Introduction
FuZi is a typical genuine medicinal material in China, and also an important
agricultural economic crop in Southwest China. It has a cultivation history of
more than 1300 years, mainly produced in Sichuan, Shaanxi, Yunnan, Guizhou,
Chongqing and other regions (Huang et al.
2011; Zhang et al. 2017a). Both the
Chinese government and Chinese companies have invested a lot in FuZi. It is a biennial herb, native to the northwest and
east of the Indian Himalayas. It is diverse in the East Asian mountains, widely
distributed from New York to the south of George Asia, and also distributed in
the divided areas of Ohio, Wisconsin and Iowa. It usually grows in woodland,
and its tuberous root is usually a therapeutic component of traditional Indian
medicine, which is used to treat dyspepsia, abdominal pain, diabetes, etc. Some
East Asian countries, such as Japan, South Korea, Mongolia and India, import
the drug from China in large quantities.
Aconitum carmichaelii, the basic plant of FuZi, is the most widely distributed species with the most
different characters, which leads to a variety of leaf types of FuZi (Gao et al.
2018). The leaf type is usually selected according to 11 leaf characteristics,
such as leaf size, leaf shape, leaf division degree, leaf surface, leaf back
and hair state etc. In the planting base of Sichuan province, FuZi has many common names such as pumpkin leaf, Dahua leaf, Xiaohua leaf, goose
palm leaf, berry leaf, oil leaf and Maoshi Miao
according to leaf shape (Xiao et al.
1991), and there is a big regional difference in the definition of plant and
leaf characters. At present, the main leaf types of FuZi
cultivation are flower leaves (including Dahua leaves
and pumpkin leaves), mixed with a small amount of Ai leaves (due to the small
yield, it belongs to the type gradually selected and eliminated by the base).
The Hua-leaf FuZi is favored by the farmers due to
its high single yield. The saying "Jiangyou FuZi is big and of high quality " refers to the type
of Hua-leaf, but the Hua-leaf FuZi is more sensitive
to external pathogens, and is very vulnerable to root rot (Gao
et al. 2017). Compared with Hua-leaf FuZi, Ai-leaf FuZi has higher
resistance to root rot and white silk disease, though its plant is weak and its
yield is small.
At present, there are some basic researches on the
biology development and intraspecific variation of FuZi
in China (Huang et al. 1980; Xiao et al. 1990; Xiao et al. 1991; Hu et al.
2008), and some explorations on the transcriptome molecular markers of leaf
type differences and stress resistance (Wen et
al. 2016; Zhang et al. 2017b).
However, there are few special academic reports on the formation of type
quality and agricultural disease resistance, and the molecular biological
mechanism is even less in this paper, we selected Dahua-leaf
and Ai-leaf FuZi, and compared their transcriptome
and expression activity of related genes, to explore the molecular biological
mechanism of leaf quality differences between the two FuZi
types from the perspective of growth metabolism network and key gene
differences in them.
Materials and Methods
Plant collection
In this study, the main cultivated leaf types of FuZi are Dahua-leaf
and Ai-leaf FuZi plants,
which were collected
in GAP base of Taiping Town, Jiang you City, Sichuan Province (N31 ° 44 '',
E104 ° 42 ') in the middle of June 2018, and the plants of FuZi were photographed
respectively (Fig. 1). The roots with soil
were extracted and brought back to the greenhouse of National Resources Bank
for planting traditional Chinese medicine of Chengdu University of Traditional
Chinese Medicine for cultivation.
According to the flora of China,
the leaves of FuZi are thin leathery or papery,
pentagonal, 6–11 cm long and 9–15 cm wide, with shallow heart-shaped trifid at
or near the base, wide rhombus in the center, sometimes obovate rhombus or
rhombus, acute, sometimes short acuminate and nearly pinnate, with about two
pairs of secondary lobes, obliquely triangular, with 1–3 teeth, occasionally or
completely, and lateral complete lobes Unequal dichroism, surface sparsely
pubescent, abaxial surface usually sparsely pubescent only along veins (Editorial Committee of flora of
China 1993). On this basis, the stems of Dahua-leaf are thick and glossy (slightly yellow in June), the
teeth of the leaves are gentle, and the leaves split shallowly; while the stems
of Ai-leaf FuZi are thin and weak, and there are more tillers in the axils of the
leaves, with small leaf surface, sharp leaf tip, narrow and long leaf bases,
and the leaves split deep to the petiole, whose shape is like that of A. argyi.
Subroot processing
After the two types of FuZi
were adapted to grow in the greenhouse for two weeks, three plants were
selected with similar growth potential for root extraction and numbered it (D1,
D2, D3 for Dahua-leaf, x1, x2, x3 for Ai-leaf ), cleaned the
surface soil with distilled water and washed with 75% alcohol. After
drying, the seeds were frozen at -80℃
immediately.
DNA extraction and
cDNA library construction
The total RNA of each sample tissue was extracted with Trizol Kit (Burlington Co. Canada) by using the
cryopreserved roots of Dahua-leaf and Ai-leaf FuZi plants, using oligo (DT) magnetic beads to separate
and purify mRNA, decomposing it into short gene segments, and then reverse
transcribing it into cDNA chains, and establishing six transcriptome libraries
of FuZi.
High throughput
sequencing, assembly and functional annotation of transcriptome
High-throughput sequencing platform Illumina Hillseq2500
of Illumina Company was used to sequence the transcriptome library of FuZi. The primers of sequencing splices were intercepted
and low-quality data of the original base sequence was filtered to obtain clean
reads. The long fragment contig was assembled with Trinity software to obtain
the long fragment set component (Unigene). BLAST
software was used to compare the Unigene sequence
with the NCBI non-redundant protein sequence (NR)ˏ Swiss-Prot
protein (Swiss-pro)tˏ Gene
Ontology (GO)ˏ Clusters of
Orthologous Groups (COG)ˏ euKaryotic Orthologous Groups (KOG)ˏ eggNOG4.5,
Kyoto Encyclopaedia of Genes and Genomes (KEGG) and other
databases. The kOBAS2.0 was used to get the Unigene
of KEGG ontology result in KEGG, and got the annotation information of Unigene.
Gene structure and
expression analysis
TransDecoder software was used
to predict the coding region sequence of Unigene and
its corresponding amino acid sequence. SSR Analysis of Unigene with more than 1 kB was screened by MISA software.
Reads of each sample were compared with Unigene sequences using STAR software, and single
nucleotide polymorphism (SNP) sites were identified through GATK’s SNP calling
process for RNA Seq.
The bowtie software was used to
compare reads obtained from sequencing with the Unigene
library, and the expression level was estimated based on the comparison results
and RESM. FPKM value is used to express the expression abundance of
corresponding Unigene. On the basis of FPKM expression
analysis, set the error rate (FDR ≤ 0.5) and absolute value log2FC
≥ 2. Used DEGseq R software
for the differential expression analysis of transcriptome samples of FuZi. Goseq R and KOBAS
software were used for enrichment analysis of differential expression genes
(DEGs), such as GO, COG, KEGG, KOG, Protein family (Pfam)
and eggNOG etc.
Fluorescence
quantitative PCR detection
Real-time quantitative polymerase chain reaction
(QRT-PCR) was used to detect the expression of the representatively
differential genes in the growth, development and metabolism of FuZi transcriptome data (D1, D2, D3, X1, X2, X3). Actin was
used as the internal reference gene, and fluorescent quantitative PCR primers
were designed according to each nucleotide sequence fragment (Table 1). SsoFastTM Eva GreenRSuperemix (Bio-Rad, USA) was used to carry out fluorescence
quantitative PCR experiment on Bio-Rad iCycler MyiQ Real-Time PCR System platform according to the
instructions. The PCR reaction mixture was 20 μL:
moderate cDNA samples, 10 μL of Ssofast TM Eva Green ® Superemix,
1.0 μL of positive and 1.0 μL of negative primers and 7 μL of DD H2O. PCR program was: 95℃, 20 s,
1 cycle; 95℃, 5 s, Tm, 20 s, 45 cycles, dissolution
curve from 65–95℃, temperature program was 0.5℃/s. Results calculation
was done using 2 − ΔΔCt method. Three
repeated experiments were designed in total.
Results
%20447-453_files/image002.jpg)
Fig. 1: The FuZi
plants with two type of leaves (A) Ai-Type
FuZi plants and (B) Dahua-Type
FuZi plants
%20447-453_files/image004.jpg)
Fig. 2: Histogram of GO classification of all assembled unigens
and DEGs
Library sequencing and assembly
A total of 52.23Gb clean data was obtained by
sequencing quality control of the transcriptome libraries of 6 samples of Dahua-leaf and Ai-leaf FuZi plants,
including 90,064,627 reads of Dahua-leaf, 85,252,760 reads of Ai-leaf and the percentage of Q30 base of each
sample was not less than 92.75%. After assembly, 52,471 unigenes were
obtained, with a total length of 49,038,560. The N50 of Unigene
was 1,364 bp, among which 15,898 Unigene were longer
than 1 kB.
Annotation
analysis of gene structure and function
The coding region of the gene is indispensable for the
growth and development. Through the structural analysis of the Dahua-leaf and Ai-leaf FuZi plants transcriptional
group, we obtained 15,568 CDs, of which 15,219 (97.75%) were within 1000 bp,
317 (2.02%) were within 1000–2000 bp, 26 (0.17%) were within 2000–3000 bp, and
6 (0.04%) were above 3000 bp. Simple
sequence repeat (SSR) and single nucleotide polymorphisms (SNP) are the
important markers types for the selection of transcriptome sequence differences
between Dahua leaf and AI leaf. In this study, 3,347
SSR markers were obtained from the single gene sequence structure analysis of
six transcriptome libraries, with single base repeat (1,700 base factors),
followed by three base repeats (1,012 genes) and two base repeats (450 genes).
In terms of single nucleotide polymorphism, there are 697,214 SNP loci in the Table 1: Details of genes and primers used in real-time
PCR
|
Gene ID |
Primer sequence（ 5'–3'） |
Tm
(℃) |
Amplification length per bp |
|
Glucan endo-1,
3-beta-glucosidase (c75304.graph_c0) |
Upstream:
CCCACTTCGGAGAGAAACTATGGGC Downstream:
TGCGTCCTCGTTCTCGTTCTCATTC |
52
|
165 |
|
Alpha-1,
4 glucan phosphorylase L isozyme (c81496.graph_c0) |
Upstream:
CTGTTCCCAGTTCAGTGTCATGGCT Downstream:
GCCAAGAGGAAGTTGACAAGGCAT |
65
|
317 |
|
beta-fructofuranosidase (c86619.graph_c1) |
Upstream:
CCGTAATCATACCTCAACCCCGTGG Downstream:
TCCCGGTTTCATTGAAGGGAAGCAA |
64
|
196 |
|
Heat
shock protein 90 (c69909.graph_c0) |
Upstream:
TCGGATGATGAGGACGAGGAAGAGA Downstream:
TGGCTTCCTCATCCAGATTGGCTTC |
65
|
165 |
|
beta-amylase
(c72189.graph_c0) |
Upstream:
ATCCTCCACCTTGGGCACATCTCTA Downstream:
CGGGTTGTCGTTTGATTCTGCATCG |
64
|
155 |
|
S-phase
kinase-associated protein (c73748.graph_c0) |
Upstream：
TGTTCGAGACGGAGGAGATCGAGAA Downstream：
CACTTTTTCACCTCCTCCTCGTCCC |
64
|
292 |
|
abscisic acid receptor PYR/PYL
(c70275.graph_c0) |
Upstream:
AACCATGGTTTCACACCCCCAGTAC Downstream:
CAGTTGGGGGTTATCGAAACGACGA |
65
|
199 |
|
ABA
responsive element binding factor (c73411.graph_c0) |
Upstream:
CGGACTTGGCCCTTGGTAGAGTAAA Downstream:
GCCAAACCAGTGATCTCCCCTTCTT |
63
|
129 |
|
Chalcone synthase
(c71781.graph_c0) |
Upstream:
GTCGGCTCCAACTATAAGTGCAGCT Downstream:
ACAAAGATGCACGTGTACTCGTCGT |
64
|
140 |
transcriptome library of three
large flower leaf samples and 693,486 SNP loci in three AI leaf samples. The
number of the two leaf type loci is similar, and the repetitive sites are
removed, 271,958 SNP loci are included in the transcriptome library.
By setting the BLAST parameter
E-value not greater than 1e-5 and HMMER parameter E-value not greater than
1e-10, 28,765 Unigenes with annotation information
were finally obtained. Among them, were 13,963 GO notes (the most
"biological process" was 7,121 "metabolic process", the
most "molecular function" was 7,009 "catalytic activity",
the most "cell component" is 6,219 "(Fig. 2), 8,460 COG notes
(the most abundant are 1,026" transcription, ribosome structure and
biosynthesis", 931 "general function prediction", and carbon
hydration 810 compounds were transcribed and metabolized, 10,443 were annotated
by KEGG (13.1% of the first three pathways were starch and sucrose metabolism,
11.7% were plant hormone signaling, and 7.6% were carbon metabolism).
Screening of
differentially expressed genes (DEGs)
Using false discovery rate (FDR) less than 0.05 and
difference multiple FC (fold change) greater than or equal to 2 as the standard
to screen the differentially expressed transcript sequences of Dahua-leaf and Ai-leaf FuZi plants, a total of
1,052 differential Unigene were obtained, of which
411 were up-regulated and 641 were down-regulated (Fig. 3). In toto 808 of these differentially expressed sequences were
annotated, with the highest up-regulation being 6.9 times (c63821. graph_ c0)
and the lowest being about 1 / 8 (c81071. graph _c0).
Among the DEGs, 371 were annotated
into the go database, and the functions related to "biological
process" were mainly enriched in "metabolic process" (GO:
0008152, 169), "cellular process" (GO: 0009987, 133), "single
tissue process" (117) etc., including glucan-1, 3-beta-glucosidase
(c75304. graph_c0), glutamate acyltransferase (c89091. graph_c0),
methyltransferase (c81444. graph_c0), etc. The functions related to "cell
components" are mainly enriched in "cells" (go: 0005623, 139),
"cell parts" (go: 0044464, 138), "membranes" (GO: 0016020,
149), such as leucine repeat (c82305. graph_c0), post-translational
modification/protein conversion chaperone (c69283. graph_c0), glycosylhydrolase (c76304. graph_c0), etc. The enrichment function
related to molecular function mainly had catalytic activity (GO: 0003824,
184)."Connection" (GO: 0005488,155),
"transport activity" (GO: 0005215,33) Among them, beet glucosides are
typical: gossypol/seed inhibitor protein (c70620. graph_c0), sucrose
–galactosyl transferase (c70620. graph_c0), LEA protein (c77003. graph_c0) and
so on. Similar to the results of GO functional enrichment, the difference
analysis of COG in the transcriptome of Dahua-leaf
and Ai-leaf showed that 263 genes were mainly enriched in "carbon
transport and metabolism" (42), "general functional prediction"
(31), "signal transduction mechanism" (30).
Enrichment
analysis of metabolic pathway of DEGs
Through KEGG enrichment and retrieval, 262 transcriptome
differential genes were found in 78 metabolic pathways. These metabolic
pathways can be classified into six categories, most of which are metabolism
(covering 90 differential genes and 53 metabolic pathways), followed by genetic
information processing (28 differential genes and 13 pathways) and
environmental signal processing (20 differential genes and 3 pathways).
In the metabolic pathway, the
most abundant differential gene was "starch and sucrose metabolism"
(ko00500), which was consistent with the results of GO differential analysis.
There are 19 different genes in this pathway. Compared with Ai-leaf FuZi, the alpha-1, 4-glucan
phosphatase (EC: 2.4.1.1, c80590.graph_c1, log2FC = 1.736955; c81496.graph_c0, log2FC
= 1.807435) was up-regulated in Dahua-leaf, the down-regulated were beta-fructofuranosidase (EC:
3.2.1.26, c86619.graph_c1, log2FC = -1.5398), beta-amylase (EC: 3.2.1.2., c66708.graph_c0, log2FC = -2.22859; c72189.graph_c0,
log2FC = -2.70389; c75146.graph_c0, log2FC= -2.61579), sucrose-phospho synthase (EC:
2.4.1.14, c90035.graph_c0, log2FC = -1.20994), trehalose-6-phosphatase (EC: 3.1.3.12., c84278.graph_c0, log2FC = -1.71061),
UTP-glucose-1-phosphouridinase
(EC:2.7.7.9, c84554.graph.c0, log2FC = -1.26431), 4-alpha-glucan transferase
(EC: 2.4.1.13., c88414.graph_c0 , log2FC =
-1.09065) etc.
In the genetic information processing pathway,
“endoplasmic reticulum protein processing” (ko04141, including 10 differential
genes) was the most abundant differentially expressed gene, and the
up-regulated gene in this pathway contain heat shock proteins 90 (Hsp90, c69909.graph_c0, log2FC = 1.08). The
down-regulated genes included kinase-related protein 1 (Skp1, c73748.graph_c0, log2FC = -1.61), endoplasmic
reticulum oxidoreductase (Ero1, c88371.graph_c0,
log2FC = -1.07), Luminal- junction protein (Bip, c85414.graph_c0, log2FC = -1.13), other heat sensitive
proteins (c72477.graph_c0, log2FC = -1.22; c73978.graph_c0,
log2FC = -3.01; c74099.graph_c0, log2FC = -2.67; c78831.graph_c0, log2FC =
-2.31; c79477.graph_c0, log2FC = -2.22; c80311.graph_c0, log2FC = -2.24) etc.
In the environmental signal processing pathway, the most abundant
differential gene was "plant hormone signal transduction" (KO04075,
including 17 differential genes). The up-regulated genes in this pathway
included ABA receptor PYR/PYL (c66471.graph_c0, log2FC = 2.20; c70275.graph_c0,
log2FC = 1.93; c79132.graph_c0, log2FC = 2.91) two-component response regulator
ARR-A, (c72468.graph_c0, log2FC = 1.35; c87201.graph_c1, log2FC = 1.96), ethylene response
transcription factor ½ (ERF1/2, c80070.graph_c0,
log2FC = 2.43), The down-regulated genes in this pathway include ABA response
element binding factor (ABF, c72751.graph_c0, log2FC = -1.27; c72819.graph_c0, log2FC
= -1.19; c73411.graph_c0, log2FC = -1.96; c75230.graph_c0, log2FC = -1.80; c89735.graph_c0,
log2FC = -2.05), Protein phosphatase 2C (PP2C, c77439.graph_c0, log2FC = -2.42;
c81169.graph_c0, log2FC = -4.32; c88118.graph_c1, log2FC = -4.91), plant
hormone response protein IAA (AUX-IAA, c79601.graph_c0, log2FC = -1.97), Serine/threonine
protein kinase 2 (SnRK2，c83136.graph_c0, log2FC
= -1.05), transducer inhibitor protein 1 (c86202.graph_c0, log2FC = -1.76) etc.
In many pathways, there were also genes that interact with the habitat.
For example, were three genes of plant pathogen interaction class (ko04626). Heat shock protein 90 (HSP90, c69909.graph_c0, log2FC =
1.08), calcium-binding protein CML (CaMCML, c81386.graph_c0,
log2FC = -1.06), RPM1 interacting protein 4 (RIN4, c88439.graph_c0, log2FC =
1.38). In addition, there were four genes related to the plant rhythm of FuZi (ko04712): chalcone synthase (CHS, EC: 2.3.1.74, c71781.graph_c0,
log2FC = 2.29; c81482.graph_c0, log2FC = -3.63), phytase inhibitor protein 1
(SPA1, c87667.graph_c0, log2FC = -1.53), two-component responder regulatory
protein 1 (TOC1, c88696.graph_c0, log2FC = -1.07).
Activity detection of DEGs
The expression of
the representative differential genes in the growth, development and metabolism
of the Dahua-leaf and Ai-leaf FuZi plants were detected by
fluorescence quantitative PCR. The results showed that the relative expression
and transformation of nine gene sequences including glucan-1, 3-glucosidase
(GG), glucan phosphatase l isoenzyme (GP), fructofuranosidase (FFS), amylase (Amy), heat shock protein 90 (Hsp90), S-phase kinase
helper protein 1 (Skp1), abscisic acid receptor PYR/PYL (ARPP), ABA responsive
element binding factor (AREB), chalcone synthetase (CHS) were basically consistent with the corresponding FPKM
change trend in the transcriptome database (Fig. 4). Among them, beta-amylase
gene had the highest transcription expression, and it is
%20447-453_files/image006.png)
Fig. 3: The KEGG classification of enriched DEGs in FuZi transcriptomes
%20447-453_files/image008.jpg)
Fig. 4: Real-time PCR verification for some important DEGs in FuZi
transcriptomes. GG: glucan-1,
3-glucosidase, GP: glucan phosphatase l isoenzyme, FFS: fructofuranosidase, AMY: amylase, Hsp90: heat shock protein 90, Skp1:S-phase
kinase helper protein 1, ARPP: abscisic acid receptor
PYR / PYL, AREB: ABA responsive element binding factor, CHS: chalcone synthetase.
also the sequence with the largest
difference in transcriptional activity between the two species.
Discussion
The clear record of cultivating FuZi
in China can be traced back to the Northern Song Dynasty, and it was only in
modern times that the original plant was FuZi. In
view of the wide adaptability and complex evolution of FuZi
plants, Chinese scholars who study the authenticity of traditional Chinese
medicine have paid attention to the influence of these complex biological
characteristics of FuZi on the quality of traditional
Chinese medical materials for a long time (Xiao et al. 1990; Xiao et al.
1991; Xiao et al. 2009). In
recent years, these plants which can visually distinguish leaf types have been
the important material basis for the cultivation of aconite varieties, and
there are many certified varieties (Hu et
al. 2008; Xia et al. 2009; Xia et al. 2014). However, there is
little research on the quality differences among several natural leaf type
varieties, and the concept of "variety" and the extension of planting
are still weak, so that the farmers in the large-scale cultivation areas such
as Sichuan, Shaanxi and Yunnan still rely on the traditional experience to
select their own production every year. In this paper, the comparative
transcriptome data analysis of Dahua-leaf and Ai-leaf FuZi plants
is helpful to accumulate the relevant basis of quality formation from the
perspective of genetic genes and habitat co-evolution, and to promote the
systematic research of FuZi.
In this study, the comparative transcriptome
analysis of aconite between Dahua-leaf and Ai-leaf FuZi
showed that the mainly enriched GO functions of 808 DEGs of two FuZi including "metabolic process" (GO:0008152),
“Cell process” (GO:0009987), “Single organization process”ˏ “Cell”
(GO:0005623), “Cell location”ˏ “membrane” (GO:0016020), “catalytic
activity” (GO:0003824), “Connect” (GO:0005488), “Transport activity” (GO:0005215)
and so on. The functional classification of COG mainly includes "carbon
transport and metabolism ", "general function prediction ",
"signal transduction mechanism", etc. The above functional categories
are basically attributed to basic metabolism (sugar and amino acid metabolism),
which contains many basic catalytic protein enzymes (such as dextran-1, 3-beta-glucosidase,
glutamate-acyltransferase, glycosyl hydrolase, sucrose-galactose transferase,
etc.), which can show significant differences in comparing the gene pool of the
transcriptome, suggesting the types and the difference of the content may be an
important aspect of the quality difference between the leaves of Dahua-leaf and Ai-leaf FuZi.
In line with the results of GO
and other differential analysis, KEGG differential analysis (especially in the
pathway ko00500) further deepened the above speculation. The
decrease in the activity of beta-fructofuranosidase
(EC: 3.2.1.26, log2fc > 0) accelerated the sucrose synthesis in FuZi, and the decrease of the activity of sucrose-phosphosynthetase (EC: 2.4.1.14, log2fc < 0)ˏ 4-alpha-glucan
transferase (EC: 2.4.1.13, log2fc < 0) also promoted the transformation of
sucrose to UDP glucose, the latter through
utp-glucose-1-phosphouridine-transferase (EC: 2.7.7.9, log2FC < 0) At the
same time, the increase in alpha-1, 4-glucan phosphatase (EC: 2.4.1.1, log2fc
> 0) transcriptional activity also promoted the transformation of the starch
to glucose-1-phosphate. In addition, the decreased expression of
trehalose-6-phosphatase (EC: 3.1.3.12, log2fc < 0), beta-amylase (EC: 3.2.1.2,
log2fc < 0) and other genes also promoted the accumulation of other small
molecular sugar (maltose, trehalose etc.).
The disease resistance is one of
the most important indexes in agricultural breeding. There have been a few
reports on the difference of disease resistance and pathogenic bacteria of FuZi. In traditional areas, the Hua-leaf FuZi is more sensitive to external pathogens and is very
vulnerable to root rot. Although the plants of Ai-leaf are thin
and the yield lesser; their resistance to root rot and white silk disease is
high (Gao et al. 2017). Compared with
the soil fungi of the two types, the results showed that the diversity and
abundance of dominant fungi, pathogenic bacteria and antagonistic bacteria on
the root surface of two kinds were significantly different, which were the
external factors of the diseases (Wang et
al. 2018).
Conclusion
As for the internal disease factors of aconite plants,
this study showed that compared with Ai-leaf, Dahua-leaf
preferred to accumulate more small molecular active sugars and amino acids. It
has been proved that sucrose, glucose, maltose and so on can obviously promote
the growth of hyphae of white silk and root rot, increase the growth of
mycelia, cause colony thickening, and increase the production of sclerotia or
conidia which is consistent with the objective situation that Dahua-leaf FuZi is more
susceptible to root rot and it is also suggested that we should further study
about the sugar types and contents in different leaves of aconite, as well as
the correlation with the quality formation and disease resistance of FuZi (Bolton et
al. 2010; Fang et al. 2011; Liebe and Varrelmann
2016; Strausbaugh 2018).
Acknowledgements
This work was supported by National Natural Science
Foundation of China (81630101, 81891010), Science and Technology Support
Program of Sichuan Province (2016JY0089), Scientific Research Project of
Sichuan Provincial Department of Education (16ZB0112), Scientific Research
Funds of Chengdu University of Traditional Chinese Medicine (030029050, ZRYY1612).
Author Contributions
Sha zhong, Yanpeng Yin
and Dingkun Zhang planned the experiments, Yanan He and Manjia Li
interpreted the results, Sha Zhong, Cheng Peng and Jihai
Gao made the write up and Min Zhang statistically
analyzed the data and made illustrations.
References
Bolton MD, L Panella, L Campbell, MF Khan
(2010). Temperature, moisture, and fungicide
effects in managing Rhizoctonia root and crown rot of sugar beet. Phytopathology 100:689‒697
Editorial Committee of flora of China (1993). Chinese Academy of Sciences. Flora of China.
Beijing Science Press, Beijing, China
Fang L, LP Wang, HY Ren, SJ Ru, ZM Feng, HR Wang (2011).
Effects of some sugars and allelochemicals on the pathogens of erysipelas and
root rot of Rhizoma Atractylodis macrocephalae. Zhejiang
Agric J 23:955‒960
Gao JH, FX Hou, YT
Ma, C Peng (2018). Molecular phylogeny and population structure of FuZi carmichaelii
(FuZi)
in western China. Intl J Agric Biol 20:826‒832
Gao JH, W Wang, XF Xie, XY Cao, C Peng (2017). Studies on the disease resistance and the diversity of
fungi on the root surface of aconite. World
Tradit Chin Med 12:2563‒2567
Hu P, YL Xia, XJ
Zhou, M Zhang, J Ding, TZ Chen, GM Shu (2008). A preliminary study on morphological polymorphism of
Aconitum Germplasm Resources. Resou Dev Market 5:448‒449, 482
Huang QW, ZY Zhou, J
Wang, RH Liu (2011). A study on the formation mode of Taoism and geography of FuZi. Chin J Tradit Chin Med 36:2599‒2601
Huang ZF, SZ Liu, GM
Yang, L Li, W G Zhong (1980). Anther culture and plantlet regeneration of aconite. Chin Herb Med 11:376‒377
Liebe S, M Varrelmann (2016). Effect of environment and sugar beet genotype
on root rot development and pathogen profile during storage. Phytopathology 106:65‒75
Strausbaugh CA (2018). Incidence, Distribution,
and Pathogenicity of Fungi Causing Root Rot in Idaho Long-Term Sugar Beet
Storage Piles. Plant Dis 102:2296‒2307
Wang W, DY Zhang, H Wen, Q Wang, C Peng, JH Gao (2018). Soil fungal biodiversity and
pathogen identification of rotten disease in FuZi carmichaelii (FuZi) roots. PLoS One 13;
Article e0205891
Wen H, C Peng, ZL Rao, DY Zhang, XF Xie, JH Gao (2016). MSAP analysis of genomic DNA methylation
modification in aconite leaves of flower leaf type and mugwort
leaf type. Chin J Tradit
Chin Med 19:3602‒3608
Xia YL, GM Shu, P Hu, M Zhang, XJ Zhou, XF Chen (2014).
Study on the comparison of the new varieties Zhongfu No.1 and Zhongfu No.2 of aconite. Chin Herb Med 37:1330‒1336
Xia YL, P Hu,
M Zhang, XJ. Zhang, XF Chen, TZ Chen, GM Shu (2009). Study on the breeding and
biological characteristics of fine varieties of aconite. Seed 28:85‒89
Xiao
XH, SL Chen, GP Yin, SY Chen (1990). Studies on the quality ecology of Aconitum (report 1). World Tradit
Chin Med
11:3‒5
Xiao
XH, SL Chen, SY Chen (1991). A preliminary study on
intraspecific variation of Aconitum. Chin Herb Med 5:18‒20
Xiao XH, SS Chen, LQ Huang, PG Xiao
(2009). Introduction
to the research of Chinese genuine medicinal materials in the past 20 years. Chin J Tradit Chin Med 34:519‒523
Zhang
DY, H Wen, W Wang, C Peng, JH Gao (2017a). Transcriptome analysis of terpenoid biosynthesis and metabolism in Aconitum. Chin J Exp Prescript 23:205‒211
Zhang DY, H Wen, W Wang, C Peng, JH
Gao (2017b). Transcriptome
study on secondary metabolism of terpenoids under abiotic stress of Aconitum. Chin Herb Med 40:2301‒2306