Orthogonal Partial Least Squares Discriminant Analysis Model Research Articles

Distinct plasma metabolomic signatures differentiate autoimmune encephalitis from drug-resistant epilepsy.

Differentiating forms of autoimmune encephalitis (AE) from other causes of seizures helps expedite immunotherapies in AE patients and informs studies regarding their contrasting pathophysiology. We aimed to investigate whether and how Nuclear Magnetic Resonance (NMR)-based metabolomics could differentiate AE from drug-resistant epilepsy (DRE), and stratify AE subtypes. This study recruited 238 patients: 162 with DRE and 76 AE, including 27 with contactin-associated protein-like 2 (CASPR2), 29 with leucine-rich glioma inactivated 1 (LGI1) and 20 with N-methyl-d-aspartate receptor (NMDAR) antibodies. Plasma samples across the groups were analyzed using NMR spectroscopy and compared with multivariate statistical techniques, such as orthogonal partial least squares discriminant analysis (OPLS-DA). The OPLS-DA model successfully distinguished AE from DRE patients with a high predictive accuracy of 87.0 ± 3.1% (87.9 ± 3.4% sensitivity and 86.3 ± 3.6% specificity). Further, pairwise OPLS-DA models were able to stratify the three AE subtypes. Plasma metabolomic signatures of AE included decreased high-density lipoprotein (HDL, -(CH2)n-, -CH3), phosphatidylcholine and albumin (lysyl moiety). AE subtype-specific metabolomic signatures were also observed, with increased lactate in CASPR2, increased lactate, glucose, and decreased unsaturated fatty acids (UFA, -CH2CH=) in LGI1, and increased glycoprotein A (GlycA) in NMDAR-antibody patients. This study presents the first non-antibody-based biomarker for differentiating DRE, AE and AE subtypes. These metabolomics signatures underscore the potential relevance of lipid metabolism and glucose regulation in these neurological disorders, offering a promising adjunct to facilitate the diagnosis and therapeutics.

Comparative study on chemical constituents of different medicinal parts of Lonicera japonica Thunb. Based on LC-MS combined with multivariate statistical analysis

Lonicerae japonicae flos (LJF), Lonicerae japonicae caulis (LJC), Lonicerae folium (LF) and Lonicerae fructus (LFR) are derived from Lonicera japonica Thunb., which are formed due to different medicinal parts. The efficacy of the 4 medicinal materials has similarities and differences. However, little attention has been paid to illustrate the differences in efficacy from the perspective of phytochemistry. In this study, ultra-high performance liquid chromatography coupled with hybrid quadrupole-orbitrap mass spectrometry (UPLC-Q-Exactive-Orbitrap-MS) was used to qualitatively analyze the ingredients in 4 herbs. A total of 86 compounds were plausibly or unambiguously identified, there were 54 common components among the 4 medicinal materials, and each kind of medicinal materials had its own unique components. On the basis of qualitative analysis, ultra-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (UPLC-QQQ-MS/MS) was used to quantitatively analyze 31 components contained in 4 medicinal materials, and principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and other multivariate statistical analysis were furtherly performed for comparing the component contents. The results showed that the samples from the same parts were clustered into one group, and the samples from different medicinal parts were significantly different. The analysis of variable importance projection (VIP) value of the OPLS-DA model showed that 10 components including chlorogenic acid, secologanic acid, isochlorogenic acid A, loganin, lonicerin, loganic acid, secoxyloganin, sweroside, luteolin and rhoifolin were the main difference components among the 4 medicinal materials. The study not only lays a solid foundation for the intrinsic quality control of 4 medicinal materials and the study of different effects of the 4 medicinal materials at the phytochemical level, but also provides a basis for more rational utilization of various parts of L. japonica and expansion of medicinal resources.

Differentiation of Polygonatum Cyrtonema Hua from Different Geographical Origins by near-Infrared Spectroscopy with Chemometrics.

The identification of the geographical origin of Polygonatum cyrtonema Hua is of particular importance because the quality and market value of Polygonatum cyrtonema Hua from different production areas are highly variable due to differences in the growing environment and climatic conditions. This study utilized near-infrared spectra (NIR) of Polygonatum cyrtonema Hua (n = 400) to develop qualitative models for effective differentiation of Polygonatum cyrtonema Hua from various regions. The models were produced under different conditions to distinguish the origins distinctly. Ten pre-processing methods have been used to pre-process the original spectra (OS) and to select the most optimal spectral pre-processing method. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed to determine appropriate models. For simplicity, the pretreated full spectrum was calculated by different wavelength selection methods, and the four most significant variables were selected as discriminant indicator variables. The results show that Polygonatum cyrtonema Hua from different regions can be effectively distinguished using spectra from a series of samples analyzed by OPLS-DA. The accuracy of the OPLS-DA model is also satisfactory, with a good differentiation rate. The study findings indicate the feasibility of using spectroscopy in combination with multivariate analysis to identify the geographical origins of Polygonatum cyrtonema Hua. The utilization of near-infrared spectroscopy combined with chemometrics exhibits high efficacy in discerning the provenance of herbal medicines and foods, thereby facilitating quality assurance measures.

Characteristics and diagnostic value of serum bile acids profile in pregnant women with intrahepatic cholestasis of pregnancy and asymptomatic hypercholanemia of pregnancy

Objective: To analyze serum bile acid profiles in pregnant women with normal pregnancy, intrahepatic cholestasis of pregnancy (ICP) and asymptomatic hypercholanemia of pregnancy (AHP), and to evaluate the application value of serum bile acid profiles in the diagnosis of ICP and AHP. Methods: The clinical data of 122 pregnant women who underwent prenatal examination in Xuzhou Maternal and Child Health Care Hospital from June 2022 to May 2023 were collected, including 54 cases of normal pregnancy group, 28 cases of ICP group and 40 cases of AHP group. Ultraperformance liquid chromatography-tandem mass spectrometry was used to measure the levels of 15 serum bile acids in each group, including cholic acid (CA), chenodeoxycholic acid (CDCA), deoxycholic acid (DCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycolcholic acid (GCA), glycochenodeoxycholic acid (GCDCA), glycodeoxycholic acid (GDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), taurocholic acid (TCA), taurochenodeoxycholic acid (TCDCA), taurodeoxycholic acid (TDCA), taurolithocholic acid (TLCA) and tauroursodeoxycholic acid (TUDCA). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to screen differential bile acids. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy of differential bile acids and combined indicators between groups. Results: (1) Compared with normal pregnancy group, the serum levels of LCA, GCA, GCDCA, GDCA, GLCA, UDCA, TCA, TCDCA, TDCA, TLCA, GUDCA and TUDCA in ICP group were significantly different (all P<0.05), while the levels of LCA, DCA, GCA, GCDCA, GDCA, GLCA, TCA, TCDCA, TDCA, TLCA, GUDCA and TUDCA in AHP group were significantly different (all P<0.05). Compared with ICP group, the serum levels of CDCA, DCA, UDCA, TDCA, GUDCA and TUDCA in AHP group were significantly different (all P<0.05). (2) In the OPLS-DA model, the differential bile acids between ICP group and AHP group were TUDCA, TCA, UDCA, GUDCA and GCA, and their variable importance in projection (VIP) were 1.489, 1.345, 1.344, 1.184 and 1.111, respectively. TCA, GCDCA, GCA, TDCA, GDCA and TCDCA were the differentially expressed bile acids between AHP group and normal pregnancy group, and their VIP values were 1.236, 1.229, 1.197, 1.145, 1.139 and 1.138, respectively. (3) ROC analysis showed that the area under the curve (AUC) of TUDCA, TCA, UDCA, GUDCA and GCA in the differential diagnosis of ICP and AHP was 0.860, and the sensitivity and specificity were 67.9% and 95.0%, respectively. The AUC of TCA, GCDCA, GCA, TDCA, GDCA and TCDCA in the diagnosis of AHP was 0.964, and the sensitivity and specificity were 95.0% and 93.1%, respectively. Conclusions: There are differences in serum bile acid profiles among normal pregnant women, ICP and AHP. The serum bile acid profiles of pregnant women have potential application value in the differential diagnosis of ICP and AHP and the diagnosis of AHP.

Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome.

To identify plasma lipid characteristics associated with premetabolic syndrome (pre-MetS) and metabolic syndrome (MetS) and provide biomarkers through machine learning methods. Plasma lipidomics profiling was conducted using samples from healthy individuals, pre-MetS patients, and MetS patients. Orthogonal partial least squares-discriminant analysis (OPLS-DA) models were employed to identify dysregulated lipids in the comparative groups. Biomarkers were selected using support vector machine recursive feature elimination (SVM-RFE), random forest (rf), and least absolute shrinkage and selection operator (LASSO) regression, and the performance of two biomarker panels was compared across five machine learning models. In the OPLS-DA models, 50 and 89 lipid metabolites were associated with pre-MetS and MetS patients, respectively. Further machine learning identified two sets of plasma metabolites composed of PS(38:3), DG(16:0/18:1), and TG(16:0/14:1/22:6), TG(16:0/18:2/20:4), and TG(14:0/18:2/18:3), which were used as biomarkers for the pre-MetS and MetS discrimination models in this study. In the initial lipidomics analysis of pre-MetS and MetS, we identified relevant lipid features primarily linked to insulin resistance in key biochemical pathways. Biomarker panels composed of lipidomics components can reflect metabolic changes across different stages of MetS, offering valuable insights for the differential diagnosis of pre-MetS and MetS.

Characterization of the effect of different cooking methods on volatile compounds in fish cakes using a combination of GC–MS and GC-IMS

In this study, gas chromatography-mass spectrometry (GC–MS) and gas chromatography-ion mobility spectrometry (GC-IMS) were used to analyze the volatiles of fish cakes obtained using five cooking methods, namely, steaming, baking, air frying, pan frying and deep frying. The odor activity value (OAV) and relative odor activity value (ROAV) were used to screen for the major aroma compounds. Orthogonal partial least squares discriminant analysis (OPLS-DA) and the variable influence on projection (VIP) were used to determine the characteristic flavor compounds in the fish cakes. A total of 72 volatile compounds were identified by GC–MS, and 41 volatile compounds were detected by GC-IMS. 3-ethyl-2,5-dimethylpyrazine and 2,5-dimethylpyrazine were not detected in either CK or SS. The OPLS-DA models for GC–MS and GC-IMS analyses were constructed based on VIP values, and 8 and 7 compounds, respectively, were screened as characteristic aroma compounds. The results of this study provide new insights into the dynamics of flavor formation in reheated fish cakes and provide a theoretical basis for the optimization of the production process of this food product.

Metabolic and elemental profiling as potential discriminating features among the black mahlab seeds (Monechma ciliatum) grown in three different regions.

Black mahlab (Monechma ciliatum) seed is a rich source of metabolites and minerals and is usually believed to have a similar composition between different areas of cultivation. Until now, no studies have assessed changes in black mahlab seeds (BMSs) to determine those constituents that help to discriminate them according to geographical origin. The present study attempted to compare the metabolomics and elemental profiles of BMSs of different geographical origins and identified the potential markers using ultrahigh-performance liquid chromatography quadrupole Orbitrap tandem mass spectrometry (UHPLC-Q-Orbitrap-MS2 ), and inductively coupled plasma mass spectrometry (ICP-MS) techniques and established the chemometric model to identify the potential markers and discriminate them according to cultivation sites. In this work, data from metabolites analysis by UHPLC-Q-Orbitrap-MS2 and multi-elemental data obtained from ICP-MS were combined with chemometrics for tracing the geographical origin of BMSs. Principal component analysis (PCA) was used to evaluate the overall grouping of samples. In contrast, partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were employed for authentication. PLS-DA and OPLS-DA models were fully validated (R2 Y and Q2 values > 0.5). Variable importance of various projections was applied to obtain valuable data about differential elements (seven markers were identified) and metabolites (23 markers were identified) with high discrimination potential. The outcomes presented in this study serve as an appropriate framework for developing novel discrimination approaches in food origin screening.

Identifying hypothermia death in a mouse model by ATR-FTIR.

The identification of hypothermia death (HD) is difficult for cadavers, especially the distinction from death due to alternative causes. A large number of studies have shown that brown adipose tissue (BAT) plays critical roles in thermoregulation of mammals. In this study, BAT of mice was used for the discrimination of HD using attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR). A modified mouse HD model conducted by Feeney DM was used in this study to obtain infrared spectra of BAT. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to establish discrimination models. The PLS-DA and OPLS-DA models exhibit prominent discriminative efficiency, and the accuracy of HD identification using fingerprint regions and ratios of absorption intensity is near 100% in both the calibration and validation sets. Our preliminary study suggests that BAT may be an extremely effective target tissue for identification of cadavers of HD, and ATR-FTIR spectra combined with chemometrics have also shown potential for cadaver identification in forensic practice in a fast and accurate manner.

UHPLC-QTOF-MS-based untargeted metabolomic authentication of Chinese red wines according to their grape varieties

Wine is a very popular alcoholic drink owing to its health benefits of antioxidant effects. However, profits-driven frauds of wine especially false declarations of variety frequently occurred in markets. In this work, an UHPLC-QTOF-MS-based untargeted metabolomics method was developed for metabolite profiling of 119 bottles of Chinese red wines from four varieties (Cabernet Sauvignon, Merlot, Cabernet Gernischt, and Pinot Noir). The metabolites of red wines from different varieties were assessed using orthogonal partial least-squares discriminant analysis (OPLS-DA) and analyzed using KEGG metabolic pathway analysis. Results showed that the differential compounds among different varieties of red wines are mainly flavonoids, phenols, indoles and amino acids. The KEGG metabolic pathway analysis showed that indoles metabolism and flavonoids metabolism are closely related to wine varieties. Based on the differential compounds, OPLS-DA models could identify external validation wine samples with a total correct rate of 90.9 % in positive ionization mode and 100 % in negative ionization mode. This study indicated that the developed untargeted metabolomics method based on UHPLC-QTOF-MS is a potential tool to identify the varieties of Chinese red wines.

Determining the geographical origin and glycogen content of oysters using portable near-infrared spectroscopy: Comparison of classification and regression approaches

Oysters are extensively cultivated worldwide. However, significant variations in chemical composition, quality, and price exist between oysters from different geographical origins. This study employed portable near-infrared spectroscopy in conjunction with chemometric analysis to determine the geographical origin and glycogen content of oysters. Pretreatment methods (multiplicative scattering correction, first derivative, and second derivative) were used to preprocess the raw spectra. Partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), and support vector machine (SVM) were then adopted to establish the qualitative models. Partial least squares regression (PLSR) and support vector machine regression (SVMR) were compared for predicting the glycogen content. The results revealed that the PLS-DA, OPLS-DA, and SVM models classified the geographical origin of oysters with 100% accuracy. For quantitative analysis, the regression equations displayed high predictive ability. The SVMR model was superior to the PLSR model for glycogen content prediction, with a coefficient of determination of prediction (R2P) of 0.9253 and a residual prediction deviation (RPD) of 3.62. Therefore, the proposed approach is suitable for the accurate and environmentally friendly determination of the geographical origin and glycogen content of oysters, thus representing an attractive alternative method for the traceability supervision and quantitative analysis of seafood products.

High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 μL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983–1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Accurate origin identification of Chinese white Chrysanthemi Flos by analysis of C, N, O, H stable isotope ratios and mineral elements combined with chemometrics

Chrysanthemum is a common medicinal and edible Chinese herbal product, and its growth environment affects its quality and market sale price. So it has high practical value and relevance to establish an efficient, rapid and accurate approach to identify the origin of chrysanthemums. In this study, we collected chrysanthemum samples from six geographical indication (GI) production areas in China and analyzed the four stable isotope ratios (δ13C, δ15N, δ18O, and δ2H) and 44 mineral element signatures of chrysanthemums to classify chrysanthemum. An analysis of variance (ANVOA) indicated that stable isotope ratios and mineral elements differed significantly between origins. The accuracies of the training set and prediction set of the random forest (RF) algorithm were 100% and 98.3%, respectively, while those of the partial least squares discriminant algorithm (PLS-DA) were 96.9% and 96.7%, respectively; the results of the RF algorithm were slightly better than those of the PLS-DA. Combining stable isotopes and mineral elements could enhance the accuracy of origin identification. Moreover, the OPLS-DA (orthogonal partial least squares discriminant analysis) models for stable isotopes and mineral elements indicated that δ2H, δ18O, Cd, Zn, Mg, Ca, Tm, As, and Tl were the elements that could most effectively distinguish the origins of chrysanthemum, and the discriminant accuracy reached 100%. Therefore, the proposed strategy can offer a simple, rapid, and dependable approach for classifying the geographic origin of chrysanthemums.

Geographical origin classification of tobacco by stable isotope and multi-elemental analysis in combination with chemometric methods

Tobacco is an important economic crop widely grown around the world. Its quality and features are closely interrelated to its geographical origin. With a view to identify the geographical origin of tobacco leaves, stable isotope ratios (δ13C, δ15N and δ2H) and 33 element contents in 260 tobacco samples collected from six provinces in China were determined by stable isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively, and analyzed with the aid of chemometric methods such as Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) and Random Forest (RF). Experimental results revealed that δ2H, Zn and Tl were significantly different in samples from different regions, which indicated their potential to be used in geographical origin classification of tobacco samples. Both OPLS-DA and RF models displayed satisfactory overall performance for the discrimination of geographical origins of tobacco samples from six cultivation areas. Particularly, the RF model built on the fused data of IRMS and ICP-MS had an exquisite sensitivity of 98.5% in comparison with the RF models on either the data set produced by IRMS or ICP-MS alone, whose sensitivity values were 92.3% and 95.4%, respectively. It was found that the top ten influential indicators for identifying the geographic origin of tobacco were δ2H, Cr, δ15N, 87/86Sr, Li, Ba, Sb, Pb, Tl, and Zn. This work may open up an avenue for geographical origin classification of tobacco.

Analysis of metabolite differences between South Korean and Chinese yellow goosefish (Lophius litulon) using capillary electrophoresis time-of-flight mass spectrometry

The yellow goosefish is a benthic fish that belongs to the family Lophiidae and order Lophiiformes and is distributed in the Yellow and East China Seas. This study aimed to distinguish between yellow goosefish from different geographical origins by analyzing their metabolites. Capillary electrophoresis time-of-flight mass spectrometry was used to analyze metabolite profiles in the muscle tissues of yellow goosefish to distinguish between Korean and Chinese yellow goosefish. In total, 271 putative metabolites were extracted using 50% acetonitrile in water. Principal component analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to distinguish different geographical origins using the metabolite profiles obtained. The R2 and Q2 values of the OPLS-DA model were 0.856 and 0.695, respectively, indicating that the model was well-fitted and had good predictability. The heat map revealed that nucleic acid and amino compounds differed between the Korean and Chinese fish, and the variable importance in the projection scores obtained from OPLS-DA showed that there were geographical differences in the primary metabolites (5′-methylthioadenosine, adenosine, uridine 5-diphosphate, guanosine 5-diphosphate, urea, homocarnosine, O-acetylcarnitine, cycloleucine, cycloleucine S-adenosylmethionine, S-adenosylhomocysteine, ethanolamine, myo-inositol 1-phosphate), which were identified as potential candidate biomarkers.

Non-Targeted Metabolomic Study of Fetal Growth Restriction.

We aimed to explore the differential metabolites in amniotic fluid and its cells from fetuses with fetal growth restriction (FGR). A total of 28 specimens of amniotic fluid were collected, including 18 with FGR and 10 controls. Differential metabolites in all samples were detected by chromatography-mass spectrometry. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to analyze the differences in metabolic spectra between the FGR and control groups through multidimensional and single-dimensional statistical analysis. The KEGG database was used for metabolic pathway enrichment analysis. Both PCA and OPLS-DA models showed a clear separation trend between FGR and control groups. We identified 27 differentially expressed metabolites in the amniotic fluid supernatant of the two groups (p < 0.05), of which 14 metabolites were up-regulated in the FGR group, and 13 metabolites, such as glutamate, phenylalanine, valine and leucine, were down-regulated. We also identified 20 differentially expressed metabolites in the amniotic fluid cell (p < 0.05), of which 9 metabolites, including malic acid, glycolic acid and D-glycerate, were up-regulated significantly and 11 metabolites, including glyceraldehyde, were down-regulated. Pathway analysis showed that most of the identified differential metabolites were involved in tricarboxylic acid cycle (TCA cycle), ABC transport, amino acid metabolism pathways and so on. The results indicated that many metabolic changes associated with FGR, which are mainly manifested by abnormal metabolism of amino acid in amniotic fluid and abnormal glucose metabolism including TCA cycle in amniotic fluid cells, respectively. Our findings provide more data for exploring the mechanism of FGR and the potential therapy targets.

A comprehensive quality evaluation of Cimicifugae Rhizoma using UPLC-Q-Orbitrap-MS/MS coupled with multivariate chemometric methods.

Cimicifugae Rhizoma, known in Chinese as Shengma, is a common medicinal material in traditional Chinese medicine (TCM), mainly used for treating wind-heat headaches, sore throat, uterine prolapse, and other diseases. An approach using a combination of ultra-performance liquid chromatography (UPLC), mass spectrometry (MS), and multivariate chemometric methods was designed to assess the quality of Cimicifugae Rhizoma. All materials were crushed into powder and the powdered sample was dissolved in 70% aqueous methanol for sonicating. Chemometric methods, including hierarchical cluster analysis (HCA), principal component analysis (PCA), and orthogonal partial least squares discriminant analysis (OPLS-DA), were adopted to classify and perform a comprehensive visualization study of Cimicifugae Rhizoma. The unsupervised recognition models of HCA and PCA obtained a preliminary classification and provided a basis for classification. In addition, we constructed a supervised OPLS-DA model and established a prediction set to further validate the explanatory power of the model for the variables and unknown samples. Exploratory work research found that the samples were divided into two groups, and the differences were related to appearance traits. The correct classification of the prediction set also demonstrates a strong predictive ability of the models for new samples. Subsequently, six chemical makers were characterized by UPLC-Q-Orbitrap-MS/MS, and the content of four components was determined. The results of the content determination revealed the distribution of representative chemical markers caffeic acid, ferulic acid, isoferulic acid, and cimifugin in two classes of samples. This strategy can provide a reference for assessing the quality of Cimicifugae Rhizoma, which is significant for the clinical practice and quality control of Cimicifugae Rhizoma.

UHPLC-QTOF-MS-based untargeted metabolomics revealing the differential chemical constituents and its application on the geographical origins traceability of lily bulbs

Lily bulbs have been historically used as an edible and medicinal homologous plant. Identifying the geographical origins of lily bulbs produced in specific origin is of great importance since the geographical origins of lily bulbs influence their quality and price greatly. In this work, an untargeted metabolomic method based on UHPLC-QTOF-MS was established for revealing the differential chemical constituents of lily bulbs among different origins and predicting the geographical origins of them by chemometric modeling. A total of 15 differential compounds were screened and identified from untargeted metabolomic data of 50 lily bulb samples by our previously developed AntDAS software. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) showed that samples from five different origins were obviously distinguished based on the differential compounds. What's more, 7 and 6 key characteristic markers were discovered by partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA), respectively. Finally, heatmap, PLS-DA and OPLS-DA models were reconstructed based on the discovered key characteristic markers, and external validation lily bulb samples were successfully discriminated, with recognition rate of 100%. This study demonstrated that the proposed strategy has great potentials for the differentiation and identification of the geographical origins of lily bulbs.

Potential preventive markers in the intracerebral hemorrhage process are revealed by serum untargeted metabolomics in mice using hypertensive cerebral microbleeds.

Hypertensive cerebral microbleeds (HCMB) may be the early stage of hypertensive intracerebral hemorrhage (HICH), which is a serious threat to health due to its high mortality and disability rates. The early clinical symptoms of HCMB may not be significant. Moreover, it is difficult to achieve early diagnosis and intervention for targeted prevention of HICH. Although hypertension (HTN) is a predisposition for HCMB, it remains unclear whether there is any difference between hypertensive patients with or without HCMB. Therefore, we carried out liquid chromatography-mass spectrometry (LC-MS) to analyze early biomarkers for HCMB in mice with hypertension and to lay the foundation for early prevention of HICH in hypertensive patients. In total, 18 C57 male mice were randomly divided into the HCMB (n = 6), HTN (n = 6), and control groups (CON, n = 6). Hematoxylin-eosin and diaminobenzidine staining were used to assess the reliability of the model. The metabolite expression level and sample category stability were tested using the displacement test of orthogonal partial least squares discriminant analysis (OPLS-DA). Significant differences in metabolites were screened out using variable importance in the projection (VIP > 1), which were determined using the OPLS-DA model and the P-value of the t-test (P < 0.05) combined with the nonparametric rank-sum test. With an area under the curve (AUC) > 0.85 and a P-value of 0.05, the receiver operating characteristic curve (ROC) was used to further screen the distinct metabolites of HCMB. Compared with the HTN and CON groups, the HCMB group had significantly higher blood pressure and lower average body weight (P < 0.05). Through untargeted LC-MS analysis, 93 distinct metabolites were identified in the HCMB (P < 0.05, VIP > 1) group. Among these potential biomarkers, six significantly decreased and eight significantly increased differential metabolites were found. Meanwhile, we found that the HCMB group had statistically distinct arginine and purine metabolism pathways (P < 0.05), and citrulline may be the most significant possible biomarker of HCMB (AUC > 0.85, P < 0.05). All of these potential biomarkers may serve as early biomarkers for HICH in hypertension.

Abstract 3338: Targeted serum metabolomics for noninvasive detection of colorectal neoplasia

Abstract Background: Using a shotgun metabolomics approach, we have previously reported distinct features in the circulating metabolome of patients with colorectal cancer (CRC) and adenoma. A clinically useful metabolomic assay requires a targeted approach that detects CRC with high sensitivity, and which also detects pre-malignant precursor lesions. Our objective was to identify a metabolomic signature for CRC, adenomatous polyps (AP), and sessile serrated adenomas (SSA), using a targeted approach. Methods: Serum from patients with all stages of CRC (N=86), AP (N=48), SSA (N=46), as well as age- and sex-matched disease-free controls (DFC; N=120) were analyzed using a targeted metabolomic assay (Biocrates MxP® Quant 500). The assay was performed on liquid chromatography-tandem mass spectrometry (LC-MS/MS), quantifying 630 metabolites across 26 biochemical classes. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to identify metabolite patterns distinguishing each neoplastic condition relative to DFCs. A small validation cohort (N=78) was additionally analyzed to test our meta-biomarkers. Results: In comparisons with DFCs, statistically significant OPLS-DA models were constructed for all three neoplastic conditions. The OPLS-DA models for CRC, AP, and SSA consisted of 39, 14, and 22 metabolites, respectively, with the most frequently represented biochemical classes being triglycerides, phosphatidylcholines, bile acids, and acylcarnitines. Based on 7-fold internal cross validation, receiver operating characteristic (ROC) analysis demonstrated an area under the curve (AUC) of 0.85, 0.89, and 0.82 for CRC, AP, and SSA, respectively. When applied to the independent validation cohort, the sensitivity and specificity of each model were: 87.0% and 87.0% for CRC; 53.3% and 93.3% for AP; and 78.6% and 57.1% for SSA. Conclusion: Metabolomic meta-biomarkers for CRC and its precursors demonstrated excellent performance based on internal validation. Importantly, these lesions were each detected with superior sensitivity compared to readily available stool-tests. A large external validation study in progress will be used to confirm these findings. Citation Format: Liam W. Fitzgerald, Dennis J. Orton, Karen A. Kopciuk, Hans J. Vogel, Robert J. Hilsden, Oliver F. Bathe. Targeted serum metabolomics for noninvasive detection of colorectal neoplasia [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3338.

Untargeted metabolomics analysis of cerebrospinal fluid in patients with leptomeningeal metastases from non-small cell lung cancer

ABSTRACT Objective: To explore and analyze the diagnostic value of metabolic markers in cerebrospinal fluid (CSF) in leptomeningeal metastases (LM) of non-small cell lung cancer (NSCLC). Methods: Forty-six CSF samples from patients with NSCLC-LM were collected. Another 48 CSF samples from patients with nonmalignant neurological diseases were selected as control group. Metabolomic analysis of CSF was performed by high-performance liquid chromatography-mass spectrometry. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were applied for modeling. A multi-criteria evaluation system (variable importance value >1, multiple of change >2 and P < 0.05 for univariate analysis) was used to find differential metabolites between two groups. The subject working characteristic curves and pathway enrichment analysis were used to screen metabolites and pathways associated with NSCLC-LM. Results: The PCA model and OPLS-DA model showed good overall data quality. Thirty endogenous differential metabolites were screened, and six potential biomarkers were further identified, including tyrosine (t = 3.37, P = 0.024, AUC = 0.967), phenylalanine (t = 3.98, P < 0.001, AUC = 0.992), pyruvate (t = 4.48, P < 0.001, AUC = 0.976), tryptophan (t = −2.5, P = 0.014, AUC = 0.935), adenosine monophosphate (t = −6.13, P < 0.001, AUC = 0.932) and glucose (t = −4.00, P < 0.001, AUC = 0.993). Thirty differential metabolites screened were subjected to metabolic pathway enrichment analysis and matched to 20 relevant metabolic pathways, of which the four most likely to cause metabolite changes were as follows: glycolysis and sugar metabolism synthesis, pyruvate metabolism, phenylalanine metabolism, and phenylalanine, tyrosine and tryptophan biosynthesis. Conclusions: Untargeted metabolomics can effectively screen for CSF metabolites specific to NSCLC-LM patients, and six potential metabolites and their metabolic pathways might be involved in the pathogenesis of NSCLC-LM.