Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer's Disease

Impact of three different peak picking software tools on the quality of untargeted metabolomics data

Data quality and control parameters are becoming more important in metabolomics. For peak picking, open-source or commercial solutions are used. Other publications consider different software solutions or data acquisition types for peak picking, a combination, including proposed and new quality parameters for the process of peak picking, does not exist. This study tries to examine the performance of three different software in terms of reproducibility and quality of their output while also considering new quality parameters to gain a better understanding of resulting feature lists in metabolomics data. We saw best recovery of spiked analytes in MS-DIAL. Reproducibility over multiple projects was good among all software. The total number of features found was consistent for DDA and full scan acquisition in MS-DIAL but full scan data leading to considerably more features in MZmine and Progenesis Qi. Feature linearity proved to be a good quality parameter. Features in MS-DIAL and MZmine, showed good linearity while Progenesis Qi produced large variation, especially in full scan data. Peak width proved to be a very powerful filtering criteria revealing many features in MZmine and Progenesis Qi to be of questionable peak width. Additionally, full scan data appears to produce a disproportionally higher number of short features. This parameter is not yet available in MS-DIAL. Finally, the manual classification of true positive features proved MS-DIAL to perform significantly better in DDA data (62 % true positive) than the two other software in either mode. We showed that currently popular solutions MS-DIAL and MZmine perform well in targeted analysis of spiked analytes as well as in classic untargeted analysis. The commercially available solution Progenesis Qi does not hold any advantage over the two in terms of quality parameters, of which we proposed peak width as a new parameter and showed that already proposed parameters such as feature linearity in samples of increasing concentration are advisable to use.

Improved analysis of grape seed extract by liquid chromatography-high resolution mass spectrometry (LC-HRMS) reveals that proanthocyanidin-protein interaction mechanisms in cream depend on degree of polymerization

This study aimed to comprehensively characterize chemical profiles of proanthocyanidins (PACs) from grape seed extract (GSE), examine their interactions with proteins in a cream system, and define the mechanisms mediating PAC-protein interactions. GSE PACs were fractionated and characterized by thiolysis followed by liquid chromatography-high resolution mass spectrometry (LC-HRMS) analysis. New PACs with a degree of polymerization (DP) up to 16 were identified by improved HRMS data processing methods. In the model cream system, high-DP PACs exhibited greater precipitation capacity and protein binding than low-DP PACs. Low-DP PACs primarily engaged in hydrogen bonding, while high-DP PACs predominantly utilized multiple hydrophobic interaction sites to form cream protein aggregates. Furthermore, particle size and viscosity measurement of cream revealed a progressively DP-dependent increase in aggregated fat globules and cream viscosity. These findings enhanced our understanding of PACs' structural intricacies and highlighted their functional role as PAC-rich natural ingredients in creating structured cream systems.

Protective signature of xanthohumol on cognitive function of APP/PS1 mice: a urine metabolomics approach by age

Alzheimer's disease (AD) has an increasing prevalence, complicated pathogenesis and no effective cure. Emerging evidences show that flavonoid compounds such as xanthohumol (Xn) could play an important role as a dietary supplement or traditional Chinese herbal medicine in the management of diseases such as AD. This study aims to analyze the target molecules of Xn in the prevention and treatment of AD, and its potential mechanism from the perspective of metabolites. APP/PS1 mice 2- and 6-months old were treated with Xn for 3 months, respectively, the younger animals to test for AD-like brain disease prevention and the older animals to address therapeutic effects on the disease. Memantine (Mem) was selected as positive control. Behavioral tests were performed to assess the course of cognitive function. Urine samples were collected and analyzed by high-performance liquid chromatography (HPLC) with tandem mass spectrometry (MS/MS) coupled with online Compound Discoverer software. Morris Water Maze (MWM) tests showed that Xn, like Mem, had a therapeutic but not a preventive effect on cognitive impairment. The expression levels of urinary metabolites appeared to show an opposite trend at different stages of Xn treatment, downregulated in the prevention phase while upregulated in the therapy phase. In addition, the metabolic mechanisms of Xn during preventive treatment were also different from that during therapeutic treatment. The signaling pathways metabolites nordiazepam and genistein were specifically regulated by Xn but not by Mem in the disease prevention stage. The signaling pathway metabolite ascorbic acid was specifically regulated by Xn in the therapeutic stage. In conclusion, dietary treatment with Xn altered the urinary metabolite profile at different stages of administration in APP/PS1 mice. The identified potential endogenous metabolic biomarkers and signal pathways open new avenues to investigate the pathogenesis and treatment of AD.

A stochastic approach for parameter optimization of feature detection algorithms for non-target screening in mass spectrometry

Feature detection plays a crucial role in non-target screening (NTS), requiring careful selection of algorithm parameters to minimize false positive (FP) features. In this study, a stochastic approach was employed to optimize the parameter settings of feature detection algorithms used in processing high-resolution mass spectrometry data. This approach was demonstrated using four open-source algorithms (OpenMS, SAFD, XCMS, and KPIC2) within the patRoon software platform for processing extracts from drinking water samples spiked with 46 per- and polyfluoroalkyl substances (PFAS). The designed method is based on a stochastic strategy involving random sampling from variable space and the use of Pearson correlation to assess the impact of each parameter on the number of detected suspect analytes. Using our approach, the optimized parameters led to improvement in the algorithm performance by increasing suspect hits in case of SAFD and XCMS, and reducing the total number of detected features (i.e., minimizing FP) for OpenMS. These improvements were further validated on three different drinking water samples as test dataset. The optimized parameters resulted in a lower false discovery rate (FDR%) compared to the default parameters, effectively increasing the detection of true positive features. This work also highlights the necessity of algorithm parameter optimization prior to starting the NTS to reduce the complexity of such datasets.

New synergistic benzoquinone scaffolds as inhibitors of mycobacterial cytochrome bc1 complex to treat multi-drug resistant tuberculosis

Through a comprehensive molecular docking study, a unique series of naphthoquinones clubbed azetidinone scaffolds was arrived with promising binding affinity to Mycobacterial Cytbc1 complex, a drug target chosen to kill multi-drug resistant Mycobacterium tuberculosis (MDR-Mtb). Five compounds from series-2, 2a, 2c, 2g, 2h, and 2j, showcased significant in vitro anti-tubercular activities against Mtb H37Rv and MDR clinical isolates. Further, synergistic studies of these compounds in combination with INH and RIF revealed a potent bactericidal effect of compound 2a at concentration of 0.39 μg/mL, and remaining (2c, 2g, 2h, and 2j) at 0.78 μg/mL. Exploration into the mechanism study through chemo-stress assay and proteome profiling uncovered the down-regulation of key proteins of electron-transport chain and Cytbc1 inhibition pathway. Metabolomics corroborated these proteome findings, and heightened further understanding of the underlying mechanism. Notably, in vitro and in vivo animal toxicity studies demonstrated minimal toxicity, thus underscoring the potential of these compounds as promising anti-TB agents in combination with RIF and INH. These active compounds adhered to Lipinski's Rule of Five, indicating the suitability of these compounds for drug development. Particular significance of molecules NQ02, 2a, and 2h, which have been patented (Published 202141033473).

Biological characteristics of pregnancy in captive Yangtze finless porpoises revealed by urinary metabolomics†

The Yangtze finless porpoises (Neophocaena asiaeorientalis a.) are an endemic and critically endangered species in China. Intensive captive breeding is essential for understanding the biology of critically endangered species, especially their pregnancy characteristics, knowledge of which is crucial for effective breeding management. Urine metabolomics can reveal metabolic differences, arising from physiological changes across pregnancy stages. Therefore, we used the urinary metabolomic technology, to explore urinary metabolite changes in pregnant Yangtze finless porpoises. A total of 2281 metabolites were identified in all samples, which including organic acids and derivatives (24.45%), organoheterocyclic compounds (20.23%), benzenoids (18.05%), organic oxygen compounds (7.73%), and phenylpropanoids and polyketides (6.48%). There were 164, 387, and 522 metabolites demonstrating differential abundance during early pregnancy, mid pregnancy, and late pregnancy, respectively, from the levels observed in nonpregnancy. The levels of pregnenolone, 17α-hydroxyprogesterone, and tetrahydrocortisone were significantly higher during all pregnancy stages, indicating their important roles in fetal development. The differential metabolites between nonpregnancy and pregnancy were mainly associated with amino acid and carbohydrate metabolism. Moreover, metabolic activity varied across pregnancy stages; steroid hormone biosynthesis was predominant in early pregnancy, and amino acid biosynthesis and carbohydrate metabolism were predominant in mid pregnancy and late pregnancy, respectively. Our results provide new insights into metabolic characteristics in the Yangtze finless porpoises' urine during pregnancy, and indicate that the differential levels of urine metabolites can determine pregnancy in Yangtze finless porpoises, providing valuable information for the husbandry and management of pregnant Yangtze finless porpoises in captivity.

Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.

Microbiome and metabolome in home-made fermented soybean foods of India revealed by metagenome-assembled genomes and metabolomics

Grep-chhurpi, peha, peron namsing and peruñyaan are lesser-known home-made fermented soybean foods prepared by the native people of Arunachal Pradesh in India. Present work aims to study the microbiome, their functional annotations, metabolites and recovery of metagenome-assembled genomes (MAGs) in these four fermented soybean foods. Metagenomes revealed the dominance of bacteria (97.80 %) with minor traces of viruses, eukaryotes and archaea. Bacillota is the most abundant phylum with Bacillus subtilis as the abundant species. Metagenome also revealed the abundance of lactic acid bacteria such as Enterococcus casseliflavus, Enterococcus faecium, Mammaliicoccus sciuri and Staphylococcus saprophyticus in all samples. B. subtilis was the major species found in all products. Predictive metabolic pathways showed the abundance of genes associated with metabolisms. Metabolomics analysis revealed both targeted and untargeted metabolites, which suggested their role in flavour development and therapeutic properties. High-quality MAGs, identified as B. subtilis, Enterococcus faecalis, Pediococcus acidilactici and B. velezensis, showed the presence of several biomarkers corresponding to various bio-functional properties. Gene clusters of secondary metabolites (antimicrobial peptides) and CRISPR-Cas systems were detected in all MAGs. This present work also provides key elements related to the cultivability of identified species of MAGs for future use as starter cultures in fermented soybean food product development. Additionally, comparison of microbiome and metabolites of grep-chhurpi, peron namsing and peruñyaan with that of other fermented soybean foods of Asia revealed a distinct difference.

Recent advances in LC-MS-based metabolomics for clinical biomarker discovery

The employment of liquid chromatography-mass spectrometry (LC-MS) untargeted and targeted metabolomics has led to the discovery of novel biomarkers and improved the understanding of various disease mechanisms. Numerous strategies have been reported to expand the metabolite coverage in LC-MS-untargeted and targeted metabolomics. To improve the sensitivity of low-abundance or poor-ionized metabolites for reducing the amount of clinical sample, chemical derivatization methods are used to target different functional groups. Proper sample preparation is beneficial for reducing the matrix effect, maintaining the stability of the LC-MS system, and increasing the metabolite coverage. Machine learning has recently been integrated into the workflow of LC-MS metabolomics to accelerate metabolite identification and data-processing automation, and increase the accuracy of disease classification and clinical outcome prediction. Due to the rapidly growing utility of LC-MS metabolomics in discovering disease markers, this review will address the recent advances in the field and offer perspectives on various strategies for expanding metabolite coverage, chemical derivatization, sample preparation, clinical disease markers, and machining learning for disease modeling.

Deep learning analysis of UPLC-MS/MS-based metabolomics data to predict Alzheimer's disease

OBJECTIVE

Metabolic biomarkers can potentially inform disease progression in Alzheimer's disease (AD). The purpose of this study is to identify and describe a new set of diagnostic biomarkers for developing deep learning (DL) tools to predict AD using Ultra Performance Liquid Chromatography Mass Spectrometry (UPLC-MS/MS)-based metabolomics data.

METHODS

A total of 177 individuals, including 78 with AD and 99 with cognitive normal (CN), were selected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort along with 150 metabolomic biomarkers. We performed feature selection using the Least Absolute Shrinkage and Selection Operator (LASSO). The H2O DL function was used to build multilayer feedforward neural networks to predict AD.

RESULTS

The LASSO selected 21 metabolic biomarkers. To develop DL models, the 21 biomarkers identified by LASSO were imported into the H2O package. The data was split into 70% for training and 30% for validation. The best DL model with two layers and 18 neurons achieved an accuracy of 0.881, F1-score of 0.892, and AUC of 0.873. Several metabolomic biomarkers involved in glucose and lipid metabolism, in particular bile acid metabolites, were associated with APOE-ε4 allele and clinical biomarkers (Aβ42, tTau, pTau), cognitive assessments [the Alzheimer's Disease Assessment Scale-cognitive subscale 13 (ADAS13), the Mini-Mental State Examination (MMSE)], and hippocampus volume.

CONCLUSIONS

This study identified a new set of diagnostic metabolomic biomarkers for developing DL tools to predict AD. These biomarkers may help with early diagnosis, prognostic risk stratification, and/or early treatment interventions for patients at risk for AD.

Surface Active Salivary Metabolites Indicate Oxidative Stress and Inflammation in Obstructive Sleep Apnea

PURPOSE

Obstructive sleep apnea (OSA), a highly prevalent and potentially serious sleep disorder, requires effective screening tools. Saliva is a useful biological fluid with various metabolites that might also influence upper airway patency by affecting surface tension in the upper airway. However, little is known about the composition and role of salivary metabolites in OSA. Therefore, we investigated the metabolomics signature in saliva from the OSA patients and evaluated the associations between identified metabolites and salivary surface tension.

METHODS

We studied 68 subjects who visited sleep clinic due to the symptoms of OSA. All underwent full-night in-lab polysomnography. Patients with apnea-hypopnea index (AHI) < 10 were classified to the control, and those with AHI ≥ 10 were the OSA groups. Saliva samples were collected before and after sleep. The centrifuged saliva samples were analyzed by liquid chromatography with high-resolution mass spectrometry (ultra-performance liquid chromatography-tandem mass spectrometry; UPLC-MS/MS). Differentially expressed salivary metabolites were identified using open source software (XCMS) and Compound Discoverer 2.1. Metabolite set enrichment analysis (MSEA) was performed using MetaboAnalyst 5.0. The surface tension of the saliva samples was determined by the pendant drop method.

RESULTS

Three human-derived metabolites (1-palmitoyl-2-[5-hydroxyl-8-oxo-6-octenoyl]-sn-glycerol-3-phosphatidylcholine [PHOOA-PC], 1-palmitoyl-2-[5-keto-8-oxo-6-octenoyl]-sn-glycerol-3-phosphatidylcholine [KPOO-PC], and 9-nitrooleate) were significantly upregulated in the after-sleep salivary samples from the OSA patients compared to the control group samples. Among the candidate metabolites, only PHOOA-PC was correlated with the AHI. In OSA samples, salivary surface tension decreased after sleep. The differences in surface tension were negatively correlated with PHOOA-PC and 9-nitrooleate concentrations. Furthermore, MSEA revealed that arachidonic acid-related metabolism pathways were upregulated in the after-sleep samples from the OSA group.

CONCLUSIONS

This study revealed that salivary PHOOA-PC was correlated positively with the AHI and negatively with salivary surface tension in the OSA group. Salivary metabolomic analysis may improve our understanding of upper airway dynamics and provide new insights into novel biomarkers and therapeutic targets in OSA.

Untargeted Metabolomics to Characterize the Urinary Chemical Landscape of E-Cigarette Users

The health and safety of using e-cigarette products (vaping) have been challenging to assess and further regulate due to their complexity. Inhaled e-cigarette aerosols contain chemicals with under-recognized toxicological profiles, which could influence endogenous processes once inhaled. We urgently need more understanding on the metabolic effects of e-cigarette exposure and how they compare to combustible cigarettes. To date, the metabolic landscape of inhaled e-cigarette aerosols, including chemicals originated from vaping and perturbed endogenous metabolites in vapers, is poorly characterized. To better understand the metabolic landscape and potential health consequences of vaping, we applied liquid chromatography-mass spectrometry (LC-MS) based nontargeted metabolomics to analyze compounds in the urine of vapers, cigarette smokers, and nonusers. Urine from vapers (n = 34), smokers (n = 38), and nonusers (n = 45) was collected for verified LC-HRMS nontargeted chemical analysis. The altered features (839, 396, and 426 when compared smoker and control, vaper and control, and smoker and vaper, respectively) among exposure groups were deciphered for their structural identities, chemical similarities, and biochemical relationships. Chemicals originating from e-cigarettes and altered endogenous metabolites were characterized. There were similar levels of nicotine biomarkers of exposure among vapers and smokers. Vapers had higher urinary levels of diethyl phthalate and flavoring agents (e.g., delta-decalactone). The metabolic profiles featured clusters of acylcarnitines and fatty acid derivatives. More consistent trends of elevated acylcarnitines and acylglycines in vapers were observed, which may suggest higher lipid peroxidation. Our approach in monitoring shifts of the urinary chemical landscape captured distinctive alterations resulting from vaping. Our results suggest similar nicotine metabolites in vapers and cigarette smokers. Acylcarnitines are biomarkers of inflammatory status and fatty acid oxidation, which were dysregulated in vapers. With higher lipid peroxidation, radical-forming flavoring, and higher level of specific nitrosamine, we observed a trend of elevated cancer-related biomarkers in vapers as well. Together, these data present a comprehensive profiling of urinary biochemicals that were dysregulated due to vaping.

Quantification of Serum Metabolites in Early Colorectal Adenomas Using Isobaric Labeling Mass Spectrometry

A major challenge in reducing the death rate of colorectal cancer is to screen patients using low-invasive testing. A blood test shows a high compliance rate with reduced invasiveness. In this work, a multiplex isobaric tag labeling strategy coupled with mass spectrometry is adopted to relatively quantify primary and secondary amine-containing metabolites in serum for the discovery of metabolite level changes of colorectal cancer. Serum samples from patients at different risk statuses and colorectal cancer growth statuses are studied. Metabolite identification is based on accurate mass matching and/or retention time of labeled metabolite standards. We quantify 40 metabolites across all the serum samples, including 18 metabolites validated with standards. We find significantly decreased levels of threonine and asparagine in the patients with growing adenomas or high-risk adenomas (p < 0.05). Glutamine levels decrease in patients with adenomas of unknown growth status or high-risk adenomas. In contrast, arginine levels are elevated in patients with low-risk adenoma. Receiver operating characteristic analysis shows high sensitivity and specificity of these metabolites for detecting growing adenomas. Based on these results, we conclude that a few metabolites identified here might contribute to distinguishing colorectal patients with growing adenomas from normal individuals and patients with unknown growth status of adenomas.

Status of Metabolomic Measurement for Insights in Alzheimer's Disease Progression-What Is Missing?

Alzheimer's disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

Simple In Vitro 18O Labeling for Improved Mass Spectrometry-Based Drug Metabolites Identification: Deep Drug Metabolism Study

The identification of drug metabolites formed with different in vitro systems by HPLC-MS is a standard step in preclinical research. In vitro systems allow modeling of real metabolic pathways of a drug candidate. Despite the emergence of various software and databases, identification of compounds is still a complex task. Measurement of the accurate mass, correlation of chromatographic retention times and fragmentation spectra are often insufficient for identification of compounds especially in the absence of reference materials. Metabolites can "slip under the nose", since it is often not possible to reliably confirm that a signal belongs to a metabolite and not to other compounds in complex systems. Isotope labeling has proved to be a tool that aids in small molecule identification. The introduction of heavy isotopes is done with isotope exchange reactions or with complicated synthetic schemes. Here, we present an approach based on the biocatalytic insertion of oxygen-18 isotope under the action of liver microsomes enzymes in the presence of ¹⁸O₂. Using the local anesthetic bupivacaine as an example, more than 20 previously unknown metabolites were reliably discovered and annotated in the absence of the reference materials. In combination with high-resolution mass spectrometry and modern methods of mass spectrometric metabolism data processing, we demonstrated the ability of the proposed approach to increase the degree of confidence in interpretating metabolism data.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Comprehensive metabolomics and lipidomics profiling uncovering neuroprotective effects of Ginkgo biloba L. leaf extract on Alzheimer's disease

Introduction: Ginkgo biloba L. leaf extract (GBLE) has been reported to be effective for alleviating cognitive and memory impairment in Alzheimer's disease (AD). Nevertheless, the potential mechanism remains unclear. Herein, this study aimed to explore the neuroprotective effects of GBLE on AD and elaborate the underlying therapeutic mechanism. Methods: Donepezil, the most widely prescribed drug for AD, was used as a positive control. An integrated metabolomics and lipidomics approach was adopted to characterize plasma metabolic phenotype of APP/PS1 double transgenic mice and describe the metabolomic and lipidomic fingerprint changes after GBLE intervention. The Morris water maze test and immunohistochemistry were applied to evaluate the efficacy of GBLE. Results: As a result, administration of GBLE significantly improved the cognitive function and alleviated amyloid beta (Aβ) deposition in APP/PS1 mice, showing similar effects to donepezil. Significant alterations were observed in metabolic signatures of APP/PS1 mice compared with wild type (WT) mice by metabolomic analysis. A total of 60 markedly altered differential metabolites were identified, including 28 lipid and lipid-like molecules, 13 organic acids and derivatives, 11 organic nitrogen compounds, and 8 other compounds, indicative of significant changes in lipid metabolism of AD. Further lipidomic profiling showed that the differential expressed lipid metabolites between APP/PS1 and WT mice mainly consisted of phosphatidylcholines, lysophosphatidylcholines, triglycerides, and ceramides. Taking together all the data, the plasma metabolic signature of APP/PS1 mice was primarily characterized by disrupted sphingolipid metabolism, glycerophospholipid metabolism, glycerolipid metabolism, and amino acid metabolism. Most of the disordered metabolites were ameliorated after GBLE treatment, 19 metabolites and 24 lipids of which were significantly reversely regulated (adjusted-p<0.05), which were considered as potential therapeutic targets of GBLE on AD. The response of APP/PS1 mice to GBLE was similar to that of donepezil, which significantly reversed the levels of 23 disturbed metabolites and 30 lipids. Discussion: Our data suggested that lipid metabolism was dramatically perturbed in the plasma of APP/PS1 mice, and GBLE might exert its neuroprotective effects by restoring lipid metabolic balance. This work provided a basis for better understanding the potential pathogenesis of AD and shed new light on the therapeutic mechanism of GBLE in the treatment of AD.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

The gastrodin biosynthetic pathway in Pholidota chinensis Lindl. revealed by transcriptome and metabolome profiling

Pholidota chinensis Lindl. is an epiphytic or lithophytic perennial herb of Orchidaceae family used as a garden flower or medicinal plant to treat high blood pressure, dizziness and headache in traditional Chinese medicine. Gastrodin (GAS) is considered as a main bioactive ingredient of this herb but the biosynthetic pathway remains unclear in P. chinensis. To elucidate the GAS biosynthesis and identify the related genes in P. chinensis, a comprehensive analysis of transcriptome and metabolome of roots, rhizomes, pseudobulbs and leaves were performed by using PacBio SMART, Illumina Hiseq and Ultra Performance Liquid Chromatography Tandem Mass Spectrometry (UPLC-MS/MS). A total of 1,156 metabolites were identified by UPLC-MS/MS, of which 345 differential metabolites were mainly enriched in phenylpropanoid/phenylalanine, flavone and flavonol biosynthesis. The pseudobulbs make up nearly half of the fresh weight of the whole plant, and the GAS content in the pseudobulbs was also the highest in four tissues. Up to 23,105 Unigenes were obtained and 22,029 transcripts were annotated in the transcriptome analysis. Compared to roots, 7,787, 8,376 and 9,146 differentially expressed genes (DEGs) were identified in rhizomes, pseudobulbs and leaves, respectively. And in total, 80 Unigenes encoding eight key enzymes for GAS biosynthesis, were identified. Particularly, glycosyltransferase, the key enzyme of the last step in the GAS biosynthetic pathway had 39 Unigenes candidates, of which, transcript28360/f2p0/1592, was putatively identified as the most likely candidate based on analysis of co-expression, phylogenetic analysis, and homologous searching. The metabolomics and transcriptomics of pseudobulbs versus roots showed that 8,376 DEGs and 345 DEMs had a substantial association based on the Pearson's correlation. This study notably enriched the metabolomic and transcriptomic data of P. chinensis, and it provides valuable information for GAS biosynthesis in the plant.

Development of LC-HRMS untargeted analysis methods for nasal epithelial lining fluid exposomics

BACKGROUND

The nasal mucosa, as a primary site of entry for inhaled substances, contains both inhaled xenobiotic and endogenous biomarkers. Nasal mucosa can be non-invasively sampled (nasal epithelial lining fluid "NELF") and analyzed for biological mediators. However, methods for untargeted analysis of compounds inhaled and/or retained in the nasal mucosa are needed.

OBJECTIVES

This study aimed to develop a high resolution LC-MS untargeted method to analyze collected NELF. Profiling of compounds in NELF samples will also provide baseline data for future comparative studies to reference.

METHODS

Extracted NELF analytes were injected to LC-ESI-MS. After spectrum processing, an in-house library provided annotations with high confidence, while more tentative annotation proposals were obtained via ChemSpider database matching.

RESULTS

The established method successfully detected unique molecular signatures within NELF. Baseline profiling of 27 samples detected 2002 unknown molecules, with 77 and 463 proposed structures by our in-house library and Chemspider matching. High confidence annotations revealed common metabolites and tentative annotations implied various environmental exposure biomarkers are also present in NELF.

SIGNIFICANCE

The experimental pipeline for analyzing NELF samples serves as simple and robust method applicable for future studies to characterize identities/effects of inhaled substances and metabolites retained in the nasal mucosa.

IMPACT STATEMENT

The nasal mucosa contains exogenous and endogenous compounds. The development of an untargeted analysis is necessary to characterize the nasal exposome by deciphering the identity and influence of inhaled compounds on nasal mucosal biology. This study established a high resolution LC-MS based untargeted analysis of non-invasively collected nasal epithelial lining fluid. Baseline profiling of the nasal mucosa (n = 27) suggests the presence of environmental pollutants, along with detection of endogenous metabolites. Our results show high potential for the analytical pipeline to facilitate future respiratory health studies involving inhaled pollutants or pharmaceutical compounds and their effects on respiratory biology.

Metabolomics as a Crucial Tool to Develop New Therapeutic Strategies for Neurodegenerative Diseases

Neurodegenerative diseases (NDs), such as Alzheimer's (AD), Parkinson's (PD), and amyotrophic lateral sclerosis (ALS), share common pathological mechanisms, including metabolism alterations. However, their specific neuronal cell types affected and molecular biomarkers suggest that there are both common and specific alterations regarding metabolite levels. In this review, we were interested in identifying metabolite alterations that have been reported in preclinical models of NDs and that have also been documented as altered in NDs patients. Such alterations could represent interesting targets for the development of targeted therapy. Importantly, the translation of such findings from preclinical to clinical studies is primordial for the study of possible therapeutic agents. We found that N-acetyl-aspartate (NAA), myo-inositol, and glutamate are commonly altered in the three NDs investigated here. We also found other metabolites commonly altered in both AD and PD. In this review, we discuss the studies reporting such alterations and the possible pathological mechanism underlying them. Finally, we discuss clinical trials that have attempted to develop treatments targeting such alterations. We conclude that the treatment combination of both common and differential alterations would increase the chances of patients having access to efficient treatments for each ND.

IDSL.IPA Characterizes the Organic Chemical Space in Untargeted LC/HRMS Data Sets

Generating comprehensive and high-fidelity metabolomics data matrices from LC/HRMS data remains to be extremely challenging for population-scale large studies (n > 200). Here, we present a new data processing pipeline, the Intrinsic Peak Analysis (IDSL.IPA) R package (https://ipa.idsl.me), to generate such data matrices specifically for organic compounds. The IDSL.IPA pipeline incorporates (1) identifying potential ¹²C and ¹³C ion pairs in individual mass spectra; (2) detecting and characterizing chromatographic peaks using a new sensitive and versatile approach to perform mass correction, peak smoothing, baseline development for local noise measurement, and peak quality determination; (3) correcting retention time and cross-referencing peaks from multiple samples by a dynamic retention index marker approach; (4) annotating peaks using a reference database of m/z and retention time; and (5) accelerating data processing using a parallel computation of the peak detection and alignment steps for larger studies. This pipeline has been successfully evaluated for studies ranging from 200 to 1600 samples. By specifically isolating high quality and reliable signals pertaining to carbon-containing compounds in untargeted LC/HRMS data sets from larger studies, IDSL.IPA opens new opportunities for discovering new biological insights in the population-scale metabolomics and exposomics projects. The package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.IPA.

Metagenomic-Metabolomic Mining of Kinema, a Naturally Fermented Soybean Food of the Eastern Himalayas

Kinema is a popular sticky fermented soybean food of the Eastern Himalayan regions of North East India, east Nepal, and south Bhutan. We hypothesized that some dominant bacteria in kinema may contribute to the formation of targeted and non-targeted metabolites for health benefits; hence, we studied the microbiome-metabolite mining of kinema. A total of 1,394,094,912 bp with an average of 464,698,304 ± 120,720,392 bp was generated from kinema metagenome, which resulted in the identification of 47 phyla, 331 families, 709 genera, and 1,560 species. Bacteria (97.78%) were the most abundant domain with the remaining domains of viruses, eukaryote, and archaea. Firmicutes (93.36%) was the most abundant phylum with 280 species of Bacillus, among which Bacillus subtilis was the most dominant species in kinema followed by B. glycinifermentans, B. cereus, B. licheniformis, B. thermoamylovorans, B. coagulans, B. circulans, B. paralicheniformis, and Brevibacillus borstelensis. Predictive metabolic pathways revealed the abundance of genes associated with metabolism (60.66%), resulting in 216 sub-pathways. A total of 361 metabolites were identified by metabolomic analysis (liquid chromatography-mass spectrophotometry, LC-MS). The presence of metabolites, such as chrysin, swainsonine, and 3-hydroxy-L-kynurenine (anticancer activity) and benzimidazole (antimicrobial, anticancer, and anti-HIV activities), and compounds with immunomodulatory effects in kinema supports its therapeutic potential. The correlation between the abundant species of Bacillus and primary and secondary metabolites was constructed with a bivariate result. This study proves that Bacillus spp. contribute to the formation of many targeted and untargeted metabolites in kinema for health-promoting benefits.

High Resolution Magic Angle Spinning Proton NMR Study of Alzheimer's Disease with Mouse Models

Alzheimer's disease (AD) is a crippling condition that affects millions of elderly adults each year, yet there remains a serious need for improved methods of diagnosis. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease; however, studies of human brain tissue metabolomics are challenging, due to sample limitations and ethical considerations. Comprehensive comparisons of imaging measurements in animal models to identify similarities and differences between aging- and AD-associated metabolic changes should thus be tested and validated for future human non-invasive studies. In this paper, we present the results of our highresolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) studies of AD and wild-type (WT) mouse models, based on animal age, brain regions, including cortex vs. hippocampus, and disease status. Our findings suggest the ability of HRMAS NMR to differentiate between AD and WT mice using brain metabolomics, which potentially can be implemented in in vivo evaluations.

Identification of cerebrospinal fluid metabolites as biomarkers for neurobrucellosis by liquid chromatography-mass spectrometry approach

Neurobrucellosis is the most morbid form in brucellosis disease. Metabolomics is an emerging method which intends to explore the global alterations of various metabolites in samples. We aimed to identify metabolites in cerebrospinal fluid (CSF) as biomarkers that were potentially unique for neurobrucellosis. CSF samples from 25 neurobrucellosis patients and 25 normal controls (uninfected patients with hydrocephalus) were collected for metabolite detection using liquid chromatography-mass spectrometry (LC-MS) approach. Inflammatory cytokines in CSF were measured with Enzyme-linked immunosorbent assay (ELISA). The base peak chromatogram in CSF samples showed that small-molecule metabolites were well separated. Principal Component Analysis (PCA) analysis exhibited the examined samples were arranged in two main clusters in accordance with their group. Projection to Latent Structures Discriminant Analysis (PLS-DA) revealed there was a noticeable separation between neurobrucellosis and normal groups. Orthogonal Partial Least-Squares-Discriminant Analysis (OPLS-DA) could responsibly illuminate the differences between neurobrucellosis and normal controls. Neurobrucellosis showed a total of 155 differentiated metabolites. Prominent potential biomarkers including 30 metabolites were then selected out, regarded as more capable of distinguishing neurobrucellosis. TNF-α and IL-6 in CSF were remarkably increased in neurobrucellosis. We presented the heatmaps and correlation analyses among the identified 30 potential biomarkers. In conclusion, this study showed that CSF metabolomics based on LC-MS could distinguish neurobrucellosis patients from normal controls. Our data offered perspectives for diagnosis and treatment for neurobrucellosis.

Serum Metabolomic and Lipidomic Profiling Reveals Novel Biomarkers of Efficacy for Benfotiamine in Alzheimer's Disease

Serum metabolomics and lipidomics are powerful approaches for discovering unique biomarkers in various diseases and associated therapeutics and for revealing metabolic mechanisms of both. Treatment with Benfotiamine (BFT), a thiamine prodrug, for one year produced encouraging results for patients with mild cognitive impairment and mild Alzheimer’s disease (AD). In this study, a parallel metabolomics and lipidomics approach was applied for the first exploratory investigation on the serum metabolome and lipidome of patients treated with BFT. A total of 315 unique metabolites and 417 lipids species were confidently identified and relatively quantified. Rigorous statistical analyses revealed significant differences between the placebo and BFT treatment groups in 25 metabolites, including thiamine, tyrosine, tryptophan, lysine, and 22 lipid species, mostly belonging to phosphatidylcholines. Additionally, 10 of 11 metabolites and 14 of 15 lipid species reported in previous literature to follow AD progression changed in the opposite direction to those reported to reflect AD progression. Enrichment and pathway analyses show that significantly altered metabolites by BFT are involved in glucose metabolism and biosynthesis of aromatic amino acids. Our study discovered that multiple novel biomarkers and multiple mechanisms that may underlie the benefit of BFT are potential therapeutic targets in AD and should be validated in studies with larger sample sizes.

Relationship of cow and calf circulating lipidomes with colostrum lipid composition and metabolic status of the cow

Newborn calves rely on lipids in colostrum for energy and immune function. The lipid concentration in colostrum, however, is highly variable, and little is known about its composition and maternal factors that influence its composition. The first objective was to measure plasma lipid composition of multiparous cows at 35 d before calving (BC; 35 ± 3 d; ± standard deviation) and 7 d BC (7 ± 2 d), their colostrum, and serum lipid composition of calves (24 h after birth) using multiple reaction monitoring profiling, which is an exploratory and highly sensitive lipidomic analysis method that screens lipids based on chemical functionality. Second, data were analyzed to determine if there were relationships between circulating lipids in the cow, colostrum lipids, and calf serum lipids. Third, relationships between markers of metabolic status of the cows and circulating and colostrum lipids were analyzed with correlation analysis. Blood was sampled and plasma prepared from multiparous cows (n = 16) at 35 and 7 d BC. Within 3 h of parturition, colostrum was collected from cows and fed to her calf. Calves received another feeding of colostrum within 12 h after birth and a serum sample was collected from each calf 24 h after the first feeding of colostrum. The metabolic status of cows was evaluated using insulin, glucose, and nonesterified fatty acid area under the curve in response to an intravenous glucose tolerance test performed at 3 wk BC. Lipids were extracted from plasma, colostrum, and calf serum and were analyzed using multiple reaction monitoring profiling. Concentration of lipids were calculated using spiked in standards and expressed as percent of lipids identified. Data were uploaded into MetaboAnalyst 5.0 for multivariate and univariate analysis. Principal component analysis indicated that circulating lipids in the cow and calf were distinct from lipids in colostrum. Phosphatidylglycerol (PG) concentration was greater in colostrum and calf serum than in cow plasma, with 23 of the 24 PG found in colostrum also found in calf serum. In response to intravenous glucose tolerance test in late gestation, nonesterified fatty acid area under the curve was positively related to total triacylglycerols lipids in 7 d BC plasma (r = 0.63) but negatively related to total membrane lipids in colostrum (r = -0.55). Thus, the metabolic status of the dam influences circulating lipids and colostrum lipid content. Moreover, the circulating lipidome of the cow and calf are similar to one another and distinct from the colostrum lipidome, except for PG, where it appears that colostrum serves as the source for PG in the calf's circulation.

Fully Automatized Detection of Phosphocholine-Containing Lipids through an Isotopically Labeled Buffer Modification Workflow

High-resolution mass spectrometry is the foremost technique for qualitative and quantitative lipidomics analyses. Glycerophospholipids and sphingolipids, collectively termed polar lipids, are commonly investigated by hyphenated liquid chromatography-mass spectrometry (LC-MS) techniques that reduce aggregation effects and provide a greater dynamic range of detection sensitivity compared to shotgun lipidomics. However, automatic polar lipid identification is hindered by several isobaric and isomer mass overlaps, which cause software programs to often fail to correctly annotate the lipid species. In the present paper, a buffer modification workflow based on the use of labeled and unlabeled acetate ions in the chromatographic buffers was optimized by Box-Behnken design of the experiments and applied to the characterization of phosphocholine-containing lipids in human plasma samples. The contemporary generation of [M + CH₃COO]^-, [M + CD₃COO]^-, and [M - CH₃]^- coupled with a dedicated data processing workflow, which was specifically set up on Compound Discoverer software, allowed us to correctly determine adduct composition, molecular formulas, and grouping, as well as granting a lower false-positive rate and streamlining the manual validation step compared to commonly employed lipidomics platforms. The proposed workflow represents a robust yet easier alternative to the existing approaches for improving lipid annotation, as it does not require extensive sample pretreatment or prior isotopic enrichment or derivatization.

Current State of Metabolomics Research in Meat Quality Analysis and Authentication

In the past decades, as an emerging omic, metabolomics has been widely used in meat science research, showing promise in meat quality analysis and meat authentication. This review first provides a brief overview of the concept, analytical techniques, and analysis workflow of metabolomics. Additionally, the metabolomics research in quality analysis and authentication of meat is comprehensively described. Finally, the limitations, challenges, and future trends of metabolomics application in meat quality analysis and meat authentication are critically discussed. We hope to provide valuable insights for further research in meat quality.

New frontiers in Alzheimer's disease diagnostic: Monoamines and their derivatives in biological fluids

Current diagnosis of Alzheimer's disease (AD) relies on a combination of neuropsychological evaluations, biomarker measurements and brain imaging. Nevertheless, these approaches are either expensive, invasive or lack sensitivity to early AD stages. The main challenge of ongoing research is therefore to identify early non-invasive biomarkers to diagnose AD at preclinical stage. Accumulating evidence support the hypothesis that initial degeneration of profound monoaminergic nuclei may trigger a transneuronal spread of AD pathology towards hippocampus and cortex. These studies aroused great interest on monoamines, i.e. noradrenaline (NA), dopamine (D) ad serotonin (5-HT), as early hallmarks of AD pathology. The present work reviews current literature on the potential role of monoamines and related metabolites as biomarkers of AD. First, morphological changes in the monoaminergic systems during AD are briefly described. Second, we focus on concentration changes of these molecules and their derivatives in biological fluids, including cerebrospinal fluid, obtained by lumbar puncture, and blood or urine, sampled via less invasive procedures. Starting from initial observations, we then discuss recent insights on metabolomics-based analysis, highlighting the promising clinical utility of monoamines for the identification of a molecular AD signature, aimed at improving early diagnosis and discrimination from other dementia.

Clinical Insights into Mitochondrial Neurodevelopmental and Neurodegenerative Disorders: Their Biosignatures from Mass Spectrometry-Based Metabolomics

Mitochondria are dynamic multitask organelles that function as hubs for many metabolic pathways. They produce most ATP via the oxidative phosphorylation pathway, a critical pathway that the brain relies on its energy need associated with its numerous functions, such as synaptic homeostasis and plasticity. Therefore, mitochondrial dysfunction is a prevalent pathological hallmark of many neurodevelopmental and neurodegenerative disorders resulting in altered neurometabolic coupling. With the advent of mass spectrometry (MS) technology, MS-based metabolomics provides an emerging mechanistic understanding of their global and dynamic metabolic signatures. In this review, we discuss the pathogenetic causes of mitochondrial metabolic disorders and the recent MS-based metabolomic advances on their metabolomic remodeling. We conclude by exploring the MS-based metabolomic functional insights into their biosignatures to improve diagnostic platforms, stratify patients, and design novel targeted therapeutic strategies.

Metabolomics and Lipidomics: Expanding the Molecular Landscape of Exercise Biology

Dynamic changes in circulating and tissue metabolites and lipids occur in response to exercise-induced cellular and whole-body energy demands to maintain metabolic homeostasis. The metabolome and lipidome in a given biological system provides a molecular snapshot of these rapid and complex metabolic perturbations. The application of metabolomics and lipidomics to map the metabolic responses to an acute bout of aerobic/endurance or resistance exercise has dramatically expanded over the past decade thanks to major analytical advancements, with most exercise-related studies to date focused on analyzing human biofluids and tissues. Experimental and analytical considerations, as well as complementary studies using animal model systems, are warranted to help overcome challenges associated with large human interindividual variability and decipher the breadth of molecular mechanisms underlying the metabolic health-promoting effects of exercise. In this review, we provide a guide for exercise researchers regarding analytical techniques and experimental workflows commonly used in metabolomics and lipidomics. Furthermore, we discuss advancements in human and mammalian exercise research utilizing metabolomic and lipidomic approaches in the last decade, as well as highlight key technical considerations and remaining knowledge gaps to continue expanding the molecular landscape of exercise biology.

Stop the rot. Enzyme inactivation at brain harvest prevents artifacts: A guide for preservation of the in vivo concentrations of brain constituents

Post-mortem metabolism is widely recognized to cause rapid and prolonged changes in the concentrations of multiple classes of compounds in brain, that is, they are labile. Post-mortem changes from levels in living brain include components of pathways of metabolism of glucose and energy compounds, amino acids, lipids, signaling molecules, neuropeptides, phosphoproteins, and proteins. Methods that stop enzyme activity at brain harvest were developed almost 50 years ago and have been extensively used in studies of brain functions and diseases. Unfortunately, these methods are not commonly used to harvest brain tissue for mass spectrometry-based metabolomic studies or for imaging mass spectrometry studies (IMS, also called mass spectrometry imaging, MSI, or matrix-assisted laser desorption/ionization-MSI, MALDI-MSI). Instead these studies commonly kill animals, decapitate, dissect out brain and regions of interest if needed, then 'snap' freeze the tissue to stop enzymatic activity after harvest, with post-mortem intervals typically ranging from ~0.5 to 3 min. To increase awareness of the importance of stopping metabolism at harvest and preventing the unnecessary complications of not doing so, this commentary provides examples of labile metabolites and the magnitudes of their post-mortem changes in concentrations during brain harvest. Brain harvest methods that stop metabolism at harvest eliminate post-mortem enzymatic activities and can improve characterization of normal and diseased brain. In addition, metabolomic studies would be improved by reporting absolute units of concentration along with normalized peak areas or fold changes. Then reported values can be evaluated and compared with the extensive neurochemical literature to help prevent reporting of artifactual data.

patRoon: open source software platform for environmental mass spectrometry based non-target screening

Mass spectrometry based non-target analysis is increasingly adopted in environmental sciences to screen and identify numerous chemicals simultaneously in highly complex samples. However, current data processing software either lack functionality for environmental sciences, solve only part of the workflow, are not openly available and/or are restricted in input data formats. In this paper we present patRoon, a new R based open-source software platform, which provides comprehensive, fully tailored and straightforward non-target analysis workflows. This platform makes the use, evaluation and mixing of well-tested algorithms seamless by harmonizing various common (primarily open) software tools under a consistent interface. In addition, patRoon offers various functionality and strategies to simplify and perform automated processing of complex (environmental) data effectively. patRoon implements several effective optimization strategies to significantly reduce computational times. The ability of patRoon to perform time-efficient and automated non-target data annotation of environmental samples is demonstrated with a simple and reproducible workflow using open-access data of spiked samples from a drinking water treatment plant study. In addition, the ability to easily use, combine and evaluate different algorithms was demonstrated for three commonly used feature finding algorithms. This article, combined with already published works, demonstrate that patRoon helps make comprehensive (environmental) non-target analysis readily accessible to a wider community of researchers.

Multiomics Integrative Analysis for Discovering the Potential Mechanism of Dioscin against Hyperuricemia Mice

Hyperuricemia is a well-known key risk factor for gout and can cause a variety of metabolic diseases. Several studies have shown that dioscin could improve metabolic symptoms and reduce the uric acid level in blood. However, there is no comprehensive metabolomic study on the anti-hyperuricemia effects of dioscin. A total of 29 adult male Kunming mice were divided into three groups: Normal (blank), PO (potassium oxonate-administrated, 200 mg/kg/day), and Dioscin (potassium oxonate + dioscin, potassium oxonate 200 mg/kg/day, dioscin 50 mg/kg/day). All mice were treated for 42 days via oral gavage. This paper implemented an untargeted metabolomics study based on ¹H NMR and LC-MS to discover the comprehensive mechanism of dioscin. Furthermore, a targeted lipidomics was fulfilled to further analyze the lipid metabolism disorder. Finally, the metabolic pathway mediated by dioscin was verified at the gene level by means of transcriptomics. The results show 53 different metabolites were closely related to the improvement of dioscin in PO-induced hyperuricemia, and 19 of them were lipids. These metabolites are mainly involved in the tricarboxylic acid cycle, lipid metabolism, amino acid metabolism, and pyrimidine metabolism. According to the transcriptomics study, the levels of 89 genes were significantly changed in the PO group compared to the normal control. Among them, six gene levels were restored by the treatment of dioscin. The six changed genes (tx1b, Tsku, Tmem163, Psmc3ip, Tcap, Tbx15) are mainly involved in the cell cycle and energy metabolism. These metabolites and genes might provide useful information for further study of the therapeutic mechanism of dioscin.

A Metabolomics Workflow for Analyzing Complex Biological Samples Using a Combined Method of Untargeted and Target-List Based Approaches

In the highly dynamic field of metabolomics, we have developed a method for the analysis of hydrophilic metabolites in various biological samples. Therefore, we used hydrophilic interaction chromatography (HILIC) for separation, combined with a high-resolution mass spectrometer (MS) with the aim of separating and analyzing a wide range of compounds. We used 41 reference standards with different chemical properties to develop an optimal chromatographic separation. MS analysis was performed with a set of pooled biological samples human cerebrospinal fluid (CSF), and human plasma. The raw data was processed in a first step with Compound Discoverer 3.1 (CD), a software tool for untargeted metabolomics with the aim to create a list of unknown compounds. In a second step, we combined the results obtained with our internally analyzed reference standard list to process the data along with the Lipid Data Analyzer 2.6 (LDA), a software tool for a targeted approach. In order to demonstrate the advantages of this combined target-list based and untargeted approach, we not only compared the relative standard deviation (%RSD) of the technical replicas of pooled plasma samples (n = 5) and pooled CSF samples (n = 3) with the results from CD, but also with XCMS Online, a well-known software tool for untargeted metabolomics studies. As a result of this study we could demonstrate with our HILIC-MS method that all standards could be either separated by chromatography, including isobaric leucine and isoleucine or with MS by different mass. We also showed that this combined approach benefits from improved precision compared to well-known metabolomics software tools such as CD and XCMS online. Within the pooled plasma samples processed by LDA 68% of the detected compounds had a %RSD of less than 25%, compared to CD and XCMS online (57% and 55%). The improvements of precision in the pooled CSF samples were even more pronounced, 83% had a %RSD of less than 25% compared to CD and XCMS online (28% and 8% compounds detected). Particularly for low concentration samples, this method showed a more precise peak area integration with its 3D algorithm and with the benefits of the LDAs graphical user interface for fast and easy manual curation of peak integration. The here-described method has the advantage that manual curation for larger batch measurements remains minimal due to the target list containing the information obtained by an untargeted approach.

A Case Report of Switching from Specific Vendor-Based to R-Based Pipelines for Untargeted LC-MS Metabolomics

Data pre-processing of the LC-MS data is a critical step in untargeted metabolomics studies in order to achieve correct biological interpretations. Several tools have been developed for pre-processing, and these can be classified into either commercial or open source software. This case report aims to compare two specific methodologies, Agilent Profinder vs. R pipeline, for a metabolomic study with a large number of samples. Specifically, 369 plasma samples were analyzed by HPLC-ESI-QTOF-MS. The collected data were pre-processed by both methodologies and later evaluated by several parameters (number of peaks, degree of missingness, quality of the peaks, degree of misalignments, and robustness in multivariate models). The vendor software was characterized by ease of use, friendly interface and good quality of the graphs. The open source methodology could more effectively correct the drifts due to between and within batch effects. In addition, the evaluated statistical methods achieved better classification results with higher parsimony for the open source methodology, indicating higher data quality. Although both methodologies have strengths and weaknesses, the open source methodology seems to be more appropriate for studies with a large number of samples mainly due to its higher capacity and versatility that allows combining different packages, functions, and methods in a single environment.

Time-resolved multi-omics analysis reveals the role of nutrient stress-induced resource reallocation for TAG accumulation in oleaginous fungus Mortierella alpina

BACKGROUND

Global resource reallocation is an established critical strategy through which organisms deal with environmental stress. The regulation of intracellular lipid storage or utilization is one of the most important strategies for maintaining energy homeostasis and optimizing growth. Oleaginous microorganisms respond to nitrogen deprivation by inducing lipid hyper accumulation; however, the associations between resource allocation and lipid accumulation are poorly understood.

RESULTS

Here, the time-resolved metabolomics, lipidomics, and proteomics data were generated in response to nutrient availability to examine how metabolic alternations induced by nitrogen deprivation drive the triacylglycerols (TAG) accumulation in M. alpina. The subsequent accumulation of TAG under nitrogen deprivation was a consequence of the reallocation of carbon, nitrogen sources, and lipids, rather than an up-regulation of TAG biosynthesis genes. On one hand, nitrogen deprivation induced the down-regulation of isocitrate dehydrogenase level in TCA cycle and redirected glycolytic flux of carbon from amino acid biosynthesis into fatty acids' synthesis; on the other hand, nitrogen deprivation induced the up-regulation of cell autophagy and ubiquitin-mediated protein proteolysis which resulted in a recycling of preformed protein nitrogen and carbon. Combining with the up-regulation of glutamate decarboxylase and succinic semialdehyde dehydrogenase in GABA shunt, and the phosphoenolpyruvate carboxykinase in the central hub involving pyruvate/phosphoenolpyruvate/oxaloacetate, the products from nitrogen-containing compounds degradation were recycled to be intermediates of TCA cycle and be shunted toward de novo biosynthesis of fatty acids. We found that nitrogen deprivation increased the protein level of phospholipase C/D that contributes to degradation of phosphatidylcholine and phosphatidylethanolamine, and supplied acyl chains for TAG biosynthesis pathway. In addition, ATP from substrate phosphorylation was presumed to be a critical factor regulation of the global resource allocation and fatty acids' synthesis rate.

CONCLUSIONS

The present findings offer a panoramic view of resource allocation by M. alpina in response to nutrient stress and revealed a set of intriguing associations between resource reallocation and TAG accumulation. This system-level insight provides a rich resource with which to explore in-depth functional characterization and gain information about the strategic combination of strain development and process integration to achieve optimal lipid productivity under nutrient stress.

Metandem: An online software tool for mass spectrometry-based isobaric labeling metabolomics

Mass spectrometry-based stable isotope labeling provides the advantages of multiplexing capability and accurate quantification but requires tailored bioinformatics tools for data analysis. Despite the rapid advancements in analytical methodology, it is often challenging to analyze stable isotope labeling-based metabolomics data, particularly for isobaric labeling using MS/MS reporter ions for quantification. We report Metandem, a novel online software tool for isobaric labeling-based metabolomics, freely available at http://metandem.com/web/. Metandem provides a comprehensive data analysis pipeline integrating feature extraction, metabolite quantification, metabolite identification, batch processing of multiple data files, online parameter optimization for custom datasets, data normalization, and statistical analysis. Systematic evaluation of the Metandem tool was demonstrated on UPLC-MS/MS, nanoLC-MS/MS, CE-MS/MS and MALDI-MS platforms, via duplex, 4-plex, 10-plex, and 12-plex isobaric labeling experiments and the application to various biological samples.

Rapid Solution-Phase Hydrogen/Deuterium Exchange for Metabolite Compound Identification

Rapid, solution-phase hydrogen/deuterium exchange (HDX) coupled with mass spectrometry (MS) is demonstrated as a means for distinguishing small-molecule metabolites. HDX is achieved using capillary vibrating sharp-edge spray ionization (cVSSI) to allow sufficient time for reagent mixing and exchange in micrometer-sized droplets. Different compounds are observed to incorporate deuterium with varying efficiencies resulting in unique isotopic patterns as revealed in the MS spectra. Using linear regression techniques, parameters representing contribution to exchange by different hydrogen types can be computed. In this proof-of-concept study, the exchange parameters are shown to be useful in the retrodiction of the amount of deuterium incorporated within different compounds. On average, the exchange parameters retrodict the exchange level with ~ 2.2-fold greater accuracy than treating all exchangeable hydrogens equally. The parameters can be used to produce hypothetical isotopic distributions that agree (± 16% RMSD) with experimental measurements. These initial studies are discussed in light of their potential value for identifying challenging metabolite species.