To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

Novel Quantitative Structure-Activity Relationship (QSAR) models of compounds' placenta (PL) permeability expressed as their log FM (fetus-to-mother blood concentration) values or binary PL1/0 (crossing/non-crossing) score were generated using a number of statistical tools: Multiple Linear Regression, Boosted Trees, Principal Component Analysis and Artificial Neural Networks, on the basis of molecular descriptors calculated by Mordred software and selected using Partial Least Squares (PLS) analysis. It was established that the most important predictor of both log FM and the binary PL1/0 score is Lipinski - a binary variable reflecting the compounds' ability to satisfy the criteria of drug-likeness according to the Lipinski's "Rule of 5". The quantitative (log FM) and qualitative (PL1/0) models of PL permeability were applied to 345 pesticides from different chemical families (triazines, carbamates, pyrethroids, organochlorine, organophosphorus and miscellaneous compounds). The ability of studied pesticides to cross the placenta was assessed; the basic physico-chemical parameters responsible for good or poor placenta transport of pesticides were identified and the relationships between the pesticides' PL permeability, blood-brain barrier (BBB) transfer and gastro-intestinal (GI) absorption were investigated. It was found (on the basis of logistic regression analysis) that the probability of a compound crossing the placenta (PL1) is inversely correlated with its lipophilicity and molar refractivity and positively correlated with the total count of oxygen and nitrogen atoms.

Pesticides, especially the newly developed neonicotinoids, are increasingly used in many countries around the world, including Cameroon, to control pests involved in crop destruction or disease transmission. Unfortunately, the pesticides also pose tremendous environmental problems because a predominant amount of their residues enter environmental matrices to affect other nontargeted species including humans. This therefore calls for continuous biomonitoring of these insecticides in human populations. The present study sought to assess the neonicotinoid insecticide exposures in two agrarian regions of Cameroon, the South-West region and Littoral region. The study involved 188 men, including 125 farmers and 63 nonfarmers. Spot urine samples were obtained from these subjects and subjected to liquid chromatographic-tandem mass spectrometric analysis for concentrations of neonicotinoid compounds, including acetamiprid, clothianidin, dinotefuran, imidacloprid, thiacloprid, nitenpyram, thiamethoxam, and N-dm-acetamiprid. Neonicotinoid compounds were detected in all study participants, and residues of all the screened pesticides were detected among participants. N-dm-Acetamiprid and imidacloprid were the most prevalent among the subjects (100.0% and 93.1%, respectively), whereas nitenpyram was less common (3.2%). The median values of imidacloprid and total urinary neonicotinoid concentrations were elevated among farmers (0.258 vs. 0.126 µg/L and 0.829 vs. 0.312 µg/L, respectively). Altogether the findings showed that both the farmer and nonfarmer study populations of Cameroon were exposed to multiple residues of neonicotinoids, with relatively higher levels of pesticides generally recorded among farmers. Although exposure levels of the neonicotinoids were generally lower than their respective reference doses, these results warrant further research on the health risk evaluation of multiple residues of the pesticides and reinforcement of control measures to minimize the exposure risks, especially among farmers. Environ Toxicol Chem 2024;43:952-964. © 2024 SETAC.

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

The purity of tandem mass spectrometry (MS/MS) is essential to MS/MS-based metabolite annotation and unknown exploration. This work presents a approach to cleaning chimeric MS/MS spectra generated in liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based metabolomics. The assumption is that true fragments and their precursors are well correlated across the samples in a study, while false or contamination fragments are rather independent. Using data simulation, this work starts with an investigation of the negative effects of chimeric MS/MS spectra on spectral similarity analysis and molecular networking. Next, the characteristics of true and false fragments in chimeric MS/MS spectra were investigated using MS/MS of chemical standards. We recognized three fragment peak attributes indicative of whether a peak is a false fragment, including (1) intensity ratio fluctuation, (2) appearance rate, and (3) relative intensity. Using these attributes, we tested three machine learning models and identified XGBoost as the best model to achieve an area under the precision-recall curve of 0.98 for a clear separation between true and false fragments. Based on the trained model, we constructed an automated bioinformatic platform, DNMS2Purifier (short for de novo ), for metabolic features from metabolomics studies. DNMS2Purifier recognizes and processes chimeric MS/MS spectra without additional sample analysis or library confirmation. DNMS2Purifer was evaluated on a metabolomics data set generated with different MS/MS precursor isolation windows. It successfully captured the increase in the number of false fragments from the increased isolation window. DNMS2Purifier was also compared to MS2Purifier, an existing MS/MS spectral cleaning tool based on the addition of data-independent acquisition (DIA) analysis. Results indicated that DNMS2Purifier uniquely recognizes false fragments, which complements the previous DIA-based approach. Finally, DNMS2Purifier was demonstrated using a real experimental metabolomics study, showing improved MS/MS spectral quality and leading to an improved spectral match ratio and molecular networking outcome.

This study focuses on the distribution of some selected organochlorine pesticides and emerging contaminants within the surface sediments of an Arctic fjord, Kongsfjorden and nearby lakes. Organochlorine pesticides (OCPs) such as dicloran, p,p'-DDT, p,p'-DDE, p,p'-DDD were studied along with five emerging contaminants namely diuron, chlorpyrifos, dicofol, pendimethalin and bifenthrin. The highest values of OCPs recorded among the fjord and lake environments during the time of study was 0.3355 ng/g (dicloran), 0.0152 ng/g (p,p'-DDT), 0.0117 ng/g (p,p'-DDE), and 0.0137 ng/g (p,p'-DDD). Except dicofol, all other pesticides were found in both the years (2018 & 2019) with an elevated concentration during 2019. The presence of fresh as well as past input of contaminants was obtained from the values of DDTs ratio. The sediment quality guidelines of DDTs confirm that the fjord and lakes are clean to marginally polluted in which the adverse effects can rarely occur at this present juncture.

INTRODUCTION:The human exposure to anticholinergic pesticides has been associated with the development of various diseases. Therefore, several biomarkers have been proposed for biomonitoring human exposure to anticholinergic pesticides. OBJECTIVE:This work evaluated the effect of human exposure to anticholinergic pesticides on β-glucuronidase (GUSB) levels. METHODS:A systematic review was performed using PubMed, Web of Science, Scopus, and EBSCO databases up to December 2021. The statistical analysis employed standardized mean differences and meta-regression. And the trial sequential analysis was performed. RESULTS:Nine studies were included. A monotonic relationship was observed between poisoning severity and GUSB. Furthermore, BuChE levels were correlated with GUSB levels. CONCLUSIONS:The results indicated that GUSB levels could be used as a possible diagnosis biomarker in poisoning related to anticholinergic pesticide exposure. However, the use of GUSB to assess the chronic exposure to anticholinergic pesticides could be only performed in recent exposure (≈ 7 days after last exposure).

Obesity, diabetes, hypertension and dyslipidemia are well-established risk factors for cardiovascular diseases (CVDs), and have been associated with exposure to persistent organic pollutants. However, studies have been lacking as regards effects of non-persistent pesticides on CVD risk factors. Here, we investigated whether background chronic exposure to polychlorinated biphenyls (PCBs) and multiclass pesticides were associated with the prevalence of these CVD risk factors in 502 Belgian and 487 Luxembourgish adults aged 18-69 years from the Nutrition, environment and cardiovascular health (NESCAV) study 2007-2013. We used hair analysis to evaluate the chronic internal exposure to three PCBs, seven organochlorine pesticides (OCs) and 18 non-persistent pesticides. We found positive associations of obesity with hexachlorobenzene (HCB), β-hexachlorocyclohexane (β-HCH) and chlorpyrifos, diabetes with pentachlorophenol (PCP), fipronil and fipronil sulfone, hypertension with PCB180 and chlorpyrifos, and dyslipidemia with diflufenican and oxadiazon, among others. However, we also found some inverse associations, such as obesity with PCP, diabetes with γ-HCH, hypertension with diflufenican, and dyslipidemia with chlorpyrifos. These results add to the existing evidence that OC exposure may contribute to the development of CVDs. Additionally, the present study revealed associations between CVD risk factors and chronic environmental exposure to currently used pesticides such as organophosphorus and pyrethroid pesticides.

In this research, aged plastic fragments collected from vineyards were characterized in terms of composition, residues of pesticides, and their potential to exchange these compounds with the aquatic media. To this end, we employed the qualitative and quantitative information provided by complementary analytical techniques, including chromatography, organic and inorganic mass spectrometry, infrared spectroscopy and electronic microscopy. Debris of weathered plastics were identified as polypropylene and polyethylene, containing different types of additives, from organic UV stabilizers to inorganic fillers, such as calcium salts. Regardless of polymer type, plastic litter collected from vineyards contained residues of pesticides, and particularly of fungicides, with total concentrations in the range of values from 114 ng g to 76.4 μg g. Data obtained under different extraction conditions suggested that a fraction of these compounds was absorbed in aged polymers, penetrating inside the material. The parallel analysis of plastic litter and vineyard soils reflected higher pesticide residues in the former matrix. Furthermore, several fungicides, considered as labile in vineyard soils (i.e. zoxamide and folpet), were those showing the highest levels in plastic litter. Simulated sorption-desorption studies, with plastic debris in contact with surface water, demonstrated the higher affinity of aged materials by moderately polar pesticides than their new counterparts. For the first time, the manuscript highlights the presence of plastic litter in vineyards soils, reflecting the accumulation of several fungicides in this matrix, in some cases, with a different stability pattern to that observed in the soil from same vineyards.

Parkinson's disease (PD) is a complex neurodegenerative disease with etiology rooted in genetic vulnerability and environmental factors. Here we combine quantitative epidemiologic study of pesticide exposures and PD with toxicity screening in dopaminergic neurons derived from PD patient induced pluripotent stem cells (iPSCs) to identify Parkinson's-relevant pesticides. Agricultural records enable investigation of 288 specific pesticides and PD risk in a comprehensive, pesticide-wide association study. We associate long-term exposure to 53 pesticides with PD and identify co-exposure profiles. We then employ a live-cell imaging screening paradigm exposing dopaminergic neurons to 39 PD-associated pesticides. We find that 10 pesticides are directly toxic to these neurons. Further, we analyze pesticides typically used in combinations in cotton farming, demonstrating that co-exposures result in greater toxicity than any single pesticide. We find trifluralin is a driver of toxicity to dopaminergic neurons and leads to mitochondrial dysfunction. Our paradigm may prove useful to mechanistically dissect pesticide exposures implicated in PD risk and guide agricultural policy.

Epidemiological studies have related exposure to pesticides to increased risk of diabetes. However, few studies have evaluated the health effects of mixed pesticides exposure, especially in an elderly population. Here, we utilized gas chromatography-tandem mass spectrometry to quantify the levels of 39 pesticides in 4 categories in a Chinese elderly population. Then we used general linear models to explore the association between individual pesticide exposure and type 2 diabetes mellitus (T2DM). Restricted cubic spline (RCS) models were fitted to identify potential non-linearities between those associations. Furthermore, stratified analysis by gender was conducted to explore the gender-specific associations. Finally, we used weighted quantile sum (WQS) regression, quantile-based g computation (qgcomp), and Bayesian kernel machine regression (BKMR) to evaluate the effects of mixed exposure to 39 pesticides. The results showed that exposure to pesticides was associated with high risk of T2DM, with β-Hexachlorocyclohexane (β-BHC) and oxadiazon being the most significant independent contributors, which was pronounced among elderly women. Moreover, the association of β-BHC and oxadiazon with T2DM was linear. These indicated that it is an urgent need to take practical measures to control these harmful pesticides.

BACKGROUND:Some pesticides have been shown to interfere with thyroid functions through changes in thyroid hormone (TH) levels. However, few human studies have explored associations between TH levels and environmental exposure to currently used pesticides, including neonicotinoids, phenylpyrazoles, phenoxy acids, and azoles. Moreover, such studies often measure biomarkers of exposure in urine or blood, and thus reveal only recent exposure. In contrast, hair has been demonstrated to be a suitable matrix for assessing chronic exposure to both persistent and nonpersistent organic pollutants. OBJECTIVES:We investigated 54 biomarkers of pollutant exposure in relation to tetraiodothyronine (T4), 3,3',5-triiodothyronine (T3), 3,3',5'-triiodothyronine (rT3), and 3,3'-diiodothyronine (T2). METHODS:In a cross-sectional study of 196 healthy Chinese women of reproductive age (25-45 years of age), concentrations of both pollutants and THs were analyzed in the first (starting from the scalp) of the hair matrix, collected in 2016. Associations between pollutants and TH levels were explored using stability-enhanced least absolute shrinkage and selection operator (lasso) by regressing all exposures against each outcome of interest, adjusted for age, body mass index, and city. RESULTS:Each TH was associated with the mixture of at least eight of the examined pesticides. We found associations of -HCH, PCP, DMP, DETP, 3Me4NP, carbofuran, , imidacloprid, 2,4-D, metolachlor, difenoconazole, and tebuconazole with THs. For example, a 2-standard deviation (SD) increase in -transformed hair DMP concentration was associated with lower hair T4 concentration [ (95% CI: , )] and higher hair T3 concentration [8.16% (95% CI: 1.73, 15.0%)] in the adjusted unpenalized regression models. We also found associations of some pesticides with T3/T4, rT3/T4, and rT3/T3 molar ratios, including PCP, DMP, 2,4-D, metolachlor, difenoconazole, and tebuconazole. DISCUSSION:Our results suggest that exposure to the low levels of pesticides examined here may disrupt thyroid homeostasis in humans. Further studies are needed to confirm our results and to evaluate the long-term consequences of these subtle interferences. https://doi.org/10.1289/EHP14378.

Healthy dietary habits encourage vegetable consumption. Although pesticide use in crops may negatively affect human health through food intake, it can also contaminate aquatic and terrestrial environments. Thus, monitoring pesticides in high-consumption matrices is crucial. This study conducted a complete workflow of analysis, including a step of target analysis of 30 widely used pesticides and a subsequent step of suspect screening. A validated QuEChERS method was employed to analyze 61 samples of fruiting vegetables and cucurbits, packaged leafy greens, and root and tuber vegetables, commercially distributed across Greece. The method proved to be highly efficient for all validation characteristics. After target analysis, the change in the residue levels detected during sample processing was evaluated as a case study using available literature data. A health risk assessment based on diet indicated acute and chronic hazard quotients (aHQ and cHQ) and chronic hazard index (cHI) values below 1. Concerning suspect screening, 53 additional identifications of pesticides and transformation products (TPs) were revealed, totaling 86 detections. Overall, 18 parent pesticide compounds and 5 TPs were identified. Ultimately, this approach is expected to provide added value in pesticide and TPs analysis of food matrices without prior data, minimizing experimental time and costs.

Emerging evidence has demonstrated the vital role of metabolism in various diseases or disorders. Metabolomics provides a comprehensive understanding of metabolism in biological systems. With advanced analytical techniques, metabolomics exhibits unprecedented significant value in basic drug research, including understanding disease mechanisms, identifying drug targets, and elucidating the mode of action of drugs. More importantly, metabolomics greatly accelerates the drug development process by predicting pharmacokinetics, pharmacodynamics, and drug response. In addition, metabolomics facilitates the exploration of drug repurposing and drug-drug interactions, as well as the development of personalized treatment strategies. Here, we briefly review the recent advances in technologies in metabolomics and update our knowledge of the applications of metabolomics in drug research and development.

This study aimed to investigate the residual characteristics of pesticides drifted by unmanned aerial spray according to buffer strip, windbreak, and morphological characteristics of non-target crops, suggest prevention for drift reduction, and finally conduct a risk analysis on pesticides exceeding the maximum residue limit (MRL) or uniform level (0.01 mg/kg) of the positive list system (PLS). Non-target crops were collected around the aerial sprayed area (paddy rice) in Boryeong, Seocheon, and Pyeongtaek after UAV spray. When pesticides were detected in more than three samples, Duncan's multiple range test was performed. In cases where pesticides were detected in only two samples, an independent sample t-test was conducted (p < 0.05). The drift rate of pesticides tends to decrease by up to 100% as the buffer distance from aerial sprayed area increases or when a windbreak, such as maize, is present between two locations. Thus, the reduction of drifted pesticides could be effective if both factors were applied near the UAV spray area. Moreover, the residue of drifted pesticides was found to be the highest in leafy vegetables such as perilla leaves or leaf and stem vegetables such as Welsh onion, followed by fruit vegetables and cucurbits, owing to the morphological characteristics of crops. Therefore, selecting pulse or cereal such as soybean or maize as a farm product near the UAV spray area can be considered to minimize the drift. For pesticides that exceed the MRL or PLS uniform level, %acceptable dietary intake is 0-0.81% with no risk. Additionally, employing pesticides approved for both paddy rice and farm products in UAV spraying can effectively minimize instances where MRL or PLS are exceeded. Therefore, this study aims to provide farmers with effective guidelines for mitigating drift. Furthermore, we strive to promote stable and uninterrupted food production while facilitating the utilization of agricultural technologies such as UAV spraying to address labor shortages and ensure sustainable food security.

The use of machine learning (ML) with metabolomics provides opportunities for the early diagnosis of disease. However, the accuracy of ML and extent of information obtained from metabolomics can be limited owing to challenges associated with interpreting disease prediction models and analyzing many chemical features with abundances that are correlated and "noisy". Here, we report an interpretable neural network (NN) framework to accurately predict disease and identify significant biomarkers using whole metabolomics data sets without feature selection. The performance of the NN approach for predicting Parkinson's disease (PD) from blood plasma metabolomics data is significantly higher than other ML methods with a mean area under the curve of >0.995. PD-specific markers that predate clinical PD diagnosis and contribute significantly to early disease prediction were identified including an exogenous polyfluoroalkyl substance. It is anticipated that this accurate and interpretable NN-based approach can improve diagnostic performance for many diseases using metabolomics and other untargeted 'omics methods.

Metabolomics, the systematic study of metabolites, is dedicated to a comprehensive analysis of all aspects of plant-based food research and plays a pivotal role in the nutritional composition and quality control of plant-based foods. The diverse chemical compositions of plant-based foods lead to variations in sensory characteristics and nutritional value. This review explores the application of the metabolomics method to plant-based food origin tracing, cultivar identification, and processing methods. It also addresses the challenges encountered and outlines future directions. Typically, when combined with other omics or techniques, synergistic and complementary information is uncovered, enhancing the classification and prediction capabilities of models. Future research should aim to evaluate all factors affecting food quality comprehensively, and this necessitates advanced research into influence mechanisms, metabolic pathways, and gene expression.

INTRODUCTION:Metabolomics and proteomics are two growing fields of science which may shed light on the molecular mechanisms that contribute to neurodegenerative diseases. Studies focusing on these aspects can reveal specific metabolites and proteins that can halt or reverse the progressive neurodegenerative process leading to dopaminergic cell death in the brain. AREAS COVERED:In this article, an overview of the current status of metabolomic and proteomic profiling in the neurodegenerative disease such as Parkinson's disease (PD) is presented. We discuss the importance of state-of-the-art metabolomics and proteomics using advanced analytical methodologies and their potential for discovering new biomarkers in PD. We critically review the research to date, highlighting how metabolomics and proteomics can have an important impact on early disease diagnosis, future therapy development and the identification of new biomarkers. Finally, we will discuss interactions between lipids and α-synuclein (SNCA) and also consider the role of SNCA in lipid metabolism. EXPERT OPINION:Metabolomic and proteomic studies contribute to understanding the biological basis of PD pathogenesis, identifying potential biomarkers and introducing new therapeutic strategies. The complexity and multifactorial nature of this disease requires a comprehensive approach, which can be achieved by integrating just these two omic studies.

In recent years, environmental epidemiology and toxicology have seen a growing interest in the environmental factors that contribute to the increased prevalence of neurodevelopmental disorders, with the purpose of establishing appropriate prevention strategies. A literature review was performed, and 192 articles covering the topic of endocrine disruptors and neurodevelopmental disorders were found, focusing on polychlorinated biphenyls, polybrominated diphenyl ethers, bisphenol A, and pesticides. This study contributes to analyzing their effect on the molecular mechanism in maternal and infant thyroid function, essential for infant neurodevelopment, and whose alteration has been associated with various neurodevelopmental disorders. The results provide scientific evidence of the association that exists between the environmental neurotoxins and various neurodevelopmental disorders. In addition, other possible molecular mechanisms by which pesticides and endocrine disruptors may be associated with neurodevelopmental disorders are being discussed.

Rural residents are exposed to both particulate and gaseous pesticides in the indoor-outdoor nexus in their daily routine. However, previous personal exposure assessment mostly focuses on single aspects of the exposure, such as indoor or gaseous exposure, leading to severe cognition bias to evaluate the exposure risks. In this study, residential dust and silicone wristbands (including stationary and personal wearing ones) were used to screen pesticides in different phases and unfold the hidden characteristics of personal exposure via indoor-outdoor nexus in intensive agricultural area. Mento-Carlo Simulation was performed to assess the probabilistic exposure risk by transforming adsorbed pesticides from wristbands into air concentration, which explores a new approach to integrate particulate (dust) and gaseous (silicone wristbands) pesticide exposures in indoor and outdoor environment. The results showed that particulate pesticides were more concentrated in indoor, whereas significantly higher concentrations were detected in stationary outdoor wristbands (p < 0.05). Carbendazim and chlorpyrifos were the most frequently detected pesticides in dust and stationary wristbands. Higher pesticide concentration was found in personal wristbands worn by farmers, with the maximum value of 2048 ng g for difenoconazole. Based on the probabilistic risk assessment, around 7.1 % of farmers and 2.6 % of bystanders in local populations were potentially suffering from chronic health issues. One third of pesticide exposures originated mainly from occupational sources while the rest derived from remoting dissipation. Unexpectedly, 43 % of bystanders suffered the same levels of exposure as farmers under the co-existence of occupational and non-occupational exposures. Differed compositions of pesticides were found between environmental samples and personal pesticide exposure patterns, highlighting the need for holistic personal exposure measurements.

Alzheimer's disease (AD) is determined by various pathophysiological mechanisms starting 10-25 years before the onset of clinical symptoms. As multiple functionally interconnected molecular/cellular pathways appear disrupted in AD, the exploitation of high-throughput unbiased omics sciences is critical to elucidating the precise pathogenesis of AD. Among different omics, metabolomics is a fast-growing discipline allowing for the simultaneous detection and quantification of hundreds/thousands of perturbed metabolites in tissues or biofluids, reproducing the fluctuations of multiple networks affected by a disease. Here, we seek to critically depict the main metabolomics methodologies with the aim of identifying new potential AD biomarkers and further elucidating AD pathophysiological mechanisms. From a systems biology perspective, as metabolic alterations can occur before the development of clinical signs, metabolomics - coupled with existing accessible biomarkers used for AD screening and diagnosis - can support early disease diagnosis and help develop individualized treatment plans. Presently, the majority of metabolomic analyses emphasized that lipid metabolism is the most consistently altered pathway in AD pathogenesis. The possibility that metabolomics may reveal crucial steps in AD pathogenesis is undermined by the difficulty in discriminating between the causal or epiphenomenal or compensatory nature of metabolic findings.

Critical ecological interactions can be disrupted by pesticides, leading to serious ecosystem and economic harm. For the most part, however, the extent and magnitude of these impacts are unknown. We argue for increased investigation of ecosystem impacts of common pesticides by scientists and scrutiny by regulators.

Exposure to pesticides during pregnancy has been associated with several serious congenital malformations, such as neural tube defects, therefore, is a cause for concern in terms of human health. This review aims to gather information related to maternal exposure during pregnancy and the risk of triggering neural tube defects in the offspring. The search strategy for the studies followed the PRISMA guidelines. We conducted a systematic search in the Science Direct, PubMed, Cochrane Library, Embase, Scopus, and Web of Science databases for all epidemiological studies that sought to associate exposure to pesticides during embryonic development with the risk of neural tube defects (NTDs). The keywords used were "pesticide", "herbicide", "congenital" and "neural". Of the 229 articles, 8 eligible ones (7 case-control and 1 cross-sectional) evaluated pesticide exposure in pregnancy. Different methods were used, including analysis of biological samples and questionnaires. The pesticides studied included insecticides, herbicides, fungicides, and nematicides. Insecticides were the most studied, with variations in concentrations between tissues and studies. Distinct levels of pesticides have been detected in maternal serum, placenta, and umbilical cord. Models were statistically adjusted for confounding factors, such as smoking and dietary supplement intakes. Concentrations were measured in different exposure windows (periconception and prenatal), related to NTDs such as anencephaly and spina bifida. Different data collection techniques, types of biological samples, and exposure windows were used, which made comparison difficult. The main pesticides studied included DDT, DDE, HCH, and endosulfan. Maternal serum showed the highest concentrations of pesticides, but detection in placental tissue and umbilical cord confirms embryonic exposure. Confounding variables were adjusted for in the analysis of the articles, but they may still contribute to the risk of NTDs. All the studies analyzed pesticide exposure and the relationship with NTDs. However, a more standardized survey would be ideal for better comparisons.

Untargeted metabolomics is an established approach in toxicology for characterising endogenous metabolic responses to xenobiotic exposure. Detecting the xenobiotic and its biotransformation products as part of the metabolomics analysis provides an opportunity to simultaneously gain deep insights into its fate and metabolism, and to associate the internal relative dose directly with endogenous metabolic responses. This integration of untargeted exposure and response measurements into a single assay has yet to be fully demonstrated. Here we assemble a workflow to discover and analyse pharmaceutical-related measurements from routine untargeted UHPLC-MS metabolomics datasets, derived from in vivo (rat plasma and cardiac tissue, and human plasma) and in vitro (human cardiomyocytes) studies that were principally designed to investigate endogenous metabolic responses to drug exposure. Our findings clearly demonstrate how untargeted metabolomics can discover extensive biotransformation maps, temporally-changing relative systemic exposure, and direct associations of endogenous biochemical responses to the internal dose.

Most epidemiological studies on the associations between pesticides exposure and semen quality have been based on a single pesticide, with inconsistent major results. In contrast, there was limited human evidence on the potential effect of pesticides mixture on semen quality. Our study aimed to investigate the relationship of pesticide profiles with semen quality parameters among 299 non-occupationally exposed males aged 25-50 without any clinical abnormalities. Serum concentrations of 21 pesticides were quantified by gas chromatography-tandem mass spectrometry (GC-MS/MS). Semen quality parameters were abstracted from medical records. Generalized linear regression models (GLMs) and three mixture approaches, including weighted quantile sum regression (WQS), elastic net regression (ENR) and Bayesian kernel machine regression (BKMR), were applied to explore the single and mixed effects of pesticide exposure on semen quality. In GLMs, as the serum levels of Bendiocarb, β-BHC, Clomazone, Dicrotophos, Dimethenamid, Paclobutrazole, Pentachloroaniline and Pyrimethanil increased, the straight-line velocity (VSL), linearity (LIN) and straightness (STR) decreased. This negative association also occurred between the concentration of β-BHC, Pentachloroaniline, Pyrimethanil and progressive motility, total motility. In the WQS models, pesticides mixture was negatively associated with total motility and several sperm motility parameters (β: -3.07∼-1.02 per decile, FDR-P<0.05). After screening the important pesticides derived from the mixture by ENR model, the BKMR models showed that the decreased qualities for VSL, LIN, and STR were also observed when pesticide mixtures were at ≥ 70th percentiles. Clomazone, Dimethenamid, and Pyrimethanil (Posterior inclusion probability, PIP: 0.2850-0.8900) were identified as relatively important contributors. The study provides evidence that exposure to single or mixed pesticide was associated with impaired semen quality.

Untargeted metabolomics is a powerful tool for investigating chemistry of complex biological systems, but its utility is compromised by the presence of uninformative features and the limited efficiency of currently available prioritization tools. More effective filtering and prioritization tools are required to address the challenges of large untargeted metabolomics datasets. Here, we introduce Metabolomics Peak Analysis Computational Tool (MPACT), a new mass spectrometry data analysis platform employing filtering based on multiple modalities, statistical techniques incorporating multilevel replication, and interactive data visualization. We demonstrate application of MPACT to uncover hidden effects of the rare earth element cerium on tunicate-associated bacterium sp. PTY087I2, culminating in characterization of two thiolated compounds including a new cysteine derivative, granaticin C, and granaticin D, recently described as mycothiogranaticin A. While we demonstrate application of MPACT to microbial natural products discovery using an elicitation approach, the platform should be readily adaptable to investigation of multipartite interactions, biomarker detection, small molecules in the environment, and a wide range of other complex sample types.

: Neurodevelopment is a fragile brain process necessary for learning from the beginning of childhood to adulthood. During the procedure, several risks could affect it, including environmental factors such as neurotoxic chemicals or environmental pollutants and, within them, exposure to pesticides. : This ecological descriptive study attempted to assess the association between environmental exposure to pesticides and neurodevelopmental disorders. This study was conducted on 4830 children diagnosed for 11 years in a total population of 119,897 children in three areas: high, medium, and low greenhouse concentrations. : Chromosomal abnormalities were the most common prenatal disorder (28.6%), while intrauterine physical factors were the least common (0.5%). Among perinatal diagnoses, gestational age less than 32 weeks was the most common (25%), while hyperbilirubinemia requiring exchange transfusion and birth complications was the least common (0.4%). Brain damage was the most common problem detected in postnatal diagnosis (36.7%), while unspecified postnatal abnormalities were the least common (3.1%). : The areas with the highest greenhouse concentration had higher incidences of neurodevelopmental disorders, particularly in boys, and lower age of referral. Chromosomal abnormalities were prevalent for prenatal diagnoses, gestational age below thirty-two weeks for perinatal diagnoses, and brain damage for postnatal diagnoses. Future studies should analyze the connection between pesticide exposure and neurodevelopmental disorders using spatial point pattern analysis.

BACKGROUND:Different types of analytical methods, with different characteristics, are applied in metabolomics and lipidomics research and include untargeted, targeted and semi-targeted methods. Ultra High Performance Liquid Chromatography-Mass Spectrometry is one of the most frequently applied measurement instruments in metabolomics because of its ability to detect a large number of water-soluble and lipid metabolites over a wide range of concentrations in short analysis times. Methods applied for the detection and quantification of metabolites differ and can either report a (normalised) peak area or an absolute concentration. AIM OF REVIEW:In this tutorial we aim to (1) define similarities and differences between different analytical approaches applied in metabolomics and (2) define how amounts or absolute concentrations of endogenous metabolites can be determined together with the advantages and limitations of each approach in relation to the accuracy and precision when concentrations are reported. KEY SCIENTIFIC CONCEPTS OF REVIEW:The pre-analysis knowledge of metabolites to be targeted, the requirement for (normalised) peak responses or absolute concentrations to be reported and the number of metabolites to be reported define whether an untargeted, targeted or semi-targeted method is applied. Fully untargeted methods can only provide (normalised) peak responses and fold changes which can be reported even when the structural identity of the metabolite is not known. Targeted methods, where the analytes are known prior to the analysis, can also report fold changes. Semi-targeted methods apply a mix of characteristics of both untargeted and targeted assays. For the reporting of absolute concentrations of metabolites, the analytes are not only predefined but optimized analytical methods should be developed and validated for each analyte so that the accuracy and precision of concentration data collected for biological samples can be reported as fit for purpose and be reviewed by the scientific community.

Parkinson's disease is a neurodegenerative illness linked to ageing, marked by the gradual decline of dopaminergic neurons in the midbrain. The exact aetiology of Parkinson's disease (PD) remains uncertain, with genetic predisposition and environmental variables playing significant roles in the disease's frequency. Epidemiological data indicates a possible connection between pesticide exposure and brain degeneration. Specific pesticides have been associated with important characteristics of Parkinson's disease, such as mitochondrial dysfunction, oxidative stress, and α-synuclein aggregation, which are crucial for the advancement of the disease. Recently, many animal models have been developed for Parkinson's disease study. Although these models do not perfectly replicate the disease's pathology, they provide valuable insights that improve our understanding of the condition and the limitations of current treatment methods. Drosophila, in particular, has been useful in studying Parkinson's disease induced by toxins or genetic factors. The review thoroughly analyses many animal models utilised in Parkinson's research, with an emphasis on issues including pesticides, genetic and epigenetic changes, proteasome failure, oxidative damage, α-synuclein inoculation, and mitochondrial dysfunction. The text highlights the important impact of pesticides on the onset of Parkinson's disease (PD) and stresses the need for more research on genetic and mechanistic alterations linked to the condition.

Arthropods represent an entry point for pesticide transfers in terrestrial food webs, and pesticide accumulation in upper chain organisms, such as predators can have cascading consequences on ecosystems. However, the mechanisms driving pesticide transfer and bioaccumulation in food webs remain poorly understood. Here we review the literature on pesticide transfers mediated by terrestrial arthropods in food webs. The transfer of pesticides and their potential for bioaccumulation and biomagnification are related to the chemical properties and toxicokinetic of the substances, the resistance and detoxification abilities of the contaminated organisms, as well as by their effects on organisms' life history traits. We further identify four critical areas in which knowledge gain would improve future predictions of pesticides impacts on terrestrial food webs. First, efforts should be made regarding the effects of co-formulants and pesticides mixtures that are currently understudied. Second, progress in the sensitivity of analytical methods would allow the detection of low concentrations of pesticides in small individual arthropods. Quantifying pesticides in arthropods preys, their predators, and arthropods or vertebrates at higher trophic level would bring crucial insights into the bioaccumulation and biomagnification potential of pesticides in real-world terrestrial food webs. Finally, quantifying the influence of the trophic structure and complexity of communities on the transfer of pesticides could address several important sources of variability in bioaccumulation and biomagnification across species and food webs. This narrative review will inspire future studies aiming to quantify pesticide transfers in terrestrial food webs to better capture their ecological consequences in natural and cultivated landscapes.

OBJECTIVE:Exposure to pesticides is a risk factor for various diseases, yet its association with biological aging remains unclear. We aimed to systematically investigate the relationship between pesticide exposure and biological aging. METHODS:PubMed, Embase and Web of Science were searched from inception to August 2023. Observational studies investigating the association between pesticide exposure and biomarkers of biological aging were included. Three-level random-effect meta-analysis was used to synthesize the data. Risk of bias was assessed by the Newcastle-Ottawa Scale. RESULTS:Twenty studies evaluating the associations between pesticide exposure and biomarkers of biological aging in 10,368 individuals were included. Sixteen reported telomere length and four reported epigenetic clocks. Meta-analysis showed no statistically significant associations between pesticide exposure and the Hannum clock (pooled β = 0.27; 95 %CI: -0.25, 0.79), or telomere length (pooled Hedges'g = -0.46; 95 %CI: -1.10, 0.19). However, the opposite direction of effects for the two outcomes showed an indication of possible accelerated biological aging. After removal of influential effect sizes or low-quality studies, shorter telomere length was found in higher-exposed populations. CONCLUSION:The existing evidence for associations between pesticide exposure and biological aging is limited due to the scarcity of studies on epigenetic clocks and the substantial heterogeneity across studies on telomere length. High-quality studies incorporating more biomarkers of biological aging, focusing more on active chemical ingredients of pesticides and accounting for potential confounders are needed to enhance our understanding of the impact of pesticides on biological aging.

Pomelo (Citrus grandis) is a highly popular and juicy member of the citrus family. However, little is known regarding the occurrence and distribution of pesticides in pomelo. In this study, we determined the levels of legacy (n = 25) and current-use pesticides (n = 2) in all parts of pomelo (i.e., epicarp, mesocarp, endocarp, pulp, and seed) and paired soil and leaf samples collected from two pomelo orchards in South China. At least one target pesticide was detected in the pomelo fruit, soil, and leaf samples, indicating that these pesticides were ubiquitous. The spatial distribution of the total concentration of pesticides in the pomelo parts was in the order of epicarp (216 ng/g) > mesocarp (9.50 ng/g) > endocarp (4.40 ng/g) > seed (3.80 ng/g) > pulp (1.10 ng/g), revealing different spatial distributions in pomelo. Principal component analysis was performed based on the concentrations of the target pesticides in the pulp and paired samples of epicarp, leaf, topsoil, and deep soil to examine the translocation pathway of the pesticides in pomelo. Close correlations were found among the target pesticides, and the pesticides in the pulp were mainly transferred from the epicarp, topsoil, or deep soil. We also explored the factors that affected such transport and found that the main translocation pathway of the non-systemic pesticide (i.e., buprofezin) into the pulp was the epicarp, whereas the systemic pesticide (i.e., pyriproxyfen) was mainly derived from the soil. The cumulative chronic dietary risks of all the pesticides resulting from pomelo consumption were much lower than the acceptable daily intake values for the general population. However, the prolonged risk of exposure to these pesticides should not be underestimated. The potential health risks posed by legacy and current-use pesticides, which are widely and frequently utilized, should be given increased attention.

Biomonitoring of pesticide exposure has become a public concern because of its potential health effects. The present study investigated the acetylcholinesterase (AChE) inhibitory levels and their associated health effects in agricultural areas in Telangana, India. This cross-sectional included 341 exposed participants and 152 control participants from agricultural areas. A structured questionnaire was completed and blood and urine samples were collected to measure pesticides, dialkyle phosphate (DAP) metabolites, and AChE activity using liquid chromatography-tandem mass spectrometry and reversed-phase high-performance liquid chromatography. twenty-eight pesticides were detected in blood samples at concentrations ranging 0.42-45.77 ng/mL. Six DAP metabolites were also measured in urine, and all DAP metabolites were significantly higher in the exposed group. AChE activity is significantly reduced in individuals exposed for >10 years, raising concerns regarding possible neurological disorders. These results emphasise the urgent need to investigate the health effects of pesticides exposure, especially in agriculture.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

Pesticides represent one of the largest intentional inputs of potentially hazardous compounds into agricultural soils. However, as an important vegetable producing country, surveys on pesticide residues in soils of vegetable production areas are scarce in China. This study presented the occurrence, spatial distribution, correlation between vegetable types and pesticides, and ecological risk evaluation of 94 current-use pesticides in 184 soil samples from vegetable production areas of Zhejiang province (China). The ecological risks of pesticides to soil biota were evaluated with toxicity exposure ratios (TERs) and risk quotient (RQ). The pesticide concentrations varied largely from below the limit of quantification to 20703.06 μg/kg (chlorpyrifos). The situation of pesticide residues in Jiaxing is more serious than in other cities. Soils in the vegetable areas are highly diverse in pesticide combinations. Eisenia fetida suffered exposure risk from multiple pesticides. The risk posed by chlorpyrifos, which exhibited the highest RQs at all scenarios, was worrisome. Only a few pesticides accounted for the overall risk of a city, while the other pesticides make little or zero contribution. This work will guide the appropriate use of pesticides and manage soil ecological risks, achieving green agricultural production.

Lettuce, a globally consumed nutritious vegetable, is often linked to concerns regarding pesticide residues. To address this issue, we conducted field trials and utilized dynamiCROP modeling to examine the uptake, distribution, translocation, and dissipation of five pesticides (λ-cyhalothrin, difenoconazole, acetamiprid, dimethomorph, and β-cypermethrin) commonly detected in lettuce. At harvest, pesticides residues were below the maximum residue limits (MRLs) at 0.05, 0.39, 0.047, 0.72, and 0.072 mg kg, respectively. Simulation results elucidated distinct behaviors of the pesticides following application to lettuce foliage across various compartments. However, all pesticides exhibited a common dissipation trend, initially stabilizing or increasing before gradually declining. For all five pesticides, the largest contribution of residues on lettuce leaves came from the leaf surface during the early period after application, and from the soil in the long term. Health risk assessments indicated negligible risks associated with consuming lettuce containing these pesticides, both in the short and long term.

AIM:To evaluate whether serum metabolomics differ between ambulatory individuals with cerebral palsy (CP) compared with individuals with typical development and whether functional capacity is associated with metabolite abundance. METHOD:Thirty-eight adolescents and young adults were enrolled (CP: n = 19; typical development: n = 19). After functional capacity testing (10-meter walk, sit-to-stand, and peak knee flexion/extension torques), blood was drawn. Targeted serum metabolomics on hydrophilic metabolites were performed by high-performance liquid chromatography coupled with high-resolution and tandem mass spectrometry. Metabolite dimensionality reduction, pathway analysis, fold change, and t-tests evaluated changes in metabolite abundance. Associations were tested between functional measures and metabolite abundance. RESULTS:Individuals with CP had a significant increase in the abundance of essential amino acids, catabolic products of protein metabolism, and tricarboxylic acid cycle substrates, such as valine, tryptophan, kynurenic acid, and pyruvate (p < 0.05). Importantly, the abundance of numerous metabolites was only highly associated with functional capacity in individuals with CP such that greater abundance was associated with greater capacity, but not in those with typical development. INTERPRETATION:Our findings show clear increases in serum metabolites in individuals with CP, which are associated with functional capacity for movement. The altered metabolite profile measured after exercise might reflect increased energy production needed for movement. Appropriate nutritional intake during exercise might be needed given increased energy requirements.

Pesticides are frequently sprayed in greenhouses to ensure crop yields, where airborne particulate matter (PM) may serve as a carrier in depositing and transporting pesticides. However, little is known about the occurrence and fate of PM-borne pesticides in greenhouses. Herein, we examined the distribution, dissipation, and transformation of six commonly used pesticides (imidacloprid, acetamiprid, prochloraz, triadimefon, hexaconazole, and tebuconazole) in greenhouse PM (PM, PM, and PM) after application as well as the associated human exposure risks via inhalation. During 35 days of experiment, the six pesticides were detected in all PM samples, and exhibited size- and time-dependent distribution characteristics, with the majority of them (>64.6%) accumulated in PM. About 1.0-16.4% of initially measured pesticides in PM remained after 35 days, and a total of 12 major transformation products were elucidated, with six of them newly identified. The inhalation of PM could be an important route of human exposure to pesticides in the greenhouse, where the estimated average daily human inhalation dose (ADD) of the six individual pesticides was 2.1-1.2 × 10 pg/kg day after application (1-35 days). Our findings highlight the occurrence of pesticides/transformation products in greenhouse PM, and their potential inhalation risks should be further concerned.

Pesticide contamination poses a significant threat to non-target wildlife, including amphibians, many of which are already highly threatened. This study assessed the extent of pesticide exposure in dead frogs collected during a mass mortality event across eastern New South Wales, Australia between July 2021 and March 2022. Liver tissue from 77 individual frogs of six species were analysed for >600 legacy and contemporary pesticides, including rodenticides. More than a third (36 %) of the liver samples contained at least one of the following pesticides: brodifacoum, dieldrin, DDE, heptachlor/heptachlor epoxide, fipronil sulfone, and 2-methyl-4-chlorophenoxyacetic acid (MCPA). Brodifacoum, a second-generation anticoagulant rodenticide, was found in four of the six frog species analysed: the eastern banjo frog (Limnodynastes dumerilii), cane toad (Rhinella marina), green tree frog (Litoria caerulea) and Peron's tree frog (Litoria peronii). This is the first report of anticoagulant rodenticide detected in wild amphibians, raising concerns about potential impacts on frogs and extending the list of taxa shown to accumulate rodenticides. Dieldrin, a banned legacy pesticide, was also detected in two species: striped marsh frog (Limnodynastes peronii) and green tree frog (Litoria caerulea). The toxicological effects of these pesticides on frogs are difficult to infer due to limited comparable studies; however, due to the low frequency of detection the presence of these pesticides was not considered a major contributing factor to the mass mortality event. Additional research is needed to investigate the effects of pesticide exposure on amphibians, particularly regarding the impacts of second-generation anticoagulant rodenticides. There is also need for continued monitoring and improved conservation management strategies for the mitigation of the potential threat of pesticide exposure and accumulation in amphibian populations.

Multiclass metabolomic studies have become popular for revealing the differences in multiple stages of complex diseases, various lifestyles, or the effects of specific treatments. In multiclass metabolomics, there are multiple data manipulation steps for analyzing raw data, which consist of data filtering, the imputation of missing values, data normalization, marker identification, sample separation, classification, and so on. In each step, several to dozens of machine learning methods can be chosen for the given data set, with potentially hundreds or thousands of method combinations in the whole data processing chain. Therefore, a clear understanding of these machine learning methods is helpful for selecting an appropriate method combination for obtaining stable and reliable analytical results of specific data. However, there has rarely been an overall introduction or evaluation of these methods based on multiclass metabolomic data. Herein, detailed descriptions of these machine learning methods in multiple data manipulation steps are reviewed. Moreover, an assessment of these methods was performed using a benchmark data set for multiclass metabolomics. First, 12 imputation methods for imputing missing values were evaluated based on the PSS (Procrustes statistical shape analysis) and NRMSE (normalized root-mean-square error) values. Second, 17 normalization methods for processing multiclass metabolomic data were evaluated by applying the PMAD (pooled median absolute deviation) value. Third, different methods of identifying markers of multiclass metabolomics were evaluated based on the CWrel (relative weighted consistency) value. Fourth, nine classification methods for constructing multiclass models were assessed using the AUC (area under the curve) value. Performance evaluations of machine learning methods are highly recommended to select the most appropriate method combination before performing the final analysis of the given data. Overall, detailed descriptions and evaluation of various machine learning methods are expected to improve analyses of multiclass metabolomic data.

Pesticides present a significant risk for both humans and the environment. However, quantitative data for a broad range of airborne pesticides in agricultural areas are missing. During or after the application, pesticides can reach the atmosphere and partition between the particulate and gaseous phase. As part of the EU project SPRINT, weekly ambient air samples were collected from two agricultural areas in Portugal (vineyard) and the Netherlands (potatoes, onions, and sugarbeet) between April 2021 and June 2022 using high-volume air samplers. The samples were analysed for 329 pesticides, of which 99 were detected. The most frequently detected compounds included the fungicides folpet, fenpropidin and mandipropamid, the insecticide chlorpyrifos-methyl, the herbicide terbuthylazine, and the metabolite prothioconazole-desthio, which were found with detection frequencies between 40 and 57 %. Pesticide concentrations ranged between 0.003 ng/m and 10 ng/m. Remarkably, 97 % of the samples contained at least one pesticide and in 95 % of the samples, pesticide mixtures were present. The calculated particle phase fractions correlated with the octanol-air partitioning coefficient for most of the investigated compounds. Furthermore, calculated daily inhalation rates for individual pesticides and pesticide mixtures were far below the Acceptable Daily Intake (ADI) with a margin of exposure (MOE) of >1000 for the highest calculated daily inhalation rate for a child. However, as this value only includes pesticide intake from food and drinking water and considering that 91 % of the detected pesticides are associated with potential adverse human health effects. These findings highlight the broad range of airborne pesticides in agricultural areas and the need for quantitative data to include the intake of mixtures of highly hazardous pesticides by inhalation in human risk assessment.

Untargeted metabolomics is a growing field, in which recent advances in high-resolution mass spectrometry coupled with liquid chromatography (LC-MS) have facilitated untargeted approaches as a result of improvements in sensitivity, mass accuracy, and resolving power. However, a very large amount of data are generated. Consequently, using computational tools is now mandatory for the in-depth analysis of untargeted metabolomics data. This article describes MetAbolomics ReSearch (MARS), an all-in-one vendor-agnostic graphical user interface-based software applying LC-MS analysis to untargeted metabolomics. All of the analytical steps are described (from instrument data conversion and processing to statistical analysis, annotation/identification, quantification, and preliminary biological interpretation), and tools developed to improve annotation accuracy (e.g., multiple adducts and in-source fragmentation detection, trends across samples, and the MS/MS validator) are highlighted. In addition, MARS allows in-house building of reference databases, to bypass the limits of freely available MS/MS spectra collections. Focusing on the flexibility of the software and its user-friendliness, which are two important features in multipurpose software, MARS could provide new perspectives in untargeted metabolomics data analysis.

Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.

Assessing a complex mixture of pesticides at the impacted sites has been challenging for risk assessors for 50 years. The default assumption is that at low concentrations, pesticides interact additively with one another; thus, the risk posed by each component of a complex mixture could be simply added up. The EPA interaction-based hazard index (HI) modifies this assumption using a binary weight-of-evidence (BINWOE). However, data gaps often preclude HI use at most sites. This study evaluated these assumptions using the BINWOE to estimate the hazard index (HI) of select pesticide mixtures. The lack of in vivo binary interaction data led us to use a cell line, SH-SY5Y, to obtain the data necessary for the BINWOE approach. In the risk assessment, we considered the most active exposure scenario inhaling a mixture of volatile pesticides from contaminated soil and groundwater. The potential interactions between pesticides in 15 binary mixtures were investigated using the MTT assay in SH-SY5Y cells. Our findings showed that 60% of the binary mixtures elicited synergism (in at least one concentration), 27% displayed antagonism, and 13% showed additive effects in SH-SY5Y cells. Combining human safety data with in vitro interaction data indicated that adults and toddlers were at the highest risk when considering industrial and commercial land use, respectively, compared to other subpopulations. Incorporating interaction data into the risk assessment either increased the risk by up to 20% or decreased the risk by 2%, depending on the mixture. Our results demonstrate the predominant synergistic interactions, even at low concentrations, altered risk characterization at the complex operating site. Most concerning, organochlorine pesticides with the same mechanism of action did not follow dose additivity when evaluated by SH-SY5Y cell lines. Based on our observations, we caution that current HI methods based on additivity assumptions may underestimate the risk of organochlorine mixtures.

Ginger is consumed as a spice and medicine globally. However, pesticide residues in ginger and their residue changes during processing remain poorly understood. Our results demonstrate that clothianidin, carbendazim and imidacloprid were the top detected pesticides in 152 ginger samples with detection rates of 17.11-27.63%, and these pesticides had higher average residues of 44.07-97.63 μg/kg. Although most samples contained low levels of pesticides, 66.45% of the samples were detected with pesticides, and 38.82% were contaminated with 2-5 pesticides. Peeling, washing, boiling and pickling removed different amounts of pesticides from ginger (processing factor range: 0.06-1.56, most <1). By contrast, pesticide residues were concentrated by stir-frying and drying (0.50-6.45, most >1). Pesticide residues were influenced by pesticide physico-chemical parameters involving molecular weight, melting point, degradation point and octanol-water partition coefficient by different ginger processing methods. Chronic and acute dietary risk assessments suggest that dietary exposure to pesticides from ginger consumption was within acceptable levels for the general population. This study sheds light on pesticide residues in ginger from market to processing and is of theoretical and practical value for ensuring ginger quality and safety.

Metabolomics is the study of metabolites present in a living system. It is a rapidly growing field aimed at discovering novel compounds, studying biological processes, diagnosing diseases, and ensuring the quality of food products. Recently, the analysis of natural samples has become important to explore novel bioactive compounds and to study how environment and genetics affect living systems. Various metabolomics techniques, databases, and data analysis tools are available for natural sample metabolomics. However, choosing the right method can be a daunting exercise because natural samples are heterogeneous and require untargeted approaches. This tutorial review aims to compile the latest technologies to guide an early-career scientist on natural sample metabolomics. First, different extraction methods and their pros and cons are reviewed. Second, currently available metabolomics databases and data analysis tools are summarized. Next, recent research on metabolomics of milk, honey, and microbial samples is reviewed. Finally, after reviewing the latest trends in technologies, a checklist is presented to guide an early-career researcher on how to design a metabolomics project. In conclusion, this review is a comprehensive resource for a researcher planning to conduct their first metabolomics analysis. It is also useful for experienced researchers to update themselves on the latest trends in metabolomics.

New hippocampal neurons are continuously generated in the adult human brain. Several studies have demonstrated that the proliferation of hippocampal cells is strongly influenced by a variety of stimuli, including pesticides exposure. These effects are particularly important because neurogenesis dysregulation could be associated with the decline of neuronal and cognitive functions and the possible development of neuropsychiatric disorders.

The throughput of mass spectrometers and the amount of publicly available metabolomics data are growing rapidly, but analysis tools such as molecular networking and Mass Spectrometry Search Tool do not scale to searching and clustering billions of mass spectral data in metabolomics repositories. To address this limitation, we designed MASST+ and Networking+, which can process datasets that are up to three orders of magnitude larger than those processed by state-of-the-art tools.

This Editorial introduces the Special Issue titled "State-of-the-Art Environmental Chemical Exposomics and Metabolomics" [...].

BACKGROUND:Organochlorine pesticides, with their environmental persistence and bioaccumulation potential, have gained significant attention. This study explores the impact of organochlorine pesticides on mortality and chronic diseases, investigates their link to inflammatory states, and examines the role of anti-inflammatory diets in mitigating adverse reactions to these pesticides. METHODS:This study, with 2,847 participants, used gas chromatography and mass spectrometry to measure organochlorine pesticide exposure in NHANES data. Conventional statistical methodologies, encompassing survival curves, Cox proportional hazards regression, regression analysis, and restricted quadratic spline analysis, were employed to investigate the association between pesticides and mortality, chronic ailments, and inflammation. Furthermore, machine learning techniques, comprising RF, AdaBoost, Extra-Trees, LightGBM, and BPNN, were leveraged to evaluate the impact of pesticides on chronic disease and mortality prognostication. RESULTS:Organochlorine pesticides were significantly and positively correlated with increased mortality (p<0.05). Additionally, these pollutants were linked to the incidence of chronic diseases such as chronic kidney disease, diabetes, and hypertension (p< 0.05). Our study, utilizing various machine learning models, also showed a notable increase in the Area Under the Curve when incorporating organochlorine pesticide indicators into the model as opposed to excluding them. Furthermore, strong correlations were observed between serum c-reactive protein (CRP) and CRP to serum albumin ratio (CAR) concentrations with these substances, demonstrating their pro-inflammatory effects at specific concentrations. Interestingly, cutting down on dietary inflammation through changes in diet effectively reduced the risk of death at high organochlorine pesticide exposure levels, but the effect was less noticeable at low to moderate exposure levels. CONCLUSIONS:Exposure to organochlorine pesticides was linked to a higher risk of mortality, likely due to an increased prevalence of chronic diseases. In this context, inflammation played a crucial role, and adopting an anti-inflammatory diet significantly reduced the mortality risk associated with these pesticides.

Among rising environmental concerns, emerging contaminants constitute a variety of different chemicals and biological agents. The composition, residence time in environmental media, chemical interactions, and toxicity of emerging contaminants are not fully known, and hence, their regulation becomes problematic. Some of the important groups of emerging contaminants are pesticides and pesticide transformation products (PTPs), which present a considerable obstacle to maintaining and preserving ecosystem health. This review article aims to thoroughly comprehend the occurrence, fate, and ecotoxicological importance of pesticide transformation products (PTPs). The paper provides an overview of pesticides and PTPs as contaminants of emerging concern and discusses the modes of degradation of pesticides, their properties and associated risks. The degradation of pesticides, however, does not lead to complete destruction but can instead lead to the generation of PTPs. The review discusses the properties and toxicity of PTPs and presents the methods available for their detection. Moreover, the present study examines the existing regulatory framework and suggests the need for the development of new technologies for easy, routine detection of PTPs to regulate them effectively in the environment.

Metabolomics, leveraging techniques like NMR and MS, is crucial for understanding biochemical processes in pathophysiological states. This field, however, faces challenges in metabolite sensitivity, data complexity, and omics data integration. Recent machine learning advancements have enhanced data analysis and disease classification in metabolomics. This study explores machine learning integration with metabolomics to improve metabolite identification, data efficiency, and diagnostic methods. Using deep learning and traditional machine learning, it presents advancements in metabolic data analysis, including novel algorithms for accurate peak identification, robust disease classification from metabolic profiles, and improved metabolite annotation. It also highlights multiomics integration, demonstrating machine learning's potential in elucidating biological phenomena and advancing disease diagnostics. This work contributes significantly to metabolomics by merging it with machine learning, offering innovative solutions to analytical challenges and setting new standards for omics data analysis.

PURPOSE:Asthma is a highly heterogeneous disease. Metabolomics plays a pivotal role in the pathogenesis and development of asthma. The main aims of our study were to explore the underlying mechanism of asthma and to identify novel biomarkers through metabolomics approach. METHODS:Serum samples from 102 asthmatic patients and 18 healthy controls were collected and analyzed using liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS) system. Multivariate analysis and weighted gene co-expression network analysis (WGCNA) were performed to explore asthma-associated metabolomics profile and metabolites. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for pathway enrichment analysis. Subsequently, 2 selected serum hub metabolites, myristoleic acid and dodecanoylcarnitine, were replicated in a validation cohort using ultra-high performance LC-MS/MS system (UHPLC-MS/MS). RESULTS:Distinct metabolomics profile of asthma was revealed by multivariate analysis. Then, 116 overlapped asthma-associated metabolites between multivariate analysis and WGCNA, including 12 hub metabolites, were identified. Clinical features-associated hub metabolites were also identified by WGCNA. Among 116 asthma-associated metabolites, Sphingolipid metabolism and valine, leucine and isoleucine biosynthesis were revealed by KEGG analysis. Furthermore, serum myristoleic acid and dodecanoylcarnitine were significantly higher in asthmatic patients than in healthy controls in validation cohort. Additionally, serum myristoleic acid and dodecanoylcarnitine demonstrated high sensitivities and specificities in predicting asthma. CONCLUSIONS:Collectively, asthmatic patients showed a unique serum metabolome. Sphingolipid metabolism and valine, leucine and isoleucine biosynthesis were involved in the pathogenesis of asthma. Furthermore, our results suggest the promising values of serum myristoleic acid and dodecanoylcarnitine for asthma diagnosis in adults.

Rural workers are disproportionally exposed to pesticides and might be at an increased risk of developing chronic diseases. Here, we investigated the impact of pesticide exposure on breast cancer (BC) risk and disease profile in rural female workers. This is a case-control study that prospectively included 758 individuals. The study was conducted in the Southwest region of Paraná state in Brazil, a region characterized by family-based agriculture and intensive use of pesticides. We found that this region has a 41% higher BC diagnosis rate and 14% higher BC mortality rate than the mean rates in Brazil, as well as a pesticide trade volume about 6 times higher than the national average. We showed substantial exposure in this population and found that even women who did not work in the fields but performed equipment decontamination and clothes washing of male partners who worked in the fields had urine samples positive for glyphosate, atrazine, and/or 2,4-D. The crude association showed a significantly higher risk of BC among women exposed to pesticides (OR: 1.58, 95% CI 1.18-2.13). Adjusted analyses showed a lower and nonstatistically significant association (OR: 1.30, 95% CI 41 0.87-1.95). Stratification on disease profile showed a significantly higher risk of lymph node metastasis (adjusted OR: 2.19, 95% CI 1.31-3.72) in women exposed to pesticides. Our findings suggest that female populations exposed to pesticides are at a higher risk of developing BC with a more aggressive profile and draw attention to the need to monitor rural populations potentially exposed to pesticides in the field or at home.

Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.com/HuanLab/AVIR). The rationale is that metabolic features with computational variation have a poor correlation between chromatographic peak area and peak height-based quantifications across the samples in a study. AVIR was trained on a set of 696 manually curated metabolic features and achieved an accuracy of 94% in a 10-fold cross-validation. When tested on various external data sets from public metabolomics repositories, AVIR demonstrated an accuracy range of 84%-97%. Finally, tested on a large-scale metabolomics study, AVIR clearly indicated features with computational variation and thus guided us to manually correct them. Our results show that 75.3% of the samples with computational variation had a relative intensity difference of over 20% after correction. This demonstrates the critical role of AVIR in reducing computational variation to improve quantitative certainty in untargeted metabolomics analysis.

Per- and polyfluoroalkyl substances (PFAS), ubiquitous environmental contaminants, pose significant challenges to ecosystems and human health. While cell cultures have emerged as new approach methodologies (NAMs) in ecotoxicity research, metabolomics is an emerging technique used to characterize the small-molecule metabolites present in cells and to understand their role in various biological processes. Integration of metabolomics with cell cultures, known as cell culture metabolomics, provides a novel and robust tool to unravel the complex molecular responses induced by PFAS exposure. In vitro testing also reduces reliance on animal testing, aligning with ethical and regulatory imperatives. The current review summarizes key findings from recent studies utilizing cell culture metabolomics to investigate PFAS toxicity, highlighting alterations in metabolic pathways, biomarker identification, and the potential linkages between metabolic perturbations. Additionally, the paper discusses different types of cell cultures and metabolomics methods used for studies of environmental contaminants and particularly PFAS. Future perspectives on the combination of metabolomics with other advanced technologies, such as single-cell metabolomics (SCM), imaging mass spectrometry (IMS), extracellular flux analysis (EFA), and multi-omics are also explored, which offers a holistic understanding of environmental contaminants. The synthesis of current knowledge and identification of research gaps provide a foundation for future investigations that aim to elucidate the complexities of PFAS-induced cellular responses and contribute to the development of effective strategies for mitigating their adverse effects on human health.

A vast range of pesticides have been routinely employed for plant protection throughout the last few decades. Pesticides can enter non-target organisms in various ways, posing health hazards. Exposure to different environmental pollutants, including pesticides, can affect the human gut flora. Metabolites generated from the gut microbiota play an essential role in the host's health by regulating metabolic homeostasis. A disruption in this equilibrium can lead to the emergence of numerous illnesses and their etiology. Pesticides have been shown in a few recent studies to harm the host's gut microbiome. As a result, there is an urgent need to investigate the impact of pesticides on gut microbiota-mediated immunity. Metabolic alterations in the host may give a better understanding of pesticide-induced harm. This review highlights the potential consequences of pesticide exposure on gut microbiota composition and function, mainly focusing on how it might alter the production of secondary metabolites with potential downstream implications for host health.

INTRODUCTION:Metabolomics produces vast quantities of data but determining which metabolites are the most relevant to the disease or disorder of interest can be challenging. OBJECTIVES:This study sought to demonstrate how behavioral models of psychiatric disorders can be combined with metabolomics research to overcome this limitation. METHODS:We designed a preclinical, untargeted metabolomics procedure, that focuses on the determination of central metabolites relevant to substance use disorders that are (a) associated with changes in behavior produced by acute drug exposure and (b) impacted by repeated drug exposure. Untargeted metabolomics analysis was carried out on liquid chromatography-mass spectrometry data obtained from 336 microdialysis samples. Samples were collected from the medial striatum of male Sprague-Dawley (N = 21) rats whilst behavioral data were simultaneously collected as part of a (±)-3,4-methylenedioxymethamphetamine (MDMA)-induced behavioral sensitization experiment. Analysis was conducted by orthogonal partial least squares, where the Y variable was the behavioral data, and the X variables were the relative concentrations of the 737 detected features. RESULTS:MDMA and its derivatives, serotonin, and several dopamine/norepinephrine metabolites were the greatest predictors of acute MDMA-produced behavior. Subsequent univariate analyses showed that repeated MDMA exposure produced significant changes in MDMA metabolism, which may contribute to the increased abuse liability of the drug as a function of repeated exposure. CONCLUSION:These findings highlight how the inclusion of behavioral data can guide metabolomics data analysis and increase the relevance of the results to the phenotype of interest.

In view of the ongoing climate change and the ever-growing world population, novel agricultural solutions are required to ensure sustainable food supply. Microbials, natural substances, semiochemicals and double stranded RNAs (dsRNAs) are all considered potential low risk pesticides. DsRNAs function at the molecular level, targeting specific regions of specific genes of specific organisms, provided that they share a minimal sequence complementarity of approximately 20 nucleotides. Thus, dsRNAs may offer a great alternative to conventional chemicals in environmentally friendly pest control strategies. Any low-risk pesticide needs to be efficient and exhibit low toxicological potential and low environmental persistence. Having said that, in the current review, the mode of dsRNA action is explored and the parameters that need to be taken into consideration for the development of efficient dsRNA-based pesticides are highlighted. Moreover, since dsRNAs mode of action differs from those of synthetic pesticides, custom-made risk assessment schemes may be required and thus, critical issues related to the risk assessment of dsRNA pesticides are discussed here.

Pesticides are chemical substances of natural or synthetic origin that are used to eradicate pests and insects. These are indispensable in the agricultural processes for better crop production. Pesticide use aims to promote crop yield and protect the crops from diseases and damage. Pesticides must be handled carefully and disposed of appropriately because they are dangerous to people and other species by default. Environmental pollution occurs when pesticide contamination spreads away from the intended plants. Older pesticides such as lindane and dichlorodiphenyltrichloroethane (DDT) may remain in water and soil for a longer time. These accumulate in various parts of the food chain and cause damage to the ecosystem. Biological techniques in the management of pest control such as importation, augmentation, and conservation, and the accompanying procedures are more efficient, less expensive, and ecologically sound than other ways. This review mainly focuses on the consequences on the targeted and non-targeted organisms including the health and well-being of humans by the use of pesticides and their toxicity. The side effects that occur when a pesticide's LD exceeds the accepted limit through oral or skin penetration due to their binding to various receptors such as estrogen receptors, GABA, EGFR, and others. These pesticide classes include carbamates, pyrethroids, organochlorides, organophosphorus, and others. The current study seeks to highlight the urgent requirement for a novel agricultural concept that includes a major reduction in the use of chemical pesticides.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

Cellular metabolism influences all aspects of cellular function and is crucial for overall organismal health. Metabolic disorders related to cardiovascular health can lead to cardiovascular diseases (CVDs). Moreover, associated comorbidities may also damage cardiovascular metabolism, exacerbating CVD and perpetuating a vicious cycle. Given the prominent role of metabolic alterations in CVD, metabolomics has emerged as an imperative technique enabling a comprehensive assessment of metabolites and metabolic architecture within the body. Metabolite profile and metabolic pathways help to deepen and broaden our understanding of complex genomic landscape and pathophysiology of CVD. Here in this review, we aim to overview the experimental and clinical applications of metabolomics in pathogenesis, diagnosis, prognosis, and management of various CVD plus future perspectives and limitations.

Parkinson's disease (PD) affects more people worldwide than just aging alone can explain. This is likely due to environmental influences, genetic makeup, and changes in daily habits. The disease develops in a complex way, with movement problems caused by Lewy bodies and the loss of dopamine-producing neurons. Some research suggests Lewy bodies might start in the gut, hinting at a connection between these structures and gut health in PD patients. These patients often have different gut bacteria and metabolites. Pesticides are known to increase the risk of PD, with evidence showing they harm more than just dopamine neurons. Long-term exposure to pesticides in food might affect the gut barrier, gut bacteria, and the blood-brain barrier, but the exact link is still unknown. This review looks at how pesticides and gut bacteria separately influence PD development and progression, highlighting the harmful effects of pesticides and changes in gut bacteria. We have examined the interaction between pesticides and gut bacteria in PD patients, summarizing how pesticides cause imbalances in gut bacteria, the resulting changes, and their overall effects on the PD prognosis.

The intensive use of pesticides in Mexican agriculture has contributed significantly to the increase in food production, but at the same time represents potential risk to biota. This situation creates a dilemma between the need to increase food production and the preservation of the environment and human health. Aquatic invertebrates play a vital role in the balance of aquatic ecosystems but are sensitive to pesticides contamination. The sensitivity of aquatic invertebrates to pesticides contamination has led them to be used to assess the potential impact of this contamination on aquatic ecosystems. In the present study, conducted in the Ayuquila-Armería basin, the following aims were achieved: 1) quantifying the presence of 20 pesticides in river sediments, 2) assessing the spatiotemporal distribution of pesticides in river sediments, 3) determining the potential risk to aquatic invertebrates, and 4) prioritizing pesticides based on their potential risk. Twelve pesticides were consistently quantified in 192 river sediments samples. The pesticides with the highest concentrations were ametrine, malathion and picloram. The temporal analysis showed seasonality in pesticide concentrations, with higher detection frequencies during the wet season. The risk assessment showed that aquatic invertebrates may be affected by the concentrations of carbofuran, malathion, diazinon and ametrine. Pesticides prioritization identified ametrine, carbofuran, and diazinon as major concerns based on the methodology that considers the Frequency and Extent of Exceedance. This study provides valuable insights into the current pesticides scenario in the Ayuquila-Armería River sediments. The findings underscore the need for sustainable alternatives to mitigate the ecological risks associated with pesticides contamination in this aquatic ecosystem.

Pesticides are frequently used to control target pests in the production of spice crops such as chives (Allium ascalonicum). However, little information is available on the responses and underlying mechanisms of pesticide exposure in this crop. Our findings revealed that the uptake, transportation, and subcellular distribution of three typical pesticides-the fungicide pyraclostrobin (PAL), insecticide acetamiprid (ATP), and herbicide pendimethalin (PND) in chives, as well as their physiological, biochemical, metabolic, and transcriptomic responses-were dependent on pesticide properties, especially hydrophobicity. The distribution of PAL and PND in chives decreased in the order root > stem > leaf, but the distribution order of ATP was the opposite. The proportion of PAL and PND in the solid phase of the root cells gradually increased, but ATP mainly existed in the cell-soluble component, indicating that the latter had an upward translocation ability and thus mainly accumulated in the leaves. Malondialdehyde levels in chive leaves were not significantly affected by exposure to these pesticides; however, the activities of superoxide dismutase (SOD) and catalase (CAT) in chive leaves increased significantly. Moreover, these pesticides exhibited critical differences in chive responses through the interaction of metabolites and regulation of differentially expressed genes. PAL dramatically influenced five carbohydrate metabolic pathways (34.35 %), disturbing the starch-to-sucrose balance. ATP strongly affected five amino acid (AC) metabolic pathways (33.38 %), enhancing four free amino acid levels. PND notably affected eight fatty acid (FA) metabolic pathways (25.38 %), increasing two unsaturated and decreasing one saturated FA. Simultaneously, PND, ATP, and PND accumulated in the chives could be detoxified through metabolic pathways mediated by cytochrome P450 (P450) and glycosyltransferase (GT)/glutathione S-transferase (GST), producing phase I (7, 4, and 5) and II (11, 13, and 10) metabolites, respectively. This study provides important molecular insights into the responses and underlying mechanisms of spice crop exposure to pesticides.

BACKGROUND:Pesticides present widespread risks to human and environmental health, yet selection criteria for end-users that factor in differences in risk between compounds are scant. We developed a system to classify pesticide risks and hazards with respect to human and environmental health and produce a minimum (lower risk) pesticide list. METHODS:We classified 659 pesticides by acute and chronic risks to human health (eg, respiratory and carcinogenic effects) and by environmental risks, including biomagnification and atmospheric ozone depletion and risks to aquatic life, terrestrial wildlife, and pollinators. From this analysis, we produced a guideline for selection of lower risk pesticides. The classification of highly hazardous and high-risk compounds has been tested in more than a million farm households in the tropics, and in US integrated pest management (IPM) programmes. The full classification, including the minimum pesticide list, has been used in management of the fall armyworm (Spodoptera frugiperda) throughout Africa and Asia. FINDINGS:Our analysis developed a stand-alone guideline for selection of lower risk pesticides. When classifying pesticides in current use against the fall armyworm in Africa, our guideline identified chemicals that are effective and of lower risk to human and environmental health. We argue that a minimum (lower risk) pesticides list, which meets IPM needs, could be developed from our classification system. INTERPRETATION:As far as we are aware, our analysis is the first to propose a method for implementing the idea of a minimum pesticide list and the first to outline lower risk candidate compounds. Currently accepted criteria for defining highly hazardous pesticides do not adequately protect human bystanders, aquatic life, terrestrial wildlife, and pollinators. FUNDING:The Sustainable Agriculture Network, the Rainforest Alliance, the US Department of Agriculture-National Institute of Food and Agriculture, the US Department of Agriculture, the Foreign Agricultural Service, the US Agency for International Development, the International Maize and Wheat Improvement Center, and the UN Food and Agriculture Organization.

Progress toward the development of efficacious therapies for Alzheimer's disease (AD) is halted by a lack of understanding early underlying pathological mechanisms. Systems biology encompasses several techniques including genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Metabolomics is the newest omics platform that offers great potential for the diagnosis and prognosis of neurodegenerative diseases as an individual's metabolome reflects alterations in genetic, transcript, and protein profiles and influences from the environment. Advancements in the field of metabolomics have demonstrated the complexity of dynamic changes associated with AD progression underscoring challenges with the development of efficacious therapeutic interventions. Defining systems-level alterations in AD could provide insights into disease mechanisms, reveal sex-specific changes, advance the development of biomarker panels, and aid in monitoring therapeutic efficacy, which should advance individualized medicine. Since metabolic pathways are largely conserved between species, metabolomics could improve the translation of preclinical research conducted in animal models of AD into humans. A summary of recent developments in the application of metabolomics to advance the AD field is provided below.

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

The Metabolomics Workbench, available at www.metabolomicsworkbench.org, is a public repository for metabolomics metadata and experimental data spanning various species and experimental platforms, metabolite standards, metabolite structures, protocols, tutorials, and training material and other educational resources. It provides a computational platform to integrate, analyze, track, deposit and disseminate large volumes of heterogeneous data from a wide variety of metabolomics studies including mass spectrometry (MS) and nuclear magnetic resonance spectrometry (NMR) data spanning over 20 different species covering all the major taxonomic categories including humans and other mammals, plants, insects, invertebrates and microorganisms. Additionally, a number of protocols are provided for a range of metabolite classes, sample types, and both MS and NMR-based studies, along with a metabolite structure database. The metabolites characterized in the studies available on the Metabolomics Workbench are linked to chemical structures in the metabolite structure database to facilitate comparative analysis across studies. The Metabolomics Workbench, part of the data coordinating effort of the National Institute of Health (NIH) Common Fund's Metabolomics Program, provides data from the Common Fund's Metabolomics Resource Cores, metabolite standards, and analysis tools to the wider metabolomics community and seeks data depositions from metabolomics researchers across the world.

Sustainable agriculture aims to meet the food needs of the growing world population while ensuring minimal impact on the environment and humans as well as productivity. Although pesticides represent the backbone of the agri-food sector in its endeavor to secure food production their application is perceived by many as an obstacle towards the achievement of sustainability; the main concerns are linked with their adverse effects on human health and the environment. Τhis review aims to present the status of chemical plant protection and provide insights into the use of pesticides within the context of sustainable agriculture. It mainly focuses on the strengthened legislation frameworks, which especially in the European Union and the United States of America ensure the placement in the market of pesticides with acceptable toxicological and environmental profiles without compromising crop production. Furthermore, the implementation of Integrated Pest Management principles plays a key role in the sustainable use of pesticides. The stringent regulatory requirements have resulted in the dramatic increase of the associated effort and costs in pesticide research and development (R&D) of improved products. Nevertheless, the investment of leading agrochemical companies in the R&D of new pesticides remains high. All the above set the ground for the sustainable use of pesticides in crop production while their successful application remains a challenge.

Pesticides are a broad group of heterogeneous chemicals that have a significant public health benefit by increasing food production productivity and decreasing food-borne and vector-borne diseases. However, depending on the agent and the exposure, they may pose health risks. Because of their behavior, acute accidental toxic exposures occur more commonly in children. Because of the dietary habits and greater intake of foods per kilogram in children and because some infants are breastfed, there is also concern about the effects on them of low-level environmental exposures. In the absence of direct conclusive evidence, consistent and relevant observations have led some investigators to infer that chronic low-dose exposure to certain pesticides might pose a potential hazard to the health and development of infants and children. Other investigators have concluded that such inferences can be neither supported nor refuted at the present time. The pediatrician has a role to play in recognizing the symptoms of acute exposure and to be able to provide appropriate treatment. It is essential to study whether there are subtle neurologic effects that may result from low-level pesticide exposures in individual patients.

Pesticides, a wide class of environmental contaminants, may cause both acute and delayed health effects in exposed subjects. These effects can range from simple irritation of the skin and eyes to more severe effects such as affecting the nervous system, the reproductive system and cancer. The molecular mechanisms underlying such effects are still under investigation. Epigenetics is the study of heritable changes in gene expression that occur without a change in the DNA sequence. Several epigenetic mechanisms, including DNA methylation, histone modifications and microRNA expression, can be triggered by environmental factors. We review current evidences indicating that epigenetic modifications may mediate pesticide effects on human health. In vitro, animal, and human investigations have identified several classes of pesticides that modify epigenetic marks, including endocrine disruptors, persistent organic pollutants, arsenic, several herbicides and insecticides. Several investigations have examined the effects of environmental exposures and epigenetic markers, and identified toxicants that modify epigenetic states. These modifications are similar to the ones found in pathological tissue samples. In spite of the current limitations, available evidence supports the concept that epigenetics holds substantial potential for furthering our understanding of the molecular mechanisms of pesticides health effects, as well as for predicting health-related risks due to conditions of environmental exposure and individual susceptibility.

Peanut oil, edible vegetable oil largely consumed in China, may be polluted with pesticides during both peanut cultivation and processing. In this study, we analyzed organochlorine pesticides, five currently used pesticides and two degradation products, in soils, seeds, peanuts, oil and dregs and systematically tracked variations of their levels in field soils and during the pressing process. The results showed that the application of metolachlor, pirimicarb and quizalofop-p-ethyl pesticides during peanut cultivation caused their concentrations in peanuts to increase. In most samples, the concentration of 3-phenoxybenzoic acid was higher than that of λ-cyhalothrin, and the variation trends of λ-cyhalothrin and 3-phenoxybenzoic acid in soil samples were similar, which indicate that after application, most λ-cyhalothrin may rapidly be degraded to 3-phenoxybenzoic acid. Regarding the pressing process of peanut oil, the sum of mass of oil and shells was less than the mass of the corresponding raw peanut. Compared with that in peanuts, the total mass of most pesticides in oil and shells was lower, while that of two degradation products was higher, an indication that the degradation products were still generated during the pressing process. Finally, the assessment of health risk of different age groups consuming the studied peanuts and peanut oil showed that the risk was very low.

Osteoporosis (OP) is a systemic disease characterized by bone metabolism imbalance and bone microstructure destruction, which causes serious social and economic burden. At present, the diagnosis and treatment of OP mainly rely on imaging combined with drugs. However, the existing pathogenic mechanisms, diagnosis and treatment strategies for OP are not clear and effective enough, and the disease progression that cannot reflect OP further restricts its effective treatment. The application of metabolomics has facilitated the study of OP, further exploring the mechanism and behavior of bone cells, prevention, and treatment of the disease from various metabolic perspectives, finally realizing the possibility of a holistic approach. In this review, we focus on the application of metabolomics in OP research, especially the newer systematic application of metabolomics and treatment with herbal medicine and their extracts. In addition, the prospects of clinical transformation in related fields are also discussed. The aim of this study is to highlight the use of metabolomics in OP research, especially in exploring the pathogenesis of OP and the therapeutic mechanisms of natural herbal medicine, for the benefit of interdisciplinary researchers including clinicians, biologists, and materials engineers.

Parkinson's disease (PD) is a neurodegenerative disorder which mainly targets motor symptoms such as tremor, rigidity, bradykinesia and postural instability. The physiological changes occur due to dopamine depletion in basal ganglia region of the brain. PD aetiology is not yet elucidated clearly but genetic and environmental factors play a prominent role in disease occurrence. Despite of various environmental factors, pesticides exposure has been convicted as major candidate in PD pathogenesis. Among various pesticides 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) has been widely investigated in PD following with paraquat (PQ), maneb (MB), organochlorines (OC) and rotenone. Effect of these pesticides has been suggested to be involved in oxidative stress, alterations in dopamine transporters, mitochondrial dysfunction, α-synuclein (αSyn) fibrillation, and neuroinflammation in PD. The present review discusses the influence of pesticides in neurodegeneration and its related epidemiological studies conducted in PD. Furthermore, we have deliberated the common pesticides involved in PD and its associated genetic alterations and the probable mechanism of them behind PD pathogenesis. Hence, we conclude that pesticides play a prominent role in PD pathogenesis and advance research is needed to investigate the alterations in genetic and mechanistic aspects of PD.

Pesticides make indispensable contributions to agricultural productivity. However, the residues after their excessive use may be harmful to crop production, food safety and human health. Although the ability of plants (especially crops) to accumulate and metabolize pesticides has been intensively investigated, data describing the chemical and metabolic processes in plants are limited. Understanding how pesticides are metabolized is a key step toward developing cleaner crops with minimal pesticides in crops, creating new green pesticides (or safeners), and building up the engineered plants for environmental remediation. In this review, we describe the recently discovered mechanistic insights into pesticide metabolic pathways, and development of improved plant genotypes that break down pesticides more effectively. We highlight the identification of biological features and functions of major pesticide-metabolized enzymes such as laccases, glycosyltransferases, methyltransferases and ATP binding cassette (ABC) transporters, and discuss their chemical reactions involved in diverse pathways including the formation of pesticide S-conjugates. The recent findings for some signal molecules (phytohomormes) like salicylic acid, jasmonic acid and brassinosteroids involved in metabolism and detoxification of pesticides are summarized. In particular, the emerging research on the epigenetic mechanisms such DNA methylation and histone modification for pesticide metabolism is emphasized. The review would broaden our understanding of the regulatory networks of the pesticide metabolic pathways in higher plants.

Metabolomics is one of the most promising 'omics' sciences for the implementation in medicine by developing new diagnostic tests and optimizing drug therapy. Since in metabolomics, the end products of the biochemical processes in an organism are studied, which are under the influence of both genetic and environmental factors, the metabolomics analysis can detect any changes associated with both lifestyle and pathological processes. Almost every case-controlled metabolomics study shows a high diagnostic accuracy. Taking into account that metabolomics processes are already described for most nosologies, there are prerequisites that a high-speed and comprehensive metabolite analysis will replace, in near future, the narrow range of chemical analyses used today, by the medical community. However, despite the promising perspectives of personalized metabolomics, there are currently no FDA-approved metabolomics tests. The well-known problem of complexity of personalized metabolomics data analysis and their interpretation for the end-users, in addition to a traditional need for analytical methods to address the quality control, standardization, and data treatment are reported in the review. Possible ways to solve the problems and change the situation with the introduction of metabolomics tests into clinical practice, are also discussed.

This review presents an overview of the dynamically developing field of mass spectrometry-based metabolomics. Metabolomics aims at the comprehensive and quantitative analysis of wide arrays of metabolites in biological samples. These numerous analytes have very diverse physico-chemical properties and occur at different abundance levels. Consequently, comprehensive metabolomics investigations are primarily a challenge for analytical chemistry and specifically mass spectrometry has vast potential as a tool for this type of investigation. Metabolomics require special approaches for sample preparation, separation, and mass spectrometric analysis. Current examples of those approaches are described in this review. It primarily focuses on metabolic fingerprinting, a technique that analyzes all detectable analytes in a given sample with subsequent classification of samples and identification of differentially expressed metabolites, which define the sample classes. To perform this complex task, data analysis tools, metabolite libraries, and databases are required. Therefore, recent advances in metabolomics bioinformatics are also discussed.

Metabolomics approaches provide an analysis of changing metabolite levels in biological samples. In the past decade, technical advances have spurred the application of metabolomics in a variety of diverse research areas spanning basic, biomedical, and clinical sciences. In particular, improvements in instrumentation, data analysis software, and the development of metabolite databases have accelerated the measurement and identification of metabolites. Metabolomics approaches have been applied to a number of important problems, which include the discovery of biomarkers as well as mechanistic studies aimed at discovering metabolites or metabolic pathways that regulate cellular and physiological processes. By providing access to a portion of biomolecular space not covered by other profiling approaches (e.g., proteomics and genomics), metabolomics offers unique insights into small molecule regulation and signaling in biology. In the following review, we look at the integration of metabolomics approaches in different areas of basic and biomedical research, and try to point out the areas in which these approaches have enriched our understanding of cellular and physiological biology, especially within the context of pathways linked to disease.

Cardiovascular diseases (CVDs) are the main cause of death globally. There is a need for the development of specific diagnostic methods, more effective therapeutic procedures as well as drugs, which can decrease the risk of deaths in the course of CVDs. For this reason, better understanding and explanation of molecular pathomechanisms of CVDs are essential. Metabolomics is focused on analysis of metabolites, small molecules which reflect the state of an organism in a certain point of time. Application of metabolomics approach in the investigation of molecular processes responsible for CVDs development may provide valuable information. In this article we overviewed recent reports employing application of untargeted and targeted metabolomic analyses in particular CVDs. Moreover, we focused on applications of various analytical platforms and metabolomics approaches which may contribute to the explanation of the pathomechanisms of different cardiovascular diseases.

Metabolomics, lipidomics, and the study of cellular metabolism are gaining increasing interest particularly in the field of immunology, since the activation and effector functions of immune cells are profoundly controlled by changes in cellular metabolic asset. Among the different techniques that can be used for the evaluation of cellular metabolism, the Seahorse Extracellular Flux Analyzer allows the real time measurement of both glycolytic and mitochondrial respiration pathways in cells of interest, through the assessment of extracellular acidification and oxygen consumption rate. Metabolomics, on the other hand, is the high-throughput analysis of metabolites, i.e., the substrates, intermediates, and products of cellular metabolism, starting from biofluids, cells or tissues. The metabolome does not include lipids as their properties are different from water-soluble metabolites and are classified under the lipidome. Lipidomics analysis allows the identification and quantification of lipid species. Metabolomics and lipidomics are currently performed with mass-spectrometry coupled with liquid or gas chromatography (LC-MS or GC-MS) and/or nuclear-magnetic resonance (NMR). Here we describe the protocol for the evaluation of metabolic rate, metabolomics, and lipidomics in T cells, examining the detailed experimental approaches.

Metabolomics based on mass spectrometry can provide quantitative and qualitative information of the pool of metabolites (metabolome) present intracellularly or extracellularly in a given biological system. A typical metabolomics workflow requires several key steps such as quick and robust sample preparations with quenching of metabolism, chemical derivatization if needed, instrumental measurement, data-processing with/without database information and further statistical analysis and interpretation. Here, we introduce general metabolomics workflows for global and targeted analyses using gas chromatography or liquid chromatography coupled with mass spectrometers.

Metabolomics has rapidly been adopted as one of the key methods in nutrition research. This review focuses on the recent developments and updates in the field, including the analytical methodologies that encompass improved instrument sensitivity, sampling techniques and data integration (multiomics). Metabolomics has advanced the discovery and validation of dietary biomarkers and their implementation in health research. Metabolomics has come to play an important role in the understanding of the role of small molecules resulting from the diet-microbiota interactions when gut microbiota research has shifted towards improving the understanding of the activity and functionality of gut microbiota rather than composition alone. Currently, metabolomics plays an emerging role in precision nutrition and the recent developments therein are discussed.

Metabolomics, one of the latest components in the suite of systems biology, has been used to understand the metabolism and physiology of living systems, including microorganisms, plants, animals and humans. Food metabolomics can be defined as the application of metabolomics in food systems, including food resources, food processing and diet for humans. The study of food metabolomics has increased gradually in the recent years, because food systems are directly related to nutrition and human health. This review describes the recent trends and applications of metabolomics to food systems, from farm to human, including food resource production, industrial food processing and food intake by humans.

Metabolomics is a truly interdisciplinary field of science, which combines analytical chemistry, platform technology, mass spectrometry, and NMR spectroscopy with sophisticated data analysis. Applied to biomarker discovery, it includes aspects of pathobiochemistry, systems biology/medicine, and molecular diagnostics and requires bioinformatics and multivariate statistics. While successfully established in the screening of inborn errors in neonates, metabolomics is now widely used in the characterization and diagnostic research of an ever increasing number of diseases. In this Review we highlight important technical prerequisites as well as recent developments in metabolomics and metabolomics data analysis with special emphasis on their utility in biomarker identification and qualification, as well as targeted metabolomics by employing high-throughput mass spectrometry.

INTRODUCTION:During the Metabolomics 2023 conference, the Metabolomics Quality Assurance and Quality Control Consortium (mQACC) presented a QA/QC workshop for LC-MS-based untargeted metabolomics. OBJECTIVES:The Best Practices Working Group disseminated recent findings from community forums and discussed aspects to include in a living guidance document. METHODS:Presentations focused on reference materials, data quality review, metabolite identification/annotation and quality assurance. RESULTS:Live polling results and follow-up discussions offered a broad international perspective on QA/QC practices. CONCLUSIONS:Community input gathered from this workshop series is being used to shape the living guidance document, a continually evolving QA/QC best practices resource for metabolomics researchers.

Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.

Determination of pesticides in cannabis facilities is increasingly important as medicinal and recreational uses of cannabis products expand rapidly. We report a method involving wipe sampling, liquid chromatography separation, and tandem mass spectrometry, which enables determination of 82 pesticides out of the 96 regulated by Health Canada. To demonstrate an application of the method, we sampled and measured pesticides in two cannabis growing facilities, representing a non-certified and a certified site. We detected 41 pesticides in surface wipe samples at the non-certified site and 6 at the certified site. This study provides the first evidence showing pesticide occurrence on common surfaces in cannabis growing facilities and points to a need for routine monitoring and strict control of pesticide use in cannabis facilities.

BACKGROUND:Almost every organ in the human body can be affected by arsenic (As) exposure associated with various industrial processes, as well as with contaminated food, drinking water and polluted air. Much is known about high exposure to inorganic As but there is little data on the metabolic changes connected to a low exposure e.g. in people living in smelter areas. OBJECTIVES:The objectives of the study were: (1) characterise urinary concentration of total arsenic (AsT) in Polish inhabitants of the vicinity of a copper smelter area, (2) speciation analysis of various forms of arsenic in girls (GL), boys (BL), women (WL) and men (ML) with a slightly elevated AsT concentration and age/sex matched groups with a substantially higher AsT concentration, (GH, BH, WH and MH - respectively), (3) comparison of metabolomics profiles of urine between the age/sex matched people with low and high AsT concentrations. METHODS:Urine samples were analysed for total arsenic and its chemical forms (As; As, methylarsonic acid, dimethylarsinic acid, arsenobetaine) using HPLC-ICP-MS. Untargeted metabolomics analysis of the urine samples was performed using UPLC system connected to Q-TOF-MS equipped with an electrospray source. The XCMS Online program was applied for feature detection, retention time correction, alignment, statistics, annotation and identification. Potentially identified compounds were fragmented and resulting spectra were compared to the spectra in the Human Metabolome Database. RESULTS:Urine concentration of AsT was, as follows: GL 16.40 ± 0.83; GH 115.23 ± 50.52; BL 16.48 ± 0.83; BH 95.00 ± 50.03; WL 16.93 ± 1.21; WH 170.13 ± 96.47; ML 16.91 ± 1.20; MH 151.71 ± 84.31 μg/l and percentage of arsenobetaine in AsT was, as follows: GL 65.5 ± 13.8%, GH 87.2 ± 4.7%, BL 59.8 ± 12.5%, BH 90.5 ± 2.4%, WL 50.8 ± 14.1%, WH 90.4 ± 3.5%, ML 53.3 ± 10.0%, MH 74.6 ± 20.2%. In the people with low and high AsT concentrations there were significant differences in the intensity of signal (is.) from numerous compounds being metabolites of neurotransmitters, nicotine and hormones transformation (serotonin in the girls and women; catecholamines in the girls, boys and women; mineralocorticoids and glucocorticoids in the boys, androgens in the women and men and nicotine in the boys, women and men). These changes might have been associated with higher is. from metabolites of leucine, tryptophan, purine degradation (in the GH, WH), urea cycle (in the WH and MH), glycolysis (in the WH) and with lower is. from metabolites of tricarboxylic acid cycle (in the BH) in comparison with low AsT matched groups. In the MH vs. ML higher is. from metabolite of lipid peroxidation (4-hydroxy-2-nonenal) was observed. Additionally, the presence of significant differences was reported in is. from food components metabolites, which might have modulated the negative effects of As (vitamin C in the girls, boys and men, vitamin B in the girls, boys and women as well as phenolic compounds in the boys and girls). We hypothesize that the observed higher is. from metabolites of sulphate (in MH) and glucoronate degradation (in BH, WH and MH) than in the matched low AsT groups may be related to the impaired glucuronidation and sulfonation and higher is. from catecholamines, nicotine and hormones. CONCLUSION:Our results indicated that even a low exposure to As is associated with metabolic changes and that urine metabolomics studies could be a good tool to reflect their wide spectrum connected to specific environmental exposure to As, e.g. in smelter areas.

Japanese radish (Raphanus sativus var. longipinnatus) plants grown under laboratory conditions were individually exposed to the same doses of atrazine (2-chloro-4-ethylamino-6-isopropylamino-1,3,5-triazine, ATR) or its main degradation products: either 2-amino-4-chloro-6-isopropylamino-1,3,5-triazine (DEA) or 2-amino-4-chloro-6-ethylamino-1,3,5-triazine (DIA) or desethyl-desisopropyl-atrazine (DEDIA) or 4-(ethylamino)-2-hydroxy-6-(isopropylamino)-1,3,5-triazine (HA), respectively. One week after treatment in plants exposed to ATR, DIA, and DEA, their concentrations were 7.8 μg/g, 9.7 μg/g, and 14.5 μg/g, respectively, while those treated with DEDIA and HA did not contain these compounds. These results were correlated with plant amino acid profile obtained by suspect screening analysis and metabolomic "fingerprint" based on non-target analysis, obtained by liquid chromatography coupled with QTRAP triple quadrupole mass spectrometer. In all cases, both ATR and its by-products were found to interfere with the plant's amino acid profile and modify its metabolic "fingerprint". Therefore, we proved that the non-target metabolomics approach is an effective tool for investigating the hidden effects of pesticides and their transformation products, which is particularly important as these compounds may reduce the quality of edible plants.

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, R = 0.9130, Q = 0.8703; for saliva samples, an accuracy of 0.9961, R = 0.9698, Q = 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.