Liquid Chromatography-mass Spectrometry Data Research Articles

Early pregnancy biomarker discovery study for spontaneous preterm birth

(1) Objectivediscover new candidate biomarkers for spontaneous preterm birth in early pregnancy samples. When fully clinically validated, early pregnancy biomarkers for sPTB give the possibility to intervene or monitor high-risk pregnancies more intensively through, as example, pelvic exams, ultrasound or sonographic cervical length surveillance. (2) Study designEarly pregnancy serum samples of eight spontaneous extreme and very preterm birth cases (<32 weeks of gestational age) without any symptoms of preeclampsia and fetal growth restriction and eight uncomplicated pregnancies were analyzed by liquid chromatography mass spectrometry (LC-MS). Thirteen proteins, which were differentially expressed according to the LC-MS data, were subsequently selected for confirmation by enzyme-linked immunosorbent assay (ELISA). (3) ResultsDifferential expression of four candidate biomarkers was confirmed by ELISA with decreased early pregnancy levels of gelsolin and fibulin-1 and increased levels of c-reactive protein and complement C5 in the preterm birth group. (4) ConclusionsThe confirmed candidate biomarkers are all to some extent related to inflammatory pathways and/or the complement system. This supports the hypothesis that both play a role in extreme and very preterm birth without any symptoms of preeclampsia and fetal growth restriction. The predictive value of complement C5, c-reactive protein, fibulin-1 and gelsolin should, therefore, be validated in another cohort with early pregnancy samples.

MetaPro: a web-based metabolomics application for LC-MS data batch inspection and library curation

IntroductionMetabolomics analysis based on liquid chromatography-mass spectrometry (LC-MS) has been a prevalent method in the metabolic field. However, accurately quantifying all the metabolites in large metabolomics sample cohorts is challenging. The analysis efficiency is restricted by the abilities of software in many labs, and the lack of spectra for some metabolites also hinders metabolite identification.ObjectivesDevelop software that performs semi-targeted metabolomics analysis with an optimized workflow to improve quantification accuracy. The software also supports web-based technologies and increases laboratory analysis efficiency. A spectral curation function is provided to promote the prosperity of homemade MS/MS spectral libraries in the metabolomics community.MethodsMetaPro is developed based on an industrial-grade web framework and a computation-oriented MS data format to improve analysis efficiency. Algorithms from mainstream metabolomics software are integrated and optimized for more accurate quantification results. A semi-targeted analysis workflow is designed based on the concept of combining artificial judgment and algorithm inference.ResultsMetaPro supports semi-targeted analysis workflow and functions for fast QC inspection and self-made spectral library curation with easy-to-use interfaces. With curated authentic or high-quality spectra, it can improve identification accuracy using different peak identification strategies. It demonstrates practical value in analyzing large amounts of metabolomics samples.ConclusionWe offer MetaPro as a web-based application characterized by fast batch QC inspection and credible spectral curation towards high-throughput metabolomics data. It aims to resolve the analysis difficulty in semi-targeted metabolomics.

Intensity-dependent mass search for improving metabolite database matches in chemical isotope labeling LC-QTOF-MS-based metabolomics

Liquid chromatography mass spectrometry (LC-MS) has been increasingly used for metabolome analysis. One of the critical steps in the LC-MS metabolome analysis workflow is related to metabolite identification. Among the measured parameters, peak mass is commonly used to search against a database for potential metabolite matches. Higher accuracy mass measurement allows the use of a narrower mass tolerance window for mass search. While various types of mass analyzers can routinely measure a peak mass with an error of less than a few ppm, mass measurement accuracy is not uniform for peaks with different intensities, particularly for quadrupole time-of-flight (QTOF) MS. Herein we present a simple and convenient method to determine the relation between peak intensity and mass error in LC-QTOF-MS-based metabolome analysis, followed by intensity-dependent mass search (IDMS) of a database for metabolite matches. This method is based on running a series of sodium formate mass calibrants, as part of the standard operating procedure (SOP) in LC-MS data acquisition, and then curve-fitting the measured mass errors and peak intensities. We show that, in two different quadrupole time-of-flight (QTOF) mass analyzers, mass accuracy is generally reduced as peak intensity decreases, which is independent of m/z values in the range commonly used for metabolite detection (e.g., m/z < 1000). We demonstrate the improvement in metabolite matches using IDMS in the analyses of dansyl labeled standards and human urine samples. We have implemented the IDMS method in the freely available MCID database at www.mycompoundid.org, which is composed of 8021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 compounds from two reactions).

A comprehensive strategy for prediction and quality evaluation of standardized planting herbs based on plant metabolomics coupled with extreme learning machine: Astragali Radix as an example.

Standardizing the planting process is an effective way to control the quality stability of herbal resources, which are susceptible to external environmental factors (e.g., moisture, soil, etc.). However, how to scientifically and comprehensively assess the effects of standardized planting on plant quality and quickly test unknown samples has not been addressed. The aim of this study was to determine and compare the metabolite levels of herbs before and after standardized planting, to quickly distinguish their sources, and to evaluate their quality, using the typical herb Astragali Radix (AR) as an example. In this study, an efficient strategy using liquid chromatography-mass spectrometry (LC-MS) based on plant metabolomics combined with extreme learning machine (ELM) has been developed to efficiently distinguish and predict AR after standardized planting. Moreover, a comprehensive multi-index scoring method has been developed for the comprehensive evaluation of the quality of AR. The results confirmed that AR after standardized planting was significantly differentiated, with a relatively stable content of 43 differential metabolites, mainly including flavonoids. An ELM model was established based on LC-MS data, and the accuracy in predicting unknown samples could reach more than 90%. As expected, higher total scores were obtained for AR after standardized planting, indicating much better quality. A dual system for evaluating the impact of standardized planting on the quality of plant resources has been established, which will significantly contribute to innovation in the quality evaluation of medicinal herbs and support the selection of optimal planting conditions.

Molecular basis of Pogostemon cablin responding to continuous cropping obstacles revealed by integrated transcriptomic, miRNA and metabolomic analyses

Pogostemon cablin (patchouli) suffers from continuous cropping obstacles in cultivation. To obtain new insights into molecular mechanisms, we performed the integrated analyses of transcriptome, miRNA and metabolite data of patchouli leaves in parallel at different levels of continuous cropping. Our results showed that continuous cropping has caused significant gene expression and metabolite changes in patchouli. A total of 49,825 genes, 57 miRNAs and 325 metabolites with significantly differential abundance were identified by pairwise comparisons samples from different planting patterns at the same growth stage. We found that continuous cropping has significantly reduced the expression levels of the late elongated hypocotyl (LHY), phytochrome (PHY), cryptochrome (CRY), and PSI and PSII complex genes, inhibiting photosynthesis and the normal growth of patchouli. miRNA-Seq analyses showed that miR172, miR319, miR397 and two novel miRNAs (novel5 and novel18) might involve in the development of continuous cropping obstacles. In addition, liquid chromatography-mass spectrometry (LC-MS) data showed that continuous cropping has significantly changed the accumulation of metabolites, such as prenol lipids, organooxygen compounds, flavonoids, carboxylic acids and derivatives, and cinnamic acids and derivatives in patchouli. The integrated transcriptomic and metabolomic analyses revealed a significant correlation between differentially expressed genes (DEGs) and differential metabolites (DEM) in the phenylpropanoid biosynthesis pathway under continuous cropping stress. The obtained results will help to elucidate potential molecular mechanisms of continuous cropping obstacles towards developing new control methods to improve the yield and quality of continuous cropping plants.

Encoding LC-MS-Based Untargeted Metabolomics Data into Images toward AI-Based Clinical Diagnosis.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides comprehensive and quantitative profiling of metabolites in clinical investigations. The use of whole metabolome profiles is a promising strategy for disease diagnosis but technically challenging. Here, we developed an approach, namely MetImage, to encode LC-MS-based untargeted metabolomics data into multi-channel digital images. Then, the images that represent the comprehensive metabolome profiles can be employed for developing deep learning-based AI models toward clinical diagnosis. In this work, we demonstrated the application of MetImage for clinical screening of esophageal squamous cell carcinoma (ESCC) in a clinical cohort with 1104 participants. A convolutional neuronal network-based AI model was trained to distinguish ESCC screening positive and negative subjects using their serum metabolomics data. Superior performances such as sensitivity (85%), specificity (92%), and area under curve (0.95) were validated in an independent testing cohort (N = 442). Importantly, we demonstrated that our AI-based ESCC screening model is not a "black box". The encoded images reserved the characteristics of mass spectra from the raw LC-MS data; therefore, metabolite identifications in key image features were readily achieved. Altogether, MetImage is a unique approach that encodes raw LC-MS-based untargeted metabolomics data into images and facilitates the utilization of whole metabolome profiles for AI-based clinical applications with improved interpretability.

Fusion of Quality Evaluation Metrics and Convolutional Neural Network Representations for ROI Filtering in LC-MS.

Region of interest (ROI) extraction is a fundamental step in analyzing metabolomic datasets acquired by liquid chromatography-mass spectrometry (LC-MS). However, noises and backgrounds in LC-MS data often affect the quality of extracted ROIs. Therefore, developing effective ROI evaluation algorithms is necessary to eliminate false positives meanwhile keep the false-negative rate as low as possible. In this study, a deep fused filter of ROIs (dffROI) was proposed to improve the accuracy of ROI extraction by combining the handcrafted evaluation metrics with convolutional neural network (CNN)-learned representations. To evaluate the performance of dffROI, dffROI was compared with peakonly (CNN-learned representation) and five handcrafted metrics on three LC-MS datasets and a gas chromatography-mass spectrometry (GC-MS) dataset. Results show that dffROI can achieve higher accuracy, better true-positive rate, and lower false-positive rate. Its accuracy, true-positive rate, and false-positive rate are 0.9841, 0.9869, and 0.0186 on the test set, respectively. The classification error rate of dffROI (1.59%) is significantly reduced compared with peakonly (2.73%). The model-agnostic feature importance demonstrates the necessity of fusing handcrafted evaluation metrics with the convolutional neural network representations. dffROI is an automatic, robust, and universal method for ROI filtering by virtue of information fusion and end-to-end learning. It is implemented in Python programming language and open-sourced at https://github.com/zhanghailiangcsu/dffROI under BSD License. Furthermore, it has been integrated into the KPIC2 framework previously proposed by our group to facilitate real metabolomic LC-MS dataset analysis.

Chemical composition and antimicrobial activity of the extracts and essential oil of Blumea balsamifera from the Philippines

Blumea balsamifera is a registered traditional medicine in the Philippines for the diuretic treatment of kidney stones. It is commonly available in tablet, capsule, and tea formats. This study aims to investigate the antibacterial activity and chemical components of local Blumea balsamifera leaf methanol extract, hexane extract, and essential oil to determine their potential application in treating bacterial urinary and digestive tract infections. The identified compounds were β-carotene and predominantly flavonoids, including quercetin, blumeatin, hyperoside, padmatin, 3', 5, 5', 7-tetrahydroxyflavanone, 3-O-methylquercetin, and 7-methyleriodictyol, from high-performance liquid chromatography (HPLC) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) data. These compounds were found in the methanol extract. Terpenes, including eudesmol, borneol, caryophyllene oxide, guaiol, (E)-caryophyllene, and α-curcumene were identified as the primary components of the essential oil by gas chromatography-mass spectrometry (GC-MS) analysis. Methanol and hexane extracts exhibited antibacterial activity against methicillin-sensitive Staphylococcus aureus with MICs of 3 and 5 mg/mL, respectively. The essential oil inhibited methicillin-sensitive and methicillin-resistant Staphylococcus aureus with respective MIC values of 2 and 3 mg/mL. However, no potential activity against other test organisms was detected. Isolation and standardization of the bioactive components from Blumea balsamifera leaf extracts can be further studied in an application to treat microbial infections.

Composition mapping of highly substituted cellulose-ether monomers by liquid chromatography–mass spectrometry and probability-based data deconvolution

Cellulose ethers (CEs) are semi-synthetic polymers produced by derivatization of natural cellulose, yielding highly substituted products such as ethyl hydroxyethyl cellulose (EHEC) or methyl ethyl hydroxyethyl cellulose (MEHEC). CEs are commonly applied as pharmaceutical excipients and thickening agents in paints and drymix mortars. CE properties, such as high viscosity in solution, solubility, and bio-stability are of high interest to achieve required product qualities, which may be strongly affected by the substitution pattern obtained after derivatization. The average and molar degree of substitution often cannot explain functional differences observed among CE batches, and more in-depth analysis is needed. In this work, a new method was developed for the comprehensive mapping of the substitution degree and composition of β-glucose monomers of CE samples. To this end, CEs were acid-hydrolyzed and then analyzed by gradient reversed-phase liquid chromatography-mass spectrometry (LC-MS) using an acid-stable LC column and time-of-flight (TOF) mass spectrometer. LC-MS provided monomer resolution based on ethylene oxide, hydroxyl, and terminating methyl/ethyl content, allowing the assignment of detailed compositional distributions. An essential further distinction of constitutional isomer distributions was achieved using an in-house developed probability-based deconvolution algorithm. Aided by differential heat maps for visualization and straightforward interpretation of the measured LC-MS data, compositional variation between bio-stable and non-bio-stable CEs could be identified using this new approach. Moreover, it disclosed unexpected methylations in EHEC samples. Overall, the obtained molecular information on relevant CE samples demonstrated the method's potential for the study of CE structure-property relationships.

A data mining tool for untargeted biomarkers analysis: Grapes ripening application

In metabolomics, data generated by untargeted approaches can be very complex due to the typically extensive number of features in raw data (with and without chemical relevance), dependence on raw data preprocessing methods, and lack of selective data mining tools to appropriately interpret these data. Extraction of meaningful information from these data is still a significant challenge in metabolomics. Moreover, currently available tools may overprocess the data, eliminating useful information. This work aims at proposing a data mining tool capable of dealing with metabolomics data, specifically liquid chromatography-mass spectrometry (LC-MS) to enhance the extraction of meaningful chemical information. The algorithm construction intended to be as general as possible in highlighting chemically relevant features, discarding non-informative signals specially background features.The proposed algorithm was applied to an LC-MS data set generated from the analysis of grapes collected over a developmental period encompassing a 4-month period. The algorithm outcome is a short list of features from metabolites that are worth to be further investigated, for example by HRMS fragmentation for subsequent identification. The performance of the algorithm in estimating potentially interesting features was compared with the commercial MZmine software. For this case study, the MZmine output yielded a final set of 37 features (out of 1543 initially identified) with noise features while the proposed algorithm identified 99 systematic features without noise. Also, the algorithm required 2 times less user-defined parameters when compared to MZmine. Globally, the proposed algorithm demonstrated a higher ability to pin-point features that may be associated with grapes developmental and maturation processes requiring minimal parameters definition, thus preventing user uncertainty and the compromise of experimental information.

Estrogenic Activity of 4-Hydroxy-Benzoic Acid from Acer tegmentosum via Estrogen Receptor α-Dependent Signaling Pathways.

Acer tegmentosum, a deciduous tree belonging to Aceraceae, has been used in traditional oriental medicine for treating hepatic disorders, such as hepatitis, cirrhosis, and liver cancer. We evaluated the estrogen-like effects of A. tegmentosum using an estrogen receptor (ER)-positive breast cancer cell line, namely MCF-7, to identify potential phytoestrogens and found that an aqueous extract of A. tegmentosum promoted cell proliferation in MCF-7 cells. Five phenolic compounds (1-5) were separated and identified from the active fraction using bioassay-guided fractionation of crude A. tegmentosum extract and phytochemical analysis. The chemical structures of the compounds were characterized as vanillic acid (1), 4-hydroxy-benzoic acid (2), syringic acid (3), isoscopoletin (4), and (E)-ferulic acid (5) based on the analysis of their nuclear magnetic resonance spectra and liquid chromatography-mass spectrometry data. All five compounds were evaluated using an E-screen assay for their estrogen-like effects on MCF-7 cells. Among the tested compounds, only 4-hydroxy-benzoic acid (2) promoted the proliferation of MCF-7 cells, which was mitigated by the ER antagonist, ICI 182,780. The mechanism underlying the estrogen-like effect of 4-hydroxy-benzoic acid (2) was evaluated via western blotting analysis to determine the expression levels of extracellular signal-regulated kinase (ERK), phosphoinositide 3-kinase (PI3K), serine/threonine kinase (AKT), and ERα. Our results demonstrated that 4-hydroxy-benzoic acid (2) induced the increase in the protein expression levels of p-ERK, p-AKT, p-PI3K, and p-Erα, concentration dependently. Collectively, these experimental results suggest that 4-hydroxy-benzoic acid (2) is responsible for the estrogen-like effects of A. tegmentosum and may potentially aid in the control of estrogenic effects during menopause.

Different software processing affects the peak picking and metabolic pathway recognition of metabolomics data

In untargeted liquid chromatography‒mass spectrometry (LC‒MS) metabolomics studies, data preprocessing and metabolic pathway recognition are crucial for screening important pathways that are disturbed by diseases or restored by drugs. Here, we collected high-resolution mass spectrometry data of serum samples from 221 coronary heart disease (CHD) patients under two different chromatographic columns (BEH amide and C18 column) and evaluated the three commonly used software programs (XCMS, Progenesis QI, MarkerView) from four aspects (including signal drift, peak number, metabolite annotation and metabolic pathway enrichment). The results showed that the data preprocessed by the three software programs have different degrees of signal drift, but the StatTarget could improve the data quality to meet the data analysis requirement after correction. In addition, XCMS surpassed other software in detection of real chromatographic peaks and Progenesis QI was the best performer in terms of the number of metabolite annotation. XCMS and Progenesis QI showed different performance in pathway enrichment. However, metabolic pathways based on the combination of XCMS and Progenesis QI had a high coincidence with Progenesis QI. In addition, we also reported that C18 and amide columns were highly complementary and have great potential for cooperation in the context of metabolic pathways. In this study, the effects of different chromatographic columns and software pretreatments on metabolomics data were evaluated based on clinical large cohort samples, which will provide a reference for the metabolomics of clinical samples and guide subsequent mechanistic research.

Metabolomic Profiling, In Vitro Antimalarial Investigation and In Silico Modeling of the Marine Actinobacterium Strain Rhodococcus sp. UR111 Associated with the Soft Coral Nephthea sp.

Malaria is a persistent illness with a great public health concern. To combat this fatal disease, developing effective antimalarial medications has become a necessity. In the present study, we described the actinomycetes associated with the Red Sea soft coral Nephthea sp. and isolated a strain that was sub-cultured in three different media (M1, ISP2, and OLIGO). Actinomycete isolate's phylogenetic analysis of the 16S rRNA gene revealed that it belongs to the genus Rhodococcus. In vitro screening of the antimalarial activity for three extracts against Plasmodium falciparum was carried out. Non-targeted metabolomics for the chemical characterization of the isolated actinomycete species UA111 derived extracts were employed using high-resolution liquid chromatography-mass spectrometry (LC-HR-MS) for dereplication purposes. Additionally, statistical analysis of the vast LC-MS data was performed using MetaboAnalyst 5.0. Finally, an in silico analysis was conducted to investigate the potential chemical compounds that could be the source of the antimalarial potential. The results revealed that ISP2 media extract is the most effective against Plasmodium falciparum, according to antimalarial screening (IC50 8.5 µg/mL), in contrast, OLIGO media extract was inactive. LC-HRMS-based metabolomics identified a range of metabolites, mainly alkaloids, from the genus Rhodococcus. On the other hand, multivariate analysis showed chemical diversity between the analyzed samples, with ISP2 extract being optimal. The docking analysis was able to anticipate the various patterns of interaction of the annotated compounds with three malarial protein targets (P. falciparum kinase, P. falciparum cytochrome bc1 complex, and P. falciparum lysyl-tRNA synthetase). Among all of the test compounds, perlolyrine (11) and 3097-B2 (12) displayed the best docking profiles. In conclusion, this work demonstrated the value of the established method for the metabolic profiling of marine actinomycetes using the data from liquid chromatography-mass spectrometry (LC-MS), which helps to streamline the difficult isolation stages required for their chemical characterization. In addition, the antimalarial efficacy of this strain has intriguing implications for future pharmaceutical development.

Comparative Analysis on the Role of 2,4-Dichlorophenoxyacetic Acid in the Expression of Bioactive Compounds in Callus of Capsicum frutescens

Plant cell culture technology serves as an effective alternative system for in vitro production of bioactive molecules, as it allows for the exploration of valuable compounds under a controlled environment. The present study was conducted to evaluate the effect of plant growth regulator (PGR); 2,4-dichlorophenoxyacetic acid (2,4-D) on the expression of compounds in coloured callus of Capsicum frutescens, a vital spice in various cuisines worldwide. The differential accumulation of compounds in the callus was analysed using liquid chromatography-mass spectrometry (LCMS) and the PGR concentration that resulted in the highest accumulation of the valuable compounds was identified. In this study, calli of various colours (cream, yellow and green) were successfully produced from C. frutescens through plant tissue culture. The increase in 2,4-D concentrations was found to increase callus growth index (GI) and specific growth rate (Sg), where the highest GI (0.5690) and Sg (0.6348 mg/week) were observed in callus produced in media supplemented with 0.5 mg/L 2,4-D. LCMS data analyses showed that 19 compounds were detected in the callus, with 8 compounds (fatty acids and phenolics) were successfully identified, while the remaining 11 compounds were reported as unknowns. Yellow-coloured callus was observed to contain the highest number of compounds (18 compounds), while green callus contained the least (14 compounds). This analysis provides valuable information on the application of biotechnological tools such as plant tissue culture as an alternative for sustainable production of compounds with high bioactivity in Capsicum frutescens.

Analysis of Primary Liquid Chromatography Mass Spectrometry Data by Neural Networks for Plant Samples Classification.

Plant samples are potential sources of physiologically active secondary metabolites and their classification is an extremely important task in traditional medicine and other fields of research. In the production of herbal drugs, different plant parts of the same or related species can serve as adulterants for primary plant material. The use of highly informative and relatively easily accessible tools, such as liquid chromatography and low-resolution mass spectrometry, helps to solve these tasks by means of fingerprint analysis. In this study, to reveal specific plant part features for 20 species from one family (Apiaceae), and to preserve the maximum information content, two approaches are suggested. In both cases, minimal raw data pretreatment, including rescaling of time and m/z axes and cutting off some uninformative regions, was applied. For the support vector machine (SVM) method, tensor unfolding was required, while neural networks (NNs) were able to work directly with squared heatmaps as input data. Moreover, five data augmentation variants are proposed, to overcome the typical problem of a lack of data. As a result, a comparable F1-score close to 0.75 was achieved by SVM and two employed NN architectures. Eight marker compounds belonging to chlorophylls, lipids, and coumarin apio-glucosides were tentatively identified as characteristic of their corresponding sample groups: roots, stems, leaves, and fruits. The proposed approaches are simple, information-saving and can be applied to a broad type of tasks in metabolomics.

Correlation of chemical profiles obtained from 1 H-NMR and LC-MS metabolomics with α-glucosidase inhibition activity for varietal selections of Ficus deltoidea.

Ficus deltoidea Jack (Moraceae) is a plant used in Malaysia to treat various ailments, including diabetes. The presence of several varieties raises essential questions regarding which is the potential bioactive variety and what are the bioactive metabolites. Here, we explored the phytochemical diversity of the seven varieties from Peninsular Malaysia using Nuclear Magnetic Resonance (NMR) and Liquid Chromatography-Mass Spectrometry (LC-MS) analyses and correlated it with the α-glucosidase inhibitory activity. The Nuclear Overhauser Effect Spectroscopy (NOESY) One-Dimensional (1D)-NMR and LC-MS data were processed, annotated, and correlated with in vitro α-glucosidase inhibitory using multivariate data analysis. The α-glucosidase results demonstrated that different varieties have varying inhibitory effects, with the highest inhibition rate being F. deltoidea var. trengganuensis and var. kunstleri. Furthermore, diverse habitats and plant ages could also influence the inhibitory rate. The heat map from NMR and LC-MS profiles showed unique patterns according to varying levels of α-glucosidase inhibition rate. The Partial Least Squares (PLS) model constructed from both NMR and LC-MS further confirmed the correlation between the α-glucosidase inhibition rate of F. deltoidea varieties and its metabolite profiles. The Variable Influence on Projection (VIP) and correlation coefficient (p(corr)) values values were used to determine the highly relevant metabolites for explaining the anticipated inhibitory action. NMR and LC-MS annotations allow the identification of flavan-3-ols and proanthocyanidins as the key bioactive factors. Our current results demonstrated the value of multivariate data analysis to predict the quality of herbal materials from both biological and chemical aspects.

An integrative cellular metabolomic study reveals downregulated tricarboxylic acid cycle and potential biomarkers induced by tetrabromobisphenol A in human lung A549 cells.

Tetrabromobisphenol A (TBBPA) is extensively utilized as a brominated flame retardant in numerous chemical products. As an environmental contaminant, the potential human toxicity of TBBPA has been attracting increasing attention. Nonetheless, the exact underlying mechanisms of toxicological effects caused by TBBPA remain uncertain. In this study, we investigated the potential mechanisms of TBBPA toxicity in vitro in the A549 cell line, one of the widely used type II pulmonary epithelial cell models in toxicology research. Cell viability was determined after treatment with varying concentrations of TBBPA. Liquid chromatography-mass spectrometry (LC-MS) metabolomics and metabolic flux approaches were utilized to evaluate metabolite and tricarboxylic acid (TCA) cycle oxidative flux changes. Our findings demonstrated that TBBPA significantly reduced the viability of cells and attenuated mitochondrial respiration in A549 cells. Additionally, LC-MS data showed significant reductions in TCA cycle metabolites including citrate, malate, fumarate, and alpha-ketoglutarate in 50 μM TBBPA-treated A549 cells. Metabolic flux analysis indicated reduced oxidative capacity in mitochondrial metabolism following TBBPA exposure. Moreover, diverse metabolic pathways, particularly alanine, aspartate, and glutamate metabolism and the TCA cycle, were found to be dysregulated. In total, 12 metabolites were significantly changed (p< .05) in response to 50 μM TBBPA exposure. Our results provide potential biomarkers of TBBPA toxicity in A549 cells and help elucidate the molecular mechanisms of pulmonary toxicity induced by TBBPA exposure.

Treatment of salon wastewater by peroxydisulfate based advanced oxidation process (PDS-AOP) under solar light: Synergy through integrated technologies

Effective technologies for degradation of organics in beauty hair salon (BHS) wastewater due to partial removal of personal care products (PCPs) from influent wastewater during wastewater treatment remain a priority in sustainable water treatment processes. The presence of these products in the environment increases the risks of surface water contamination and associated issues. In the present study, three types of BHS wastewater samples; raw, rapid granular multimedia (RGM) filtered, and RGM and carbon nanotube polyethersulfone (CNT/PES) ultrafiltration (UF) membrane filtered samples were exposed to PDS under solar light radiation and dark conditions. The main aim of this study was to determine the ideal treatment options and sequence of the mentioned treatment technologies. In the RGM and membrane filtered water samples the acidic conditions (pH 2–6) and corresponding lower organic loads were found to be both beneficial for PDS efficacy to degrade organics. TOC removal values of 18.32 %, 53.31 %, 89.74 % at the highest PDS dosage for the BHS, RGM filtered, and RGM and membrane filtered samples, respectively, were observed after 7 h of solar/PDS treatment. Moreover, solar/PDS treatment demonstrated enhanced efficiency in the degradation of organics in the RGM, and RGM and membrane prefiltered samples as confirmed by marked reduction in peak intensities of the organics in quadrupole time-of-flight liquid chromatography mass spectrometry (QTOF LC MS) data sets. Evidently, solar activated PDS for 7 h of the RGM and membrane filtered BHS samples showed the highest load reduction of organics. This study facilitates further insight into a promising multistep technology approach for the efficient treatment of BHS wastewater to help ensure a healthy aqueous environment.

A comparison between major chemical markers of the cultivated and wild harvested Siphonochilus aethiopicus, African ginger, from Mpumalanga, South Africa, using Liquid Chromatography-Mass Spectrometry

Siphonochillus aethiopicus, known as African ginger, is indigenous to South Africa and has multiple traditional uses in health to treat human diseases. The multiple traditional uses of African ginger have exacerbated the over-harvesting of the plant species from the wild for trade on the traditional medicine market. The wild populations of African ginger have almost completely depleted from the wild and a few African ginger cultivation sites have been established in South Africa, to conserve the plant species. The aim of the study was to compare the major chemical markers of the cultivated and wild harvested African ginger from Mpumalanga using Liquid Chromatography-Mass Spectrometry (LC-MS). The Council for Scientific and Industrial Research (CSIR) wild-harvested African ginger dated 2010 was used as a reference sample for comparison purposes. The LC-MS data generated from the ethanol extracts of the cultivated African ginger detected the presence of 4,4a,5,8a,9-tetrahydro-3,5,8a-trimethylnaptho[2,3-b]furan-8. This chemical marker was also detected in the wild harvested African ginger as compared to a previous study, which auto oxidised in the referenced sample over time. This study supports the efforts to conserve African ginger through cultivation for further development in commercialisation.

Bioautography-guided HPTLC-MS as a rapid hyphenated technique for the identification of antimicrobial compounds from selected South African Combretaceae species.

Many species within Combretaceae are traditionally used for the treatment of bacterial infections. The similarity in chemistry and antimicrobial activities within the family pose a challenge in selecting suitable species for herbal drug development. This study aimed at rapidly identifying antimicrobial compounds using bioautography-guided high-performance thin-layer chromatography coupled with mass spectrometry (HPTLC-MS). Hierarchical cluster analysis of ultra-performance liquid chromatography-mass spectrometry data from the methanol extracts of 77 samples, representing four genera within Combretaceae, was carried out. Based on groupings on the dendrogram, 15 samples were selected for bioautography analysis against four pathogens (Staphylococcus aureus, Bacillus cereus, Escherichia coli and Salmonella typhimurium). Active compounds were identified using HPTLC-MS analysis of bands corresponding to the inhibition zones. Bioautography revealed 15 inhibition zones against the four pathogens, with the most prominent present for Combretum imberbe. Analysis of the active bands, using HPTLC-MS indicated that flavonoids, triterpenoids and combretastatin B5 contributed to the antibacterial activity. The compounds corresponding to molecular ions m/z 471 (Combretum imberbe) and 499 (Combretum elaeagnoides) inhibited all four pathogens, and were identified as imberbic acid and jessic acid, respectively. Chemotaxonomic analysis indicated that arjunic acid, ursolic acid and an unidentified triterpenoid (m/z 471) were ubiquitous in the Combretaceae species and could be responsible for their antibacterial activities. Application of HPTLC-MS enabled the rapid screening of extracts to identify active compounds within taxonomically related species. This approach allows for greater efficiency in the natural product research workflow to identify bioactive compounds in crude extracts.