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\begin{document}

\title{Breeding microbes: How genetic diversity shapes gut microbial
communities in the critically endangered European mink (Mustela
lutreola)}



\author[1]{Pauline Van Leeuwen}%
\author[1]{Albrecht  Schulte-Hostedde}%
\author[2]{Christine Fournier-Chambrillon}%
\author[3]{M.Carmen  Aranda}%
\author[4]{Laurie Berthomieu}%
\author[2]{Pascal Fournier}%
\author[5]{Johan Michaux}%
\affil[1]{Laurentian University}%
\affil[2]{Groupe de Recherche et d'Etudes pour la Gestion de l'Environnement (GREGE)}%
\affil[3]{Fundación para la Investigación en Etología y Biodiversidad}%
\affil[4]{Zoodyssée}%
\affil[5]{Université de Liège}%


\vspace{-1em}



  
  \date{May 21, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Host's fitness can be affected by its genotype and gut microbiota,
defined as the microbes living in the host's intestinal tract. This
study explored how the genetic diversity of the host influences its
bacterial communities in the context of captive breeding programs, for
the critically endangered European mink (Mustela lutreola). As stated by
the ecosystem on a leash model, mechanisms such as inbreeding depression
may lead to changes in immunomodulation and will therefore induce
modifications of the gut microbiota. We investigated variation in the
gut bacteria through 16S rRNA metabarcoding, related to the genetic
diversity of European mink held in captivity in two breeding centers
representing separate breeding stocks originating from the western and
eastern populations. The genetic diversity of the host was assessed
through diversity analysis of the adaptive MHC class I and II genes as
well as neutral microsatellite markers. Results indicate lower diversity
in neutral and MHC class I genes for the western population, and the
opposite for MHC class II. A lower MHC class II gene variability led to
an increase in microbial phylogenetic diversity and in abundance
depending on the presence of specific MHC-II motifs. This shows the
importance of integrating both neutral and adaptive markers when
investigating genetic variation in the context of ex situ conservation,
as well as gut microbial community assessment. We advocate for more
natural mating systems in captive breeding program to foster genetic
diversity as a whole to mitigate the effects of genetic drift on those
small, isolated populations.%
\end{abstract}%



\sloppy


\textbf{Title:} Breeding microbes: How genetic diversity shapes gut
microbial communities in the critically endangered European mink
(\emph{Mustela lutreola} )

\textbf{Authors and affiliations:} Pauline van
Leeuwen\textsuperscript{1,2}, Albrecht I.
Schulte-Hostedde\textsuperscript{1}, Christine
Fournier-Chambrillon\textsuperscript{3}, M. Carmen
Aranda\textsuperscript{4}, Laurie Berthomieu\textsuperscript{5}, Pascal
Fournier\textsuperscript{3,} Johan R. Michaux\textsuperscript{2}

\textsuperscript{1} Department of Biology, Laurentian University,
Sudbury, ON, Canada;

\textsuperscript{2} Conservation Genetics Laboratory, University of
Li\selectlanguage{ngerman}ège, Liège, Belgium;

\textsuperscript{3} Groupe de Recherche et d'Etude pour la Gestion de
l'Environnement, Villandraut, France

\textsuperscript{4} Fundación para la Investigación en Etología y
Biodiversidad, Madrid, Spain

\textsuperscript{5} Zoodyssée, Villiers-en-Bois, France

Corresponding author: Pauline van Leeuwen pvan\_leeuwen@laurentian.ca

Running title: Gut bacteria \& genetics of the European mink

\textbf{Abstract} :

Host's fitness can be affected by its genotype and gut microbiota,
defined as the microbes living in the host's intestinal tract. This
study explored how the genetic diversity of the host influences its
bacterial communities in the context of captive breeding programs, for
the critically endangered European mink (\emph{Mustela lutreola} ). As
stated by the ecosystem on a leash model, mechanisms such as inbreeding
depression may lead to changes in immunomodulation and will therefore
induce modifications of the gut microbiota. We investigated variation in
the gut bacteria through 16S rRNA metabarcoding, related to the genetic
diversity of European mink held in captivity in two breeding centers
representing separate breeding stocks originating from the western and
eastern populations. The genetic diversity of the host was assessed
through diversity analysis of the adaptive MHC class I and II genes as
well as neutral microsatellite markers. Results indicate lower diversity
in neutral and MHC class I genes for the western population, and the
opposite for MHC class II. A lower MHC class II gene variability led to
an increase in microbial phylogenetic diversity and in abundance
depending on the presence of specific MHC-II motifs. This shows the
importance of integrating both neutral and adaptive markers when
investigating genetic variation in the context of \emph{ex
situ}conservation, as well as gut microbial community assessment. We
advocate for more natural mating systems in captive breeding program to
foster genetic diversity as a whole to mitigate the effects of genetic
drift on those small, isolated populations.

\textbf{Keywords} : Mustela lutreola, microbiota, MHC, immunogenetics,
breeding program, genetic diversity, captivity

\textbf{Introduction}

More than 5,800 animal species to date are endangered, as the Earth
experiences a mass extinction event (Ceballos et al., 2015).
Conservationists have multiple factors to consider in order to arrest
population declines. Extrinsic of population declines include habitat
loss and degradation, over-exploitation, emerging infectious diseases,
invasive species, and climate change (\selectlanguage{english}Butchart et al. 2010). Intrinsic
drivers of extinction, such as genetic factors, also play a key role for
population viability, especially when species are reduced to small,
isolated populations that can be negatively affected by genetic load
(Hedrick, 2001). In this scenario, finding a suitable mate is
challenging and reproduction with related individuals can occur, leading
to inbreeding depression. Inbreeding has largely been documented in
small populations in the wild (Hedrick, 2001; reviewed in Spurgin \&
Gage, 2019), impacting individual and population fitness through the
fixation of detrimental alleles. An increase of detrimental alleles in
endangered species increases their susceptibility to extrinsic
ecological drivers of extinction (Frankham, 2005). One \emph{ex
situ}conservation tool used to mitigate the decrease of genetic
diversity in endangered species are Captive Breeding Programs (CBPs).
Captive populations of endangered species have the difficult goal of
ensuring the survival of stable, self-sustaining populations for later
reintroduction into the native habitat (Mallinson, 1995). A key
challenge of CBPs is to maintain genetic diversity and avoid inbreeding
depression with a small number of founders (Bouman, 1977; Ralls et al.,
1979).

The majority of captive breeding schemes rely on studbooks that document
pedigree information within the CBPs. While studbooks can be useful to
minimize inbreeding effects (Pelletier et al., 2009; Witzenberger \&
Hochkirch, 2011), information from pedigrees can be flawed in some
captive populations (Bowling et al., 2003; Marshall et al., 1999; Signer
et al., 1994). Molecular genetic analyses can provide more insights into
the relationships within captive populations and their genetic
structure. Recently, genetic studies of endangered species have
increased, using highly variable loci non-coding for fitness traits such
as microsatellite markers (Witzenberger \& Hochkirch, 2011).
Microsatellites are known to be highly informative for small populations
that have reached bottleneck events and are considered a tool to measure
neutral genetic variation, and generally represent the extent and
pattern of molecular variation within a population (Selkoe \& Toonen,
2006). However, both empirical and simulated data indicate \selectlanguage{english}that
patterns of variation and divergence in adaptive traits are not always
associated with concomitant variation in neutral markers (Hedrick, 2001;
Larson, 2012; Reed \& Frankham, 2001), and some conservation biologists
advocate for genetic diversity analysis for adaptive variation in CBPs
(Hughes, 1991; Sommer, 2005). One targeted adaptive region is the Major
Histocompatibility Complex (MHC) because its genes play a crucial role
in the adaptive immune system. \selectlanguage{english}Historical events such as bottlenecks
and founder effects, but also constraints of the mating system, such as
limited sexual selection in CBPs (Schulte-Hostedde and Mastromonaco
2015), can be reflected in low numbers of MHC alleles (Schad et al.,
2004; Hapke et al., 2004). However, in some free-ranging populations,
genetic variation at the MHC might persist due to balancing selection,
through heterozygote and/or rare allele fitness advantage, despite low
levels of variability shown by neutral markers (Jarvi et al., 2004; Rico
et al., 2016). These studies support the importance of balancing
selection as a mechanism to maintain variation in small populations, and
the difficulty of using neutral markers as surrogates for variation in
fitness-related loci. Furthermore, MHC genes, particularly of class I,
contribute to participate in pheromone recognition, playing a role in
sexual selection as well (Penn, 2002).

MHC genes have a crucial role in adaptive immunity in jawed vertebrates,
by encoding proteins that bind peptide antigens and present them at the
cell surface to lymphocytes for their activation (Ujvari \& Belov,
2011). T and B lymphocytes are known to interact directly with gut
microbial communities to prevent invasion, pathogenesis, and detrimental
immune response towards commensals (Ost \& Round, 2018). T cells react
to foreign molecules via cell co-receptors: glycoproteins CD8 and CD4
encoded by MHC class II and class I genes, respectively (Penn \& Potts
1999). MHC-I molecules are present on all host nucleated cells and
antigen presenting cells, and are known to act at the intracellular
level, while MHC-II molecules are strictly found on antigen presenting
cells and target extracellular microbes (Ost \& Round, 2018). MHC
presentation of antigens to T cells is thus the basis for initiating
antigen specificity and, immune responses are considered specific to the
organisms they target.

MHC genes are considered one of the most diverse loci in jawed
vertebrates and good candidates for genetic diversity analysis in
endangered species (Hughes, 1991). High genetic diversity in these loci
could allow targeting more combinations of gut microbes, reflected in
variable immunity or tolerance among individuals through rare allele and
heterozygous advantage in balancing selection. Bolnick et al. (2014)
examined the role of MHC-II motifs (amino acid sequences) in gut
microbial community variation in sticklebacks \selectlanguage{english}(\emph{Gasterosteus
aculeatus} ) and found that common MHC motifs were linked to increases
in microbial abundance and diversity, and rare motifs had the opposite
impacts. Similarly, the microbiota was less phylogenetically diverse in
individuals with high MHC-II diversity in the plumage of blue petrels
(\selectlanguage{english}\emph{Halobaena caerulea} , Leclaire et al., 2018), the gut of
laboratory mouse strains (\selectlanguage{english}BALB/c, Khan et al., 2019), and the fur
microbiota of fur seals (\selectlanguage{english}\emph{Arctocephalus gazella} , Grosser et al.,
2019). However, no study to date has investigated the MHC-gut microbiota
relationships in endangered species under CBPs.

While the host environment has a strong impact on its gut microbial
community, the genetics and biology of the host should also be taken
into account to fully understand the complex dynamics that occur in this
system (Koskella et al., 2017; Spor et al., 2011). This is especially
true when considering the gut microbiota of endangered species, where a
small number of founders in CBPs are likely to experience overall low
genetic variation and diversity. Transition from natural to captive
settings for breeding can become more challenging in these conditions
when considering host-associated microbes (Trevelline et al., 2019; West
et al., 2019). Within this context, Foster et al. (2017) proposed a
theoretical framework known as the leash model, which posits that the
host is under strong selection to evolve mechanisms to keep the
microbiota under control, or ``on a leash''. The presence of a
genetically diverse microbiota leads to the dominance of the fastest
growing microbes instead of the microbes that are most beneficial to the
host (Foster et al., 2017). The targeting of microbial taxa to either
promote or limit their proliferation could thus be beneficial to the
host, through its adaptive immune response. We therefore hypothesize
that less host control, expressed by more genetically diverse gut
microbes, should happen in individuals with reduced genetic diversity in
both neutral and adaptive markers. To test this hypothesis, we
investigated the genetic diversity and gut microbial community
assemblages in the critically endangered European mink (\emph{Mustela
lutreola} ).

The European mink (E-mink) is a semi-aquatic carnivore from
the\emph{Mustelidae} family. Once widespread throughout Europe, it was
evaluated as ``critically endangered'' in 2011 (Maran et al., 2016).
There have been drastic declines in population and range, historically
due to overexploitation and still today driven by habitat loss,
degradation and fragmentation, road collisions, and the impacts of the
alien American mink (\emph{Mustela vison} ). E-mink populations are now
restricted to enclaves in western France and northern Spain (referred as
the western population), the delta of the Danube in Romania, and Ukraine
and Russia (referred as the eastern population, Maran et al., 2016), the
latter being the focal origin of a captive breeding effort in Estonia
with successful reintroduced populations on Hiiumaa Island.

Two major studies have documented the genetic diversity of the
free-ranging E-mink populations (Michaux et al., 2005; Cabria et al.,
2015). The western population was characterized by a single
mitochondrial DNA haplotype against 17 haplotypes in the overall eastern
population. Western E-mink had also a much lower microsatellite genetic
diversity and allelic richness compared to the eastern population. The
authors concluded that the western free-ranging population reached a
recent bottleneck, and potentially inbreeding depression due to
geographic isolation. However, no proof of fitness reduction in this
population through inbreeding has been reported as of yet (Carbonell et
al., 2015). \selectlanguage{english}The antigen-binding site, encoded by exon 2 of the DRB MHC
class II gene, was also investigated in the eastern captive population
by Becker et al. (2009). They detected nine alleles within the 20
individuals investigated, estimating low to moderate variability when
comparing to other endangered species in similar situations to the
E-mink. However, no comparison is yet available for the captive western
population.

Both populations are currently in a CBP. The majority of the eastern
breeding stock is held at the Tallinn Zoo and extending to other zoos in
Europe such as Zoodyss\selectlanguage{ngerman}ée in France and is only composed of captive-born
individuals for over thirty of generations (Maran, pers. comm., 2021).
This stock is managed under an EAZA Ex situ Program (EEP). On the other
hand, the western breeding stock is held in facilities in Spain. Due to
the recent implementation of a CBP in Spain, most of the breeding stock
originates from the free-ranging western population captures in Spain
within the last seven years (i.e. seven generations), and wild-born
individuals from Spain are still being introduced as founders to this
date. Those populations are considered as two distinct stocks and are
bred separately, although few cross breeds are currently being conducted
in Spanish facilities and at the Tallinn Zoo.

Both captive E-mink populations therefore offer a range of variation in
neutral and adaptive genetic diversity. Due to extreme population
variation over time and the emergence of small and isolated populations,
the E-mink provides a unique framework to study the relationship between
host genetics and gut microbial communities. Following the ecosystem on
a leash model, the aims of this study were to (i) characterize the
genetic diversity in the two captive E-mink populations with neutral and
adaptive genetic markers as well as their gut microbial communities,
(ii) examine the relationship between gut microbial diversity and
genetic diversity, and (iii) investigate if gut microbial community
structure and composition is linked to specific MHC motifs.

\textbf{Methods}

\emph{2.1 Sample collection and DNA extraction}

Samples were collected from captive live animals from two populations in
2020. Ten E-mink were sampled in captive settings at the Fundación para
la Investigación en Etología y Biodiversidad (FIEB) breeding center in
Spain {[}originated from Spanish free-ranging population (western
population){]} and fourteen E-mink from the conservation breeding center
at Zoodyssée in France {[}originated from the captive EEP population
(representing the eastern population), Table S1{]}. Two mink sampled in
the Spanish breeding center were wild-born individuals but spent at
least a year in captivity. One individual sampled in the Spanish
breeding center was the result of a crossbreed between western and
eastern mink. For MHC and microsatellite markers analysis, hair samples
were collected using sterilized tweezers from each animal during a
routine procedure. For the microbiota, fresh fecal samples were
collected in the enclosure of each animal separately using sterilized
tools and kept in 96\% ethanol tubes at 4°C until further processing. As
the E-mink's diet in captivity varies by day, samples were collected at
four occasions depending on the item fed to the animal the previous day.
The diet of the E-mink from both breeding centers relied on 3 types of
food: trout, mice and chicken.

DNA from hair samples were extracted using the \selectlanguage{english}DNeasy Blood and Tissue
Kit from Qiagen using the manufacturer's protocol. DNA extractions from
the fecal samples collected were conducted in duplicates using the
QiaAmp Mini Kit with Inhibitex (Qiagen, Germany) following the
manufacturer's instructions. Two blank extractions were made to control
for contamination during the extraction process. A mock community sample
(HM-783D, BEI resources) containing genomic DNA from 20 bacterial
strains, at concentrations ranging from 0.6 to 1400 pg/\selectlanguage{greek}μ\selectlanguage{english}l, was also
added in each library to confirm the reliability of our method.

\emph{2.2 Microsatellite analysis}

Multilocus genotypes were obtained by PCR amplification of 16 autosomal
microsatellites (Fleming et al., 1999; Cabria et al., 2007). The forward
primer of each locus was 5'-end labeled with a fluorescent dye. The
following three multiplex sets were designed: mix 1 (Mvi 114, MLUT 25,
MLUT 27, Mvis 099, Mvi 4001), mix 2 (Mvi 087, MLUT 32, MLUT 35, Mvis022,
Mvi 1341) and mix 3 (MLUT 04, MER009, Mvis075, Mvis072, MER41, MER022).
PCR and genotyping steps were carried out following Pigneur et al.
(2019). Length variation determination (alleles and genotypes) was
performed using Genemapper 4.0 (Applied Biosystems). To construct
consensus multilocus genotypes, an allele was only accepted if observed
at least twice. We thus accepted heterozygous genotypes that were
observed twice. A homozygote was accepted after three positive PCRs gave
the same single allele.

The genetic structure of both sampled captive breeding centers was
inferred using Bayesian clustering analysis with Structure 2.3 software
(Pritchard et al., 2000). We ran 10 iterations for each K value from 1
to 5 using the admixture model. A total of 10\textsuperscript{6} MCMC
repetitions were performed after a burn-in period of 20\%. The results
of the 5 iterations for each K value were summarized and averaged using
the Clumpp method (Jakobsson \& Rosenberg, 2007). The optimal number of
clusters was investigated using the \selectlanguage{greek}Δ\selectlanguage{english}K method (Evanno et al., 2005).
F-statistics (pairwise F\textsubscript{ST}, D\textsubscript{Jost} and
F\textsubscript{is}), allelic richness (Ar), the expected (He) and
observed (Ho) heterozygosity, as well as inbreeding coefficient (F) and
multilocus heterozygosity (MLH) were calculated for each defined
group/individual in R version 3.5.2 (R Development Core Team, 2008). A
R\textsubscript{ST} genetic distance estimation matrix between
individuals was generated using GenAlex 6.5 (\selectlanguage{english}Peakall \& Smouse, 2006).

\emph{2.3 Amplification, sequencing and analysis of MHC genes}

The fragment of DRB gene from exon 2 of MHC class II (Beta 1, 231 bp)
was targeted using the primers designed by Becker et al. (2009)
for\emph{Mustela lutreola} and a fragment from exon 2 (alpha 1) from MHC
class I, using the primers \selectlanguage{english}Meme-MHC-Iex2F and \selectlanguage{english}PpLAa1L250 designed by
Sin et al. (2012) for mustelids. PCRs were carried out in 25 \selectlanguage{greek}μ\selectlanguage{english}l volumes
containing 0.9 \selectlanguage{greek}μ\selectlanguage{english}l of primer mix, 5 \selectlanguage{greek}μ\selectlanguage{english}l of GoTaq reaction buffer
(Promega), 2 \selectlanguage{greek}μ\selectlanguage{english}l of MgCl\textsubscript{2},0.04 \selectlanguage{greek}μ\selectlanguage{english}l of BSA, 0.8 \selectlanguage{greek}μ\selectlanguage{english}l of
dNTPs, 0.125 \selectlanguage{greek}μ\selectlanguage{english}l of GoTaq G2 DNA polymerase (Promega, France) and 3 \selectlanguage{greek}μ\selectlanguage{english}l of
DNA. The specific protocol was used for PCR: annealing with touchdown
protocol from 65\selectlanguage{ngerman}°C to 56°C for 30 s. Amplified DNA in duplicates were
pooled after quantification using \selectlanguage{english}the Quant-iT PicoGreen dsDNA Assay
Kit (Thermo Fisher Scientific Inc., Austria). The library preparation
and sequencing were performed by Novogene (UK). Using their designated
library protocol, 2\selectlanguage{ngerman}×250 bp paired\selectlanguage{english}-end sequencing with a depth of 50,000
reads/sample for MHC genotyping and was completed using an Illumina
NovaSeq platform (Illumina Biotechnology Co., Novogene, UK).

To analyze MHC-I and MHC-II amplicon sequences, we used the three-step
pipeline AmpliSAS (Sebastian et al., 2015). Low-quality sequences with
Phred scores lower than 20 were removed and clustering was conducted
using the default parameters for Illumina sequences. Already identified
alleles of MHC-II DRB for E-mink were extracted from NCBI (Becker et
al., 2009), as well as sequences from closely related species
(\emph{Mustela putorius} and \emph{Mustela itatsi} ) for MHC-I exon 2.
If NCBI blast reveled 100\% of sequence identify between the discovered
alleles in this study and already identified one, their name was
replaced by the accession number of these sequences. For the subsequent
analysis, we focused on the amino acid translated sequences (referred as
MHC motifs) as they are in direct contact with bacteria. We measured
motif richness as the number of sequences per individual for each locus.
We calculated functional distances between individuals following the
approach described in Strandh et al. (2012). \selectlanguage{english}A maximum-likelihood tree
was constructed based on the chemical binding properties of the amino
acids, as described by five physico-chemical descriptor variables
(z-descriptors) for each amino acid, using sequences of \emph{Meles
meles} , \emph{Meles leucurus} , \emph{Meles anakuma} and \emph{Martes
zibelina} as out-group retrieved through NCBI blast (Figure S1). The
trees were used as reference from which the functional distances between
individuals were calculated using unweighted UniFrac for both genes
(Lozupone \& Knight, 2005). Following Bolnick et al. (2014), the genetic
distance between each amino acid sequences within each individual
(Faith's PD) were calculated, and further defined as motif divergence.

\emph{2.5 Statistical analysis for MHC and microsatellites markers
between populations}

For subsequent analyses, individuals were sorted according to their
population origin (western and eastern) that corresponded to structure
results (admixed individuals (q \textless{} 0.9) were excluded = 0
individuals). Non-parametric Kruskal-Wallis rank sum tests were used to
investigate neutral genetic variation with F and MLH calculated from
microsatellite markers analysis, between the two E-mink populations and
host sex. Adaptive genetic diversity and variation between E-mink
populations and individual sex was also observed using the same approach
for motif richness for both MHC genes and divergence.

\selectlanguage{english}Permutational multivariate analyses of variance (PERMANOA)
models\emph{adonis} from the vegan package were constructed with 9,999
permutations with reported F, R2, and p- values, to determine whether
there were significant differences in genetic distance matrices for
neutral and adaptive markers between E-mink population, individual sex
and birth location. Pearson's pairwise correlation tests between the
presence of MHC motifs, present in at least three individuals, were
conducted to potentially represent haplotype blocks for the two genes
investigated. The significance cutoff was set to p-value \textless{} .05
for each test. Mantel tests were also conducted between each neutral,
MHC-I and MHC-II genetic distance matrices with 1,000 permutations to
investigate correlation between each marker.

\emph{2.6 Microbiota data generation and processing}

After DNA extraction, the targeted gene for bacterial taxonomic
affiliation using broad bacterial primers of the region V4 of the 16S
rRNA gene (515F-806R, 390 bp) was amplified through PCRs. Amplification,
library preparation and sequencing were carried out in a similar manner
to MHC genotyping, with a depth of 100,000 reads/samples of two
libraries composed of 48-52 samples.

The quality controls of the demultiplexed paired-end reads were
performed through the software FastQC (Andrews, 2010). Demultiplexed
sequence reads denoising and amplicon sequence variants (ASVs) picking
steps were done with the QIIME2 tool (Bolyen et al., 2018; v. 2019.1),
using the DADA2 pipeline (Callahan et al., 2016; Callahan et al., 2017).
Samples were pooled by individuals to limit bias from diet foods prior
to rarefaction. Rarefaction was conducted at 27,000 reads/samples in
sampling depth. ASVs---or also referred to as bacterial
phylotypes---were then screened to the 97\% 16S rRNA gene full-length
reference sequences from the Silva v.132 database (Pruesse et al., 2007)
for taxonomical association using the VSEARCH classifier implemented in
QIIME2 (Bokulich et al., 2018). Sequence alignment and phylogeny
building were conducted in QIIME2 for the construction of UniFrac
distance matrices.

\selectlanguage{greek}\emph{2.7 Statistical analysis for α-diversity of gut bacteria according
to host information}\selectlanguage{english}

All statistical analyses were conducted using the phyloseq and
microbiome packages for manipulation of data. Total observed number of
bacterial taxa, Chao1, Shannon's diversity index and Faith's PD in each
sample were used as metrics to measure the \selectlanguage{greek}α\selectlanguage{english}-\selectlanguage{english}diversity of gut bacteria
between samples. Chao1 characterizes the overall phylotype richness
within a host, Shannon's diversity index takes into account richness and
abundance, and Faith's PD is an indicator of genetic diversity within a
sample. Differences in the indices according to E-mink populations, sex
and birth location of the host were analyzed using Kruskal-Wallis rank
sum tests. Linear regression models were also conducted with the
different measures of the microsatellites and MHC analysis (MLH, F for
microsatellite data, MHC motif richness and divergence) as predictors,
and microbial richness indexes as response variable. Homogeneity of
variance assumptions were tested using Levene tests and normality of the
residuals with Shapiro-Wilk tests and visual representations.

\selectlanguage{greek}\emph{2.8 Statistical analysis for β-diversity of gut bacteria between
population and differential abundance}\selectlanguage{english}

Weighted and unweighted UniFrac distance matrix between samples were
used to investigate differences in gut microbial communities between
E-mink population and host sex. These metrics take into account the
differences in phylogenetic distance and abundance of each bacterial
community between samples, pairwise, emphasizing on rare (unweighted) or
abundant microbial taxa (weighted). PERMANOVA tests were conducted in
similar conditions than PERMANOVA tests conducted on MHC matrixes.
Additionally, models were constructed with adaptive genetic variables:
motif richness and divergence from both MHC genes. To test the
hypothesis that both neutral and adaptive distances are correlated with
gut microbial composition, we employed Mantel tests between each genetic
distance and Unifrac distance.

A differential abundance analysis was conducted on the raw ASVs count
(after filtering, prior to rarefaction) that were present in more than
10\% of all the samples and that had a relative abundance of more than
5\% among all taxa. It corresponds to the core microbiota of the
dataset, represented by 1203 phylotypes. The abundance analysis was made
at the ASV level with the DESeq2 package, using a negative binomial Wald
test to test significance in contrast between each E-mink population and
each common MHC motifs that were present in at least two individuals.
Only microbial ASV with a significance level (\selectlanguage{greek}α\selectlanguage{english}) below .001 after false
discovery rate (FDR) corrections were considered using the
Benjamini--Hochberg method (Love et al., 2014). From the same core
microbiota, we also tested for Pearson correlations between microbial
genera' relative abundance per individual and continuous genetic
variables (MLH, motif richness and PD for both MHC class I and II) with
FDR corrections, for genera that were encountered at least in three
individuals.

\textbf{Results}

\emph{3.1 Genetic diversity of the European mink captive populations}

\selectlanguage{english}The microsatellite markers analysis demonstrated an overall population
allelic richness per locus of 2.69 with an average of 2.49 in the
western population, and 2.82 in the eastern population. Heterozygosity
values were lower in the western population compared to eastern
population (Table 1). Bayesian assignment recovered two genetic clusters
within our population, and no admixture pattern were detected. All
individuals clustered according to populations, corresponding to the two
different breeding facilities. The offspring with parents of each
population was assigned to the western population according to
clustering (p(K\textsubscript{western})=0.975). Multilocus
heterozygosity was slightly higher in eastern than western populations
(Kruskal-Wallis: \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=3.4761; p-value=0.0623), but the
inbreeding coefficient (F) was not (Kruskal-Wallis:
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=0.085714; p-value=0.7697). Overall, sex and birth
location had no significant effect on neutral markers' diversity and
richness. PERMANOVA on genetic distance based on the microsatellite
markers detected no variation according to E-mink population, sex and
birth location (Table 2).

Raw MHC amplicon sequencing data consisted of 3,084,478 raw reads with
an average length of 230 base pairs for MCH-I and 130 base pairs for
MHC-II. After processing, we characterized 13 MHC-I motifs (amino acid
sequences) and 6 MHC-II motifs. The average number of motifs per
individual was 5.3 and 3.08 (range: 2-9; 2-4) for MHC-I and MHC-II genes
respectively, indicating the presence of at least five and two copies
for the two regions. For the MHC-I gene, three motifs were strictly
present in the eastern E-mink, and one motif in the western population.
Comparatively, no motifs were unique to eastern E-mink for MHC-IIex2
gene, and three were strictly found in western E-mink (Figure 1).
Spearman correlation tests allowed us to detect haplotype blocks for
both genes, mostly attributed to the eastern population (with the motifs
Mulu:MHC-I*0003, Mulu:MHC-I*0008, Mulu:MHC-I*0012, Mulu:MHC-I*0013 and
Mulu:MHC-I*0015 for MHC-I and Mulu:DRB*90701, EU263553 for MHC-II) and
western E-mink (Mulu:MHC-I*0007, Mulu:MHC-I*0009 and Mulu:MHC-I*0011 for
MHC-I, KM371114\_EU263551, EU263558\_LC055119, EU263550\_EU263557 and
EU263554\_EU263552\_EU263556 for MHC-II, Figure S2 \& Table S2). Most of
the variation encountered in both genes was expressed in amino acid
residues that influence the binding of CD4 and CD8 glycoproteins
involved in antigen presentation for adaptive immunity (Figure S3).

Motif richness and divergence (Faith's PD) were significantly higher in
the western population compared to eastern E-mink for MHC-II gene
(Kruskal-Wallis: \selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=13.456, p-value=0.0002;
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=8.0614, p-value=0.0045; respectively). However, for
MHC-I, divergence was higher in eastern E-mink compared to the western
population, but not motif richness (Kruskal-Wallis:
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=5.0097, p-value=0.0252;
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=1.5456, p-value=0.2138, respectively). No changes
in motif richness nor divergence were observed according to sex for the
two genes. However, we did observe significant variation in MHC-II
richness according to birth location (Kruskal-Wallis:
\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}=10.854, p-value=0.0125), and a Dunn test with
Benjamini-Hochberg correction only detected higher motif richness for
the MHC-II gene in captive-born E-mink in Spain compared to the EEP
(Dunn: Z=-2.748, adjusted p-value=0.0358). PERMANOVA detected a
significant influence of E-mink sex for MHC-I genetic distance, as well
an influence of mink population close to the significance threshold
(Figure S5), whereas E-mink population was the only variable influenced
MHC-II composition variation (Table 2). Finally, Mantel tests showed a
positive correlation between MHC class I and neutral markers distances
(Mantel: r=0.2761, p-value=0.001).

\selectlanguage{greek}\emph{3.2 α-diversity of gut bacteria according to host information}\selectlanguage{english}

A sample of mock community containing known concentrations of genomic
DNA from 20 bacterial strains was sequenced. 19 of the 20 different
strains originally included in the sample were detected. The undetected
strain was present at the lowest concentration. Therefore, our protocol
allowed bacterial DNA detection and identification to the genus level as
long as its concentration in the DNA extract was at least 2.8 pg/\selectlanguage{greek}μ\selectlanguage{english}l, and
provided that the sequence was included in the reference database.

After reads processing, a total read count of 624,796 was obtained for
gut microbial communities in captive E-mink, with an average counts per
sample of 26,033 after rarefaction to limit sequencing depth artifacts.
A total number of 5703 ASVs -- or phylotypes -- were distinguished in
the samples. The gut microbiota of the E-mink was mostly composed of
the\emph{Firmicute} phylum (74\%), which was dominated by
the\emph{Clostridiaceae} and \emph{Peptostreptococcaceae} families,
followed by \emph{Proteobacteria} (14\%) with \emph{Enterobacteriaceae}
,\emph{Moraxellaceae} and \emph{Pseudomonadaceae} families (Figure 2).

Despite an overall observation of lower microbial richness in the
western compared to the eastern populations, no significant results were
observed in multiple microbial richness indices (Shannon index, Chao1;
Figure 3). However, we did observe slightly lower Faith's PD in western
compared to eastern individuals (\selectlanguage{greek}χ\selectlanguage{english}\textsuperscript{2}= 2.8834, p-value =
0.0895). Western females had significantly lower microbial phylogenetic
diversity compared to males (Figure 3). Despite not reaching statistical
significance (R\textsuperscript{2}=0.1626, F=0.5502, p-value=0.7633),
linear regression models with alpha diversity measures as response
variables showed negative correlations with adaptive genetic richness
measures, and particularly strong estimates for MHC-I richness (Figure
S4).

\selectlanguage{greek}\emph{3.3 β-diversity of gut bacteria between E-mink and differential
abundance}\selectlanguage{english}

\selectlanguage{english}Beta diversity analyses revealed that bacterial communities were
significantly different in composition according to E-mink population
only when considering unweighted Unifrac distances, whereas no
significant differences in microbial community composition were found
between host sexes nor birth locations (Models 4 and 6, Table 2).
Moreover, MHC-II gene richness had a small significant influence on gut
microbial composition (Model 5, Table 2). This is reflected in the
results of the PCoA, which clustered individuals with differences in
number of MHC-II motifs (Figure 4). Mantel tests also shown a
significant positive correlation between unweighted Unifrac distance and
MHC-II genetic distance (Mantel: r=0.4811, p-value=0.019), and despite
not reaching statistical significance, a negative correlation with MHC-I
genetic distance (Mantel: r=-0.0823, p-value=0.862) and close to zero
for neutral markers' distance (Mantel: r=0.0065, p-value=0.229).

We recovered several bacterial genera \selectlanguage{english}and families whose relative
abundances were significantly correlated with MHC-I and MHC-II richness
and divergence, while some marginally correlated with multilocus
heterozygosity of neutral markers (Table 3). A majority (65\%) of the
Pearson correlations appeared to be negative between bacterial genera
and genetic indexes. Differential abundance analysis in microbial
families according to presence of MHC motifs for both genes between
E-mink populations detected a significant increase in abundance for 13
families in the eastern population, for 8 in western E-mink (Figure 5).

From the differential abundance analysis, 22 phylotypes were found to be
significantly different in abundance according to MHC motifs presence.
MHC-I motifs explained the variation of 3 phylotypes, and only impacted
the \emph{Clostridiaceae} family (Clostridium sensu stricto 1). MHC-II
motifs presence was attributed to the altered the abundance of 14
microbial phylotypes and those were mostly more abundant in the eastern
E-mink as 13 phylotypes were more abundant in eastern E-mink against one
in the western E-mink. MHC-II motifs impacted over twelve genera
compared to one for MHC-I motifs. It is also worth noting that both
MHC-I and MHC-II motifs presence were observed to alter taxa abundance
for 5 phylotypes, mostly from the Proteobacteria phylum (Table 4). The
MHC-II motifs KM371114\_EU263551, EU263554\_EU263552\_EU263556, and
Mulu:DRB*90701 respectively took part in the variation in abundance for
17, 15 and 12 phylotypes respectively. MHC-I motif Mulu:MHC-I*00008 was
significantly involved in the variation for 4 phylotypes, whereas the
other motifs had relatively low impact, as they took part in abundance
variation of 1-2 families.

\textbf{Discussion}

\emph{Genetic variation in the two E-mink captive breeding programs}

Our results show low genetic polymorphism in both captive E-mink
populations. Microsatellite markers for both populations exhibited low
allelic richness and heterozygosity indices, with the western population
having the lowest values, in line with previously published results
(Michaux et al., 2005; Cabria et al., 2007; Cabria et al. 2015).
However, the eastern E-mink sampled in this study might not reflect the
full genetic variation within the entire population, as collected E-mink
originated from a subset of the EEP since its start 25 years ago (Becker
et al., 2009). Conversely, western E-mink sampled came from wild-born
and captive-born individuals from a recent breeding program. \selectlanguage{english}Our
Bayesian clustering analysis suggests the existence of at least two main
genetic units of E-mink defined by their origin with the captive
programs, validating our use of the two E-mink groups for studying their
genetic and gut microbial variation.

Nonetheless, the two MHC genes investigated revealed differential
variation between the two E-mink populations, the MHC-I gene being more
divergent in eastern E-mink and the MHC-II gene exhibiting more richness
and divergence in western E-mink. Interestingly, the adaptive genetic
diversity followed the neutral markers trend only for one gene and not
the other, making the assessment of genetic diversity in captive
breeding complex. The maintenance of genetic variation in neutral
markers through non-selective evolutionary forces (genetic drift,
inbreeding) depend on the number of founders in a population, as well as
the breeding system of the species. However, balancing selection is
believed to counteract those non-selective evolutionary forces in
functional genes (Hedrick, 1999), resulting in an excess of
heterozygotes in small, isolated populations for MHC-II loci. This
pattern has been observed in several isolated populations (Aguilar et
al., 2004; Jarvi et al., 2004; Schad et al., 2004), but all species
investigated were free ranging, implying less restrictions in the mating
system compared to CBPs and therefore stronger sexual selection.

In line with previous evidence of the role of sexual selection for MHC
pattern distribution in vertebrates (Edwards \& Hedrick, 1998), we
observed that sex had an influence on MHC-I gene composition. It has
been shown that MHC class I genes may be involved in pheromone
recognition, and that mate preferences can be reflected in dissimilarity
of MHC patterns (Penn, 2002). In the case of the E-mink, captive-bred
males are less successful breeders compared to wild-born males (Kiik et
al., 2013). Therefore, mate pairing based only on pedigree might not
provide enough information and might be hindered by MHC-I similarities
between potential mates. It is particularly striking knowing that the
only successful breeding of an eastern pair in 2019 at the French
facility was composed of mates having dissimilar MHC-I gene composition
(Zazu-Rosin, Figure S5; L. Berthomieu pers. comm., 2020). Variation at
neutral markers may thus not accurately reflect variation at potentially
relevant genes, particularly those under selection like the MHC (Ujvari
\& Belov, 2011), and a global genetic assessment should be taken in
consideration in conservation genetics for management decisions (Mardsen
et al., 2013).

\emph{The ecosystem on a leash model in mammalian gut microbiota}

\selectlanguage{english}The hypothetical framework presented by the ecosystem on a leash model
(Foster et al., 2017) suggests that more host control in distantly
related microbes, illustrated by the Faith's PD index and Unifrac
distances, should be found in individuals with high MHC diversity. This
pattern was observed, although weakly, in alpha diversity analysis for
the western population, highlighting the importance of the MHC class II
gene above MHC class I and microsatellite markers. It is also worth
noting that this result only involves distantly related microbes,
emphasizing the fact that a phylogenetically diverse microbiota could
lead to the dominance of the fastest growing microbes instead of the
microbes that are most beneficial to the host, lending support to the
ecosystem on a leash model.

Similar more robust trends were found in beta-diversity analysis, where
gut microbial composition was different according to the number of
MHC-II motifs a E-mink possessed and the more distant two E-mink are in
MHC-II haplotype, the more different in rare gut microbiota composition
as well. Mostly negative correlations were observed between microbial
abundance and MHC genes richness and divergence, likewise suggesting
more host control in individuals with high adaptive genetic variation.
This also supports an advantage in balancing selection despite strong
genetic drift. The differential abundance analysis also revealed
stronger host control in the western population, mostly explained by the
presence of specific MHC-II motifs. Moreover, the MHC-II gene was more
likely to impact a wide range of microbial taxa. These results are in
line with previous studies conducted on fish, mice and birds (Bolnick et
al., 2014; Khan et al., 2019; Leclaire et al., 2018). However, we did
observe that one motif of MHC-II present in the eastern population also
impacted the abundance of several bacterial families, indicating that
the captive eastern population still possess interesting motifs for host
control.

Because the MHC-I gene targets intracellular non-self-molecules
recognition, it would impact a smaller number of bacteria compared to
MHC-II (Ost \& Round, 2018). Other taxonomic groups such as viruses and
protists would need to be targeted for further investigation, and
eastern population might be more equipped to recognize them, given the
increased diversity for this MHC-I gene (Kubinak et al., 2012). This is
of particular importance knowing the circulation of several viruses in
free-ranging western E-mink (Fournier-Chambrillon et al., 2004; Philippa
et al., 2008; Ma\selectlanguage{ngerman}ñas et al., 2016). In particular the canine distemper
virus, which results in a high mortality rate in E-mink, is currently
re-emerging in many wild carnivore populations in Europe (Origgi et al.,
2012) and has had a major impact on population of E-mink in Navarre,
Spain (Fournier-Chambrillon et al., 2021).

Overall, our prediction that less host control will be observed in mink
with lower genetic diversity is supported by both alpha and beta
diversity for the E-mink. However, both populations have low genetic
diversity, and the MHC class II DRB gene seemed to have a stronger
influence in gut microbes than other markers. To further validate our
results, replicating the study to see if those differences are
observable when individuals from the two populations are kept in the
same facility to control for the influence of the external environment
should be conducted. Given that we only had access to samples from a
small fraction of the captive eastern population, our results might also
not be representative of the entire captive breeding stock. Despite the
gut microbiota variation being a complex puzzle, our study gives more
importance to host immunogenetics in the context of species
conservation.

\emph{Adaptation to captivity and management practices}

For MHC genes, rare allele and heterozygous advantage are two types of
balancing selection that have been suggested to be important in
maintaining high levels of adaptive genetic diversity (Sommer, 2005).
Assuming that rare and divergent MHC genotypes are more likely to induce
host control on gut microbes, giving a fitness advantage to the host,
the co-evolutionary arm race with gut microbes will foster adaptation
from microorganisms to not be targeted by common MHC alleles (Kubinak et
al., 2012). However, the evolutionary time lag of these antagonistic
responses can lead to variation in fitness in a cyclic manner and
microbe-driven selection could vary over time and space and between
E-mink populations. This mechanism could be of influence in the western
E-mink population, given that the breeding program started in 2013 and
individuals from the wild are still being captured to increase founder
size in the program from the natural habitat. Moreover, low MHC class II
gene diversity in the eastern population might indicate that
non-evolutionary forces overshadow balancing selection for this locus,
which could be mainly explained by extensive constrains in the mating
system for a long period of time.

Inadvertent genetic adaptation to captivity for endangered species has
been documented over recent years (reviewed in Frankham, 2008). This has
been related to a fitness reduction when animals are released in the
wild environment, increasing with the numbers of captive-bred
generations, including changes in reproductive success, morphology and
behavior (Williams \& Hoffman, 2009; Willoughby \& Christie, 2019).
Becker et al. (2009) previously investigated the MHC-II DRB gene in the
captive eastern E-mink population, and detected nine alleles,
representing 6 motifs. However, ten years later, we observed 3 motifs in
the eastern group. The EEP in Estonia started in 1992 and has not been
supplemented by wild individuals for at least 25 generations (T. Maran,
pers. comm., 2021). Moreover, given the moderate success of the breeding
program due to captive-born male behavior (Kiik et al., 2013), this
suggests that high number of generations in captivity led to loss of
genetic diversity and deleterious genetic fixation took place for this
population (Woodworth et al., 2002; Frankham, 2008; Witzenberger \&
Hochkirch, 2011; Parmar et al., 2017). Even though 90\% of the initial
gene diversity has been maintained through studbook calculations (T.
Maran, pers. comm., 2021), it is likely that studbook measurements might
not reflect this trend for all E-mink genes. However, different
management strategies have been proposed to mitigate fitness reduction
for future reintroduction (reviewed in Williams \& Hoffman, 2009) that
could be implemented for the E-mink.

One strategy is to limit the number of generations captivity through the
use of artificial insemination (AI) and gametes cryopreservation. AI
allows to balance the genetic contribution between males, even with
individuals showing abnormal breeding behavior. Gametes cryopreservation
could extend the generation intervals, only requiring mature females
kept in captivity. These tools have been conducted in the CBP of the
black footed ferret (\emph{Mustela nigripes} ) and benefited the
program, notably by using sperm that were stored as long as 20 years
(Howard et al., 2015). However, these technics present serious
limitations as they are expensive to put in place, demand expertise and
investigation for success in every single species, and it is therefore
unlikely that they become tools on a regular basis for the E-mink
programs at this time.

Another strategy is to translocate animals between breeding centers for
reproduction to prevent loss of genetic diversity. Similar to the
western captive population of E-mink, these translocations could be
composed of wild-born individuals, free of captive selection pressure
(Schulte-Hostedde \& Mastromonaco, 2015). Occasional translocations from
western to eastern captive populations could also be conducted and would
potentially mitigate the modest reproductive success within the program.
It is worth noting that wild-born animals have been out of reach from
the EEP breeding stock so far. However, conducting preliminary MHC
variation assessment on reintroduced animals from the eastern stock
present in Hiiumaa island, as they no longer face captivity for a number
of generations, could be used to identify potential assets to the
current breeding stock.

Captivity has been shown to alter gut microbial communities (McKenzie et
al., 2017). Combined with this traditional conservation efforts,
microbial rescue could also help improve success of managing at-risk
populations. For example, the most common cause of mortality in captive
cheetahs (\selectlanguage{english}\emph{Acinonyx jubatus} ) is bacterial infection, possibly
because of an increase in pathogenic taxa compared to wild conspecifics
(Wasimuddin et al., 2017). Microbial rescue, using probiotics, can
stabilize the composition of the gut microbiota of dolphin in captivity
(\selectlanguage{english}\emph{Lagenorhynchus obliquidens} , Cardona et al., 2018).
Implementing wild-like diet-based enrichment could also mitigate
captivity effects on gut microbial communities in the same way as
captive selection (Mueller et al., 2019; Trevelline et al., 2019; van
Leeuwen et al., 2020).

These types of strategies could increase adaptive genetic diversity
related to immunomodulation and therefore a fitness advantage to the
mink once reintroduced. Coupled with a more in-depth investigation on
the gut microbiota of the E-mink according to diet and environment
manipulation, these technics can have synergetic effects and foster the
success of the CBPs (Gould et al., 2018; West et al., 2019). This first
look into the connection between management strategies, genetic
diversity and gut bacteria within the CBPs of the E-mink allowed
preliminary assessment of the current situation. It also offers many
axes of further research and potential strategies with the on-going
challenges that many \emph{ex situ} conservation programs face to
mitigate species extinction.

\textbf{Declarations}

Funding: Funding was supported by the NSERC CREATE grant, ReNewZoo and
Conversation Genetics Laboratory of the University of Li\selectlanguage{ngerman}ège

Conflict of interest: All authors certify that they have no affiliations
with or involvement in any organization or entity with any financial
interest or non-financial interest in the subject matter or materials
discussed in this manuscript.

Data availability: Supporting information has been made available
online. Pooled 16S rRNA sequences available on NCBI accession number:
SUB9956843. ASV table, taxonomy table and mapping file have been
uploaded, as well as MHC-1, MHC-2 and microsatellite data: Dryad link
XXXX, see reference list.

Authors' contributions: J.M., A.S.H. and P.V.L. planned and designed the
study. P.V.L. with the help of C.F.-C., P.F., C.A. and L.B. performed
the sample preparation for sequencing. J.M. provided sequencing services
and together with A.S.H. advised on laboratory and sampling procedures.
P.V.L. performed bioinformatics, statistical analyses and the
interpretation of results with feedbacks provided by A.S.H. and J.M..
P.V.L. wrote the manuscript with input from all authors.

\textbf{Acknowledgments} : FIEB (Ciprian Petrescu), Madis Podra, OFB
(Christelle Bellanger, Maylis Fayet), Zoodyssée (Mathilde Picard, Suzon
Berton, Guillaume Romano), Lise Marie Pigneur, Alice Mouton, Adrien
André, EEP program for the European mink, Tiit Maran and the Ministry
for the Ecological Transition and the Demographic Challenge (Spain).

\textbf{Tables and figures}

\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/733008/articles/710989-breeding-microbes-how-genetic-diversity-shapes-gut-microbial-communities-in-the-critically-endangered-european-mink-mustela-lutreola}

Table 1. Measures of neutral genetic diversity through microsatellite
marker analysis by mink population. N: number of individuals, A: total
number of alleles, Ar: mean allelic richness per locus, Ho: observed
heterozygosity, He: expected heterozygosity, Fis: mean inbreeding
coefficient. Overall mean value for each population across the 16
markers.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/733008/articles/710989-breeding-microbes-how-genetic-diversity-shapes-gut-microbial-communities-in-the-critically-endangered-european-mink-mustela-lutreola}

Table 2. Model results of PERMANOVAs for 9,999 permutations for each
distance matrix according to variables of interest. Values in bracket
are p-values from Marti Anderson's PERMDISP2 procedure for the analysis
of multivariate homogeneity of group dispersions (variances). UWU:
Unweighted Unifrac, WU: Weighted Unifrac distances.

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/733008/articles/710989-breeding-microbes-how-genetic-diversity-shapes-gut-microbial-communities-in-the-critically-endangered-european-mink-mustela-lutreola}

Table 3. \selectlanguage{english}Genera that were significantly correlated with or differed
across variables associated with gut microbial communities. \selectlanguage{english}Italicized
P-values were marginally significant after False Discovery Rate (FDR)
correction of p-values.

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/733008/articles/710989-breeding-microbes-how-genetic-diversity-shapes-gut-microbial-communities-in-the-critically-endangered-european-mink-mustela-lutreola}

Table 4. \selectlanguage{english}Summary of significantly enriched phylotypes among the two
mink group according to presence of each MHC motif in each class I and
class II genes from the DESEq2 analysis. If multiple motifs explained
the variation of one ASV, there were accounted for once if it varied
only according to motifs from the same gene.

\textbf{Hosted file}

\verb`image5.emf`  available at \url{https://authorea.com/users/733008/articles/710989-breeding-microbes-how-genetic-diversity-shapes-gut-microbial-communities-in-the-critically-endangered-european-mink-mustela-lutreola}

Figure 1. Distribution of MHC motifs in both mink population. Grey
shading shows the presence of each motif for each individual. Newly
discovered motifs were named following the nomenclature, and already
identified motifs were named according to the accession number found
through NCBI blast.

\textbf{Hosted file}

\verb`image6.emf`  available at \url{https://authorea.com/users/733008/articles/710989-breeding-microbes-how-genetic-diversity-shapes-gut-microbial-communities-in-the-critically-endangered-european-mink-mustela-lutreola}

Figure 2. \selectlanguage{english}The relative abundance of common bacterial families across 24
E-mink individuals from the two breeding populations. The pie chart
represents the mean proportion of relative abundance for each microbial
phylum for all individuals.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}

Figure 3. \selectlanguage{english}Alpha diversity indexes for microbial taxa richness are shown
as boxplots (with median, interquartile range (IQR) and whiskers
extending to the last data points). Individual values appear as light
blue dots for the female mink and dark blue triangle for the males, for
each mink populations. Shannon index (A) expresses changes in taxa
richness and abundance, Chao1 (B) variation in strict richness, and
Faith's PD (C) for phylogenetic diversity. Results from respective
Kruskal-Wallis tests are documented.

\textbf{Hosted file}

\verb`image8.emf`  available at \url{https://authorea.com/users/733008/articles/710989-breeding-microbes-how-genetic-diversity-shapes-gut-microbial-communities-in-the-critically-endangered-european-mink-mustela-lutreola}

Figure 4. \selectlanguage{english}Differences in E-mink microbial community composition
visualized by principal coordinates analysis of unweighted UniFrac
distances based on rarefied OTU counts. Individuals are labelled
according to their name their population or origin (animal names from
the eastern population are underlined, not underlined for western).
Clusters correspond to number of MHC-II motifs for each individual where
the value is indicated at the centroid.

\textbf{Hosted file}

\verb`image9.emf`  available at \url{https://authorea.com/users/733008/articles/710989-breeding-microbes-how-genetic-diversity-shapes-gut-microbial-communities-in-the-critically-endangered-european-mink-mustela-lutreola}

Figure 5. Mean relative abundance of each family that experienced
significant differential abundance between the two mink populations
(orange: Western; blue: Eastern, mean relative abundance per population
+ standard error). Colored circles next to the family name correspond to
which MHC motifs absence/presence the variation in abundance was
significant for (yellow: MHCII, blue: MHCI, green: both MHCI and MHCII).

\textbf{References}

{[}dataset{]} Van Leeuwen, P., Schulte-Hostedde, A.,
Fournier-Chambrillon, C., Aranda, M.C., Berthomieu, L., Fournier, P.,
Michaux, J.; 2021; Pooled 16S rRNA sequences SUB9956843; GenBank NCBI;
DOI to be determined

{[}dataset{]} Van Leeuwen, P., Schulte-Hostedde, A.,
Fournier-Chambrillon, C., Aranda, M.C., Berthomieu, L., Fournier, P.,
Michaux, J.; 2021; ASV table, taxonomy table and mapping file have been
uploaded, MHC-1, MHC-2 and microsatellite data; Dryad; DOI to be
determined

 \selectlanguage{english}Aguilar, A., Roemer, G., Debenham, S., Binns, M., Garcelon, D., \&
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