AABArchives Animal BreedingAABArch. Anim. Breed.2363-9822Copernicus PublicationsGöttingen, Germany10.5194/aab-63-155-2020Screening the key genes of hair follicle growth cycle in Inner Mongolian Cashmere goat based on RNA sequencingScreening the key genes of hair follicle growth cycleSuRuiGongGaoZhangLingtianYanXiaochunWangFenghongZhangLeiQiaoXianLiXiaokaiLiJinquanlijinquan_nd@126.comCollege of Animal Science, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia Autonomous Region, 010018, ChinaKey Laboratory of Animal Genetics, Breeding and Reproduction, Hohhot, Inner Mongolia Autonomous Region, 010018, ChinaKey Laboratory of Mutton Sheep Genetics and Breeding, Ministry of Agriculture and Rural Affairs, Hohhot, 010018, ChinaEngineering Research Center for Goat Genetics and Breeding, Hohhot, Inner Mongolia Autonomous Region, 010018, China
Inner Mongolian Cashmere goat is an excellent local breed
selected for the dual-purpose of cashmere and meat. There are three lines of Inner
Mongolian Cashmere goat: Erlangshan, Alashan and Aerbasi. Cashmere is a kind
of precious textile raw material with a high price. Cashmere is derived from
secondary hair follicle (SHF), while hair is derived from primary hair
follicle (PHF). The growth cycle of SHF of cashmere goat is 1 year, and it
can be divided into three different stages: anagen, catagen and telogen. In
this study, we tried to find some important influence factors of SHF growth
cycle in skin tissue from Inner Mongolian Cashmere goats by RNA sequencing
(RNA-Seq). Three female Aerbasi Inner Mongolian Cashmere goats (2 years old)
were used as experimental samples in this study. Skin samples were collected
in September (anagen), December (catagen) and March (telogen) at dorsal side
from cashmere goats. Results showed that over 511 396 044 raw reads and
487 729 890 clean reads were obtained from sequence data. In total, 51
different expression genes (DEGs) including 29 downregulated genes and 22 upregulated genes were enriched in anagen–catagen comparing group. The 443 DEGs
contained 117 downregulated genes and 326 upregulated genes that were enriched
in catagen–telogen comparing group. In telogen–anagen comparing group, 779
DEGs were enriched including 582 downregulated genes and 197 upregulated
genes. The result of gene ontology (GO) annotation showed that DEGs are in
different growth cycle periods, and enriched GO items are mostly related to the
transformation of cell and protein. The Kyoto Encyclopedia of Genes and Genomes
(KEGG) enrichment result indicated that metabolic process has a great impact on
SHF growth cycle. Based on the results of a comprehensive analysis of
differentially expressed genes, GO enrichment and KEGG enrichment, we found
that FGF5, FGFR1 and RRAS had an effect on the hair follicle growth cycle. The results of
this study may provide a theoretical basis for further research on the
growth and development of SHF in Inner Mongolian Cashmere goats.
Introduction
China is the largest cashmere producer in the world, with the output of
cashmere accounting for about 50 % of the world's total production. In
addition, the production of cashmere in Inner Mongolia accounts for about
40 % of the total output of the whole country. Owing to the excellent
quality, cashmere from Inner Mongolian Cashmere goats is very expensive and
competitive in the textile industry. Inner Mongolian Cashmere goat is a local
breed that provides both cashmere and meat, which can be divided into three
lines: Erlangshan, Alashan and Aerbasi. These goats live tenaciously in
semi-arid steppe of the Inner Mongolia Autonomous Region. Because of the harsh
living environment, the fiber diameter of cashmere is usually less than
16 µm, which can keep a body warm in cold winter.
Cashmere is derived from secondary hair follicle (SHF) of cashmere goat, while hair is derived from
primary hair follicle (PHF). Hair follicle is a special tissue in the skin which has its own growth
cycle and affects the growth of cashmere. A growth cycle mainly consists of
three distinct stages: hair follicles begin to develop from growth stage
(anagen), stop growing during regression stage (catagen), and then atrophy
at rest stage (telogen); finally, hair follicles re-enter a new cycle of growth (Botchkarev et al., 2003 ; Chen et al., 2016; Miao et al., 2016). The
growth cycle of hair follicles is influenced by heredity, environment,
climate, nutrition and so on. Generally, SHF growth cycle of Inner Mongolian
Cashmere goats is 1 year. Li et al. (2008) observed skin tissue sections of Inner
Mongolian Cashmere goats for 1 year and concluded that SHF anagen is from
April to November, catagen is from December to January, and
telogen is from February to March. However, the growth cycle of PHF in
cashmere goats is different from that of the SHF. In order to meet the needs
of the market, scientific methods were used to select less fineness and
higher yield cashmere goats for breeding purposes in order to obtain more
high-quality cashmere. With the continuous development of science and
technology, molecular breeding will help scientists to speed up the breeding
process of Inner Mongolian Cashmere goat.
RNA-Seq is a novel high-throughput sequencing-based approach for global
transcriptome mapping, which was first proposed and applied in yeast in 2008 (Nagalakshmi et al., 2008). Transcriptome has temporal and spatial specificity, which means gene
expression varies in different tissues or different periods. In the past
decade, RNA-Seq technology has been applied in many species, and scientists
have developed several methods to analyze these differences that explain some
biological phenomena. Researchers have found many important factors
affecting the growth cycle of cashmere goats, such as the MAPK signaling
pathway, Wnt signal transduction pathway, fibroblast growth factor (FGF)
family, bone morphogenetic protein (BMP) family, Notch signal transduction
pathway and so on Geng et al. (2013), Wang et al. (2016), and Jin et al. (2016).
In this study, Inner Mongolian Cashmere goat skin samples were sequenced, and
the influencing factors and their interactions were explored. By comparing
different expression genes (DEGs) among anagen, catagen and telogen, we tried to reveal a fresh viewpoint
of growth cycle of hair follicle of cashmere goats. Functional annotation
analysis was used to locate influence factors. The real-time quantitative
polymerase chain reaction (qRT-PCR) was used to verify the DEGs, and a network diagram of the
interaction of various factors was constructed.
Material and methodsEthics statement
In this study, skin samples were collected in accordance with the International
Guiding Principles for Biomedical Research Involving Animals and were
approved by the Animal Ethics Committee of the Inner Mongolia Academy of
Agriculture and Animal Husbandry Sciences that is responsible for animal
care and use in the Inner Mongolia Autonomous Region of China. In our study,
no specific permissions were required for these activities, and the animals
did not involve endangered or protected species.
Skin sample preparation for RNA-seq and qRT-PCR validation
Three female Aerbasi Inner Mongolian Cashmere goats at 2 years old from a stud
farm (Inner Mongolia Jin Lai Livestock Technology Company, Hohhot, Inner
Mongolia) were used in this study. All cashmere goats were raised by feeding
practices according to the cashmere goat standard. Skin samples were
collected in September (anagen), December (catagen) and March (telogen) of
SHF from cashmere goats, the sampling site was the upper one-third of the left
scapula along the mid-dorsal and mid-abdominal line. For each goat, we used
procaine for local anesthesia to reduce animal pain. After hair shearing and
alcohol disinfection, approximately 1 cm2 of skin tissue was grasped
with sterile forceps and quickly cut near the tip using sterile scalpel
blades. Each clipping was obtained immediately adjacent to the location of
the previous shearing. Yunnan Baiyao powder (Yunnan Baiyao Group Co., Ltd.,
China) was applied immediately to stop the bleeding. Then the
samples were quickly put into the liquid nitrogen and finally stored at -80 ∘C until RNA
extraction.
RNA extraction, quantification and qualification
Total RNA was extracted by TRIzol (Invitrogen) under the protocol. In
addition, RNA degradation and contamination were monitored on 1 % agarose
gels. The purity was checked using the NanoPhotometer®
spectrophotometer (IMPLEN, CA, USA). RNA concentration was measured using
Qubit® RNA Assay Kit in a Qubit® 2.0 flurometer
(Life Technologies, CA, USA). RNA integrity was assessed using the RNA Nano
6000 Assay Kit of the Bioanalyzer 2100 system (Agilent Technologies, CA,
USA).
Library preparation for transcriptome sequencing
A total amount of 3 µg of RNA per sample was used as input material for
the RNA sample preparations. Sequencing libraries were generated using
NEBNext® Ultra™ RNA Library Prep Kit for
Illumina® (NEB, USA) following manufacturer's recommendations
and index codes were added to attribute sequences to each sample. Briefly,
mRNA was purified from total RNA using poly-T oligo-attached magnetic beads.
Fragmentation was carried out using divalent cations under elevated
temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). First
strand complementary DNA (cDNA) was synthesized using random hexamer primer and M-MuLV Reverse
Transcriptase (RNase H-). Second strand cDNA synthesis was subsequently
performed using DNA Polymerase I and RNase H. Remaining overhangs were
converted into blunt ends via exonuclease or polymerase activities. After
adenylation of 3′ ends of DNA fragments, NEBNext Adaptor with hairpin loop
structure was ligated to prepare for hybridization. In order to select cDNA
fragments of preferentially 250∼300 bp in length, the library
fragments were purified with AMPure XP system (Beckman Coulter, Beverly,
USA). Then 3 µL USER Enzyme (NEB, USA) was used with size-selected,
adaptor-ligated cDNA at 37 ∘C for 15 min followed by 5 min at 95 ∘C before PCR. Then PCR was performed with Phusion high-fidelity
DNA polymerase, Universal PCR primers and Index (X) primer. At last, PCR
products were purified (AMPure XP systemc) and library quality was assessed
on the Agilent Bioanalyzer 2100 system.
Clustering and sequencing
The clustering of the index-coded samples was performed on a cBot cluster
generation system using TruSeq PE Cluster Kit v3-cBot-HS (Illumina) according
to the manufacturer's instructions. After cluster generation, the library
preparations were sequenced on an Illumina Hiseq platform and 125 bp or 150 bp
paired-end reads were generated.
Data analysis and quality control
Raw data (raw reads) of fastq format were firstly processed through in-house
perl scripts. In this step, clean data (clean reads) were obtained by
removing reads containing adapter, reads containing ploy-N and low-quality
reads from raw data. At the same time, Q20, Q30 and GC (Q20 and Q30 are Phred scores, which represent sequencing quality, and GC represents the percentage of bases G and C in the sequencing) contents of the clean
data were calculated. All the downstream analyses were based on the clean
data with high quality.
Reads mapping to the reference genome
Reference genome and gene model annotation files were downloaded from the genome
website directly. Index of the reference genome was built using Bowtie
v2.2.3 (Langmead et al., 2012) and paired-end clean reads were aligned to the reference genome
using TopHat v2.0.12 (Kim et al., 2013). We selected TopHat as the mapping tool because
TopHat can generate a database of splice junctions based on the gene model
annotation file and thus a better mapping result than other non-splice
mapping tools.
Quantification of gene expression level
HTSeq v0.6.1 was used to count the read numbers mapped to each gene. And
then the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) of each gene was calculated based on the
length of the gene and read count mapped to this gene. FPKM, expected
number of Fragments Per Kilobase of transcript sequence per Million base
pairs sequenced, considers the effect of sequencing depth and gene length
for the read count at the same time, and it is currently the most commonly
used method for estimating gene expression levels (Trapnell et al., 2010).
Differential expression analysis
Differential expression analysis of three conditions or groups (three biological
replicates per condition) was performed using the DESeq R package (1.18.0)
(Wang et al., 2010). DESeq provides statistical routines for determining differential
expression in digital gene expression data using a model based on the
negative binomial distribution. The resulting P values were adjusted using
the approach by Benjamini and Hochberg for controlling the false discovery
rate. Genes with an adjusted P value < 0.05 found by DESeq were
assigned as differentially expressed.
GO and KEGG enrichment analysis of differentially expressed
genes
GO enrichment analysis of differentially expressed genes was implemented by
the GOseq R package, in which gene length bias was corrected. GO terms with
corrected P value (q value) less than 0.05 were considered significantly
enriched by differential expressed genes. KEGG is a database resource for
understanding high-level functions and utilities of the biological system,
such as the cell, the organism and the ecosystem, from molecular-level
information, especially large-scale molecular datasets generated by genome
sequencing and other high-throughput experimental technologies
(http://www.genome.jp/kegg/, last access: 29 November 2019). We used KOBAS software to test the statistical
enrichment of differential expression genes in KEGG pathways.
Validate RNA-seq data and gene expression level
We used qRT-PCR to validate the RNA-seq data and gene expression level in
this study. After we extracted total RNA (TRIzol, Invitrogen) from experimental
goat skin samples in three periods, we synthesized cDNA
(Prime Script™ RT reagent kit with gDNA Eraser Perfect Real Time, TaKaRa)
from mRNA. Then, the primers we used were designed and synthesized by Sangon
Biotech, depending on the mRNA sequences published on the NCBI database. SYBR
Green (TaKaRa) was used in qRT-PCR. β-actin acted as internal
reference and the DEG expression level was calculated by 2-ΔΔct (where ct is The threshold cycle) (Livak et al., 2001).
Reaction system and condition of the qRT-PCR reaction was based on the
protocol. The annealing temperature (TM) was based on the primer design.
Results were analyzed by SAS 9.2.
Venn diagram of the construction of DEGs.
Cluster analysis of all DEGs from the nine sequenced
samples. The color scale bar, ranging from blue to red, represents the low-to-high expression, respectively.
Results
After extracting total RNA from three female Inner Mongolian Cashmere goats in the key
stages of anagen, catagen and telogen, we totally constructed nine
transcriptome libraries of cashmere goat skin samples and sequenced the
RNA. Over 511 396 044 raw reads and 487 729 890 clean reads were obtained in
sequence data. Quality control result showed Q20 of each sample was more
than 93 % and Q30 more than 85 %, GC content was between 54.77 % and
57.75 % (Table 1). Numbers 1–3 in the sample names represent the 1–3 cashmere goats.
Quality control results indicated that the sequencing results were reliable and
could be used for subsequent data analysis.
After we mapped the clean reads to goat reference genome (Capra hircus ARS1), shown in
Table 2, we compared each growth cycle stage. There were three comparing
groups in our research: (1) anagen to catagen, (2) catagen to telogen and (3) telogen to anagen. By limiting the q value to < 0.05, we found, in total, 51 DEGs
including 29 downregulated genes and 22 upregulated genes in the first group.
In the second group, there were 443 DEGs in total, containing 117 downregulated
genes and 326 upregulated genes. In the third group, there were 779 DEGs
including 582 downregulated genes and 197 upregulated genes. In the second
group, most DEGs were upregulated, while in the third group downregulated
genes play a greater part. A Venn diagram of the construction of DEGs is shown
in Fig. 1; it can be seen that the number of DEGs in anagen and telogen is
the most, and the number of DEGs in anagen and catagen is the least. These
analysis data show that when the hair follicle is in telogen, the gene
expression changes greatly compared with other periods. To analyze the
expression patterns of genes showing conserved expression between anagen,
catagen and telogen, we performed hierarchical clustering to group the genes
according to similarities in their patterns of gene expression (Fig. 2).
Through hierarchical clustering, it can be found that anagen and catagen are
clustered together, and the gene expression patterns are similar, while the
gene expression patterns of telogen are quite different from those of anagen
and catagen.
KEGG pathway analysis of DEGs. (a) DEGs between anagen and
catagen. (b) DEGs between catagen and telogen. (c) DEGs between telogen and
anagen.
After comparing the DEGs between each group, GO annotations were analyzed (see the Supplement). In
the first group, we found, for biological process, that upregulated DEGs were mostly
enriched in fatty acid beta-oxidation using acyl-CoA dehydrogenase
(GO:0033539), acute inflammatory response (GO:0002526) and acute-phase
response (GO:0006953); downregulated DEGs were mostly enriched in
regulation of cell shape (GO:0008360), injection of substances into other
organism (GO:0035737) and envenomation, resulting in modification of
morphology or physiology of other organisms (GO:0035738). Interestingly, both up- and downregulated DEGs were enriched in protein
complex assembly (GO:0006461) and protein complex biogenesis (GO:0070271).
For the cellular component, both up- and downregulated DEGs were mainly
enriched in cytoskeletal part (GO:0044430), cytoskeleton (GO:0005856),
intermediate filament (GO:0005882) and intermediate filament cytoskeleton
(GO:0045111). Then, for molecular function, most DEGs were enriched in metal
ion binding (GO:0046872) and cation binding (GO:0043169). RNA-directed
RNA polymerase activity (GO:0003968), RNA polymerase activity (GO:0034062)
and serine-type endopeptidase activity (GO:0004252) only have upregulated
DEGs. In the second group that has DEGs between catagen and telogen, upregulated
DEGs enriched in protein import into nucleus (GO:0006606) in biological
process, intermediate filament (GO:0005882) and dynein binding (GO:0045502).
A few downregulated DEGs were enriched in cytoplasmic transport (GO:0016482)
in biological process, intracellular non-membrane-bounded organelle
(GO:0043232) in cellular component and dynein binding (GO:0045502) in
molecular function. Finally, in the third group DEGs were mainly enriched in
protein import into nucleus (GO:0006606) in biological process, intermediate
filament (GO:0005882) in cellular component and dynein binding (GO:0045502)
in molecular function. These results showed that DEGs are in different growth cycle
periods and GO items mostly related to the transformation of cell and protein.
FGF5,
FGFR1 and RRAS were
detected by qRT-PCR, and the trend was similar to RNA-seq. **p value < 0.01, *p value < 0.05.
In order to collect the molecular interaction, reaction and relation of
the DEGs of the three groups, the KEGG pathway was also analyzed. DEGs in these three
groups were mainly enriched in the pathways related to metabolism (Fig. 3),
while most DEGs in the first and second groups were enriched in the metabolic
pathway (chx01100). However, most DEGs in third group were enriched in
phagosome (chx04145). The results of KEGG enrichment indicated that
the metabolic pathway had a great impact on SHF growth cycle.
Interactive network control chart. We find that FGF5 may
influence FGFRL1 and can directly regulate and then indirectly affect RRAS, ultimately
affecting proliferation and differentiation.
Together with DEGs, GO and KEGG analysis data, we identified three genes
including fibroblast growth factor 5(FGF5), fibroblast growth factor receptor
1(FGFR1) and RAS related (RRAS), which may be related to hair follicle growth and
development in MAPK signaling pathway and verified their expression level by
qRT-PCR. The primer sequences information is shown in Table 3. Results
showed the expression trend in qRT-PCR of these three genes was basically the
same with RNA-Seq (Fig. 4). It can be seen that the expression trend of
FGF5 is opposite to that of FGFR1 and RRAS. It is possible that there is a negative
regulatory relationship between FGF5 and FGFR1, while a positive regulatory
relationship between FGFR1 and RRAS. FGF5 was highly expressed in anagen and expressed lower in telogen. FGFR1 was different from FGF5, while telogen expressed higher
and anagen expressed lower. Depending on the expression of genes and MAPK
pathway, we can draw an interactive network control chart (Fig. 5). The interactive
network control chart showed that FGF5, FGFR1 and RRAS were located at essential places in
the MAPK signal pathway. Firstly, together with other factors, FGF5 directly
activated FGFR1 which was a receptor on membrane. FGFR1 may combine with GRB2 and SOS, then
activate RRAS. Finally, after a series of activation and phosphorylation
interactions, cell proliferation and differentiation were affected and
then regulated the periodic growth of hair follicles.
Since the 1960s, researchers tried to explore the periodic changes in SHF in
cashmere goats, from phenotype to the molecular mechanism. The expression
gene at RNA level varies in time and tissue, so transcriptome sequencing is
a direct way to explore gene expression changes. Therefore, we studied the
growth cycle of SHF in cashmere goats from the perspective of transcriptome. In
a recent study, we chose hair follicle, pulled out from the dorsal side, as
experimental samples to analyze the SHF and PHF in catagen and telogen
growth cycles of cashmere goats, while the effect of other factors were
excluded. We identified a set of differentially expressed known and novel genes
in hair follicles, such as STC2, ROR2 and VEGFA, which may be related to
hair cycle growth and other physiological functions (Su et al., 2018). However, in a recent
study, researchers found that stem cells in hair follicle were regulated by
the intra-follicle adjacent micro-environmental niche, while this niche was
also modulated dynamically by extra-follicular macro-environmental signals
(Chen et al., 2016). Therefore, we used skin in anagen, catagen and telogen as sample to
sequence the transcriptome in this study.
Geng et al. (2013) studied the hair follicle development and cycling of Shaanbei
white cashmere goat. It was found that a large number of DEGs were mainly
related to the cellular process, cell and cell part, binding, biological
regulation and metabolic process among the different stages of hair follicle
development. In addition, Wnt, Shh, TGF-β and Notch signal pathways
may be involved in the development of hair follicles (Geng et al., 2013). In this study,
there were 51 DEGs between anagen and catagen, 443 DEGs between catagen and
telogen, and 779 DEGs between telogen and anagen. The most DEGs were gained
between telogen and anagen, while fewest DEGs were between anagen and
catagen. This may mean that, from growth stage to resting phase of hair
follicle, changes were gradually taking place inside the skin. In addition,
in hair follicle going from resting phase into a new round of cycle growth phase, the
internal molecular microstate of the skin has undergone a great change. We
also found that upregulated genes between catagen and telogen played a great
count, while downregulated genes accounted for the majority between telogen
and anagen. This may indicate that the expression level of most genes in
skin was consistent with the hair follicle activity.
The mechanism of SHF growth of cashmere goats is still in the exploratory
stage. Hair follicle growth and development is an extremely complicated
process, and it is influenced by many internal and external factors. It has been
reported that hormone level, light duration and nutrition, as well as some other
important factors, had an important effect on the growth and development of
hair follicles in cashmere goats. Melatonin is one of the most important
hormones that affects the growth and development of SHF in cashmere goats. It
could promote the initiation and maturation of secondary follicles and
increased their population, while the beneficial effect of melatonin on
secondary follicle population remained throughout the cashmere goat's whole
life (Yang et al., 2019). Recently, the mechanism of melatonin effect on SHF of cashmere
goats was reported. Included in the enhancement of activities of antioxidant
enzymes are, for example, superoxide dismutase and glutathione peroxidase (GSH-
Px), elevated total antioxidant capacity, upregulated anti-apoptotic
Bcl-2 expression, and downregulated expression of the pro-apoptotic proteins,
Bax and caspase-3 (Yang et al., 2019). Daily light exposure also played a large role in SHF
growth. Exposure to a short photoperiod extended the anagen phase of the
cashmere goat hair follicle to increase cashmere production. Assessments of
tissue sections indicated that the short photoperiod significantly induced
cashmere growth (Liu et al., 2016). When reducing the daily light exposure for cashmere goats to
7 h, the SHF activity and the melatonin concentration in July and the
cashmere fiber length and fiber weight in October were significantly
increased compared to natural daily photoperiod (Zhang et al., 2019). From a nutritional aspect,
researchers found that there was a tendency or a significant interaction effect of
Cu and Mo on cashmere growth (P=0.076) or diameter (P<0.05), which might be
accomplished by changing the number of secondary follicle and active
secondary follicle, as well as secondary-to-primary follicle ratio (Zhang et al., 2011).
Researchers also found that dietary supplement of Essential Oils-Cobalt
(EOC) significantly promoted cashmere goat hair fiber quality (P<0.05) (Lei et al., 2018).
Because the growth and development of SHF of cashmere goats are a very
complex process, many scientists are still exploring the important factors
it affects and its growth and development mechanism.
In this study, the expression of some genes from the MAPK signal pathway was
different in different periods. Therefore, it can be predicted that these
DEGs would have an impact on cell proliferation and differentiation through the
MAPK signal pathway, and then it affected the growth and development of hair
follicles. Previous studies found the MAPK signal pathway was an important
pathway for the growth and development of hair follicle in mammal. Akilli
Öztürk et al. demonstrated an essential role of Gab1 upstream of
MAPK in the regulation of the hair cycle and the self-renewal of hair
follicle stem cells in mouse (Akilli Öztürk et al., 2015). Liu et al. found inhibition of
MAPK-ERK-Mfn2 axis abrogated the protective effects of Sirt1
on hair follicle stem cell survival, migration and proliferation (Liu et al., 2018).
Zhang et al. analyzed the transcriptome of cashmere goat and milk goat and
discovered that the MAPK signal pathway was involved in hair follicle cycling
in both cashmere and milk goat (Zhang et al., 2020). Jin et al. suggested that LAMTOR3 influences
the character of cashmere fiber, and it might regulate the development of
hair follicle and cashmere growth by inducing the MAPK signaling pathway
(Jin et al., 2019). Platelet-rich plasma (PRP) was an innovative treatment of androgenic
alopecia in the early stages of development; PRP might promote
proliferation of dermal papilla cells by activated MAPK and Akt signal
pathways (Xiao et al., 2019). Recently, researchers found that the MAPK signal pathway not only
affected hair follicle growth and development but also affected poultry
feather growth and development. Fang et al. revealed that the altered genes
or targets of altered miRNAs were involved in multiple biological processes
and pathways, including the MAPK signal pathway that related to feather growth
and development (Fang et al., 2018). This research all demonstrated MAPK signal
pathway's importance in hair follicle growth and development. Our study
indicated that FGF5, FGFRL1 and RRAS influenced the growth and development of hair
follicle in Inner Mongolian Cashmere goat through MAPK signal pathway. This
will provide a theoretical basis for further research on the growth and
development of SHF in Inner Mongolian Cashmere goats.
Conclusions
In this study, we tried to find some important influencing factors of SHF
growth cycle in skin tissue from Inner Mongolian Cashmere goats by RNA-Seq.
As results, over 511 396 044 raw reads and 487 729 890 clean reads were
obtained from sequence data. In total, 51 DEGs including 29 downregulated
genes and 22 upregulated genes were enriched between anagen and catagen.
After comparing catagen to telogen, we got 443 DEGs in total containing 117
downregulated genes and 326 upregulated genes. In the telogen–anagen comparing
group, 779 DEGs including 582 downregulated genes and 197 upregulated
genes were found. DEGs were annotated in different growth cycle periods by GO
analysis in each comparing group. The GO items were mostly related to the
transformation of cell and protein. KEGG enrichment result indicated that
the metabolic process had a great impact on SHF growth cycle. Comprehensive analysis
results of DEGs, GO enrichment and KEGG enrichment that our study excavated
indicated that FGF5, FGFRL1 and RRAS influenced the growth and development of hair
follicle in Inner Mongolian Cashmere goats through MAPK signal pathway.
Data availability
All data files are available from the SRA database (https://www.ncbi.nlm.nih.gov/sra, last access: 18 May 2020, accession number
SUB6509124; Gong, 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/aab-63-155-2020-supplement.
Author contributions
RS and JL designed the experiments, GG and XQ
carried them out, LZ and XY analyzed the data. RS
and GG prepared the article with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors are grateful to the staff of Inner Mongolia Jinlai Livestock
Technology Co for providing assistance.
Financial support
This work was financially supported by National Natural Science Foundation of China (grant nos. 31860637 and 31660639); Inner Mongolia Autonomous Region Science and Technology Plan Project Funding (no. 2019GG243); China Agriculture Research System (grant no. CARS-39-06); Talent cultivation project of “double first-class” discipline innovation team construction in Inner Mongolia Agricultural University (grant no. NDSC2018-01).
Review statement
This paper was edited by Steffen Maak and reviewed by two anonymous referees.
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