome inhibition.
Gastroenterology 2004; 127: 1474-87.

Piazzon MC, Naya-Catala F, Perera E, Palenzuela O, Sitja-Bobadilla A,
Perez-Sanchez J. Genetic selection for growth drives differences in
intestinal microbiota composition and parasite disease resistance in
gilthead sea bream. Microbiome 2020; 8: 168.

Polianciuc SI, Gurzau AE, Kiss B, Stefan MG, Loghin F. Antibiotics in
the environment: causes and consequences. Med Pharm Rep 2020; 93:
231-240.

Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A
human gut microbial gene catalogue established by metagenomic
sequencing. Nature 2010; 464: 59-65.

Rosado D, Xavier R, Severino R, Tavares F, Cable J, Perez-Losada M.
Effects of disease, antibiotic treatment and recovery trajectory on the
microbiome of farmed seabass (\emph{Dicentrarchus labrax} ). Sci Rep
2019; 9: 18946.

Rosshart SP, Vassallo BG, Angeletti D, Hutchinson DS, Morgan AP, Takeda
K, et al. Wild Mouse Gut Microbiota Promotes Host Fitness and Improves
Disease Resistance. Cell 2017; 171: 1015-1028 e13.

Ruiz VE, Battaglia T, Kurtz ZD, Bijnens L, Ou A, Engstrand I, et al. A
single early-in-life macrolide course has lasting effects on murine
microbial network topology and immunity. Nat Commun 2017; 8: 518.

Rutten NB, Gorissen DM, Eck A, Niers LE, Vlieger AM, Besseling-van der
Vaart I, et al. Long Term Development of Gut Microbiota Composition in
Atopic Children: Impact of Probiotics. PLoS One 2015; 10: e0137681.

Ryu AH, Eckalbar WL, Kreimer A, Yosef N, Ahituv N. Use antibiotics in
cell culture with caution: genome-wide identification of
antibiotic-induced changes in gene expression and regulation. Sci Rep
2017; 7: 7533.

Sadeghi J, Chaganti SR, Shahraki AH, Heath DD. Microbial community and
abiotic effects on aquatic bacterial communities in north temperate
lakes. Sci Total Environ 2021; 781: 146771.

Sereno D, Muller WEG, Bausen M, Elkhooly TA, Markl JS, Wiens M. An
evolutionary perspective on the role of mesencephalic astrocyte-derived
neurotrophic factor (MANF): At the crossroads of poriferan innate immune
and apoptotic pathways. Biochem Biophys Rep 2017; 11: 161-173.

Shahraki AH, Chaganti SR, Heath D. Assessing high-throughput
environmental DNA extraction methods for meta-barcode characterization
of aquatic microbial communities. J Water Health 2019; 17: 37-49.

Sommer F, Anderson JM, Bharti R, Raes J, Rosenstiel P. The resilience of
the intestinal microbiota influences health and disease. Nat Rev
Microbiol 2017; 15: 630-638.

Stevick RJ, Sohn S, Modak TH, Nelson DR, Rowley DC, Tammi K, et al.
Bacterial Community Dynamics in an Oyster Hatchery in Response to
Probiotic Treatment. Front Microbiol 2019; 10: 1060.

Stoddard M, Huang C, Enyedi B, Niethammer P. Live imaging of leukocyte
recruitment in a zebrafish model of chemical liver injury. Sci Rep 2019;
9: 28.

Surana NK, Kasper DL. Moving beyond microbiome-wide associations to
causal microbe identification. Nature 2017; 552: 244-247.

Tabassum T, Mahamud ASU, Acharjee TK, Hassan R, Snigdha TA, Islam T, et
al. Probiotic supplementations improve growth, water quality,
hematology, gut microbiota and intestinal morphology of Nile tilapia.
Aquaculture Reports 2021; 21: 100972.

Talwar C, Nagar S, Lal R, Negi RK. Fish Gut Microbiome: Current
Approaches and Future Perspectives. Indian J Microbiol 2018; 58:
397-414.

Team RC. R: A language and environment for statistical computing. 2013.

Thaiss CA, Levy M, Korem T, Dohnalova L, Shapiro H, Jaitin DA, et al.
Microbiota Diurnal Rhythmicity Programs Host Transcriptome Oscillations.
Cell 2016; 167: 1495-1510 e12.

Toews SD, Wellband KW, Dixon B, Heath DD. Variation in juvenile Chinook
salmon (Oncorhynchus tshawytscha) transcription profiles among and
within eight population crosses from British Columbia, Canada. Mol Ecol
2019; 28: 1890-1903.

Tomosada Y, Villena J, Murata K, Chiba E, Shimazu T, Aso H, et al.
Immunoregulatory effect of bifidobacteria strains in porcine intestinal
epithelial cells through modulation of ubiquitin-editing enzyme A20
expression. PLoS One 2013; 8: e59259.

Uren Webster TM, Consuegra S, Hitchings M, Garcia de Leaniz C.
Interpopulation Variation in the Atlantic Salmon Microbiome Reflects
Environmental and Genetic Diversity. Appl Environ Microbiol 2018; 84.

Willms RJ, Jones LO, Hocking JC, Foley E. A cell atlas of
microbe-responsive processes in the zebrafish intestine. Cell Reports.
2022 Feb 1;38(5):110311.

Wu ZB, Gatesoupe FJ, Li TT, Wang XH, Zhang QQ, Feng DY, et al.
Significant improvement of intestinal microbiota of gibel carp
(Carassius auratus gibelio) after traditional Chinese medicine feeding.
J Appl Microbiol 2018; 124: 829-841.

Xia Y, Sun J. Hypothesis Testing and Statistical Analysis of Microbiome.
Genes Dis 2017; 4: 138-148.

Xiao S, Fei N, Pang X, Shen J, Wang L, Zhang B, et al. A gut
microbiota-targeted dietary intervention for amelioration of chronic
inflammation underlying metabolic syndrome. FEMS Microbiol Ecol 2014;
87: 357-67.

Yao Z, Yang K, Huang L, Huang X, Qiuqian L, Wang K, et al. Disease
outbreak accompanies the dispersive structure of shrimp gut bacterial
community with a simple core microbiota. AMB Express 2018; 8: 120.

Zarrinpar A, Chaix A, Xu ZZ, Chang MW, Marotz CA, Saghatelian A, et al.
Antibiotic-induced microbiome depletion alters metabolic homeostasis by
affecting gut signaling and colonic metabolism. Nat Commun 2018; 9:
2872.

Zhang Z, Li D, Xu W, Tang R, Li L. Microbiome of Co-cultured Fish
Exhibits Host Selection and Niche Differentiation at the Organ Scale.
Front Microbiol 2019; 10: 2576.

Table 1. Multivariate statistical testing (PERMANOVA) of effects of
treatment, dams, and sires (nested within dams) on microbial community
beta diversity (Bray-Curtis dissimilarity matrix).\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Source & df & SS & MS & Pseudo-F & P(perm)\tabularnewline
\midrule
\endhead
Treatment & 2 & 41246 & 20623 & 6.1 & \textbf{0.001}\tabularnewline
Dams & 5 & 23505 & 4700 & 1.1 & 0.22\tabularnewline
Sires (Dams) & 6 & 24259 & 4043 & 1.3 & 0.06\tabularnewline
Res & 151 & 4.6 & 3107.6 & - & -\tabularnewline
Total & 186 & 6.5 & - & - & -\tabularnewline
\bottomrule
\end{longtable}

Table 2. Comparison of gene expression levels and differentially
expressed gene distributions between the treatment groups
(\textbar{}log2 Fold Change\textbar{} \textgreater{} 1 and FDR P value
\textless{} 0.05).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Genes}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Gene name}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Gene abbreviation}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{Base Mean}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{log2 FD}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{pvalue}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{padj}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Antibiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Antibiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Antibiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Antibiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Antibiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Antibiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Antibiotic}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112234113\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
transmembrane protein 220-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{tmem220}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
40.0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.68\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.09E-08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
ubr4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
ubiquitin protein ligase E3 component n-recognin 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{ubr4}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
149.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-2.75\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.55E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.005\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112248188\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
annexin A1-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{anxa1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
123.0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.56\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.34E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.005\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112241821\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
kinase D-interacting substrate of 220 kDa B-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{kidins220}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
558.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-1.12\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.35E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.007\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112241820\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
NTPase KAP family P-loop domain-containing protein 1-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{nkpd1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
422.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-1.36\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.20E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.007\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112255867\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
neuronal acetylcholine receptor subunit alpha-3-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{chrna3}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
88.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.73E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.008\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112220969\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
macrophage-stimulating protein receptor-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{mst1r}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
47.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-3.38\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.10E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.008\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112248679\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
golgin subfamily A member 7-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{golga7}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-6.86\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.97E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.008\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112245911\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{vacuolar protein sorting-associated protein 33A}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{vps33a}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
57.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.09\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.59E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.009\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112216100\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{cyclin-dependent kinase 11B}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{cdk11b}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
32.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-4.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.38E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.03\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112231356\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{free fatty acid receptor 2-like}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{ffar2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
21.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-3.66\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.05E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.009\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112259970\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
vacuolar protein-sorting-associated protein 25-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{vps25}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-3.52\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.82E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.009\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112258258\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
~septin-8-A\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{SEPTIN8}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
42.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.84\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.24E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.008\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
xrra1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
X-ray radiation resistance associated 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{xrra1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2201.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.86E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.011\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112246816\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
HCLS1 binding protein 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{hs1bp3}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
160.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-4.37\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.66E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.011\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
nsrp1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
nuclear speckle splicing regulatory protein 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{nsrp1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
21.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-3.91\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.33E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.011\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112239977*\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
apoptosis inducing factor mitochondria associated 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{aifm3}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
14.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.00\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.54E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.011\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112232613\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
peroxisomal biogenesis factor 14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{pex14}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
17.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-4.31\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7.21E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.011\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
cfap58\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
cilia and flagella associated protein 58\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{cfap58}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
302.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.73\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.04E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.014\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112218626\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
WAS protein family homolog 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{wash1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
58.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.22\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.11E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.014\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112215983\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
natriuretic peptide B\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{nppb}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
34.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.61\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.06E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.014\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112261371\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
echinoidin\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
echinoidin\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
58.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.18E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.014\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112245791\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
multidrug resistance-associated protein 7-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{mrp7}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
28.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-4.01\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.57E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.014\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112234485\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
uncharacterized\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
uncharacterized\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
192.0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-4.76\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.23E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.015\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112243336\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
SET domain containing 2, histone lysine methyltransferase\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{setd2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
86.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-1.29\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.37E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.016\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112251319\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
phosphatase and actin regulator 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{phactr1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
23.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-3.97\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.82E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.030\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112265673\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
serine protease 16\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{prss16}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
41.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.69\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.34E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.043\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112249106\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
piezo-type mechanosensitive ion channel component 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{piezo1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
41.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.69\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.34E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.043\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112232636\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
ER degradation enhancer, mannosidase alpha-like 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{edem2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
29.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.52\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.83E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.047\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Probiotics}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Probiotics}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Probiotics}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Probiotics}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Probiotics}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Probiotics}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Control Vs Probiotics}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112242158\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Sorting nexin-10A\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{snx10b}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7818.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.24\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.94E-10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112232343\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Heat shock factor-binding protein 1-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{hsbp1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
131.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-6.57\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.91E-08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112245911\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Vacuolar protein sorting-associated protein 33A\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{vps33a}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
57.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.40\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.37E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112246837\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
COMM domain containing 10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{commd10}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
31.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.88\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.12E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112241820\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
NTPase KAP family P-loop domain-containing protein 1-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{nkpd1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
422.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-1.35\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.06E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112220855\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
COP9 signalosome complex subunit 6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{cops6}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
138.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.74\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.03E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112218768\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
WEE2 oocyte meiosis inhibiting kinase\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{wee2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
353.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.15\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.19E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.003\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112253929\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
homeobox protein PKNOX1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{pknox1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
18.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.02\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
8.06E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.003\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112234113\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
transmembrane protein 220-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{tmem220}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
40.0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.60\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.73E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.004\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
prmt3*\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
protein arginine methyltransferase 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{prmt3}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
138.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.25\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.34E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.007\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112255055\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LSM3 homolog, U6 small nuclear RNA and mRNA degradation associated\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{lsm3}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
114.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-7.27\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.00001\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112264620\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
THO complex subunit 4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{alyref}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
138.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.25\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.000003\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.007\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112248608\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
B-cell linker protein-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{blnk}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
37.6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-4.01\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.57E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.007\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112246700\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
cytochrome b-c1 complex subunit 6, mitochondrial-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{uqcrh}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
97.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.75\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
9.35E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.015\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112219606\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
transmembrane protein 38B\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{tmem38b}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
114.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-7.27\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.11E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112249934\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
syndecan-1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{sdc1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
71.6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-3.41\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.27E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112214559\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
lysophospholipid acyltransferase\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{lpcat4}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
42.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.46\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.27E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112217687\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
R-spondin-3-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{rspo3}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
90.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.38\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.33E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.028\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112264739\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
proteasome subunit beta type-4-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{psmb4}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
97.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-4.25\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.29E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.034\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112261503\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
sodium-dependent multivitamin transporter\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{slc5a6}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
291.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.80\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.44E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.035\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112263481\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
CD151 antigen\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{cd151}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
18.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.89E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.037\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112232610\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
phosphogluconate dehydrogenase\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{pgd}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
155.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-3.97\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.62E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.040\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
rabep2*\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
rab GTPase-binding effector protein 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{rabep2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
46.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-3.05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.56E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.040\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
sidt2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
SID1 transmembrane family, member 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{sidt2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
19.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.66\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.79E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.040\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112248204\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
low-density lipoprotein receptor-related protein 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{lrp2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
12.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.16\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.71E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.045\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112242147\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
uncharacterized\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
uncharacterized\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
291.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.44E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.034\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112254714\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
ras-related protein Rab-34-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
rab34b\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
558.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-1.03\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.59E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.005\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112234485\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
uncharacterized\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
uncharacterized\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
191.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.59\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7.11E-08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.0007\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112241821\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
kinase D-interacting substrate of 220 kDa B-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{kidins220}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
558.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-1.03\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.59E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.005\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Antibiotic Vs Probiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Antibiotic Vs Probiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Antibiotic Vs Probiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Antibiotic Vs Probiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Antibiotic Vs Probiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Antibiotic Vs Probiotic}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textbf{Antibiotic Vs Probiotic}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112216146\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
FERM domain containing 4Bb\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{frmd4bb}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
24.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.93\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.97E-08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.001\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112232343\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
heat shock factor-binding protein 1-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{hsbp1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
131.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-6.47\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.22E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112220855\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
COP9 signalosome complex subunit 6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{cops6}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
138.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-6.27\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.05E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112248188\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
annexin A1-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{anxa1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
123.0\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.74\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.91E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112265459\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
arsenite methyltransferase-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{as3mt}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
57.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-4.33\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.39E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112232636\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
ER degradation enhancer, mannosidase alpha-like 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{edem2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
29.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.67\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.23E-07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.002\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112218768\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
WEE2 oocyte meiosis inhibiting kinase\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{wee2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
353.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.19\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.16E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.004\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112232613\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
peroxisomal biogenesis factor 14\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{pex14}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
17.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.82\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.14E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.004\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112252883\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
transient receptor potential cation channel subfamily V member 5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{trpv5}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
160.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-1.01\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.43E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.005\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
LOC112222087*\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\section*{mesencephalic astrocyte-derived neurotrophic
factor}

{\label{mesencephalic-astrocyte-derived-neurotrophic-factor}}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{manf}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
43.9\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
-2.96\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1.98E-06\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
0.006\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112246837\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
COMM domain containing 10\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{commd10}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
31.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-5.39\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.10E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.010\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
nsrp1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
nuclear speckle splicing regulatory protein 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{nsrp1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
21.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.00\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.35E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.010\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112239977\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
apoptosis inducing factor mitochondria associated 3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{aifm3}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
14.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.17\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.30E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.010\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112249580\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
transcription factor 12\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{tcf12}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
40.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-1.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.96754E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.01\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112220311\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Occludin a\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{ocln}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2096.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
7.94E-06\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.016\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112231356\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
free fatty acid receptor 2-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{ffar2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
21.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.47\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.10E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.020\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112264739\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
proteasome subunit beta type-4-like\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{psmb4}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
97.9\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-4.59\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.06E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.033\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112237710\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
~dispanin subfamily A member 2b\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{dspa2b}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
780.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.89\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.69E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.035\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112262831\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
polyubiquitin\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{ub}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
687.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.32\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.65E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.035\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112245658\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
trafficking kinesin-binding protein 1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{trak1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
36.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.52\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.38E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.035\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112215983\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
natriuretic peptide B\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{nppb}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
34.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-1.54\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.60E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.035\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112218750\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
protein mono-ADP-ribosyltransferase\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{parp12}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
39.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.13\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.94E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.036\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112225266\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
interferon alpha/beta receptor 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{ifnar2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
158.6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.07\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.60E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.043\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112217407\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
protein PML\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{pml}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
442.7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.36\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.40E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.048\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
ano7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
anoctamin 7\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{ano7}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
62.4\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-3.31\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.93E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.049\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112245441\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
uncharacterized\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
uncharacterized\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
20.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.37\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.77E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.049\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
LOC112225425\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
T cell differentiation protein 2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{mal2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
155.6\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
6.50E-05\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.049\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

* Indicate that these genes were also significant in our OpenArray
high-throughput qRT-PCR analysis.

Table 3: LMM model of PC1-9 (Eigenvalue \textgreater{} 1, and \%
variance explained \textgreater{} 2\%) on the qPCR data for the 48
selected genes test for the effect of treatment.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
PCA axes & Type III Sum of Squares & df & Mean Square & F &
Sig.\tabularnewline
\midrule
\endhead
PC1 & 64.65 & 2 & 32.32 & 1.75 & 0.17\tabularnewline
PC2 & 5.19 & 2 & 2.59 & 0.44 & 0.64\tabularnewline
PC3 & 7.91 & 2 & 3.95 & 1.42 & 0.285\tabularnewline
PC4 & 27.45 & 2 & 13.72 & 9.59 & 0.0002
***\textsuperscript{a}\tabularnewline
PC5 & 31.93 & 2 & 15.96 & 14.44 & 1.038e-05 ***\tabularnewline
PC6 & 12.64 & 2 & 6.32 & 5.99 & 0.0097 **\tabularnewline
PC7 & 12.19 & 2 & 6.09 & 4.44 & 0.01 *\tabularnewline
PC8 & 3.16 & 2 & 1.58 & 1.64 & 0.23\tabularnewline
PC9 & 10.62 & 2 & 5.31 & 5.93 & 0.003 **\tabularnewline
\bottomrule
\end{longtable}

Signif. codes: 0.01 \textless{} P [?] 0.05*, 0.001 \textless{} P [?] 0.01**,
P [?] 0.001***

Table 4: Results of the LMM analysis for significance levels for
treatment, dam, sire (nested in dam), tank (nested in sire nested in
dam) effects for each. Body weight, dam, treatment\selectlanguage{ngerman}×dam, treatment×sire
effects were nonsignificant before FDR correction and were removed from
the model. Treatment was considered as fixed effects, with body weight,
dam, and sire effects as random effects. The dependent variable was log
transformed \selectlanguage{greek}Δ\selectlanguage{english}C\textsubscript{T}.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
Genes & Probiotics vs Control & Antibiotics VS Control & Treatment &
Sire (nested within dam) & Tank (sire(dam))\tabularnewline
\midrule
\endhead
\emph{uqcrh} & 0.9 & 0.14 & 0.11 & 0.036 * & 0.32\tabularnewline
\emph{sidt2} & 0.07 & 0.17 & 0.08 & 0.09 & 0.43\tabularnewline
\emph{rabep2} & 0.05*\textsuperscript{a} & 0.60 & 0.015* & 0.78 &
0.45\tabularnewline
\emph{piezo1} & 0.06 & 0.25 & 0.18 & 1.00 & 1.00\tabularnewline
\emph{ffar2} & 0.71 & 0.14 & 0.09 & 0.83 & 0.89\tabularnewline
\emph{trpv5} & 0.52 & 0.88 & 0.62 & 0.33 & 0.98\tabularnewline
\emph{aifm3} & 0.04* & 0.89 & \textbf{0.002} **\textsuperscript{b} &
0.009 ** & 0.99\tabularnewline
\emph{ub} & 0.78 & 0.14 & 0.05* & 0.62 & 1.00\tabularnewline
\emph{dspa2b} & 0.4 & 0.20 & 0.06 & 0.07 & 1.00\tabularnewline
\emph{pml} & 0.58 & 0.39 & 0.60 & 0.01 * & 0.99\tabularnewline
\emph{nkpd1} & 0.27 & 0.33 & 0.47 & 0.04 * & 1.00\tabularnewline
\emph{tmem38b} & 0.41 & 0.02* & 0.07 & 0.13 & 0.98\tabularnewline
\emph{pknox1} & 0.63 & 0.44 & 0.43 & 1.00 & 1.00\tabularnewline
\emph{manf} & \textbf{0.0001}*** & 0.87 & \textbf{5.6e-06} *** & 1.00 &
0.14\tabularnewline
\emph{ifitm3} & 0.44 & 0.35 & 0.25 & 0.17 & 0.49\tabularnewline
\emph{ifnar2} & 0.4 & 0.80 & 0.77 & 0.02 * & 0.99\tabularnewline
\emph{anxa1} & 0.57 & 0.31 & 0.19 & 0.13 & 0.02*\tabularnewline
\emph{prmt3} & \textbf{0.0027}** & 0.16 & \textbf{0.001} *** & 0.37 &
1.00\tabularnewline
\bottomrule
\end{longtable}

a. Significant codes: 0.01 \textless{} \emph{P}  [?] 0.05*,
0.001 \textless{} P [?] 0.01**, \emph{P}  [?] 0.001***

b. Significant bold \emph{P} value indicates significant after
\emph{P}value correction.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1: Experimental design and the number of samples collected for
each experiment.

Figure 2: Panel A: Pie charts showing BC diversity from tank water
samples for the three treatments with the relative abundances of the
most abundant bacteria families (15 families). Other less abundant taxa
(here less than 1\%) were merged and renamed as ``Others'' in the pie
chart. Panel B: Scatterplot of the first two axes from the PCoA of the
tank water BC where the treated fish were held. Treatment is shown by
colour with the 95\% ellipses.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3. Panel (A) Pie charts showing BC diversity from Chinook salmon
hindgut samples for the three treatments with the relative abundances of
the most abundant bacteria families (15 families). Other less abundant
taxa (here less than 1\%) were merged and renamed as ``Others'' in the
pie chart. Panel (B) Scatterplot of the first two axes from the PCoA of
the Chinook salmon gut BC. Treatment is shown by colour with the 95\%
ellipses.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

Figure 4. Scatterplot of the first two axes from the PCoA of the Chinook
salmon gut as well as water BC. Sample type is shown by colour with the
95\% ellipses.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

Figure 5. Hierarchical clustering of the 7 core bacterial taxa and
association with gene expression. Columns correspond to the 7 core
bacterial taxa; rows correspond to 9 selected differentially expressed
genes. Red and blue denote positive and negative associations,
respectively. The intensity of the colors represents the degree of
association between the genus abundance and bacterial taxa without
considering treatment effect. Numbers in each square represent
significant \emph{P} -values (unadjusted) with treatment and Family ID
effects included in our model.

\selectlanguage{english}
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