Lunzhao Yi

Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS-LDA

Metabolic profiling has increasingly been used as a probe in disease diagnosis and pharmacological analysis. Herein, plasma fatty acid metabolic profiling including non‐esterified fatty acid (NEFA) and esterified fatty acid (EFA) was investigated using gas chromatography/mass spectrometry (GC/MS) followed by multivariate statistical analysis. Partial least squares‐linear discrimination analysis (PLS‐LDA) model was established and validated to pattern discrimination between type 2 diabetic mellitus (DM‐2) patients and health controls, and to extract novel biomarker information. Furthermore, the PLS‐LDA model visually represented the alterations of NEFA metabolic profiles of diabetic patients with abdominal obesity in the treated process with rosiglitazone. The GC/MS‐PLS‐LDA analysis allowed comprehensive detection of plasma fatty acid, enabling fatty acid metabolic characterization of DM‐2 patients, which included biomarkers different from health controls and dynamic change of NEFA profiles of patients after treated with medicine. This method might be a complement or an alternative to pathogenesis and pharmacodynamics research.

Anti-inflammatory effect of Houttuynia cordata injection

Abstract Houttuynia cordata (Saururaceae) injection (HCI) is a traditional Chinese medicine used in China. It was chosen as one of eight types of traditional Chinese medicine that play a unique role in severe acute respiratory syndrome (SARS) owing to the effect of curbing inflammation. In order to validate this plausible anti-inflammatory property, the chemical composition of HCI has been analysed by GC/MS, 22 components were identified, and the inflammation induced by carrageenan in the rat pleurisy model and by xylene in the mice ear edema model was adopted to study the anti-inflammatory activity of HCI. Injection of carrageenan into the pleural cavity elicited an acute inflammatory response characterized by protein rich fluid accumulation and leukocyte infiltration in the pleural cavity. The peak inflammatory response was obtained at 24h when the fluid volume, protein concentration, C-reactive protein and cell infiltration were maximums. The results showed that these parameters were attenuated by HCI at any dose and touched bottom at dose of 0.54ml/100g, although less strong than dexamethasone. This drug was also effective in inhibiting xylene induced ear edema, and the percentage of inhibition came to 50% at dose of 80μl/20g. The results clearly indicate that HCI have anti-inflammatory activity.

Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

This review focuses on recent and potential advances in chemometric methods in relation to data processing in metabolomics, especially for data generated from mass spectrometric techniques. Metabolomics is gradually being regarded a valuable and promising biotechnology rather than an ambitious advancement. Herein, we outline significant developments in metabolomics, especially in the combination with modern chemical analysis techniques, and dedicated statistical, and chemometric data analytical strategies. Advanced skills in the preprocessing of raw data, identification of metabolites, variable selection, and modeling are illustrated. We believe that insights from these developments will help narrow the gap between the original dataset and current biological knowledge. We also discuss the limitations and perspectives of extracting information from high-throughput datasets.

Multistimuli‐Responsive Supramolecular Gels: Design Rationale, Recent Advances, and Perspectives

This manuscript presents a brief overview of recent advances in multistimuli-responsive supramolecular gels (MRSGs). The synthesis of MRSGs with faster and smarter responsive abilities to a variety of external stimuli, such as redox reagents, pH changes, ligands, and coupling reagents, is one key issue for the upgrade of current molecular motors, signal sensors, shape memory devices, drug delivery systems, display devices, and other devices. However, the design rules of MRSGs are still not well understood. The lack of information about the relationship between the spatial structure and gelation behavior of existing gelators means that the knowledge required to design new gelators by the addition of functional moieties to well-known gelators is lacking. Insights into the gelation pathway of known gelators may bring inspiration to researchers who want to exploit elegant designs and specific building blocks to obtain their own MRSGs with predictable stimuli-responsive abilities.

Exploring metabolic syndrome serum profiling based on gas chromatography mass spectrometry and random forest models

Metabolic syndrome (MetS) is a constellation of the most dangerous heart attack risk factors: diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure. Analysis and representation of the variances of metabolic profiles is urgently needed for early diagnosis and treatment of MetS. In current study, we proposed a metabolomics approach for analyzing MetS based on GC-MS profiling and random forest models. The serum samples from healthy controls and MetS patients were characterized by GC-MS. Then, random forest (RF) models were used to visually discriminate the serum changes in MetS based on these GC-MS profiles. Simultaneously, some informative metabolites or potential biomarkers were successfully discovered by means of variable importance ranking in random forest models. The metabolites such as 2-hydroxybutyric acid, inositol and d-glucose, were defined as potential biomarkers to diagnose the MetS. These results obtained by proposed method showed that the combining GC-MS profiling with random forest models was a useful approach to analyze metabolites variances and further screen the potential biomarkers for MetS diagnosis.

A new strategy to prevent over-fitting in partial least squares models based on model population analysis

Partial least squares (PLS) is one of the most widely used methods for chemical modeling. However, like many other parameter tunable methods, it has strong tendency of over-fitting. Thus, a crucial step in PLS model building is to select the optimal number of latent variables (nLVs). Cross-validation (CV) is the most popular method for PLS model selection because it selects a model from the perspective of prediction ability. However, a clear minimum of prediction errors may not be obtained in CV which makes the model selection difficult. To solve the problem, we proposed a new strategy for PLS model selection which combines the cross-validated coefficient of determination (Qcv(2)) and model stability (S). S is defined as the stability of PLS regression vectors which is obtained using model population analysis (MPA). The results show that, when a clear maximum of Qcv(2) is not obtained, S can provide additional information of over-fitting and it helps in finding the optimal nLVs. Compared with other regression vector based indictors such as the Euclidean 2-norm (B2), the Durbin Watson statistic (DW) and the jaggedness (J), S is more sensitive to over-fitting. The model selected by our method has both good prediction ability and stability.

A novel kernel Fisher discriminant analysis: constructing informative kernel by decision tree ensemble for metabolomics data analysis.

Large amounts of data from high-throughput metabolomics experiments become commonly more and more complex, which brings an enormous amount of challenges to existing statistical modeling. Thus there is a need to develop statistically efficient approach for mining the underlying metabolite information contained by metabolomics data under investigation. In the work, we developed a novel kernel Fisher discriminant analysis (KFDA) algorithm by constructing an informative kernel based on decision tree ensemble. The constructed kernel can effectively encode the similarities of metabolomics samples between informative metabolites/biomarkers in specific parts of the measurement space. Simultaneously, informative metabolites or potential biomarkers can be successfully discovered by variable importance ranking in the process of building kernel. Moreover, KFDA can also deal with nonlinear relationship in the metabolomics data by such a kernel to some extent. Finally, two real metabolomics datasets together with a simulated data were used to demonstrate the performance of the proposed approach through the comparison of different approaches.

Exploring metabolic syndrome serum free fatty acid profiles based on GC-SIM-MS combined with random forests and canonical correlation analysis.

Metabolic syndrome (MetS) is a cluster of metabolic abnormalities associated with an increased risk of developing cardiovascular diseases or type II diabetes. Till now, the etiology of MetS is complex and still unknown. Metabolic profiling is a powerful tool for exploring metabolic perturbations and potential biomarkers, thus may shed light on the pathophysiological mechanism of diseases. In this study, fatty acid profiling was employed to exploit the metabolic disturbances and discover potential biomarkers of MetS. Fatty acid profiles of serum samples from metabolic syndrome patients and healthy controls were first analyzed by gas chromatography-selected ion monitoring-mass spectrometry (GC-SIM-MS), a robust method for quantitation of fatty acids. Then, the supervised multivariate statistical method of random forests (RF) was used to establish a classification and prediction model for MetS, which could assist the diagnosis of MetS. Furthermore, canonical correlation analysis (CCA) was employed to investigate the relationships between free fatty acids (FFAs) and clinical parameters. As a result, several FFAs, including C16:1n-9c, C20:1n-9c and C22:4n-6c, were identified as potential biomarkers of MetS. The results also indicated that high density lipoprotein-cholesterol (HDL-C), triglycerides (TG) and fasting blood glucose (FBG) were the most important parameters which were closely correlated with FFAs disturbances of MetS, thus they should be paid more attention in clinical practice for monitoring FFAs disturbances of MetS than waist circumference (WC) and systolic blood pressure/diastolic blood pressure (SBP/DBP). The results have demonstrated that metabolic profiling by GC-SIM-MS combined with RF and CCA may be a useful tool for discovering the perturbations of serum FFAs and possible biomarkers for MetS.

Metabolomic identification of novel biomarkers of nasopharyngeal carcinoma

This paper introduces a new identification strategy of novel metabolic biomarkers for nasopharyngeal carcinoma (NPC). Here, we combined gas chromatography-mass spectrometry (GC-MS) metabolic profiling with three partial least squares-discriminant analysis (PLS-DA) based variable selection methods to screen the metabolic biomarkers of NPC. We found that the variable importance on projection (VIP) method exhibited better efficiency than the coefficients β and the loadings plot for the metabolomics data set of 39 NPC patients and 40 healthy controls. In addition, we proved that the area under receiver operating characteristic curve (AUC) was more sensitive than the correct rate to evaluate the discrimination ability of the classical models. Therefore, three novel candidate biomarkers, glucose, glutamic acid and pyroglutamate were identified, with a correct rate of 97.47% and an AUC value of 97.40%. Our results suggested that the metabolic disorders of NPC were mainly reflected in the glycolysis and glutamate metabolism; in addition, metabolic levels of the related metabolic pathways may affect each other, such as the TCA cycle and lipid metabolism. We believe that the findings of these novel metabolites will be very helpful for early-diagnosis and subsequent pathogenesis research of NPC.

From supramolecular hydrogels to functional aerogels: a facile strategy to fabricate Fe3O4/N-doped graphene composites

This manuscript introduces a simple method to fabricate hybrid aerogels with Fe3O4 nanocrystals/nitrogen-doped graphene (Fe3O4/N-GAs) through one-shot self-mineralization of ferrocenoyl phenylalanine/graphene oxide (Fc-F/GO) supramolecular hydrogels. We found that GO could trigger a sol–gel transition of Fc-F gelators below the critical gelation concentration and the electron microscopic results revealed that the self-assembled Fc-F fibrils tightly bound onto graphene sheets. Upon hydrothermal reaction, Fc moieties in these fibrils could be locally oxidized to Fe3O4 nanocrystals by GO, remaining on the top of reduced GO (RGO) sheets and therefore inhibiting the self-aggregation of graphene nanosheets. After drying, the remains of the supramolecular hybrid hydrogels are presented as the three-dimensional (3D) framework of ultra-thin graphene sheets on which Fe3O4 nanoparticles (NPs) are uniformly immobilized as single crystals. Since the new born Fe3O4 nanocrystals are closely anchored on the graphene sheets, the as-prepared Fe3O4/N-GAs complex shows excellent electrocatalytic activity for the oxygen reduction reaction (ORR, compared to commercial Pt/C). Notably, the Fc-F/GO supramolecular hydrogels act as multifunctional reagents, such as capping agents for preventing graphene nanosheets from stacking and Fe and N sources for Fe3O4/N-GAs. We expect that this intriguing strategy can provide a useful archetypical example in designing nonprecious metal oxides/carbon hybrid materials to serve as substitutes for noble metal catalysts.