Orthogonal Projections To Latent Structures Discriminant Analysis Model Research Articles

Spectroscopic optical coherence tomography at 1200nm for lipid detection.

Spectroscopic analysis of optical coherence tomography (OCT) data can yield added information about the sample's chemical composition, along with high-resolution images. Typical commercial OCT systems operate at wavelengths that may not be optimal for identifying lipid-containing samples based on absorption features. The main aim of this study was to develop a 1200nm spectroscopic OCT (SOCT) for the classification of lipid-based and water-based samples by extracting the lipid absorption peak at 1210nm from the OCT data. We developed a 1200nm OCT system and implemented a signal processing algorithm that simultaneously retrieves spectroscopic and structural information from the sample. In this study, we validated the performance of our OCT system by imaging weakly scattering phantoms with and without lipid absorption features. An orthogonal projections to latent structures-discriminant analysis (OPLS-DA) model was developed and applied to classify weakly scattering samples based on their absorption features. The OCT system achieved an axial resolution of and a sensitivity of 95dB. The calibrated OPLS-DA model on weakly scattering samples with lipid and water-based absorption features correctly classified 19/20 validation samples. The 1200nm SOCT system can discriminate the lipid-containing weakly scattering samples from water-based weakly scattering samples with good predictive ability.

Multivariate regression modelling for gender prediction using volatile organic compounds from hand odor profiles via HS-SPME-GC-MS.

The efficacy of using human volatile organic compounds (VOCs) as a form of forensic evidence has been well demonstrated with canines for crime scene response, suspect identification, and location checking. Although the use of human scent evidence in the field is well established, the laboratory evaluation of human VOC profiles has been limited. This study used Headspace-Solid Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS) to analyze human hand odor samples collected from 60 individuals (30 Females and 30 Males). The human volatiles collected from the palm surfaces of each subject were interpreted for classification and prediction of gender. The volatile organic compound (VOC) signatures from subjects' hand odor profiles were evaluated with supervised dimensional reduction techniques: Partial Least Squares-Discriminant Analysis (PLS-DA), Orthogonal-Projections to Latent Structures Discriminant Analysis (OPLS-DA), and Linear Discriminant Analysis (LDA). The PLS-DA 2D model demonstrated clustering amongst male and female subjects. The addition of a third component to the PLS-DA model revealed clustering and minimal separation of male and female subjects in the 3D PLS-DA model. The OPLS-DA model displayed discrimination and clustering amongst gender groups with leave one out cross validation (LOOCV) and 95% confidence regions surrounding clustered groups without overlap. The LDA had a 96.67% accuracy rate for female and male subjects. The culminating knowledge establishes a working model for the prediction of donor class characteristics using human scent hand odor profiles.

Global metabolomics study on the pathogenesis of pediatric medulloblastoma via UPLC- Q/E-MS/MS.

Medulloblastoma is one of the most frequent malignant brain tumors in infancy and childhood. Early diagnosis and treatment are quite crucial for the prognosis. However, the pathogenesis of medulloblastoma is still not completely clarified. High-resolution mass spectrometry has enabled a comprehensive investigation on the mechanism of disease from the perspective of metabolism. Herein, we compared the difference of metabolic profiles of serum between medulloblastoma (n = 33) and healthy control (HC, n = 16) by using UPLC-Q/E-MS/MS. Principal component analysis and orthogonal projections to latent structures discriminant analysis (OPLS-DA) intuitively revealed the significantly distinct metabolic profiles between medulloblastoma and HC (p < 0.01 for permutation test on OPLS-DA model). Total of 25 significantly changed metabolites were identified. ROC analysis reported that six of them (Phosphatidic acid (8:0/15:0), 3'-Sialyllactose, Isocoproporphyrin, Acetylspermidine, Fructoseglycine and 3-Hydroxydodecanedioate) showed high specificity and precision to be potential diagnosis biomarkers (AUC > 0.98). Functional analysis discovered that there are four pathways notably perturbed for medulloblastoma. These pathways are related with the dysfunction of arachidonic acid metabolism, steroid hormone biosynthesis, and folate-related metabolism. The target intervention on these pathways may reduce the mortality of medulloblastoma.

Comprehensive metabolomics study on the pathogenesis of anaplastic astrocytoma via UPLC-Q/TOF-MS.

Anaplastic astrocytoma (AA) is a malignant carcinoma whose pathogenesis remains to be fully elucidated. System biology techniques have been widely used to clarify the mechanism of diseases from a systematic perspective.The present study aimed to explore the pathogenesis and novel potential biomarkers for the diagnosis of AA according to metabolic differences. Patients with AA (n = 12) and healthy controls (n = 15) were recruited. Serum was assayed with untargeted ultraperformance liquid chromatography-quadrupole/time-of-flight-mass spectrometry (UPLC-Q/TOF-MS) metabolomic techniques. The data were further evaluated using multivariate analysis and bioinformatic methods based on the KEGG database to determine the distinct metabolites and perturbed pathways.Principal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) identified the significance of the distinct metabolic pattern between patients with AA and healthy controls (P < .001) in both ESI modes. Permutation testing confirmed the validity of the OPLS-DA model (permutation = 200, Q2 < 0.5). In total, 24 differentiated metabolites and 5 metabolic pathways, including sphingolipid, glycerophospholipid, caffeine, linoleic acid, and porphyrin metabolism, were identified based on the OPLS-DA model. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide were recognized as potential biomarkers with excellent sensitivity and specificity (area under the curve > 98%).These findings indicate that the perturbed metabolic pattern related to immune regulation and cellular signal transduction is associated with the pathogenesis of AA. 3-Methylxanthine, sphinganine, LysoPC(18:1), and lactosylceramide could be used as biomarkers of AA in future clinical practice. This study provides a therapeutic basis for further studies on the mechanism and precise clinical diagnosis of AA.

Assessment of periportal fibrosis in Schistosomiasis mansoni patients by proton nuclear magnetic resonance-based metabonomics models.

BACKGROUNDThe evaluation of periportal fibrosis (PPF) is essential for a prognostic assessment of patients with Schistosomiasis mansoni. The WHO Niamey Protocol defines patterns of fibrosis from abdominal ultrasonography, 1H-nuclear magnetic resonance (NMR)-based metabonomics has been employed to assess liver fibrosis in some diseases.AIMTo build 1H-NMR-based metabonomics models (MM) to discriminate mild from significant periportal PPF and identify differences in the metabolite profiles.METHODSA prospective cross-sectional study was performed on schistosomiasis patients at a University Hospital in Northeastern Brazil. We evaluated 41 serum samples from 10 patients with mild PPF (C Niamey pattern) and 31 patients with significant PPF (D/E/F Niamey patterns). MM were built using partial least squares-discriminant analysis (PLS-DA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) formalisms.RESULTSPLS-DA and OPLS-DA resulted in discrimination between mild and significant PPF groups with R2 and Q2 values of 0.80 and 0.38 and 0.72 and 0.42 for each model, respectively. The OPLS-DA model presented accuracy, sensitivity, and specificity values of 92.7%, 90.3%, and 100% to discriminate significant PPF. The metabolites identified as responsible by discrimination were: N-acetylglucosamines, alanine, glycolaldehyde, carbohydrates, and valine.CONCLUSIONMMs discriminated mild from significant PPF patterns in patients with Schistosomiasis mansoni through identification of differences in serum metabolites profiles.

Prediction of Low-Dose Aspirin-Induced Gastric Toxicity Using Nuclear Magnetic Resonance Spectroscopy-Based Pharmacometabolomics in Rats.

Background: Low-dose aspirin (LDA) is the backbone for secondary prevention of coronary artery disease, although limited by gastric toxicity. This study aimed to identify novel metabolites that could predict LDA-induced gastric toxicity using pharmacometabolomics. Methods: Pre-dosed urine samples were collected from male Sprague-Dawley rats. The rats were treated with either LDA (10 mg/kg) or 1% methylcellulose (10 mL/kg) per oral for 28 days. The rats’ stomachs were examined for gastric toxicity using a stereomicroscope. The urine samples were analyzed using a proton nuclear magnetic resonance spectroscopy. Metabolites were systematically identified by exploring established databases and multivariate analyses to determine the spectral pattern of metabolites related to LDA-induced gastric toxicity. Results: Treatment with LDA resulted in gastric toxicity in 20/32 rats (62.5%). The orthogonal projections to latent structures discriminant analysis (OPLS-DA) model displayed a goodness-of-fit (R2Y) value of 0.947, suggesting near-perfect reproducibility and a goodness-of-prediction (Q2Y) of −0.185 with perfect sensitivity, specificity and accuracy (100%). Furthermore, the area under the receiver operating characteristic (AUROC) displayed was 1. The final OPLS-DA model had an R2Y value of 0.726 and Q2Y of 0.142 with sensitivity (100%), specificity (95.0%) and accuracy (96.9%). Citrate, hippurate, methylamine, trimethylamine N-oxide and alpha-keto-glutarate were identified as the possible metabolites implicated in the LDA-induced gastric toxicity. Conclusion: The study identified metabolic signatures that correlated with the development of a low-dose Aspirin-induced gastric toxicity in rats. This pharmacometabolomic approach could further be validated to predict LDA-induced gastric toxicity in patients with coronary artery disease.

P060 Unravelling the microbial-derived metabolic alterations in a T-bet-/- x RAG2-/- mouse colitis model

Abstract Background T-bet (Tbx21) is a T-box transcription factor that can regulate immune cells’ differentiation and function. Loss of T-bet leads to the absence of adaptive immunity. T-bet-/- x RAG2-/- mice develop spontaneous ulcerative colitis (TRUC) that resembles human colitis, thus they are widely used as a colitis model in recent years. While many researchers studied the host and gut microbiota interaction in immune dysregulation, how the host and bacteria metabolites regulate the immune response is less understood. Methods TRUC and Rag2-/- (as a control group) mice models were employed. When TRUC mice developed anorectal prolapse and rectal inflammation, mice colon tissue and faeces were collected for gene expression analysis and metabolic profiling, respectively. Metabolites were measured by Nuclear Magnetic Resonance and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) was performed to determine differential metabolites. Metabolite enrichment analysis was done using MetaboAnalyst. RNA-seq was employed for colon gene expression. Differential genes were generated with DESeq2 package and enrichment analysis was conducted with clusterProfiler package. Results Principle component analysis of the metabolic profile showed a clear separation between Rag2-/- and TRUC group (Fig 1A). The OPLS-DA model between the two groups is robust (R2 = 0.55, Q2 = 0.89, p-value = 0.03). 12 metabolites were significantly changed. Alanine, butyrate, beta-glucose, glycerol, lysine, pyruvate, gamma-aminobutyrate, dimethylamine, galactose, hypoxanthine and aspartate were significantly increased in TRUC mice compared to Rag2-/- mice, while tyramine was decreased (Fig 1B&amp;C). Metabolite enrichment analysis showed that glycolysis, glucose-alanine cycle, gluconeogenesis, etc. were among the most enriched pathways (Fig 1D). KEGG module over-representation analysis also identified these glucose-related metabolic pathways as top enriched pathways with many genes increased in TRUC mice (Fig 2). In addition to metabolic pathway perturbance, gene set enrichment analysis suggested that cytokine production and regulation were notably altered in TRUC mice, as well as the response to molecules of bacterial origin (Fig 3). Conclusion TRUC mice with ulcerative colitis have significantly affected gut-host metabolism. The up-regulation of glucose-related metabolic pathways suggested increased energy consumption. Increase of butyrate, dimethylamine and enriched genes in response to bacteria implied the host-bacteria interaction process. Further investigation of gut microbiota alteration and integration of bacteria-metabolites-host immune response axis is warranted to determine whether targeting certain pathways can be harnessed as a novel therapy to colitis.

Metabolite Fingerprinting Using 1H-NMR Spectroscopy and Chemometrics for Classification of Three Curcuma Species from Different Origins.

Curcuma longa, Curcuma xanthorrhiza, and Curcuma manga have been widely used for herbal or traditional medicine purposes. It was reported that turmeric plants provided several biological activities such as antioxidant, anti-inflammatory, hepatoprotector, cardioprotector, and anticancer activities. Authentication of the Curcuma species is important to ensure its authenticity and to avoid adulteration practices. Plants from different origins will have different metabolite compositions because metabolites are affected by soil nutrition, climate, temperature, and humidity. 1H-NMR spectroscopy, principal component analysis (PCA), and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) were used for authentication of C. longa, C. xanthorrhiza, and C. manga from seven different origins in Indonesia. From the 1H-NMR analysis it was obtained that 14 metabolites were responsible for generating classification model such as curcumin, demethoxycurcumin, alanine, methionine, threonine, lysine, alpha-glucose, beta-glucose, sucrose, alpha-fructose, beta-fructose, fumaric acid, tyrosine, and formate. Both PCA and OPLS-DA model demonstrated goodness of fit (R2 value more than 0.8) and good predictivity (Q2 value more than 0.45). All OPLS-DA models were validated by assessing the permutation test results with high value of original R2 and Q2. It can be concluded that metabolite fingerprinting using 1H-NMR spectroscopy and chemometrics provide a powerful tool for authentication of herbal and medicinal plants.

Development of a lipid metabolism-related gene model to predict prognosis in patients with pancreatic cancer.

BACKGROUNDPancreatic cancer is a highly heterogeneous disease, making prognosis prediction challenging. Altered energy metabolism to satisfy uncontrolled proliferation and metastasis has become one of the most important markers of tumors. However, the specific regulatory mechanism and its effect on prognosis have not been fully elucidated.AIMTo construct a prognostic polygene signature of differentially expressed genes (DEGs) related to lipid metabolism.METHODSFirst, 9 tissue samples from patients with pancreatic cancer were collected and divided into a cancer group and a para-cancer group. All patient samples were subjected to metabolomics analysis based on liquid tandem chromatography quadrupole time of flight mass spectrometry. Then, mRNA expression profiles and corresponding clinical data of pancreatic cancer were downloaded from a public database. Least absolute shrinkage and selection operator Cox regression analysis was used to construct a multigene model for The Cancer Genome Atlas.RESULTSPrincipal component analysis and orthogonal projections to latent structures-discriminant analysis (OPLS-DA) based on lipid metabolomics analysis showed a clear distribution in different regions. A Euclidean distance matrix was used to calculate the quantitative value of differential metabolites. The permutation test of the OPLS-DA model for tumor tissue and paracancerous tissue indicated that the established model was consistent with the actual condition based on sample data. A bar plot showed significantly higher levels of the lipid metabolites phosphatidylcholine (PC), phosphatidyl ethanolamine (PE), phosphatidylethanol(PEtOH), phosphatidylmethanol (PMeOH), phosphatidylserine (PS) and diacylglyceryl trimethylhomoserine (DGTS) in tumor tissues than in paracancerous tissues. According to bubble plots, PC, PE, PEtOH, PMeOH, PS and DGTS were significantly higher in tumor tissues than in paracancerous tissues. In total, 12.3% (25/197) of genes related to lipid metabolism were differentially expressed between tumor tissues and adjacent paracancerous tissues. Six DEGs correlated with overall survival in univariate Cox regression analysis (P < 0.05), and a 4-gene signature model was developed to divide patients into two risk groups, with patients in the high-risk group having significantly lower overall survival than those in the low-risk group (P < 0.05). ROC curve analysis confirmed the predictive power of the model.CONCLUSIONThis novel model comprising 4 lipid metabolism-related genes might assist clinicians in the prognostic evaluation of patients with pancreatic cancer.

Geographical Differentiation of Hom Mali Rice Cultivated in Different Regions of Thailand Using FTIR-ATR and NIR Spectroscopy

Although Hom Mali rice is considered the highest quality rice in Thailand, it is susceptible to adulteration and substitution. There is a need for rapid, low-cost and efficient analytical techniques for monitoring the authenticity and geographical origin of Thai Hom Mali rice. In this study, two infrared spectroscopy techniques, Fourier-transform infrared spectroscopy with attenuated total reflection (FTIR-ATR) and near-infrared (NIR) spectroscopy, were applied and compared for the differentiation of Thai Hom Mali rice from two geographical regions over two production years. The Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model, built using spectral data from the benchtop FTIR-ATR, achieved 96.97% and 100% correct classification of the test dataset for each of the production years, respectively. The OPLS-DA model, built using spectral data from the portable handheld NIR, achieved 84.85% and 86.96% correct classification of the test dataset for each of the production years, respectively. Direct NIR analysis of the polished rice grains (i.e., no sample preparation) was determined as reliable for analysis of ground rice samples. FTIR-ATR and NIR spectroscopic analysis both have significant potential as screening tools for the rapid detection of fraud issues related to the geographical origin of Thai Hom Mali rice.

Abstract 2352: Place of birth influences the metabolomic profile and preliminary effectiveness of a weight loss intervention among Hispanic men from the ANIMO pilot study

Abstract Background: The effect of intrinsic and extrinsic factors (e.g. the environment and genetics) on the metabolome may have implications for disease risk, health disparities, and effectiveness of clinical interventions. Purpose: This study investigated whether plasma metabolomic profiles differ between U.S. born and foreign-born Hispanics participating in a weight loss intervention. Secondarily, we sought to examine if country of origin had an effect on the intervention effectiveness. Methods: Participants were overweight and obese Hispanic men (N=42) aged 18 to 64 years enrolled in a 24-week weight loss intervention. Metabolomic profiles were acquired from plasma using ultra performance liquid chromatography-mass spectroscopy (UPLC-MS) at baseline, 12 and 24 weeks. Models were visualized using supervised orthogonal projections to latent structures-discriminant analysis (OPLS-DA); R2X cut points of 0.75, 0.50, and 0.25 were used to describe substantial, moderate, or weak levels of predictive accuracy, respectively. Score plots (S-plots) were utilized to select metabolites that influenced the OPLS-DA model. Progenesis QI was used for peak integration and metabolite identification in addition to the Human Metabolite Data Base (HMDB). The effect of place of birth on weight loss was examined using multiple linear regression. Results: Foreign-born participants were older (P = 0.0086), had less education (P &amp;lt; 0.001), were mostly Spanish-preferred speakers (P &amp;lt; 0.001), and had less acculturation (P &amp;lt; 0.001) compared to U.S. born participants. The metabolome between U.S. born vs foreign-born were moderately (R2X =0.65), weakly (R2X= 0.33), and substantially (R2X =0.79) different at baseline, 12 weeks, and 24 weeks. Endogenous metabolites such as alpha-keto and amino acids, phospholipids, fatty acids, as well as some dietary compounds were characteristic of the foreign-born participants, but not the U.S. born, at baseline. Post-intervention, metabolites from dietary sources (cereals, herbs and spices, mushrooms, chives, onions, and beer), and lipids (diacylglycerols, cholesteryl and glycerolipids) where characteristic of the foreign-born group, whereas saponins and metabolites from green vegetables, avocados, alcoholic beverages, coffee, and green tea were characteristic of the U.S. born group. Models comparing study arm or weight loss status were weak. At 24 weeks, U.S. born participants had more weight loss than foreign born (P = 0.023). Conclusion: Plasma metabolomic profiles from Hispanic men differ by country of origin. Analyses to determine whether the greater intervention benefit among U.S. born Hispanic males can be explained by their baseline metabolome are pending. Future studies should determine factors that influence differences in metabolomic profiles in order to tailor interventions. Citation Format: Melissa Lopez-Pentecost, David O. Garcia, Xiaoxiao Sun, Cynthia A. Thomson, H-H. Sherry Chow, Wade M. Chew, Jessica A. Martinez. Place of birth influences the metabolomic profile and preliminary effectiveness of a weight loss intervention among Hispanic men from the ANIMO pilot study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2352.

A Novel Dried Blood Spot Detection Strategy for Characterizing Cardiovascular Diseases.

Cardiovascular diseases (CVDs) are the leading cause of death in China. Conventional diagnostic methods are dependent on advanced instruments, which are expensive, inaccessible, and inconvenient in underdeveloped areas. To build a novel dried blood spot (DBS) detection strategy for imaging CVDs, in this study, a total of 12 compounds, including seven amino acids [homocysteine (Hcy), isoleucine (Ile), leucine (Leu), valine (Val), phenylalanine (Phe), tyrosine (Tyr), and tryptophan (Trp)], three amino acid derivatives [choline, betaine, and trimethylamine N-oxide (TMAO)], L-carnitine, and creatinine, were screened for their ability to identify CVD. A rapid and reliable method was established for the quantitative analysis of the 12 compounds in DBS. A total of 526 CVD patients and 200 healthy volunteers in five provinces of China were recruited and divided into the following groups: stroke, coronary heart disease, diabetes, and high blood pressure. The orthogonal projection to latent structures-discriminant analysis (OPLSDA) model was used to characterize the difference between each CVD group. Marked differences between the groups based on the OPLSDA model were observed. Based on the model, the patients in the three training sets were mostly accurately categorized into the appropriate group. In addition, the receiver operating characteristic (ROC) curves and logistic regression of each metabolite chosen by the OPLSDA model had an excellent predictive value in both the test and validation groups. DBS detection of 12 biomarkers was sensitive and powerful for characterizing different types of CVD. Such differentiation may reduce unnecessary invasive coronary angiography, enhance predictive value, and complement current diagnostic methods.

Chinese medicine Bazi Bushen capsule improves lipid metabolism in ovariectomized female ApoE-/- mice.

Dyslipidemia is one of the mechanisms of atherosclerosis (AS). Depletion of estrogen plays a key role in the pathogenesis of postmenopausal AS in women, and the blood lipid levels of women are closely related to endogenous estrogen levels. Phytoestrogens (PEs) exert estrogenic effects, including protection against AS, without the adverse effects of estrogen administration. Bazi Bushen capsule (BZBS) is a traditional Chinese medicine herbal compound prescription that has been shown to contain 11 unique PEs. In the present study, we assessed the effects of BZBS against lipid metabolism disorders. All ApoE-/- mice underwent ovary ligation and bilateral ovariectomy (Ovx) to induce surgical menopause (Ovx/ApoE-/- mice), whereas the C57BL/6J mice underwent sham surgery (needle threading). Ovx/ApoE-/- mice were given a high-fat diet without estrogen and C57BL/6J mice were given a normal diet for 12 weeks. Ovx/ApoE-/- mice treated with G1, a highly selective G-protein-coupled estrogen receptor1 (GPER1) agonist with proven activity against AS, were used as positive controls. Estrogen levels were measured and uterine atrophy index was calculated to determine the success of the model. Serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) were measured in each group. The orthogonal projections to latent structures discriminant analysis (OPLS-DA) model was used to separate the groups, MetaboAnalyst was then used to analyze the metabolic pathway, and the most representative metabolites were finally identified. Removal of bilateral ovaries resulted in successful surgical menopause models, where BZBS increased estrogen levels but did not increase the risk of uterine proliferation. BZBS attenuated dyslipidemia, including decreased TG, TC, and LDL-C levels, but increased HDL-C levels. The OPLS-DA model successfully distinguished the groups with good predictive ability and revealed their tendency to separate from each other. MetaboAnalyst suggested that both the G1 group and high-dose BZBS (HD-BZ) could improve the effect of lipid metabolism: the glycerophospholipid metabolism pathway was mainly improved by the G1 group, while the inositol phosphate metabolism pathway was mainly improved by the HD-BZ group. For the four compounds with the highest content, the concentrations of docosahexaenoic acid (DHA), 3-hydroxybutyric acid, and 5(Z), 8(Z), 11(Z)-eicosatrienoic acid were dramatically lower in the model group compared to the control group. Lysophosphatidylethanolamine (18:0) was higher in the model group than in the control group. BZBS corrected these effects. BZBS treatment reduced serum lipid levels and improved fatty acid metabolism in high-fat diet-fed, surgically induced menopausal ApoE-/- mice.

How do cancer-sniffing dogs sort biological samples? Exploring case-control samples with non-targeted LC-Orbitrap, GC-MS, and immunochemistry methods

Early identification of disease onset is regarded as an important factor for successful medical intervention. However, cancer and other long-term latency diseases are rare and may take years to manifest clinically. As such, there are no gold standards with which to immediately validate proposed preclinical screening methodologies. There is evidence that dogs can sort samples reproducibly into yes/no categories based on case-control training, but the basis of their decisions is unknown. Because dogs are sniffing air, the distinguishing chemicals must be either in the gas-phase or attached to aerosols and/or airborne particles. Recent biomonitoring research has shown how to extract and analyze semi- and non-volatile compounds from human breath in exhaled condensates and aerosols. Further research has shown that exhaled aerosols can be directly collected on standard hospital-style olefin polypropylene masks and that these masks can be used as a simple sampling scheme for canine screening. In this article, detailed liquid chromatography-high resolution mass spectrometry (LC-HR-MS) with Orbitrap instrumentation and gas chromatography-mass spectrometry (GC-MS) analyses were performed on two sets of masks sorted by consensus of a four-dog cohort as either cancer or control. Specifically, after sorting by the dogs, sample masks were cut into multiple sections and extracted for LC-MS and GC-MS non-targeted analyses. Extracts were also analyzed for human cytokines, confirming the presence of human aerosol content above levels in blank masks. In preliminary evaluations, 345 and 44 high quality chemical features were detected by LC-MS and GC-MS analyses, respectively. These features were used to develop provisional orthogonal projection to latent structures-discriminant analysis (OPLS-DA) models to determine if the samples classified as cancer (case) or non-cancer (control) by the dogs could be separated into the same groups using analytical instrumentation. While the OPLS-DA model for the LC-HR-MS data was able to separate the two groups with statistical significance, although weak explanatory power, the GC-MS model was not found to be significant. These results suggest that the dogs may rely on the less volatile compounds from breath aerosol that were analyzed by LC-HR-MS than the more volatile compounds observed by GC-MS to sort mask samples into groups. These results provide justification for more expansive studies in the future that aim to characterize specific chemical features, and the role(s) of these features in maintaining homeostatic biological processes.

Profiling of Small Molecular Metabolites in Nostoc flagelliforme during Periodic Desiccation.

The mass spectrometry-based metabolomics approach has become a powerful tool for the quantitative analysis of small-molecule metabolites in biological samples. Nostoc flagelliforme, an edible cyanobacterium with herbal value, serves as an unexploited bioresource for small molecules. In natural environments, N. flagelliforme undergoes repeated cycles of rehydration and dehydration, which are interrupted by either long- or short-term dormancy. In this study, we performed an untargeted metabolite profiling of N. flagelliforme samples at three physiological states: Dormant (S1), physiologically fully recovered after rehydration (S2), and physiologically partially inhibited following dehydration (S3). Significant metabolome differences were identified based on the OPLS-DA (orthogonal projections to latent structures discriminant analysis) model. In total, 183 differential metabolites (95 up-regulated; 88 down-regulated) were found during the rehydration process (S2 vs. S1), and 130 (seven up-regulated; 123 down-regulated) during the dehydration process (S3 vs. S2). Thus, it seemed that the metabolites’ biosynthesis mainly took place in the rehydration process while the degradation or possible conversion occurred in the dehydration process. In addition, lipid profile differences were particularly prominent, implying profound membrane phase changes during the rehydration–dehydration cycle. In general, this study expands our understanding of the metabolite dynamics in N. flagelliforme and provides biotechnological clues for achieving the efficient production of those metabolites with medical potential.

Chemometric Analysis of the Volatile Compounds Generated by Aspergillus carbonarius Strains Isolated from Grapes and Dried Vine Fruits.

Ochratoxin A (OTA) contamination in grape production is an important problem worldwide. Microbial volatile organic compounds (MVOCs) have been demonstrated as useful tools to identify different toxigenic strains. In this study, Aspergillus carbonarius strains were classified into two groups, moderate toxigenic strains (MT) and high toxigenic strains (HT), according to OTA-forming ability. The MVOCs were analyzed by GC-MS and the data processing was based on untargeted profiling using XCMS Online software. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) was performed using extract ion chromatogram GC-MS datasets. For contrast, quantitative analysis was also performed. Results demonstrated that the performance of the OPLS-DA model of untargeted profiling was better than the quantitative method. Potential markers were successfully discovered by variable importance on projection (VIP) and t-test. (E)-2-octen-1-ol, octanal, 1-octen-3-one, styrene, limonene, methyl-2-phenylacetate and 3 unknown compounds were selected as potential markers for the MT group. Cuparene, (Z)-thujopsene, methyl octanoate and 1 unknown compound were identified as potential markers for the HT groups. Finally, the selected markers were used to construct a supported vector machine classification (SVM-C) model to check classification ability. The models showed good performance with the accuracy of cross-validation and test prediction of 87.93% and 92.00%, respectively.

Metabolomic profiling of matured coconut water during post-harvest storage revealed discrimination and distinct changes in metabolites.

The metabolites of coconut water stored at room temperature were analyzed using UPLC-MS/MS and multivariate statistical analysis to identify the differential biomarkers and metabolic pathways during post-harvest storage. Principal component analysis (PCA), partial least squares-discriminate analysis (PLS-DA), and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were employed to analyze the UPLC-MS/MS data set of 34 matured coconut water samples collected after 0, 1, 2, 3, 4, and 5 months of storage (MOS); moreover, the p-value and fold change were chosen to identify the differential biomarkers; furthermore, a KEGG pathway was applied to analyze the metabolic pathways. All samples were discriminated well in the OPLS-DA model and were divided into two clusters: groups A (0 MOS, and so on), B, C, and D were in one cluster, and groups E and F were in another. A total of 18 biomarkers were identified among all groups and 12 biomarkers between groups A and E, from which we concluded that the post-harvest storage life of matured coconut water shall not exceed 3 months and the pathways of the TCA cycle, protein hydrolysis from coconut meat, and interconversion among amino acids were mainly enriched during the post-harvest storage.

Chemometric evaluation of the efficacy of locally administered chlorhexidine in patients with periodontal disease

The process of assessment of drug efficacy produces multivariate data which are difficult to interpret. The interpretation and extraction of relevant data requires application of chemometric algorithms for multivariate data analysis. The aim of our study was evaluation of the efficacy of local treatment with chlorhexidine (CHX) in patients suffering from periodontal disease by chemometric algorithms for multivariate data analysis. Several algorithms were used: principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal projection to latent structures discriminant analysis (OPLS-DA). The PCA models identified the examined variables as suitable for monitoring the periodontal disease progression at the same time revealing mutual relationship among them. The developed PLS-DA model successfully distinguished patients treated with CHX from non-treated patients. The OPLS-DA model revealed differences in the mechanism of action of the two widely applied treatments in periodontal disease, local administration of CHX and local administration of doxycycline (DOX). The approach presented in this study opens the possibility of application of chemometric algorithms for multivariate data analysis for assessment of treatment efficacy.

PLS-Based and Regularization-Based Methods for the Selection of Relevant Variables in Non-targeted Metabolomics Data

Non-targeted metabolomics constitutes a part of the systems biology and aims at determining numerous metabolites in complex biological samples. Datasets obtained in the non-targeted metabolomics studies are high-dimensional due to sensitivity of mass spectrometry-based detection methods as well as complexity of biological matrices. Therefore, a proper selection of variables which contribute into group classification is a crucial step, especially in metabolomics studies which are focused on searching for disease biomarker candidates. In the present study, three different statistical approaches were tested using two metabolomics datasets (RH and PH study). The orthogonal projections to latent structures-discriminant analysis (OPLS-DA) without and with multiple testing correction as well as the least absolute shrinkage and selection operator (LASSO) with bootstrapping, were tested and compared. For the RH study, OPLS-DA model built without multiple testing correction selected 46 and 218 variables based on the VIP criteria using Pareto and UV scaling, respectively. For the PH study, 217 and 320 variables were selected based on the VIP criteria using Pareto and UV scaling, respectively. In the RH study, OPLS-DA model built after correcting for multiple testing, selected 4 and 19 variables as in terms of Pareto and UV scaling, respectively. For the PH study, 14 and 18 variables were selected based on the VIP criteria in terms of Pareto and UV scaling, respectively. In the RH and PH study, the LASSO selected 14 and 4 variables with reproducibility between 99.3 and 100%, respectively. In the light of PLS-based models, the larger the search space the higher the probability of developing models that fit the training data well with simultaneous poor predictive performance on the validation set. The LASSO offers potential improvements over standard linear regression due to the presence of the constrain, which promotes sparse solutions. This paper is the first one to date utilizing the LASSO penalized logistic regression in untargeted metabolomics studies.

PCA as a practical indicator of OPLS-DA model reliability.

Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) are powerful statistical modeling tools that provide insights into separations between experimental groups based on high-dimensional spectral measurements from NMR, MS or other analytical instrumentation. However, when used without validation, these tools may lead investigators to statistically unreliable conclusions. This danger is especially real for Partial Least Squares (PLS) and OPLS, which aggressively force separations between experimental groups. As a result, OPLS-DA is often used as an alternative method when PCA fails to expose group separation, but this practice is highly dangerous. Without rigorous validation, OPLS-DA can easily yield statistically unreliable group separation. A Monte Carlo analysis of PCA group separations and OPLS-DA cross-validation metrics was performed on NMR datasets with statistically significant separations in scores-space. A linearly increasing amount of Gaussian noise was added to each data matrix followed by the construction and validation of PCA and OPLS-DA models. With increasing added noise, the PCA scores-space distance between groups rapidly decreased and the OPLS-DA cross-validation statistics simultaneously deteriorated. A decrease in correlation between the estimated loadings (added noise) and the true (original) loadings was also observed. While the validity of the OPLS-DA model diminished with increasing added noise, the group separation in scores-space remained basically unaffected. Supported by the results of Monte Carlo analyses of PCA group separations and OPLS-DA cross-validation metrics, we provide practical guidelines and cross-validatory recommendations for reliable inference from PCA and OPLS-DA models.