ArticlePDF Available

Leveraging an existing whole genome resequencing population dataset to characterize toll‐like receptor gene diversity in a threatened bird

Authors:

Abstract

Species recovery programs are increasingly using genomic data to measure neutral genetic diversity and calculate metrics like relatedness. While these measures can inform conservation management, determining the mechanisms underlying inbreeding depression requires information about functional genes associated with adaptive or maladaptive traits. Toll‐like receptors (TLRs) are one family of functional genes, which play a crucial role in recognition of pathogens and activation of the immune system. Previously, these genes have been analyzed using species‐specific primers and PCR. Here, we leverage an existing short‐read reference genome, whole‐genome resequencing population dataset, and bioinformatic tools to characterize TLR gene diversity in captive and wild tchūriwat'/tūturuatu/shore plover (Thinornis novaeseelandiae), a threatened bird endemic to Aotearoa New Zealand. Our results show that TLR gene diversity in tchūriwat'/tūturuatu is low, and forms two distinct captive and wild genetic clusters. The bioinformatic approach presented here has broad applicability to other threatened species with existing genomic resources in Aotearoa New Zealand and beyond.
Mol Ecol Resour. 2022;00:1–16.
|
1wileyonlinelibrary.com/journal/men
|
Advances in high- throughput sequencing technologies and bio-
informatic tools are enabling scientists to generate and analyse
whole- genome resequencing data (Auwera et al., 2013 ; Ekblom &
Wolf, 2 014). This progress has largely increased the measurement
of neutral genetic diversity in nonmodel organisms often to inform
the conservation management of threatened species (Allendorf
et al., 2010; Angeloni et al., 2012; Primmer, 2009). However,
studies suggest that neutral genetic diversity may be a poor prox y
for functional diversity (Grueber et al., 2015; Marsden et al., 2013;
Sommer, 2005). Further, whereas neutral genetic diversity can be
used to measure inbreeding and assess inbreeding depression, de-
termining the genetic mechanisms that underlie inbreeding depres-
sion requires information about specific functional loci associated
with adaptive or maladaptive traits (Kohn et al., 2006; Mable, 2019;
Ouborg et al., 2010; Won et al., 2021). When whole- genome rese-
quencing data and a reference genome for a population of interest
Received: 10 January 2022 
|
Revised: 29 Ap ril 2022 
|
Accepted: 26 May 2022
DOI : 10.1111/1755-0998.13 656




1|1|2|1|
3|4|1
1School of Biologica l Sciences, Universit y
of Canterbury, Chris tchurch, New Zealand
2Tea Break Bioi nformatic s, Ltd,
Palmerston North, New Zealand
3Department of Conservation,
Biodive rsity Group, Auckland, New
Zealand
4Instit ute of Veterinar y, Animal, and
Biomedical Sciences, Wildbase, Massey
University, Palmerston North, New
Zealand

Molly Magid, School of Biological
Science s, University of Cante rbury, 20
Kirkwood Avenue, Upper Riccarton,
Christchurch NZ 8140, New Zealand.
Email: mollycmagid@gmail.com

New Zealand Department of
Conser vation; New Zealan d Ministr y of
Business, Innovation and Employment
Endeavour Fund, Grant/Award Number:
UOCX1602; Fulbright New Zealand;
Ministry of Busine ss, Innovat ion and
Employment; University of Canterbury
 Paul A. Hohenlohe

Species recovery programs are increasingly using genomic data to measure neutral
genetic diversity and calculate metrics like relatedness. While these measures can
inform conservation management, determining the mechanisms underlying inbreed-
ing depression requires information about functional genes associated with adaptive
or maladaptive traits. Toll- like receptors (TLRs) are one family of functional genes,
which play a crucial role in recognition of pathogens and activation of the immune
system. Previously, these genes have been analysed using species- specific primers
and PCR. Here, we leverage an existing short- read reference genome, whole- genome
resequencing population data set, and bioinformatic tools to characterize TLR gene
diversity in captive and wild tchūriwat’/tūturuatu/shore plover (Thinornis novaeseelan-
diae), a threatened bird endemic to Aotearoa New Zealand. Our results show that TLR
gene diversity in tchūriwat’/tūturuatu is low, and forms two distinct captive and wild
genetic clusters. The bioinformatic approach presented here has broad applicability to
other threatened species with existing genomic resources in Aotearoa New Zealand
and beyond.

conservation genomics, immune genes, shore plover, toll- like receptors, whole- genome
sequences
This is an op en access ar ticle under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction
in any medium, provide d the original work is properly cited an d is not used for co mmercial purposes.
© 2022 The Authors. Molecular Ecology Resources published by John Wiley & Sons Ltd.

|
 MAGID et Al.
already exists, bioinformatic tools may be used to analyse these data
to characterize functional genetic diversity (Hoelzel et al., 2 019;
Kohn et al., 2006).
There is a growing interest in characterizing the functional di-
versity of immune genes within intensively managed species to im-
prove outcomes and reduce the burden of wildlife disease (Morris
et al., 2015; Zhu et al., 2020). Low immune gene diversity increases
susceptibility to a variety of pathogens (Spielman et al., 2004).
Inbreeding leads to an overall decrease in genetic diversity at im-
mune genes, loss of rare and potentially advantageous alleles, and
a decreased ability to adapt to novel or rapidly evolving pathogens
(Altizer et al., 2003; Spielman et al., 200 4). Research from both
wild and laboratory populations show that inbreeding contributes
to an increased parasite load, a greater susceptibility to pathogens,
a higher likelihood that individuals will act as disease reservoirs,
and ultimately, higher rates of mortalit y due to disease (Acevedo-
Whitehouse et al., 2003; Ross- Gillespie et al., 2007; Whitehorn
et al., 2011; Whiteman et al., 2005). Further, increased immune gene
diversit y may help populations respond more rapidly to pathogens
(Bonneaud et al., 2012; Davies et al., 2021).
Toll- like receptor (TLR) genes are an innate immune gene family
involved in pathogen recognition and immune response (Acevedo-
Whitehouse & Cunningham, 2006; Grueber et al., 2012; V inkler &
Albrecht, 2009). TLRs are an ancient part of the immune system,
present in nearly all multicellular organisms (Singh et al., 2003).
They are located either on the cell membrane or intracellularly on
the membrane of lysosomes or endosomes within innate immune
or somatic cells (Behzadi et al., 2021; Takeda & Akira, 2005). TLRs
recognize conserved pat terns of pathogens by binding residues,
known as antigens, from pathogens that enter the body (Singh
et al., 2003). Pathogen recognition by TLRs is necessary for the
proper activation and direction of the adaptive immune response
(Chen et al., 2021; Clark & Kupper, 2005; Kawasaki & Kawai, 2014;
Pasare & Medzhitov, 2005; Xia et al., 2021). Studies have found that
TLR gene diversity tends to be low in small, highly threatened pop-
ulations compared to large populations of species of least concern
(Alcaide & Edwards, 2 011; Dalton et al., 2016; Knafler et al., 2017;
Morrison et al., 2020). In previous studies, having higher TLR gene
diversit y has been associated with greater disease resistance, sur-
vival, and reproduction (Antonides et al., 2019; Bateson et al., 2016;
Davies et al., 2021; Heng et al., 2011; Podlaszczuk et al., 2021;
Quéméré et al., 2021). Having a robust TLR gene diversity within a
population allows for selection of the most beneficial variant(s) to
effectively deal with pathogens. Also, immune gene diversity may
not correlate with neutral measures of genome- wide diversity, so
directly characterizing TLR genes will explicitly reveal how diver-
sity at innate immune genes are affected in highly threatened avian
populations (Grueber et al., 2015; Hartmann et al., 2014; Marsden
et al., 2013).
To date, TLR gene diversity has been mostly characterized using
species- specific TLR primers and PCR protocols to perform SNP
genotyping of individuals in a population (Allendorf et al., 2010;
Dalton et al., 2016; Grueber et al., 2015; Lara et al., 2020; Morrison
et al., 2020). However, these approaches can be expensive and time-
consuming. When whole- genome resequencing data and a refer-
ence genome for the species of interest already exists, researchers
can use bioinformatic tools to analyse these existing resources to
identify TLR genes and characterize TLR gene diversity (Hoelzel
et al., 2019; Wang et al., 20 05).
TLR genes may be more suited for identification using bioinfor-
matic tools, in comparison to other immune genes like MHC, be-
cause they are relatively conserved and gene duplications are rare
and well- defined (Gr ueber et al., 2015). The TLR protein is composed
of three protein domains: the extracellular binding domain, trans-
membrane protein, and intracellular toll/interleukin 1 (TIR) signalling
domain (Yilmaz et al., 2005). There is mostly conserved evolution
and synonymous substitutions within TLR sequences, and the ma-
jority of variation is within the region coding for the extracellular
binding protein, which is what comes into contact with antigens
(Werling et al., 2009). The binding domain has a pattern of leucine-
rich repeats, and so is also known as the leucine- rich repeat (LRR)
domain (Alcaide & Edwards, 2011). Inserts of lecuines within the LRR
domain may impact pathogen recognition (Offord et al., 2010), and
single nucleotide polymorphisms (SNPs) within this region can also
affect the binding affinity of TLRs (Keestra et al., 2008; Matsushima
et al., 20 07). In contrast, the TIR signalling domain is largely con-
served across the TLR family, and phylogenetically among related
species (Beutler & Rehli, 2002; Narayanan & Park, 2015; Yilmaz
et al., 2005). The implications of these protein structures are two-
fold: (1) the pattern of repeats within the LRR domain and conserved
TIR domain are ideal for bioinformatic identification due to the simi-
larity of sequences within taxonomically related species, and (2) the
sequences within the LRR region are variable and may contain SNPs
that are adaptive for the recognition of par ticular pathogens.
Advances in bioinformatic tools utilizing comparative genom-
ics and sequence similarity comparison and the growth of online
repositories and databases like NCBI with extensive genomic data
that includes functional genes from a diversity of species, together
facilitate the identification of functional genes within the genomes
of nonmodel organisms (Feng et al., 2020; Grueber, 2015; Ren
et al., 2021). However only a few studies to date have used bioinfor-
matic tools to identify functional genes and characterize functional
genetic diversity within a whole- genome resequencing population
data set (Brandies et al., 2020; Zhang et al., 2 014). Previous research
on avian TLRs have used tools such as NCBI Basic Local Alignment
Search Tool (blast) to design primer sequences to amplify these genes
(Chávez- Treviño, 2017; Grueber et al., 2015; Morrison et al., 2020)
and to compare sequences among closely- related species (Raven
et al., 2017), so using established methods for primer design may
also help in the identification of TLR genes (Yilmaz et al., 2005). A
study on immune gene diversity in Tasmanian devils used bioinfor-
matics to characterize diversity of several immune genes previously
identified through a combination of transcriptome analysis and
comparative genomes (Morris et al., 2015). Research using genomes
from the B10K consortium highlights the efficacy of comparative
genomics to identify orthologues and conser ved regions using

|

MAGID et A l.
bird species in the same taxonomic class (Feng et al., 2020; Zhang
et al., 2014). Increasingly, immune gene databases are available to
identif y and annotate immune genes within specific taxonomic
groups (Grueber, 2015; Mueller et al., 2020; Wong et al., 2 011).
These resources provide an avenue for characterizing immune gene
diversity in nonmodel organisms.
Tchūriwat’/tūturuatu/shore plover (Thinornis novaeseelandiae)
is a threatened shorebird endemic to Aotearoa New Zealand
(Robertson et al., 2021). Tchūriwat’/tūturuatu is thought to have
been widespread on the coastal areas of mainland Aotearoa,
but populations decreased dramatically during the 19th cen-
tury because of introduced mammalian predators (Davis, 1994).
Tchūriwat’/tūturuatu was eventually extirpated from the main-
land and the remaining wild population is now restricted to the
Chatham Islands archipelago (Davis, 1994). The current wild popu-
lation totals 234 birds and is mostly confined to a single predator-
free island, Hokorereoro/Rangatira (Department of Conservation,
Shore Plover Recovery Group herein DOC SPRG). A conservation
breeding program was established between 1991– 1996 with eggs
brought from Hokorereoro/Rangatira and was augmented in 2003
by a single adult male from Western Reef (Dowding et al., 2005).
At the time of this study, the captive population had not been aug-
mented since 2003 and the number of individuals in the captive
population was 41. Based on the pedigree of the captive popula-
tion, individuals were highly related to one another (average mean
kinship = 0.119) and mean inbreeding was relatively high (0.0 48)
(SPRG, unpublished data).
Captive birds are also highly vulnerable to contracting avian
pox (DOC SPRG). Avian pox is caused by avipoxvirus (APV), a large
dsDNA virus with hundreds of strains that can infect a wide range
of bird species (Bolte et al., 1999; Boyle, 20 07). Infection with APV
causes lesions on the body at the site where it enters the skin and
on feather- free areas of the body. APV is generally transmitted
through insect vectors, but if there is a break in epithelial integ-
rity, it can also spread through contact with an infected bird or via
contact with shared objects like feeders used by infected birds
(Hansen, 1999). In rare instances, APV can also be transmitted
through the inhalation of viral particles (Hansen, 1999). Aspects
of captive breeding programmes like shared aviaries and resources
make them particularly vulnerable for the spread of infection (van
Riper & Forrester 2007).
In addition to being highly transmissible within a captive setting,
avian pox infections in captive birds are often severe, can last for
several months, and may never resolve (DOC SPRG). In contrast,
wild tchūriwat’/tūturuatu contract mild avian pox infections which
are most often cleared in 1– 2 weeks (DOC SPRG). Longer infection
periods for captive birds allow pox lesions more time to develop,
making it more likely that birds will contract secondar y bacterial in-
fections which increase the severit y of and rate of mor tality from
APV (Alley & Gartrell, 2019; Gartrell et al., 2002; Hansen, 1999; Weli
& Tryland, 2011). While death from the virus remains low, the sever-
ity of infection is high. Also, chicks and juveniles are more likely to
contract APV (DOC SPRG), and without a fully developed adaptive
immune system, the innate immune system may be especially im-
portant for young birds in fighting off poxvirus infections (Fellah
et al., 2008; Palacios et al., 2009). Protecting juveniles from infec-
tion is important for the timely release of young birds into the wild
(Dowding, 2013, DOC SPRG).
Ongoing research in an aligned project based on approximately
50 K single nucleotide polymorphisms (SNPs) generated using a
reduced- representation sequencing approach shows that wild and
captive tchūriwat’/tūturuatu populations are genetically distinct (I.
Cubrinovska, unpublished data). These SNPs reflect genome- wide
diversit y, but may also be indicative of differences at the gene level,
including functional genes like TLRs that contribute to the immune
response to poxvirus. If the wild population– which appears to be
less susceptible to avian pox (DOC SPRG)– has higher immune gene
diversity, then genetic rescue of the captive population through
augmentation with wild individuals may improve future disease
outcomes.
Sourcing individuals from genetically diverse or different pop-
ulations is recognized as a way to improve both genome- wide di-
versity and immune gene diversity (Glassock et al., 2021; Grueber
et al., 2017; Heber et al., 2013; McLennan et al., 2020). Leveraging
existing genomic resources and bioinformatic tools, we identify
TLR genes and characterize TLR gene diversity in captive and wild
tchūriwat’/tūturuatu. In addition to informing future research to de-
termine whether a recent translocation of tchūriwat’/tūturuatu from
the wild to captivity has increased TLR gene diversit y and an asso-
ciated immune response to the avipox infection, the bioinformatic
approach presented here is broadly applicable to threatened birds
with existing genomic resources.
|
 | 
Originally generated for an aligned project (see Benefit- sharing
statement), we used short- read sequence data for 66 tchūriwat’/
tūturuatu (39 captive, 27 wild), including a wild male named Maui
that was chosen for the tchūriwat’/tūturuatu reference genome.
Blood samples for captive birds were collected during routine
health checks by Brett Gartrell and Isaac Conservation and Wildlife
Trust staff. Blood samples for wild birds were collected as part of
the 2018/2019 breeding season juvenile banding trip to Maung’ Rē/
Mangere and Hokorereoro/Rangatira in the Chathams Island ar-
chipelago. Blood was taken from juveniles and adults and stored in
lysis buffer for shipment. All samples were subsequently stored at
−0°C until extraction. High quantity and quality DNA was extracted
using a tailored lithium chloride extraction method (Galla, 2019).
Extractions were assessed for quality by running 2 μl of DNA on a
2% agarose gel. A Qubit 2.0 fluorometer (Fisher Scientific) was used
for DNA quantification. Blood samples and DNA extractions are
stored at the University of Canterbury on behalf of the Shore Plover
Recovery Programme.

|
 MAGID et Al.
Libraries were prepared with the Nextera DNA Flex Library
Prep Kit according to the manufacturer's specifications and se-
quenced across one lane of an Illumina Novaseq 6000 to achieve
an average coverage depth of approximately 10× (Galla et al.,
2020), excluding the individual used for the reference genome
which was sequenced to an average depth of approximately 100×.
Libraries for 21 individuals that had low depth of coverage were
subsequently resequenced as above to achieve an average cover-
age depth of at least 10×.
 | 
fastqc version 0.11.8 (FastQC, 2015) was used to evaluate the qualit y
of the raw Illumina data and assess potential sample contamination.
Initial read trimming was performed using trimgalore version 0.6.6
(Kreuger, 2021), using pair- end mode, a minimum length of 54 bp, and
with the - - nextseq two- colour chemistry option. A kmer abundance
plot was created using jellyfish 2.3.0 (Marçais & Kingsford, 2011)
with a kmer length of 31 prior to assembly to assess heterozygo-
sity and contamination. The genome was assembled in two stages
using two assemblers, masurca version 3.3.4 (Zimin et al., 2013), and
meraculous version 2.2.6 (Goltsman et al., 2017 ). While the assem-
blies with MaSuRCA give the best contiguity, the assembler can have
problems with two- colour chemistry Illumina data when finding runs
of low- quality bases or reads with a high proportion of “G” bases (De-
Kayne et al., 2020). In those cases, the assembler tends to create
regions where the consensus is simply a string of “G” bases and/or
unknown “N” bases, as a result of both the error- correction module
and the superread creation during the assembly process. Meraculous
is much less prone to these artefact s due to their agnostic approach
to error correction, which relies on coverage and bubble resolution
in de Bruijn graphs, at the expense of contiguity when compared to
MaSuRCA. Thus, both assemblers were combined.
In the first stage, MaSuRCA was used to create the main as-
sembly, using the trimmed reads padded with low quality “N”
bases to a uniform length of 150 bp. Parameter adjustments in-
clude a grid batch size of 500,000,0 00, a Jellyfish hash size of
8,00 0,000,000, use of the Celera assembler for the final step,
and the inclusion of scaffold gap closing. All other parameters
were set to default for nonbacterial Illumina assemblies. For
Meraculous, a kmer size of 61 with a minimum depth cutoff of five
was selec ted, and the assembly was run on Diploid mode 1 (to
create a single haploid reference). Finally the Meraculous assem-
bly was used to correct the ambiguities and gaps in the MaSuRCA
assembly by first aligning both genomes using Last version 980
(Kielbasa et al., 2011), filtering for contigs with low quality regions
(due to the presence of poly- C/poly- G regions right next to a po-
ly- N region) using awk, then using the merge_and_replace.pl Perl
script available at https://github.com/Lanil en/SemHe lpers. Th e
hardware used for the assembly was a 12- core, 24- thread work-
station with 128 Gb of RAM. Assembly took 5 days for MaSuRCA
and 10 h for Meraculous.
 | 

Figure 1 provides an overview of the methods used for TLR iden-
tification and characterization. We used a virtual machine (VM;
16 vCPUs, 9 TB of memory, 128 GB of R AM) in the host Research
Compute Cluster (RCC) at the University of Canterbur y.
 | 
2.4.1  |  blast alignment
To identify TLR genes, we used NCBI Basic Local Alignment
Search Tool (blast), given its prior use to design primer se-
quences that amplify TLR genes (Chávez- Treviño et al., 2017;
Yilmaz et al., 2005) and previous comparisons of TLR sequences
among closely- related avian species (Mueller et al., 2020; Raven
et al., 2017). We used bird TLR nucleotide (BLASTn) and protein
(tBLASTn) sequences from close relatives and/or species with
high quality genome assemblies to search for similar nucleotide
sequences within the reference genome for tchūriwat’/tūturuatu
(Madden, 2013). Unlike other analyses that focused solely on the
most variable LRR binding region, we chose to identify the en-
tire sequence for each TLR, because it provided additional con-
firmation that the whole TLR was captured. For inclusion in our
search through the tchūriwat’/tūturuatu reference genome, TLR
sequences were either characterized for other species in the labo-
ratory through targeted amplification and sequencing, or identi-
fied using the NCBI genome annotation pipeline (Table 1).
BLAST uses an algorithm to map input query sequences to the
most similar region within the tchūriwat’/tūturuatu genome. If
BLAST is able to map the input quer y sequence to a region or re-
gions within the database, it outputs the region(s) as a list. The list
is ordered by the expect value (e- value) of each alignment, which is
the number of alignments with a similar score that are expected by
chance. The lower the e- value of the alignment, the less likely that
the alignment is due to chance and the more likely it is that the
alignment reflects a biological similarity between the sequences
(NCBI, 2020). In addition to the e- value, the percent quer y cover
and percent identity of the alignment help the user to judge the
quality of each alignment. The percent query cover is percent of
the quer y sequence that is aligned to a database sequence, out of
the whole length of the query sequence, regardless of the iden-
tity of the bases within a sequence. The percent identity is how
many of the bases within the query sequence alignment match the
bases within the database sequence, out of the whole length of
the query sequence. The closer both of these percentages are to
100%, the greater the likelihood that the alignment is biologically
relevant.
To further ensure that agreement between the query se-
quence and individual target sequences was not due to chance,
two strategies were implemented: (1) a comparative approach

|

MAGID et A l.
 Work flow schematic to show how bioinformatic tools and genomic resources are used to identify and characterize TLR genes
in threatened birds. See text for details. bcf, BIM collaboration format; LRR, leucine rich repeat; ORF, open reading frame; SNP, single
nucleotide polymorphism; TLR, toll- like receptor. All remaining abbreviations refer to names of bioinformatic tools utilized
TLR Identification
Genomic Resources Bioinformatic tools Workflow
Short-read reference
genome, reference
species TLR sequences,
reference species
genomes
1. BLASTn, tBLASTn
2. ORF finder
3. LRRsearch
4. SMART protein
visualiser
TLR Characterisation
Genomic Resources Bioinformatic tools Workflow
Short-read reference
genome, short-read
population resequencing
data
5. FastQC
6. TrimGalore
7. Burrows-Wheeler
aligner (bwa)
8. SAMtools
9. BCFtools
10. VCFtools
11. Geneious
12. Beagle
13. DNAsp
14. PopART
15. Adegenet
To analyse protein sequence and visualise protein structure,
use LRR finder and SMART protein visualiser (3, 4)
To identify preliminary TLR sequences, align reference
species TLR sequences to reference genome, use BLASTn
and tBLASTn (1)
To perform reciprocal best hit test, use (1) to align
preliminary TLR sequences against well-annotated reference
species genomes
To identify cross-alignment and false positives, use (1) to
align preliminary TLR sequences against short-read
reference genome
To find open reading frames and transcribe species TLR
sequences, use ORF Finder (2)
To phase SNPs for haplotype analysis, use Beagle (12) and
to produce consensus files for each allele within an
individual, use (9, 10)
To concatenate individual bcf files into a population vcf file
use (9) and to filter SNPs for the file, use BCFtools (9) and
VCFtools (10)
To trim raw resequencing reads, use TrimGalore (6), and to
analyse quality of trimmed reads, use (5)
To align trimmed resequencing reads to the reference
genome use bwa (7), and to produce alignment files for
downstream analyses, use SAMtools (8)
To run mpilutp call SNPs at the population level through
comparison to the reference genome, use BCFtools (9)
To analyse synonymous and non-synonymous SNPs,
use Geneious (11)
To calculate haplotypes statistics, use DNAsp (13), to create
haplotype networks use PopART (14), and to create a PCA
use Adegenet (15)
To analyse quality of resequencing files, use FastQC (5)

|
 MAGID et Al.
seeking agreement among multiple species' TLR sequences align-
ing to a sp ecific reg ion in th e tch ūriwat’/tū tur uat u genome; an d (2)
the reciprocal best hit (RBH) test. In the first instance, if the same
region in the tchūriwat’/tūturuatu genome was being mapped to
with the same type of TLR gene from different bird species, there
was greater confidence in that region. In the second, the RBH test
is an approach that has been used within comparative genomics to
confirm the identity of orthologs in nonmodel species using well-
annotated reference genomes (Kristensen et al., 2011). This limited
the test to just chicken and zebra finch genomes, since both are
high- qualit y genome assemblies and are well- annotated. This test
involves taking the “best hit” region identified within tchūriwat’/
tūturuatu using the TLR sequence from a reference species and
BLASTing it against the genome of that reference species (Irizarry
et al., 2016). If the “best hit” of this BLAST search matches the
original reference TLR, it provides greater support that the genes
are orthologs of one another (Kristensen et al., 2011). Each pre-
liminary TLR sequence identified in tchūriwat’/tūturuatu passed
the RBH test, mapping to the original chicken and zebra finch ref-
erence TLR sequences within each genome. This helped to con-
firm not only that a TLR gene was likely identified, but also that
the specific TLR gene of interest had been located (i.e., a BLAST
search with preliminar y TLR3 sequence brought up only the TLR3
sequence in reference species).
After conducting these tests of gene alignment, each pre-
liminar y TLR sequence was BLASTed against the remainder of
the tchūriwat’/tūturuatu genome to find whether there was
alignment of this sequence to other regions within the genome.
This test was conducted to determine whether there were
nontarget sequences within the tchūriwat’/tūturuatu genome
that may align to these TLR sequences during the process of
whole- genome alignment. There was little cross- alignment of
one type of TLR mapping to other TLR genes (e.g., TLR1A refer-
ence aligning to the TLR3 region). When this did happen, it was
often within pairs of duplicated TLRs ( TLR1A & TLR1B, TLR2A
& TLR2B), and it was only a partial alignment (at most 50%),
so these cross alignment s were easy to distinguish from the
true alignment. False positives were also investigated within
the genome, to find out whether there were TLR sequences
aligning to other, non- TLR regions. There were no false positive
sequences within the tchūriwat’/tūturuatu genome. Based on
these tests, the Burrows- Wheeler Aligner version 0.7.17 (bwa)
(Li & Durbin, 2009) with a default maximum mismatch value
of 4% for read alignments (allowing only 4% of bases to differ
in identity within sequence alignments) would be sufficient to
prevent the cross- alignment of duplicated TLRs.
2.4.2  |  Protein analysis
Once the preliminary TLR regions passed these quality control
measures, each sequence was entered into the NCBI Open Reading
Frame (ORF) finder (https://www.ncbi.nlm.nih.gov/orffi nder). This
tool searches for reading frames within the query DNA sequence
and transcribes it into a protein sequence with each possible reading
frame. The result is a graphic showing the protein sequence result-
ing from each reading frame that is used for transcription (Wheeler
et al., 2003). Then we used BL ASTp searches with the result ant pro-
tein as the input sequence and the BLA ST protein database as com-
parison sequences. When the correct reading frame for the protein
was identified, the search would bring up the reference TLR protein
sequences that we had originally aligned with.
Two additional web applications were used to examine TLR
protein sequences and investigate the protein products of TLR
genes. The first was LRRfinder (Of ford et al., 2010), which uses a
database of toll- like receptor TLR sequences acquired from NCBI
to identif y the LRR regions within a TLR (LRRs, LRRNT, LRRCT,
transmembrane protein, TIR signalling domain). The search tool
focuses on predicting potential LRR regions, because this region
is the most variable and may affect the binding specificity and af-
finity of TLRs (Keestra et al., 2008; Matsushima et al., 2007). All
searches revealed multiple LRR regions within the sequences, a
transmembrane protein region in almost all sequences, and a TIR
domain in all sequences. Also the LRR identifications matched the
   
Killdeer (Charadrius vociferus)Family Charadriidae NCBI genome annotationa
Black- headed Gull
(Chroicocephalus ridibundus)
Order Charadriiformes Targeted amplification and
sequencingb
Chicken (Gallus gallus)Class Aves Targeted amplification and
sequencingc
Zebra Finch (Taeniopygia
guttata)
Class Aves NCBI genome annotation
excluding targeted
amplification and
sequencing of TLR4d
aThe NCBI Eukaryotic Genome Annotation Pipeline, https://www.ncbi.nlm.nih.gov/genom e/annot
ation_euk/proce ss/.
bPodlaszc zuk et al. (2021).
cYilmaz et al. (2005).
dVinkler et al. (2009).
 Reference species for toll- like
receptor ( TLR) identification in tchūriwat’/
tūturuatu, listed alongside the taxonomic
group shared with tchūriwat’/tūturuatu.
For each referece species, TLR sequences
used for comparison were either identfied
through the use of the NCBI annotation
pipelinea or through amplification and
sequencing of targeted TLRs in the
laboratory

|

MAGID et A l.
type of TLR gene they were from (i.e., all LRRs for TLR3 matched
TLR3 LRRs in reference species).
To further visualize these protein products, we used the sim-
ple modular architecture research tool (smart version 9.0) (Letunic
et al., 2021; Schult z et al., 1998). SMART contains a protein data-
base that uses markov modes to identify protein domains within an
input protein sequence by calculating the expected value (e- value)
SWise score, which is the output of the established SWise protein
search algorithm (Birney et al., 1996), for each alignment between
the quer y sequence and sequences in the SMART database (Letunic
et al., 2021; Schult z et al., 1998). The protein domains assigned
with low e- values are less likely to be assigned by chance, and at
a predetermined e- value threshold, the protein domain will appear
as a visual block on a 2D schematic of the protein. This visualiza-
tion revealed whether we had captured all expected protein do-
mains within the TLR protein sequences and had not missed parts
of the sequence. This also facilitated a visual comparison between
the tchūriwat’/tūturuatu TLR protein schematics to the TLR SMART
protein schematics in chicken (Temperley et al., 2008).
 | 
2.5.1  |  Population resequencing
alignment and analysis
Once TLR regions were identified within t he genome, whole- genome
alignment of population resequencing data to the tchūriwat’/
tūturuatu reference genome was performed to characterize TLR SNP
diversit y. To assess read quality, fastqc version 0.11.9 (FastQC, 2015)
was run to identify low quality regions and baseline quality scores,
and then trim galore version 0.6.5 (Kreuger, 2021) was used to trim
Illumina paired end 150 bp reads and remove low quality reads. The
two- colour chemistr y option was chosen, which supports trimming
and removal of low- quality sequences of non- G bases and improves
the removal of low quality G's by removing them regardless of their
quality (Kreuger, 2021).
Trimmed reads were aligned to the reference genome with
bwa version 0.7.17 (Li & Durbin, 2009). samtools version 1.10. (Li
et al., 2009) was used to sort BAM files prior to SNP discovery.
A custom perl script (“split_bamfiles_tasks.pl”) (Moraga, 2018)
was used to split the bam files into chunks that could be pro-
cessed more quickly with bcftools version 1.11 (Li et al., 2009).
bcftools mpileup was run with annotations GT, PL, DP, SP, ADF,
ADR, AD to allow for downstream filtering. vcftools version 0.1.16
(Danecek et al., 2011) was used to filter the data set as follows:
minor allele frequency (maf ) ≥0.05, Phred- score (quality) >20,
max- missingness = 0.90, minimum depth >5, minGQ >10, and
maximum depth <200. b cftools was also used to filter the vcf for
strand bias using the parameter of strand- bias adjusted Phred-
score <60. Hardy– Weinberg equilibrium filtering was not applied
because two assumptions are likely to be violated in this study
(no selection and random mating). In addition, vcfto ols was used
to examine the site and individual depth and missingness and to
remove individuals with low depth and high missingness from fur-
ther analysis (n = 1 captive individual). Final SNPs were analysed in
Geneious Prime 2020 (https://www.genei ous.com/) to determine
whether they made synonymous or nonsynonymous changes to
TLR protein products. Nonsynonymous SNPs were further inves-
tigated in Geneious to analyse the amino acid change that resulted
from each SNP. Then, a preliminary analysis of nonsynonymous
SNPs was conducted to determine which physiochemical changes
that might occur due to the change in amino acid identity, using the
well- defined physicochemical attributes of charge and polarity.
beagle version 5.2 (Browning et al., 2021) was used with default
settings to phase haplotypes for both populations. Each TLR gene
was phased separately by phasing along the whole contig that con-
tained that TLR gene. For each individual, genotype sequences were
generated using bcftools. To increase quality and decrease errors
within haplotypes, sites with low depth and high strand bias were
set to missing using bcftools +setgt with options - i ‘FORMAT/DP<5’
and ‘FORMAT/SP>60’. Then, the filtered VCF was used to produce
TLR consensus sequences for each individual in the population. sam-
tools faidx was used to target each TLR region in the genome, and
bcftools consensus was used with parameters - M to output any miss-
ing genotypes as “N”, and - H 1pIu and 2pIu to produce both phased
haplotypes for an individual. If individuals had any sites within the
TLR sequence where they were missing a genot ype, they were not
included in the haplotype analysis for that TLR gene. While the
accepted standard for SNP analysis is that nonsynonymous SNPs
have significant influence on the functionality of a protein, synon-
ymous SNPs may also change protein function (Sauna & Kimchi-
Sarf aty, 2011), and within TLR genes, synonymous SNPs have been
associated with disease resistance and immune response (Cho
et al., 2013; Junjie et al., 2012). Given this, both synonymous and
nonsynonymous SNPs were used in the construction of haplotypes.
The resulting TLR consensus sequence for each individual was
output into a population mega file for either the captive or wild
population, and the process was repeated for every polymorphic
TLR gene. The resulting mega files were imported into dnasp v. 6.12
(Rozas, 2017) and were analysed to calculate haplotype diversity,
nucleotide diversity (𝛑), and Tajima's D. Haplotype nexus files for
each TLR were also created in DNAsp and imported into popart
(Leigh & Br yant, 2015) to construct minimum- joining haplotype net-
works. We also visualized differences in TLR haplotypes between
populations with a PCA. Adegenet (Jombart, 2008) was used to con-
struct the PCA with scaled mean haplotype frequencies for all TLR
genes, using code adapted from the introductory tutorial to ade-
genet (Jombart, 2015).
|
Nine TLRs were identified within the tchūriwat’/tūturuatu genome:
TLR1A, TLR1B, TLR2A, TLR2B, TLR3, TLR4, TLR5, TLR7, and TLR21.
A partial sequence of the TLR15 gene was identified within the

|
 MAGID et Al.
genome, but was not included in the analysis. The protein schematic
structure for each TLR identified is shown in Figure 2.
There were 28 SNPs in the TLR genes of the captive tchūri-
wat’/tūturuatu population (n = 38) ( Table 2). The SNPs were
unevenly distributed among TLR genes, with two that were mono-
morphic (TLR2B, TLR 21), three with one SNP (TLR1A , TLR1B,
TLR2A), one with two SNPS (TLR4), one with three SNPs (TLR3),
one with 7 SNPs (TLR5), and one with 13 SNPs (TLR7). Out of the
 SMART protein schematics for each tchūriwat /tūturuatu toll- like receptor identified (available via licence: CC BY 2.0). Key
shows the visual representation for each protein domain. The pink boxes are areas of low compositional complexity
 Tchūriwat’/tūturuatu toll- like receptor gene diversit y statistics and comparisons for captive (n = 38) and wild (n = 26)
tchūriwat’/tūturuatu populations
 
    𝛑 
              
TLR1A 2289 (761) 1 (0:1) 3 (2:1) 1 (0:1) 1 (0:1) 3 (2:1) 1 (0:1) 2 (35) 3 (25) 1 0.00011 0.00042 0. 248 0.483 0 .244 03 0.91554
TLR1B 1857 (592) 1 (1:0) 1 (1: 0) 1 (1:0) 1 (1:0) 1 (1:0) 1 (1:0) 2 (35) 2 (25) 2 0.00024 0.00023 0.437 0.429 1.37017 1.23557
TLR2A 2423 (806) 1 (1:0) 0 0 1 (1:0) 0 0 2 (37) 1 (26) 10.00018 00.425 01.4606 0
TLR2B 2156 (717) 01 (1 :0) 0 0 0 0 1 (38) 2 (26) 1 0 0.0001 00.216 00.0466
TLR3 2367 (783) 3 (3:0) 2 (2:0) 2 (2:0) 1 (1 :0) 1 (1: 0) 1 (1: 0) 4 (38) 3 (26) 20.00045 0.00029 0.478 0.521 0.79912 0.46712
TLR4 22 59 (752) 2 (1:1) 1 (1:0 ) 1 (0:1) 1 (0:1) 0 0 3 (38) 2 (24) 20.00027 0.00013 0.537 0.284 0. 7749 6 0.35171
TLR5 2585 (860) 7 (4:3) 8 (4:4) 7 (4:3) 4 (2:2) 4 (2:2) 4 (2:2) 3 (35) 4 (23) 3 0.00126 0.00146 0. 551 0. 673 3.08884* 2.8 9296*
TLR7 3141 (1045 ) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 6 (38) 6 (26) 60.000206 0.00206 0.665 0.6 81 4.0516 4* 3.69 096*
TLR21 16 49 (548) 0 0 0 0 0 0 1 (38) 1 (26) 1 0 0 0 0 0 0
Total Private 3 (2:1) 4 (3:1) 2 (1 :1) 2 (2:0) 5 5
Tot al 28 (14:14) 29 (15:14) 25 (11:14) 22 (9:13) 22 (10:12 ) 20 (8 :12) 24 24 19
Note: As terisks denote signific ance at p < .01.
Abbreviations: bp(aa), base pairs (amino acids); syn:nsyn, ratio of synonymous to nonsynonymous SNPs.

|

MAGID et A l.
total SNPs, half (14) were synonymous, and the remaining half (14)
were nonsynonymous. The majority of SNPs (22) were within the
LRR binding domain. The majority of nonsynonymous SNPs (12)
were also located in the LRR region and half of them (7) caused a
physicochemical change due to the change in identity of the amino
acid (Table 3). A total of 24 haplot ypes were observed across all
TLR genes in the captive population, with the TLR genes that
have higher SNP diversity also having a higher number of inferred
haplotypes (Table 2). Tajima's D was nonsignificant for most loci,
except for TLR5 and TLR7, which were both significantly positive
(p< .01).
A total of 29 SNPs were observed in the TLR genes of the wild
tchūriwat’/tūturuatu population (n = 26) (Table 2). There were two
monomorphic genes (TLR2B, TLR 21), three with one SNP each
(TLR1B, TLR2A, TLR4), one with two SNPS (TLR3), one with three
SNPs (TLR1A), one with eight SNPs (TLR5), and one with 13 SNPs
(TLR7). Out of the total SNPs, slightly more than half (15) were syn-
onymous, and the remaining 14 were nonsynonymous. The majority
of SNPs (22) were within the LRR binding domain. The majorit y of
nonsynonymous SNPs (12) were also located in the LRR region and
half of them (7) caused a physicochemical change due to the change
in identit y of the amino acid (Table 3). A total of 24 haplotypes
were obser ved across all TLR genes in the wild population (Table 2).
Tajima's D was nonsignificant for most loci, except for TLR5 and
TLR7, which were both significantly positive (p< .01).
The wild and captive populations of tchūriwat/tūturuatu both
had low overall TLR SNP and haplotype diversity. Each had a similar
ratio of nonsynonymous to synonymous SNPs. Across all loci, the
populations shared 25 SNPs and 19 haplotypes (Table 2). The cap-
tive population had three private SNPs and five private haplotypes
in four genes (TLR1A, TLR2A, TLR3, TLR4). The wild population had
four private SNPs and five private haplot ypes in four genes (TLR1A,
TLR2B, TLR4, TLR5). Haplot ype networks reveal there are closely
related haplotypes for each TLR gene, and that many of the TLR hap-
lotypes shared between captive and wild tchūriwat’/tūturuatu occur
at different frequencies with each population (Figure S1). The PC A
reveals two relatively distinct genetic clusters corresponding to wild
and captive populations (Figure 3).
|
The bioinformatic approach developed here was successful for
identif ying TLR genes and characterizing TLR gene diversity in
tchūriwat’/tūturuatu. This research provides a critical first step to-
wards using TLR gene diversity to inform conservation action for a
threatened Aotearoa New Zealand endemic bird. We were able to
identif y and charac terize the same number of or more TLR genes
as compared to similar studies done using amplicon sequencing
of TLRs within threatened species (Dalton et al., 2016; Grueber
et al., 2015; Morrison et al., 2020). Fur ther, as the generation of
whole- genome resequencing data for threatened species becomes
routine (Allendor f et al., 2010 ; Forcina & Leonard, 2020), identify-
ing and characterizing TLRs using existing genomic resources will
be more cost effective and more efficient than traditional amplicon
sequencing.
All complete TLRs identified display the structure of TLR pro-
teins in other species: a region of leucine rich repeats (the bind-
ing domain), then a transmembrane protein (carboxyl- terminal
tail) where the TLR sits within the cell or lysosome membrane,
and then the TIR (toll- like/interleukin receptor) signalling domain
(Kannaki et al., 2010; Yilmaz et al., 2005). While LRRs vary in num-
ber between TLR sequences, the overall structure and approximate
amount of LRRs within each type of TLR is similar to what is seen in
chicken TLRs (Temperley et al., 2008). A partial sequence of TLR 15
was identified, but this sequence did not include the TIR signalling
 Tchūriwat/tūturuatu toll- like receptor gene diversit y statistics and comparisons for captive (n = 38) and wild (n = 26)
tchūriwat/tūturuatu populations
 
    𝛑 
              
TLR1A 2289 (761) 1 (0:1) 3 ( 2:1) 1 (0:1) 1 (0 :1) 3 (2:1) 1 (0 :1) 2 (35) 3 (25) 1 0.00011 0.00042 0.248 0.483 0 .24403 0.91 554
TLR1B 1857 (592) 1 (1:0) 1 (1:0) 1 (1:0) 1 (1:0) 1 (1:0) 1 (1:0) 2 (35) 2 (25) 2 0.0 0024 0.00023 0.437 0.429 1.370 17 1.23557
TLR2A 2423 (806) 1 (1:0) 0 0 1 (1:0) 0 0 2 (37 ) 1 (26) 10.00018 00.425 01.4606 0
TLR2B 2156 (717) 01 (1 :0) 0 0 0 0 1 (38) 2 (26) 1 0 0.0001 00.216 0−0.0466
TLR3 2367 (783) 3 (3:0) 2 (2:0) 2 (2:0) 1 (1 :0) 1 (1: 0) 1 (1: 0) 4 (38) 3 (26) 20.00045 0.00029 0.478 0.521 0.79912 0.46712
TLR4 22 59 (752) 2 (1:1) 1 (1:0 ) 1 (0:1) 1 (0:1) 0 0 3 (38) 2 (24) 20.00027 0.00013 0.537 0.284 0. 7749 6 0.35171
TLR5 2585 (860) 7 (4:3) 8 (4:4) 7 (4:3) 4 (2:2) 4 (2:2) 4 (2:2) 3 (35) 4 (23) 3 0.00126 0.00146 0. 551 0. 673 3.08884* 2.8 9296*
TLR7 3141 (1045 ) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 13 (4:9) 6 (38) 6 (26) 60.000206 0.00206 0.665 0.6 81 4.0516 4* 3.69 096*
TLR21 16 49 (548) 0 0 0 0 0 0 1 (38) 1 (26) 1 0 0 0 0 0 0
Total Private 3 (2:1) 4 (3:1) 2 (1 :1) 2 (2:0) 5 5
Tot al 28 (14:14) 29 (15:14) 25 (11:14) 22 (9:13) 22 (10:12 ) 20 (8 :12) 24 24 19
Note: As terisks denote signific ance at p < .01.
Abbreviations: bp(aa), base pairs (amino acids); syn:nsyn, ratio of synonymous to nonsynonymous SNPs.

|
 MAGID et Al.
domain. The sequence ended prematurely at the end of a contig, so
the scaf folding of the short- read genome may have prevented iden-
tification of the full gene. Morris et al. (2015) encountered a similar
problem identifying immune gene sequences when they were frag-
mented or split between contigs in the genome assembly.
In nonpasserine avian species, there are most often 10 avian TLRs,
eight of which are orthologous to other ver tebrate TLRs (TLR1A/B,
TLR2A/B, TLR3, TLR4, TLR5, TLR7), one that is orthologous to bony
fish and Xenopus (TLR21), and one that is unique to reptiles and birds
(TLR15) (Alcaide & Edwards, 2011; Grueber et al., 2014). Studies of
avian TLR evolution show a pattern of both gene loss and duplication
in these regions (Kannaki et al., 2010 ; Temperley et al., 2008; Velová
et al., 2018). Recent research suggests there is also a duplication of
TLR7 in some avian taxa. The duplicated TLR7 is thought to have
a similar function, though with slight difference, and this is an area
of ongoing research (Raven et al., 2017). To date, it has been found
in Charadriiformes, Cuculiformes, Mesiornithiformes, and some
Passeriiformes (Velová et al., 2018). Tūturuatu are within the order
Charad riiformes, and t his species is likely to h ave a duplication of TLR7
and thus have 11 TLRs total (Raven et al., 2017; Velová et al., 2018).
 Principal component
analysis genetic clustering of captive
(n = 38) and wild (n = 26) tchūriwat’/
tūturuatu. Produced using scaled mean
allele frequencies for each individual (see
text for details)
 Amino acid analysis of nonsynonymous SNPs within TLR genes of captive and wild tchūriwat’/tūturuatu populations
 

  

 
TLR 1A Bacteria, liproproteins 503 LRR As p/His Charge Captive, Wild
TLR 4 LPS, gram negative bacteria 372 LRR Asn/Asp Charge Captive
TLR 5 Flagellin 9LRR Phe/Leu None Captive, Wild
326 LRR Val/I le Charge Wild
664 TIR Hi s/Arg None Captive, Wild
843 TIR Lys/G lu Charge Captive, Wild
TLR7 ssRNA, virus 4LRR Ala/Pro None Captive, Wild
79 LRR Th r/Il e Polarity Captive, Wild
100 LRR Met /Leu None Captive, Wild
411 LRR Leu/Phe None Captive, Wild
459 LRR Gln/His Charge Captive, Wild
465 LRR Ala/Val None Captive, Wild
467 LRR Glu/Gly Charge, Polarity Captive, Wild
469 LRR Asn/Ser None Captive, Wild
755 LRR Gln/Lys Charge Captive, Wild
Abbreviations: AA, amino acid, standard abbreviations for amino acids used; LRR, leucine rich repeat binding domain; SNPs, single nucleotide
polymorphisms; TIR, TIR signalling domain; TLR- toll- like receptor.
aAlcaide and Edwards (2011).

|

MAGID et A l.
However, we did not find evidence of the duplication of TLR7
within tchūriwat’/tūturuatu. After identifying the first gene coding
for TLR7, this region was removed from the genome and the BLAST
search with reference TLR7 sequences was repeated with the re-
mainder of the genome. This second search was done to ensure
that the reference TLR sequence would not align to the previously
identified TLR7 region. There was no additional alignment to other
sequences in the genome. It is possible that the duplication of TLR7
within the reference genome is not fully resolved, meaning the du-
plicated genes are collapsed into one region. Alternatively, the du-
plicated region may be incomplete with gaps in the sequence or it
may be of low quality, so that it is either partially or fully cut out of
the assembly. In the case of a low quality or par tial assembly, the
duplicated region would not be easily identified through the use
of BLAST. In either case, only one TLR7 gene was identified. If the
duplication does exist and is of low quality within the tchūriwat’/
tūturuatu genome, this means the population resequencing data
may not properly align to the low- qualit y TLR7 gene and may instead
align to the other TLR7 gene. In this case, SNPs found within TLR7
may not be true SNPs, but instead may be artefacts of the duplicated
TLR7 reads misaligned to this region.
Although it was not possible to make direct comparisons to
all birds with TLR data, our findings are consistent with the lower
TLR gene diversity found within small, isolated populations of
other threatened species (African penguins (Spheniscus demer-
sus); Dalton et al., 2016; Mohua (Mohoua ochrocephala) Grueber
et al., 2015; Orange- bellied parrot (Neophema chrysogaster);
Morrison et al., 2020).
Comparing captive and wild tchūriwat’/tūturuatu reveals that
both populations have low TLR gene diversity. These findings are
consistent with Dalton et al. (2016) which show that both captive
and wild populations of African penguins (Spheniscus demersus) have
low levels of TLR SNP diversity. Unlike tchūriwat’/tūturuatu, captive
African penguin populations have a lower number of nonsynonymous
SNPs compared to wild populations (Dalton et al., 2016). However,
it is also important to note that there is a dif ference in sample size
between the captive and wild tchūriwat’/tūturuatu populations, be-
cause the captive population (n = 38) has more samples than the wild
population (n = 26). The inclusion of more wild individuals may re-
veal additional TLR diversity, especially if there are rare TLR SNPs or
haplotypes in the wild population. Both captive and wild populations
have nonsynonymous SNPs that mostly fall within the LRR binding
region, which may have an effe ct on binding aff inity and TLR response
(Keestra et al., 2008; Mat sushima et al., 2007). Additionally, some of
the nonsynonymous SNPs do cause physiochemical changes in the
amino acid sequence, but further research is needed to determine
how these SNPs may influence functionality of TLR proteins.
In captive and wild populations, genetic diversity is unevenly dis-
tributed across TLR genes. Most TLR genes have a low number of
SNPs and haplotypes. SNP diversity within TLR7 was the highest of
all TLR genes. However, this result may not reflect the true diversity
of TLR7 within each population, if there is a TLR7 duplication that
is not well resolved within the reference genome. Both captive and
wild tchūriwat’/tūturuatu populations also had two TLRs (TLR5 and
TLR7) with a value of Tajima's D that was significant and positive,
indicating that rare alleles are scarce. The positive, significant values
may reflect a rapid decrease in population size and/or balancing se-
lection (Tajima, 1989). Since this result was the same for both wild
and captive populations, it may reflect the overall history of species
decline, given that the size and distribution of the species decreased
dramatically in a shor t amount of time after mammalian predators
were introduced (Davis, 1994) and has remained small and restricted
for more than 100 years (DOC SPRG). Nevertheless, it is also possi-
ble that this result reflects balancing selection on one or both TLRs
(but see below).
The finding that captive and wild tchūriwat/tūturuatu fall into
two relatively distinct genetic clusters based on TLR haplotype di-
versity is consistent with a PCA based on 50 K SNPs (I. Cubrinovska,
unpublished data). While most alleles are shared bet ween captive
and wild populations, the frequencies of these alleles differ between
each population, and there are a small number of private alleles in
both populations. Given that there has been little to no genetic ex-
change between the populations for decades, these differences can
most likely be attributed to genetic drift. Previous research in other
bird species has also found genetic differentiation in both neutral
(microsatellites) and TLR loci, which generally indicates that genetic
drift may be stronger than selection (Gonzalez- Quevedo et al., 2015;
Grueber et al., 2013; Knafler et al., 2016). However, it is possible that
selection at TLR loci may contribute to the genetic differentiation be-
tween populations (Grueber et al., 2013; Knafler et al., 2016, 2017 ).
Further analysis is nece ssary to determine the impac ts of genetic drif t
and selection on TLR gene diversity in tchūriwat/tūturuatu. In the
meantime, low overall TLR gene diversity combined with the genetic
differentiation of captive and wild tchūriwat/tūturuatu lends support
to the recent decision to augment the captive population with birds
from the wild. Future research will investigate how this augmentation
affects TLR gene diversity in the captive population and whether it
leads to increased immune response in captive individuals.
|
Below we highlight a few technical considerations that will influence
the broad applicability of our bioinformatic approach to other bird
species. If the species reference genome is relatively incomplete,
it may not be suf ficient for identifying all TLR genes. Undertaking
strategies to improve completeness of the genome may help, includ-
ing alignment of multiple assemblies to fill in sequencing gaps. It may
also be necessary to undertake deliberate sequencing strategies
to maximize depth and minimize missingness in resequencing data.
For example, if preliminary screening of resequencing data shows
low depth and high missingness, it may be necessary to invest in
resequencing of relevant libraries. Also, if there are individuals who
are particularly important, it may be beneficial to (re)sequence them
to greater depth. Further, we acknowledge that our approach was
used to identify TLR genes and characterize TLR gene diversity in a

|
 MAGID et Al.
threatened bird species, so it is unknown whether it may be useful
in other species with more complex genomes or heterozygous popu-
lations. Nevertheless, advancing technologies such as long- read
sequencing may provide opportunities to apply our approach to spe-
cies with different levels of heterozygosity and genome complexity
(Bayer et al., 2020; Pollard et al., 2018; Tettelin et al., 20 05). Despite
these caveats, we reiterate that if a whole- genome resequencing
population data set is readily available for a species of interest, these
genomic resources can be used to provide a first look at TLR genes
and TLR gene diversity. Further, even if TLR gene identification and
characterization is incomplete or wholly unsuccessful, a bioinfor-
matic approach is likely to produce useful information about TLR
regions that can inform targeted sequencing of TLR genes (e.g., even
partial information about TLR sequences can inform primer design).

We acknowledge Hokotehi Moriori Trust and Ngāti Mutunga o
Wharekauri, especially Susan Thorpe and Gail Amaru, for their guid-
ance and support. We also thank the students of Kaingaroa and Te
One for selecting Maui as the individual used for the tchūriwat’/
tūturuatu reference genome. We are grateful to all members and
associates of the the New Zealand Department of Conser vation
Shore Plover Recovery Group (formerly Specialist Group), including
Troy Makan (DOC), Kate McInnes (DOC), Rose Collen (DOC), John
Dowding (DM Consultants), Anne Richardson, and Leigh Percasky
(Isaacs Conservation and Wildlife Trust), and Todd Jenkinson and
Mireille Hicks (Pūkaha National Wildlife Centre). Many thanks also to
Francois Bissey for access to the University of Canterbur y's Research
Compute Cluster, Natalie Forsdick and Levi Collier- Robinson for as-
sistance with bioinformatic scripts, and to Stephanie Galla for out-
reach support on Rēkohu/Wharekauri. This research was funded by
the New Zealand Ministr y of Business, Innovation and Employment
Endeavour Fund (UOCX1602 awarded to TES), the New Zealand
Department of Conservation (contract awarded to TES) and Fulbright
New Zealand (Fulbright US Graduate Award awarded to MM). Open
access publishing facilitated by University of Canterbury, as part of
the Wiley - University of C anterbur y agreement via the Council of
Australian University Librarians.

Author declare no conflicts of interest.
    
Scripts for TLR identification and resequencing pipeline have been
made available on GitHub (https://github.com/UC- ConSE RT/Magid_
et_al.git). Tchūriwat ’/tūturuatu are culturally significant to Moriori
and Māori (the Indigenous Peoples of the Chatham Islands archipel-
ago and Aotearoa New Zealand, respectively), all genomic data ob-
tained from species like tchūriwat’/tūturuatu is culturally significant
in its own right (Collier- Robinson et al., 2019). To ensure that the ac-
cessibility of these data are consistent with the CARE data principles
(Carroll et al., 2020, 2021), the short- read reference genome, unfil-
tered population vcf file, and associated metadata used in this study
will be made available from a local genome browser (http://www.
uccon sert.org/data/) on the recommendation of Hokotehi Moriori
Trust and Ngāti Mutunga o Wharekauri Iwi Trust.

Tchūriwat’/tūturuatu/shore plover is an endemic shorebird clas-
sified as Nationally Critical according to the New Zealand Threat
Classification System criteria (Robertson et al., 2021). This cocreated
research is part of a long- standing research par tnership between the
Conservation Systematics and Evolution Research Team (ConSERT,
including TES, MM, IC) and Aotearoa New Zealand's Department of
Conser vation Shore Plover Recovery Group, which is a team of con-
servation scientists and prac titioners (including DH, BG, TES) that
provide guidance regarding the development and execution of the
Shore Plover Recovery Plan to ensure the recovery of tchūriwat’/
tūturuatu in the wild. This research was also conducted in collabo-
ration with Hokotehi Moriori Trust, Ngāti Mūtunga o Wharekauri
Iwi Trust, and the local community on Rēkohu/Wharekauri in the
Chatham Island archipelago. For example, following engagement
with both trusts regarding the selection of an individual bird for
the tchūriwat’/tūturuatu reference genome, IC and Stephanie Galla
visited the two schools on Rēkohu/Wharekauri: They first visited
Kaingaroa School (one class, Years 1– 8) and asked students to name
both candidate birds for the reference genome. Then they visited
Te One School (three classes, Years 1– 2, Years 3– 5, Years 6– 8), and
asked students to vote on whether “Kina” or “Maui” should be used
for the reference genome, and Maui won. MM also developed an im-
mune system outreach module that highlights our ongoing research
with tchūriwat’/tūturuatu that TES subsequently shared with imi
and iwi, and the principals of both schools during a recent visit to
Rēkohu/Wharekauri.

Molly Magid https://orcid.org/0000-0002-6598-2530
Jana R. Wold https://orcid.org/0000-0001-8095-4282
Roger Moraga https://orcid.org/0000-0001-7806-1135
Brett D. Gartrell https://orcid.org/0000-0002-8062-9313
Tammy E. Steeves https://orcid.org/0000-0003-2112-5761

Acevedo- Whitehouse, K., & Cunningham, A. (2006). Is MHC
enough for understanding wildlife immunogenetics? Trends in
Ecology & Evolution, 21(8), 433– 438. https://doi.org/10.1016/j.
tree.2006.05.010.
Acevedo- Whitehouse, K.,Gulland, F.,Greig, D., & Amos, W. (2003).
Disease susceptibility in California sea lions. Nature, 422(6927),
35– 35. https://doi.org/10.1038/422035a.
Alcaide, M., & Edwards, S. V. (2011). Molecular evolution of the toll- like
receptor multigene family in birds. Molecular Biology and Evolution,
28(5) , 1703– 1715. https://doi.org/10.1093/molbe v/msq351
Allendorf, F. W., Hohenlohe, P. A., & Luikart, G . (2010). Genomics and the
future of conservation genetics. Nature Reviews Genetics, 11(10),
69 7– 709. https://doi.org/10.1038/nrg284 4
Alley, M., & Gartrell, B. (2019). Wildlife diseases in New Zealand: Recent
findings and future challenges. New Zealand Veterinary Journal,
67(1), 1– 11. ht tps://doi.org/10.1080/0 0480 169.2018.1520656

|

MAGID et A l.
Altizer, S.,Harvell, D., & Friedle, E. (2003). Rapid evolutionary dynamics
and disease threats to biodiversity. Trends in Ecology & Evolution,
18(11), 589– 596. https://doi.org/10.1016/j.tree.2003.08.013.
Angeloni, F.,Wagemaker, N.,Vergeer, P., & Ouborg, J. (2012). Genomic
toolboxes for conservation biologists. Evolutionary Applications,
5(2), 130– 143. h ttp s://doi.org /10.1111/ j.1752- 4571.2011. 00217.x
Antonides, J., Mathur, S., Sundaram, M., Ricklef s, R., & DeWoody, J. A.
(2019). Immunogenetic response of the bananaquit in the f ace of
malarial parasites. BMC Evolutionary Biology, 19(1), 107. ht t p s :// d oi .
org /10.1186/s128 6 2- 019- 1435- y
Auwera, G. A.,Carneiro, M. O., Hartl, C .,Poplin , R.,delAngel, G., Levy-
Moonshine, A.,Jordan, T.,Shakir, K.,Roazen, D.,Thibault, J.,Banks,
E.,Garimella, K. V.,Altshuler, D.,Gabriel, S., & DePristo, M. A. (2013).
From FastQ data to high- confidence variant calls: The genome anal-
ysis toolkit best practices pipeline. Current Protocols in Bioinformatics,
43(1), 11. https://doi.org/10.10 02/0 4712 50953.bi111 0 s43
Bateson , Z. W., Hammerly, S. C., Johnson, J. A., Morrow, M. E.,
Whittingham, L. A., & Dunn, P. O. (2016). Specific alleles at im-
mune genes, rather than genome- wide heterozygosity, are related
to immunity and survival in the critically endangered At twater's
prairie- chicken. Molecular Ecology, 25(19), 4730– 474 4. ht t p s: //d o i .
org /10.1111/mec.13793
Bayer, P. E., Golicz, A . A., Scheben, A., Batley, J., & Edwards, D. (2020).
Plant pan- genomes are the new reference. Nature Plants, 6(8), 914–
920. https://doi.org/10.1038/s4147 7- 020- 0733- 0
Behzadi, P., García- Perdomo, H. A., & Karpiński, T. M. (2021). Toll-
like receptors: General molecular and struc tural biology.
Journal of Immunology Research, 2021, e9914854. ht t p s : //d oi .
org/10.1155/2021/9914854
Beutler, B., & Rehli, M. (20 02). Evolution of the TIR, tolls and TLRs:
Functional inferences from computational biology. In B. Beutler &
H. Wagner (Eds.), Toll- like receptor family members and their ligands
(pp. 121). Springer.
Birney, E., Thompson, J. D., & Gibson, T. J. (1996). PairWise and
SearchWise: Finding the optimal alignment in a simultaneous com-
parison of a protein profile against all DNA translation frames.
Nucleic Acids Research, 24(14), 27 30 – 2739. h ttps://doi.org/10.1093/
nar/24.14.2730
Bolte, A . L., Meurer, J., & Kaleta, E. F. (1999). Avian host spectrum of
avipoxviruses. Avian Pathology, 28(5), 415– 432. h t t p s :// d oi .
org /10.1080/03079 45999 44 34
Bonnea ud, C., Balenger, S. L., Zhang, J., Edwards, S. V., & Hill, G . E. (2012).
Innate immunity and the evolution of resistance to an emerging
infectious disease in a wild bird. Molecular Ecology, 21(11), 2628–
26 39. https://doi .org /10.1111/j.136 5- 294X .20 12.05551.x
Boyle, D. B. (2007). Genus Avipoxvirus. In A. A. Mercer, A. Schmidt, & O.
Weber (Eds.), Poxviruses. Birkhäuser Advances in Infectious Diseases.
Birkhäuser.
Brandies, P. A., Wright, B. R., Hogg, C . J., Grueber, C. E., & Belov, K.
(2020). Characterisation of reproductive gene diversity in the en-
dangered Tasmanian devil. Molecular Ecology Resources, 21, 721
732. ht tps://doi.o rg/10 .1111/1755- 099 8.13295
Browning, B. L., Tian, X., Zhou , Y., & Browning, S. R . (2021). Fast two-
stage phasing of large- scale sequence data. The American Journal
of Human Genetics, 108(10), 188 0– 1890. htt ps://doi.o rg/10.1016/j.
ajhg.2021.08.005
Carroll, S. R., Garba, I., Figueroa- Rodríguez, O. L., Holbrook , J., Lovett,
R., Materechera, S ., Parsons, M., Raseroka, K., Rodriguez-
Lonebear, D., Rowe, R., Sara, R., Walker, J. D., Anderson, J., &
Hudson, M. (2020). The CARE principles for indigenous data gov-
ernance. Data Science Journal, 19, 43. https://doi.org/10.5334/
dsj- 2020- 043
Carroll, S. R., Herczog, E., Hudson, M., Russell, K., & St all, S. (2021).
Operationalizing the CARE and FAIR principles for indigenous data
futures. Scientific Data, 8(1), 108. ht tps://doi.org/10.1038/s4159 7-
021- 00892 - 0
Chávez- Treviño, A ., Canales- del- Castillo, R., Ruvalcaba- Or tega, I.,
Reséndez- Pérez, D., González- Rojas, J. I., Guzmán- Velasco, A.,
& Panjabi, A. O. (2017). Primer development for amplif ication of
toll- like genes for the assessment of adaptive genetic diversit y in
vulnerable grassland bird species. Conservation Genetics Resources,
9(3), 385– 387. https://doi.org/10.1007/s1268 6- 017- 0690- 8
Chen, A . Y.- A ., Huang, C .- W., Liu, S.- H., Liu, A.- C., & Chaung, H.- C.
(2021). Single nucleotide polymorphisms of immunity- related
genes and their effects on immunophenotypes in different
pig breeds. Genes, 12(9), 1377. https://doi.org/10.3390/genes
12091377
Cho, P., Gelinas, L., Corbett, N. P., Tebbutt, S. J., Turvey, S. E., Fortuno,
E. S., & Kollmann, T. R. (2013). Association of common single-
nucleotide polymorphisms in innate immune genes with differ-
ences in TLR- induced cytokine production in neonates. Genes &
Immunity, 14(4), 199– 211. https://doi.org/10.1038/gene.2013.5
Clark, R., & Kupper, T. (2005). O ld meets new: The interac-
tion bet ween innate and adaptive immunity. Journal of
Investigative Dermatology, 125(4), 629– 637. ht t p s : //d o i.
org/10.1111/j.0022- 202X.2005.23856.x
Collier- Robinson, L., Rayne, A., Rupene, M., Thoms, C., & Steeves, T.
(2019). Embedding indigenous principles in genomic research
of culturally significant species: A conservation genomics case
study. New Zealand Journal of Ecology, 43(3), 1– 9. ht t p s: //d o i .
org /10.2 0417/ nzje c ol.43. 36
Dalton, D. L ., Vermaak, E., Smit- Robinson, H. A., & Kot ze, A. (2016). Lack
of diversity at innate immunity toll- like receptor genes in the criti-
cally endangered white- winged flufftail (Sarothrura ayresi). Scientific
Reports, 6(1), 1– 8. https://doi.org/10.1038/srep3 6757
Danecek , P., Auton, A ., Abecasis, G ., Albers, C. A ., Banks, E., DePristo, M.
A., Handsaker, R. E., Lunter, G., Marth, G. T., Sherr y, S. T., McVean,
G., & Durbin, R. (2011). The variant call format and VCFtools.
Bioinformatics, 27(15), 2156– 2158. https://doi.org/10.1093/bioin
forma tics/btr330
Davies, C ., Taylor, M., Hammers, M., Burke, T., Komdeur, J., Dugdale,
H., & Richardson, D. (2021). Contemporary evolution of the viral-
sensing TLR3 gene in an isolated vertebrate population [Preprint].
Preprints. https://doi.org/10.22541/ au.16099 2924.47833 640/v1
Davis, A . (1994). Status, distribution, and population trends of the New
Zealand shore plover. Notornis, 41, 1 79 – 19 4.
De- Kayne, R ., Frei, D., Greenway, R., Mendes, S. L., Retel, C., &
Feulner, P. G. D. (2020). Sequencing platform shifts provide op-
portunities but pose challenges for combining genomic data
sets. Molecular Ecology Resources, 21(3), 653– 660. h t t p s: //d o i .
org /10.1111/1755- 0 998.1330 9
Dowding, J. E., Collen, R.,Davis, A. M., O'Connor, S. M., & Smith, M.
H.. (2005). Gains and losses in the New Zealand Shore Plover
(Thinornis novaeseelandiae). Recovery Programme 1993– 2003,
Volume: Internaional Wader Studies17: 36– 42 Conference:Status and
Conser vation of Shorebirds in the East Asian- Australasian Flyway.
Dowding, J. E. (2013). Reducing the risk of extinction of a globally threat-
ened shorebird: Translocations of the shore plover (Thinornis novae-
seelandiae), 1990– 2012. 15.
Ekblom, R., & Wolf, J. B. W. (2014). A field guide to whole- genome se-
quencing, assembly and annotation. Evolutionary Applications, 7(9),
1026– 1042. https://doi.org/10.1111/eva.12178
FastQC (2010). FastQC: A Quality Control Tool for High Throughput
Sequence Data. In S. Andrews (Ed.), Brabraham Bioinformatics.
(Version 0.11.8) [Online]. Available online at : http://www.bioin
forma tics.babra ham.ac.uk/proje cts/fastq c/
Fellah, J. S ., Jaffredo, T., & Dunon, D. (2008). Development of the avian
immune system. In K. A. Schat, B. Kaspers, & P. Kaiser (Eds.), Avian
immunology (pp. 5166). Elsevier.
Feng, S., Stiller, J., & Deng, Y. (2020). Dense sampling of bird diversity
increases power of comparative genomics. Nature, 587, 252– 257.
ttps://doi.org/10.1038/s4158 6- 020- 2873- 9

|
 MAGID et Al.
Forcina, G., & Leonard, J. A. (2020). Tools for monitoring genetic diversity
in mammals: Past, present, and future. In J. Or tega & J. E. Maldonado
(Eds.), Conservation genetics in mammals: Integrative research using
novel approaches (pp. 13– 27). Springer International Publishing.
Galla, S. J.,Moraga, R.,Brown, L.,Cleland, S.,Hoeppner, M. P.,Maloney, R.
F.,Richardson, A.,Slater, L.,Santure, A. W., & Steeves, T. E. (2020).
A comparison of pedigree, genetic and genomic estimates of re-
latedness for informing pairing decisions in two critically endan-
gered birds: Implications for conservation breeding programmes
worldwide. Evolutionary Applications, 13 (5), 991– 1008. ht t p s: //d o i .
org /10.1111/eva.12916
Galla, S. J. (2019). Conservation Genomic Management of Two Critically
Endangered New Zealand Birds. PhD Thesis. Un iversity of C anterbur y
School of Biological S ciences.
Gartrell, B. D., Alley, M. R., Pauli, J., Collen, R ., & Barlow, K. (2002). An
outbreak of poxvirus infection in captive shore plovers. Ko kako,
9(2 ) , 9– 11 .
Glassock, G. L., G rueber, C. E., Belov, K., & Hogg, C. J. (2021). Reducing
the extinction risk of populations threatened by infectious dis-
eases. Diversity, 13(2), 63. https://doi.org/10.3390/d1302 0063
Goltsman, E., Ho, I., & Rokhsar, D. (2017). Meraculous- 2D: Haplotype-
sensitive assembly of highly heterozygous genomes. arXiv. ht t ps : //d o i .
org /10.4 8550/ AR XIV.170 3.0 9852
Gonzalez- Quevedo, C ., Spurgin, L. G., Illera, J. C., & Richardson, D. S.
(2015). Drift, not selection, shapes toll- like receptor variation
among oceanic Island populations. Molecular Ecology, 24(23), 5852–
5863. https://doi .org /10.1111/mec.13437
Grueber, C. E. (2015). Comparative genomics for biodiversity conserva-
tion. Computational and Structural Biotechnology Journal, 13, 370–
375. https://doi.org/10.1016/j.csbj.2015.05.003
Grueber, C. E., Knafler, G. J., King, T. M., Senior, A. M., Grosser, S.,
Robertson, B., Wes ton, K. A ., Brekke, P., Harris , C. L. W., &
Jamieson, I. G. (2015). Toll- like receptor diversity in 10 threat-
ened bird species: Relationship with microsatellite heterozygosity.
Conservation Genetics, 16(3), 595– 611. htt ps://doi.org/10.1007/
s1059 2- 014- 06 85- x
Grueber, C. E., Sutton, J. T., Heber, S., Briskie, J. V., Jamieson, I. G., &
Robertson, B. C . (2017). Reciprocal translocation of small numbers
of inbred individuals rescues immunogenetic diversity. Molecular
Ecology, 26(10), 2660– 2673. http s://doi. org /10.1111/mec.14 063
Grueber, C. E., Wallis, G . P., & Jamieson, I. G. (2013). Genetic drif t out-
weighs natural selec tion at toll- like receptor (TLR) immunity loci
in a re- introduced population of a threatened species . Molecular
Ecology, 22(17 ), 4470– 4482. https://doi.org/10.1111/mec.12404
Grueber, C. E., Wallis, G . P., & Jamieson, I. G. (2014). Episodic positive
selection in the evolution of avian toll- like receptor innate immu-
nity genes. PLoS One, 9(3), e89632. https://doi.org/10.1371/journ
al.pone.0089632
Grueber, C. E., Wallis, G . P., King, T. M., & Jamieson, I. G. (2012). Variation
at innate immunity toll- like receptor genes in a bottlenecked popu-
lation of a New Zealand Robin. PLoS One, 7(9), e45011. ht t p s: //d o i .
org/10.1371/journ al.pone.0045011
Hansen, W. (1999). Avian pox. Report no. 1999– 0001; information and
technology repor t (pp. 163– 169). USGS Publications Warehouse.
http://pubs.er.usgs.gov/publi catio n/2001161
Hart mann, S. A., S chaefer, H. M., & Sege lbacher, G. (2014). Genet ic deple-
tion at adaptive but not neutral loci in an endangered bird specie s.
Molecular Ecology, 23 (23), 5712– 5725. https: //doi .org/10.1111/
mec .12975
Heber, S., Varsani, A., Kuhn, S., Girg , A., Kempenaers, B., & Briskie, J.
(2013). The genetic rescue of two bottlenecked south Island robin
populations using translocations of inbred donors. Proceedings
of the Royal Society B: Biological Sciences, 280(1752), 20122228.
https://doi.org/10.1098/rspb.2012.2228
Heng, J., Su, J., Huang, T., Dong, J., & Chen, L. (2011). The poly morphism
and haplotype of TLR3 gene in grass carp (Ctenopharyngodon
idella) and their associations with susceptibility/resistance to grass
carp reovirus. Fish & Shellfish Immunology, 30(1), 45– 50. h t tp s : //d o i.
org/10.1016/j.fsi.2010.09.004
Hoelzel, A. R.,B ruford, M. W., & Fleischer, R. C. (2019). Conservation of
adaptive potential and functional diversity. Conservation Genetics,
20(1), 1– 5. https://doi.org/10 .1007/s1059 2- 019- 01151 - x.
Irizarry, K. J. L., Bryant, D., Kalish, J., Eng, C., Schmidt, P. L., Barrett,
G., & Barr, M. C. (2016). Integrating genomic data sets for knowl-
edge discovery: An informed approach to Management of Captive
Endangered Species. International Journal of Genomics, 2016,
e2374610. h tt ps: //doi .org/10.1155/2016/2374610
Jombar t, T. (2008). Adegenet: A R package for the multivariate analysis
of genetic markers. Bioinformatics, 24(11), 14 03– 1405. h t t p s :// do i .
org/10.1093/bioin forma tics/btn129
Jombart, T. (2015). An introduction to adegenet 2.0.0 (p. 79). Imperial
College London. MRC Centre for Outbreak Analysis and
Modelling.
Junjie, X ., Songyao, J., Minmin, S., Yanyan, S., Baiyong, S., Xiaxing, D.,
Jiabin, J., Xi, Z., & Hao, C . (2012). The association bet ween toll-
like receptor 2 single- nucleotide polymorphisms and hepatocel-
lular carcinoma susceptibility. BMC Cancer, 12, 57. ht t p s :// d oi .
org /10.1186/1471- 2407- 12- 57
Kannaki, T. R., Reddy, M. R., Shanmugam, M., Verma, P. C., & Shar ma,
R. P. (2010). Chicken toll- like receptors and their role in immu-
nity. World's Poultry Science Journal, 66(4), 727– 738. h t t p s :// do i .
org/10.1017/S0043 93391 0000693
Kawasaki, T., & Kawai, T. (2014). Toll- like receptor signaling path-
ways. Frontiers in Immunology, 5, 461. https://doi.org/10.3389/
fimmu.2014.00461
Keestra, A. M., de Zoete, M. R., van Aubel, R. A. M. H., & van Putten, J.
P. M. (2008). Func tional characterization of chicken TLR5 reveals
species- specific recognition of flagellin. Molecular Immunology,
45(5), 1298– 1307. https://doi.org/10.1016/j.molimm.2007.09.013
Kielbasa, S. M., Wan, R ., Sato, K., Horton, P., & Frith, M. C. (2011).
Adaptive seeds tame genomic sequence comparison. Genome
Research, 21(3), 487– 493. ht tps ://doi.o rg/10.1101/gr.113985.110
Knafler, G., Grueber, C., Sutton, J., & Jamieson, I. (2017). Differential
patterns of diversit y at microsatellite, MHC, and TLR loci in bottle-
necked south Island saddleback populations. New Zealan d Journal of
Ecology, 41(1), 98– 106. https://doi.org/10.20417/ nzjec ol.41.8
Knafler, G. J., Ortiz- Catedral, L., Jackson, B., Varsani, A., Grueber, C.
E., Robertson, B . C., & Jamieson, I. G. (2016). Comparison of beak
and feather disease virus prevalence and immunity- associated ge-
netic diversity over time in an Island population of red- crowned
parakeets. Archives of V irology, 161(4), 811– 820. ht t ps : //d o i .
org /10.10 07/s0070 5- 015- 2717- 3
Kohn, M. H., Murphy, W. J., Ostrander, E. A., & Wayne, R. K. (20 06).
Genomics and conservation genetics. Trends in Ecology & Evolution,
21(11), 629– 637. https://doi.org/10.1016/j.tree.2006.08.001
Kreuger, F. (2021). Trim Galore (0.6.4) [Computer software]. GitHub re-
pository, https://github.com/Felix Krueg er/TrimG alore
Kristensen, D. M., Wolf, Y. I., Mushegian, A. R., & Koonin, E . V. (2011).
Computational methods for gene Orthology inference. Briefings
in Bioinformatics, 12(5), 379– 391. https://doi.org/10.1093/bib/
bbr030
Lara, C. E., Grueber, C. E., Holtmann, B., Santos, E. S. A ., Johnson, S. L.,
Robertson, B. C ., Castaño- Villa, G. J., Lagisz, M., & Nakagawa, S.
(2020). Assessment of t he dunnocks' introduc tion to New Zealand
using innate immune- gene diversity. Evolutionary Ecology, 34, 803–
820. https://doi.org/10.1007/s1068 2- 020- 10070 - 0
Leigh, J. W., & Br yant, D. (2015). Popart: Full- feature software for hap-
lotype network construction. Methods in Ecology and Evolution, 6(9),
1110– 1116. h ttp s://doi. org /10.1111/2 041- 210X .12410
Letunic , I., Khedk ar, S., & Bork, P. (2021). SMART: Recent updates, new
developments and status in 2020. Nucleic Acids Research, 49(D1),
D458– D46 0. https://doi.org/10.1093/nar/gkaa937

|

MAGID et A l.
Li, H., & Durbin, R. (2009). Fast and accurate shor t read alignment with
Burrows- Wheeler transform. Bioinformatics, 25(14), 1754– 1760.
https://doi.org/10.1093/bioin forma tics/btp324.
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth,
G., Abe casis, G., & Durbin, R. (2009). The sequence alignment/map
format and SAMtools. Bioinformatics, 25(16), 2078– 2079. ht t ps : //
doi.org/10.1093/bioin forma tics/btp352
Mable, B . K. (2019). Conservation of adaptive potential and function al di-
versity: Integrating old and new approaches. Conservation Genetics,
20(1), 89– 100. ht tps://doi.org/10 .1007/s1059 2- 018- 1129- 9
Madden, T. (2013). The BLAST sequence analysis tool. In The NCBI
handbook [internet] (2nd ed.). National Center for Biotechnolog y
Information (US). https://www.ncbi.nlm.nih.gov/books/ NBK15
33 87/
Marçais, G., & Kingsford, C. (2011). A fast, lock- free approach for ef-
ficient parallel counting of occurrences of k- mers. Bioinformatics,
27(6), 764– 770. https://doi.org/10.1093/bioin forma tics/btr011
Marsden, C. D., Verberkmoes, H., Thomas, R., Wayne, R. K., & Mable,
B. K. (2013). Pedigrees, MHC and micros atellites: An integrated
approach for genetic management of captive African wild dogs
(Lycaon pictus). Conservation Genetics, 14(1), 171– 183. h t t ps : //d o i .
org /10.1007/s1059 2- 012- 0440 - 0
Matsushima, N., Tanaka, T., Enkhbayar, P., Mikami, T., Taga, M., Yamada,
K., & Kuroki, Y. (2007). Comparative sequence analysis of leucine-
rich repeats (LRRs) within vertebrate toll- like receptors. BMC
Genomics, 8(1), 124. htt ps://doi.org /10.1186/1471- 2164- 8 - 124
McLennan, E. A., Grueber, C. E., W ise, P., Belov, K., & Hogg, C. J. (2020).
Mixing genetically differentiated populations successfully boosts
diversity of an endangered carnivore. Animal Conservation, 23(6),
70 0 – 71 2 . http s://doi. org /10.1111/acv.125 89
Moraga, R. (2018). SubSampler_SNPcaller. Tea Break Bioinformatics.
[Computer software] Available at: ht tps://github.com/Lanil en/
SubSa mpler_SNPca ller
Morris, K. M., Wright , B., Grueber, C. E., Hogg , C., & Belov, K. (2015).
Lack of genetic diversity across diverse immune genes in an endan-
gered mammal, the Tasmanian devil (Sarcophilus harrisii). Molecular
Ecology, 24(15), 386 0– 3872. https: //doi .org/10.1111/mec.13291
Morrison, C. E., Hogg, C. J., Gales, R., Johnson, R. N ., & Grueber, C. E.
(2020). Low innate immune- gene diver sity in the critically en-
dangered orange- bellied parrot (Neophema chrysogaster). Emu -
Austral Ornithology, 120(1), 56– 6 4. https://doi. org/10 .1080/01584
197.2019.1686994
Mueller, R. C., Mallig, N., Smith, J., Eöery, L., Kuo, R. I., & Kraus, R. H. S.
(2020). Avian immunome DB: An example of a user- friendly inter-
face for extracting genetic information. BMC Bioinformatics, 21(1),
502. https://doi.org/10.1186/s1285 9- 020- 03764 - 3
Narayanan, K. B., & Park, H. H. (2015). Toll/interleukin- 1 receptor (TIR)
domain- mediated cellular signaling pathways. Apoptosi s, 20(2),
196 – 20 9. ht tps://doi.org/10.1007/s1049 5- 014- 1073- 1
NCBI (2020). BLAST results: Expect values, part 1. The National Library
of Medicine. https://www.youtu be.com/watch ?v=ZN3Rr XAe0uM
O’Connor, E. A .,Westerdahl, H.,Burri, R., & Edwards, S. V. (2019). Avian
MHC evolution in the era of genomics: Phase 1.0. Cells, 8(10), 1152.
https://doi.org/10.3390/cells 8101152.
Offor d, V., Coffey, T. J., & Werling, D. (2010). LR Rfinder: A we b applicati on
for the identification of leucine- rich repe ats and an integrative toll-
like receptor database. Developmental & Comparative Immunology,
34(10), 10 35– 1041. https://doi.org/10.1016/j.dci.2010.05.004
Ouborg, N. J., Pertoldi, C., Loeschcke, V., Bijlsma, R. K., & Hedrick, P. W.
(2010). Conservation genetics in transition to conservation genom-
ics. Trends in Genetics, 26(4), 177– 187. htt ps://doi.o rg/10.1016/j.
tig.2010.01.001
Palacios, M. G., Cunnick, J. E., Vleck, D., & Vleck, C. M. (20 09). Ontogeny
of innate and adaptive immune defense components in free- living
tree swallows, Tachycineta bicolor. Developmental & Comparative
Immunology, 33(4), 456– 463. https://doi.org/10.1016/j.
dci.2008.09.006
Pasare, C., & Medzhitov, R. (2005). Toll- like receptors: Linking innate and
adaptive immunity. In S. G upta, W. E. Paul, & R. Steinman (Eds.),
Mechanisms of lymphocyte activation and immune regulation X (pp.
11– 18). Springer US.
Podlaszc zuk, P., Indykiewic z, P., Kamiński, M., & Minias, P. (2021).
Physiological condition reflects polymorphism at the toll- like re-
ceptors in a colonial waterbird. Ornitholog y, 138, ukab052. ht t p s ://
doi.org/10.1093/ornit holog y/ukab052
Pollard, M. O., Gurdasani, D., Mentzer, A. J., Porter, T., & Sandhu, M. S.
(2018). Long reads: Their purpose and place. Human Molecular
Genetics, 27(R2), R234– R241. https://doi.org/10.1093/hmg/ddy177
Primmer, C. R. (2009). From conservation genetic s to conservation ge-
nomics. Annals of the New York Academy of Sciences, 1162 (1), 357–
368. https://doi.org/10.1111/j.1749- 6632.2009.04444.x
Quéméré, E., Hessenauer, P., Galan, M., Fernandez, M., Merlet, J., Chaval,
Y., Morellet, N., Verheyden, H., Gilot- Fromont, E., & Char bonnel, N.
(2021). Pathogen- mediated selection favours the maintenance of
innate immunity gene polymorphism in a widespread wild ungu-
late. Journal of Evolutionary Biology, 34(7), 1156– 1166 . h t t p s :// do i .
org /10.1111/jeb.13876
Raven, N., Lisovski, S., Klaassen, M., Lo, N., Madsen, T., Ho, S. Y. W., &
Ujvari, B. (2017). Purif ying selec tion and concerted evolution of
RNA- sensing toll- like receptors in migrator y waders. Infection,
Genetic s and Evolution, 53, 135– 145. https://doi.org/10.1016/j.
meegid.2017.05.012
Ren, Y., MacPhillamy, C., To, T.- H., Smith, T. P. L., Williams, J. L., &
Low, W. Y. (2021). Adaptive selection signatures in river buffalo
with emphasis on immune and major histocompatibility complex
genes. Genomics, 113 , 3599– 3609. https://doi.org/10.1016/j.
ygeno.2021.08.021
Robertson, H. A .,Baird, K. A .,Elliott, G. P.,Hitchmough , R. A.,Mcarthur, N.
J.,Mak an, T. D.,O'Do nnell, M. C., Sagar, P. M.,Scof ield, R. P.,Taylor, G.
A., & Michel, P. (2021). Conservation status of birds in Aotearoa New
Zealand, 2021. Report number: New Zealand classification series 36.
Affiliation: New Zealand Department of Conservation.
Ross- Gillespie, A .,O’Riain, M. J., & Keller, L. F. (2007). Viral epi-
zootic reveals inbreeding depression in a habitually in-
breeding mammal. Evolution, 61(9), 2268– 2273. ht t p s : //d oi .
org /10.1111/j.1558- 5646. 2007.00177.x.
Rozas, J.,Ferrer- Mata, A.,Sánchez- DelBarrio, J. C.,Guirao- Rico,
S.,Librado, P.,Ramos- Onsins, S. E., & Sánchez- Gracia, A . (2017).
DnaSP 6: DNA sequence polymorphism analysis of large data set s.
Molecular Biology and Evolution, 34(12), 3299– 3302. h t t ps : //d o i .
org /10.10 93/molbe v/msx24 8.
Sauna, Z. E., & Kimchi- Sarfaty, C. (2011). Understanding the contribu-
tion of synonymous mut ations to human disease. Nature Reviews
Genetics, 12(10), 683– 691. https://doi. org/10 .1038/nr g3051
Schult z, J., Milpet z, F., Bork, P., & Ponting, C . P. (1998). SMART, a simple
modular architecture research tool: Identification of signaling do-
mains. Proceedings of the National Academy of Sciences of the United
States of America, 95(11), 5857– 5864. https://doi. org/10 .1073/
pnas.95.11.5857
Singh, B. P., Chauhan, R. S., & Singhal, L. K. (2003). Toll- like receptors and
their role in innate immunity. Current Science, 85(8), 115 6– 1164.
Sommer, S. (2005). The importance of immune gene variability (MHC)
in evolutionary ecology and conservation. Frontiers in Zoolog y, 2(1),
1– 18 . h ttp s://doi.org/10.1186/1742- 99 94- 2- 16.
Spielman, D.,Brook, B. W.,Briscoe, D. A., & Frank ham, R. (20 04). Does
Inbreeding and loss of genetic diversity decrease disease re-
sistance? Conservation Genetics, 5(4), 439– 448. ht t p s: //d o i .
org /10.1023/b:coge. 00000 41030.76598.cd.
Tajima, F. (1989). Statistical method for tes ting the neutral mutation hy-
pothesis by DNA polymorphism. Genetics, 123(3), 585– 595.

|
 MAGID et Al.
Takeda, K., & Akira, S. (20 05). Toll- like receptors in innate immunit y.
International Immunology, 17(1), 1– 14. https ://doi.org/10.1093/
intim m/dxh186
Temperley, N. D., Berlin, S., Paton, I. R., Griffin, D. K ., & Burt, D. W.
(2008). Evolution of the chicken toll- like receptor gene family: A
story of gene gain and gene loss. BMC Genomics, 9, 62. h t tp s : //d o i.
org /10.1186/1471- 2164- 9- 62
Tettelin, H., Masignani, V., Cieslewicz, M. J., Donati, C ., Medini, D., Ward,
N. L., Angiuoli, S. V., Crabtree, J., Jones, A. L ., Durkin, A. S., DeBoy, R.
T., Davidsen, T. M., Mora, M., Scarselli, M., Ros, I. M. y, Peterson, J. D.,
Hauser, C. R ., Sundaram, J. P., Nelson, W. C., … Fraser, C. M. (2005).
Genome analysis of multiple pathogenic isolates of Streptococcus
agalactiae: Implications for the microbial “pan- genome”. Proceedings
of the National Academy of Sciences of the United States of America,
102(39), 13950. https ://doi.org/10.1073/pnas.050 67 58102, 13955
van Riper, C., & Forrester, D. J. (2007). Avian pox. In N. J. Thomas , D. B.
Hunter, & C. T. Atkinson (Eds.), Infectious diseases of wild birds (pp.
131– 176). John Wiley & Sons, Ltd.
Velová, H., G utowska- Ding, M. W., Burt, D. W., & Vinkler, M. (2018).
Toll- like receptor evolution in birds: Gene duplication, pseudoge-
nization, and diversifying selection. Molecular Biolog y and Evolution,
35(9), 2170– 2184. h ttp s://doi. org /10.109 3/mol be v/ms y119
Vinkler, M., & Albrecht, T. (2009). The question waiting to be asked:
Innate immunity receptors in the perspective of zoological re-
search. Folia Zoologica, 58, 15– 28.
Vinkler, M.,Bryjová, A., Albrecht , T., & Bryja, J. (20 09). Identification of
the first Toll- like receptor gene in passerine birds: TLR4 orthologue
in zebra finch (Taeniopygia guttata). Tissue Antigens, 74 (1), 32– 41.
https://doi.org/10.1111/j.1399- 0039.2009.01273.x.
Wang, X., Ishimori, N., Korstanje, R., Rollins, J., & Paigen, B. (2005).
Identifying novel genes for atherosclerosis through mouse- human
comparative genetics. The American Journal of Human Genetics,
77(1), 1– 15. ht tps://doi.o rg/10.1086/43165 6
Weli, S. C., & Tryland, M. (2011). Avipoxviruses: Infection biology and
their use as vaccine vectors. Virology Journal, 8(1), 49. ht t p s : //d oi .
org /10.1186/1743- 42 2X- 8- 49
Werling, D., Jann, O. C., Of ford, V., Glass, E. J., & Coffey, T. J. (2009).
Variation matters: TLR s tructure and species- specific pathogen
recognition. Trends in Immunology, 30(3), 124– 130. ht t p s : //d o i.
org /10.1016/j.it. 200 8.12.001
Wheeler, D. L., Church, D. M., Federhen, S., Lash, A . E., Madden, T. L.,
Pontius, J. U., Schuler, G. D., Schriml, L. M., Sequeira, E., Tatusova, T.
A., & Wagner, L. (2003). Database resources of the National Center
for Biotechnology. Nucleic Acids Research, 31(1), 28– 33.
Whitehorn, P. R., Tinsley, M. C., Brown, M. J. F., Dar vill, B., & Goulson, D.
(2011). Genetic diversity, parasite prevalence and immunity in wild
bumblebees. Proceedings of the Royal Society B: Biological Sciences,
278(1709), 1195– 1202. ht tps://doi.org/10.1098/rs pb.2010.1550
Whiteman, N. K.,Matson, K. D.,Bollmer, J. L., & Parker, P. G. (2005).
Disease e cology in the Ga lápagos Hawk (Bute o galapagoensis): h ost
genetic diversity, parasite load and natural antibodies. Proceedings
of the Royal Society B: Biological Sciences, 273(1588), 797– 804.
https://doi.org/10.1098/rspb.2005.3396.
Won, H., Jeon, H.- B., K im, D.- Y., & Suk, H . Y. (2021). Differential patterns
of diversity at neutral and adaptive loci in endangered Rhodeus
pseudosericeus populations. Scientific Reports, 11 (1), 15953.
htt ps://doi.o rg/10.10 38/s4159 8 - 021- 95385 - w
Wong, E. S., Papenfuss, A. T., & Belov, K. (2011). Immunome database for
marsupials and monotremes. BMC Immunology, 12(1), 48. ht t p s : //
doi .org /10.1186/1471- 2172- 12- 48
Xia, P., Wu, Y., Lian, S., Yan, L ., Meng, X., Duan, Q., & Zhu, G. (2021).
Research progress on toll- like receptor signal transduction and its
roles in antimicrobial immune responses. Applied Microbiology and
Biotechnology., 105, 5341– 5355. htt ps://doi.o rg/10.10 07/s0025 3-
02 1- 114 0 6 - 8
Yilmaz, A., Shen, S., Adelson, D. L., Xavier, S., & Zhu, J. J. (2005).
Identification and sequence analysis of chicken toll- like receptors.
Immunogenetics, 56(10), 743– 753. htt ps://doi.o rg/10.1007/s002 5
1- 004- 0740- 8
Zhang, G ., Li, C., Li, Q., Li, B., Larkin, D. M., Lee, C., Storz, J. F., Antunes,
A., Greenwold, M. J., Meredith, R. W., Odeen, A ., Cui, J., Zhou, Q.,
Xu, L., Pan, H., Wang, Z., Jin, L., Zhang, P., Hu, H., … Froman, D. P.
(2014). Comparative genomics reveals insights into avian genome
evolution and adaptation. Science, 346(6215), 1311– 1320. ht t p s ://
doi.org/10.1126/scien ce.1251385
Zhu, Y., Grueber, C., Li, Y., He, M., Hu, L., He, K., Liu, H., Zhang, H., &
Wu, H. (2020). MHC- associated Baylisascaris schroederi load in-
forms the giant panda reintroduction program. International Journal
for Parasitology: Parasites and Wildlife, 12, 113– 120. h t t p s: //d o i .
org/10.1016/j.ijppaw.2020.05.010
Zimin, A . V.,Marçais, G.,Puiu, D.,Roberts, M.,Salzberg, S. L., & Yorke, J.
A. (2013). The MaSuRCA genome assembler. Bioinformatics, 29(21),
2669– 2677. https://doi.org/10.1093/bioin forma tics/btt476.

Additional supporting information may be found in the online
version of the article at the publisher ’s website.
Magid, M., Wold, J. R., Moraga, R .,
Cubrinovska, I., Houston, D. M., Gar trell, B. D., & Steeves, T.
E. (2022). Leveraging an existing whole- genome
resequencing population data set to characterize toll- like
receptor gene diversity in a threatened bird. Molecular
Ecology Resources, 00, 1–16. https: //doi .org/10.1111/1755-
0998 .13656
ResearchGate has not been able to resolve any citations for this publication.
Article
Full-text available
Toll-like receptors (TLRs) are a crucial component of vertebrate innate immune response. Despite their importance, associations of TLR diversity with fitness-related traits have rarely been examined in wild animal populations. Here, we tested for associations of TLR polymorphism with physiological condition in a colonial waterbird, the Black-headed Gull (Chroicocephalus ridibundus). Physiological condition and polymorphism at 4 TLR loci were assessed in 60 gulls from a breeding colony in northern Poland. We found that blood hemoglobin and plasma albumin concentrations were positively associated with TLR diversity across all genotyped loci. Plasma concentrations of albumin and triglycerides were also associated with the presence of specific TLR variants and locus-specific diversity. All significant associations between physiological condition and TLRs were primarily apparent at the level of nucleotide, rather than amino acid allelic variants. Although the exact molecular mechanisms responsible for these associations require further investigation, our study provides strong correlational support for links between TLR diversity and physiological condition in a wild avian population, and it adds to the growing, but still modest, body of evidence for the fitness-related consequences of individual TLR repertoire in wild birds.
Article
Full-text available
Enhancing resistance and tolerance to pathogens remains an important selection objective in the production of livestock animals. Single nucleotide polymorphisms (SNPs) vary gene expression at the transcriptional level, influencing an individual’s immune regulation and susceptibility to diseases. In this study, we investigated the distribution of SNP sites in immune-related genes and their correlations with cell surface markers of immune cells within purebred (Taiwan black, Duroc, Landrace and Yorkshire) and crossbred (Landrace-Yorkshire) pigs. Thirty-nine SNPs of immune-related genes, including 11 cytokines, 5 chemokines and 23 Toll-like receptors (TLRs) (interferon-α and γ (IFN-α, γ), tumor necrosis factor-α (TNF-α), granulocyte-macrophage colony-stimulating factor (GM-CSF), Monocyte chemoattractant protein-1 (MCP-1) and TLR3, TLR4, TLR7, TLR8, and TLR9) were selected, and the percentages of positive cells with five cell surface markers of CD4, CD8, CD80/86, MHCI, and MHCII were analyzed. There were 28 SNPs that were significantly different among breeds, particularly between Landrace and Taiwan black. For instance, the frequency of SNP1 IFN-α -235A/G in Taiwan black and Landrace was 11.11% and 96.15%, respectively. In addition, 18 SNPs significantly correlated with the expression of cell surface markers, including CD4, CD8, CD80/86, and MHCII. The percentage of CD4+ (39.27%) in SNP33 TLR-8 543C/C was significantly higher than those in A/C (24.34%), at p < 0.05. Together, our findings show that Taiwan black pigs had a unique genotype distribution, whereas Landrace and Yorkshire had a more similar genotype distribution. Thus, an understanding of the genetic uniqueness of each breed could help to identify functionally important SNPs in immunoregulation.
Article
Full-text available
River buffalo is an agriculturally important species with many traits, such as disease tolerance, which promote its use worldwide. Highly contiguous genome assemblies of the river buffalo, goat, pig, human and two cattle subspecies were aligned to study gene gains and losses and signs of positive selection. The gene families that have changed significantly in river buffalo since divergence from cattle play important roles in protein degradation, the olfactory receptor system, detoxification and the immune system. We used the branch site model in PAML to analyse single-copy orthologs to identify positively selected genes that may be involved in skin differentiation, mammary development and bone formation in the river buffalo branch. The high contiguity of the genomes enabled evaluation of differences among species in the major histocompatibility complex. We identified a Babesia-like L1 LINE insertion in the DRB1-like gene in the river buffalo and discuss the implication of this finding.
Article
Full-text available
Given the fact that threatened species are often composed of isolated small populations, spatial continuity or demography of the populations may be major factors that have shaped the species’ genetic diversity. Thus, neutral loci have been the most commonly-used markers in conservation genetics. However, the populations under the influence of different environmental factors may have evolved in response to different selective pressures, which cannot be fully reflected in neutral genetic variation. Rhodeus pseudosericeus , a bitterling species (Acheilognathidae; Cypriniformes) endemic to the Korean Peninsula, are only found in some limited areas of three rivers, Daecheon, Han and Muhan, that flow into the west coast. Here, we genotyped 24 microsatellite loci and two loci (DAB1 and DAB3) of MHC class II peptide-binding β1 domain for 222 individuals collected from seven populations. Our microsatellite analysis revealed distinctive differentiation between the populations of Daecheon and Muhan Rivers and the Han River populations, and populations were structured into two subgroups within the Han River. Apparent positive selection signatures were found in the peptide-binding residues (PBRs) of the MHC loci. The allelic distribution of MHC showed a degree of differentiation between the populations of Daecheon and Muhan Rivers and the Han River populations, partially similar to the results obtained for microsatellites, however showed rather complex patterns among populations in the Han River. Considering the apparent differences in the distribution of supertypes obtained based on the physicochemical differences induced by the polymorphisms of these PBRs, the differentiation in DAB1 between the two regional groups may result in the differences in immune function. No differentiation between these two regions was observed in the supertyping of DAB3, probably indicating that only DAB1 was associated with the response to locally specialized antigenic peptides.
Article
Full-text available
When microorganisms invade a host, the innate immune system first recognizes the pathogen-associated molecular patterns of these microorganisms through pattern recognition receptors (PRRs). Toll-like receptors (TLRs) are known transmembrane PRRs existing in both invertebrates and vertebrates. Upon ligand recognition, TLRs initiate a cascade of signaling events; promote the pro-inflammatory cytokine, type I interferon, and chemokine expression; and play an essential role in the modulation of the host’s innate and adaptive immunity. Therefore, it is of great significance to improve our understanding of antimicrobial immune responses by studying the role of TLRs and their signal molecules in the host’s defense against invading microbes. This paper aims to summarize the specificity of TLRs in recognition of conserved microbial components, such as lipoprotein, lipopolysaccharide, flagella, endosomal nucleic acids, and other bioactive metabolites derived from microbes. This set of interactions helps to elucidate the immunomodulatory effect of TLRs and the signal transduction changes involved in the infectious process and provide a novel therapeutic strategy to combat microbial infections.
Article
Full-text available
Background/aim: Toll-like receptors (TLRs) are pivotal biomolecules in the immune system. Today, we are all aware of the importance of TLRs in bridging innate and adaptive immune system to each other. The TLRs are activated through binding to damage/danger-associated molecular patterns (DAMPs), microbial/microbe-associated molecular patterns (MAMPs), pathogen-associated molecular patterns (PAMPs), and xenobiotic-associated molecular patterns (XAMPs). The immunogenetic molecules of TLRs have their own functions, structures, coreceptors, and ligands which make them unique. These properties of TLRs give us an opportunity to find out how we can employ this knowledge for ligand-drug discovery strategies to control TLRs functions and contribution, signaling pathways, and indirect activities. Hence, the authors of this paper have a deep observation on the molecular and structural biology of human TLRs (hTLRs). Methods and materials: To prepare this paper and fulfill our goals, different search engines (e.g., GOOGLE SCHOLAR), Databases (e.g., MEDLINE), and websites (e.g., SCOPUS) were recruited to search and find effective papers and investigations. To reach this purpose, we tried with papers published in the English language with no limitation in time. The iCite bibliometrics was exploited to check the quality of the collected publications. Results: Each TLR molecule has its own molecular and structural biology, coreceptor(s), and abilities which make them unique or a complementary portion of the others. These immunogenetic molecules have remarkable roles and are much more important in different sections of immune and nonimmune systems rather than that we understand to date. Conclusion: TLRs are suitable targets for ligand-drug discovery strategies to establish new therapeutics in the fields of infectious and autoimmune diseases, cancers, and other inflammatory diseases and disorders.
Article
Full-text available
As big data, open data, and open science advance to increase access to complex and large datasets for innovation, discovery, and decision-making, Indigenous Peoples’ rights to control and access their data within these data environments remain limited. Operationalizing the FAIR Principles for scientific data with the CARE Principles for Indigenous Data Governance enhances machine actionability and brings people and purpose to the fore to resolve Indigenous Peoples’ rights to and interests in their data across the data lifecycle.
Article
Haplotype phasing is the estimation of haplotypes from genotype data. We present a fast, accurate, and memory-efficient haplotype phasing method that scales to large-scale SNP array and sequence data. The method uses marker windowing and composite reference haplotypes to reduce memory usage and computation time. It incorporates a progressive phasing algorithm that identifies confidently phased heterozygotes in each iteration and fixes the phase of these heterozygotes in subsequent iterations. For data with many low-frequency variants, such as whole-genome sequence data, the method employs a two-stage phasing algorithm that phases high-frequency markers via progressive phasing in the first stage and phases low-frequency markers via genotype imputation in the second stage. This haplotype phasing method is implemented in the open-source Beagle 5.2 software package. We compare Beagle 5.2 and SHAPEIT 4.2.1 by using expanding subsets of 485,301 UK Biobank samples and 38,387 TOPMed samples. Both methods have very similar accuracy and computation time for UK Biobank SNP array data. However, for TOPMed sequence data, Beagle is more than 20 times faster than SHAPEIT, achieves similar accuracy, and scales to larger sample sizes.
Article
Toll‐like Receptors (TLR) play a central role in recognition and host frontline defence against a wide range of pathogens. A number of recent studies have shown that TLR genes (Tlrs) often exhibit large polymorphism in natural populations. Yet, there is little knowledge on how this polymorphism is maintained and how it influences disease susceptibility in the wild. In previous work, we showed that some Tlrs exhibit similarly high levels of genetic diversity as genes of the Major Histocompatibility Complex (MHC), and signatures of contemporary balancing selection in roe deer (Capreolus capreolus), the most abundant cervid species in Europe. Here, we investigated the evolutionary mechanisms by which pathogen‐mediated selection could shape this innate immunity genetic diversity by examining the relationships between Tlr (Tlr2, Tlr4 and Tlr5) genotypes (heterozygosity status and presence of specific alleles) and infections with Toxoplasma and Chlamydia, two widespread intracellular pathogens known to cause reproductive failure in ungulates. We showed that Toxoplasma and Chlamydia exposures vary significantly across year and landscape structure with few co‐infection events detected, and that the two pathogens exert antagonistic selection on Tlr2 polymorphism. By contrast, we found limited support for Tlr heterozygote advantage. Our study confirmed the importance of looking beyond Mhc genes in wildlife immunogenetic studies. It also emphasized the necessity to consider multiple pathogen challenges and their spatiotemporal variation to improve our understanding of vertebrate defence evolution against pathogens.