Honors & Awards

  • 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)

Professional Education

  • Doctor of Science, University of Potsdam (2017)


  • Si Wu. "United States Patent WO2021034771A1 Surrogate of VO2 Max Test", Leland Stanford Junior University, May 21, 2021

All Publications

  • Genomic basis underlying the metabolome-mediated drought adaptation of maize. Genome biology Zhang, F., Wu, J., Sade, N., Wu, S., Egbaria, A., Fernie, A. R., Yan, J., Qin, F., Chen, W., Brotman, Y., Dai, M. 2021; 22 (1): 260


    BACKGROUND: Drought is a major environmental disaster that causes crop yield loss worldwide. Metabolites are involved in various environmental stress responses of plants. However, the genetic control of metabolomes underlying crop environmental stress adaptation remains elusive.RESULTS: Here, we perform non-targeted metabolic profiling of leaves for 385 maize natural inbred lines grown under well-watered as well as drought-stressed conditions. A total of 3890 metabolites are identified and 1035 of these are differentially produced between well-watered and drought-stressed conditions, representing effective indicators of maize drought response and tolerance. Genetic dissections reveal the associations between these metabolites and thousands of single-nucleotide polymorphisms (SNPs), which represented 3415 metabolite quantitative trait loci (mQTLs) and 2589 candidate genes. 78.6% of mQTLs (2684/3415) are novel drought-responsive QTLs. The regulatory variants that control the expression of the candidate genes are revealed by expression QTL (eQTL) analysis of the transcriptomes of leaves from 197 maize natural inbred lines. Integrated metabolic and transcriptomic assays identify dozens of environment-specific hub genes and their gene-metabolite regulatory networks. Comprehensive genetic and molecular studies reveal the roles and mechanisms of two hub genes, Bx12 and ZmGLK44, in regulating maize metabolite biosynthesis and drought tolerance.CONCLUSION: Our studies reveal the first population-level metabolomes in crop drought response and uncover the natural variations and genetic control of these metabolomes underlying crop drought adaptation, demonstrating that multi-omics is a powerful strategy to dissect the genetic mechanisms of crop complex traits.

    View details for DOI 10.1186/s13059-021-02481-1

    View details for PubMedID 34488839

  • metID: a R package for automatable compound annotation for LC-MS-based data. Bioinformatics (Oxford, England) Shen, X., Wu, S., Liang, L., Chen, S., Contrepois, K., Zhu, Z., Snyder, M. 2021


    SUMMARY: Accurate and efficient compound annotation is a long-standing challenge for LC-MS-based data (e.g., untargeted metabolomics and exposomics). Substantial efforts have been devoted to overcoming this obstacle, whereas current tools are limited by the sources of spectral information used (in-house and public databases) and are not automated and streamlined. Therefore, we developed metID, an R package that combines information from all major databases for comprehensive and streamlined compound annotation. metID is a flexible, simple, and powerful tool that can be installed on all platforms, allowing the compound annotation process to be fully automatic and reproducible. A detailed tutorial and a case study are provided in Supplementary Materials.AVAILABILITY AND IMPLEMENTATION: https://jaspershen.github.io/metID.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

    View details for DOI 10.1093/bioinformatics/btab583

    View details for PubMedID 34432001

  • Mass spectrometry-based metabolomics: a guide for annotation, quantification and best reporting practices. Nature methods Alseekh, S., Aharoni, A., Brotman, Y., Contrepois, K., D'Auria, J., Ewald, J., C Ewald, J., Fraser, P. D., Giavalisco, P., Hall, R. D., Heinemann, M., Link, H., Luo, J., Neumann, S., Nielsen, J., Perez de Souza, L., Saito, K., Sauer, U., Schroeder, F. C., Schuster, S., Siuzdak, G., Skirycz, A., Sumner, L. W., Snyder, M. P., Tang, H., Tohge, T., Wang, Y., Wen, W., Wu, S., Xu, G., Zamboni, N., Fernie, A. R. 2021; 18 (7): 747-756


    Mass spectrometry-based metabolomics approaches can enable detection and quantification of many thousands of metabolite features simultaneously. However, compound identification and reliable quantification are greatly complicated owing to the chemical complexity and dynamic range of the metabolome. Simultaneous quantification of many metabolites within complex mixtures can additionally be complicated by ion suppression, fragmentation and the presence of isomers. Here we present guidelines covering sample preparation, replication and randomization, quantification, recovery and recombination, ion suppression and peak misidentification, as a means to enable high-quality reporting of liquid chromatography- and gas chromatography-mass spectrometry-based metabolomics-derived data.

    View details for DOI 10.1038/s41592-021-01197-1

    View details for PubMedID 34239102

  • A Customizable Analysis Flow in Integrative Multi-Omics. Biomolecules Lancaster, S. M., Sanghi, A., Wu, S., Snyder, M. P. 2020; 10 (12)


    The number of researchers using multi-omics is growing. Though still expensive, every year it is cheaper to perform multi-omic studies, often exponentially so. In addition to its increasing accessibility, multi-omics reveals a view of systems biology to an unprecedented depth. Thus, multi-omics can be used to answer a broad range of biological questions in finer resolution than previous methods. We used six omic measurements-four nucleic acid (i.e., genomic, epigenomic, transcriptomics, and metagenomic) and two mass spectrometry (proteomics and metabolomics) based-to highlight an analysis workflow on this type of data, which is often vast. This workflow is not exhaustive of all the omic measurements or analysis methods, but it will provide an experienced or even a novice multi-omic researcher with the tools necessary to analyze their data. This review begins with analyzing a single ome and study design, and then synthesizes best practices in data integration techniques that include machine learning. Furthermore, we delineate methods to validate findings from multi-omic integration. Ultimately, multi-omic integration offers a window into the complexity of molecular interactions and a comprehensive view of systems biology.

    View details for DOI 10.3390/biom10121606

    View details for PubMedID 33260881

  • Molecular Choreography of Acute Exercise. Cell Contrepois, K. n., Wu, S. n., Moneghetti, K. J., Hornburg, D. n., Ahadi, S. n., Tsai, M. S., Metwally, A. A., Wei, E. n., Lee-McMullen, B. n., Quijada, J. V., Chen, S. n., Christle, J. W., Ellenberger, M. n., Balliu, B. n., Taylor, S. n., Durrant, M. G., Knowles, D. A., Choudhry, H. n., Ashland, M. n., Bahmani, A. n., Enslen, B. n., Amsallem, M. n., Kobayashi, Y. n., Avina, M. n., Perelman, D. n., Schüssler-Fiorenza Rose, S. M., Zhou, W. n., Ashley, E. A., Montgomery, S. B., Chaib, H. n., Haddad, F. n., Snyder, M. P. 2020; 181 (5): 1112–30.e16


    Acute physical activity leads to several changes in metabolic, cardiovascular, and immune pathways. Although studies have examined selected changes in these pathways, the system-wide molecular response to an acute bout of exercise has not been fully characterized. We performed longitudinal multi-omic profiling of plasma and peripheral blood mononuclear cells including metabolome, lipidome, immunome, proteome, and transcriptome from 36 well-characterized volunteers, before and after a controlled bout of symptom-limited exercise. Time-series analysis revealed thousands of molecular changes and an orchestrated choreography of biological processes involving energy metabolism, oxidative stress, inflammation, tissue repair, and growth factor response, as well as regulatory pathways. Most of these processes were dampened and some were reversed in insulin-resistant participants. Finally, we discovered biological pathways involved in cardiopulmonary exercise response and developed prediction models revealing potential resting blood-based biomarkers of peak oxygen consumption.

    View details for DOI 10.1016/j.cell.2020.04.043

    View details for PubMedID 32470399

  • Extensive Posttranscriptional Regulation of Nuclear Gene Expression by Plastid Retrograde Signals PLANT PHYSIOLOGY Wu, G., Meyer, E. H., Wu, S., Bock, R. 2019; 180 (4): 2034–48


    Retrograde signals emanate from the DNA-containing cell organelles (plastids and mitochondria) and control the expression of a large number of nuclear genes in response to environmental and developmental cues. Previous studies on retrograde signaling have mainly analyzed the regulation of nuclear gene expression at the transcript level. To determine the contribution of translational and posttranslational regulation to plastid retrograde signaling, we combined label-free proteomics with transcriptomic analysis of Arabidopsis (Arabidopsis thaliana) seedlings and studied their response to interference with the plastid gene expression pathway of retrograde signaling. By comparing the proteomes of the genomes uncoupled1 (gun1) and gun5 mutants with the wild type, we show that GUN1 is critical in the maintenance of plastid protein homeostasis (proteostasis) when plastid translation is blocked. Combining transcriptomic and proteomic analyses of the wild type and gun1, we identified 181 highly translationally or posttranslationally regulated (HiToP) genes. We demonstrate that HiToP photosynthesis-associated nuclear genes (PhANGs) are largely regulated by translational repression, while HiToP ribosomal protein genes are regulated posttranslationally, likely at the level of protein stability without the involvement of GUN1. Our findings suggest distinct posttranscriptional control mechanisms of nuclear gene expression in response to plastid-derived retrograde signals. They also reveal a role for GUN1 in the translational regulation of several PhANGs and highlight extensive posttranslational regulation that does not necessitate GUN1. This study advances our understanding of the molecular mechanisms underlying intracellular communication and provides new insight into cellular responses to impaired plastid protein biosynthesis.

    View details for DOI 10.1104/pp.19.00421

    View details for Web of Science ID 000477951400024

    View details for PubMedID 31138622

    View details for PubMedCentralID PMC6670084

  • Characterization of three different classes of non-fermented teas using untargeted metabolomics FOOD RESEARCH INTERNATIONAL Zhang, Q., Wu, S., Li, Y., Liu, M., Ni, K., Yi, X., Shi, Y., Ma, L., Willmitzer, L., Ruan, J. 2019; 121: 697–704


    Non-fermented teas, which are widely consumed in China, Japan, Korea, and elsewhere, have refreshing flavors and valuable health benefits. Various types of non-fermented teas look and taste similar and have no obvious differences in appearance, making their classification challenging. To date, there are very few reports about characterization and discrimination of different types of non-fermented teas. To characterize non-fermented teas and build a standard model for their classification based on their chemical composition, we employed multi-platform-based metabolomics to analyze primary and secondary metabolites in three main categories of non-fermented teas (green, yellow, and white), using 96 samples collected from China. Five hundred and ninety unique tea metabolites were identified and quantified in these three types of teas. Moreover, a partial least squares discriminant analysis (PLS-DA) model was established based on metabolomics data, in order to classify non-fermented teas into these three classes. Furthermore, our results speculate that the health benefits (e.g., antioxidant content) of these three types of non-fermented tea differ primarily because of variation in their metabolic components (e.g., ascorbate, vitexin).

    View details for DOI 10.1016/j.foodres.2018.12.042

    View details for Web of Science ID 000470048200074

    View details for PubMedID 31108798

  • Mapping the Arabidopsis Metabolic Landscape by Untargeted Metabolomics at Different Environmental Conditions MOLECULAR PLANT Wu, S., Tohge, T., Cuadros-Inostroza, A., Tong, H., Tenenboim, H., Kooke, R., Meret, M., Keurentjes, J. B., Nikoloski, Z., Fernie, A. R., Willmitzer, L., Brotman, Y. 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 Alseekh, S., Wu, S., Brotman, Y., Fernie, A. R., Antonio, C. 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 PLOS GENETICS Wu, S., Alseekh, S., Cuadros-Inostroza, A., Fusari, C. M., Mutwil, M., Kooke, R., Keurentjes, J. B., Fernie, A. R., Willmitzer, L., Brotman, Y. 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 Wu, S., Chen, S., Dong, X., Tan, G., Li, W., Lou, Z., Zhu, Z., Chai, Y. 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 Tan, G., Liu, M., Dong, X., Wu, S., Fan, L., Qiao, Y., Chai, Y., Wu, H. 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 MOLECULAR BIOSYSTEMS Chen, S., Wu, S., Li, W., Chen, X., Dong, X., Tan, G., Zhang, H., Hong, Z., Zhu, Z., Chai, Y. 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 Cai, Y., Gao, Y., Tan, G., Wu, S., Dong, X., Lou, Z., Zhu, Z., Chai, Y. 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 PLOS ONE Wu, S., Tan, G., Dong, X., Zhu, Z., Li, W., Lou, Z., Chai, Y. 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 Wu, S., Gao, Y., Dong, X., Tan, G., Li, W., Lou, Z., Chai, Y. 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 Li Wu-Hong, Zhang Xin-Rong, Wu Si, Tan Guang-Guo, Liu Chao-Mei, Zhu Zhen-Yu, Chai Yi-Feng 2012; 40 (7): 1031–36