Integration of serum metabolomics and network pharmacology reveals the immunoenhancing mechanisms of Qishenbuqi capsules

Abstract

Introduction

Qishenbuqi capsule (QSBQC), a listed Chinese patent prescription, comprises of 4 herbs. Clinically, it has been shown to improve immune functions.

Methods

Subjects with Qi deficiency and non-Qi deficiency were recruited, who then took QSBQC for 4 weeks. Traditional Chinese medicine (TCM) syndrome scores and the levels of white blood cells, CD3⁺ T cells (CD3⁺), CD4⁺ T cells (CD3⁺CD4⁺), CD8⁺ T cells (CD3⁺CD8⁺), and CD4⁺/CD8⁺ were determined. Serum metabolomics was used to explore the metabolic mechanisms of QSBQC on improving immunity. Meanwhile, the potential active ingredients, targets, and pathways of QSBQC on enhancing immunity were screened by network pharmacology.

Results

QSBQC significantly improved TCM syndrome scores and increased the number of CD8⁺ T cells of both Qi deficiency and non-Qi deficiency subjects. Serum metabolomics revealed that QSBQC regulated 18 differential metabolites and 8 metabolic pathways of Qi deficiency, and 12 differential metabolites and 7 metabolic pathways of non-Qi deficiency subjects. The “herbs-compounds-pathways” diagram showed that PQ-2, cimifugin, and divaricatol were the main active components. Pathways in cancer and arginine and proline metabolism could be the most important pathways.

Conclusion

Our research revealed the immunoenhancing mechanisms of QSBQC and improved the combination of TCM theory and modern western medicine theory.

Graphical Abstract

Graphical Abstract.

Introduction

With the rapid spreading of coronavirus disease 2019 (COVID-19) globally, more and more people are now playing attentions to immunity. Over 164 million people worldwide have been infected by the virus.¹ T cell immunity plays a critical role in human immunity in terms of providing protection against virus.^2,3 Alternatively, natural medicines have been widely used to enhance immunity. Among others, traditional Chinese medicines (TCMs) and TCM prescriptions are receiving more and more attentions.

Various symptoms of patients infected with COVID-19 indicate the importance of immune regulation in human body. People with lower immunity are more likely to be infected with virus.⁴ Nowadays, people are increasingly awaring the importance of improving immunity. TCM prescriptions have accumulated abundant clinical experiences in enhancing immunity, showing great therapeutic potentials and scientific research values. In TCM theory, Qi is an important factor in the occurrence, development, and treatment of diseases. When the defensive functions of Qi are weakened, the body's ability to resist diseases will decrease. In the other words, according to TCM theory, people with Qi deficiency are more susceptible to diseases than the normal. As such, invigorating Qi is one of the basic principles of TCMs on improving immunity.

Qishenbuqi capsule (QSBQC) is a listed Chinese patent prescription produced by Guangshengyuan traditional Chinese medicine Co., Ltd, which is derived from Yupingfeng, a classic and famous TCM prescription. QSBQC is comprised of 4 herbs including Astragali radix, Atractylodis macrocephalae rhizoma, Saposhnikoviae radix, and Panacis quinquefolii radix, nourishing Qi and strengthening the exterior. Clinically, it has been reported that QSBQC prevents colds, treats upper respiratory tract infections, and improves immune functions, cough, and wheezing symptoms of subjects with Qi deficiency syndrome, and so forth.^5,6 However, the underlying mechanisms of QSBQC on subjects with Qi deficiency syndrome in terms of enhancing immunity are still unclear. Meanwhile, concerning the characteristics of QSBQC, i.e. multi-component, multi-target, and multi-pathway features, novel technologies and approaches are needed.

As an emerging system biology technology, metabolomics qualitatively and quantitatively demonstrates dynamic changes of endogenous small-molecule metabolites in living organisms⁷ and further reflects the variations of metabolic pathways of the entire metabolic network of an organism. More importantly, the features of metabolomics are consistent with the concept of “holism” in TCM theory.⁸ In recent years, metabolomics has been widely used in revealing efficacies and underlying mechanisms of TCMs and TCM prescriptions.⁹ Meanwhile, network pharmacology, a newly emerging discipline based on multi-disciplinary technologies including system biology, multi-directional pharmacology, computational biology, and network analysis, is able to explore the pathogenesis of diseases and reveal the interactions between targets and drugs in vivo in terms of constructing related networks from the perspective of biological systems. Network pharmacology makes it possible to reveal the complex and integral mechanisms of TCMs from a perspective of systems and at a molecular level.¹⁰

The present study aimed to demonstrate the mechanisms of QSBQC on subjects with Qi deficiency syndrome in terms of improving immunity from the perspective of NMR-based serum metabolomics and network pharmacology. First, we included 12 subjects with Qi deficiency and 15 subjects with biased syndromes (i.e. non-Qi deficiency) determined by “Guiding principles of clinical researches on new drugs in Chinese medicines.” And then, immune indicators were tested and analyzed. Furthermore, a NMR-based metabolomics approach was applied to screen and identified the differential metabolites and the corresponding metabolic pathways before and after taking QSBQC. In addition, network pharmacology was performed to identify the potential bioactive compounds involving in the effects of QSBQC on enhancing immunity, and to elucidate the molecular mechanisms of QSBQC underlying. The current results will not only provide experimental support for deeply understanding the mechanisms of QSBQC on improving the immunity of subjects with Qi deficiency and non-Qi deficiency, but also expand the clinical applications of QSBQC.

Materials and methods

Subjects

This single-center clinical trial was conducted for 1 month from 2020 Oct 18, until 2020 Nov 22, at the community hospital of Yidian community (Taiyuan, Shanxi, China). A total of 120 volunteers participated in this study. Finally, 27 subjects were enrolled, including 12 subjects with Qi deficiency syndrome and 15 non-Qi deficiency syndrome subjects.

Doctors explained the study details to all subjects and ensured written informed consent of each subject. The protocol of this study was reviewed and approved by the Ethical Committee of Shanxi University (No. 2020070008).

Inclusion, exclusion, and rejection criteria

Inclusion criteria

According to ``Guiding principles of clinical researches on new drugs in Chinese medicines'', the diagnosis criteria of Qi deficiency syndrome include main symptoms, e.g. panting, fatigued spirit and lack of strength, feeble pulse, and secondary symptoms, e.g. spontaneous sweating, disinclined to talk, and pale tongue. The non-Qi deficiency syndrome subjects include other biased syndromes except for Yin-deficiency and Fire-hyperactivity syndromes.

Exclusion criteria

People were excluded who received or took any medicine or measurement to prevent influenza or pneumonia in the past month, suffering from serious cardiovascular and cerebrovascular diseases, liver and kidney diseases, blood diseases, endocrine diseases, lung diseases, neuropsychiatric diseases, alcohol or other drug abuse, and lactating or pregnancy.

Rejection criteria

Subjects who violated inclusion criteria or meeting the exclusion criteria, and who missed the clinical data and could not be statistically analyzed were removed.

Qishenbuqi capsules

Qishenbuqi capsules (QSBQCs) were provided by Guangshengyuan traditional Chinese medicine Limited Company (Datong City, Shanxi Province, China, CFDA approval number: B20020329). All QSBQCs that used in this study were acquired from the same batch (Lot number: 200310), to minimize the variation. Each subject consumed 3 capsules, 3 times per day for 4 weeks. No other Chinese herbal medication or western medicine was taken during this period.

The herbal drugs and the voucher numbers of QSBQCs are presented in Table 1. Briefly, 780 g A. radix, 260 g A. macrocephalae rhizoma, 260 g S. radix, and 39 g P. quinquefolii radix were extracted by MilliQ water. After being concentrated, powders were crushed into 1,000 capsules. All contents were carefully identified, authenticated, and standardized by the Quality Control Department of Guangshengyuan TCM Limited Company.

Table 1.

The Chinese herbs contained in Qishenbuqi capsules.

Latin names	English names	Chinese names	Plant parts	Processing	Voucher numbers
Astragalus membranaceus (Fisch.) Bge.	Astragali Radix	Huang Qi	Root	dried	YP031
Atractylodes macrocephala Koidz.	Saposhnikoviae Radix	Fang Feng	Root	dried	YP030
Saposhnikovia divaricata (Turcz.) Schischk.	Atractylodis Macrocephalae Rhizoma	Bai Zhu	Rhizome	dried	YP347
Panax quinquefolius L.	Panacis Quinquefolii Radix	Xi Yang Shen	Root	dried	ZY029

Thin-layer chromatography (TLC) was applied for quality control of QSBQCs. TLC-Silica Gel plate was used for TLC analysis. Chloroform: methanol: water at the ratio of 13: 7: 2 was used as the developer system, and 10% sulfuric acid ethanol was used as the color-developing agent. Accordingly, the content of astragaloside IV (C₄₁H₆₈O₁₄) was determined as the standard. For each capsule of QSBQCs, the content of astragaloside IV should not be less than 81 μg (see details in the Supplementary Fig. S1, Supplementary Table S1).

Measurement of immune indexes and TCM syndromes

Plasma samples were collected from the subjects before and after treatment. The levels of white blood cells (WBCs) of peripheral blood, CD3⁺ T cells (CD3⁺), CD4⁺ T cells (CD3⁺CD4⁺), CD8⁺ T cells (CD3⁺CD8⁺), and CD4⁺/CD8⁺ were measured by Shanxi Shangning High-tech Medical Testing Center (Taiyuan, China).

The score table of TCM syndromes was used to score TCM syndromes of subjects before and after treatment. According to ``Guiding principles of clinical researches on new drugs in Chinese medicines'', the reduction rate of syndrome scores was calculated by the following formula, so that to quantitatively assess the clinical efficacy of QSBQCs:

The reduction rate of syndrome scores ≥70% and ≥30% means that the efficacy is significantly effective and effective, respectively. In contrast, <30% indicates invalid efficacies.

¹H NMR-based serum metabolomics

Samples collections and preparation

About 5 mL of fasting venous blood was collected for each subject in the morning both before and after QSBQCs administration. Blood samples were then centrifuged at 3,000 r/min for 10 min. Afterward, supernatant of each sample was collected in an EP tube, and stored at −80 °C.

A volume of 450-μL serum samples was mixed with 350-μL deuterium oxide (D₂O), and then vortex oscillation for 30 s. Centrifugation was carried out at 4 °C and 13,000 rpm for 20 min. Finally, 550-μL supernatant of each sample was transferred into a 5-mm nuclear magnetic tube for testing.

¹H NMR spectroscopy analysis

¹H NMR spectra of serum samples were recorded on a Bruker 600-MHz AVANCE III NMR spectrometer (Bruker Biospin, Rheinstetten, Germany) operating at an ¹H NMR frequency of 600 MHz and a temperature of 298 K. A 1-dimensional Carr-Purcell-Meiboom-Gill pulse sequence with water suppression and a total spin–spin relaxation delay of 320 ms were used to attenuate the broad signals from proteins and lipoproteins. The spectrum width was set as 12, 019.2 Hz, the number of scans was 64, the acquisition time was 2.7263 s, and the spectral size was 65,536.

Data processing and multivariate data analysis

¹H NMR spectrum processing

All spectra were processed by MestReNova software (version 11.0, Mestrelab Research, Santiago de Compostella, Spain). The spectra of serum samples were referenced to the chemical shift of creatinine at δ 3.04 ppm. All spectra were manually phased and baseline adjusted. The region of δ 4.67–5.06 ppm was removed to eliminate the impacts of residual water. The spectra were divided and the signal integral computed in 0.01 ppm intervals across the region δ 0.00–8.50 ppm. The area of integral data was normalized, generating a data matrix, which was then imported into Excel.

Multivariate data analysis

SIMCA-P software (Version 13.0, UmetricsAB, Sweden) was applied to conduct multivariate statistical analysis to screen and identify potential biomarkers and metabolic pathways involved in Qi deficiency syndrome and non-Qi deficiency syndrome, as well as the protective effects of QSBQCs. Principal component analysis (PCA), an unsupervised approach, was performed to present natural interrelations among observations. With each dot representing an individual sample, clusters that showed in a score plot correspond to metabolic patterns of differential groups. Partial least squares discriminant analysis (PLS-DA), a supervised pattern recognition approach, was applied to maximize the differences between 2 groups by incorporating known classification. The cumulative values of the total Y explained variance (R²) and the predicable variation (Q²) were calculated to assess the models constructed. R² and Q² values indicated the goodness and the predictive ability of a model, respectively. R²X and Q² were used to evaluate PCA model. The validation of OPLS-DA models was assessed by analysis of variance testing of cross-validated residuals test.

Potential biomarkers between 2 groups were screened by OPLS-DA. Specifically, the corresponding S-plot and VIP (variable importance in the projection) were combined with t-test to screen differential metabolites (VIP > 1). Furthermore, differential metabolites were identified by Human Metabolome Database (http://www.hmdb.) and Biological Magnetic Resonance Data Bank (http://www.bmrb.wisc.edu).

In addition, metabolic pathway analysis with Metabo-Analyst 5.0 website (https://www.metaboanalyst.ca/faces/ModuleView.xhtml) was performed to explore the most relevant pathways involved in protective effects of QSBQCs on Qi deficiency and non-Qi deficiency subjects. P-value < 0.05 and impact value greater than 0.1 were used.

Rreceiver operating characteristic analysis

Receiver operating characteristic (ROC) curve analysis was performed by using SPSS (version 26, Chicago, IL, USA). Area under the curve (AUC) is usually used to evaluate the results of ROC analysis. In case of AUC > 0.5, the closer the AUC is to 1, the better the diagnosis is. ROC prediction has a lower accuracy when AUC ranges in 0.5–0.7, a medium accuracy in 0.7–0.9, and higher over 0.9.

Network pharmacology analysis

Collection and identification of potential active compounds of QSBQCs

We identified the chemical components of QSBQCs by using Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP, http://lsp.nwu.edu.cn./tcmsp.php). Candidate active components with oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18 were regarded to own high OB and high DL values, which were selected as active compounds in the current study.¹¹ We used PubChem database (https://www.ncbi.nlm.nih.gov) to retrieve the structure information of bioactive components of QSBQCs. And then, the chemical structures and information of components of each herb contained in QSBQCs were input into Swiss Target Prediction (http://www.swisstargetprediction.ch/) and BATMAN-TCM databases (http://bionet.ncpsb.org/batman-tcm/index.php) for obtaining potential targets of QSBQCs.

The prediction of potential targets for immunity

Genes related to immunity were acquired from GenCLiP3 (http://ci.smu.edu.cn/genclip3/analysis.php), OMIM (https://omim.org/), PharmGKB (https://www.pharmgkb.org/), and GeneCards (https://www.genecards.org/) databases, where ``immunity'' and ``Homo sapiens'' were set as the keyword and the attribute, respectively. The collated targets were then imported into Uniprot database (http://www.uniprot.org/), along with a list of target gene names. We restricted our analyses to human beings, and all nonhuman targets were removed. All correct genes were adjusted to their official gene symbols. After removing redundant entries, we merged all data. Finally, the targets for both bioactive components and immunity were intersected to identify specific targets of QSBQCs on enhancing immunity.

Gene ontology enrichment and KEGG pathway analysis

The obtained specific targets of QSBQCs on enhancing immunity were imported into Database for Annotation, Visualization, and Integrated Discovery database (DAVID, https://david.ncifcrf.gov/). Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed on the direct targets of QSBQCs. GO enrichment analysis included biological process (BP), cellular components (CCs), and molecular function (MF). GO/KEGG pathway enrichment terms of putative proteins with P ≤ 0.05 were considered to be significant, and of interest.

Joint pathway analysis of key targets and differential metabolites

Metabo-Analyst 5.0 website (https://www.metaboanalyst.ca/faces/ModuleView.xhtml) was applied to jointly analyze the crucial metabolism pathways between targets of QSBQCs on improving immunity and differential metabolites of Qi deficiency and non-Qi deficiency subjects.¹²

Statistical analysis

All experimental data were expressed as the mean ± standard deviation (SD). Metabolites highlighted by S-plot and VIP values were analyzed using an independent samples t-test. A value of P < 0.05 was considered to be statistically significant. SPSS 26.0 software (Chicago, IL, USA) was used for ROC analysis.

Results

A total of 120 community volunteers participated in the study period, among which 48 and 35 subjects were excluded due to the exclusion criteria and their constitution of harmony, respectively. Consequently, 27 subjects were enrolled, including 12 subjects with Qi deficiency syndromes and 15 subjects with non-Qi deficiency syndromes (Fig. 1). There was no difference in age, gender, body mass index between the subjects with or without Qi deficiency syndromes (P > 0.05) (Table 2).

Fig. 1.

Flowchart for the screening and the enrollment of subjects of this study.

Table 2.

Demographic details of subjects with or without Qi deficiency syndromes.

	Qi deficiency	Non-Qi deficiency	P-value
Sample size	12	15
Male/Female	3/9	3/12	0.767
Age ( year)	53.8 ± 10.8	55.5 ± 9.2	0.663
BMI ( kg/m2)	23.4 ± 2.3	24.4 ± 3.7	0.428

QSBQCs significantly improved TCM syndrome scores of both Qi deficiency and non-Qi deficiency subjects

Before taking QSBQCs, TCM syndrome scores of Qi deficiency subjects were higher than that of non-Qi deficiency subjects. After taking QSBQCs for 4 weeks, TCM syndrome scores of both Qi deficiency subjects and non-Qi deficiency subjects were significantly decreased (P < 0.01). Moreover, the differences in TCM syndrome scores before and after taking QSBQCs in Qi deficiency subjects were greater than that of non-Qi deficiency subjects (Table 3).

Table 3.

TCM syndrome scores and the immune indexes of subjects before and after taking Qishenbuqi capsules.

Indicator	Qi deficiency (n = 12)		non-Qi deficiency (n = 15)
	Before	After	Before	After
TCM syndrome scores	18.08 ± 4.76	6.83 ± 3.19**	14.00 ± 7.03	5.3 ± 3.84##
WBCs (109/L)	5.26 ± 1.26	5.04 ± 1.45	6.05 ± 1.18	5.58 ± 1.31
CD3+ T cells (%)	66.82 ± 8.77	69.08 ± 7.08	70.09 ± 7.79	70.38 ± 8.22
CD4+ T cells (%)	39.18 ± 8.57	37.43 ± 7.50	44.02 ± 6.21	41.07 ± 8.10
CD8+ T cells (%)	23.55 ± 5.65	26.18 ± 4.77*	22.09 ± 5.85	24.84 ± 4.34##
CD4+/CD8+	1.79 ± 0.67	1.49 ± 0.45*	2.16 ± 0.79	1.72 ± 0.54##

Note: values were presented as mean ± SD.

^*Compared with Qi deficiency + before group, ^*P < 0.05, ^**P < 0.01.

^#Compared with non-Qi deficiency + before group, ^#P < 0.05, ^##P < 0.01.

After taking QSBQCs, 5 and 6 subjects in Qi deficiency group were significantly effective (42%) and effective (50%), respectively, while only one subject was ineffective (8%). As for non-Qi deficiency group, the percentage of significantly effective, effective, and ineffective subjects was 33%, 60%, and 7%, respectively (Fig. 2).

Fig. 2.

The effects of Qinshenbuqi capsules (QSBQCs) on Qi deficiency subjects (a) and non-Qi deficiency subjects (b). Red, blue, and yellow colors indicate significantly effective, effective, and ineffective subjects, respectively.

QSBQCs significantly improved the subjects’ immunity

We analyzed the population of WBCs of peripheral blood, CD3⁺ T cells (CD3⁺), CD4⁺ T cells (CD3⁺ CD4⁺), CD8⁺ T cells (CD3⁺ CD8⁺), and CD4⁺/CD8⁺ of subjects before and after taking QSBQCs. QSBQCs significantly increased the immune indexes of both Qi deficiency group and non-Qi deficiency group, showing the same trend. The percentage of CD8⁺ T cells significantly increased, while the ratio of CD4⁺/CD8⁺ significantly decreased. The percentage of CD3⁺ T cells had increased, which was not significant (Table 3).

¹H NMR-based metabolomics analysis

The representative ¹H NMR spectra of serum samples collected from subjects were shown in Supplementary Fig. S2. The assignments of metabolites were referred to both published data and databases. In total, 31 serum metabolites were identified (Supplementary Table S2).

QSBQCs significantly improved the metabolic profiles and the serum metabolites of Qi deficiency and non-Qi deficiency subjects

For all groups, PCA score plot showed that both Qi deficiency and non-Qi deficiency subjects had a significant separation of serum metabolic profiles before and after taking QSBQCs (Supplementary Fig. S3). Meanwhile, both Qi deficiency and non-Qi deficiency groups showed the same tendency after taking QSBQCs, which might be attributed to the protective effects of QSBQCs.

Firstly, PCA score plot showed that Qi deficiency group was separated from non-Qi deficiency group. Moreover, PLS-DA model between Qi deficiency subjects and non-Qi deficiency subjects was assessed by the random permutation test (n = 200). Calculations indicated that there was a significant difference in the metabolic profiles between Qi deficiency and non-Qi deficiency subjects (R²Y = 0.812, Q² = 0.566). OPLS-DA and S-plot were utilized for identifying differential metabolites that responsible for the separation between 2 groups. Compared with Qi deficiency subjects, 13 differential metabolites were found to be involved in non-Qi deficiency, including higher levels of lipids, as well as lower levels of 3-hydroxybutyric acid, alanine, creatine, glycine, lysine, methanol, taurine, valine, glucose, betaine, choline, and acetate (Fig. 3a, Table 4).

Fig. 3.

PCA score plot, statistical validation of corresponding PLS-DA model by permutation analysis (200 times), and S-plot of ¹H NMR serum spectra between QD + before and NQD + before (a), between QD + before and QD + after (b), and between NQD + before and NQD + after (c). QD, Qi deficiency; NQD, non-Qi deficiency.

Table 4.

Differential metabolites between qi deficiency (QD) and non-qi deficiency (NQD) subjects before taking Qishenbuqi capsules (QSBQC) and the changes of differential metabolites in the QD and NQD subjects before and after taking QSBQC.

NO.	Metabolites	Before taking QSBQ	QD subjects	NQD subjects
		QD vs NQD	Before vs after taking QSBQ	Before vs after taking QSBQ
1	Lipids			–
2	3-hydroxybutyric acid
3	Alanine			–
4	Creatine			–
5	Glycine
6	Lysine
7	Methanol
8	Taurine			–
9	Valine
10	Glucose			–
11	Betaine			–
12	Choline		–	–
13	Acetate		–
14	Glutamine	–
15	Glutamate	–
16	Citric acid	–		–
17	Glycerol	–
18	Histidine	–		–
19	Isoleucine	–
20	Proline	–
21	Threonine	–	–

“–”: not significant

“”: increased; “”: decreased

Concerning Qi deficiency, the result can be visualized by PCA scores plot, showing that serum metabolic profiles before and after taking QSBQCs were overtly separated. PLS-DA score plot showed a good discrimination between the 2 groups. The established model presented an excellent model with predictive capabilities (R²Y = 0.920, Q² = 0.819), suggesting that serum metabolites significantly differed between the 2 groups. Using OPLS-DA and S-plot analysis, the levels of 18 metabolites were found to be significantly changed in subjects after taking QSBQCs, including higher levels of lipids, glutamate, glutamine, citric acid, isoleucine, proline, together with lower levels of 3-hydroxybutyric acid, alanine, creatine, glycine, lysine, methanol, taurine, valine, glucose, betaine, glycerol, and histidine (Fig. 3b, Table 4).

As for non-Qi deficiency, PCA score plot showed that non-Qi deficiency subjects were significantly separated before and after taking QSBQCs. A random permutation test (200 times) of corresponding PLS-DA model was performed to evaluate the robustness of model, as exhibited by the steep R² and Q² regression lines between R² and Q² (R²Y = 0.862, Q² = 0.643), indicating that QSBQCs had significant effects on serum metabolites of non-Qi deficiency subjects. OPLS-DA and S-plot were applied to distinguish the differential metabolites of serum before and after taking QSBQCs. The results showed that 12 differential metabolites were found, including increased levels of glutamate, glutamine, isoleucine, and proline, while decreased levels of 3-hydroxybutyric acid, acetate, glycerol, glycine, lysine, methanol, threonine, and valine (Fig. 3c, Table 4).

The shared and the unique differential metabolites and metabolic pathways that associated with Qi deficiency group and non-Qi deficiency group before and after taking QSBQCs

In common, a total of 10 differential metabolites were commonly regulated by QSBQCs in both subjects with Qi deficiency and non-Qi deficiency. In contrast, the unique differential metabolites of Qi deficiency included lipids, citric acid, alanine, creatine, taurine, glucose, betaine, and histidine, while non-Qi deficiency included acetate and isoleucine. These unique differential metabolites may be the key biomarkers which were involved in specific effects of QSBQCs on subjects with different TCM syndromes (Fig. 4).

Fig. 4.

Differential metabolites and the corresponding pathways that involved in the effects of Qishenbuqi capsules (QSBQCs) on Qi deficiency and non-Qi deficiency subjects. Blue font and black font indicated the up-regulated and the down-regulated metabolites in serum with QSBQCs, respectively. 1, D-glutamine and D-glutamate metabolism; 2, alanine, aspartate, and glutamate metabolism; 3, glycine, serine, and threonine metabolism; 4, histidine metabolism; 5, arginine and proline metabolism; 6, arginine biosynthesis; 7, glutathione metabolism; and 8, glyoxylate and dicarboxylate metabolism.

QSBQCs regulated 8 metabolic pathways in serum samples of Qi deficiency subjects, including D-glutamine and D-glutamate metabolism, alanine, aspartate and glutamate metabolism, glycine, serine, and threonine metabolism, histidine metabolism, arginine and proline metabolism, arginine biosynthesis, glutathione metabolism, glyoxylate, and dicarboxylate metabolism (Fig. 4).

As for non-Qi deficiency subjects, 7 metabolic pathways were identified and associated with the regulatory effect of QSBQCs. Except for histidine metabolism pathway, metabolic pathways regulated of QSBQCs on non-Qi deficiency subjects are the same as those on Qi deficiency subjects. Therefore, histidine metabolism pathway is a unique metabolic pathway of Qi deficiency, indicating that it may be a key pathway for treating symptoms of Qi deficiency by QSBQCs (Fig. 4).

Related metabolic biomarkers of the effects QSBQCs on Qi deficiency and non-Qi deficiency

ROC analysis was further applied to evaluate the potential diagnostic abilities of 18 metabolites with Qi deficiency and 12 metabolites with non-Qi deficiency. The results showed that isoleucine, valine, 3-hydroxybutyric, alanine, lysine, glutamate, proline, glutamine, citric acid, methanol, glycerol, and glucose (AUC > 0.9) could well distinguish Qi deficiency subjects before and after taking QSBQCs. Isoleucine, lysine, and glutamate (AUC > 0.9) have a good prediction in distinguishing non-Qi deficiency subjects before and after taking QSBQCs (Fig. 5).

Fig. 5.

ROC curve of differential metabolites for discriminating QD + after from QD + before (a) and NQD + after from NQD + before (b). QD, Qi deficiency; NQD, non-Qi deficiency.

Network pharmacology analysis

Active compounds and potential targets of QSBQCs on improving immunity

A total of 39 compounds of QSBQCs were identified by TCMSP database and PubChem database. Also, with searching literatures, the following components as important active components in corresponding herbs were also included in the subsequent analyses of network pharmacology: 7 components in A. radix, including astragaloside I, astragaloside II, astragaloside III, astragaloside IV, calycosin-7-O-β-D-glucoside, ononin, 7,2-dihydroxy-3,4-dimethoxyisoflavan; 4 components in A. macrocephalae rhizoma, including atractylenolide I, atractylenolide II, atractylenolide III, atractylone; 4 components in S. radix, including hamaudol, cimifugin, 4-O-β-D-glucosyl-5-O-methylvisamminol, prim-o-glucosylcimifugin; and 5 components in P. quinquefolii radix, including ginsenoside Rb1, ginsenoside Rc, ginsenoside Rd, ginsenoside Re, ginsenoside Rg1. In total, 59 compounds were included, among which the number of compounds in A. radix, A. macrocephalae rhizoma, S. radix, and P. quinquefolii radix was 10, 11, 22, and 16, respectively (Supplementary Table S3).

As a result, after eliminating redundancy, 109 targets were associated with the improved effects of QSBQCs on immunity (Fig. 6).

Fig. 6.

The network diagram of ``herbs-ingredients-targets'' of Qishenbuqi capsules (QSBQCs) on enhancing immunity (a), and the Venn diagram of potential targets of 4 herbs contained in QSBQCs (b). In a, herbs are represented as orange diamonds. The components are represented as V, in which components of A. macrocephalae rhizoma, A. radix, S. radix, and P. quinquefolii radix are represented in yellow, green, blue, and pink, respectively. Meanwhile, the same components of S. radix and P. quinquefolii radix are represented in red. The targets are represented as purple rectangles. The edges between nodes represent their interactions.

GO analysis and pathway enrichment analysis

A total of 569 GO terms were identified by DAVID software (P < 0.05), including 75% for BPs, 15% for CCs, and 10% for MF. The top 20 enriched terms for BP, CC, and MF terms of QSBQCs are shown in Fig. 7a. Among them, BP mainly involved signal transduction, positive regulation of transcription from RNA polymerase II promoter, negative regulation of the apoptotic process. CC mainly included the cytoplasm, plasma membrane, and nucleus. MF mainly included protein binding, ATP binding, and identical protein binding.

Fig. 7.

A matrix diagram of GO and KEGG enrichment analysis of Qishenbuqi capsules on improving immunity. (a) GO functional classification analysis and (b) KEGG enrichment analysis. BP, biological process; CC, cellular components; MF, molecular function.

To explore the potential signaling pathways of QSBQCs, we conducted KEGG pathway enrichment, by which 111 signal pathways were identified. The top 20 potential signaling pathways are listed in Fig. 7b, including pathways in cancer, PI3K-Akt signaling pathway, hepatitis B, proteoglycans in cancer, HTLV-I infection, and so forth.

The “herbs-components-pathways” network construction

To further characterize the molecular mechanisms of immunity improvement of QSBQCs, ``herbs-compounds-pathways'' relationship diagram was drawn based on components of herbs contained, proteins involved, and their signaling pathways (Fig. 8). As shown in Fig. 8, 20 compounds in 4 herbs regulating 20 pathways play important roles in enhancing immunity of QSBQCs. Among them, PQ-2, cimifugin, divaricatol, etc., were the main active components. Pathways in cancer, PI3K-Akt signaling pathway, and hepatitis B could be the most important pathways.

Fig. 8.

The “herbs-compounds-pathways” network of enhancing effects of Qishenbuqi capsules on immunity. The blocks on left, the middle blocks, and the blocks on right represent herbs, key compounds, and the top 20 pathways of KEGG enrichment, respectively. The width of line for herbs-compounds links is proportional to the number of compounds in herbs, and that for compounds-pathways links is proportional to the number of targets for compounds that map to the pathways.

Joint pathway analysis of key targets and metabolites

To explore the crucial metabolic pathways, joint pathway analysis was conducted on 18 differential metabolites from Qi deficiency subjects and 12 differential metabolites from non-Qi deficiency subjects with 109 targets by Metabo-Analyst 5.0, respectively. The results showed that arginine and proline metabolism, arginine biosynthesis, and purine metabolism were simultaneously enriched with targets screened by network pharmacology and differential metabolites screened and identified by serum metabolomics (Fig. 9).

Fig. 9.

Joint pathway analysis of key targets and differential metabolites. The red rectangle node and gray rectangle node represent presence and un-presence, respectively. QD, Qi deficiency; NQD, non-Qi deficiency.

Discussion

During the epidemic of COVID-19, although physical-distancing and other transmission-mitigation strategies implemented have prevented most individuals from being infected,² it was difficult to completely cut off the transmission route because of the numerous transmission modes of COVID-19. Therefore, for the majority of susceptible people, enhancing their immunity and improving the body's ability to resist viruses are crucial to prevent infection. Based on preventive thoughts and clinical practices, TCMs have been used for “preventive treatment of diseases” for a long time, Zhi Wei Bing () in Chinese, in terms of strengthening the body's vital Qi and improving the population's ability to prevent diseases.¹³ Concerning this, the applications of TCMs can be valuable.

In present study, for the first time, the combination of metabolomics and network pharmacology was integrally applied to reveal the mechanisms of QSBQCs, a classical TCM prescription of nourishing Qi and strengthening the exterior, which contains 4 herbs, i.e. A. radix, A. macrocephalae rhizoma, S. radix, and P. quinquefolii radix. We found that QSBQCs could significantly improve TCM syndrome scores, improve immunity, and ameliorate metabolic profiles of both subjects with Qi deficiency and non-Qi deficiency.

QSBQCs improved TCM syndrome scores of subjects enrolled

Qi deficiency is the main syndrome type in TCM theory, mainly manifested as fatigue, shortness of breath, and spontaneous sweating. In clinic, many subhealth symptoms, such as chronic fatigue, susceptibility to colds, and other disease symptoms, often occur due to Qi deficiency. Moreover, their intrinsic changes mainly include low-energy metabolism and low-immune functions.¹⁴ The formation of Qi deficiency is complicated involving various factors, including but not limiting, the stress of long-term life, poor lifestyles, such as heavy, mental work, and night work.^15,16 Worse, Qi deficiency is frequently combined with other biased symptoms, such as blood stasis, phlegm dampness, etc., consequently resulting in shortness of breath, fatigue, and spontaneous sweating. Herein, we found that after taking QSBQCs, TCM symptoms of subjects with Qi deficiency and non-Qi deficiency were significantly improved, which suggested that QSBQCs can not only significantly improve Qi deficiency, but also improve the biased syndromes related.

QSBQCs enhance immunity via regulating serum metabolites and metabolic pathways

In this study, we found that QSBQCs increased the total number of T cells in all subjects and significantly increased the number of CD8⁺ T cells. Serum metabolomics analysis showed that QSBQCs significantly improved the abnormalities in glucose and fat utilization of Qi deficiency subjects. Glucose metabolism not only provides energy for physical activities, but also mediates a variety of physiological functions by forming complex signaling networks.¹⁷ In general, the main source of body productivity is aerobic decomposition. Glucose is decomposed into pyruvate through glycolysis, and then decomposed into acetyl-CoA, which enters TCA cycle. TCA cycle is the central metabolic pathway for all aerobic metabolic processes.¹⁸ In this study, we found a downward trend of citrate cycle of Qi deficiency subjects, indicating that aerobic catabolism was weakened. Qi deficiency subjects are likely to experience an insufficient supplyment of oxygen. Furthermore, insufficient supplement of oxygen leads to a decrease in aerobic metabolism. As a consequence, glucose cannot be used effectively, which in turn causes energy metabolism disorders. After taking QSBQCs, glucose content of Qi deficiency subjects decreased, while the contents of citric acid and lipids increased. It indicated that QSBQCs promoted the decomposition and the utilization of glucose, thus generating more energy. This is a unique metabolic characteristic of Qi deficiency subjects, as which was not observed for non-Qi deficiency subjects. Overall, QSBQCs specifically improves the insufficient energy supplement of Qi deficiency subjects.

Amino acids as the substrate of protein synthesis are essential for human function.¹⁹ The human body utilizes amino acids to synthesize some biologically active substances, e.g. immune antibodies, digestive enzymes, plasma proteins, growth hormones, and so on. Therefore, they are important components of the body's immune mechanism and are vital to the body’s immune functions. QSBQCs significantly influenced amino acid metabolism of both Qi deficiency and non-Qi deficiency subjects. After taking QSBQCs, the levels of isoleucine, proline, glutamate, and glutamine of Qi deficiency subjects increased, while the levels of glycine, lysine, valine, alanine, histidine, and taurine decreased. As for the subjects with non-Qi deficiency, the levels of isoleucine, proline, glutamate, and glutamine also increased, while the levels of glycine, lysine, valine, and threonine decreased.

Isoleucine, a kind of branched-chain amino acid plays a critical role in immune functions, e.g. maintaining the development of immune organs and cells, and stimulating the secretion of immune molecules substances.^20–23 According to a clinical trial, dietary supplementation of isoleucine can relieve acute diarrhea induced by malnourishment in children, which is related to the production of host defense peptides induced by isoleucine.²⁴ Thus, isoleucine has the capacity of preventing invasion of pathogens via the increase of immunity.

Glutamine is the most abundant and versatile amino acid in body. Studies in vitro and in vivo have demonstrated that glutamine is an essential nutrient for lymphocyte proliferation and cytokine production, macrophage phagocytic plus secretory activities, and neutrophil bacterial killing.²⁵ The glutamine level of subjects with Qi deficiency is low, which may be related to the decline of body’s immune function.²⁶ After taking QSBQCs, glutamine's content increased, indicating that immunity was improved. As both glutamine and proline are converted from glutamate, the increased levels of glutamine, proline, and glutamate indicate that QSBQCs could significantly increase the level of glutamate, and promote such a conversion of glutamate to glutamine and proline. The decrease in contents of most amino acids indicates that Qi deficiency is mainly caused by low-energy metabolism of the body, which slows down the metabolism rates of amino acids. QSBQCs can accelerate the metabolism rates of amino acids and provide sufficient energy to body, thus significantly relieving and/or reducing fatigue symptoms.

QSBQCs significantly regulated 8 metabolic pathways of Qi deficiency subjects, 7 of which are the same as that of non-Qi deficiency subjects. D-glutamine and D-glutamate metabolism is the most significant of metabolic pathways regulated by QSBQCs. Studies have shown that glutamine can induce the polarization of M2 macrophages through glutamine-UDP-N-acetylglucosamine pathway and α-ketoglutarate pathway, while the production of succinate or γ-aminobutyric acid induces the polarization of M1 macrophages.^27,28 In addition, metabolic pathway of glutamine may also affect the activation of CD8⁺ T cells.²⁹

Histidine metabolic pathway is a unique metabolic pathway involving in the effect of QSBQCs on Qi deficiency subjects. Histidine can be transformed by histidine decarboxylase as histamine, a well-known proinflammatory mediator.³⁰ This reaction occurs in enterochromaffin-like cells of stomach, in the mast cells of immune system, and in various regions of brain where histamine may serve as a neurotransmitter.³¹ Therefore, Qi deficiency not only results in weakness of energy as abovementioned, but also affects gastrointestinal functions, immune system, and even functions of neurotransmitters of brain. In contrast, QSBQCs can significantly regulate these disorders and prevent them from getting worse.

Potential active ingredients and signaling pathways involving in the enhancing immunity effects of QSBQCs

Network pharmacology is an effective approach to explore drug–target interactions and to identify potential active ingredients of TCMs.¹⁷ The present results showed that calycosin-7-O-β-D-glucoside, divaricatol, ginsenoside rh2, wogonin, astragaloside IV, etc., are the core components of compound-target network of QSBQCs. As previously suggested, many of these components have been confirmed to be able to improve immunity of body. For instance, wogonin can not only increase the levels of cytokines IL-1α and TNF-α in the culture medium of WBCs in a dose-dependent manner, but also increase the transcription levels of IL-1α and TNF-α mRNA, as well as inhibit expression levels of p65 and I-κB of NF-κB.³² Astragalosides can promote the phagocytic function of cells, increase the content of lysosomal hydrolase, and enhance the ability to process and deliver antigens, while inhibit productions of IL-1 and TNF-α by peritoneal macrophages.³³ Ginsenoside Rh2 suppressed T-cell acute lymphoblastic leukemia by blocking PI3K/Akt/mTOR signaling pathway and enhanced immunity in spleen by regulating immune factors.³⁴

The results of KEGG analysis showed that QSBQCs could regulate a variety of signaling pathways including pathways in cancer, PI3K-Akt signaling pathway, hepatitis B, etc. Among them, pathways in cancer with the highest enrichment score could be regarded as one of the most crucial signal pathways involving in the enhancing effects of QSBQCs on immunity. It is a complex pathway composed of multiple signaling pathways, including mTOR signaling pathway, and MAPK signaling pathway, etc. mTOR pathway is inhibited by rapamycin, a well-known immunosuppressive drug, and integrates different inputs that promote translation and cell division, including hormones (insulin, IGF-1), growth factors, ATP, and amino acid availability.³⁵ AKT1 is the main effector molecule of PI3K-AKT signal transduction by inhibiting NF-κB activation and IRF3 activity, which can inhibit MyD88-dependent signal transduction mediated by Toll-like receptor and IFN-β (TRIF)-dependent signal transduction induced by adapter of Toll/IL-1R domain, thus inhibiting inflammation and improving immunity.³⁶

Potential targets and metabolic pathways involving in enhancing effects of QSBQCs on immunity

Finally, according to the results of joint pathways analysis, arginine and proline metabolism was selected as the most crucial metabolic pathway. NOS2 was considered as the only target from network pharmacology to be associated with this metabolic pathway. NOS2 enzyme can produce NO as long as the molecule is intact and its substrate arginine is available. NOS2 activation is considered as a hallmark of ``classically activated macrophages'', i.e. macrophages that are mediators of delayed-type hypersensitivity response and are endowed with antitumor properties.³⁷ The enzyme arginase could hydrolyze arginine to ornithine and urea. Arginase pathway limits arginine availability for NO synthesis. Ornithine itself can further feed into the important downstream pathways of polyamine and proline syntheses, which are important for cellular proliferation and tissue repair.³⁸

The present study was subject to some limitations. First, differential metabolites identified were not verified by further experiments. Second, as the sample size of this study was relatively small, further studies including a larger number of subjects with Qi deficiency may be required to validate current findings in the future.

Conclusion

This study demonstrated, for the first time, the enhancement effects of QSBQCs on immunity of both Qi deficiency subjects and non-Qi deficiency subjects, as well as the underlying mechanisms, by integrally using serum metabolomics and network pharmacology. QSBQCs significantly improved the subjects' TCM symptoms and increased the total number of T cells and the number of CD8⁺ T cells, enhancing immunity of the body. Furthermore, as suggested by metabolomics and network pharmacology, the immunity-enhancing effect of QSBQCs is likely to be related to improve the abnormality of serum metabolites and to enhance amino acid metabolism and energy metabolism by regulating pathways in cancer, PI3K-Akt signaling pathway, and other pathways. QSBQCs are of significance in preventing COVID-19 in terms of improving the immunity of susceptible people. The current results also expand the application scope of QSBQCs in clinic. Furthermore, our research improves the combination of TCM theory and modern western medicine theory, in terms of combining TCM symptoms of Qi deficiency with comprehensive applications of metabolomics and network pharmacology.

Authors’ contribution

XL and XQ designed the outline of study. ZZ and YF contributed to the drafting and revision of manuscript. YF and YC contributed to serum metabolomics. LY contributed to network pharmacology. YW, YY, PZ, and RZ performed the clinical study. JJ and SX contributed to acquisition and the accuracy of the data. XL and XQ revised and proof-read the manuscript. All authors reviewed the manuscript and approved its submission.

Funding

Epidemic Special Project (COVID-19) of Shanxi Provincial Education Department, and the Key Laboratory of Effective Substances Research and Utilization in TCM of Shanxi Province (202105D121009).

Conflicts of interest: The authors declare that they have no conflict of interest.

Abbreviations

AUC, area under the curve; BP, biological process; CC, cellular components; COVID-19, coronavirus disease 2019; DL, drug-likeness; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function; NQD, non-Qi deficiency; OB, oral bioavailability; QD, Qi deficiency; QSBQC, Qishenbuqi capsule; ROC, receiver operating characteristic; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; TCM, traditional Chinese medicine; WBC, white blood cell

Data availability statement

The data that support the findings of this study are available upon request.