Honors & Awards
Excellent Master Foundation for innovative projects, Second Military Medical University (03.2012)
4th National Pharmaceutical Analysis and Pharmaceutical Metabolism Excellent Thesis Exchange, National Pharmaceutical Analysis Society (07.2013)
EMBO grant for Metabolomics course, EMBO (02.2015)
Science Foundation Ireland Student Travel Award at Metabolomics conference, Metabolomics society (06.2016)
Doctor of Science, University of Potsdam (2017)
Master of Medicine, Second Military Medical College (2013)
Bachelor of Science, Huazhong University Of Science & Technology (2010)
Mapping the Arabidopsis Metabolic Landscape by Untargeted Metabolomics at Different Environmental Conditions
2018; 11 (1): 118–34
Metabolic genome-wide association studies (mGWAS), whereupon metabolite levels are regarded as traits, can help unravel the genetic basis of metabolic networks. A total of 309 Arabidopsis accessions were grown under two independent environmental conditions (control and stress) and subjected to untargeted LC-MS-based metabolomic profiling; levels of the obtained hydrophilic metabolites were used in GWAS. Our two-condition-based GWAS for more than 3000 semi-polar metabolites resulted in the detection of 123 highly resolved metabolite quantitative trait loci (p ≤ 1.0E-08), 24.39% of which were environment-specific. Interestingly, differently from natural variation in Arabidopsis primary metabolites, which tends to be controlled by a large number of small-effect loci, we found several major large-effect loci alongside a vast number of small-effect loci controlling variation of secondary metabolites. The two-condition-based GWAS was followed by integration with network-derived metabolite-transcript correlations using a time-course stress experiment. Through this integrative approach, we selected 70 key candidate associations between structural genes and metabolites, and experimentally validated eight novel associations, two of them showing differential genetic regulation in the two environments studied. We demonstrate the power of combining large-scale untargeted metabolomics-based GWAS with time-course-derived networks both performed under different abiotic environments for identifying metabolite-gene associations, providing novel global insights into the metabolic landscape of Arabidopsis.
View details for DOI 10.1016/j.molp.2017.08.012
View details for Web of Science ID 000419468100010
View details for PubMedID 28866081
Guidelines for Sample Normalization to Minimize Batch Variation for Large-Scale Metabolic Profiling of Plant Natural Genetic Variance
PLANT METABOLOMICS: METHODS AND PROTOCOLS
2018; 1778: 33–46
Recent methodological advances in both liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS) have facilitated the profiling highly complex mixtures of primary and secondary metabolites in order to investigate a diverse range of biological questions. These techniques usually face a large number of potential sources of technical and biological variation. In this chapter we describe guidelines and normalization procedures to reduce the analytical variation, which are essential for the high-throughput evaluation of metabolic variance used in broad genetic populations which commonly entail the evaluation of hundreds or thousands of samples. This chapter specifically deals with handling of large-scale plant samples for metabolomics analysis of quantitative trait loci (mQTL) in order to reduce analytical error as well as batch-to-batch variation.
View details for DOI 10.1007/978-1-4939-7819-9_3
View details for Web of Science ID 000443077900004
View details for PubMedID 29761429
Combined Use of Genome-Wide Association Data and Correlation Networks Unravels Key Regulators of Primary Metabolism in Arabidopsis thaliana
2016; 12 (10): e1006363
Plant primary metabolism is a highly coordinated, central, and complex network of biochemical processes regulated at both the genetic and post-translational levels. The genetic basis of this network can be explored by analyzing the metabolic composition of genetically diverse genotypes in a given plant species. Here, we report an integrative strategy combining quantitative genetic mapping and metabolite‒transcript correlation networks to identify functional associations between genes and primary metabolites in Arabidopsis thaliana. Genome-wide association study (GWAS) was used to identify metabolic quantitative trait loci (mQTL). Correlation networks built using metabolite and transcript data derived from a previously published time-course stress study yielded metabolite‒transcript correlations identified by covariation. Finally, results obtained in this study were compared with mQTL previously described. We applied a statistical framework to test and compare the performance of different single methods (network approach and quantitative genetics methods, representing the two orthogonal approaches combined in our strategy) with that of the combined strategy. We show that the combined strategy has improved performance manifested by increased sensitivity and accuracy. This combined strategy allowed the identification of 92 candidate associations between structural genes and primary metabolites, which not only included previously well-characterized gene‒metabolite associations, but also revealed novel associations. Using loss-of-function mutants, we validated two of the novel associations with genes involved in tyrosine degradation and in β-alanine metabolism. In conclusion, we demonstrate that applying our integrative strategy to the largely untapped resource of metabolite-transcript associations can facilitate the discovery of novel metabolite-related genes. This integrative strategy is not limited to A. thaliana, but generally applicable to other plant species.
View details for DOI 10.1371/journal.pgen.1006363
View details for Web of Science ID 000386683300030
View details for PubMedID 27760136
View details for PubMedCentralID PMC5070769
Lipidomic profiling reveals significant alterations in lipid biochemistry in hypothyroid rat cerebellum and the therapeutic effects of Sini decoction
JOURNAL OF ETHNOPHARMACOLOGY
2015; 159: 262–73
Hypothyroidism is known to be closely associated with lipid metabolism. Although our previous serum and urine metabonomics studies have provided some clues about the molecular mechanism of hypothyroidism at the metabolic level, the precise mechanism underlying the pathogenesis of hypothyroidism remains elusive, especially from the aspect of lipid metabolism. In the present study, we applied an ultra high performance liquid chromatography/time-of-flight mass spectrometry (UHPLC/TOF-MS)-based lipidomics method to analyze the global lipid profiles of hypothyroidism in rat cerebellum. Using unsupervised analysis and multivariate statistical analysis, we separated the Sham and hypothyroid groups clearly and screened out 23 potential lipid biomarkers related to hypothyroidism that were primarily involved in sphingolipid metabolism, glycerophospholipid metabolism and β-oxidation of fatty acid. Subsequently, we conducted computational analysis to build and simulate the lipid network of hypothyroidism, knowing that it would be useful to elucidate the pathological mechanism of hypothyroidism. Based on the selected 23 lipid biomarkers, we systematically evaluated the therapeutic effects of Sini decoction (SND) and the positive drug T4. The results showed that both SND and T4 can to some extent convert the pathological status of hypothyroidism through different pathways. Overall, this investigation illustrates that lipidomic profiling approach is powerful in giving a complementary view to the pathophysiology of hypothyroidism and offers a valuable tool for systematic study of the therapeutic effects of SND on hypothyroidism at lipid level.
View details for DOI 10.1016/j.jep.2014.11.033
View details for Web of Science ID 000348271500028
View details for PubMedID 25435288
A strategy for rapid analysis of xenobiotic metabolome of Sini decoction in vivo using ultra-performance liquid chromatography-electrospray ionization quadrupole-time-of-flight mass spectrometry combined with pattern recognition approach
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
2014; 96: 187–96
Xenobiotic metabolome identificatioqn of Chinese herbal prescription in biological systems is a very challenging task. In the present work, a reliable strategy based on the combination of ultra-performance liquid chromatography-electrospray ionization quadrupole-time-of-flight mass spectrometry (UHPLC-ESI-Q-TOFMS) and pattern recognition approach such as principal component analysis (PCA) and partial least squared discriminant analysis (PLS-DA) was proposed to rapidly discover and analyze the xenobiotic metabolome from Sini decoction (SND). Using the S- and VIP-plots of PLS-DA, 96 and 112 interest ions from positive and negative ion datasets were extracted as SND metabolome in rat urine following oral administration of SND. Among them, 53 absorbed prototype components of SND and 49 metabolites were identified, which provided essential data for further studying the relationship between the chemical components and pharmacological activity of SND. Our results indicated that hydrolysis and demethylation were the major metabolic pathways of diterpenoid alkaloids, while glucuronidation, sulfation, hydrolysis, reduction, demethylation, and hydroxylation were the main metabolic pathways of flavonoids, and hydrolysis was the metabolic pathway of gingerol-related compounds. No saponin-related metabolites were detected.
View details for DOI 10.1016/j.jpba.2014.03.028
View details for Web of Science ID 000336774400022
View details for PubMedID 24759592
Investigation of the therapeutic effectiveness of active components in Sini decoction by a comprehensive GC/LC-MS based metabolomics and network pharmacology approaches
2014; 10 (12): 3310–21
As a classical formula, Sini decoction (SND) has been fully proved to be clinically effective in treating doxorubicin (DOX)-induced cardiomyopathy. Current chemomics and pharmacology proved that the total alkaloids (TA), total gingerols (TG), total flavones and total saponins (TFS) are the major active ingredients of Aconitum carmichaelii, Zingiber officinale and Glycyrrhiza uralensis in SND respectively. Our animal experiments in this study demonstrated that the above active ingredients (TAGFS) were more effective than formulas formed by any one or two of the three individual components and nearly the same as SND. However, very little is known about the action mechanisms of TAGFS. Thus, this study aimed to use for the first time the combination of GC/LC-MS based metabolomics and network pharmacology for solving this problem. By metabolomics, it was found that TAGFS worked by regulating six primary pathways. Then, network pharmacology was applied to search for specific targets. 17 potential cardiovascular related targets were found through molecular docking, 11 of which were identified by references, which demonstrated the therapeutic effectiveness of TAGFS using network pharmacology. Among these targets, four targets, including phosphoinositide 3-kinase gamma, insulin receptor, ornithine aminotransferase and glucokinase, were involved in the TAGFS regulated pathways. Moreover, phosphoinositide 3-kinase gamma, insulin receptor and glucokinase were proved to be targets of active components in SND. In addition, our data indicated TA as the principal ingredient in the SND formula, whereas TG and TFS served as adjuvant ingredients. We therefore suggest that dissecting the mode of action of clinically effective formulae with the combination use of metabolomics and network pharmacology may be a good strategy.
View details for DOI 10.1039/c4mb00048j
View details for Web of Science ID 000344373900029
View details for PubMedID 25315049
Myocardial lipidomics profiling delineate the toxicity of traditional Chinese medicine Aconiti Lateralis radix praeparata
JOURNAL OF ETHNOPHARMACOLOGY
2013; 147 (2): 349–56
The lateral root of Aconitum has been popularly used in traditional Chinese medicine (TMC) known as Fuzi which is beneficial for the treatment of various diseases, such as rheumatism, painful joints, syncope and bronchial asthma. However, it has a potential carditoxicity with a relatively narrow margin of safety.This paper was designed to explore the mechanisms of Fuzi's toxicity and find out potential tissue-specific biomarkers of toxic effects.A myocardial lipidomics based on ultraperformance lipid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC/Q-TOF MS) was developed to compare three cardiac lipid extraction methods and investigate the changes of lipids in mice heart of three different dosage groups. In addition, we concurrently inspected the biochemical parameters in plasma, observed the histology of the heart and recorded the electrocardiogram (ECG).The cardiotoxicity of Fuzi was dose-dependent, and the high-dose group obviously manifested the heart damage in histology and a certain degree of arrhythmia. Significant changes of 14 lipid metabolites which primarily involved in phospholipid metabolism, sphingolipid metabolism, saturated fatty acid oxidation and unsaturated fatty acid peroxidation were identified and considered as the potential biomarkers of Fuzi toxicity.The lipidomics approach is helpful to search potential tissue-specific biomarkers and understand the underlying mechanisms of Fuzi toxicity on the heart.
View details for DOI 10.1016/j.jep.2013.03.017
View details for Web of Science ID 000319180700011
View details for PubMedID 23541933
Metabolic Profiling Provides a System Understanding of Hypothyroidism in Rats and Its Application
2013; 8 (2): e55599
Hypothyroidism is a chronic condition of endocrine disorder and its precise molecular mechanism remains obscure. In spite of certain efficacy of thyroid hormone replacement therapy in treating hypothyroidism, it often results in other side effects because of its over-replacement, so it is still urgent to discover new modes of treatment for hypothyroidism. Sini decoction (SND) is a well-known formula of traditional Chinese medicine (TCM) and is considered as efficient agents against hypothyroidism. However, its holistic effect assessment and mechanistic understanding are still lacking due to its complex components.A urinary metabonomic method based on ultra performance liquid chromatography coupled to mass spectrometry was employed to explore global metabolic characters of hypothyroidism. Three typical hypothyroidism models (methimazole-, propylthiouracil- and thyroidectomy-induced hypothyroidism) were applied to elucidate the molecular mechanism of hypothyroidism. 17, 21, 19 potential biomarkers were identified with these three hypothyroidism models respectively, primarily involved in energy metabolism, amino acid metabolism, sphingolipid metabolism and purine metabolism. In order to avert the interference of drug interaction between the antithyroid drugs and SND, the thyroidectomy-induced hypothyroidism model was further used to systematically assess the therapeutic efficacy of SND on hypothyroidism. A time-dependent recovery tendency was observed in SND-treated group from the beginning of model to the end of treatment, suggesting that SND exerted a recovery effect on hypothyroidism in a time-dependent manner through partially regulating the perturbed metabolic pathways.Our results showed that the metabonomic approach is instrumental to understand the pathophysiology of hypothyroidism and offers a valuable tool for systematically studying the therapeutic effects of SND on hypothyroidism.
View details for DOI 10.1371/journal.pone.0055599
View details for Web of Science ID 000315157200042
View details for PubMedID 23409005
View details for PubMedCentralID PMC3567130
Serum metabonomics coupled with Ingenuity Pathway Analysis characterizes metabolic perturbations in response to hypothyroidism induced by propylthiouracil in rats
JOURNAL OF PHARMACEUTICAL AND BIOMEDICAL ANALYSIS
2013; 72: 109–14
A serum metabonomic profiling method based on ultra-performance liquid chromatography/time-of-flight mass spectrometry (UHPLC/TOF-MS) was applied to investigate the metabolic changes in hypothyroid rats induced by propylthiouracil (PTU). With Significance Analysis of Microarray (SAM) for classification and selection of biomarkers, 13 potential biomarkers in rat serum were screened out. Furthermore, Ingenuity Pathway Analysis (IPA) was introduced to deeply analyze unique pathways of hypothyroidism that were primarily involved in sphingolipid metabolism, fatty acid transportation, phospholipid metabolism and phenylalanine metabolism. Our results demonstrated that the metabonomic approach integrating with IPA was a promising tool for providing a novel methodological clue to systemically dissect the underlying molecular mechanism of hypothyroidism.
View details for DOI 10.1016/j.jpba.2012.09.030
View details for Web of Science ID 000311819100014
View details for PubMedID 23146233
- Chiral Separation of New Triazole Antifungal Active Compounds by Capillary Electrophoresis and Molecular Modeling Study of Chiral Recognition Mechanisms CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2012; 40 (7): 1031–36