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.

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.

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.

Non-targeted analysis based on quantitative prediction and toxicity assessment for emerging contaminants in tire particle leachates.

Non-targeted analysis (NTA) has great potential to screen emerging contaminants in the environment, and some studies have conducted in-depth investigation on environmental samples. Here, we used a NTA workflow to identify emerging contaminants in used tire particle (TP) leachates, followed by quantitative prediction and toxicity assessment based on hazard scores. Tire particles were obtained from four different types of automobiles, representing the most common tires during daily transportation. With the instrumental analysis of TP leachates, a total of 244 positive and 104 negative molecular features were extracted from the mass data. After filtering by a specialized emerging contaminants list and matching by spectral databases, a total of 51 molecular features were tentatively identified as contaminants, including benzothiazole, hexaethylene glycol, 2-hydroxybenzaldehyde, etc. Given that these contaminants have different mass spectral responses in the mass spectrometry, models for predicting the response of contaminants were constructed based on machine learning algorithms, in this case random forest and artificial neural networks. After five-fold cross-validation, the random forest algorithm model had better prediction performance (MAECV = 0.12, Q2 = 0.90), and thus it was chosen to predict the contaminant concentrations. The prediction results showed that the contaminant at the highest concentration was benzothiazole, with 4,875 µg/L in the winter tire sample. In addition, the joint toxicity assessment of four types of tires was conducted in this study. According to different hazard levels, hazard scores increasing by a factor 10 were developed, and hazard scores of all the contaminants identified in each TP leachate were summed to obtain the total hazard score. All four tires were calculated to have relatively high risks, with winter tires having the highest total hazard score of 40,751. This study extended the application of NTA research and led to the direction of subsequent targeting studies on highly concentrated and toxic contaminants.

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.

Mechanism of Takifugu bimaculatus Skin Peptides in Alleviating Hyperglycemia in Rats with Type 2 Diabetic Mellitus Based on Microbiome and Metabolome Analyses.

In this study, we aimed to explore the hypoglycemic effects of a hydrolysate on Takifugu bimaculatus skin (TBSH). The effect of the dipeptidyl peptidase-IV (DPP-IV) inhibitory activities from different TBSH fractions was investigated on basic indexes, gut hormones, blood lipid indexes, viscera, and the gut microbiota and its metabolites in rats with type 2 diabetes mellitus (T2DM). The results showed that the <1 kDa peptide fraction from TBSH (TBP) exhibited a more potent DPP-IV inhibitory effect (IC50 = 0.45 ± 0.01 mg/mL). T2DM rats were induced with streptozocin, followed by the administration of TBP. The 200 mg/kg TBP mitigated weight loss, lowered fasting blood glucose levels, and increased insulin secretion by 20.47%, 25.23%, and 34.55%, respectively, rectified irregular hormonal fluctuations, lipid metabolism, and tissue injuries, and effectively remedied gut microbiota imbalance. In conclusion, TBP exerts a hypoglycemic effect in rats with T2DM. This study offers the potential to develop nutritional supplements to treat T2DM and further promote the high-value utilization of processing byproducts from T. bimaculatus. It will provide information for developing nutritional supplements to treat T2DM and further promote the high-value utilization of processing byproducts from T. bimaculatus.

Impacts of maternal microbiota and microbial metabolites on fetal intestine, brain, and placenta.

BACKGROUND: The maternal microbiota modulates fetal development, but the mechanisms of these earliest host-microbe interactions are unclear. To investigate the developmental impacts of maternal microbial metabolites, we compared full-term fetuses from germ-free and specific pathogen-free mouse dams by gene expression profiling and non-targeted metabolomics. RESULTS: In the fetal intestine, critical genes mediating host-microbe interactions, innate immunity, and epithelial barrier were differentially expressed. Interferon and inflammatory signaling genes were downregulated in the intestines and brains of the fetuses from germ-free dams. The expression of genes related to neural system development and function, translation and RNA metabolism, and regulation of energy metabolism were significantly affected. The gene coding for the insulin-degrading enzyme (Ide) was most significantly downregulated in all tissues. In the placenta, genes coding for prolactin and other essential regulators of pregnancy were downregulated in germ-free dams. These impacts on gene expression were strongly associated with microbially modulated metabolite concentrations in the fetal tissues. Aryl sulfates and other aryl hydrocarbon receptor ligands, the trimethylated compounds TMAO and 5-AVAB, Glu-Trp and other dipeptides, fatty acid derivatives, and the tRNA nucleobase queuine were among the compounds strongly associated with gene expression differences. A sex difference was observed in the fetal responses to maternal microbial status: more genes were differentially regulated in male fetuses than in females. CONCLUSIONS: The maternal microbiota has a major impact on the developing fetus, with male fetuses potentially more susceptible to microbial modulation. The expression of genes important for the immune system, neurophysiology, translation, and energy metabolism are strongly affected by the maternal microbial status already before birth. These impacts are associated with microbially modulated metabolites. We identified several microbial metabolites which have not been previously observed in this context. Many of the potentially important metabolites remain to be identified.

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.

Exploring the Mechanism of H2S Synthesis in Male Bactrian Camel Poll Glands Based on Data Independent Acquisition Proteomics and Non-Targeted Metabolomics.

During estrus, the poll glands of male Bactrian Camels (Camelus Bactrianus) become slightly raised, exuding a large amount of pale yellow watery secretion with a characteristic odor that may contain hydrogen sulfide (H2S). However, whether H2S can be synthesized in the poll glands of male Bactrian Camels and its role in inducing camel estrus remains unclear. This study aimed to identify differentially expressed proteins (DEPs) and signaling pathways in the poll gland tissues of male Bactrian Camels using data independent acquisition (DIA) proteomics. Additionally, gas chromatography-mass spectrometry (GC-MS) was performed to identify differentially expressed metabolites (DEMs) in the neck hair containing secretions during estrus in male Bactrian Camels, to explore the specific expression patterns and mechanisms in the poll glands of camels during estrus. The results showed that cystathionine-γ-lyase (CTH) and cystathionine-ß-synthase (CBS), which are closely related to H2S synthesis in camel poll glands during estrus, were mainly enriched in glycine, serine, and threonine metabolism, amino acid biosynthesis, and metabolic pathways. In addition, both enzymes were widely distributed and highly expressed in the acinar cells of poll gland tissues in camels during estrus. Meanwhile, the neck hair secretion contains high levels of amino acids, especially glycine, serine, threonine, and cystathionine, which are precursors for H2S biosynthesis. These results demonstrate that the poll glands of male Bactrian Camels can synthesize and secrete H2S during estrus. This study provides a basis for exploring the function and mechanism of H2S in the estrus of Bactrian Camels.

Feasibility of organic micropollutant removal from municipal wastewaters by algal treatment vs. activated sludge treatment: Comparison based on non-targeted organic compound analysis.

Sustainability and life-cycle concerns about the conventional activated sludge (CAS) process for wastewater treatment have been driving the development of energy-efficient, greener alternatives. Feasibility of an algal-based wastewater treatment (A-WWT) system has been demonstrated recently as a possible alternative, capable of simultaneous nutrient and energy recovery. This study compared capabilities of the A-WWT and CAS systems in removing organic micropollutants (OMP). Initial assessments based on surrogate organic measures and fluorescence excitation-emission matrix (FEEM) scans revealed that the A-WWT system achieved higher removals of organics than the CAS system. However, effluents of both systems contained residual organic matter, necessitating further OMP assessment for a rigorous comparison. A novel ultrahigh-performance liquid chromatography- Fourier transform mass spectrometry (UPLC-FTMS)-based non-targeted screening approach was adopted here for residual OMP analysis. This approach confirmed that the A-WWT system resulted in better OMP removal, eliminating 329 compounds and partially reducing 472 compounds, compared to 206 eliminations and 410 partial reductions by the CAS system. Mass spectra signal corresponding to some OMPs increased with treatment while some transformation products were observed following treatment. Higher OMP reduction in the A-WWT system with concurrent reductions of biodegradable carbon, nutrients, and pathogens in a single-step while producing energy and nutrient rich algal biomass underscore its potential as a greener alternative for wastewater treatment.

Comprehensive Comparison of Three Different Medicinal Parts of Eupatorium lindleyanum DC. Using the RRLC-Q-TOF-MS-Based Metabolic Profile and In Vitro Anti-Inflammatory Activity.

Eupatorium lindleyanum DC. (EL) is a traditional Chinese herb known for its phlegm-reducing, cough-relieving and asthma-calming properties. It is widely used for treating cough and bronchitis. However, preliminary experiments have revealed wide variations in the composition of its different medicinal parts (flowers, leaves and stems), and the composition and efficacy of its different medicinal parts remain largely underexplored at present. In this study, non-targeted rapid resolution liquid chromatography coupled with a quadruple time-of-flight mass spectrometry (RRLC-Q-TOF-MS)-based metabolomics approach was developed to investigate the differences in the chemical composition of different medicinal parts of EL. We identified or tentatively identified 9 alkaloids, 11 flavonoids, 14 sesquiterpene lactones, 3 diterpenoids and 24 phenolic acids. In addition, heatmap visualization, quantitative analysis by high-performance liquid chromatography (HPLC-PDA) and ultra-high-performance liquid chromatography-triple quadrupole tandem mass spectrometry (UPLC-MS/MS) showed particularly high levels of sesquiterpene lactones, flavonoids and phenolic acids in the flowers, such as eupalinolide A and B and chlorogenic acid, among others. The leaves also contained some flavonoid sesquiterpene lactones and phenolic acids, while the stems were almost absent. The findings of in vitro activity studies indicated that the flowers exhibited a notable inhibitory effect on the release of the inflammatory factors TNF-α and IL-6, surpassing the anti-inflammatory efficacy observed in the leaves. Conversely, the stems demonstrated negligible anti-inflammatory activity. The variations in anti-inflammatory activity among the flowers, leaves and stems of EL can primarily be attributed to the presence of flavonoids, phenolic acids and sesquiterpene lactones in both the flowers and leaves. Additionally, the flowers contain a higher concentration of these active components compared to the leaves. These compounds mediate their anti-inflammatory effects through distinct biochemical pathways. The results of this study are anticipated to provide a scientific basis for the rational and effective utilization of EL resources.

A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize.

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

Cyanoacetohydrazide as a Novel Derivatization Agent for the Determination of UHPLC-HRMS Steroids in Urine.

The possibility of cyanoacetohydrazide usage as a novel derivatizing agent is demonstrated in the presented article, and a comparison with hydroxylamine as the most commonly used reagent is provided. Optimal conditions for steroid derivatization with cyanoacetohydrazide are provided. According to the collected data, the maximum yield of derivatives was observed at pH 2.8 within 70 min at 40 °C with 5 ng/mL limit of detection for all investigated analytes. It was shown that cyanoacetohydrazide derivatives produces both syn- and anti-forms as well as hydroxylamine, and their ratios were evaluated and shown in presented work. An efficiency enchantment from two to up to five times was achieved with a novel derivatization reagent. Its applicability for qualitative analysis of steroids in urine was presented at real samples. Additionally, the reproducible fragmentation of the derivatizing agent in collision-induced dissociation offers opportunities for simplified non-targeted steroidomic screening. Furthermore, cyanoacetohydrazide increases ionization efficiency in positive mode, which can eliminate the need for redundant high-resolution instrument runs required for both positive and negative mode analyses.

High-Resolution Mass Spectrometry Non-Targeted Detection of Per- and Polyfluoroalkyl Substances in Roe Deer (Capreolus capreolus).

Among wildlife species, roe deer stands out as a valuable indicator of environmental pollution due to its ecological significance and role as a game animal. The assessment of poly- and perfluoro substances (PFASs) bioaccumulation is of the utmost importance, relying on the liver and muscles as the main organs of interest. The study concerned the identification of 60 PFAS through a non-target workflow analysis based on HPLC Q-Exactive Orbitrap High-Resolution Mass Spectrometry in a homogeneous group of 18 female roe deer species. The developed strategy allowed us to individuate the 60 PFAS compounds with different levels of confirmation. Apart from seven PFASs identified via analytical standards, the remaining fifty-three features were identified with CL 2 or 3. Moreover, by applying a differential statistic approach, it was possible to distinguish the bioaccumulation patterns in the liver and muscle, identifying 12 PFAS upregulated in the muscle and 20 in the liver. The analysis reveals that specific PFAS compounds present exclusively in either the muscle or in the liver. The study emphasises the specificity of the liver and muscle as significant bioaccumulation sites for PFAS, raising questions about the underlying mechanisms of this process. In conclusion, the presented non-targeted PFAS analysis workflow evidenced promising and reliable results, successfully demonstrating its feasibility in the field of environmental research.