Multivariate Pattern Recognition Analysis Research Articles

Integrating untargeted and targeted LC-MS-based metabolomics to identify the serum metabolite biomarkers for tuberculosis.

Given the limitations of untargeted metabolomics in precise metabolite quantification, our current research employed a novel approach by integrating untargeted and targeted metabolomics utilizing ultra-high-performance liquid chromatography quadrupole time-of-flight tandem mass spectrometry (UHPLC-QTOF-MS/MS) to analyze the metabolic profile and potential biomarkers for tuberculosis (TB). A cohort of 36 TB patients and 36 healthy controls (HC) was enlisted to obtain serum samples. Multivariate pattern recognition and univariate statistical analysis were employed to screen and elucidate the differential metabolites, whereas dot plots and receiver operating characteristic (ROC) curves were established for the identification of potential biomarkers of TB. The results indicated a distinct differentiation between the two groups, identifying 99 metabolites associated with five primary metabolic pathways in relation to TB. Of these, 19 metabolites exhibited high levels of sensitivity and specificity, as evidenced by the area under curve values approaching 1. Following targeted quantitative analysis, three potential metabolites, namely, L-asparagine, L-glutamic acid, and arachidonic acid, were demonstrated excellent discriminatory ability as evidenced by the results of the ROC curve, dot plots, and random forest model. Particularly noteworthy was the enhanced diagnostic efficacy of the combination of these three metabolites compared to singular biomarkers, suggesting their potential utility as serum biomarkers for TB diagnosis.

Multiplatform Metabolomics to Understand the Imidacloprid-Induced Toxicity in Drosophila.

Neonicotinoids, the class of insecticides used for crop protection, are subjected to vigilance due to their pernicious impacts. Imidacloprid (IMD) is one of the most representative insecticides of the neonicotinoid family, which has shown unfriendly consequences for non-target species. Metabolomics, a multidisciplinary approach, is being used in toxicological research to understand the metabolic responses to toxicant exposure by utilizing modern analytical techniques. Yet, no solitary analytical technique can cover the broad metabolite spectrum, but a multi-technique metabolomics platform can aid in analyzing the majority of the metabolites. In the present study, an effort has been made to identify the differential metabolites in Drosophila after exposure to IMD at 2.5 and 25 ng/mL using liquid chromatography-high-resolution mass spectrometry (LC-HRMS), gas chromatography-MS (GC-MS), and NMR-based untargeted metabolomics. Multivariate pattern recognition analysis helped in identifying/recognizing 19 (LC-HRMS), 7 (GC-MS), and 13 (NMR) differential metabolites mainly belonging to the category of amino acids, sugars, fatty acids, and organic acids. The pathway analysis of differential metabolites predominantly showed impact on aminoacyl-tRNA biosynthesis, amino acid metabolism, and glycerophospholipid metabolism. Among these, arginine and proline metabolism was observed to be the common metabolic pathway perturbed in Drosophila due to IMD exposure. The multiplatform metabolomics based on LC-HRMS, GC-MS, and NMR analysis with an advanced level of statistical analysis can provide insights into potential perturbations in the metabolome of IMD-exposed Drosophila.

Machine learning-assisted fluorescence sensor array for qualitative and quantitative analysis of pyrethroid pesticides

The simultaneous detection of multiple residues of pyrethroid pesticides (PPs) on vegetables and fruits is still challenging using traditional nanosensing methods due to the high structural similarity of PPs. In this work, sensor arrays composed of three nanocomposite complexes (rhodamine B-CD@Au, rhodamine 6G-CD@Au, and coumarin 6-CD@Au) were constructed to discriminate between structurally similar PPs. Four PPs, deltamethrin, fenvalerate, cyfluthrin, and fenpropathrin, were successfully discriminated. The ability of these sensor units was derived from the different affinity between receptor/analyte and receptor/dye, as well as the non-linear relationship between fluorescence signal and analyte concentration. Upon multivariate pattern recognition analysis, the array performed high-throughput identification of four PPs in unknown samples with 100% classification accuracy. In addition, good accuracy of predicting concentration using the “stepwise prediction” strategy combined with the machine learning method was achieved.

Emotion regulation in bipolar disorder type-I: multivariate analysis of fMRI data

BackgroundBipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD patients showed increased activations in inferior frontal gyrus when instructed to decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data to examine if we could classify conditions within-group as well as HC versus BD.MethodsWe reanalyzed explicit emotion regulation data using a multivariate pattern recognition approach, as implemented in PRONTO software. The original experimental paradigm consisted of a full 2 × 2 factorial design, with valence (Negative/Neutral) and instruction (Look/Decrease) as within subject factors.ResultsThe multivariate models were able to accurately classify different task conditions when HC and BD were analyzed separately (63.24%–75.00%, p = 0.001–0.012). In addition, the models were able to correctly classify HC versus BD with significant accuracy in conditions where subjects were instructed to downregulate their felt emotion (59.60%–60.84%, p = 0.014–0.018). The results for HC versus BD classification demonstrated contributions from the salience network, several occipital and frontal regions, inferior parietal lobes, as well as other cortical regions, to achieve above-chance classifications.ConclusionsOur multivariate analysis successfully reproduced some of the main results obtained in the previous univariate analysis, confirming that these findings are not dependent on the analysis approach. In particular, both types of analyses suggest that there is a significant difference of neural patterns between conditions within each subject group. The multivariate approach also revealed that reappraisal conditions provide the most informative activity for differentiating HC versus BD, irrespective of emotional valence (negative or neutral). The current results illustrate the importance of investigating the cognitive control of emotion in BD. We also propose a set of candidate regions for further study of emotional control in BD.

Authentication of the production season of Xinyang Maojian green tea using two-dimensional fingerprints coupled with chemometric multivariate calibration and pattern recognition analysis

This study aimed to authenticate the production season of Xinyang Maojian green tea, and screen and identify its characteristic components through non-targeted metabonomics methods based on two-dimensional fingerprints combined with chemometric analysis. Firstly, two-dimensional fingerprints of spring and autumn teas were obtained through HPLC-DAD analysis to form a three-dimensional array (retention time × absorption wavelength × sample). Subsequently, the multiple co-elution peaks and spectral profiles in two-dimensional HPLC-DAD fingerprints were resolved by using alternating trilinear decomposition assisted multivariate curve resolution (ATLD-MCR) algorithm. We obtained the relative concentration matrix C (24 × 122), which was further used to distinguish the production season of Xinyang Maojian green tea through chemometric pattern recognition analysis. The evaluation results of both orthogonal partial least squares-discriminant analysis (OPLS-DA) and partial least squares-discriminant analysis (PLS-DA) models were better than those of PCA models, and could effectively distinguish the production season of Xinyang Maojian green teas. Moreover, 5 variables were selected through VIP method to build new UV-scaling and Par-scaling OPLS-DA models. In conclusion, the following characteristic components were identified in accordance with the analytical standards and published data: gallocatechin (GC), theobromine (THB), epigallocatechin gallate (EGCG), gallocatechin gallate (GCG), and epicatechin gallate (ECG).

Serum Biomarkers for Chronic Renal Failure Screening and Mechanistic Understanding: A Global LC-MS-Based Metabolomics Research.

Chronic kidney disease, including renal failure (RF), is a global public health problem. The clinical diagnosis mainly depends on the change of estimated glomerular filtration rate, which usually lags behind disease progression and likely has limited clinical utility for the early detection of this health problem. Now, we employed Q-Exactive HFX Orbitrap LC-MS/MS based metabolomics to reveal the metabolic profile and potential biomarkers for RF screening. 27 RF patients and 27 healthy controls were included as the testing groups, and comparative analysis of results using different techniques, such as multivariate pattern recognition and univariate statistical analysis, was applied to screen and elucidate the differential metabolites. The dot plots and receiver operating characteristics curves of identified different metabolites were established to discover the potential biomarkers of RF. The results exhibited a clear separation between the two groups, and a total of 216 different metabolites corresponding to 13 metabolic pathways were discovered to be associated with RF; and 44 metabolites showed high levels of sensitivity and specificity under curve values of close to 1, thus might be used as serum biomarkers for RF. In summary, for the first time, our untargeted metabolomics study revealed the distinct metabolic profile of RF, and 44 metabolites with high sensitivity and specificity were discovered, 3 of which have been reported and were consistent with our observations. The other metabolites were first reported by us. Our findings might provide a feasible diagnostic tool for identifying populations at risk for RF through detection of serum metabolites.

Division of Labor among Worker Bees Is Associated with the Lipidomic Plasticity in Their Brains

The division of labor is a dominant characteristic of honeybees and is accompanied by behavioral specialization and cognitive enhancement. As the central nervous system to control the labor-specific behaviors of honeybee, the brain is richest in lipid in terms of both diversity and abundance. In this study, an in-depth LC-MS/MS-based lipidomic method was applied to systematically characterize the brain lipid compositions of worker bees with three labor stages: newly emerged bee (NEB), nurse bee (NB), and forager bee (FB). A total number of 337 lipid species that assigned to 20 lipid classes were analyzed. The association of the brain lipidomes with the division of labors was suggested by the results of both the unsupervised and supervised multivariate pattern recognition analysis. More than 68% of the identified lipid species were found to be significantly changed in at least one comparison between NEB, NB, and FB. A total of 81 lipid species were identified as the potential labor-featured molecules with VIP > 1 and p-adj < 0.05. The labor-featured lipids of FA(18:2), FA(18:3), FA(26:0), PC(18:0_18:3), PS(18:1_18:1), SM(d38:1), CoQ10, and CoQ9, as well as their interactions with 12 behavior-related genes, including AmEST-6, AmFABP, AmE75, AmDGAT2, AmLSD1, AmNPC1, AmABCA1, AmNMDAR1, AmHTT, AmNOS, etc., were revealed by the further IPA analysis. These findings demonstrate for the first time that the brain lipidomes of worker bees are associated with the stable differences in their labors, which help understand the function of brain lipids on the labor-dependent behaviors of honeybees.

Insight into the differences in the toxicity mechanisms of dinotefuran enantiomers in zebrafish by UPLC-Q/TOF-MS.

Dinotefuran is a chiral insecticide widely used to control Nilaparvata lugens in agriculture. However, little is known about the toxic effects of dinotefuran enantiomers on aquatic organisms. In this study, zebrafish were exposed to 1.00 and 10.00mg/L dinotefuran enantiomers for 96h, after which multivariate pattern recognition, metabolite identification, and pathway analysis were performed. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were then conducted to reveal the metabolic perturbations caused by dinotefuran enantiomers. Metabolic pathway analysis revealed the perturbation of five main pathways, including phenylalanine, tyrosine and tryptophan biosynthesis; phenylalanine metabolism; retinol metabolism; arginine and proline metabolism; and glycerophospholipid metabolism. These disturbed metabolic pathways were strongly correlated with energy, amino acid metabolism, and lipid metabolism. Pathway analysis also indicated that the metabolic pathway changes induced by the same level of R and S-dinotefuran were enantioselective. Our research may provide better insight into the risk of chiral dinotefuran in aquatic organisms in the environment.

The Imprinted PARAFILM as a New Carrier Material for Dried Plasma Spots (DPSs) Utilizing Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) in Phospholipidomics.

The application of desorption electrospray ionization mass spectrometry (DESI-MS) and dried blood spot (DBS) sampling has been successfully implemented several times. However, the difficulty of combining DBS sampling with DESI-MS is still the carrier material used for the blood samples. In this study, a new, easily obtained, and cost-effective carrier substrate for dried plasma spot (DPS) sampling and DESI-MS analysis and its application in phospholipidomics studies was described. First, the effects of several carrier materials, including cellulose-based materials (31 ET paper and filter paper) and non-cellulose-based materials (PARAFILM and its shape-modified material, PTFE-printed glass slide and polyvinylidene fluoride film), were tested. Second, a method combining DPS sampling with DESI-MS for phospholipidomics analysis was established, and parameters affecting compound signal intensities, such as sample volume and sprayer solvent system, were optimized. In conclusion, the total signal intensity obtained from shape-modified PARAFILM was the strongest. The suitable plasma sample volume deposited on PARAFILM carriers was 5 μl, and acetonitrile (ACN) was recommended as the optimal spray solvent for phospholipid (PL) profiling. Repeatability (87.5% of compounds with CV < 30%) and stability for data acquisition (48 h) were confirmed. Finally, the developed method was applied in phospholipidomics analysis of schistosomiasis, and a distinguished classification between control mice and infected mice was observed by using multivariate pattern recognition analysis, confirming the practical application of this new carrier material for DPS sampling and DESI-MS analysis. Compared with a previously reported method, the rapid metabolomics screening approach based on the implementation of DPS sampling coupled with the DESI-MS instrument developed in this study has increased analyte sensitivity, which may promote its further application in clinical studies.

Rapid and sensitive screening of multiple polycyclic aromatic hydrocarbons by a reusable fluorescent sensor array

Simple and rapid sensing of polycyclic aromatic hydrocarbons (PAHs) remains a great technical challenge due to their chemical stability and structural similarity. Here, a simple, sensitive and cost-effective sensing strategy is proposed to detect multiple PAHs by utilizing the inner filter effect (IFE) and a reusable fluorescent sensor array consisting of four polyvinyl alcohol (PVA) composite carbon quantum dots (CDs) film sensors. The CDs/PVA films have a wide and tunable excitation range, which provide sufficient spectral overlap with PAHs and ensure the efficient occurrence of IFE. Under different excitations, the diverse UV absorption capacities of PAHs resulted in diverse spectral responses, enabling a unique chemical fingerprint for each PAH. Upon multivariate pattern recognition analysis, the array rendered high-throughput discrimination and sensitive quantification of 16 priority PAHs with 100% classification accuracy and detection limit as low as 57 nM. Moreover, the rapid and accurate screening of multiple environmental samples were also realized with the results consistent with high-performance liquid chromatography. This IFE-based reusable array is readily prepared, green and feasible, which exhibits great potential in environmental analysis and brings an advanced strategy to high-throughput sensing of more pollutants with similar structures and lack of recognition sites.

Support vector machine-based classification of schizophrenia patients and healthy controls using structural magnetic resonance imaging from two independent sites.

Structural brain alterations have been repeatedly reported in schizophrenia; however, the pathophysiology of its alterations remains unclear. Multivariate pattern recognition analysis such as support vector machines can classify patients and healthy controls by detecting subtle and spatially distributed patterns of structural alterations. We aimed to use a support vector machine to distinguish patients with schizophrenia from control participants on the basis of structural magnetic resonance imaging data and delineate the patterns of structural alterations that significantly contributed to the classification performance. We used independent datasets from different sites with different magnetic resonance imaging scanners, protocols and clinical characteristics of the patient group to achieve a more accurate estimate of the classification performance of support vector machines. We developed a support vector machine classifier using the dataset from one site (101 participants) and evaluated the performance of the trained support vector machine using a dataset from the other site (97 participants) and vice versa. We assessed the performance of the trained support vector machines in each support vector machine classifier. Both support vector machine classifiers attained a classification accuracy of >70% with two independent datasets indicating a consistently high performance of support vector machines even when used to classify data from different sites, scanners and different acquisition protocols. The regions contributing to the classification accuracy included the bilateral medial frontal cortex, superior temporal cortex, insula, occipital cortex, cerebellum, and thalamus, which have been reported to be related to the pathogenesis of schizophrenia. These results indicated that the support vector machine could detect subtle structural brain alterations and might aid our understanding of the pathophysiology of these changes in schizophrenia, which could be one of the diagnostic findings of schizophrenia.

Brain structural abnormalities in obesity: relation to age, genetic risk, and common psychiatric disorders

Emerging evidence suggests that obesity impacts brain physiology at multiple levels. Here we aimed to clarify the relationship between obesity and brain structure using structural MRI (n = 6420) and genetic data (n = 3907) from the ENIGMA Major Depressive Disorder (MDD) working group. Obesity (BMI > 30) was significantly associated with cortical and subcortical abnormalities in both mass-univariate and multivariate pattern recognition analyses independent of MDD diagnosis. The most pronounced effects were found for associations between obesity and lower temporo-frontal cortical thickness (maximum Cohen´s d (left fusiform gyrus) = −0.33). The observed regional distribution and effect size of cortical thickness reductions in obesity revealed considerable similarities with corresponding patterns of lower cortical thickness in previously published studies of neuropsychiatric disorders. A higher polygenic risk score for obesity significantly correlated with lower occipital surface area. In addition, a significant age-by-obesity interaction on cortical thickness emerged driven by lower thickness in older participants. Our findings suggest a neurobiological interaction between obesity and brain structure under physiological and pathological brain conditions.

S219. SINGLE-SUBJECT PREDICTION OF FUNCTIONAL OUTCOMES ACROSS DIAGNOSTIC GROUPS USING CLINICAL DATA

BackgroundPsychotic disorders are associated with serious deterioration in functioning even before the first psychotic episode. Also on clinical high risk (CHR) states of developing a first psychotic episode, several studies reported a decreased global functioning. In a considerable proportion of CHR individuals, functional deterioration remains even after (transient) remission of symptomatic risk indicators. Furthermore, deficits in functioning cause immense costs for the health care system and are often more debilitating for individuals than positive symptoms. However in the past, CHR research has mostly focused on clinical outcomes like transition. Prediction of functioning in CHR populations has received less attention. Therefore, the current study aims at predicting functioning in CHR individuals at a single subject level applying multi pattern recognition to clinical data. Patients with a first depressive episode who frequently have persistent functional deficits comparable to patients in the CHR state were investigated in addition.MethodsPRONIA (‘Personalized Prognostic Tools for Early Psychosis Management’) is a prospective collaboration project funded by the European Union under the 7th Framework Programme (grant agreement n°602152). Considering a broad set of variables (MRI, clinical data, neurocognition, genomics and other blood derived parameters) as well as advanced statistical methods, PRONIA aims at developing an innovative multivariate prognostic tool enabling an individualized prediction of illness trajectories and outcome. 11 university centers in five European countries and in Australia (Munich, Basel, Birmingham, Cologne, Düsseldorf, Münster, Melbourne, Milan, Udine, Bari, Turku) participate in the evaluation of three clinical groups (subjects clinically at high risk of developing a psychosis [CHR], patients with a recent onset psychosis [ROP] and patients with a recent onset depression [ROD]) as well as healthy controls.In the current study, we analysed data of 114 CHR and 106 ROD patients. Functioning was measured by the ‘Global Functioning: Social and Role’ Scales (GF S/R). In a repeated, nested cross validation framework we trained a l1-regularized SVM to predict good versus bad outcome. Multivariate pattern recognition analysis allowed to identify most predictive variables from a multitude of clinical, environmental as well as sociodemographic potential predictors assessed in PRONIA.ResultsBased on the 5 to 20 identified most predictive features, prediction models revealed a balanced accuracy (BAC) up to 77/72 for social functioning in CHR/ROD patients and up to 73/69 for role functioning. These models showed satisfying performance of BACs up to 69/63 for social functioning and 67/60 for role functioning in an independent test sample. As expected, prior functioning levels were identified as main predictive factor but also distinct protective and risk factors were selected into the prediction models.DiscussionResults suggest that especially prediction of the multi-faceted construct of role functioning could benefit from inclusion of a rich set of clinical variables.To the best of our knowledge this is the first study that has validated clinical prediction models of functioning in an independent test sample. Identification of predictive variables enables a much more efficient prognostic process. Moreover, understanding the mechanisms underlying functional decline and its illness related pattern might enable an improved definition of targets for intervention. Future research should aim at further maximisation of prediction accuracy and cross-centre generalisation capacity. In addition, other functioning outcomes as well as clinical outcomes need to be focused on.

Metabolomics approach based on utra-performance liquid chromatography coupled to mass spectrometry with chemometrics methods for high-throughput analysis of metabolite biomarkers to explore the abnormal metabolic pathways associated with myocardial dysfunction.

Ultra-performance liquid chromatography/mass spectrometry-based metabolomics can been used for discovery of metabolite biomarkers to explore the metabolic pathway of diseases. Identification of metabolic pathways is key to understanding the pathogenesis and mechanism of disease. Myocardial dysfunction induced by sepsis (SMD) is a severe complication of septic shock and represents major causes of death in intensive care units; however its pathological mechanism is still not clear. In this study, ultrahigh-pressure liquid chromatography with mass spectrometry-based metabolomics with chemometrics anaylsis and multivariate pattern recognition analysis were used to detect urinary metabolic profile changes in a lipopolysaccharide-induced SMD mouse model. Multivariate statistical analysis including principal component analysis and orthogonapartial least squares discriminant analysis for the discrimination of SMD was conducted to identify potential biomarkers. A total of 19 differential metabolites were discovered by high-resolution mass spectrometry-based urinary metabolomics strategy. The altered biochemical pathways based on these metabolites showed that tyrosine metabolism, phenylalanine metabolism, ubiquinone biosynthesis and vitamin B6 metabolism were closely connected to the pathological processes of SMD. Consequently, integrated chemometric analyses of these metabolic pathways are necessary to extract information for the discovery of novel insights into the pathogenesis of disease.

ELEMENTAL PROFILE OF NON-COMMERCIAL WINES IN CHANGING TRADITIONAL RURAL REGIONS FROM EASTERN EUROPE

Traditional winemaking for subsistence is at the heart of rural households in traditional cultural landscapes where the bioclimatic conditions allow wine production. As these wines are typically not assessed for their elemental content, their consumers could be exposed to various contaminants. Our goals were the followings: (i) to understand the elemental composition of non-commercial traditional wines from three cultural regions of Eastern Europe (Romania) and (ii) to find the main chemical factors which contribute to the differentiation of the wines produced in these regions. Our assessment was based on 62 homemade white and red wines. We used the ICP-OES technique to analyze 21 elements. We found that the wines contained low levels of elemental pollutants. The differentiation of the wines by geographic regions using multivariate pattern recognition analysis was efficient based on magnesium (Mg), strontium (Sr), iron (Fe). Our study shows that the non-commercial wines from the traditional cultural landscapes such as those from Eastern Europe are of good quality with regard to the metallic components. Monitoring these wines will reveal important changes in the quality of these wines as the rural landscapes undergo massive social-ecological changes.

In vivo solid-phase microextraction gas chromatography-mass spectrometry (SPME-GC-MS) assay to identify epicuticular profiles across task groups of Apis mellifera ligustica workers

Colony integrity and survival in honeybees is regulated by chemical signals that are actively produced by glands and synergically transmitted between the members. A number of these signals regulate the typical age-related division of labor among the worker bees performing different tasks at different ages. In this study, the analysis of the cuticular profiles in Apis mellifera ligustica Spinola, 1806 workers at various tasks (newly emerged, nurse and forager bees) was performed using in vivo solid-phase microextraction gas chromatography-mass spectrometry (SPMEGC- MS). The use of in vivo SPME shed new light on the complexity of the molecular pattern on the epicuticle of honeybees. The identified molecules are mainly hydrocarbons, saturated and unsaturated carboxylic acids, and to a less extent, esters, sterols, aldehydes, and alcohols. Their relative abundance between the three task groups was evaluated using descriptive statistics and multivariate pattern recognition analysis (i.e., principal component analysis, PCA, and linear discriminant analysis, LDA). Eleven molecules namely nonacosane, pentacosane, (Z)-12-pentacosene, 11-tricosene, 11-methylnonacosane, squalene, 13-methylheptacosane, heptacosane, heneicosane, docosane and tricosane, occur with high frequency in newly emerged, nurse, and forager bees. The compounds that contributed the most for the separation of the three task groups in the PCA were pentacosane, (Z)-12-pentacosene, 13-methylheptacosane and squalene; while for LDA, nonacosane, 11-methylnonacosane and pentacosane were the molecules that contributed most to the discrimination.

Saliva and urine metabolic profiling reveals altered amino acid and energy metabolism in male farmers exposed to pesticides in Madhya Pradesh State, India

Globally, the human population is exposed to low doses of pesticides due to its extensive use in agriculture. The chronic exposure to pesticides can lead to cancer, depression, anxiety, Parkinson's and Alzheimer's diseases etc. Here, we have made an attempt to use mass spectrometry based metabolomics to investigate the metabolic perturbations induced by the pesticides in the urine and saliva samples of farmers from the Madhya Pradesh State of India. The study was aimed to establish non-invasive matrices like urine and saliva as alternative diagnostic matrices to the occupational exposure studies. Saliva and urine samples were collected from 51 pesticides applicators and acquired metabolic profiles of urine and saliva samples using gas chromatography-mass spectrometry (GC-MS). Multivariate pattern recognition and pathway analysis were used to analyze and interpret the data. Investigation of endogenous metabolic profiles revealed remarkable discrimination in both saliva and urine samples of the exposed population strongly suggesting the changes in metabolic composition within the identified metabolites (for urine samples: accuracy 0.9766, R2 = 0.9130, Q2 = 0.8703; for saliva samples, an accuracy of 0.9961, R2 = 0.9698, Q2 = 0.9637). Thirteen metabolites of urine samples and sixteen metabolites of saliva samples were identified as differential metabolites specific to pesticide exposure. Pathway analysis of differential metabolites revealed that amino acid metabolism, energy metabolism (glycolysis and TCA cycle) and glutathione metabolism (oxidative stress) were found to affect in pesticide exposed population. The present study suggested that GC-MS based metabolomics can help to reveal the metabolic perturbations in human population after pesticides exposure.

Near infrared hyperspectral images and pattern recognition techniques used to identify etiological agents of cotton anthracnose and ramulosis

Hyperspectral imaging near infrared (HSI-NIR) has the potential to be used as a non-destructive approach for the analysis of new microbiological matrices of agriculture interest. This article describes a new method for accurately and rapidly classifying the etiological agents Colletotrichum gossypii (CG) and C. gossypii var. cephalosporioides (CGC) grown in a culture medium, using scattering reflectance HSI-NIR and multivariate pattern recognition analysis. Five strains of CG and 46 strains of CGC were used. CG and CGC strains were grown on Czapek-agar medium at 25 °C under a 12-hour photoperiod for 15 days. Molecular identification was performed as a reference for the CG and CGC classes by polymerase chain reaction of the intergenic spacer region of rDNA. The scattering coefficient µs and the absorption coefficient µa were obtained, which resulted in a µs value for CG of 1.37 × 1019 and for CGC of 5.83 × 10–11. These results showed that the use of the standard normal variate was no longer essential and reduced the spectral range from 1000–2500 nm to 1000–1381 nm. The results evidenced two type II errors for the CG 457-2 and CGC 39 samples in the soft independent modelling model of the analogy model. There were no classification errors using the algorithm of the successive projections for variable selection in linear discriminant analysis (SPA-LDA). A parallel validation of the results obtained with SPA-LDA was performed using a box plot analysis with the 11 variables selected by SPA, in which there were no outliers for the HSI-NIR models. The new HSI-NIR and SPA-LDA procedures for the classification of CG and CGC etiological agents are noted for their greater analytical speed, accuracy, simplicity, lower cost and non-destructive nature.

Nano-sized TiO2 (nTiO2) induces metabolic perturbations in Physarum polycephalum macroplasmodium to counter oxidative stress under dark conditions

Nano-sized TiO2 (nTiO2) exerts an oxidative effect on cells upon exposure to solar or UV irradiation and ecotoxicity of the nTiO2 is an urgent concern. Little information is available regarding the effect of TiO2 on cells under dark conditions. Metabolomics is a unique approach to the discovery of biomarkers of nTiO2 cytotoxicity, and leads to the identification of perturbed metabolic pathways and the mechanism underlying nTiO2 toxicity. In the present study, gas chromatography mass spectrometry (GC/MS)-based metabolomics was performed to investigate the effect of nTiO2 on sensitive cells (P. polycephalum macroplasmodium) under dark conditions. According to the multivariate pattern recognition analysis, at least 60 potential metabolic biomarkers related to sugar metabolism, amino acid metabolism, nucleotide metabolism, polyamine biosynthesis, and secondary metabolites pathways were significantly perturbed by nTiO2. Notably, many metabolic biomarkers and pathways were related to anti-oxidant mechanisms in the living organism, suggesting that nTiO2 may induce oxidative stress, even under dark conditions. This speculation was further validated by the biochemical levels of reactive oxygen species (ROS), 8-hydroxy-2-deoxyguanosine (8-OHdG), and total soluble phenols (TSP). We inferred that the oxidative stress might be related to nTiO2-induced imbalance of cellular ROS. To the best of our knowledge, the present study is the first to investigate the nTiO2-induced metabolic perturbations in slime mold, provide a new perspective of the mechanism underlying nTiO2 toxicity under dark conditions, and show that metabolomics can be employed as a rapid, reliable and powerful tool to investigate the interaction among organisms, the environment, and nanomaterials.

Untargeted Metabolomics Reveal Lipid Alterations upon 2-Deoxyglucose Treatment in Human HaCaT Keratinocytes.

The glucose analogue 2-deoxyglucose (2-DG) impedes cancer progression in animal models and is currently being assessed as an anticancer therapy, yet the mode of action of this drug of high clinical significance has not been fully delineated. In an attempt to better characterize its pharmacodynamics, an integrative UPLC-Q-Exactive-based joint metabolomic and lipidomic approach was undertaken to evaluate the metabolic perturbations induced by this drug in human HaCaT keratinocyte cells. R-XCMS data processing and subsequent multivariate pattern recognition, metabolites identification, and pathway analyses identified eight metabolites that were most significantly changed upon a 3 h 2-DG exposure. Most of these dysregulated features were emphasized in the course of lipidomic profiling and could be identified as ceramide and glucosylceramide derivatives, consistently with their involvement in cell death programming. Even though metabolomic analyses did not generally afford such clear-cut dysregulations, some alterations in phosphatidylcholine and phosphatidylethanolamine derivatives could be highlighted as well. Overall, these results support the adequacy of the proposed analytical workflow and might contribute to a better understanding of the mechanisms underlying the promising effects of 2-DG.