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.

Detection of metabolic syndrome with ATR-FTIR spectroscopy and chemometrics in blood plasma

Metabolic Syndrome (MetS) is a constellation of 3 or more risk factor (abdominal obesity, high triglycerides, low HDL-c, high blood pressure, and elevated blood glucose) for atherosclerotic cardiovascular disease. Considering these systemic metabolic changes in the biochemical pathways of all biomolecules, Attenuated Total Reflection-Fourier Transform Infrared (ATR-FTIR) spectroscopy is a rapid, low-cost, and reagent-free alternative technique capable of identifying spectral biomarkers that differentiate subjects with MetS from control. In this study, plasma samples from 74 subjects (14 MetS, 60 control) were analyzed on the ATR-FTIR spectrophotometer. The objective was to differentiate subjects with MetS from control with supervised chemometrics modeling (Orthogonal Partial Least Squares-Discriminant Analysis, OPLS-DA). Additionally, the inflammatory status of subjects with MetS and control (supervised by C-reactive protein - CRP, leptin, and cell-free DNA - cfDNA) was verified. The OPLS-DA model achieved 100% sensitivity and specificity in cross-validation. For 1 latent variable (93.4% of variance), RMSECV < 0.002, PRESS CV < 0.0001, and R² > 0.9999 was obtained. Significant spectrochemical differences (p < 0.05) were found between MetS and control subjects in the following biomolecular regions (cm^-1): 1717-1703 [ν(CO) and δ(NH)], 1166-1137 [ν(C-OH) + ν(CO) and ν(CC) + δ(OH) + ν(CO)], 1113-1040 [ν(PO₂^-) and ν(C-OH)], and 1027-1008 [ν(CO) and v(CH₂OH)]. In the OPLS-DA model loadings, amide I [1720-1600 cm^-1, ν(CO)] and amide II [1570-1480 cm^-1, δ(NH) + ν(CH)] had significantly greater weight than all other regions. There was a significant difference in inflammatory status between MetS patient and control (p < 0.05 for CRP and leptin, and p < 0.01 for cfDNA).

Rapid and low-cost liquid biopsy with ATR-FTIR spectroscopy to discriminate the molecular subtypes of breast cancer

Breast cancer (BC) is the most prevalent cancer worldwide. The prognosis and survival of these patients are directly related to the diagnostic stage. Even so, the gold standard screening method (mammography) has a long waiting period, high rates of false positives, anxiety for patients, and consequently delays the diagnosis by core needle biopsy (invasive method). Alternatively, the Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy is a noninvasive, low-cost, rapid, and reagent-free technique that generates the spectral metabolomic profile of biomolecules. This makes it possible to assess systemic repercussions, such as the BC carcinogenesis process. Blood plasma samples (n = 56 BC and n = 18 controls) were analyzed in the spectrophotometer in the ATR-FTIR mode. For the exploratory analysis of the data, interval Principal Component Analysis (iPCA) was used, and for predictive chemometric modeling, the Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) algorithm with validation by leave-one-out cross-validation. iPCA in the region of 1118-1052 cm^-1 (predominantly DNA/RNA bands) showed significant clustering of molecular subtypes and control. The OPLS-DA model achieved 100% accuracy with only 1 latent variable and Root Mean Square Error of Cross-Validation (RMSECV) < 0.005 for all molecular subtypes and control. The wavenumbers (cm^-1) with the highest iPCA peaks (loadings: 1117, 1089, 1081, 1075, 1057, and 1052) were used as input to MANOVA (Wilks' Lambda, p < 0.001 between molecular subtypes and control). The rapid and low-cost detection of BC molecular subtypes by ATR-FTIR spectroscopy would plausibly allow initial screening and clinical management, improving prognosis, reducing mortality and costs for the health system.

Discrimination of dyslipidemia types with ATR-FTIR spectroscopy and chemometrics associated with multivariate analysis of the lipid profile, anthropometric, and pro-inflammatory biomarkers

BACKGROUND

Obesity, dyslipidemia, and low-grade inflammatory state form a triad of self-sustaining metabolic dysfunction. Attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy is a simple, rapid, and non-destructive technique that generates spectral fingerprints of biomolecules that can be correlated with metabolic changes. We verified the efficiency of ATR-FTIR spectroscopy in blood plasma (n = 74) to discriminate the types of dyslipidemias and suggest metabolic inflammatory changes.

METHODS

Principal Component Analysis (PCA) was performed on the biochemical and anthropometric data to verify whether the dyslipidemia types share a similar biochemical profile plausible of discrimination in chemometric modeling. To discriminate the types of dyslipidemias based on spectral data, Orthogonal Partial Least-Squares Discriminant Analysis (OPLS-DA) was used and validated with leave-one-out cross-validation.

RESULTS

Although no significant difference was obtained between the types of dyslipidemia and normal subjects by CRP, leptin, and cfDNA, there was a significant difference between normal subjects vs combined hyperlipidemia (CH) + hypercholesterolemia (HCL) + hypertriglyceridemia (HTG) (p < 0.05) by the 1245 cm^-1 peak [ν_as(PO₂^-)] (possible indication of chronic inflammation by increased cfDNA). The area under the curve of the region between 1770 and 1720 cm^-1 was significantly increased for CH in relation to other dyslipidemias and normal subjects. Furthermore, there were significant differences for the main representative peaks of lipids, proteins, carbohydrates, and nucleic acids between the types of dyslipidemias and between the types of dyslipidemias and normal subjects. The OPLS-DA model achieved 100 % accuracy with 1 latent variable and Standard Error of Cross-Validation (SECV) < 0.004 for all types of dyslipidemia  and the control group.

CONCLUSIONS

Our results suggest that ATR-FTIR spectroscopy associated with chemometric modeling is a plausible applicant for screening the types of dyslipidemias. However, more extensive studies should be conducted to verify the real applicability in clinical analysis laboratories or medical clinics.

Predictive models built upon annotated and validated intake biomarkers in urine using paired or unpaired analysis helped to classify cranberry juice consumers in a randomized, double-blinded, placebo-controlled, and crossover study

Intake biomarkers of cranberry juice in women can assess consumption in clinical trials. Discriminant biomarkers in urine may explain urinary tract infection (UTI) preventive activities. We hypothesized that validated and annotated discriminant metabolites in human urine could be used as intake biomarkers in building predictive multivariate models to classify cranberry consumers. Urine samples were collected from 16 healthy women aged 18 to 29 years at baseline and after 3- and 21-day consumption of cranberry or placebo juice in a double-blind, crossover study. Urine metabolomes were analyzed using ultra high-performance liquid chromatography coupled with Orbitrap mass spectrometry. Paired and unpaired multivariate analyses were used to annotate or identify discriminant metabolic features after cranberry consumption. Twenty-six discriminant metabolic features (paired analysis) and 27 (unpaired analysis) after cranberry consumption in an open-label intervention were rediscovered in the blinded study. These metabolites included exogenous (quinic acid) and endogenous ones (hippuric acid). The paired analysis showed better model fitting with partial least-square discriminant analysis models built on all metabolites than the unpaired analysis. Predictive models built on shared metabolites by the unpaired analysis were able to classify cranberry juice consumers with 84.4% to 100% correction rates, overall better than the paired analysis (50%-100%). The double-blind study validated discriminant metabolites from a previous open-label study. These urinary metabolites may be associated with the ability of cranberries to prevent UTIs and serve as potential cranberry intake biomarkers. It reveals the importance of selecting the right predictive models to classify cranberry consumers with higher than 95% correction rates.

Quality assessment of fried licorice based on fingerprints and chemometrics

Fried licorice is obtained by frying licorice without using any auxiliary materials, and it is widely used both as food and medicine in China. To explore the influence of licorice origin on the quality of fried licorice, a method based on fingerprinting and chemometrics was developed. Twenty batches of licorice were selected from four locations. The reference chromatograms of each location were established via similarity analysis. Chemometric methods, such as cluster, principal component, and orthogonal partial least squares discriminant analyses were used to evaluate the changes in the composition of fried licorice, predict its origin, and reflect its quality. Mass spectrometry was used to identify the chemical components. Finally, an origin prediction function was established via discriminant analysis to trace the origin of licorice. The model was demonstrated to be stable, reliable, and accurate in predicting licorice origin and to provide a reference for origin traceability.

The impacts of brewing in glass tumblers and thermos vacuum mugs on the aromas of green tea (Camellia sinensis)

This study investigated the effect of brewing apparatus on the aromatic feature of tea infusion. Huangshan Maofeng tea infusion was brewed under glass tumblers (GT) or thermos vacuum mugs (TVM) for up to 180 min. Tea infusion sensory attributes were evaluated using quantitative descriptive analysis and the composition of volatiles were analyzed using headspace solid phase microextraction coupled with gas chromatography-mass spectrometry. Results showed that GT tea infusion at each brewing duration possessed stronger 'Pure', 'Fresh' and 'Grassy' attributes than TVM tea infusion, whereas TVM tea infusion showed a higher intensity on 'Steamed' aroma. A total of 74 volatiles were detected in tea infusion, and aldehydes and alcohols appeared to be the major volatiles. Total aldehydes concentration percentage decreased in tea infusion with brewing process, whereas an increase on total alcohol percentage was found. Principal component analysis indicated that brewing duration and apparatus played vital roles in altering the volatile composition in tea infusion, whereas orthogonal partial least squares discriminant analysis (OPLS-DA) revealed that GT tea infusion samples were separated from TVM tea infusion samples. OPLS-DA also screened 20 volatiles that significantly contributed to the differentiation of GT and TVM tea infusion.