Abstract
The porcine immune system has an important role in pre-clinical studies
together with understanding the biological response mechanisms before
entering into clinical trials. The size distribution of the Korean
minipig is an important feature that make this breed ideal for
biomedical research and safe practice in post clinical studies. The
extremely tiny (ET) minipig serves as an excellent model for various
biomedical research studies, but the comparatively frail and vulnerable
immune response to the environment over its Large (L) size minipig
breed leads to additional after born care. To overcome this pitfall,
comparative analysis of the genomic regions under selection in the L
type breed could provide a better understanding at the molecular level
and lead to the development of an enhanced variety of ET type minipig.
In this study, we utilized whole genome sequencing (WGS) to identify
traces of artificial selection and integrated them with transcriptome
data generated from blood samples to find strongly selected and
differentially expressed genes of interest. We identified a total of 35
common genes among which 7 were differentially expressed and showed
selective sweep in the L type over the ET type minipig breed. The
stabilization of these genes were further confirmed using nucleotide
diversity analysis, and these genes could serve as potential biomarkers
for the development of a better variety of ET type pig breed.
Introduction
Pre-clinical trials are the most effective measures taken to reduce the
risk of any human calamities from a new treatment, and the animal model
has an important role in serving such objectives. The significant
information gathered from experimentation with lower mammals ultimately
helps in the validation of hypothesis and is the true success of an
experiment. The selection of suitable animals for the development of
pre-clinical safe trials is a necessary prerequisite that would enable
a strong foundation to pursue safe human-related trials [[36]1, [37]2].
For these pre-clinical trials various animal models have been used,
such as rodents, non-rodents, and non-human primates including minipigs
which largely are considered the best animal model for the these
studies due to various advantages [[38]3–[39]5]. Pigs as an animal
model have been proven to have few ethical problems in studying pig
organs for transplantation compared to pet animals (like the dogs) or
large primates (like chimpanzees, orangutans, or gorillas), and the
organ size is also an important advantage of minipigs that make the
breed attractive for various biomedical research projects [[40]6,
[41]7]. Moreover, pigs as an experimental animal can be kept in a
gnotobiotic/germfree chamber [[42]8]. Such gnotobiotic pigs are an
excellent model to examine kidney problems (hemolytic uremic syndrome)
that occur after oral infection with enterohemorrhagic Escherichia coli
[[43]9, [44]10]. Furthermore, the immune system of minipigs is more
than 80% similar to humans while the similarity of the mouse immune
system to human is limited to 10%. Therefore, these advantages have
popularized these pigs for feasible use in biomedical research [[45]10,
[46]11]. Minipigs are one of the key breeds anatomically and
physiologically similar to humans that are used to understand various
mechanisms and to evaluate the efficacy and safety of experimental
therapies, drugs, and modalities in healthcare studies [[47]12,
[48]13]. Recent progress in genetic engineering also makes the minipig
as ideal candidate to be used as a prospective organ donor for
xenotransplantation in humans. There are different breeds of minipigs,
e.g., Minnesota, Yucatan, Hanford, Mini-Lewe, and the widely used
Göttingen minipigs [[49]14, [50]15]. Among the different breeds of
minipigs, only the Korean miniature-pig breed, Mini-Pig, registered
with the United Nations, and Agricultural Food Organization (FAO) as a
medical/laboratory species located in South Korea, and it is used for
various biomedical research studies like xenografts, efficacy
evaluation and biomaterial studies in different regions of the world.
The Korean miniature pig varies from ET with an average body weight
(18-26kg) to a L size minipig with an average body weight (37–85.6kg)
range from birth to full maturation. Among the ET and L-type Minipig,
the ET breed has been considered as one of the most suitable animal
models, but due to a more prone immune system, the ET breed comes with
the pitfall of extensive after birth care compared to the L type breed.
Identification of genomic regions which undergo positive selection in
one breed is a potent approach to delineate genes that help with
adaptation to environmental factors and are responsible for the
phenotype diversity.
In the last decade, many genome wide analyses with sound statistical
approaches have been conducted to pin down significant results from the
driven data [[51]16, [52]17]. Using the existing WGS knowledge and
understanding of molecular architecture, we oversaw the development of
a breed with an enhanced trait value [[53]18, [54]19]. These approaches
already helped in the identification of different genomic regions with
selection signals, suggesting the contribution of the region in
influencing different traits related to phenotypic or genotypic
composition [[55]20, [56]21]. Similarly, with a better understanding of
the genetic architecture and using advanced molecular breeding
approaches these pitfalls can be overcome and a more stable ET breed
can be developed with an L type immunogenicity response.
In the pursuit of identifying potential genes and their roles in
different pathways, we performed WGS data analysis to distinguish
selective sweep genes in the L-type over the ET type and RNA-seq
analysis for their gene expression patterns. Here, we present an
unbiased approach by integrating WGS and RNA-seq data and utilizing
statistically established methods such as cross-population extended
haplotype homozygosity (XP-EHH) [[57]22], Integrated Haplotype Score
(iHS) [[58]23] and Z-scores of the pooled heterozygosity (ZHp) [[59]24]
statistics were used to detect the selection signatures from the ET and
L type breeds followed by a comparative nucleotide diversity analysis
on the identified genes of interest performed in the L vs ET type
minipigs using vcftools to observe the stability in the region
[[60]25].
Methods
The analysis was implemented to identify selective sweep genes in the
minipig pig breed variety using re-sequencing data following the
identification of differentially expressed genes by utilizing the
RNA-seq data.
Sampling and data collection
All the experimental procedures were verified and approved by the
National Institute of Animal Science (NIAS) approval no: NIAS20181295
and carried out in consent with the ARRIVE guidelines [[61]26, [62]27].
All the minipigs used in this study were male, and the average birth
weight of the ET and L type was (0.18–0.3kg) and (0.5–0.7kg),
respectively, and the average body weight at 12/24 months was 18/26kg
and 37–85.76kg. The pigs were euthanized with an anesthetic injection
given into the ear vein with an overdose of Alfaxan (0.7mg/kg), and
blood samples were collected from post-harvested minipigs (N = 4)
[[63]28, [64]29]. Subsequently, the samples were stored in a sterile
container and immediately frozen at −70°C until further analysis.
RNA-seq data were generated for the minipigs (N = 4) with pair-end
data after isolation of the blood samples using TRIzol (Invitrogen) and
a RNeasy RNA Purification Kit with DNase treatment (Qiagen) following
the manufacturers’ instruction manual. One microliter of cleaned total
RNA was used to check the RNA quality using BioAnalyzer with an RNA
chip (RIN > 7 and 28S:18S ratio > 1.0). The library was constructed
with random cDNA fragments and acquired adapter-fragments of the cDNA
using the TruSeq Stranded Total RNA Sample Prep Kit (Illumina, San
Diego, CA, USA) following the manufacturer’s instructions. The
constructed library was used to perform sequencing on the Illumina
novaseq and paired-end reads were generated. and reported earlier
[[65]20, [66]30]. The selective sweep genes were identified from the
data derived using WGS analysis or re-sequencing analysis performed by
collecting the blood samples from the ET and L type Minipig breed.
Similarly, differentially expressed genes were identified using
applying the same blood sample for the RNA-seq analysis to obtain their
role in the biological process.
Sequence mapping and SNP calling
The raw reads were aligned with the reference genome of the pig
(Sscrofa11.1) downloaded from the NCBI. SAMTOOLS was further used to
clean low-quality map reads in the BAM files with permissive quality
cutoffs [flag-sat–bS and–bF 4] [[67]31]. To perform variant calling and
snp/indels extraction, we used the Genome Analysis Toolkit 4.0 (GATK)
pipeline based on best practices defined by the Broad Institute
[[68]32], and the Picard tool was used to filter potential PCR
duplicates. Subsequently, the reference BAM file was indexed using
SAMtools. Furthermore, the HaplotypeCaller, CombineGVCF and
“SelectVariant” argument of GATK were used for the identification of
single nucleotide polymorphism (SNPs). VariantFilteration was adopted
from GATK to avoid possible false positive with the following
parameters: SNPs with mapping quality (MQ) < 40.0, MQRankSum < − 12.5,
ReadPosRankSum < − 8.0 and quality depth (unfiltered depth of
non-reference samples; low scores are indicative of false positives and
artifacts) < 2.0 were filtered [[69]33]. Haplotype phasing and
imputation of missing alleles for the entire set of swine populations
were performed using BEAGLE version 4.1 [[70]34]. After all the
filtering processes, a total of 24,665,965 SNPs were retained and used
for further analysis.
To perform differentially expressed gene analysis, the PE reads were
first analyzed for the quality assessment using FastQC [[71]35], and
low-quality reads were removed using Trimmomatic tools [[72]36] with
parameters leading:3, trailing:3, slidingwindow:4:15, headcrop:13, and
minlen:36 before proceeding to the sequence alignment. All
quality-filtered PE reads were aligned to the Sscrofa genome
(Sscrofa11.1) at the University of California Santa Cruz (UCSC) using
Hisat2 [[73]37, [74]38], and reads were counted using FeatureCount
[[75]39]. Finally, DESeq2 was used to identify differentially expressed
genes [[76]40].
Selective sweep gene analysis
To determine a genome wide pattern of positive selection using the
whole SNP set identified from the ET and L type breeds, we first phased
the SNPs data with a beagle and extracted each chromosome. Afterwards,
we divided them into PopA and PopB and applied three statistically
established methods, XP-EHH [[77]22], ZHp [[78]24], and iHS [[79]23],
to detect the genome wide selective sweep regions. Here, each method
based on different approaches such as the XP-EHH assesses haplotype
differences between two populations. It is designed to detect alleles
that have an increase in frequency to the point of fixation or near
fixation in one of the two populations (A and B) being compared, by
calculating the extended haplotype homozygosity (EHH) and log-ratio iHH
between PopulationA and PopulationB as shown in Eq ([80]1).
[MATH:
XP−EHH=<
mfrac>ln(<
mi>iHHA
iHHB)−E[ln(<
mi>iHHA
iHHB)]SD[ln(<
mi>iHHA
iHHB)] :MATH]
(1)
Similarly, the iHS test is a program that identifies selected sweep
genes by searching the locus where allele resides on a longer haplotype
than the ancestral allele and compares the EHH between the derived and
ancestral alleles as shown in Eq ([81]2). This approach makes the
method less affected by the demographic history and enabled us to
identify incomplete sweeps, where the selected sweep is not fixed in
the sample, and then, did a comparison between iHH[A] and iHH[D]
denoted as ancestral and derived alleles [[82]23].
[MATH:
iHS=ln(<
mi>iHHA
iHHD)−E[ln(<
mi>iHHA
iHHD)]SD[ln(<
mi>iHHA
iHHD)] :MATH]
(2)
To calculate ZHp we first obtained the expected heterozygosity (Hp)
score at each position to scan the selection signals. The Hp values of
individual SNPs were first calculated according to Eq ([83]3) where
∑nMAJ and ∑nMIN represent the sums of the numbers of the major and
minor alleles at each locus. Subsequently, to detect selection signals,
the Hp values were then Z-transformed using Eq ([84]4) [[85]24,
[86]41].
[MATH: Hp=2∑nMAJ∑nMIN/(∑nMAJ+∑nMIN)2 :MATH]
(3)
[MATH:
ZHp=(Hp−μ<
mi>Hp)/σ
Hp :MATH]
(4)
The genomic coordinates of the regions with a high XP-EHH, ZHp, and iHS
score for the 10k window with a 10k bin size were computed using an
in-house python script, and then, it was used as input data to fetch
the gene_id information of the respective regions.
Gene ontology analysis
Lists of differentially expressed genes with p.adj ≤ 0.05 in the
Minipig w.r.t. L vs ET type breed were compiled and submitted to DAVID
v6.8 server [[87]42] for functional annotation and enrichment analysis.
Subsequently, list of degs visualized using Cytoscape program with
string plugin [[88]43, [89]44]. For each list, enriched Gene Ontology
(GO) was performed for the 3 categories: Biological Processes,
Molecular functions, and Cellular Compartments analysis. These terms
were then clustered semantically using the ReviGO server [[90]45].
Enriched functions throughout the whole transcriptome of the minipig
with an elevated GO-term function and clustered lower-level GO-terms
were visualized using treemap.
Results
Blood samples from 4 minipig breeds (L and ET type), respectively, were
collected, and we performed re-sequencing, which enabled us to obtain
the complete genetic variation and to identify the genes potentially
involved in genomic selection. Subsequently, RNA-seq analysis was
performed which enabled us to identify differentially expressed genes.
Population structure analysis
The principal component analysis (PCA) plot reveals the distribution of
the two breeds in a two-dimensional view. The present PCA analysis from
the WGS data was performed using the R package SNPRelate with 4 samples
from each pig breed, and we obtained a clear separation of the
respective breeds. Similarly, a clear separation was observed from the
RNA-seq data after batch correction studies ([91]Fig 1A and 1B).
Fig 1.
[92]Fig 1
[93]Open in a new tab
Principal component analysis: (a) & (b) representing the distribution
of pig breed variety (L vs ET) in 2-d view. There a clear separation
between the breeds can be visualized. (c) Differentially expressed
genes visualization was performed using enhanced volcano plot with
p.adj ≤ 0.05 and Log2FC ≥±1.5, here NS signifies non-significant genes.
(d) Common genes in different condition viz. iHS, ZHp, XP-EHH, and DEGs
were visualized using Venny, and 35 common genes were identified.
Genome wide artificial selection
Based on the high-quality SNPs, three tests were performed for the
identification of positively selected genes in different genomic
regions of the chromosomes of the L-type minipig breed. We identified
positive selective sweep genes in the L-type minipig with p ≤ 0.05 and
respective scores of ≥ ±1.5 and obtained 855 genes in XP-EHH, 3650
genes in iHS, and 2949 genes in the ZHp statistical analysis ([94]S1
File).
Differentially expressed gene analysis and common genes identification
The differential expression analysis was performed in R package DESEq2
after obtaining the gene expression count using featureCounts [[95]39].
A cutoff value of the fold change ≥ ±1.5 and adjusted p-value ≤ 0.05
were selected to obtain differentially expressed genes (DEGs) between
the respective breeds. The overall relationship differentially
expressed pattern was further visualized by Volcano Plot ([96]Fig 1C)
[[97]46], and to capture the information, representation of the gene
interaction role was identified with Cytoscape [[98]43] using the
string database plugin [[99]44] and presented as the network ([100]Fig
2D). Next, common genes were identified among the iHS, XP-EHH, ZHp, and
DEGs and the results were limited to 35 genes that were used for
further analysis ([101]Fig 1D, and [102]S2 File). Amongst them, 7 were
identified as differentially expressed and selective sweep genes in the
L-type breed ([103]Table 1).
Fig 2.
[104]Fig 2
[105]Open in a new tab
(a and b) Gene ontology study was done to identify the contribution and
significance of differentially expressed upregulated and downregulated
genes in minipig with p ≤ 0.05. (c) KEGG pathway enrichment analysis
after functional annotation with p < 0.01. Enriched pathway in L-type
minipig was performed by dot-plot analysis. (d) protein-protein
interaction analysis was done to visualize the upregulated and
downregulated genes. Here, Blue nodes represent the downregulated genes
and purple nodes represent the upregulation of genes.
Table 1. Identification of differentially expressed selective sweep genes.
Gene_Id ENS_id Chr XP-EHH iHS ZHp log2FC FDR
AIF1L ENSSSCG00000034178 1 2.649219181 2.6971365 -2.5781349 1.7994906
0.02328175
DNAH9 ENSSSCG00000018015 12 4.629307125 4.1900572 -3.3738688 2.1705897
0.0000756
GABBR2 ENSSSCG00000027558 1 3.922787712 5.2696931 -2.5781349 -2.523233
0.01536176
GRTP1 ENSSSCG00000009554 11 3.34265521 3.69934 -4.2762313 1.5207297
0.0000014
HBB ENSSSCG00000014725 9 3.111822546 3.0945901 -1.866248 -3.382391
0.02276081
HECW1 ENSSSCG00000036443 18 3.32804312 5.2894497 -3.963867 1.9930709
3.87E-10
HTR4 ENSSSCG00000014428 2 2.788343178 2.2548874 -3.9885142 1.7281431
0.00707621
[106]Open in a new tab
Gene ontology (GO) and gene regulatory network studies
The information inferred from the existing literature reported a close
relationship between human and pig organs [[107]6, [108]47, [109]48].
KEGG pathway analysis of the identified commonly selective sweep genes
from iHS, XP-EHH, and zHP was undertaken by filtering the data based on
the score and a significant p-value ≤ 0.05. The aims were to study the
significance of the identified selective sweep genes in pigs and to
comprehend the crucial functions shared by these genes in humans to
extensively analyze and understand the molecular mechanisms shared by
the species. Different genes were observed sharing common pathways such
as the regulation of autophagy (ATG16L2, ATG7, and ATG13), Fc epsilon
RI signaling pathway (MAP2K4, MAPK8, GAB2, and VAV2), Insulin
resistance (MLXIPL, MAPK8, PRKCE, CREB3L1, and PTPRF), Axon guidance
(ROBO2, EPHA5, DPYSL5, SRGAP1, and ROBO1), and cAMP signaling pathway
(CAMK2B, MAPK8, CREB3L1, HTR4, VAV2, and RAPGEF4) ([110]Table 2, and
[111]S1 Fig). Amongst them, the commonly enriched pathways were axon
guidance (GO:0007411), synaptic vesicle endocytosis (GO:0048488),
microtubule cytoskeleton organization (GO:0000226), negative regulation
of cell migration (GO:0030336), protein localization to basolateral
plasma membrane (GO:1903361), camera-type eye photoreceptor cell
differentiation (GO:0060219), hippocampus development (GO:0021766), and
vesicle-mediated transport (GO:0016192) in Biological process.
Similarly, cell-cell junction (GO:0005911), synapse (GO:0045202),
basolateral plasma membrane (GO:0016323), and axoneme (GO:0005930) were
enriched in the cellular component and transferase activity, and
transferring glycosyl groups (GO:0016757) was identified in the
molecular function ([112]S3 File).
Table 2. Comparison of KEGG pathways enriched in Pig and Human for selective
sweep genes.
SNO KEGG Number KEGG Pathway Pig Genes Human Genes
1 ssc04360: hsa04360 Axon guidance ROBO2, EPHA5, DPYSL5, SRGAP1, ROBO1
ROBO2, EPHA5, DPYSL5, SRGAP3, SRGAP1, NTN1, EPHB1, ROBO1
2 ssc04024: hsa04024 cAMP signaling pathway CAMK2B, MAPK8, CREB3L1,
HTR4, VAV2, RAPGEF4 CAMK2B, GABBR2, MAPK8, GRIN3A, CREB3L1, HTR4, VAV2,
RAPGEF4
3 ssc04664: hsa04664 Fc epsilon RI signaling pathway MAP2K4, MAPK8,
GAB2, VAV2 MAP2K4, MAPK8, GAB2, VAV2
4 ssc04931: hsa04931 Insulin resistance MLXIPL, MAPK8, PRKCE, CREB3L1,
PTPRF MLXIPL, MAPK8, CREB3L1, PRKCE, PTPRF
5 ssc04140: hsa04140 Regulation of autophagy ATG16L2, ATG7, ATG13
ATG16L2, ATG13, ATG7
6 ssc04012 ErbB signaling pathway CAMK2B, MAP2K4, MAPK8, CBLB
7 ssc04911 Insulin secretion CAMK2B, CREB3L1, KCNMA1, RAPGEF4
8 hsa04520 Adherens junction GUCY1A2, KCNMA1, CLCA1, ITPR2
9 hsa04514 Cell adhesion molecules (CAMs) CAMK2B, MAPK8, PRKCE, ITPR2,
IL1RAP
10 hsa04971 Gastric acid secretion PTPRM, PTPRJ, CTNNA3, CTNNA2, PTPRF
11 hsa04724 Glutamatergic synapse GALNT14, GALNT13, GALNT10
12 hsa04750 Inflammatory mediator regulation of TRP channels CNTNAP2,
CDH2, PTPRM, CDH15, NEO1, PTPRF
13 hsa00512 Mucin type O-Glycan biosynthesis CAMK2B, KCNK10, SLC26A7,
ITPR2
14 hsa04924 Renin secretion GRIN3A, GRM8, ITPR2, DLGAP1, SHANK2
[113]Open in a new tab
Similarly, a separate analysis was performed for the enrichment
analysis in positively and negatively expressed DEGs in the L-type
minipig. We observed signaling pathways such as the TNF signaling
pathway, NF−kappa B signaling pathway, Rap1 signaling pathway,
Neurotrophin signaling pathway, Cytokine−cytokine receptor interaction,
and Toll−like receptor signaling pathway. Among them, the major
pathways were enriched in the upregulated condition ([114]Fig 2C).
Likewise, the functional annotations of genes were tagged into three
groups: molecular function, cellular component, and biological process.
The most significant GO terms in the upregulated condition were as
follows: regulation of cell shape (GO:0009360), platelet-derived growth
factor receptor signaling pathway (GO:0048008), positive regulation of
protein kinase B signaling (GO:0051897), antigen processing and
presentation of endogenous peptide antigen via MHC class I
(GO:0019885), and innate immune response (GO:0045087). The significant
GO terms in the downregulation were translation (GO:0006412),
cytoplasmic translation (GO:0002181), ribosomal small subunit assembly
(GO:0000028) etc. [115]Fig 2A and 2B show the biological processes.
Likewise, extracellular exosome (GO:0070062), cytosol (GO:0005829), and
cytoplasm (GO:0005737) were among the enriched terms in cellular
component and GTPase activator activity (GO:0005096), non-membrane
spanning protein tyrosine kinase activity (GO:0004715), and zinc ion
binding (GO:0008270) were among the enriched terms in Molecular
function ([116]S2 Fig and [117]S4 File).
Discussion
A robust immune response to outside challenges could help in the
survival of a biological entity, and the blood is an important
component that has a key role in the development of the immune system.
Blood circulates throughout the tissues, recognizes foreign bodies and
subsequently acts through the T and B-cells [[118]49–[119]51]. The L
type pig breed has been reported to have a better immune system over
the ET type minipig breed, and hence, the emphasis was given to
overcome this issue by development of a better ET type pig breed which
can be used in various biomedical studies. Genomic selection methods
have been successfully beneficial in various studies to understand the
molecular mechanism involved in trait specific features and phenotypic
characteristics [[120]33]. These selection methods are based on a
strong statistical foundation to predict an accurate gene selection and
are widely used to improve the trait characteristics by understanding
the mechanisms involved in adapting to the situation according to
environmental changes and other factors for better survival
[[121]52–[122]54]. Although, these methods have enabled us to identify
genes of interest from huge data in the form of positive selective
sweep, a differentially expressed gene could help us in identifying
potential markers and therefore have an imperative role in the
development of better breed to prevail over the existing problems.
Henceforth, we integrated the results obtained from the WGS with the
RNA-seq data to identify potential genes involved in the development of
the L type pig breed over the ET type.
The sweep genes among the L vs ET minipig variety exhibiting a positive
signature were identified using XP-EHH and iHS and ZHp statistical
test. The identified common genes expressed in the blood samples were
limited to a total number of genes to 35. Among them, there were 7
(AIF1L, DNAH9, GABBR2, GRTP1, HBB, HECW1, and HTR4) differentially
expressed (upregulated and downregulated) genes with log2FC ≥ ±1.5 and
p.adj ≤ 0.05 in the L type minipig. Among them,, the identified key
genes HECT, C2 and WW Domain Containing E3 Ubiquitin Protein Ligase 1
(HECW1) also known as NEDD4-like ubiquitin protein ligase 1 (NEDL1)
protein-coding gene identified as a positively selected gene with GO
annotations related to this gene include a ubiquitin-protein ligase
activity. They regulate the bone morphogenetic protein signaling
pathway during embryonic development and bone remodeling [[123]55,
[124]56]. From a complex protein-protein interaction network, it has
been identified as actively involved with the transforming growth
factor-beta (TGFB) signaling pathway and directly interacts with SMAD
family proteins responsible for regulating cell development and growth
[[125]57]. These SMAD family proteins have been strongly correlated
with the immune response. that the SMAD pathway regulates the
production of IgA by B cells, maintains the protective mucosal barrier,
and promotes the balanced differentiation of CD4+ T cells into
inflammatory T helper type and suppressive FOXP3+ T regulatory cells
[[126]58–[127]60].
The identified selective sweep gene AIF1L has been identified as an
important molecule that has an essential role in cell survival and is
actively involved in proinflammatory activities of immune cells such as
monocytes/macrophages and activated T lymphocytes [[128]61, [129]62].
Besides this, DNAH9 gene also identified as a selectively sweep and a
positively expressed differentially expressed gene is known to have a
crucial role in the cytoplasmic movement of organelles, also known as
cytoplasmic dyneins and the bending of cilia and flagella with the help
of molecular motor axonemal dyneins [[130]63]. These motors also enable
the response to a broad array of signals including phosphorylation,
Ca2+, redox changes, and mechanical activation [[131]64, [132]65]. It
is also reported that the respiratory tract is lined with cilia which
keep inhaled dust, smog, and potentially harmful microorganisms from
entering the lungs and overexpression of such key genes could help in
the survival of eukaryotes at the cellular level by ejecting dust and
foreign bodies entering the cells [[133]66]; additionally, it creates
the water currents necessary for respiration and circulation in sponges
and coelenterates as well. Whereas, GRTP1 also known as Growth
hormone-regulated TBC protein 1, is an upregulated gene in the L type
found to be involved in a growth related function [[134]67].
Moreover, the identified HTR4 (5-Hydroxytryptamine Receptor 4), a
positively selected gene found on chromosome 2, is a Protein Coding
gene that is a member of the family of serotonin receptors, which are G
protein coupled receptors that stimulate cAMP production in response to
serotonin. Serotonin stimulates monocytes and lymphocytes, which
influence the secretion of cytokines and is also reported for utilizing
functions in the innate and adaptive immunity [[135]68, [136]69].
HBB, a selective sweep gene identified as three-fold down regulated in
the L-type mini breed located on chromosome 9 on the Sscrofa11.1 genome
assembly is a Hemoglobin Subunit Beta protein coding gene. It is
directly involved with the innate immune system and associated with
important pathways in biological processes such as oxygen transport,
receptor-mediated endocytosis, blood coagulation, etc. [[137]70,
[138]71]. Among the identified key genes, GABBR2 is one of the GPCR
family proteins via γ-aminobutyric acid signaling pathway reported to
have a key role in neurodevelopment phenotypes [[139]72]. The
characterization of this gene at the molecular level could help us to
better understand how its downregulation helps the development of the L
type minipig breed. Gene annotation studies also revealed a close
association and active involvement of these genes in the various
biological processes important in the immune system and in the
development of cells at various stages ([140]Fig 2A–2C). These
identified genes were further mapped to their genomic positions using
the Manhattan plot ([141]Fig 3A and 3B). Afterwards, the identified
genes were further analyzed for nucleotide diversity which also
presented strong evidence of the stabilization effect in these genes in
terms of selection ([142]Fig 3C). In conclusion, we have identified 35
key genes among which 7 were differentially expressed and positively
selected in the L-type Korean minipig. Nucleotide diversity analysis
showed strong evidence for the stability of identified genes, and the
gene ontology analysis revealed an association with the immune response
associated pathways, regulation of autophagy, signaling pathways, etc.
A comparative analysis with human states shows the importance of the
minipig as a suitable animal model for various research. A comparative
analysis was done to understand the similarity between the pathway
association with humans as a reference source. The results clearly
present evidence of a close association of different pathways at the
molecular level and a strong association between them. The identified
genes could be used as potential markers in molecular breeding
processes and could enhance the immune response in the relative ET type
minipig breed.
Fig 3.
[143]Fig 3
[144]Open in a new tab
(a) Manhattan plot was generated to map the coordinates of identified
genes on respective location of the chromosomes in iHS and XP-EHH. (b)
These identified genes were further analyzed for nucleotide
distribution comparison analysis in LvsET minipig and understanding the
stability and gene level.
Supporting information
S1 File. Different tabs provided the statistical analysis result from
iHS, XP-EHH, and ZGp score with differentially expressed genes with
p.adj ≤ 0.05.
(XLSX)
[145]Click here for additional data file.^ (617.6KB, xlsx)
S2 File. Common genes identified using statistical test integrated with
DEGs.
(XLSX)
[146]Click here for additional data file.^ (11.8KB, xlsx)
S3 File. Gene ontology analysis of selective sweep genes consist of
(BP, CC, and MF) with background pig and human.
(XLSX)
[147]Click here for additional data file.^ (32.4KB, xlsx)
S4 File. Gene ontology analysis of DEGs consist of (BP, CC, MF, and
KEGG) with from upregulated and downregulated genes in L-type minipig.
(XLSX)
[148]Click here for additional data file.^ (64.3KB, xlsx)
S1 Fig. Comparative KEGG pathway analysis of selective sweep genes with
background pig and human to identify the commonly associated pathways
using dot plot analysis.
(TIF)
[149]Click here for additional data file.^ (382.9KB, tif)
S2 Fig. Gene ontology analysis viz cellular component and molecular
function terms associated with upregulated and downregulated genes in
L-type minipig was performed using REVIGO.
(TIF)
[150]Click here for additional data file.^ (384KB, tif)
Data Availability
All relevant data are within the paper and its [151]Supporting
Information files or can be retrieved underlying the results presented
in the study on request from:
[152]https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE184912.
Funding Statement
This work was supported by Korea Post-Genome Project (Project title:
Deciphering the reference genome and the discovery of trait-associated
genes in Nanchukmacdon and mini pigs). Project No. PJ013343 of the
National Institute of Animal Science, Rural Development Administration,
Republic of Korea. This study was supported by 2020 the RDA Fellowship
Program of National Institute of Animal Science, Rural Development
Administration, Republic of Korea. This funding helped in successfully
performing all the sample analysis and provided financial assistance to
D.A. The funding bodies played no role in the design of the study and
collection, analysis, and interpretation of data and in writing the
manuscript”.
References