Abstract
The analysis of the differences in metabolic profiles between naturally
Ophiocordyceps sinensis (NO) and cultivated Ophiocordyceps sinensis
(CO) is an essential process for the medicinal value mining of
Ophiocordyceps sinensis. Non-targeted metabolomics was used to compare
the differences in metabolite composition and abundance between NO and
CO. Total metabolite composition found that NO is rich in organic acids
and derivatives, and CO is rich in lipids and lipid-like molecules. HCA
found that organooxygen compounds, cinchona alkaloid, and fatty acyls
had different abundances in NO and CO. The variable importance in
projection value and quantitative analysis of metabolites found that NO
was rich in l-iditol, malate, linoleic acid, and oleic acid; CO is rich
in sucrose, perseitol, hydroquinidine, nonanoic acid,
1-hydroxy-2-naphthoic acid, hymol-β-d-glucoside, and gly-his-lys. these
compounds have the potential to be biomarkers of NO and CO. KEGG
enrichment analysis showed that ascorbate and aldarate metabolism,
carbon metabolism, pyrimidine metabolism, and fatty acid biosynthesis
were the most different metabolic pathways between NO and CO.
Therefore, the analysis of the characteristics of NO and CO metabolites
has reference value for finding their different medicinal functions.
Subject terms: Microbiology, Molecular biology
Introduction
Ophiocordyceps sinensis is a mixture of dead insects and fungi formed
by Chinese fungus infecting Hepialus larvae^[38]1. In China, NO is
mainly distributed near the Qinghai-Tibet Plateau, at an altitude of
more than 3500 m in the alpine region^[39]2. The climate is
characterized by low temperature, low oxygen content, high ultraviolet,
and variable temperature^[40]3,[41]4. These extreme climatic
characteristics make NO have many special metabolites, which are widely
used in the field of medicine^[42]5,[43]6. Studies suggest that NO rich
superoxide dismutase may inhibit the excessive production of
oxygen-free radicals and treat cerebral ischemia by increasing the
activity of superoxide dismutase (SOD), glutathione peroxidase
(GSH-PX), and catalase (CAT) in brain tissue^[44]7. Modern pharmacology
found that cordyceps polysaccharide can inhibit lipid peroxidation in
hepatocytes and protect the liver^[45]8. Nucleosides may enhance the
function of macrophages, showing anti-tumor immune regulation^[46]9. In
addition, in daily life, NO, ginseng, and velvet antler are known as
the three treasures of traditional Chinese medicine and have extremely
high nutritional value^[47]10,[48]11. Therefore, it is more and more
popular among consumers and medical scientists and has attracted the
interest of scientists. However, the production of NO is gradually
unable to meet the needs of consumers^[49]12. Herdsmen obeyed the
profit-seeking mentality and blindly expanded the collection of NO,
resulting in grassland degradation, reduced ecosystem diversity, and
gradual loss of a suitable living environment for O. sinensis. The
natural reserves have also been reduced year by year^[50]13–[51]15.
Therefore, the cultivation of CO has become the academic peak that
scientists compete to occupy. Methodologically, the cultivation of CO
is divided into three processes. The first is the domestication and
cultivation of host insects. Scientists have completed the complete
life history of mating, oviposition, hatching, pupation, and eclosion
of adults captured in the field in the laboratory^[52]16,[53]17. The
host bat moth has been reared for multiple life histories to adapt to
laboratory feeding conditions. When its survival rate exceeds 80%, it
is considered that the domestication of the species is initially
completed^[54]18. The second is the isolation of Chinese fungus with a
high infection rate and high activity^[55]19. The most common method is
to inoculate its stroma, mycelia, or sclerotia on the medium from NO in
the laboratory, and obtain Chinese fungus by adjusting the medium
scheme and culture conditions^[56]20. Finally, Chinese fungus was
artificially inoculated into the host insects by feeding method,
epidermal injection method, or spray method^[57]21,[58]22. After some
time, the larvae died to form a dead insect, the stroma germinated from
the top of the larvae, and the CO culture was completed. Due to the
lack of a variety of environmental factors, the composition and content
of CO metabolites may be different from NO. This indirectly leads to
the preference of NO and CO in medicinal value and health care
function. At present, CO cultivation technology is gradually improving,
and scientists hope to use CO as an alternative to NO in the field of
medical and physical health care^[59]23,[60]24. Therefore, the
differences in metabolites and medicinal functions of CO and NO are one
of the urgent problems to be solved.
When used as a Chinese herbal medicine, NO has two medicinal forms,
namely naturally fresh O. sinensis (NFO) and naturally dired O.
sinensis (NDO). In the existing research, NFO and CFO are more used for
research, ignoring the medicinal value of NDO and CDO. Therefore, NFO,
cultivated fresh O. sinensis (CFO), NDO, and cultivated dried O.
sinensis (CDO) were included in this study. In this study, the
differences in metabolites between NFO and CFO (NFO vs CFO), and
between NDO and CDO (NDO vs CDO) were compared to provide valuable
metabolite data for the development of their medicinal functions.
Results
Overview of metabolites
The metabolic profile showed that a total of 2,641 metabolites were
identified and annotated, including 1,229 species that could be
classified by KEGG, which were divided into 18 superclasses and 108
classes (Table [61]S2). The top 6 superclasses were organic acids and
derivatives (300 metabolites, 25.64%), lipids and lipid-like molecules
(218 metabolites, 18.63%), organoheterocyclic compounds (168
metabolites, 14.36%), benzenoids (151 metabolites, 12.91%),
phenylpropanoids and polyketides (140 metabolites, 11.97%), and organic
oxygen compounds (139 metabolites, 11.88%) (Fig. [62]1).
Figure 1.
[63]Figure 1
[64]Open in a new tab
KEGG identified the composition and proportion of metabolites.
The top 6 classes were carboxylic acids and derivatives (249
metabolites, 20.26%), organooxygen compounds (138 metabolites, 11.23%),
fatty acyls (105 metabolites, 8.54%), benzene and substituted
derivatives (97 metabolites, 7.89%), prenol lipids (67 metabolites,
5.45%), and flavonoids (56 metabolites, 4.56%).
Multivariate statistical analysis
Multivariate statistical analysis was used to determine the differences
in metabolites between NO and CO. Principal component analysis (PCA)
results showed that CDO, CFO, NDO, and NFO were significantly divided
into 4 groups and the distance was far (Fig. [65]2A), indicating that
the metabolites of NO and CO were quite different. The clustering and
correlation analysis of the samples (Fig. [66]2B, C), all support that
CDO and NDO are clustered into one branch, CFO and NFO are clustered
into one branch, indicating that there is a significant difference in
the metabolic spectrum between fresh and dry O. sinensis. It also shows
that the inclusion of dry O. sinensis in the research scope of this
study has important reference value for the discovery of their
medicinal functions.
Figure 2.
[67]Figure 2
[68]Open in a new tab
Multivariate statistical analysis of 4 O. sinensis samples. (A) PCA
analysis of 4 groups samples. (B) Cluster analysis of 4 groups samples.
(C) Correlation analysis between 4 groups samples.
In the OPLS-DA score plot, different species of O. sinensis were
significantly distinguished. The OPLS-DA model had high
interpretability (
[MATH: RX(NFOVSCFO)2 :MATH]
=0.716,
[MATH: RY(NFOVSCFO)2 :MATH]
=1,
[MATH: RX(NDOVSCDO)2 :MATH]
=0.718, and
[MATH: RY(NDOVSCDO)2 :MATH]
=1) and predictability (
[MATH: QNFOVSCFO2 :MATH]
=0.997, and
[MATH: QNDOVSCDO2 :MATH]
=0.993). It shows that the model has good fitting and strong
predictability (Figure [69]S1A, 1B). To prevent over-fitting of the
model, this study used 200 permutation tests for verification. The
results show that the verification intercepts of R^2 and Q^2 are
[MATH: RNFOVSCFO2 :MATH]
=0.77,
[MATH: QNFOVSCFO2 :MATH]
=0.04,
[MATH: RNDOVSCDO2 :MATH]
=0.73, and
[MATH: QNDOVSCDO2 :MATH]
=− 0.09 (Figure [70]S1C, 1D), respectively, indicating that the model
is not over-fitting.
Among the identified metabolites, NFO had more abundant lpc 18:2,
glycerophosphocholine, acetyl coenzyme a, phosphorylcholine, oleamide,
and fenpropidin than CFO. CFO is rich in bis (2-ethylhexyl) adipate,
tyramine, spermine, l-arginine, n6, n6, n6-trimethyl-l-lysine, and
paminoazobenzene (Figure [71]S1E). Compared with CDO, NDO is rich in
leu-pro, medermycin, traumatic acid, and l-carnitine. CDO is rich in
2-amino-1-phenylethanol,
2-thio-s-acetyl-sn-glyceryl-3-phosphorylcholine, acetyl coenzyme a,
paminoazobenzene, glycerophosphocholine, lpc 18:2, fluvoxamine,
chlormadinone acetate, N6, N6. N6-trimethyl-l-lysin, and
l-o-hexadecyl-2-deoxy (Figure [72]S1F). The results showed that N6, N6,
N6-trimethyl-l-lysin, and paminoazobenzene were abundant in CO (CDO and
CFO), but not in NO (NFO and NDO), which may be potential biomarkers of
NO and CO.
Identification and abundance analysis of differentially accumulated
metabolites (DAMs)
By comparing the metabolites in wild and artificial O. sinensis samples
(NFO vs CFO, and NDO vs CDO), VIP ≥ 1 was used to screen differential
metabolites (Fig. [73]3A). In NDO vs CDO, a total of 508 metabolites
were screened, of which 200 were up-regulated and 308 were
down-regulated (Fig. [74]3B), mostly belonging to organic acids and
derivatives (88 metabolites, 17.32%) and lipids and lipid-like
molecules (55 metabolites, 10.83%). In NFO vs CFO, a total of 492
metabolites were screened, of which 331 were up-regulated and 161 were
down-regulated (Fig. [75]3C), most of which belonged to organic acids
and derivatives (79 metabolites, 16.06%) and lipids and lipid-like
molecules (49 metabolites, 11.42%).
Figure 3.
[76]Figure 3
[77]Open in a new tab
Differentially expressed metabolites screening. (A) Differential VIP
plots the top 20 DAMs in CDO, CFO, NDO, and NFO. (B) Volcano plot of
DAMs in NDO vs CDO. (C) Volcano plot of DAMs in NFO vs CFO.
It was found that NO is rich in organic acids and derivatives, and CO
is rich in lipids and lipid-like molecules. Quantitative analysis of
metabolites showed that NO was rich in linoleic acid and oleic acid,
and CO was rich in thymol-β-d-glucoside, gly-his-lys, and
hydroquinidine (Fig. [78]4). The discovery of these DAMs not only
effectively identifies NO and CO, but also provides a reference for the
medicinal research of NO and CO.
Figure 4.
[79]Figure 4
[80]Open in a new tab
Comparative analysis of the abundance of DAMs in the top 20 species of
4 species of O. sinensis. At the level of P < 0.05, the representation
of different capital letters was significantly different.
Hierarchical cluster analysis
In NDO vs CDO, DAMs were clustered into 2 groups, and both cluster 1
and cluster 2 contained organooxygen compounds and cinchona alkaloids.
In organooxygen compounds, CDO is rich in sucrose and perseitol, and
NDO is rich in l-iditol. In cinchona alkaloid, CDO is rich in
hydroquinidine, and NDO is rich in malate (Fig. [81]5A). This indicated
that in NDO and CDO, sucrose and perseitol showed opposite accumulation
with l-iditol, and hydroquinidine and malate also showed opposite
accumulation. In NFO vs CFO, DAMs were clustered into two groups, and
both cluster 1 and cluster 2 included fatty acyls. Oleic acid was more
abundant in NFO, while nonanoic acid and 1-hydroxy-2-naphthoic acid
were more abundant in CFO (Fig. [82]5B). This indicated that Oleic acid
and 1-hydroxy-2-naphthoic acid accumulated oppositely in NFO and CFO.
Figure 5.
[83]Figure 5
[84]Open in a new tab
HCA is based on the top 20 DAMs. (A) NDO vs CDO, (B) NFO vs CFO.
NO has more abundant l-iditol, malate, and oleic acid, while CO is rich
in sucrose, perseitol, hydroquinidine, nonanoic acid, and
1-hydroxy-2-naphthoic acid. The differential accumulation of these
compounds may be one of the main reasons for the differences in the
metabolic profiles of NO and CO, and the similar DAMs often have
functional similarities or complementarities in biology, which will
promote the mining of different medicinal functions of NO and CO.
KEGG pathway enrichment analysis of DAMs
DAMs were annotated by KEGG, and the results showed that DAMs were also
enriched in a total of 94 metabolic pathways such as phenylalanine
metabolism, glycerolipid metabolism, lysine biosynthesis, and arginine
biosynthesis in NFO vs CFO (Table [85]S2). In NDO vs CDO, DAMs were
enriched in 86 metabolic pathways such as glutathione metabolism,
cysteine and methionine metabolism, and biosynthesis of amino acids
(Table [86]S3). The top 20 enrichment pathways were selected for
analysis. It was found that ascorbate and aldarate metabolism, carbon
metabolism, pyrimidine metabolism, fatty acid biosynthesis were the
main enrichment pathways of NO and CO (Figure [87]6A, and B ).
Figure 6.
[88]Figure 6
[89]Open in a new tab
KEGG enrichment circle diagram of DAMs. (A) NDO vs CDO; (B) NFO vs CFO.
Among them, the first circle from outside to inside is the pathway of
the first 20 enrichment, and the outside circle is the coordinate scale
of the number of differential metabolites. Different colors represent
different superclasses. The second circle is the number of differential
metabolites in the pathway and the −log[10]Q value. The more the number
of differential metabolites, the longer the strip, the smaller the
−log[10]Q value, and the redder the color. The third circle is the bar
chart of the proportion of up-regulated and down-regulated differential
metabolites. Dark purple represents the proportion of up-regulated
differential metabolites, and light purple represents the proportion of
down-regulated differential metabolites. The specific values are shown
below. The fourth circle is the Rich Factor value of each pathway, the
background grid line, each grid represents 0.1.
Discussion
O. sinensis has a long history as a medicinal material^[90]25. In
recent years, scientists have successfully cultured CO in the
laboratory^[91]26,[92]27. Scientists have carried out a lot of research
on whether CO can be a substitute for NO^[93]28,[94]29. However, there
is a lack of sufficient metabolite evidence in the scientific
literature to effectively distinguish NO from CO, especially the
difference between NDO and CDO. In this study, the metabolite
characteristics of NFO vs CFO, and NDO vs CDO were compared and
analyzed, and the differences in metabolic profiles of NO and CO were
found, which not only provided data basis for the development of the
medicinal value of O. sinensis, but also provided guidance for
consumers to choose NO and CO.
In NO, the abundance of organic acids and derivatives is higher,
specifically organooxygen compounds, and cinchona alkaloids. Studies
have shown that O. sinensis is rich in alkaloids (3'-deoxyadenosine,
pyrimidines, adenosine, etc.)^[95]30,[96]31. We observed that they used
NFO, which can effectively inhibit tumor cell growth in a
concentration-dependent manner^[97]32,[98]33. This study found that
although the overall content of organic acids and derivatives in NO was
higher, its nucleoside abundance was lower than that of CO, and more
leu-pro, medermycin, traumatic acid, and l-carnitine were detected in
NDO. This proves that in NO, the preservation process promotes the
accumulation of nucleosides, which may lead to different medicinal
functions of NFO and NDO, especially NDO may play a better role in
antitumor function. This study complements the missing data on the
metabolites of post-storage O. sinensis (NDO and CDO), and more animal
model experiments are needed to verify their detailed medicinal
functions. In addition, adenosine was used for NO quality control
standards at the same time^[99]34,[100]35, this study found that it can
also effectively distinguish CO. Adenosine has been reported to trigger
and mediate ischemic preconditioning to reduce myocardial
ischemia-reperfusion injury and has a good protective effect on the
myocardium^[101]36–[102]38. Therefore, NO may have better myocardial
protection. In addition, this study suggests that glycine, histidine,
l-lysine, linoleic acid, nonanoic acid, oleic acid, sucrose, l-iditol,
and perseitol are also potential biomarkers of NO and CO.
Lipids and lipid-like molecules were more abundant in CO. Lipids such
as unsaturated FFAs and eicosanoids have been reported to have multiple
biological activities, such as improving the anti-inflammatory ability
of organisms, improving memory, and improving cognitive
deficits^[103]39–[104]41. Modern pharmacology believes that O. sinensis
contains at least eight essential amino acids^[105]42,[106]43, which
play an important role in the treatment of nervous system diseases,
inhibition of bacteria, and enhancement of immune
function^[107]44–[108]47. Glutamate, tryptophan, and tyrosine have been
reported to have immune-enhancing physiological
activities^[109]48,[110]49. This study found that CO is rich in
glycine, histidine, and l-lysine. Therefore, CO may have a better
effect in improving the nervous system and regulating the immune
function of organisms. In NO and CO, there was no significant
difference in the content and composition of antibacterial active
substances, cordyceps polysaccharides, and sterols. This indicates that
CO can be an alternative to NO when these compounds need to be used.
Methods
Sample collection
CFO (6 repetitions) was purchased from Shenzhen Dongyang Industrial
Development Co., Ltd., China, and NFO (6 repetitions) was purchased
from Baohuitang Co., Ltd., Qinghai Province, China, from Zaduo County
(95° 33′ 29′′E, 33° 12′ 31′′N, Altitude: 4568 m), Yushu City, Qinghai
Province, China. On July 17, 2023, CFO and NFO were placed in a petri
dish covered with filter paper in the dark, dried at natural
temperature, weighed once every 3 h, until the weight did not change
significantly twice in a row. It was considered that the drying was
completed, and CDO (6 repetitions) and NDO (6 repetitions) were
obtained respectively. Four samples were divided into two groups, the
NO group included NDO and NFO, and the CO group included CDO and CFO.
Sample pretreatment and UPLC-MS/MS
Analysis Based on the existing research results, this study improved
the determination method in the reference^[111]50,[112]51. Four groups
were washed three times with sterile distilled water, and then the
stroma and sclerotia mixed liquid nitrogen was ground. (1) Six samples
were taken for each component, and 50 mg of each sample was weighed and
placed in a 2 mL EP tube. The medium-sized grinding beads were added to
assist grinding, and 250uL 4 °C pre-cooled liquid extractant (methanol:
water = 4:1) was added for the extraction of metabolites. (2)
Homogenate in the tissue disruptor, add 1 mL 4 °C pre-cooled extract,
ice bath ultrasonic extraction for 20 min, and then stand at − 20 °C
for 1 h. (3) Using a low-temperature high-speed centrifuge, the
parameters were set to 15,000 g, 4 °C, and extracted for 20 min. The
supernatant was taken for UPLC-MS/MS analysis. Methods are as follows,
column: agilent 1290 infinity LC (100 nm × 2.1 mm, 1.7 um), flow rate:
0.4 mL/min, column temperature: 40 °C, injection volume: 2 μL. The
mobile phase was ultrapure water (containing 0.04% acetic acid) and
acetonitrile (containing 0.04% acetic acid). Elution gradient:
0.0–0.5 min water: acetonitrile = 95:5 (V/V); 0.5–7.0 min, 5:95;
7.0–8.0 min, 5:95; 8.1–12.0 min, 95:5. Mass spectrometry conditions
were as follows: electrospray ionization (ESI) temperature was 500 °C;
the mass spectrometry voltage was 5500 V; the helium pressure was
25psi; the collision activation dissociation (CAD) parameter is set to
high. In the triple quadrupole (QQQ) system, each ion pair is scanned
based on the optimized clustering potential (DP) and collision energy
(CE). In addition, 10 uL was taken from each sample and mixed well to
make QC samples, and the same analysis was performed.
Data processing and metabolite identification
The original mass spectrometry data was converted into MzXML format
using Analyst 1.6.3 software and imported into XCMS format for
processing. The XCMS parameters are as follows: for peak picking,
centWave m/z = 10 ppm, peakwidth = c (10,60), prefilter = c (10,100).
For peak grouping, bw = 5, mzwid = 0.025, minfrac = 0.5. Firstly,
baseline filtering, peak recognition, integration, peak alignment and
retention time correction were performed. The characteristic peaks with
relative standard deviation (RSD) > 30% in QC samples were filtered to
obtain the data matrix of retention time (RT), mass-to-charge ratio
(m/z) and peak intensity. The MS and MSMS data were matched and
annotated with commonly used metabolic databases (KEGG, HMDB, Metlin,
MoNA and self-built databases) to obtain metabolite information. The
total peak area normalization and log10 logarithm of the response
intensity of the sample mass spectrum peak were performed to reduce the
error caused by the sample preparation process and the instrument, and
the data matrix for subsequent analysis was obtained. The content of
metabolites was imported into OriginPro 2018 software for principal
component analysis, orthogonal partial least squares discriminant
analysis (OPLS-DA) and S-Plot analysis difference multiple analysis.
The screening criteria for differential metabolites also met the
following conditions: P ≤ 0.05, VIP > 1. The relative quantitative
hierarchical clustering of metabolites and differential metabolites was
performed by heatmap package in Rv3.3.2. KEGG pathway enrichment
([113]http://www.kegg.jp/kegg/kegg1.html)^[114]52–[115]54 analysis of
metabolites was performed through the OmicShare cloud platform
([116]https://www.omicshare.com).
Conclusions
In this study, non-targeted metabolomics was used to compare the
metabolic profiles of NO (NDO and NFO) and CO (CDO and CFO). The
differences in their metabolic profiles were mainly derived from
Organic acids and derivatives (NO>CO) and lipids and lipid-like
molecules (NO