Metabolomics of repetitive myocardial stunning in chronic multivessel coronary artery stenosis: Effect of non-selective and selective ß1-receptor blockers.

Chronic coronary artery stenosis can lead to regional myocardial dysfunction in the absence of myocardial infarction by repetitive stunning, hibernation or both. The molecular mechanisms underlying repetitive stunning-associated myocardial dysfunction are not clear. We used non-targeted metabolomics to elucidate responses to chronically stunned myocardium in a canine model with and without ß-adrenergic blockade treatment. After development of left ventricular systolic dysfunction induced by ameroid constrictors on the coronary arteries, animals were randomized to 3 months of placebo, metoprolol or carvedilol. We compared these two ß-blockers with their different ß-adrenergic selectivities on myocardial function, perfusion and metabolic pathways involved in tissue undergoing chronic stunning. Control animals underwent sham surgery. Dysfunction in stunned myocardium was associated with reduced fatty acid oxidation and enhanced ketogenic amino acid metabolism, together with alterations in mitochondrial membrane phospholipid composition. These changes were consistent with impaired mitochondrial function and were linked to reduced nitric oxide and peroxisome proliferator-activated receptor signalling, resulting in a decline in adenosine monophosphate-activated protein kinase. Mitochondrial changes were ameliorated by carvedilol more than metoprolol, and improvement was linked to nitric oxide and possibly hydrogen sulphide signalling. In summary, repetitive myocardial stunning commonly seen in chronic multivessel coronary artery disease is associated with adverse metabolic remodelling linked to mitochondrial dysfunction and specific signalling pathways. These changes are reversed by ß-blockers, with the non-selective inhibitor having a more favourable impact. This is the first investigation to demonstrate that ß-blockade-associated improvement of ventricular function in chronic myocardial stunning is associated with restoration of mitochondrial function. KEY POINTS: The mechanisms responsible for the metabolic changes associated with repetitive myocardial stunning seen in chronic multivessel coronary artery disease have not been fully investigated. In a canine model of repetitive myocardial stunning, we showed that carvedilol, a non-selective ß-receptor blocker, ameliorated adverse metabolic remodelling compared to metoprolol, a selective ß1-receptor blocker, by improving nitric oxide synthase and adenosine monophosphate protein kinase function, enhancing calcium/calmodulin-dependent protein kinase, probably increasing hydrogen sulphide, and suppressing cyclic-adenosine monophosphate signalling. Mitochondrial fatty acid oxidation alterations were ameliorated by carvedilol to a larger extent than metoprolol; this improvement was linked to nitric oxide and possibly hydrogen sulphide signalling. Both ß-blockers improved the cardiac energy imbalance by reducing metabolites in ketogenic amino acid and nucleotide metabolism. These results elucidated why metabolic remodelling with carvedilol is preferable to metoprolol when treating chronic ischaemic left ventricular systolic dysfunction caused by repetitive myocardial stunning.

Non-targeted GC-MS metabolomics-based differences in Indica rice seeds of different varieties.

Rice seeds of different varieties exhibited distinct metabolic profiles in our study. We analyzed the metabolites in seeds of six rice varieties (CH, HM, NX, YX, HY, and MX) using non-targeted GC-MS. Our findings revealed that amino acids, sugars, and organic acids were predominant in all varieties, with significant differences observed in CH compared to the others. Specifically phenylalanine and glycine content differed notably in NX and YX, respectively. Additionally, 1,5-anhydroglucitol content in NX, and glutamate, aspartate, and lactulose in NX, YX, HM, HY, and MX were up-regulated. Due to the biological functions of these amino acids and sugars, these indicated that compared to CH, rice of NX were more conducive to metabolism of carbohydrate and fat, and healthy growth maintenance in the human body, but mightThese variations suggest that NX rice may be more beneficial for carbohydrate and fat metabolism and overall health maintenance compared to CH. However, it may not be suitable for diabetic patients. YX rice may not be an ideal glycine supplement, rice ofwhile HM, HY, and MX rice could serve as potential lactulose sources. Furthermore, NX and YX rice exhibited higher levels of main storage proteins compared to CH. This study offers valuable insights into the metabolic differences among various rice varieties.

Exploring the role of CBLB in acute myocardial infarction: transcriptomic, microbiomic, and metabolomic analyses.

BACKGROUND: Specific alterations in gut microbiota and metabolites have been linked to AMI, with CBLB potentially playing an essential role. However, the precise interactions remain understudied, creating a significant gap in our understanding. This study aims to address this by exploring these interactions in CBLB-intervened AMI mice using transcriptome sequencing, 16 S rDNA, and non-targeted metabolite analysis. METHODS: To probe the therapeutic potential and mechanistic underpinnings of CBLB overexpression in AMI, we utilized an integrative multi-omics strategy encompassing transcriptomics, metabolomics, and 16s rDNA sequencing. We selected these particular methods as they facilitate a holistic comprehension of the intricate interplay between the host and its microbiota, and the potential effects on the host's metabolic and gene expression profiles. The uniqueness of our investigation stems from utilizing a multi-omics approach to illuminate the role of CBLB in AMI, an approach yet unreported to the best of our knowledge. Our experimental protocol encompassed transfection of CBLB lentivirus-packaged vectors into 293T cells, followed by subsequent intervention in AMI mice. Subsequently, we conducted pathological staining, fecal 16s rDNA sequencing, and serum non-targeted metabolome sequencing. We applied differential expression analysis to discern differentially expressed genes (DEGs), differential metabolites, and differential microbiota. We performed protein-protein interaction analysis to identify core genes, and conducted correlation studies to clarify the relationships amongst these core genes, paramount metabolites, and key microbiota. RESULTS: Following the intervention of CBLB in AMI, we observed a significant decrease in inflammatory cell infiltration and collagen fiber formation in the infarcted region of mice hearts. We identified key changes in microbiota, metabolites, and DEGs that were associated with this intervention. The findings revealed that CBLB has a significant correlation with DEGs, differential metabolites and microbiota, respectively. This suggests it could play a pivotal role in the regulation of AMI. CONCLUSION: This study confirmed the potential of differentially expressed genes, metabolites, and microbiota in AMI regulation post-CBLB intervention. Our findings lay groundwork for future exploration of CBLB's role in AMI, suggesting potential therapeutic applications and novel research directions in AMI treatment strategies.

Nitrogen-fixing bacteria promote growth and bioactive components accumulation of Astragalus mongholicus by regulating plant metabolism and rhizosphere microbiota.

BACKGROUND: The excessive application of chemical fertilizers in the cultivation of Astragalus mongholicus Bunge results in a reduction in the quality of the medicinal plant and compromises the sustainable productivity of the soil. PGPB inoculant is a hot topic in ecological agriculture research. In the cultivation of Astragalus mongholicus, the screened nitrogen-fixing bacteria can promote plant growth, however, whether it can promote the accumulation of main bioactive components remains unknown. In this study, mixed inoculants containing 5 strains of growth promoting bacteria (Rhizobium T16 , Sinorhizobium T21 , Bacillus J1 , Bacillus G4 and Arthrobacter J2) were used in the field experiment. The metabolic substances in the root tissues of Astragalus mongholicus were identified during the harvest period by non-targeted metabolomics method, and the differential metabolites between groups were identified by statistical analysis. Meanwhile, high-throughput sequencing was performed to analyze the changes of rhizosphere soil and endophytic microbial community structure after mixed microbial treatment. RESULTS: The results of non-targeted metabolism indicated a significant increase in the levels of 26 metabolites after treatment including 13 flavonoids, 3 saponins and 10 other components. The contents of three plant hormones (abscisic acid, salicylic acid and spermidine) also increased after treatment, which presumed to play an important role in regulating plant growth and metabolism. Studies on endosphere and rhizosphere bacterial communities showed that Rhzobiaceae, Micromonosporaceae, and Hypomicrobiaceae in endophytic, and Oxalobactereae in rhizosphere were significantly increased after treatment. These findings suggest their potential importance in plant growth promotion and secondary metabolism regulation. CONCLUSIONS: This finding provides a basis for developing nitrogen-fixing bacteria fertilizer and improving the ecological planting efficiency of Astragalus mongholicus.

Combustion-Derived Pollutants Linked with Kidney Disease in Low-Lying Flood-Affected Areas in the Balkans.

Tens of thousands of people in southern Europe suffer from Balkan endemic nephropathy (BEN), and four times as many are at risk. Incidental ingestion of aristolochic acids (AAs), stemming from the ubiquitousAristolochia clematitis(birthwort) weed in the region, leads to DNA adduct-induced toxicity in kidney cells, the primary cause of BEN. Numerous cofactors, including toxic organics and metals, have been investigated, but all have shown small contributions to the overall BEN relative to non-BEN village distribution gradients. Here, we reveal that combustion-derived pollutants from wood and coal burning in Serbia also contaminate arable soil and test as plausible causative factors of BEN. Using a GC-MS screening method, biomass-burning-derived furfural and coal-burning-derived medium-chain alkanes were detected in soil samples from BEN endemic areas levels at up to 63-times and 14-times higher, respectively, than in nonendemic areas. Significantly higher amounts were also detected in colocated wheat grains. Coexposure studies with cultured kidney cells showed that these pollutants enhance DNA adduct formation by AA, - the cause of AA nephrotoxicity and carcinogenicity. With the coincidence of birthwort-derived AAs and the widespread practice of biomass and coal burning for household cooking and heating purposes and agricultural burning in rural low-lying flood-affected areas in the Balkans, these results implicate combustion-derived pollutants in promoting the development of BEN.

Identification of Hydrocarbon Sulfonates as Previously Overlooked Transthyretin Ligands in the Environment.

Incidences of thyroid disease, which has long been hypothesized to be partially caused by exposure to thyroid hormone disrupting chemicals (TDCs), have rapidly increased in recent years. However, known TDCs can only explain a small portion (∼1%) of in vitro human transthyretin (hTTR) binding activities in environmental samples, indicating the existence of unknown hTTR ligands. In this study, we aimed to identify the major environmental hTTR ligands by employing protein Affinity Purification with Nontargeted Analysis (APNA). hTTR binding activities were detected in all 11 indoor dust and 9 out of 10 sewage sludge samples by the FITC-T4 displacement assay. By using APNA, 31 putative hTTR ligands were detected including perfluorooctanesulfonate (PFOS). Two of the most abundant ligands were identified as hydrocarbon surfactants (e.g., dodecyl benzenesulfonate). Moreover, another abundant ligand was surprisingly identified as a disulfonate fluorescent brightener, 4,4'-bis(2-sulfostyryl)biphenyl sodium (CBS). CBS was validated as a nM-affinity hTTR ligand with an IC50 of 345 nM. In total, hydrocarbon surfactants and fluorescent brighteners explain 1.92-17.0 and 5.74-54.3% of hTTR binding activities in dust and sludge samples, respectively, whereas PFOS only contributed <0.0001%. Our study revealed for the first time that hydrocarbon sulfonates are previously overlooked hTTR ligands in the environment.

Nontargeted Analysis of Coumarins in Source Water and Their Formation of Chlorinated Coumarins as DBPs in Drinking Water.

Coumarin was detected as one of the most abundant compounds by nontargeted analysis of natural product components in actual water samples prior to disinfection. More importantly, prechlorination of humic acid generated 3-hydroxycoumarin and monohydroxy-monomethyl-substituted coumarin with a total yield of ≤10.1%, which suggested the humic substance in raw water is an important source of coumarins. 7-Hydroxycoumarin, 6-hydroxy-4-methylcoumarin, 6,7-dihydroxycoumarin, and 7-methoxy-4-methylcoumarin were identified in raw water by high-performance liquid chromatography-tandem high-resolution mass spectrometry because only some coumarin standards were commercially available. Their chlorination generated monochlorinated and polychlorinated coumarins, and their structures were confirmed by the synthesized standards. These products could form at various dosages of chlorine and pH levels, and some with a concentration of 600 ng/L can be stable in tap water for days. 3,6,8-Trichloro-7-hydroxycoumarin, 3-chloro-7-methoxy-4-methylcoumarin, and 3,6-dichloro-7-methoxy-4-methylcoumarin were first identified in finished water with concentrations of 0.0670, 78.1, and 14.7 ng/L, respectively, but not in source water, suggesting that they are new DBPs formed during disinfection. The cytotoxicity of 3-chloro-7-methoxy-4-methylcoumarin in CHO-K1 cells was comparable to those of 2,6-dibromo-1,4-benzoquinone and 2,6-dichloro-1,4-benzoquinone in TIC-Tox analyses, suggesting that further investigation of their occurrence and control in drinking water systems is warranted.

Target and Nontarget Screening to Support Capacity Scaling for Substance Use Assessment through a Statewide Wastewater Surveillance Network in New York.

Wastewater-based epidemiology (WBE) has been widely implemented around the world as a complementary tool to conventional surveillance techniques to inform and improve public health responses. Currently, wastewater surveillance programs in the U.S. are evaluating integrated approaches to address public health challenges across multiple domains, including substance abuse. In this work, we demonstrated the potential of online solid-phase extraction coupled with liquid chromatography-high-resolution mass spectrometry to support targeted quantification and nontargeted analysis of psychoactive and lifestyle substances as a step toward understanding the operational feasibility of a statewide wastewater surveillance program for substance use assessment in New York. Target screening confirmed 39 substances in influent samples collected from 10 wastewater treatment plants with varying sewershed characteristics and is anticipated to meet the throughput demands as the statewide program scales up to full capacity. Nontarget screening prioritized additional compounds for identification at three confidence levels, including psychoactive substances, such as opioid analgesics, phenethylamines, and cathinone derivatives. Consumption rates of 12 target substances detected in over 80% of wastewater samples were similar to those reported by previous U.S.-based WBE studies despite the uncertainty associated with back-calculations. For selected substances, the relative bias in consumption estimates was sensitive to variations in monitoring frequency, and factors beyond human excretion (e.g., as indicated by the parent-to-metabolite ratios) might also contribute to their prevalence at the sewershed scale. Overall, our study marks the initial phase of refining analytical workflows and data interpretation in preparation for the incorporation of substance use assessment into the statewide wastewater surveillance program in New York.

Identification and Prioritization of Organic Pollutants in Human Milk from the Yangtze River Delta, China.

Pollutants in human milk are critical for evaluating maternal internal exposure and infant external exposure. However, most studies have focused on a limited range of pollutants. Here, 15 pooled samples (prepared from 467 individual samples) of human milk from three areas of the Yangtze River Delta (YRD) in China were analyzed by gas chromatography quadrupole time-of-flight mass spectrometry. In total, 171 compounds of nine types were preliminarily identified. Among these, 16 compounds, including 2,5-di-tert-butylhydroquinone and 2-tert-butyl-1,4-benzoquinone, were detected in human milk for the first time. Partial least-squares discriminant analysis identified ten area-specific pollutants, including 2-naphthylamine, 9-fluorenone, 2-isopropylthianthrone, and benzo[a]pyrene, among pooled human milk samples from Shanghai (n = 3), Jiangsu Province (n = 6), and Zhejiang Province (n = 6). Risk index (RI) values were calculated and indicated that legacy polycyclic aromatic hydrocarbons (PAHs) contributed only 20% of the total RIs for the identified PAHs and derivatives, indicating that more attention should be paid to PAHs with various functional groups. Nine priority pollutants in human milk from the YRD were identified. The most important were 4-tert-amylphenol, caffeine, and 2,6-di-tert-butyl-p-benzoquinone, which are associated with apoptosis, oxidative stress, and other health hazards. The results improve our ability to assess the health risks posed by pollutants in human milk.

Fragmentation Pattern-Based Screening Strategy Combining Diagnostic Ion and Neutral Loss Uncovered Novel para-Phenylenediamine Quinone Contaminants in the Environment.

Identifying transformed emerging contaminants in complex environmental compartments is a challenging but meaningful task. Substituted para-phenylenediamine quinones (PPD-quinones) are emerging contaminants originating from rubber antioxidants and have been proven to be toxic to the aquatic species, especially salmonids. The emergence of multiple PPD-quinones in various environmental matrices and evidence of their specific hazards underscore the need to understand their environmental occurrences. Here, we introduce a fragmentation pattern-based nontargeted screening strategy combining full MS/All ion fragmentation/neutral loss-ddMS2 scans to identify potential unknown PPD-quinones in different environmental matrices. Using diagnostic fragments of m/z 170.0600, 139.0502, and characteristic neutral losses of 199.0633, 138.0429 Da, six known and three novel PPD-quinones were recognized in air particulates, surface soil, and tire tissue. Their specific structures were confirmed, and their environmental concentration and composition profiles were clarified with self-synthesized standards. N-(1-methylheptyl)-N'-phenyl-1,4-benzenediamine quinone (8PPD-Q) and N,N'-di(1,3-dimethylbutyl)-p-phenylenediamine quinone (66PD-Q) were identified and quantified for the first time, with their median concentrations found to be 0.02-0.21 µg·g-1 in tire tissue, 0.40-2.76 pg·m-3 in air particles, and 0.23-1.02 ng·g-1 in surface soil. This work provides new evidence for the presence of unknown PPD-quinones in the environment, showcasing a potential strategy for screening emerging transformed contaminants in the environment.

Nontargeted Identification of Organic Components in Fine Particulate Matter Related to Lung Tumor Metastasis Based on an Adverse Outcome Pathway Strategy.

Emerging studies implicate fine particulate matter (PM2.5) and its organic components (OCs) as urgent hazard factors for lung cancer progression in nonsmokers. Establishing the adverse outcome pathway (AOP)-directed nontargeted identification method, this study aimed to explore whether PM2.5 exposure in coal-burning areas promoted lung tumor metastasis and how we identify its effective OCs to support traceability and control of regional PM2.5 pollution. First, we used a nude mouse model of lung cancer for PM2.5 exposure and found that the exposure significantly promoted the hematogenous metastases of A549-Luc cells in lung tissues and the adverse outcomes (AOs), with key events (KEs) including the changed expression of epithelial-mesenchymal transition (EMT) markers, such as suppression of E-cad and increased expression of Fib. Subsequently, using AOs and KEs as adverse outcome directors, we identified a total of 35 candidate chemicals based on the in vitro model and nontargeted analysis. Among them, tributyl phosphate (C12H27O4P), 2-bromotetradecane (C14H29Br), and methyl decanoate (C11H22O2) made greater contributions to the AOs. Finally, we clarified the interactions between these OCs and EMT-activating transcription factors (EMT-ATFs) as the molecular initiation event (MIE) to support the feasibility of the above identification strategy. The present study updates a new framework for identifying tumor metastasis-promoting OCs in PM2.5 and provides solid data for screening out chemicals that need priority control in polluted areas posing higher lung cancer risk.

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage.

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Nontargeted Analysis of Reactive Nitrogenous Compounds in Suwannee River Standard Reference Materials and Authentic River Water Samples.

Concerns over toxic nitrogenous disinfection byproducts (N-DBPs) necessitate identifying their precursors in source water. Natural organic amino compounds are known precursors to N-DBPs. Three Suwannee River (SR) standard reference materials (SRMs), humic acids (HA), fulvic acids (FA), and natural organic matter (NOM), are commonly used to study DBP formation, but the chemical makeup of amino compounds in SRSRMs remains largely unknown. To address this, we combined stable hydrogen/deuterium isotope labeling, HDPairFinder bioinformatics, and nontargeted high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS) to characterize these compounds in SRSRMs. This method classifies reactive amines, provides accurate masses and MS/MS spectra, and quantifies intensities. We identified 2707 high-quality features with primary and/or secondary amines in SRSRMs and 75% of them having an m/z < 300. Across all three SRSRMs, 327 amino features were detected, while 856, 794, and 200 unique features were found in SRNOM, SRHA, and SRFA, respectively. In North Saskatchewan River (NSR) samples, a total of 6449 amino features were detected, 818 of them matched those in SRSRMs, and 87% of them were different between the two rivers. Using chemical standards, we confirmed 10 compounds and tentatively identified 5 more. This study highlights similarities and differences in reactive N-precursors in SRSRMs and local river water, enhancing the understanding of geo-differences in reactive N-precursors in different source waters.

Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening.

Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.

Improving predictions of compound amenability for liquid chromatography-mass spectrometry to enhance non-targeted analysis.

Mass-spectrometry-based non-targeted analysis (NTA), in which mass spectrometric signals are assigned chemical identities based on a systematic collation of evidence, is a growing area of interest for toxicological risk assessment. Successful NTA results in better identification of potentially hazardous pollutants within the environment, facilitating the development of targeted analytical strategies to best characterize risks to human and ecological health. A supporting component of the NTA process involves assessing whether suspected chemicals are amenable to the mass spectrometric method, which is necessary in order to assign an observed signal to the chemical structure. Prior work from this group involved the development of a random forest model for predicting the amenability of 5517 unique chemical structures to liquid chromatography-mass spectrometry (LC-MS). This work improves the interpretability of the group's prior model of the same endpoint, as well as integrating 1348 more data points across negative and positive ionization modes. We enhance interpretability by feature engineering, a machine learning practice that reduces the input dimensionality while attempting to preserve performance statistics. We emphasize the importance of interpretable machine learning models within the context of building confidence in NTA identification. The novel data were curated by the labeling of compounds as amenable or unamenable by expert curators, resulting in an enhanced set of chemical compounds to expand the applicability domain of the prior model. The balanced accuracy benchmark of the newly developed model is comparable to performance previously reported (mean CV BA is 0.84 vs. 0.82 in positive mode, and 0.85 vs. 0.82 in negative mode), while on a novel external set, derived from this work's data, the Matthews correlation coefficients (MCC) for the novel models are 0.66 and 0.68 for positive and negative mode, respectively. Our group's prior published models scored MCC of 0.55 and 0.54 on the same external sets. This demonstrates appreciable improvement over the chemical space captured by the expanded dataset. This work forms part of our ongoing efforts to develop models with higher interpretability and higher performance to support NTA efforts.

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion.

Data-independent acquisition-all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples.

Size-resolved molecular characterization of water-soluble organic matter in atmospheric particulate matter from northern China.

Atmospheric particulate matter (PM) affects visibility, climate, biogeochemical cycles and human health. Water-soluble organic matter (WSOM) is an important component of PM. In this study, PM samples with size-resolved measurements at aerodynamic cut-point diameters (Dp) of 0.01-18 µm were collected in the rural area of Baoding and the urban area of Dalian, Northern China. Non-targeted analysis was adopted for the characterization of the molecule constitutes of WSOM in different sized particles using Fourier transform-ion cyclotron resonance mass spectrometry. Regardless of the location, the composition of WSOM in Aitken mode particles (aerodynamic diameter <0.05 µm) was similar. The WSOM in accumulation mode particles (0.05-2 µm) in Baoding was predominantly composed of CHO compounds (84.9%), which were mainly recognized as lignins and lipids species. However, S-containing compounds (64.2%), especially protein and carbohydrates species, accounted for most of the WSOM in the accumulation mode particles in Dalian. The CHO compounds (67.6%-79.7%) contributed the most to the WSOM in coarse mode particles (>2 µm) from both sites. Potential sources analysis indicated the WSOM in Baoding were mainly derived from biomass burning and oxidation reactions, while the WSOM in Dalian arose from coal combustion, oxidation reactions, and regional transport.

Metabolic profiling of peri-implant crevicular fluid in peri-implantitis.

OBJECTS: This study aims to explore the etiology of peri-implantitis by comparing the metabolic profiles in peri-implant crevicular fluid (PICF) from patients with healthy implants (PH) and those with peri-implantitis (PI). MATERIALS AND METHODS: Fifty-six patients were enrolled in this cross-sectional study. PICF samples were collected and analyzed using both non-targeted and targeted metabolomics approaches. The relationship between metabolites and clinical indices including probing depth (PD), bleeding on probing (BOP), and marginal bone loss (MBL) was examined. Additionally, submucosal microbiota was collected and analyzed using 16S rRNA gene sequencing to elucidate the association between the metabolites and microbial communities. RESULTS: Significant differences in metabolic profiles were observed between the PH and PI groups, with 179 distinct metabolites identified. In the PI group, specific amino acids and fatty acids were significantly elevated compared to the PH group. Organic acids including succinic acid, fructose-6-phosphate, and glucose-6-phosphate were markedly higher in the PI group, showing positive correlations with mean PD, BOP, and MBL. Metabolites that increased in the PI group positively correlated with the presence of Porphyromonas and Treponema and negatively with Streptococcus and Haemophilus. CONCLUSIONS: This study establishes a clear association between metabolic compositions and peri-implant condition, highlighting enhanced metabolite activity in peri-implantitis. These findings open avenues for further research into metabolic mechanisms of peri-implantitis and their potential therapeutic implications.

Nontargeted metabolomics reveals sequential changes in amino acid and ferroptosis-related metabolism in Parkinson's disease.

Parkinson's disease (PD) is inseparable from metabolic disorders but lacks assessment of specific metabolite alteration. To explore the sequential metabolic changes in PD progression, we evenly divided 78 C57BL/6 mice (10 weeks) into six groups (one control group and five experimental groups) and collected the hippocampus tissue of mice after treating with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, and probenecid (twice a week) at five periods (1, 2, 3, 4, and 5 weeks) for metabolome analysis. Our study identified 567 differentially abundant metabolites (DAMs) (total 4348 metabolites). Compared with controls, 145, 146, 171, 208, and 213 DAMs were obtained from the five experimental groups, respectively. Notably, 40 shared DAMs were present in five experimental groups, of which 22 shared DAMs formed a new metabolic network based on amino acid metabolism. Compared with group W3, 84 DAMs were identified in group W5, including 12 unique DAMs. DAMs in different stages of PD were significantly enriched in amino acid metabolism pathway, lipid metabolism pathway, and ferroptosis pathway. l-Glutamine, spermidine, and l-tryptophan were the key hubs in the whole metabolic process of PD. N-Formyl-l-methionine gradually increased in abundance with PD progression, whereas 5-methylcytosine gradually decreased. The study emphasized the sequential changes in DAMs in PD progression, stimulating subsequent studies.

Exploring the impact of polyphenolic compounds on the chromatic characteristics in flowers of Rhododendron arboreum Sm. collected from different altitudinal locations.

INTRODUCTION: Rhododendron arboreum Sm. flowers grow in the Himalayan region and have traditionally been used in beverages and food. These wild edible Himalayan flowers are known for their sweet-sour flavor and beautiful scarlet red color. The primary pigments responsible for the scarlet red color of these flowers are anthocyanins. OBJECTIVE: In the present study, we conducted chemo-profiling and elucidated the chromatic characteristics of R. arboreum flower petals growing in the wild in different altitudinal areas. METHODOLOGY: The content of anthocyanins, phenolics, and other flavonoids was determined in R. arboreum flower petals collected from 38 different locations in two provinces in India (Himachal Pradesh and Uttarakhand) to obtain a distinguishable chemical index. A UHPLC method has also been developed and validated for the quantitative analysis. Besides, the color characteristics of each collected floral sample were also analyzed. RESULTS: Chemometric analysis (principal component analysis [PCA] and heatmap analysis) revealed that floral samples collected from different altitudes exhibited similar chemical diversity, whereas statistical analysis (bivariate linear correlation) revealed a positive correlation between the color parameter a*/b* and cyanidin glycosides. Besides, non-targeted metabolomics analysis was carried out, which resulted in the tentative identification of 150 metabolites. CONCLUSION: The results revealed that there is a direct influence of accumulated anthocyanins to color parameter a*/b* values in the floral samples irrespective of altitude.