Genetic linkage analysis of stable qtls in Gossypium hirsutum ril population revealed function of GhCesA4 in fiber development

Genetic linkage analysis of stable QTLs in Gossypium hirsutum RIL
population revealed function of GhCesA4 in fiber development
Ruìxián Liú
a
,
b
, Xiànghu
ı Xia¯o
a
,
b
,
c
, Jw Go¯ng
a
,
d
, Jùnwén Lıˇ
a
,
d
, Hàoliàng Yán
a
, Qún Geˇ
a
,
d
, Quánweˇi Lú
c
,
Péngta¯o Lıˇ
c
, Jìngta¯o Pa¯n
a
, Haˇihóng Sha¯ng
a
,
d
, Yùzhe¯n Shí
a
, Qúanjia¯ Chén
b
,
⇑
, Yoˇulù Yuán
a
,
b
,
d
,
⇑
,
Wànkuí Goˇng
a
,
⇑
a
National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Institute of Cotton Research, Chinese Academy of Agricultural Sciences, Anyang 455000, Henan, China
b
Engineering Research Centre of Cotton, Ministry of Education, College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi 830052, Xinjiang, China
c
College of Biotechnology and Food Engineering, Anyang Institute of Technology, Anyang 455000, Henan, China
d
Zhengzhou Research Base, National Key Laboratory of Cotton Bio-breeding and Integrated Utilization, Zhengzhou University, Zhengzhou 450001, Henan, China
h i g h l i g h t s
Genome-wide mining of QTLs of fiber
yield and fiber quality traits were
beneficial to better understanding
their genome level distributions.
A total of 64 stable QTLs of fiber yield
and 75 stable QTLs of fiber quality
were identified, which formed 33
clusters, indicating that the
correlations between fiber yield and
quality traits have complex genetic
components.
These QTLs formed expression
network, via their candidate genes, to
orchestrate fiber development and
the formation fiber yield and quality.
The candidate gene, GH_D07G2262,
an upland cotton cellulose synthase 4
(GhCesA 4) gene had pleiotropic
functions during cotton fiber
development, which positively
regulates fiber length and strength,
and negatively lint percentage.
g r a p h i c a l a b s t r a c t
a r t i c l e
i n f o
Article history:
Received 31 May 2023
Revised 27 August 2023
Accepted 2 December 2023
Available online xxxx
Keywords:
Gossypium hirsutum
High-density genetic map (HDGM)
Fiber quality and yield trait
Quantitative trait loci (QTLs), GhCesA4
a b s t r a c t
Introduction: Upland cotton is an important allotetrapolyploid crop providing natural fibers for textile
industry. Under the present high-level breeding and production conditions, further simultaneous
improvement of fiber quality and yield is facing unprecedented challenges due to their complex negative
correlations.
Objectives: The study was to adequately identify quantitative trait loci (QTLs) and dissect how they
orchestrate the formation of fiber quality and yield.
Methods: A high-density genetic map (HDGM) based on an intraspecific recombinant inbred line (RIL)
population consisting of 231 individuals was used to identify QTLs and QTL clusters of fiber quality
and yield traits. The weighted gene correlation network analysis (WGCNA) package in R software was uti-
lized to identify WGCNA network and hub genes related to fiber development. Gene functions were
https://doi.org/10.1016/j.jare.2023.12.005
2090-1232/
Ó 2023 The Authors. Published by Elsevier B.V. on behalf of Cairo University.
This is an open access article under the CC BY-NC-ND license (
http://creativecommons.org/licenses/by-nc-nd/4.0/
).
⇑
Corresponding authors.
E-mail addresses:
chqjia@126.com
(Q. Chén),
yuanyoulu@caas.cn
(Y. Yuán),
gongwankui@caas.cn
(W. Goˇng).
Journal of Advanced Research xxx (xxxx) xxx
Contents lists available at
ScienceDirect
Journal of Advanced Research
j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j a r e
Please cite this article as: R. Liú, X. Lıˇ, Hàoliàng Yán et al., Genetic linkage analysis of stable QTLs in Gossypium hirsutum RIL population revealed function of
GhCesA4 in fiber development, Journal of Advanced Research,
https://doi.org/10.1016/j.jare.2023.12.005
verified via virus-induced gene silencing (VIGS) and clustered regularly interspaced short palindromic
repeats (CRISPR)/Cas9 strategies.
Results: An HDGM consisting of 8045 markers was constructed spanning 4943.01 cM of cotton genome. A
total of 295 QTLs were identified based on multi-environmental phenotypes. Among 139 stable QTLs,
including 35 newly identified ones, seventy five were of fiber quality and 64 yield traits. A total of 33
QTL clusters harboring 74 QTLs were identified. Eleven candidate hub genes were identified via
WGCNA using genes in all stable QTLs and QTL clusters. The relative expression profiles of these hub
genes revealed their correlations with fiber development. VIGS and CRISPR/Cas9 edition revealed that
the hub gene cellulose synthase 4 (GhCesA4, GH_D07G2262) positively regulate fiber length and fiber
strength formation and negatively lint percentage.
Conclusion: Multiple analyses demonstrate that the hub genes harbored in the QTLs orchestrate the fiber
development. The hub gene GhCesA4 has opposite pleiotropic effects in regulating trait formation of fiber
quality and yield. The results facilitate understanding the genetic basis of negative correlation between
cotton fiber quality and yield.
Ó 2023 The Authors. Published by Elsevier B.V. on behalf of Cairo University. This is an open access article
under the CC BY-NC-ND license (
http://creativecommons.org/licenses/by-nc-nd/4.0/
).
Introduction
Allotetraploid cotton, which produces a large amount of natural
fiber for textile industry, is one of the economic crops widely
planted in the world
[1,2]
. It includes two cultivated species,
Gossypium hirsutum, which shares 90 % of total fiber production,
features broader adaptability, high yield potential, and fibers of
attractive quality, and G. barbadense, which has exceptionally
high-quality fibers, occupying less than 10 % of fiber yield in pro-
duction
[3]
. Thus, developing high-yield and high-quality fiber of
G. hirsutum cultivars plays a critical role in cotton production.
However cotton fiber quality and yield traits are genetically com-
plex, which are controlled by multi-genes. Previous studies reveal
that fiber quality and yield are negatively correlated
[4,5]
. During
breeding
improvement
practices,
this
negative
correlation
between traits will lead to a negative selection effect (trade-off
effect) of a trait when the improvement of another trait is imposed
[6]
. Under current agricultural conditions, both cotton breeding
and production in major cotton producing countries have reached
a high level of practice. These factors render it a challenge for the
breeders to further improve cotton fiber quality and yield simulta-
neously. Quantitative trait loci (QTLs) mapping of these complex
quantitative traits, which has been applied to the genetic architec-
ture dissection of crops, was proven effective in marker assisted
selection (MAS) in cotton breeding
[7]
. Following the improvement
of sequencing technology and the reduction in cost, high quality
drafts of cotton genomes, which includes G. raimondii, G. arboretum
(two diploid species), and G. hirsutum, G. barbadense (two allote-
traploid species), had been assembled, which could further acceler-
ate cotton biotechnological improvement
[3,8–14]
. In addition,
large collections of cotton accessions had been genotyped by
high-throughput re-sequencing strategies or by CottonSNP63K or
CottonSNP80K arrays
[15,16]
. The important QTLs and candidate
genes of fiber quality and yield traits were thus identified by GWAS
analysis of natural populations or by linkage analysis based on
high-density genetic map (HDGM) of segregating populations
using the low-cost and large-scale single nucleotide polymorphism
(SNP) genotyping strategies
[15–20]
.
Improving and producing high quality fibers have always been
the primary tasks of cotton breeders and other participants in cot-
ton breeding and production system
[2,20]
. Based on the morpho-
logical characteristics of developing fibers, previous studies had
assorted cotton fiber development into four stages, which includes
initiation (0–3 days post anthesis, DPA), elongation (3–20 DPA),
secondary cell wall (SCW) biosynthesis (15–40 DPA), and matura-
tion (40–50 DPA)
[21,22]
. A large number of genes are thought to
be involved in the complex process of cotton fiber development
and formation, and some of these genes may also affect genetic
diversity in fiber quality across cultivars and species in Gossypium
[2,3,23]
. Recent studies revealed that complex multi-group signal
pathways, including plant hormones
[24]
, plant transcription fac-
tor family
[25]
, lipids
[26]
, chitinase-like (CTL) protein
[27]
,
a
-
expansin
[28]
, aquaporins
[29]
, ROS signal transduction
[30]
, may
involve in fiber development.
In this study, a population consisting of 231 recombinant inbred
lines (231 RILs) was developed from a cross between Lumianyan
28 (LMY28) of high yield cultivar and Xinluzao 24 (XLZ24) a high
fiber quality cultivar. The fiber quality traits, which include fiber
length (FL), fiber strength (FS) and fiber micronaire (FM), and fiber
yield traits, which include boll weight (BW) and lint percentage
(LP), of the RILs were investigated across nine environments, while
seed index (SI) seven environments. After the population was
genotyped by SLAF-seq strategy, together with the previous geno-
typing result of it by CottonSNP80K array
[17]
, an HDGM was con-
structed aiming to identify QTLs of fiber quality and fiber yield
traits. Eleven candidate genes were identified through weighted
gene correlation network analysis (WGCNA), which was conducted
using the genes in all stable QTLs and QTL clusters. The relative
expression profiles of these candidate genes in RNA-seq data and
qRT-PCR validation during fiber development revealed that they
played orchestrating roles in the formation of fiber quality and
yield traits. Thus, the mechanism of how these QTLs regulate the
formation of fiber yield and quality, as well as their integrating
and restricting relationships were analyzed. Silencing via virus-
induced gene silencing (VIGS) strategy and editing via clustered
regularly interspaced short palindromic repeats (CRISPR)/Cas9 sys-
tem of a hub gene cellulose synthase 4 (GhCesA4, GH_D07G2262)
revealed that it has a pleiotropic effect and is involved in the for-
mation of LP, as well as FL and FS during cotton fiber development.
The results will be of great significance for candidate gene cloning
and functional analysis, as well as for dissecting how candidate
genes coordinate cotton fiber development through interactions.
Materials and methods
Plant materials and phenotype evaluation
The experimental materials included two parental G. hirsutum
cultivars, LMY28 and XLZ24, as well as a RIL population consisting
of 231 RILs derived from a cross of LMY28
XLZ24, which was
described in a previous study
[17]
. Briefly the cross was made in
Anyang in 2008 summer. F
2
harvested 237 individual plants, and
F
2:3
harvested 231 lines and from thence, they were proliferated
via self-pollinating. From F
2
to F
2:6
, they were alternatively planted
in the growing seasons in Sanya, Hainan and Anyang, Henan
respectively. At F
2:6
, a single plant was selected from each line to
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
2
compose F
6:7
generation. F
6:8
and later generations were consid-
ered as a RIL population.
From 2013 to 2016, the field phenotypes of FL, FS, FM, BW, and
LP were evaluated under nine environments, which included four
in Anyang (13AY, 14AY, 15AY, and 16AY), two in Linqing (13LQ
and 14LQ), and three in Quzhou (13QZ), Kuerle (14KEL), and Alaer
(15ALE). SI was evaluated in seven environments, same as above-
mentioned except 14AY and 14LQ. The field experiment design
and management, and phenotype evaluation were as described in
previous study
[17]
. Briefly, thirty naturally opened bolls from each
line were hand harvested and the phenotype evaluation of all the
target traits was based on these bolls. The phenotypic evaluation
procedures briefly involved air-drying, weighing seed cotton, gin-
ning, and weighing lint and cottonseeds. BW refers to the average
single boll weight of the 30 bolls, SI the weight of 100 seeds, and LP
the percentage of lint in seed cotton weight. FL, FS and FM were
measured using HFT9000 (Premier Evolvics Pvt. Ltd., India) instru-
ments with HVICC Calibration.
DNA extraction and library construction and genotyping
Total genomic DNA samples of XLZ24, LMY28, and 231 RILs
were extracted using MiniBEST Plant Genomic DNA Extraction kit
(Code: 9768, Takara, Beijing). Two strategies were applied to geno-
type the experimental materials, SLAF-seq strategy and CHIP strat-
egy. The CHIP strategy was performed with cottonSNP80K array,
which consisted of 77,774 SNPs
[16,17]
. The genotyping procedure
was initially delineated in a previous study
[17]
, briefly the geno-
typing was performed following the protocols provided in the
manufacturer’s instruction (Illumina Inc., San Diego, CA, United
States)
[15]
and the raw data were reanalyzed following the proce-
dure described in a previous report
[16]
. The SLAF-seq strategy was
performed following the procedures described in previous studies
[31,32]
on the Illumina HiSeq platform. Briefly, the reference gen-
ome of G. hirsutum (TM-1)
[3]
was used in silico to simulate the
digestion results to select appropriate restriction endonucleases
or their combination to generate optimum SLAF marker distribu-
tion across G. hirsutum genome. A combination of two endonucle-
ases, HaeIII and SspI (New England Biolabs, NEB, USA), was selected
to digest the genomic DNA of the experimental materials to con-
struct Illumina libraries. SLAF-seq was performed on the Illumina
HiSeq platform. The identification and genotyping of SLAF markers
were based on Sun et al.
[31]
and Ali et al.
[33]
. Raw data were fil-
tered according to the criteria of minimum read depth less than 10
fold in parental lines and less than 1 fold in RILs to obtain clean
reads. The clean reads were aligned to TM-1 reference genome
[3]
by BWA software
[34]
. After SNPs and Indels were detected
using software GATK (Genome Analysis Toolkit)
[35]
. They were
further filtered to generate SLAF markers according to the follow-
ing standards: 1) SNPs and Indels that had more than 30 % missing
data and 2) segregation distortion that reached p < 0.005 signifi-
cant level in RILs were removed.
HDGM construction and QTL mapping
The HDGM was constructed by Lep-Map3 software
[36]
using
Kosambi mapping function
[37]
. QTLs of the target traits were
identified using the composite interval mapping (CIM) method
through the Windows QTL Cartographer 2.5 software
[38]
. A QTL
was declared to exist if where the logarithm of odds (LOD) reached
the threshold of p
0.05 significance level after 1000 permutations
[17]
. For a target trait, QTLs identified in different environments
with fully or partially overlapping confidence intervals were con-
sidered the same QTL. QTLs that were identified across at least
two environments were regarded as stable QTLs
[17]
. If a QTL
has positive additive effect, it means that the favorable allele of
that QTL is contributed by XLZ24, while negative additive effect
by LMY28. MapChart 2.2
[39]
was utilized to display the genetic
linkage map and the QTL positions on the map.
To compare QTLs of current study with those of previous stud-
ies, the physical interval of the target QTLs reported in previous
studies were realigned to the TM-1 reference genome
[3,40]
. If
the physical marker interval of a QTL partially or fully overlapped
with that of a reported QTL in previous studies, the QTL was
regarded as a common one. Previous studies of both linkage anal-
ysis of segregating populations and GWAS of natural populations
were used to compare the QTL results of current study.
Gene identification and annotation
The physical interval of a stable QTL or QTL cluster was deter-
mined based on the physical position of all markers of that QTL
or QTL cluster on the TM-1 reference genome
[3]
. All genes in
the physical intervals of stable QTLs and QTL clusters were fetched
from the CottonFGD database (
https://cottonfgd.org/
). All gene
annotation information was retrieved from Cotton Omics Database
(
https://cotton.zju.edu.cn/
).
RNA-Seq dataset collection
RNA-Seq data of developing fiber samples of sGK156 (P1), 901–
001 (P2), MBZ70-053 (L1), and MBZ70-236 (L2), which were sam-
pled on 5, 10, 15, 20, 25, 30 days post anthesis (DPA), had been
reported previously
[41]
. L1 and L2 are two lines of RIL population
developed from a cross of P1
P2. The fiber quality, especially FL
and FS, of P2 and L1 were better than that of P1 and L2.
Identification of co-expression modules during fiber development
Weighted gene correlation network analysis (WGCNA) package
in R software
[42]
was utilized to identify the gene co-expression
modules relating to fiber development. Initially, all genes within
the physical intervals of stable QTLs and QTL clusters were filtrated
to obtain their expression profiles according to the criterion of
average FPKM
0.5 for further analyses. Then the modular scale
independence and mean connectivity with varying power values
were analyzed. When R
2
value reached 0.9, the optimal soft thresh-
old was determined for module analysis, followed by setting the
optimal soft threshold, minimum module size, and merge cut
height to 8, 30, and 0.25, respectively in stepwise. The modules,
which had a highly significant correlation to a fiber development
stage, were selected to perform intra-modular analysis to identify
both gene significance (GS) and module membership (MM) in the
modules of interest. GS represents the significance of gene expres-
sion correlation with the fiber development stage, while MM rep-
resents the correlation between the eigengene of each module and
the module itself. Subsequently, a scatter plot was created to
depict the correlation between GS and MM in the given modules.
Enrichment analysis of protein–protein interaction (PPI) network and
screen of candidate genes
To identify candidate genes for fiber development, the following
steps were performed: 1) the modules, which had a correlation
coefficient > 0.80 at significant level of p_value < 0.01, were
selected; 2) The genes, which had GS
0.56 and MM 0.93 in
the significant modules, were selected; 3) Analyses of gene ontol-
ogy (GO) and PPI network were performed with Metascape
(
https://metascape.org/
).
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
3
Analysis of real-time quantitative polymerase chain reaction (qRT-
PCR)
Cotton developing fibers of XLZ24 and LMY28 were sampled
from 5, 10, 15, 20, 25, 30 DPA ovules with tweezers on ice. Then,
total RNA samples were isolated from aforementioned fiber sam-
ples with the RNAprep pure plant plus kit (for samples abundance
of polysaccharides & polyphenolics) (DP441, TianGen Biotech, Bei-
jing, China). cDNAs were synthesized utilizing 1
l
g total RNA sam-
ples and the HiScript III RT superMix for qPCR (+gDNA wiper)
reverse transcription (R323-01AA, Vazyme, Biotech, Nanjing,
China). The qRT-PCR analyses were performed on an applied
biosystems 7500 fast real-time PCR system (ABI) utilizing the
chamQ universal SYBR qPCR master mix (Q711-02-AA, Vazyme,
Biotech, Nanjing, China). An internal control (actin gene) was uti-
lized to normalize the variance of samples. The relative expression
level was determined utilizing the 2
DD
CT
method
[43]
, and the
analysis was performed on three independent biological replica-
tions. All primer sequences are presented in
Supplementary
Table S1
.
VIGS and CRISPR/Cas9 verification of GH_D07G2262
VIGS experiment was conducted basically following protocols
described in previous study
[44]
. A 300-bp fragment of CDs of
GH_D07G2262, spanning from +2662 to +2961 bp from the start
codon, was cloned into the pCLCrV-A vector using restriction
endonucleases SpeI and AscI. The vector pCLCrV-GH_D07G2262
and the helper vector pCLCrV-B were transformed into LBA4404,
an Agrobacterium tumefaciens strain. The transformed Agrobac-
terium was propagated and reactivated in YEB medium, and was
collected in infection buffer, which contained 10 mM 2-(N-
morpholine)-ethanesulphonic acid (MES), 10 mM MgCl
2
and
0.2 mM acetosyringone, pH 5.8, When the cotyledons of cotton
seedlings of TM-1 were fully unfolded, Agrobacterium was injected
into the two cotyledons. The seedlings injected with pCLCrV-
CHLI500 were used as positive control. Then, the injected seedlings
were kept in darkness for 24–36 h, and were grown in greenhouse
(25
℃, 16-h light/8-h dark). At boll setting stage, newly unfolded
leaves were sampled to evaluate GH_D07G2262 expression level.
Naturally opened bolls were sampled to evaluated FM and FL
phenotypes.
Two single-guide RNA (sgRNA) sequences were designed from
the sequence of GH_D07G2262 and cloned into CRISPR/Cas9 vector
using the primer sequences presented in
Supplementary Table S1
.
The CRISPR/Cas9 vectors were then transformed into GV3101, an A.
tumefaciens strain. The cotton transformation was conducted fol-
lowing previous descriptions
[45]
, and the resulting putative trans-
genic plants were removed to a greenhouse with a 16-hour light/8-
hour dark cycle at 30
℃. Following selection based on kanamycin
test and transgene PCR amplification test, the genomic DNA and
total RNA of developing fibers of positive plants were extracted.
The
editing
target
site
was
cloned
and
sequenced,
and
GH_D07G2262 cDNA of developing fiber was synthesized and
cloned and sequenced to confirm the CRISPR/Cas9 system editing
result. The fiber quality phenotypes were evaluated in T
2
genera-
tion of the edited transgenic cotton plants.
Paraffin cross-sectioning of natural mature fibers
To observe the secondary cell wall of fiber cells, dried mature
fibers were treated and observed following the protocol
[46]
with
some modifications. Briefly, the fiber samples were fixed by
formaldehyde-acetic acid–ethanol (FAA) for 24 h, and then placed
in a dehydration chamber for dehydration treatment with a series
of different concentrations of 75 %, 85 %, 90 %, 95 %, 100 %, and
100 % ethanol, with each step treated for 4 h, 2 h, 1.5 h, 1.5 h,
30 min-1 h, 30 min-1 h, respectively, and then washed three times
with ethanol + xylene (1:1), xylene, and xylene for 10–20 min in
each step. After the pretreatment, the samples were embedded in
paraffin three times in step-wise for 1–2 h in each step. After the
paraffin solidified, the excessive paraffin was removed and
trimmed. The cross sections of the samples were sliced to a thick-
ness of 4
l
m, dried, and stored at room temperature. The cross-
sections of fiber cells were stained with toluidine blue for 2–
5 min, and finally observed under a microscope Nikon Eclipse
E100 (Nikon, Japan) and scanned using Pannoramic MIDI
Ⅱ (3DHIS-
TECH, Japan). Cell wall thickness of cotton fibers was analyzed with
SlideViewer software provided by the manufacturer along with
Pannoramic MIDI.
Results and analysis
Phenotypic assessment of traits of fiber quality and yield
The correlation analyses of the traits of fiber quality and yield
were preliminary reported in a previous study (
Table S2
)
[17]
.
The correlations analyses showed that the trait pairs of FL-FS, FS-
SI and FL-SI had significant or extremely significant positive corre-
lations across all environments; FS-FM and FS-LP had extremely
significant negative correlations; FM-LP, BW-SI and FM-BW had
extremely significant positive correlations in most environments;
LP-SI, LP-BW and FM-SI had significant or extremely significant
negative correlations in most environments.
Construction of HDGM
An HDGM containing 8045 markers, including 2934 SLAF-
markers and 5111 CHIP-markers (CottonSNP80K array), was con-
structed, which spans 4943.01 cM with an average marker interval
of 0.61 cM (
Fig. 1
a;
Table 1
;
Table S3
). A
t
sub-genome consisted of
5929 markers, including 2336 SLAF-markers and 3593 CHIP-
markers, which spans 2904.16 cM with an average marker interval
of 0.49 cM. D
t
sub-genome consisted of 2116 markers, including
598
SLAF-markers
and
1518
CHIP-markers,
which
covers
2038.85 cM with an average marker interval of 0.96 cM. The long-
est chromosome was A12, which spanned 303.00 cM and consisted
of 758 markers with an average interval of 0.40 cM. The shortest
chromosome was D03, which spanned 105.41 cM and contained
27 markers with an average interval of 3.90 cM. The chromosome
that contained the largest number of markers was A13, which con-
tained 1006 markers with coverage of 267.58 cM, and an average
interval of 0.27 cM. The chromosome that contained the least num-
ber of markers was D03, which contained 27 markers with cover-
age of 105.41 cM and an average interval of 3.90 cM. The largest
linkage gap was 41.02 cM on D07. The number of linkage gaps
(
20 cM) on D
t
sub-genome was more than that on A
t
sub-
genome. Collinearity analysis showed that the absolute value of
the spearman coefficient of 26 chromosomes was at least 0.83, sug-
gesting that the linkage map and the cotton reference genome
(TM-1) had relatively high collinearity (
Fig. 1
b;
Table 1
).
QTLs and QTL clusters
A total of 295 QTLs of traits of fiber quality (140 QTLs) and yield
(155 QTLs) (
Table S4
) were identified on 26 chromosomes, explain-
ing 3.38–19.41 % of phenotype variance (PV). Of them, 156 QTLs
had positive additive effect on PV and 139 QTLs negative additive
effects, indicating that both XLZ24 and LMY28 contributed favor-
able alleles to increase the trait PV of fiber quality and yield respec-
tively. A total of 139 stable QTLs, including 75 fiber quality QTLs
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
4
(16 of FL, 26 of FS, and 33 of FM) and 64 yield QTLs (29 of BW, 18 of
LP, and 17 of SI), were identified simultaneously in at least two
environments on 26 chromosomes, which could explain 3.38–
13.18 % of PV (
Table S5
;
Fig. 2
). Two chromosomes A07 and A12
harbored the largest number of stable QTLs, twelve and eleven
respectively, which could explain 3.38–11.42 % of PV. Among these
stable QTLs, 69 had positive additive effect, indicating that XLZ24
contributed favorable alleles to improve fiber quality and yield;
and 70 had negative additive effect indicating that LMY28 con-
tributed favorable alleles to improve fiber quality and yield. In
addition, a total of 201 QTLs, including 92 fiber quality QTLs and
109 yield QTLs, were identified in A
t
sub-genome, which was more
than twice the number of 94 QTLs identified in the D
t
sub-genome,
including 48 fiber quality and 46 yield QTLs. Similarly, 88 stable
QTLs, including 46 fiber quality traits and 42 yield traits, in A
t
sub-genome were also nearly twice as many as 51 stable QTLs,
including 29 fiber quality QTLs and 22 yield QTLs, identified in
the D
t
sub-genome.
Among all QTLs identified in current study, 214 QTLs were in 74
QTL clusters, which were distributed on 25 chromosomes (exclud-
ing D09), 51 of the clusters were in A
t
sub-genome and 23 of them
in D
t
sub-genome (
Table S6
). A07 harbored six QTL clusters, which
was the largest number of QTL clusters in a chromosome, followed
by A02, A04, A08, A12, and D10, each of which harbored five QTL
clusters. Thirty-three of the 74 clusters (
Table S7
) harbored at least
two stable QTLs of different traits, which were regarded as QTL
cluster hotspots and were distributed on 20 chromosomes (exclud-
ing A03, D03, D06, D08-D09, and D13). Four of the thirty-three QTL
clusters exclusively harbored stable QTLs of yield trait (BW, LP, and
SI) on A02, A04, A10, and A13. Six of the thirty-three QTL clusters
exclusively harbored stable QTLs for fiber quality (FL, FS, and FM)
traits on A05, A08, D01, D10, and D12. Twenty-three of the
thirty-three QTL clusters harbored stable QTLs of both fiber quality
(FL or FS or FM) and yield (BW or LP or SI) traits on A01-A02, A05-
A13, D02, D04-D05, D07, and D10-D11. Nine of the twenty-three
QTL clusters harbored at least three stable QTLs. The cluster on
Fig. 1. High density genetic-linkage map (HDGM) construction and evaluation. a. diagram of the map. b. collinearity between the genetic map (black font) and the physical
map (red font) of A
t
and D
t
sub-genomes. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
5
A07, Clu-A07-1, harbored three stable QTLs of fiber quality (FL or
FS or FM) and two stable QTLs of yield trait (BW and SI).
Qtls common to those of previous studies
Upon comparing the stable QTLs identified in the current study
with those from previous studies, the results revealed that 93
stable QTLs were common to previous ones, and that 46 stable
QTLs were newly identified QTLs. In the 93 stable QTLs, 39 ones,
including seven of FL, nine of FS, sixteen of FM, two of BW, two
of LP, and three of SI, were common to those in previous linkage
analysis of segregating populations (
Table S8
with references); 69
ones, including seven of FL, fourteen of FS, nine of FM, thirteen of
BW, fifteen of LP, and eleven of SI, were common to those in previ-
ous GWAS of natural populations (
Table S9
with references).
Seventeen of them were common to those of both linkage analysis
and GWAS.
In addition, comparing with expression QTL (eQTL) hotspots of
fiber development in previous reports
[47]
revealed that 39 stable
QTLs overlap with the eQTL hotspots (
Table S10
), of which 26 are
the abovementioned common QTLs. Five of them, qFL-D03-1,
qFL-D05-3, qFS-D10-2, qFM-D03-1, and qBW-A05-1, were simulta-
neously identified in both linkage analysis and GWAS.
Construction and identification of the gene co-expression modules of
stable QTLs and clusters in fiber development
To dissect the potential acting mechanism of the functional
genes during the trait formation of cotton fiber quality and yield,
a total of 15,167 genes were fetched from the physical region of
confidence interval of the stable QTLs and QTL clusters in TM-1 ref-
erence genome
[3]
. Filtration of these genes under the criterion of
average FPKM
0.5 resulted in 6275 effectively expressed genes
(EEGs) during fiber development. WGCNA of these EEGs generated
14 distinct gene co-expression modules (
Fig. 3
a;
Fig. S1
). The high
correlation between the module eigengenes and sample of differ-
ent fiber developing stage indicated that the module of MEpurple
displayed a significant positive correlation with 10 DPA, MEsky-
blue with 15 DPA, MEgreen with 25 DPA, and MEblack with 30
DPA (
Fig. 3
a). The scatter plots of GS versus MM of above selected
modules were presented in
Fig. 3
b. At the condition of threshold of
GS
0.56 and MM 0.92, a total of 650 genes were screened from
the above modules with significant correlation with fiber develop-
ing stages.
Screening for candidate genes
GO enrichment analysis of the 650 genes revealed that plant-
type secondary cell wall biogenesis (GO:0009834), fatty acid
biosynthetic process (GO:0006633), and very long-chain fatty acid
biosynthetic process (GO:0042761) were enriched (
Fig. 3
c). PPI
analysis of these 650 genes identified 9 hub nodes in the network,
of which five were in plant-type secondary cell wall biogenesis and
four were in fatty acid biosynthetic process (
Fig. 3
d). These nine
hub nodes corresponded to eleven G. hirsutum homologous genes,
which were regarded as putative candidate genes orchestrating the
fiber development in this study (
Table 2
). The qRT-PCR result of
these eleven candidate genes in XLZ24 and LMY28 showed that
except for GH_A03G0921, all other genes had differential expres-
sion at least at an important stage during fiber development
(
Fig. 4
).
Silencing verification of GH_D07G2262 function in cotton
GH_D07G2262 gene was identified in the QTL cluster Clu-D07-2,
which consists of multiple QTLs of traits of both fiber quality and
yield.
Its
physical
interval
was
from
53,125,583
bp
to
54,911,218 bp, harbored 134 genes. GH_D07G2262 (CesA4) gene
belongs to cellulose synthase gene superfamily in Gossypium
species (
Fig. S2
). To investigate the function GH_D07G2262 gene
Table 1
Detailed information of HDGM of LMY28
XLZ24 RIL population.
Chr
Genetic length (cM)
No. makers
Average marker interval (cM)
Collinearity coefficient
No. > 20 cM gaps
Largest gap (cM)
Total
SLAF-seq
Cotton80K array
A01
137.43
307
78
229
0.45
0.96
–
10.77
A02
199.20
100
31
69
1.99
0.91
3
26.75
A03
210.24
516
210
306
0.41
0.86
2
28.78
A04
237.87
264
36
228
0.90
0.83
2
22.12
A05
259.19
484
139
345
0.54
0.85
–
19.81
A06
217.32
263
65
198
0.83
0.93
–
15.64
A07
275.13
532
305
227
0.52
0.89
1
26.47
A08
222.41
849
303
546
0.26
0.91
–
15.88
A09
216.33
410
143
267
0.53
0.92
–
12.14
A10
134.89
199
88
111
0.68
0.95
–
15.17
A11
223.58
241
38
203
0.93
0.88
2
37.24
A12
303.00
758
247
511
0.40
0.86
–
17.09
A13
267.58
1006
653
353
0.27
0.93
–
19.05
A
t
2904.16
5929
2336
3593
0.49
–
10
–
D01
175.87
137
13
124
1.28
0.93
1
28.49
D02
154.40
365
55
310
0.42
0.87
–
17.82
D03
105.41
27
8
19
3.90
0.95
2
24.79
D04
159.41
110
15
95
1.45
0.83
2
30.90
D05
225.53
315
71
244
0.72
0.96
1
21.34
D06
126.18
99
13
86
1.27
0.96
2
33.79
D07
177.62
157
103
54
1.13
0.83
3
41.02
D08
118.99
115
13
102
1.03
0.90
1
23.17
D09
161.93
142
46
96
1.14
0.94
3
29.38
D10
182.07
253
148
105
0.72
0.89
1
20.57
D11
207.80
231
62
169
0.90
0.94
1
26.75
D12
134.39
87
29
58
1.54
0.87
–
14.69
D13
109.27
78
22
56
1.40
0.88
2
30.29
D
t
2038.85
2116
598
1518
0.96
–
19
–
Total
4943.01
8045
2934
5111
0.61
–
29
–
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
6
in cotton fiber development, both VIGS and CRISPR/Cas9 strategies
were employed to silence it using upland cultivars TM-1 and
Jin668 respectively (
Fig. 5
). Analysis of the reverse transcription
quantitative polymerase chain reaction (RT-qPCR) data revealed
that the expression of GH_D07G2262 was significantly lower in
VIGS plants than in control TM-1 plants (
Fig. 5
a). When cotton
bolls naturally mature, they were harvested and FL of the bolls
was measured (
Fig. 5
b-c). The results revealed that average FL of
silenced plants were 27.06 mm and 28.18 mm, significantly shorter
than that of the control plant (30.79 mm), suggesting that silencing
of GH_D07G2262 significantly inhibited cotton fiber elongation.
The CRISPR/Cas9 editing obtained three mutants, all of which were
detected the expected mutations guided by sgRNA at the target
location of GH_D07G2262 (
Fig. 5
d). The observed results revealed
that the FL and FS of all T
1
transgenic knockout plants were signif-
icantly shorter and weaker than those of control Jin668 plants
(
Table S11
). The FL and FS of knockout plants were 25.00–
27.90 mm and 27.20–29.70 cN
tex
1
, significantly shorter and
weaker than those of WT, which were 28.21 mm (FL) and 30.40
cN
tex
1
(FS), respectively. In T
2
homologous GH_D07G2262-
knockout lines, Both FL and FS were significantly lower and weaker
than those of WT lines (
Fig. 5
e and 5f). Observation of cross-
sections of fiber cells revealed that the average cell wall thickness
WT, and three edited lines, GhCesA4-1, GhCesA4-2, and GhCesA4-3
was 4.43, 3.26, 3.53, and 3.13
l
m, respectively. Statistical analysis
revealed that CRISPR/Cas9 editing of GH_D07G2262 significantly
reduced fiber cell wall thickness (
Fig. 5
g and 5 h). Interestingly,
LP of knockout lines were significantly increased than that of WT
(
Fig. 5
f), suggesting that GH_D07G2262 may have pleiotropic func-
tions during cotton fiber development.
Discussion
HDGM construction and QTL identification
High-throughput sequencing and genotyping strategies are an
effective approach to evaluate the genetic diversities or to dissect
the genetic makeup of important traits of a given population
[16]
. In cotton, most of QTLs of fiber quality and yield traits were
detected through analyses of linkage and GWAS based on segrega-
tion populations
[5,6,48,49]
and nature accessions
[47,50–52]
,
Fig. 2. Identification and distribution of stable QTLs of fiber quality and yield traits on 26 chromosomes.
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
7
respectively. To fully identify the QTLs of traits relating to fiber
quality and yield harbored in the RILs of the present study, an
HDGM was constructed incorporating previous genotyping results
through CottonSNP80K array strategy
[17]
and molecular markers
de novo developed through SLAF-Seq strategy. Comparison of the
two maps revealed that the HDGM of current study had better cov-
erage of and a much more improved collinearity to the physical
reference genome map of upland cotton than the previous one
[17]
(
Fig. 1
). Compared with the previous QTL identification results
in this population, the present study detected a total of 139 stable
QTLs, of which 62 were detected de novo and the remaining 77
were duplicated with the previous results of CottonSNP80K array
strategy
[17]
. Of these 77 QTLs, 39 were also stable ones while
38 were detected in a single environment in previous results. These
findings confirmed that the improved HDGM has higher value and
reliability in linkage analysis and QTL identification.
In reviewing the previous reported literatures, it was observed
that 272 of the total 295 QTLs identified in this population, includ-
ing 130 of the 139 stable ones, were common to those in previous
reports and nine stable QTLs were unique loci in this population
[17]
. When comparing the current results of this population with
previous analyses of both linkage and associations, it was observed
Fig. 3. WGCNA of genes in stable QTL and QTL clusters. a. The heat map of correlations between the modules and sample of different fiber developmental stages. Modules are
displayed on the left, and each column corresponds to a fiber developmental stage. The number in each cell represents the pearson correlation coefficient, the p_value of the
corresponding module-trait is exhibited in parentheses. The color of cells indicates the degree of correlation; b. Scatterplot of correlations between module membership
(MM) and gene significance (GS) of the cells of MEpurple/MEgreen/MEblack/MEskyblue showing high correlation between module and fiber developmental stage. The
correlation coefficient between MM and GS and p_value is listed on top of each scatterplot. One dot represents one gene in the plot. c. The enrichment analysis of 650 genes
(GS
0.56 and MM 0.92) in above four modules. d. Protein-protein interaction network and MCODE (molecular complex detection) components of GO terms (GO:0009834:
plant-type secondary cell wall biogenesis GO:0006633: fatty acid biosynthetic process).
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
8
that 39 stable QTLs, including 32 of fiber quality traits and seven of
yield traits, were common to previous linkage analysis of segregat-
ing populations (
Table S8
), and 69 stable QTLs, including 30 of fiber
quality traits and 39 of yield traits, were found in GWAS of natural
populations (
Table S9
). Seventeen of the above stable QTLs were
simultaneously identified both in segregating and natural popula-
tions. An eQTL mapping study
[47]
revealed that 39 stable QTLs of
traits relating fiber quality and yield of present study were
expressed, 26 of which were also identified in previous segregation
or nature populations. These QTLs common to previous studies
suggest the reliability of QTL identification of current study and
that they may play important roles in regulating the trait forma-
tion of fiber quality or yield. The QTLs specific in the population
of current study may suggest new insight in the genetic architec-
ture that underlies the trait formation of fiber quality and yield.
These results may also imply a complex genetic mechanism regu-
lating fiber development. One QTL cluster Clu-A07-1, for example,
which harbored a stable QTL of FL, FS, FM, BW, and SI
[6,53]
, was
identified two candidate genes Gh_A07G1749 (leucine-rich repeat
protein kinase family protein; LRR RLK) and GH_A07G2179 (GhSI7;
transcriptional regulator STERILE APETALA) by fine mapping in G.
hirsutum intraspecific F
2
population
[53,54]
.
QTL effect direction of each trait in QTL clusters and simultaneous
improvement of the clustered traits
The pairwise correlations between traits in multi-trait studies
were frequently analyzed in previous segregation population
reports
[6,17]
. The correlation analysis (
Table S2
) indicated that
the trait pairs of FL-FS, SI-FL/FS, FM-LP/BW, and BW-SI were posi-
tively correlated at significant or extremely significant level and
that the trait pairs of FM-FL/FS, LP-FL/FS, FM-SI, BW-LP and SI-LP
were negatively correlated at significant or extremely significant
level in most of the experimental environments, while the trait
pair of FS-BW showed extremely significant positive correlations
in some environments and extremely significant negative correla-
tions in other environments. The current study summarized 74 QTL
clusters among multi-traits based on the confidence intervals and
Table 2
Eleven candidate genes associated with fiber development.
MCODE
Modules
Gene IDs
Abbr.
Gene annotation in A. thaliana
G. hirsutum
A. thaliana
Plant-type secondary cell wall
biogenesis
Skyblue
GH_A11G3703
AT5G03170
FLA11
FASCICLIN-like arabinogalactan-protein 11
GH_D03G1117
AT5G15630
IRX6
COBRA-like extracellular glycosyl-phosphatidyl inositol-anchored protein
family
GH_A11G1050
AT2G46770
NST1
NAC (No Apical Meristem) domain transcriptional regulator superfamily
protein
Green
GH_D03G0812
AT5G05170
CEV1
Cellulose synthase family protein
GH_A07G2317
AT5G44030
CesA4
cellulose synthase A4
GH_D07G2262
AT5G44030
CesA4
cellulose synthase A4
Fatty acid biosynthetic process
GH_A04G1111
AT5G05580
FAD8
fatty acid desaturase 8
GH_A03G0921
AT1G19440
KCS4
3-ketoacyl-CoA synthase 4
GH_A05G1932
AT1G19440
KCS4
3-ketoacyl-CoA synthase 4
GH_D03G0938
AT4G23850
LACS4
AMP-dependent synthetase and ligase family protein
Black
GH_A04G1519
AT5G60620
GPAT9
glycerol-3-phosphate acyltransferase 9
Genetic linkage analysis of stable QTLs in Gossypium hirsutum RIL population revealed function of GhCesA4 in fiber development.
Fig. 4. Quantitative RT-PCR of the expression of 11 candidate genes during fiber development. Fiber sample at 5 DPA of L28 was used as control for all tests.
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
9
their corresponding physical intervals of these multi-trait QTLs
(
Fig. 2
). In linkage analysis of a segregation population, the effect
direction of a QTL represents which of the two parents of the pop-
ulation provides the favorable allele of the locus for the trait. The
QTL effect direction of the traits in a QTL cluster corresponds to
the phenotypic correlation direction between traits. In the current
study, 33 of 74 QTL clusters harbored at least two stable QTLs
(
Tables S6, S7
). Among the positively correlated trait pairs in each
of the clusters, the favorable alleles at this locus for these traits are
all provided by the same parent, such as FL-FS, FL/FS-SI, FM-LP/BW
(
Tables S6, S7
). These positively correlated traits usually share
same additive effect direction, which infers that they can be
improved synchronously via simply selecting the common markers
in the clusters of these traits through MAS. However, balancing of
the yield and quality was always a problem in crop genetic
improvement. Between the negatively correlated fiber quality
and yield traits, it is always difficult to further improve one with-
out allowing a certain trade-off of the other
[6,55–57]
. In the pre-
sent study, the results revealed that 25 of the total 74 QTL clusters
harbored two stable QTLs, in which 14 clusters had stable QTL pairs
with opposite additive effects, while nine clusters harbored at least
three stable QTLs (
3), in which there were 6 stable QTL pairs,
namely, LP-FL/FS, FM-FL/FS, and SI-FM/LP, had opposite additive
effects. These cluster loci are the keys to resolve the trade-off
effects between the negatively correlated traits. Some studies reck-
oned that the balancing of complex trade-off was caused by gene
pleiotropy and linkage drags
[55]
. A recent study suggested that
the QTL clusters formed by the negatively correlated traits and that
in the clusters, the alleles favorable for the trait development con-
tributed by different parents, can explain the trade-off effect
[6]
,
which makes it difficult to achieve simultaneous improvement of
both traits
[55–57]
. However, there are currently two possible gene
acting mechanisms involved in the trade-off effect, namely linkage
drags and gene pleiotropy. In such case, it is necessary to first con-
duct further genetic dissect of the cluster to clarify which of the
two mechanisms is at work, and then use corresponding strategies
Fig. 5. GH_D07G2262 positively regulates cotton fiber elongation but negatively regulates lint percentage in knockout experiments. a. The relative expression level of
GH_D07G2262 in control, VIGS-1 and VIGS-2 plants. b. Fiber length (FL, mm) of control and VIGS plants. c. Mature fiber of control and VIGS plants. d. Sanger sequencing-based
detection of GH_D07G2262-knockout plants obtained after CRISPR/Cas9 gene editing. Nucleotide deletions at the target site are represented by dots. e. Phenotypes of fiber
samples of GH_D07G2262-knockout and wild type (WT) plants. f. Analysis of variance of FL, fiber strength (FS, cN
tex
1
), and lint percentage (LP, %) of WT and CRISPR/Cas9
edited lines. g. Cell wall thickness of mature fibers of the WT and CRISPR/Cas9 edited lines. h. Statistical analysis of the mean values of cell wall thickness of mature fibers.
Error bars indicate the standard errors of biological replicates.
***
P_value < 0.001.
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
10
to improve the two traits based on the mechanism at work. For
linkage drags, via finely mapping the linked causal genes and then
recombining them through conventional breeding strategies, the
problem of linkage drags can be solved. While for gene pleiotropy,
it can be solved by editing cis-regulatory regions (CRRs) via a tiling-
deletion-based CRISPR/Cas9 screening strategy
[55]
. Researchers
have found that variations on CRRs can regulate the gene expres-
sion at different levels
[58–61]
and thus regulating the quantitative
traits. Accumulating the amount of genetic variations in CRRs and
screening the favorable variations at different levels would achieve
the optimum effects of overcoming trade-off among quantitative
traits. However, it still remains unclear which of the two mecha-
nisms, linkage drag or pleiotropic genes, is at work in these clus-
ters, and further dissection is needed to clarify this issue.
GhCesA4 may regulate cotton fiber development
Previous study indicated that plant homomeric cellulose syn-
thase (CesA) trimers comprising CesA4/7/8 are in charge of the
biosynthesis of the secondary cell walls (
Fig. 6
)
[62]
. These homo-
meric CesA trimers may form complexes (CSCs) to perform their
function. After assembled in the Golgi apparatus, CSCs are subse-
quently transported to the plasma membrane (PM), where they
catalyze cellulose synthesis
[63]
and assemble them into microfib-
rils of the secondary cell wall (SCW)
[64]
. Integrity of CSCs is very
important for cellulose biosynthesis, mutation of any member of
the complexes may leads to rapid CesA proteasome degradation
[65]
. Knock-out of any monomer of GhCesA 4, 7, and 8 will result
in the loss-of-function of the cellulose synthase supercomplex
(CSS) and the production of fiber cells without SCW thickening
[64]
. In rice, different CSCs comprising different OsCesA members
may be responsible for biosynthesis of primary cell wall or SCW
[66]
. The heterotrimeric complex comprising OsCesA4/7/9 is
responsible for synthesizing rice SCW cellulose
[67,68]
. While in
Arabidopsis, AtCesA4, 7, 8 are highly co-expressed in regulating cel-
lulose synthesis of SCW
[69,70]
. Existing studies have elaborated
the mechanisms of regulating CSCs and the action modes of CSCs
in the synthesis of cellulose in SCWs. Some results demonstrate
that the wall thickening activator NACs could differentially binds
at the promoter region of CesA4
[69]
. GhKNL1, a class
Ⅱ KNOX pro-
tein, which is reckoned to be a transcription factor regulating plant
SCW formation, could directly bind to the promoter elements of
GhCesA4-2/4–4/8–2 and GhMYB46 to catalyze SCW cellulose syn-
thesis during cotton fiber development
[71]
. A study identified a
rice mutant of OsCesA4, fc17, which significantly reduces cellulose
crystallinity, and thus enhances biomass saccharification and lodg-
ing resistance
[72]
(
Fig. 6
). Another mutant Bc19 of OsCesA4 is
regarded as a semi-dominant brittle allele, which affects cellulose
synthesis in a dosage-dependent manner
[73]
. AtCesA8 promoter
can drive overexpression of OsSUS3 to regulate carbon partitioning
between cellulose and other polysaccharide biosynthesis
[74]
.
Increased wall thickness may coincide with reduced cellulose crys-
tallinity
[74]
. Meanwhile, a sucrose-free environment accelerated
the relocation of PM-localized GFP–CesA3 towards the periphery
of the Golgi apparatus through the clathrin-rich trans-Golgi net-
work
[75]
, mediated by clathrin-dependent mechanisms.
Cotton fiber is a highly specialized single cell that develops from
the epidermal layer of the ovule after fully elongated and its sec-
ondary cell wall thickened, which assembles a large quantity of
cellulose
[64]
. A recent study identified homozygous GhCesA4 (A
t
:
GH_A08G0515, D
t
: GH_D08G0525) mutants via CRISPR/Cas9 editing,
which displayed significant reduction in both fiber yield and FL
(mid panel) in comparison with those of control plants (
Fig. S2
)
[64]
. During cotton fiber SCW synthesis, GhCesA4, 7, and 8 assem-
ble into heteromers in 36-mer-like CSS
[64]
, which infers that the
pathway and modulation of cellulose synthesis of SCW in develop-
ing cotton fiber cells may adopt similar mechanisms. In this study,
we identified two new GhCesA4 members, GH_A07G2317 and
GH_D07G2262, within the physical intervals of Clu-A07-1 and
Clu-D07-2 respectively, which was identified via genetic linkage
analysis (
Fig. 2
). Silencing GH_D07G2262 via both VIGS technology
of TM-1 and CRISPR/Cas9 editing of Jin668 resulted shorter FL and
higher LP than those of wild control (
Fig. 5
). In a previous study, it
was observed that when the homologous pair of GhCesA4,
GH_A08G0515 and GH_D08G0525, were edited, the secondary cell
wall of fibers was damaged
[64]
. Here we demonstrated that edit-
ing of GH_D07G2262 reduced the secondary cell wall thickness of
cotton fiber, implying that the homologous pair of GhCesA4 might
additively co-regulate secondary cell wall thickening during fiber
development.
We also identified some other candidate genes in the stable
QTLs or clusters, including FLA11 (fasciclin-like Arabinogalactan
protein 11, GH_A11G3703 in Clu-A11-3), IRX6 (irregular xylem6,
GH_D03G1117 in Clu-D03-1), CEV1 (constitutive expression of the
vegetative storage protein1, GH_D03G0812 in Clu-D03-1), and
NST1
(NAC
secondary
wall
thickening
promoting
factor1,
GH_A11G1050 in qFM-A11-1), which are correlated to plant-type
SCW biogenesis, form hub nodes in their protein interaction net-
work (
Fig. 3
d). How these hub node genes orchestrate the cellulose
biosynthesizes and the development of fiber quality is still open to
elucidation, however, previous studies revealed the involvement of
them in cellulose biosynthesis in SCW. The cev1 is testified as an
allele of AtCesA3 and mutations of cev1 allele lead to less cellulose
deposition in its roots than wild-type roots
[76]
. AtCesA3 needs to
be assembled into functional CSCs to catalyze cellulose in plant
SCWs
[77]
. Arabinogalactan proteins (AGPs) are a class of glycopro-
teins that are present throughout the plant kingdom and play a
crucial role in various aspects of plant development
[78]
.
Fasciclin-like AGPs (FLAs) are an important class of AGPs
[79]
,
which maintain conservative fasciclin functional domains, suggest-
ing their functional conservations in fundamental aspects of
embryogenesis and seed development
[80]
, including fiber initia-
tion and elongation
[78]
. The expression of FLA11 and FLA12 is
SCW-specific and highly correlated to cellulose content and
microfibril angle
[81]
.
The irx6 mutant was identified to be COBL4 gene defect, which
exhibits reduced cellulose content
[82]
, In willow, SxCOBL4 is
involved in cellulose microfibril deposition
[83]
, it may regulate
the structure of cell walls via enhancing cellulose content and pre-
serving cellulose crystallinity
[84]
, which infers that fiber quality is
Fig. 6. The regulatory schematic model of CesAs in the synthesis and accumulation
of cellulose in secondary cell walls during fiber development. The cellulose synthase
complexes (CSCs) formed from homomeric CesA trimers are assembled in the Golgi
apparatus; subsequently they are transported to the plasma membrane, where they
catalyze cellulose synthesis. The assembly of celluloses into microfibrils of the
secondary cell wall is affected by both cellulose accumulation and crystallinity.
R. Liú, X. Lıˇ, Hàoliàng Yán et al.
Journal of Advanced Research xxx (xxxx) xxx
11
dependent to not only cellulose deposition but also cellulose crys-
tallinity in cotton. The current study also identified that Fatty acids
(FAs) biosynthesis pathway plays a vital role in orchestrating cot-
ton fiber development, of which the hub node genes included
long-chain acyl-coA synthetase4 (LACS4, GH_D03G0938 in Clu-D03-
1), fatty acid denaturase8 (FAD8, GH_A04G1111 in Clu-A04-3),
ketoacyl-CoA synthase4 (KCS4, GH_A03G0921 in Clu-A03-1), and
glycerol-3-phosphate acyltransferase9 (GPAT9, GH_A04G1519 in
Clu-A04-5) (
Fig. 3
). The Acyl-CoA synthetase (ACS) family plays a
crucial role in both FAs synthesis and degradation, and long-
chain ACSs (LACSs) comprise a small subgroup of this family
[85]
. Ketoacyl-CoA synthase (KCS) is a rate-limiting enzyme
required for the synthesis of very-long-chain fatty acids (VLCFA),
and several members of this enzyme family have been found to
|