Full Abstract Posters

Womxn in Metabolomics (WomiX) was established to promote the professional development and engagement of womxn in metabolomics through creating a sense of community and providing opportunities for networking and mentorship. WomiX is committed to the advancement of womxn in metabolomics and explicitly welcomes all members including trans women, transfeminine, genderfluid, nonbinary, and allies of womxn. Our primary mandate is to provide opportunities for members to engage with and learn from the metabolomics community and in our first few years we have established a series of web-based seminars called Images of Success. Our talented and diverse range of invited speakers have discussed topics including science communication and publishing, advice for students and core facility researchers, and the transition from academia to industry positions. We also established a formal mentorship program, which pairs early career members with experienced researchers and provides professional development opportunities through group workshops. Every year during Women’s Week in March, we highlight the achievements of WomiX members through our social media platforms. This year we are excited to introduce the annual WomiX Mentorship Award, which recognizes the exceptional mentorship and leadership efforts of a womxn researcher in metabolomics. Future WomiX initiatives include an internship program to provide early career members with opportunities to engage in diverse metabolomics research experiences. WomiX is currently over 100 members strong and we are actively finding new ways to engage with more members of the metabolomics community.

One of the biggest issues in our society today is metabolic syndrome, a disease that encompasses diabetes, obesity, insulin resistance, hypertension, and more. High fat diets have become increasingly common and tend to lead to complications associated with metabolic syndrome. Metabolic syndrome is also associated with kidney disease, although its role in causing and progressing kidney disease has yet to be discovered. NMR spectroscopy is widely used in metabolic profiling because it is intrinsically quantitative, non-destructive, highly reproducible, requires no elaborate sample preparation, and has a unique ability to differentiate small molecules based on their distinct spectral location. Most animal studies in dietary metabolomics research tend to only focus on one sex, however it is important to note that there is a difference between male and female metabolism in humans, as well as other animals. For this study, male and female Nile grass rats were given an adlibitum chow diet and an adlibitum high-fat diet. The purpose of this study is to analyze the lipidomic effects of high-fat diets on the kidney metabolome in male and female Nile grass rats. Lipids were extracted from kidneys and scanned using a JEOL 400-MHz spectrometer. Two-way ANOVA of lipid concentrations showed no significant differences between the two diets. However, statistical analysis did find significant difference between male and female samples with p-values ranging from 0.001-0.008 for several of the lipids that were analyzed such as total fatty acids, polyunsaturated fatty acids, unsaturated fatty acids phosphatidylcholine, docosahexaenoic acid, and lysophosphatidylcholine. Further study of these results and of the differences in kidney aqueous metabolites between these two diets is under investigation..

Uremic solutes are ubiquitous compounds that are retained in the blood of patients with defective kidney clearance. Accumulation of uremic solutes in the blood can lead to the development of a condition known as uremia.  At low abundance, most uremic solutes are relatively harmless, however at high levels, some uremic solutes can contribute to diabetes, liver failure, heart disease, memory loss, kidney disease, jaundice, and a variety of intractable skin conditions. These pathogenic uremic solutes can be further called uremic toxins. To date, the methods published for uremic solute characterization are only able to quantify a handful of uremic solutes. Given the importance of uremic solutes in health and disease, we believe that an improved method for quantifying a larger panel of uremic solutes is needed. Here we describe a new assay based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) that allows us to identify and quantify 88 small-molecule uremic solutes in plasma and serum. Among these compounds, many of them were previously unmeasured and are potentially important uremic toxins. In developing the assay, we carefully checked its accuracy, sensitivity, and reproducibility. The accuracies of quality control solutions and the recovery rates of spiked NIST SRM 1950 human serum samples at 3 different concentrations were in the range of 100 ± 10% and 100 ± 20%, respectively. We further demonstrate the utility of this uremic toxin assay by reporting the levels of the targeted uremic solutes in over 500 serum samples of patients undergoing renal dialysis. Compared with the age- and gender-matched control samples, the levels of 80 of the 88 targeted metabolites were significantly altered in the patients. As far as we are aware, this is the most comprehensive, quantitative assay that has been reported for the analysis of uremic solutes.

Introduction: Accurate integration of acquired mass spectral data from numerous analytical batches is critical to enhance statistical power and facilitate lipid identification. Currently, most retention time correction tools either look at intra-batch drift or use a default setting with pre-defined parameters that are often not optimized for lipidomics data collected from a LC gradient method. Herein, we developed an integrated pipeline to account for retention time shift among batches, thus improving the feature alignment accuracy, data imputation, and batch-effect correction.
Methods and Results: Retention alignment was performed utilizing identified endogenous lipids as internal references combined with gradient information. By utilizing multiple internal references, retention time shifts can be mapped among batches and adjustment of the features can be performed. After retention time shift is addressed, alignment is performed using matched retention time and calculated mass accuracy. After features alignment, correction of technical variations were done with a developed workflow.
To evaluate this model, we utilized data acquired from serum samples from a large cohort of Pakistani children with environmental enteropathy (EE) and malnutrition. Untargeted UPLC-MS was performed on 421 samples analyzed across 11 batches with each batch individually pre-processed in XCMS. Feature alignment in the negative ion mode across all 11 batches resulted in over 500 common features. An additional ~400 features were missed in only 1 batch and another ~400 features missed in 2 batches. Normal machine learning data imputation methodologies performed adequately for these features, allowing an additional ~800 features into the data that would initially be excluded. After the addition of these features, batch effect correction was applied to remove the technical variation.
Conclusion: This clinical study of malnutrition highlights the usability of this tool in the feature alignment of large-scale untargeted metabolomics data analysis. The created R toolkit provides a convenient user-friendly interface for data analysis.

Higher fat diets are associated with higher rates of colorectal cancer (CRC). Recently, it has been shown that bacterially derived metabolites impact the formation of aberrant crypt foci (ACF), a precursor of CRC. However, it is not yet known whether bacterially-derived metabolites altered in high fat diets are associated with ACF. We hypothesize that distinct microbe-metabolite relationships exist in the ACF present and absent groups. We also hypothesize that XGBoost machine learning models trained on microbe-metabolite relationships will be more predictive of ACF formation than models trained only on bacterial or metabolite relative abundances. Thus, we assessed bacterial-metabolite relationships in previously published bacteriome (16S) and metabolome (HILIC UPLC-MS) data generated in a murine model of lifetime obesity, with samples split into two groups: ACF present and ACF absent (Chatelaine et al. 2022). IntLIM-2.0 was used to identify significant omics relationships (microbe-metabolite, metabolite-metabolite, and microbe-microbe) by comparing the line of best fit slopes for each relationship across ACF presence. We compared the ability of XGBoost to predict ACF when all relationships were used vs. when only IntLIM-identified relationships were used. Boruta, an automated feature selection algorithm, was used to confirm if the microbe-metabolite, metabolite-metabolite, and microbe-microbe relationships found using XGBoost were important.
We found distinct differences in the microbe-metabolite and metabolite-metabolite relationships between the ACF phenotypes. Most notably, we found previously unreported relationships between potentially pro-inflammatory bacteria (Prevotella, Clostridium) and phospholipid species, which may provide insights into cell signaling alterations in ACF. Consistent with our hypothesis, we also found that the XGBoost models derived from IntLIM significant metabolites were more predictive than the models built only on relative metabolite abundances. These predictive modeling approaches can be used to investigate omic relationships to better understand complex biological processes, such as the development of ACF formation.

With an estimated 19.3 million new cases and over 10 million deaths in 2020, cancer remains a leading cause of death worldwide. Early diagnosis is associated with better outcomes in the majority of cancers, making screening essential. Many screening programs exist but are often inhibited by remarkably low participation. Herein we propose a novel approach for screening multiple cancers in a single analysis by combining serum metabolomic profiling with ensemble machine learning (EML) algorithms. Samples were collected from a total of 332 cancer patients (98 with colorectal cancer [CRC], 99 with breast cancer [BrCa], 37 with endometrial cancer [EC], 98 with other cancers) and 50 controls. In an effort to address class imbalance, an additional 282 control observations were generated through adaptive synthetic sampling. Untargeted metabolomic analysis was performed via gas chromatography-mass spectrometry. The EML model was trained to recognize the presence of a tumor using 7 classification models (validated via cross validation [CV]): decision tree, naive Bayes, random forest, deep learning, generalized linear model, fast large margin and gradient boosted tree. Classification proposals were weighted according to CV accuracy and classification confidence and then combined in a voting scheme to calculate an EML score for every sample. Ensemble model accuracy was 95.8%(100%sensitivity, 91%specificity). Three other EML models were built in order to sequentially subclassify the positive samples according to cancer localization: a) the first one was trained to distinguish between subjects with CRC and those with all other forms of cancer (100% accuracy); b) the second to discriminate BrCa patients from other types of cancer (78.5% accuracy); c) the third to distinguish EC subjects from other cancers (87. 5% accuracy). These results support the application of metabolomics as a promising and multi-comprehensive cancer screening tool.  Additional studies with more subjects will improve the accuracy of cancer detection and differentiation.

Performing untargeted metabolite identification on HRMS systems, using information dependent acquisition/data dependent acquisition (IDA/DDA) suffers from gaps in MS/MS coverage or poor-quality MS/MS. In previous work, we showed that activation of the Zeno trap increased the MS/MS signal and peak area for polar metabolite fragments by up to 14x, leading to improved quantification and identification.
Here we explored the wider utility of using the Zeno trap with SWATH compared to IDA/DDA and MRMHR. We initially used the rat urine samples to make the comparison by looking at MS/MS quality using the different acquisition strategies and sample loading volumes while assessing raw signal and the number of identified metabolites. The first analysis compared a 0.2 µL injection of 1:10 diluted rat urine using Zeno MRMHR and Zeno SWATH acquisition and a 2 µL injection using SWATH acquisition. Cyclic AMP, which showed a 2-fold (Log2) change in the biological set, was chosen and extracted for the m/z 136.0602 fragment ion (10 mDa mass extraction window, MEW). The XIC peak intensities were 4.8e4, 5.6e4 and 5.5e4 cps, respectively. The 0.2 µL injection using Zeno MRMHR and Zeno SWATH with the enhanced duty cycle showed the same sensitivity as the 2 µL injection without the Zeno trap, highlighting the 10-fold gain in sensitivity.
Another benefit of the increased sensitivity of Zeno MS/MS with SWATH acquisition is flexibility in the number and distribution of the variable windows due to fast acquisition rates. The variable window method employed here is 80 windows between m/z 80 and 650, with accumulation times of 5 ms. A larger number of smaller windows increases selectivity while high scan rates maintain the number of data points across the LC peak, which was ~10 points across 6-second peak widths.

Heparin is a highly sulfonated polysaccharide applied in clinic for treatment of several diseases. Heparin disaccharide I-S tetrasodium salt purchased from various vendors have shown differences in target-binding activity as large as 10-fold, although 100% HPLC purity is provided by vendor’s certificate of analysis. The biological activity is thought to be closely related to its molecular structure, e.g., chirality. Trapped ion mobility spectrometry (TIMS) has the ion mobility separation power to differentiate enantiomers of heparin disaccharide. MetaboScape® is an integrated software supporting non-targeted screening, compound identification, and statistics. In this study, a 4D-Metabolomics data acquisition and analysis workflow is used to determine the differences among heparin disaccharide samples with ranging target-binding activities, which can be utilized for quality control of this material.
Heparin disaccharide I-S tetrasodium salt standards from five different vendor were directly infused to timsTOF instrument, three major peaks of the protonated ion of heparin disaccharide were observed from the extracted ion mobilogram, indicating more than one enantiomer present in the samples.LC-MS acquisitions were performed by injecting each sample in triplicates for non-targeted and statistical analysis. LC-MS base peak chromatogram overlays showed differences between samples at several retention time regions.
To further determine the compounds responsible for difference in biological activity, untargeted analysis was performed using MetaboScape® to extract and annotate features, followed by statistical analysis. In PCA, more than 50% variance is covered by PC1 and PC2, indicating difference compositions in samples obtained from different vendors despite similar HPLC purity results from vendor COAs. In PLS, compounds with intensities trending or reverse-trending with biological activities and annotated by the SmartFormula module and MetaboBASE 3.0 Spectral Library.
Correlating the target-binding activity with 4D profiles of heparin disaccharide from different vendors highlights the application of timsTOF and MetaboScape® to provide guidance in quality control of heparin disaccharide materials.

Microbiome, metabolomics and health
The elevated blood 4-cresol concentration, a gut microbial-derived metabolite, was shown to decrease type 2 diabetes (T2D) risk in a Lebanese cardiometabolic disease (CMD) cohort. In CMD model animals, the administration of a non-toxic dose of 4-cresol improved glucose homeostasis and the function of pancreatic islet β-cell. To investigate the health impacts of 4-cresol in the general Japanese population, we conducted a phenome-wide association study of 4-cresol sulfate, the primary conjugate of 4-cresol in blood, with health-related phenotypes. 
We measured plasma 4-cresol sulfate of 3,645 individuals enrolled in the Nagahama Study, a community-based prospective genome cohort, by liquid chromatography-mass spectrometry and performed regression analysis with 934 traits. We identified significant associations (p ≤ 5.35 x10-5) of plasma 4-cresol sulfate levels with 34 intermediate phenotypes related to blood pressure regulation, liver and renal function, sleep quality, eye pressure, ion regulation, glucose and fatty acid metabolism and dietary habits. Among them, two typical biomarkers for metabolic syndrome (MetS), namely, urinary glucose (p = 1.75 x10-28) and free fatty acid levels in the blood (p = 5.31 x10-16), showed the strongest and negative associations. In addition, fourteen other MetS-related traits (13 blood pressure indices and blood triglyceride levels) were inversely associated. Furthermore, the Wilcoxon test revealed that individuals diagnosed as MetS according to Japanese diagnostic criteria had significantly lower plasma 4-cresol sulfate levels than the others. 
These results indicated possible protective effects of 4-cresol sulfate against MetS in humans and illustrated the impacts of 4-cresol sulfate on the health of the general Japanese population. It is the first phenome-wide association study of a target metabolite to investigate its pleiotropic effects on various human traits. These findings strongly suggest that the gut microbiome is vital for the host's overall health.

The profiling and quantitation of lipids and their changing levels in biological samples are crucial to understand the role of these important biomolecules in health and disease. The object of this study is to optimize an approach for comprehensive, high-throughput, and reliable identification and quantification of lipids. Mass spectrometry (MS), with its powerful capability for analyzing biomolecules, is the technique of choice for characterizing lipids at the level of molecular species. However, the wide diversity and concentration range of lipids in complex biological samples pose challenges to untargeted lipidomics analyses. Such studies often require extensive and time-consuming manual examination of MS and MS/MS spectra and chromatograms to ensure accurate identification and quantitation of lipid molecular species. Here we present a robust workflow of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) that combines automatic data processing and limited manual verification to reliably identify and quantify hundreds of lipid species. A set of samples known to differ in lipid composition, including mouse plasma, mouse liver, and olive oil, was used as the model. Lipids were extracted from these samples and analyzed by LC-MS/MS. The raw data obtained were processed with commercial software for peak filtering, aligning, and integrating, as well as identification of lipid molecular species and relative quantitation across samples. Lipid identification was based on accurate masses, MS/MS spectra, and retention times, while quantification was based on peak areas of extracted ion chromatograms normalized by a set of internal standards. By adjusting several parameters for peak alignment, smoothing, mass and retention time tolerance, and adduct clustering, along with the use of a curated approval database, we have developed a robust and versatile lipidomics workflow that enables high-throughput and accurate lipid identification for routine analysis of various samples.

Mass spectrometry (MS), generally coupled to liquid chromatography (LC), is a powerful tool for qualitative and quantitative lipid analysis. High-resolution mass analysis of molecular ions followed by the analysis of the fragment ions after collision induced dissociation (CID) can provide not only the exact mass of the analyte but also its structural information. Nevertheless, elucidating the complete structures of lipids with MS is still limited. For example, the identification of the C=C bond location in fatty acyl chains is still challenging with commercial mass spectrometers. Low-energy CID of protonated or deprotonated lipids seldom yields specific fragment ions indicative of the C=C position.  Here, we present a pseudo MS3 method, employing in-source fragmentation (ISF) followed by CID, to identify the position of the C=C bond. Epoxy-phospholipids, produced by reaction with m-CPBA (meta-chloroperoxybenzoic acid), are introduced into the ionization source where in-source fragmentation is activated. The epoxy-fatty acyl group, generated by ISF, undergoes CID and produces diagnostic ion pairs that are 16 Da apart. Because this method does not require a specific mass analyzer for MS3 or any instrument modification, it can be used in any tandem mass spectrometer. To validate the versatility of this pseudo-MS3 approach, we performed ISF and CID in three different mass spectrometers, with CID performed in the linear ion trap of a Thermo Orbitrap XL mass spectrometer, in the HCD cell of the Thermo Q-Exactive Plus, and in the collision cell of the Agilent Q-TOF 6545. Work to date was performed by infusion, with the pseudo MS3 approach showing better sensitivity than the conventional MS3 method. However, as we are not able to differentiate the fragments of co-eluting analytes with the pseudo MS3 method, separation techniques like LC are required. In continuing work, we will apply LC prior to the pseudo MS3 method.

Preterm birth is defined as any birth before 37 weeks of completed gestation. Impacting approximately 8% of all newborns, preterm birth can pose adverse health risks to both mothers and infants. Objectives of the present study were to assess the predictive value of serum metabolomics analysis for preterm birth. Maternal serum was collected at 28-32 weeks gestation (onset of the 3rd trimester) from primigravida’s (18+ years) with a singleton pregnancy from the All Our Families Cohort (Alberta, Canada). Serum from 27 preterm and 50 term births were analyzed by untargeted metabolomic analysis by liquid chromatography/mass spectrometry (QTOF 6550i, Agilent). Pattern recognition algorithms were used to identify and isolate significant metabolites predictive of preterm birth based on their observed mass to charge ratios. Multivariate modeling was conducted using SIMCA (v17). OPLS-DA demonstrated distinct separation between the groups with validation scores of 45%, 75%, and 18% for R2X, R2Y, and Q2Y, respectively. Network and pathway analysis was completed using MetaboAnalyst 5.0 to identify compounds involved in several metabolic pathways and to be strongly associated with preterm birth in primigravida. Lysine and arginine pathways were altered with pre-term birth (VIP>1). In addition, women who delivered preterm had significantly elevated levels of various oxidized phospholipids including phosphatidylserine, phosphatidylinositol and phosphatidylethanolamine. While additional investigation is necessary to validate these findings, the identified metabolomic biomarkers maybe promising predictors in the identification of those at risk for preterm birth. Such markers may serve as additional evaluation tools for physicians to administer timely prevention, monitoring, and intervention to avoid harmful consequences to both mother and child.

BACKGROUND: Amino acid analysis (AAA) is used to determine the amino acid (AA) composition of peptides and protein-containing samples and can be used for absolute quantitation of the peptides. Acid hydrolysis can be performed in either the vapour or liquid phase using 6M HCl. Reversed-phase (RP) chromatographic separation of non-derivatized AA is challenging. Several derivatization methods and sample pre-treatments are commonly employed to address this issue. Here, we could quantify non-derivatized AA from peptide hydrolysates, including methionine and cysteine, by RP-UPLC-MRM-MS without any special pre-treatment.
METHODS: Samples were spiked with certified isotopically labeled (13C, 15N) AA as internal standards. Liquid phase hydrolysis was employed using 6 M HCl and heating for 24 hours. The released AA were then analyzed by RP-UPLC-MRM-MS and quantified using the analyte/internal standard area ratios. Peptide quantitation was performed based on the sum of the individual AA from the known peptide sequences. The analysis was performed using the Shimadzu Nexera XR UHPLC system coupled to the Sciex QTrap 6500+ mass spectrometer (+ESI). Chromatographic separation was performed using a Zorbax Eclipse Plus C18 column (2.1 × 150 mm, 1.8 μm) and a 6 minutes 0-50%B gradient (A: 0.1% FA in water; B: 0.1% FA in acetonitrile).
RESULTS: In this study, a reversed-phase high-performance liquid chromatography-based method was used to separate all AA, including isobaric compounds leucine and isoleucine, before detection by multiple reaction monitoring with LC-MRM-MS. The sulphur-containing AA: cysteine and methionine were also detected and quantified. A robust method was developed for absolute protein/peptide quantification and AA compositional analysis.
CONCLUSIONS: In this study, we separated AA using the C-18 column without needing a derivatization step. Also, we were able to detect and quantify the oxidized forms of Methionine (M) and Cysteine (C). Moreover, we used certified stable isotope label standards for each AA for the quantitation.

Background and methods
Targeted metabolomics is used in hypothesis-driven research to annotate and quantify a biologically relevant subset of known metabolites. However, targeted analysis has limited coverage of the metabolome. In comparison, untargeted metabolomics offers a wider overview of metabolites and their relative levels, providing the opportunity to find unexpected changes not part of the original goals. But the varying physiochemical properties of the metabolome require specific detection criteria for different metabolite groups. Moreover, untargeted analysis can suffer from signal bias and mass drift introduced by matrix effects, which complicates metabolite identification and reduces overall sensitivity.
Semi-targeted metabolomics recently emerged as a promising alternative, offering researchers a middle ground between targeted and untargeted approaches in one single experiment. Semi-targeted workflows begin with annotation and quantifying a pre-selected group of metabolites in a sample. In addition, the data can then be reanalyzed (retro-mined) to look for global metabolic changes that were not part of the original focus, therefore, identifying other biologically meaningful metabolite changes.
Here, we developed a semi-targeted metabolomics workflow on a Thermo Scientific™ Orbitrap Exploris™ 240 mass spectrometer by targeting identified metabolites (i.e., amino and organic acids) in animals and plant-based milk. The data was, then, reanalyzed using an untargeted approach to assess other metabolic variations among the different samples utilizing Thermo Scientific™ Compound Discoverer™ 3.3 software for data processing, unknown identification, and differential analysis.
High data quality, reliability, and robustness of measurement were observed by evaluating the isotopically labeled internal standards to assess instrument performance using metrics including retention time, mass accuracy, and signal response. Minimal chromatographic shift and consistent signal responses were observed as evidenced by low % CV for quality control and sample replicates.
Conclusion
Semi-targeted metabolomics enables the ability to perform targeted and untargeted analysis in a single sample injection allowing scientists to gain more knowledge about biological samples.

Alzheimer’s Disease (AD) is a complex, multifactorial, progressive, irreversible neurodegenerative disease which results in cognitive, functional, and behavioral impairment. To date, no therapies are available which directly treat the disease and one reason for this is the misstratification of the disease.
Current studies predominantly focus on uncovering the etiopathogenesis of AD using single omics platforms such as genomics, proteomics, metabolomics, lipidomics or epigenomics. We combined comprehensive metabolomics with methylation arrays to better understand the possible etiology and pathogenesis of the disease by analyzing those brains from individuals who died from AD (n=30), mild-AD (n=14) and compare them with age- and gender-matched controls (n=30). A targeted LC-MS/MS, 1H NMR and the Illumina Infinium Methylation EPIC Bead Chip assay. We identified differentially abundant metabolites as well as differentially methylated cytosines using robust linear regression. We interrogated correlation of methylation level and metabolite concentration.
20 metabolites were significantly different concentrations when we compared AD against controls (FDR q Overall, our findings demonstrate intricate relationship between methylation changes and metabolite concentrations which underlines the utility of combining metabolomics and other omics-based platforms such as epigenetics for the study of AD and related dementias.

Introduction
Untargeted mass spectrometry-based metabolomics aims to detect and measure as many compounds as possible from a biospecimen and enables researchers to make new discoveries and generate new hypotheses. Informatics is essential for understanding big data, which in some studies may involve thousands of samples and thousands of peaks generated from the untargeted analysis. Informatics is used to preprocess raw LC-MS and GC-MS data to extract compound-relevant signals, annotate the resulting signals to associate the signals with one or more compounds, and determine the statistical significance of signals or compounds that differentiate phenotypes. Most existing software tools either are not equipped with the capability to handle big data and/or cannot seamlessly carry out all these steps.
Methods
We have developed an integrated informatics pipeline that addresses the big data metabolomics workflow. The pipeline consists of three tools that communicate with each other in the background. The first is ADAP-BIG, a desktop software tool that preprocesses raw LC-MS and GC-MS big data and then passes the extracted signals to ADAP-KDB. ADAP-KDB is an online resource that annotates the signals by matching the user’s experimental information (accurate mass, retention time, fragmentation spectra) against in-house or public compound/spectral libraries and then prioritizing the unknown signals in the context of other relevant studies in the public domain. In the meantime, the relative compound quantitation information from ADAP-BIG is passed to ADAP-ML, a microservice-based resource for statistical and machine learning analysis. The results from ADAP-ML are returned to ADAP-BIG to package the analysis results for users to review.
Conclusions
The ADAP informatics pipeline provides an integrated solution for researchers to preprocess and analyze metabolomics big data from various studies, which may be generated from large studies including epidemiological studies.

Volatile organic compounds (VOCs) are an important class of plant metabolites. These molecules, among other things, are used to communicate with insects. These communications can be, for example, the attraction of pollinators or to discourage pests from attacking the plant.  Plant VOCs belong to various chemical classes and they may be generated spontaneously, or in response to some stimulus. The study of these metabolites is important in understanding relationships between plants and pests as well as other aspects of plant biology. Most plant VOC studies have relied on either solid-phase microextraction (SPME) or active sampling onto sorbent tubes, which are then desorbed either with solvent or via thermal desorption (TD). coupled to GC-MS or occasionally GC×GC-TOFMS. In this study, we demonstrate the improved variety and detectability of plant VOCs which can be observed when relying on TD-GC×GC-TOFMS vs. other techniques using in vivo sampling of four plants including one variety of mint (Mojito sp.), one blueberry (Patriot Spec) plant, and two different varieties of tomato (little Napoli and sugar rush sp.).

Short-chain fatty acids (SCFAs) and medium-chain fatty acids (MCFAs) are key to energy metabolism, carbohydrate/lipid regulation, and signaling. Aberrant levels can cause inflammation and insulin resistance. There is a need for the combined analysis of SCFAs and MCFAs, because it can provide crucial insights into gut microbiome, diet, and health status.
We developed a unique LC/MS-MS assay validated for human feces and serum/plasma covering the broadest panel (19 analytes) of SCFAs and MCFAs. Approximately 200 mg wet fecal matter from 15 individuals were weighed and homogenized in isopropanol (approx. 600 µL; 3:1 dilution) using a Precellys® homogenizer with ceramic beads. After centrifugation, 10 µL supernatant were transferred to a 96-deep-well plate for derivatization using 3-nitrophenylhydrazine (NPH) and N-(3-dimethylaminopropyl)-N′-ethylcarbodiimide (EDC). For human plasma/serum, 50 µL were applied. Analysis was performed on a Waters® UPLC Acquity I-class system, coupled to a Xevo® TQ-S mass spectrometer. Absolute quantification was carried out using a 7-point external calibration with corresponding isotope-labeled internal standards.
In feces, SCFAs and MCFAs showed variances between different positions on the individual sample. The analysis of three different positions resulted in a median coefficient of variance (CV) of 11.3%. Pooling of the three positions reduced the median CV to 2.3%. A multi-spot sampling approach of fecal samples is, therefore, strongly recommended to minimize intra-sample variance.
Altogether, sample collection, handling, and storage (pre-analytics) proved to be key in delivering meaningful, reproducible SCFAs and MCFAs results, especially for fecal samples.

Breath-based metabolomics is becoming more popular; breath is collected and transferred to a sorbent tube that is then analyzed by thermal-desorption (TD) coupled to either GC-MS or GC×GC-MS. Breath is a very attractive biosample as it contains many volatile organic compounds (VOCs), its collection is non-invasive, and non-threatening for participants, there is essentially no further sample preparation, and large volumes / replicate samples can be collected in a short time. This study will compare two popular and inexpensive commercial options for collecting breath samples using Tedlar gas sampling bags in either 1 or 3 L volumes that collect the entire breath sample, and the Markes Bio-VOC 2 device which captures only the last 129 mL of a breath sample.
In this research, breath samples from a group of healthy volunteers were collected in parallel using both 3 L Tedlar bags and the Bio-VOC 2 device. Breath samples were then loaded onto TD tubes and analyzed by TD-GC×GC-ToFMS. The two methods were compared according to analytical performance (diversity of compounds, analyte responses, etc), as well as practical considerations (sampling time, ease of use, etc). Blanks of each sampling device were collected prior to the analysis of a human breath sample.

Spheroids are 3D cell cultures reported to be more reliable models than the classical 2D cell model in cancer research. They can be combined with omic technologies, such as metabolomics to serve as a powerful tool in biomarker and drug discovery. However, to get a high coverage, untargeted cell metabolomic studies usually require a large number of cells (e.g., ≥106 cells). Since spheroids typically contain ≤50000 cells, this constitutes a challenge for untargeted metabolomic studies. We developed a comprehensive method to produce spheroids, extract metabolites, and analyze samples for untargeted single-spheroid metabolomics. A549 cells were cultivated in F-12K medium with 10% FBS in T-75 flasks until they reached ≥70% confluency. Spheroids were grown in 96-well plates coated with agarose gel. The cells were then incubated at 37˚C with 95% humidity and 5% CO2. Metabolites were extracted from a single spheroid using the newly developed cold-solvent extraction coupled with glass bead-assisted cell lysis and were analyzed using the previously developed CIL LC-MS method. The obtained data were processed with an in-house software developed explicitly for this application, which includes peak picking, alignment, filtering, metabolite identification, and quality control.
Using the optimized culturing technique, we were able to produce spheroids with uniform shapes and sizes, allowing us to achieve reproducible results, which is crucial in biomarker and drug discovery. In addition, the application of the CIL LC-MS method allowed us to identify 1000-1500 metabolites from a single spheroid. Hence, for this work, we will explore the application of single-spheroid metabolomics to biomarker and drug discovery. The spheroids will be exposed to known anticancer drugs, such as paclitaxel and cisplatin to investigate how their metabolomic profile change and identify which metabolites are significantly affected. The results we present will validate the potential of untargeted single-spheroid metabolomics for biomarker and drug discovery.

The automation of nuclear magnetic spectroscopy (NMR) remains a pressing challenge in the field of metabolomics. Here, we report the development of a software program, called MagMet, that automates the processing and quantification of 1D 1H NMR in human fecal extracts. To optimize the program we identified 82 potential fecal metabolites using 1D 1H NMR of six human fecal extracts using manual profiling and from a literature review of known fecal metabolites. We acquired pure compounds corresponding to those metabolites and then recorded and processed 1D 1H NMR spectra of these compounds at 700 MHz to generate a fecal metabolite spectral library for MagMet. The fitting of these metabolites by MagMet was iteratively optimized to replicate manual profiling by a human expert. We validated MagMet’s automated processing using a test set of fecal extracts, obtaining correct identifications for >90% of compounds and 1H NMR spectral profiling of fecal samples. It is also validated to profile serum and wine samples. Magmet is able to accurately identify and quantify up to 58 serum metabolites and 63 wine metabolites.  MagMet is available at https://www.magmet.ca

Fetal malformations occur in about 3% of all pregnancies. Currently, non-invasive screening tests, such as free circulating fetal DNA, only allow the identification of chromosomal anomalies, which account for less than 50% of all congenital defects, while the others can usually be detected through ultrasound evaluation in later stages of pregnancy. In this context, metabolomics may open new scenarios for innovative and comprehensive screening tests. Our research group has already demonstrated that an Ensemble Machine Learning (EML) model based on serum metabolomic profiles of pregnant women at a gestational age of 20 weeks can be trained to discriminate between mothers carrying malformed fetuses and those with normal fetuses. In the present study we report the diagnostic performances applying the same system at an earlier gestational age (15± 1 weeks'). Five hundred serum samples were randomly selected from the study cohort of the New Zealand subset of the SCOPE study and analyzed through gas chromatography-mass spectrometry. The untargeted metabolomics profile was used to query the pre-trained EML based on a cross validation and classification confidence weighted scheme of 9 classification models (partial least squares discriminating analysis, linear discriminating analysis, naïve bayes, decision tree, random forest, k-nearest neighbor, artificial neural network, support vector machine and logistic regression). The EML showed an accuracy of 99.2% ±0.6% (specificity = 99.9±0.1% [correctly identified 491/493 controls] with a sensitivity = 88±8% [correctly identified 14/16 fetal malformations]). Thanks to the accuracy of cross-validation and the classification confidence of individual models, metabolomic analysis represents a valid tool for the detection of congenital defects. Other benefits include the low price and the absence of risk. The identification of malformations requires further investigation, with the aim to extend the application of the method to larger study populations, demonstrating the practicality of the method.

BioTransformer4.0 is the successor to BioTransformer,  an in silico metabolism prediction tool which is developed using both machine learning and knowledge approaches and has been widely used by metabolism researchers since it’s released in 2019. BioTransformer4.0 contains six main modules, which are the abiotic metabolism module, promiscuous enzyme (EC-based) metabolism module, CYP450 (Phase I) metabolism module, Phase II metabolism module, gut microbiota metabolism module, and environment microbial metabolism module. In order to reduce predicted false positive metabolites, we introduce more machine learning methods in Phase I and Phase II modules, add filters that determine if the metabolites are end products, and allow users to select if BioTransformer4.0 will retrieve known metabolites for well-studied compounds stored in HMDB5.0 or not. The CYP450 prediction has been improved by an average of about 23%, and the sulfation and glucuronidation of Phase II metabolism have been improved by 43% in terms of Jaccard score.
We have also created the online webserver for BioTransformer4.0, which is freely available at http://biotransformer.ca/

DSM is one of the world leading producers of essential nutrients like vitamins, carotenoids, human milk oligosaccharides, nutritional lipids. In the last few years, omics studies including metabolomic has been widely applied and providing insights to many DSM projects including vitamin bioprocessing, animal and human nutrition, and personal care.
In 2022, we have established the first DSM in-house metabolomic platform to support its on-going research needs in clinical trials, and in vivo screening studies. This platform consists of five components: sample extraction, data acquisition, data analysis, data annotation/dereplication and biological interpretation. More specifically, (1) matrix specific extractions protocols are developed for microbes, cecal samples and skin strips; (2) six LCMS methods using three different column chemistry are designed to cover large diverse chemical classes including polar metabolites (amino acids, nucleic acids, organic acids, etc) and non-polar metabolites (phospholipids, triglycerides, ceramides, fatty acids, etc); (3) connecting six commercial and in-house software from peak picking to finding significant metabolites; (4) six commercial and in-house database are combined to provide high confidence metabolite annotation; lastly (5) pathway/chemical enrichments and networking are used highlight biological relevance.
To this date, this metabolomic platform has been successfully applied to four diverse research areas in DSM and providing insights in bioprocessing monitoring, helping to understand modes-of-action and down select DSM’s human and animal nutrition and care product.

Object: Machine learning based approach to predict small molecules metabolism. Metabolite structures were elucidated by in silico fragmentation, MS/MS spectral library and comparison of acquired to reference or predicted CCS values using a novel CCS prediction algorithm. Together, each of these steps forms a fully integrated workflow that utilizes the four dimensional data to ensure low level drug metabolites can be annotate
Research question: Fast and accurate identification and characterization of drug metabolites play a critical role in preclinical and clinical development stages to assist lead compound structure optimization, screening drug candidates, and finding active or potentially toxic metabolites. In this work, a DDA nonta rgeted LC-timsTOF Pro PASEF metabolomics workflow was conducted to profile and characterize drug metabolites
Methods :A time-series experiment was conducted by spiking human liver microsomes (HLM, Promega) and fentanyl (Sigma) into a prei ncubated NADPH regeneration system at 370C; 100 µL of reaction solution at 0, 5, 15, 30, 45, 60, 90 and 120 min was aliquoted; the reactions were stopped by adding cold acetonitrile; all samples were centrifuged at 12,000 rpm at 40C for 10 min; the supernatant was transferred into sample insert vial and 5 µL was injected (n=3) for each of the two biological replicates. Analysis was performed by Elute UHPLC timsTOF Pro (Bruker) with PASEF data acquisition and ESI positive mode. Data analysis was conducted in DataAnalysis 5.3 and MetaboScape
Summary 
In vitro HLM/fentanyl drug metabolism analysis by TIMS enabled timsTOF Pro PASEF metabolomics workflow
 Data Analysis was performed in MetaboScape 2022b on metabolite profiling and characterization
Integrated software addresses common needs for advancing pharma, metabolomics, lipidomics, non-targeted screening and exposome research

L-carnitine has been identified as a useful metabolite biomarker. In literature, it is reported that changes in serum/plasma L-carnitine levels along with other metabolite markers can be used to detect pregnancy in animals including sheep as well as various diseases in humans. The development of a portable carnitine detection system could be particularly useful for on-farm or in-clinic applications. There are several manufacturers that sell carnitine assay kits, but they are expensive and require multiple pipetting steps that are difficult to perform outside of a lab. Further, they are not designed for easy-to-use, one-step testing in a dry assay format stored at room temperature. Here, we describe a new carnitine assay that overcomes all mentioned challenges. This new carnitine acetyltransferase assay, which uses 5,5′-dithiobis-(2-nitrobenzoic acid) (DTNB) as the colorimetric reagent, has been developed to provide an easy-to-handle, inexpensive colorimetric assay for carnitine detection and quantification in serum or plasma. Chromogen DTNB reacts with Coenzyme A (CoA) produced from the carnitine acetyltransferase reaction with the substrate L-carnitine to form a strong yellow color that can be spectrophotometrically measured at 450 nm. In addition, an economical method of deproteinization which utilizes polyacrylic acid to rapidly precipitate proteins has been developed which is used in combination with the one-step L-carnitine assay when testing sheep serum samples. The dry reaction L-carnitine assay has a dose-response curve in both Milli-Q water and sheep serum with a coefficient of determination of 0.99 and performs 79% more effectively than the liquid assay. Reaction mixture and carnitine acetyltransferase enzyme were prepared separately with 25% w/v sucrose added to the reagents for lyophilizing.  We believe this new, simplified carnitine assay has the potential to be taken to the field as a pen-side instrument that could be used by farmers, veterinarians, physicians, and even the general public.

Background: Patients with mild SARS-CoV-2 infection may develop chronic and debilitating post-viral fatigue, termed long-COVID. After initial infection with related viruses (SARS-CoV-1 and MERS-CoV), patients exhibited altered lipid metabolism. These alterations suggest an unknown pathologic mechanism following infection of novel coronaviruses. We hypothesize this is caused by peroxisomal damage, inhibiting the catabolism of very long and branched-chain fatty acids, leading to decreased oxylipin synthesis. Decreasing these potent crucial immune- metabolic mediators would lead to pathophysiology seen in long-COVID patients. Methods: We used the Syrian hamster model with a sham group and an infected group using live SARS-CoV-2. Samples were taken on days 0, 3, and 6. These time points recapitulate human pathophysiology in the hamster (day zero= no infection, day 3= severe pathology in the lungs and peak viral load, day 6= post-infection). Brain, muscle, and liver samples were collected at each time point. Comprehensive nontargeted metabolomics was carried out with four assays: (1) lipidomics (RPLC-MSMS) ; (2) biogenic amines (HILIC-MSMS) ; (3) primary metabolism (GC-TOF MS) and (4) targeted analysis of oxylipins and endocannabinoids.
Results: IHC showed a drastic decrease of peroxisomes in infected tissues compared to controls. Metabolomic results yielded absolute quantification of over 80 lipid mediators and more than 900 structurally characterized metabolites and lipids that were matched by retention times, accurate mass, and MS/MS spectra to MassBank.us and NIST20 libraries. Large changes were observed, specifically for plasmenyl- and plasmanyl- ether lipids and a range of other lipid classes. We will present the statistical and network analyses of these results and the implications for PVF experienced by long-COVID patients.
Conclusions: Drastic metabolome changes were detected in Hamster organs following SARS- CoV2 infections led to peroxisome loss. If viral infections might cause similar organelle dysfunction in humans, certain covid19 (and ME/CFS) phenotypes might become explainable.

Small peptides (2-5 amino acids) have recently attracted attention because of their biological functions, such as human dipeptidyl peptidase IV inhibitory, angiotensin-converting enzyme inhibitory and antioxidative. By calculating the number of possible combinations of 20 types of a-amino acids, there can be 400 potential dipeptides with a wide range of physiochemical properties. Therefore, accurate identification and quantification of each dipeptide remains challenge. In this study, we developed a chemical isotope labeling (CIL) liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS) approach for comprehensively profiling and accurately quantifying dipeptides in different types of samples. An optimized LC gradient ensures the separation of 12C-dansyl chloride labeled dipeptide isomers, such as leucine and isoleucine dipeptides. Tandem-MS collision energy was optimized to provide useful information for dipeptides sequencing. A CIL library consisting of retention time and tandem-MS information of 360 12C-reagent labeled dipeptides was constructed with the optimized conditions. For sample analysis, data dependent acquisition (DDA) was carried out for both qualitative and quantitative analysis. MS2 of DDA provides informative and clean tandem-MS spectra for accurate identification of dipeptides. Meanwhile, the MS1 containing peak pair information of DDA data was extracted and processed using an in-house developed software, which includes peak pair picking, filtering and alignment. A Japanese sake sample and a human serum sample were used for method validation. For each type of sample, 12C and 13C-reagent labeled products were mixed and analyzed with the developed method. We successfully identified more than 200 dipeptides in the Japanese sake sample and around 80 dipeptides in the human serum sample. For quantitative analysis, the peak pair ratio of dipeptides ranges from 0.8-1.2 and more than 90% of dipeptides with RSD less than 10%, which shows good performance of relative quantification.

Qualitative capabilities of high-resolution mass spectrometry (HRMS), such as automated LC-MS/MS workflows using collision induced dissociation (CID), have been crucial for pharmaceutical drug development to investigate the metabolism of candidate modalities at the early stages of development. Recent HRMS technology advancements, including improvements in the duty cycle, enabled the application of electron activated dissociation (EAD) on LC timescales and the integration of this complementary MS/MS fragmentation mechanism into LC-MS/MS workflows, providing a more confident characterization of the compounds of interest. This presentation reports on the utility of “orthogonal” MS/MS fragmentation of complementary TOF MS precursors for a more comprehensive characterization of metabolites in rat hepatocytes incubated with 1 µM Verapamil, buspirone and nefazodone for  0-, 30- and 120-minutes. The samples were analyzed in data-dependent mode using Zeno CID IDA and Zeno EAD IDA on a ZenoTOF 7600 system and data were analyzed in Molecule Profiler software. For the metabolites that have structures sufficiently different from the parent drug and thus yield significantly different fragmentation fingerprints, the annotation of fragments with respect to a hypothetical structure is used without relying on the annotation of the MS/MS spectrum of parent drug. To date, from 15 studied metabolites that covered hydroxylations and glucuronide conjugations either on parent drug or a cleavage product, the automated structure proposal gave the correct answer at rank 1 for 14 metabolites. Reconciling the evidence for a site of modification from the Zeno CID and Zeno EAD MS/MS data reduced the ambiguity in the structure proposal and enabled confident elucidation of positional isomers. We found that for the glucuronide conjugates, the EAD data provided more specific MS/MS fragments than CID and was instrumental in correctly pinpointing the site of modificationQualitative capabilities of high-resolution mass spectrometry (HRMS), such as automated LC-MS/MS workflows using collision induced dissociation (CID), have been crucial

Tissue metabolomics is a promising approach to understand the insights of biological systems and enable discovery of new disease biomarkers. Comprehensive analysis of tissue metabolomics is challenging in terms of quantitative methodology and detection sensitivity. The goal of this project is to develop an efficient analytical method of performing tissue metabolomics with high coverage and sufficient spatial information. With small amounts of tissue (0.5-1 mg), it is difficult to collect small sizes of samples, handle metabolite extraction and detect many metabolites. By using LC-MS Orbitrap and dansyl labelling for analyzing amine/phenol metabolites, we successfully developed an analytical method for metabolome analysis of over 1,000 peaks pairs or metabolites, with over 200 metabolites high-confidently identified and around 500 metabolites putatively identified. In this method, small sizes of samples were obtained consistently from an organ such as a chicken liver at identical size (0.25 mm ID x 0.25 mm thickness) by using a tissue slicer and micro-punches. Each piece of tiny tissue was subjected to a freeze-thaw cycle, followed by metabolite extraction with methanol-water. The metabolites were labeled and then analyzed using LC-MS. All data was processed by our in-house software which provides the relative quantification information on each metabolite from different tiny tissues and performs statistics analysis such as Volcalno plot, PCA plot, etc.  We successful applied this method to analyze different parts of chicken liver and chicken heart. The developed method was demonstrated to be highly reproducible and efficient in metabolome analysis of very small amounts of tissue samples.
This approach can be a foundation to demonstrate spatial information of the metabolites on tissue slices. This developed method can overcome the limitation of low coverage of mass spectrometry imaging (MSI) methods, but still provide sufficient spatial information for visualization by software which we will develop in the near future.

Coronavirus disease 2019 (COVID-19) is mortality disease triggered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. In the last two-years, COVID-19 caused over 5 million of death worldwide, and now this disease entries into next phase with huge social attention so called “Long-COVID-19”. Long-COVID-19 is characterized by multiple symptoms in the individual experienced SARS-CoV-2 infection. However, effective strategies for prediction and diagnosis haven’t been established yet. Therefore, we proposed a possibility that comprehensive metabolomic analysis is able to identify specific metabolome profile in Long-COVID-19. We analyzed plasma samples from healthy, acute COVID-19, post COVID-19 (non Long-COVID-19) and long-COVID-19 individuals using Chemical Isotope Labeling (CIL) LC-MS, which is a high-coverage and accurate quantification metabolomic profiling approach.
In total, 81 samples, including quality control (QC) samples, were analyzed. Among them, 15 samples were from healthy control group, 15 samples were from acute group, 15 samples were from recovered group, 30 samples were from “long-COVID-19” group, and 6 samples were from QC. On average, 2038 ± 25 peak pairs (putative metabolites) per sample were detected. On the PCA plot, the tightly clustered QC demonstrates good performance of LC-MS. The acute group was clearly separated from other three groups, indicating the huge metabolome alternation in acute patients. On the PLS-DA plot, the four groups separated at different degrees. Besides acute group, long-COVID-19 group was also separated from healthy control and recovered group, which shows the impact of long-COVID-19 on the plasma metabolome.  The volcano plots were also generated to confirm the multivariate analysis results. Compared with healthy control, 772, 137, and 226 significantly changed (fold change>1.5, q-value < 0 .25) metabolites were detected from acute, long-COVID-19, and recovered group, respectively. Among them, 28 unique metabolites with significant change were detected from long-COVID-19 group. These metabolites may be used as biomarkers for prediction and diagnosis in Long-COVID-19.

Coenzyme A (CoA) is an essential cofactor for dozens of reactions in intermediary metabolism. Dysregulation of CoA synthesis or acyl CoA metabolism can result in metabolic diseases. Although several methods use tandem mass spectrometry coupled to reversed phase chromatography to quantify acyl CoA levels in biological samples, none uses hydrophilic interaction chromatography (HILIC) for simultaneous profiling of short and long chain acyl CoAs. A HILIC based metabolomics method was developed to profile acyl CoA’s ranging from C0 - C22:6 in quadrupole time of flight-mass spectrometry, as well leaving the opportunity to perform discovery-based CoA profiling when an analytical standard is not present in the calibration curve. The method showed good separation for all the CoA analysed (C0 - C22:6) and validated with good linearity, precision and accuracy. The developed validated method was applied further to profile acyl CoAs in various biological samples (cell extracts, brain tissue, liver tissue, muscle tissue and plasma) successfully.

The Metabolomics Innovation Centre (TMIC) specializes in quantitative metabolomics assays for human, animal, plant and microbial samples. In the past few years, TMIC has developed and adapted several quantitative assays to expand its list of detectable metabolites and has also successfully applied the platforms to different biofluid samples by adjusting the calibration concentration ranges. These assays are based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) in multiple reaction monitoring (MRM) mode. For example, we have developed several quantitative assays capable of measuring 143 metabolites biomedically relevant metabolites (including amino acids, acylcarnitines, biogenic amines, lysolipids, organic acids, vitamins and uremic toxins), in serum and urine, etc. Most recently, these assay platforms have also been expanded to include more metabolite classes as well as the number of metabolites in each class, e.g., includes up to 900 metabolites and ratios, and covers 21 chemical classes including organic acids, amino acids, nucleotides/nucleosides, ketone and keto acids, and lipids, etc.; and have been fully validated for serum, urine and fecal extracts, separately. The recovery rates of spiked samples with three different concentration levels are in the range of 80% to 120% with satisfactory precision values of less than 20%. These assays were used to successfully analyze human serum, urine and fecal samples, with results closely matching those reported in the literature, and are in development to be used for more different types of samples such as cerebrospinal fluid and saliva.The Metabolomics Innovation Centre (TMIC) specializes in quantitative metabolomics assays for human, animal, plant and microbial samples. In the past few years, TMIC has developed and adapted several quantitative assays to expand its list of detectable metabolites and has also successfully applied the platforms to different biofluid samples by adjusting the calibration concentration ranges. These assays are based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) in multiple reaction

Coronavirus disease 2019 continues to spread rapidly with high mortality. Research exploring the development and outcome of COVID-19 infections has led to the need to find better diagnostic and prognostic biomarkers. We performed serum metabolomics profiling of critically ill coronavirus disease 2019 patients to understand better the underlying pathologic processes and pathways, and to identify potential diagnostic/prognostic biomarkers. Here we studied two cohorts from Canada and Mexico. For the Canadian cohorts blood samples were collected from patients until either testing was confirmed negative on ICU day 3 (COVID negative) or until ICU day 10 if the patient tested positive (COVID positive). For the Mexican cohort, blood specimens were collected within two days after admission on average. Targeted quantitative metabolomics was used to identify and determine the concentration of different endogenous metabolites including amino acids, biogenic amines and derivatives, acylcarnitines, lipids and organic acids using a reverse-phase liquid chromatography-mass spectrometry (LC-MS)/MS custom assay. For both cohorts, metabolomics profiling with feature classification easily distinguished both healthy control subjects and COVID negative patients from Covid positive patients. Arginine/kynurenine ratio and kynurenine: tryptophan ratio accurately identified coronavirus disease status, whereas creatinine/arginine ratio accurately predicted COVID associated death. Administration of tryptophan (kynurenine precursor), arginine, sarcosine, and/or lysophosphatidylcholines may be considered as potential adjunctive therapies. Through our analysis we were able to propose a COVID-19 diagnostic panel consisting of three metabolites including the kynurenine: tryptophan ratio, LysoPC a C260, and pyruvic acid to discriminate controls from not hospitalized with a high AUC (0.947

High-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR)-based metabolomics has demonstrated its utility in studies of biofluids for various diseases. HRMAS NMR spectroscopy is uniquely well-suited for analyzing human blood samples due to the small quantity of sample and minimal preparation required. To develop this methodology into standardized clinical protocols, evaluations of the method’s quality assurance (QA) and quality control (QC) are critical. This study aims to assess the QA/QC measured from human blood specimens in the form of serum and plasma through within-subject and between-subject comparisons, as well as stability and consistency comparisons over several freezing-thawing cycles of sample storage conditions, and most importantly, the agreement of pooled control samples against individual samples.
Forty-five blood plasma (30 cross-sectional, and 15 longitudinal from five subjects with three replicates two weeks apart for each subject), and 12 blood serum (in two groups of six subjects each) samples were studied. Six pooled plasma samples were generated, with equal volumes from each of the 30 individual plasma, and three pooled serum samples with equal volumes from each of the six samples were generated for each of the two groups. Pooled samples were tested against their respective individual samples with linear association models. Longitudinal plasma samples were used to evaluate within-subject variations over time vs. between-subject alterations; and pooled serum samples were tested for potential metabolomics alterations through multiple freezing-thawing cycles with global Wald tests.
Our results demonstrated both sample stability and system reproducibility for analysis of biological specimens. The tests and the procedures designed for these evaluations are critical for the development of NMR based metabolomics for various disease of concern. The present study of QA/QC, illustrates examples of how to approach this process; more precise QA/QC procedures for a specific potential clinical application will need to be designed and tested individually.

Waterfowl populations may decline because of anthropogenic and environmental changes (stressors). In response to stressors, the hypothalamic-pituitary-adrenal (HPA) axis releases corticosterone (CORT), initiating physiological processes to provide energy, restore homeostasis and increase survival. Corticosterone has been used to monitor the impacts of stress but CORT fluctuates amongst life-history stages and may not be a reliable indicator of adverse conditions. A better approach may be to examine CORT along with the metabolic responses to identify physiological changes associated with stress. The objective of this study was to utilize CORT and metabolomics to recognize stress in ducks. We predicted that fecal CORT and metabolomics could be used to differentiate ducks subjected to a stressor from unstressed ducks. To test this prediction, we surgically implanted CORT (n=15) or placebo (n=10) pellets into mallard ducks (Anas platyrhynchos). Fecal samples were collected prior to (Days -1, 0) and after implantation (Days 1, 2, 3, 5, 5, 7, 10, 15). H1 Nuclear Magnetic Resonance (NMR) spectroscopy was used to analyze metabolites, and CORT was analyzed by radioimmunoassay.  Corticosterone concentrations were significantly elevated during CORT implantation (Friedman Test P>0.001). However, CORT concentration peaked on days 1, and 2 post-implantation, then decreased during the rest of the implantation period. This indicates a negative feedback response of the HPA axis to elevated exogenous CORT. Fecal metabolite profiles distinguished CORT implanted ducks from control individuals.  
Fecal metabolomic profiles did not differ between groups during the baseline period (Days -1, 0). Fecal metabolite profiles began to differ after implantation (Days 1-3) but PCA score plots show almost complete separation in the metabolite profile separation at Day 4. Metabolites (Fructose, sorbitol, ribose, and galactonate) were higher in the CORT implanted ducks and are associated with energy production. Metabolites higher in the placebo ducks were associated with growth and energy maintenance. Fecal metabolomics shows promise as a non-invasive novel tool in identifying and characterizing physiological responses associated with large-scale environmental changes in wild birds.

Bacterial metabolic interactions play a vital role in our overall health. Metabolic cross-talk between microbes within an environment can have a large impact on the individual interacting species. Due to the complexity of these interactions, our understanding of the individual metabolic contributions of each microbe to the overall community is limited. We have developed a new platform to assess the dynamic metabolic contributions of individual microbes within a complex community, and we tested this platform using three pathogens associated with cystic fibrosis polymicrobial infections: Haemophilus influenzae (HI), Pseudomonas aeruginosa (PA), and Staphylococcus aureus (SA). This platform represents a single assay that separates individual metabolism from community metabolism and could help better understand the metabolic underpinnings of polymicrobial infections.  Microbial metabolism is the product of mass action across the microbial community; therefore, metabolic read-outs are representative of the community, not individual phenotypes. We performed simultaneous metabolomics analyses for monocultures and community cultures and then performed flux balance calculations and network analysis to determine the flow of nutrients in the community setting. We identified several key metabolic pathways, including nucleotide metabolism, the TCA cycle, isoprenoid biosynthesis, and the urea cycle, which were affected by cross-feeding interactions between all three microbes. We found that HI produced succinate throughout the 22 hour experimental period, and this carbon source was taken up by both PA and SA. We also found that PA was adept at scavenging purine bases from the other microbes and did not use de novo purine synthesis. It instead consumed adenine and guanine cross-fed from the other microbes, and it did so to such an extent that it depleted the levels of those purines from the shared microenvironment. In addition, we were able to determine the overall metabolic contribution of each individual species to the community metabolite pool. 

Objective and background. Precision or personalized medicine appreciates the influence of inter-individual differences contributing in treatment success. As antibiotics (ABX) use induces dysbiosis and has drastic consequences on the metabolome, this study aimed at delineating the biomarker signature of ABX-induced dysbiosis by using both targeted and untargeted metabolomics strategies.
Method. To identify the altered metabolome due to different antibiotics in a female C57BL/6 mouse model, broad-spectrum ampicillin and neomycin, Gram-positive targeting (vancomycin), anaerobic targeting (metronidazole), clinically most prescribed (ciprofloxacin and Bactrim) and a cocktail of ABX were added to their drinking water for 14 days. The feces and serum samples were collected from individual mice and the metabolites were extracted and divided into two fractions: a derivatized extract for 12 short-chain and medium-chain fatty acids (C2 to C8) in a targeted manner; and a dry extract reconstituted in the running buffer for untargeted LC-MS analysis in both positive and negative ion modes.
Results. Oral administration of either ampicillin, vancomycin, or the cocktail of ABX, induced dysbiosis as measured by cecum size, and consequently reduced the amount of butyric acid in the serum (P treated with ampicillin, vancomycin and the cocktail of ABX.
Conclusion. This study identified individual metabolite signatures of dysbiosis induced by different ABX and has the diagnostic potential of identifying different stages of dysbiosis induced by antibiotics or other external factors.

Honey is an important commodity, renowned for its nutritional properties and health benefits. The composition of honey depends on the nectar source and there is increasing demand for monofloral sources. However, honey is one of the most adulterated foods. A further challenge is to certify the claimed floral or geographical origin. In this regard, the characterization of floral markers could help to determine if bees feed on flowers of a particular source and protect consumers against deceptive marketing practices.
This work used untargeted fingerprinting to determine levels of specific chemical markers in honey from different floral sources (Vaccinium sect. Cyanococcus; Rubus armeniacus; Rubus idaeus; Trifolium genus; Melilotus officinalis; Fagopyrum esculentum; Vaccinium subg. Oxycoccus; Chamaenerion angustifolium and “wildflowers”).
Pure honey was diluted 100X in 20% acetonitrile containing 0.1% (v/v) of formic acid, followed by 2 ??L loop injection using a LC-HR-MS system in a one-minute chromatographic run.  Full scan mode operating a range of m/z 50-1300 was acquired. Minor chemical compounds depending on the floral origin are the key to authentication and traceability. From over 3,100 deconvoluted molecular features recorded, only 1350 presented the quality required for further analysis (i.e., not present in the experimental control, isotopic pattern recorded, and CV Significance was declared at p?0.05. From this analysis, 193 molecular features were significantly different across groups. Honey obtained from buckwheat flowers presented the most discriminant features (121) followed by sweet clover, blackberry, blueberry, clover, firewood, wildflowers, cranberry, and raspberry with 21, 12, 10, 10, 10, 4,3, and 2 discriminant features, respectively. Some of these molecular features were tentatively annotated as phenolics and flavonoids.
Future efforts will focus on increasing the number of samples per group and floral sources as well as dereplication analysis of key unknown compounds by screening against in-house and commercial databases.

Plant metabolomics field has been proven to be a rich contribution to several research areas such as agriculture, environmental sciences, food assessment, drug development, natural products identification and production, etc. Thus, efficient extraction techniques are crucial to augment the identification number of the myriad compounds found in plant samples. Moreover, many plant metabolites have not been reported yet, growing a high interest in upgrading metabolite libraries.  Therefore, substantial identification results in plant LC-MS-based metabolomics experiments will help to address these issues. In this study, different types of plant tissues were employed to develop a homogenization technique using stainless steel lysis beads. The tissue samples were frozen at -80 °C and 100 mg of tissue was weighed, then homogenized and extracted using 4:1 (v/v) MeOH/H2O. Samples were dried using N2, and finally resuspended with water. The extracted metabolites were further analyzed using the Chemical Isotope Labeling (CIL) LC-MS method, which is a powerful technique that improves the overall performance of metabolomics analysis. In this method, metabolites are labeled with a pair of isotopic labeling reagents followed by LC-MS analysis. With the rational design of the labeling reagents (e.g. dansyl chloride), the separation, detection sensitivity, and quantification accuracy and precision can be greatly enhanced. The labeled samples are analyzed using ultra high performance liquid chromatography combined with high resolution mass spectrometer (UHPLC-HRMS). Data analysis was performed using an IsoMS Pro that executes peak picking, alignment, filtering, metabolite identification, and quality control. The results show this procedure provides higher throughput than conventional approaches such as mortar and pestle for metabolite extraction. The results also show that many plant metabolites were identified in different types of samples.

Bacteria is well-known for its diversity and capability of metabolisms with close relation to human health. To study these complex biological activities, it is important to have a robust and accurate method for metabolite screening and quantification. However, target analyses usually focus on certain types of metabolites with limited numbers. Here, we present a comprehensive and quantitative microbiome metabolite assay, based on the chemical isotope labelling (CIL) LC-MS technique and global metabolomics, to target microbiome-related metabolites and other microbiota-host-related endogenous metabolites. Two derivatization reagents were used: dansyl chloride for amine/phenol-containing metabolites and DmPA bromide for carboxyl-containing metabolites. The technique provides better sensitivity, separation, and quantification. The signal ratio between the light reagent-labelled sample and the heavy reagent-labelled pooled reference sample was used for relative quantification. A comprehensive microbiome metabolite list was prepared by collecting information from various databases and literature. IsoMS Pro and our new software IsoMS Target were used to locate the metabolites in the acquired LC-MS data for further pathway and statistical analysis. 
A set of mouse fecal sample data was tested with this method. In the list of 1230 target metabolites, we identified 470 microbiome-related metabolites and 549 other endogenous metabolites. These metabolites were further categorized with their structures and pathways. Categories related to the microbiome showed high identification coverage. For example, short and medium-chain fatty acid, bile acid, and aromatic amino acids had identification rates of 92.6%(25/27), 82.4%(42/51), and 89.2%(123/135), respectively. With high metabolite coverage from the CIL technique and the flexible target list, this method can be applied to various sample types and compounds of interest. 

Theme: Metabolomics in chronic diseases
Background: The bacterium Mycobacteria tuberculosis is the biggest killer of human beings of all time. Even today, it kills, on average, 1 to 1.5 million people each year. However, most of the cases of tuberculosis (TB) are curable with a drug course of antibiotics for six months. So, why hasn’t this disease been eradicated? Diagnosis. The PCR-based diagnostics that work pretty well for most TB cases take days to generate a result. During that time infected people are still spreading this airborne disease. In children, diagnostics are incredibly challenging, so those TB cases are often not identified (typically less than 40% are diagnosed). If there were a rapid diagnostic – and better yet, a non-invasive diagnostic – then the eradication of TB could proceed at lightning pace.
Methods: Here, we present the use of breath and sputum volatile organic compounds (VOCs), as potential diagnostic biomarkers, sampled from patients with Mycobacterial infections from clinics in South Africa, Haiti, and the USA. Breath was concentrated onto thermal desorption tubes or sputum headspace volatiles were concentrated onto solid phase fibers. VOCs were analyzed using GC×GCtofMS. Chromatographic data were aligned and compounds were assigned putative names based on spectral library match. The statistical analyses were performed in R.
Results: A set of putative biomarkers for adults with TB disease (compared to controls who presented with TB disease symptoms but were ultimately diagnosed with a different lung disease) will be presented as well as a set of four unique biomarkers specific to children. In addition, disease adjacent data (from other Mycobacterial lung infections) will also be presented for additional context and perspective, including how breath biomarkers change during treatment.
Conclusion:
Breath provides a rich source of diagnostic information for Mycobacterial diseases.
During successful treatment, breath biomarkers revert to a less inflammatory breathprint.

Adulteration of natural health and food products is a significant concern for consumer health and safety. Extra-virgin olive oil (EVOO), popular for its healthy characteristics, including strong antioxidant activity and high concentration of monounsaturated fatty acids, is of particular concern as a commonly adulterated commercial product. Recently, the Canadian Food Inspection Agency’s 2020-2021 Food Fraud Annual Report has reported in the analysis of 49 EVOO samples, 12.2% resulted in unsatisfactory sterol and fatty acid profiles based on the International Olive Council Standard (Government of Canada, 2022). Consequently, simple and effective authentication methods for EVOO samples are of high interest. Current methods of EVOO authentication use methods that are often expensive, rely heavily on well maintained infrastructure, and require specialized technicians to acquire high quality -omics data. A method built with low-field benchtop NMR in mind, could be more accessible to a wider range of technician skill levels, scalable for high sample-throughput, and comparatively inexpensive in both the short and long term. Our objective was to develop and validate a low-field NMR method for the authentication of EVOO. To meet this objective, two technicians acquired low-field benchtop NMR spectral data from the same 49 commercial EVOO and 45 non-EVOO edible oil samples. The spectral data were processed, centered, and pareto scaled separately; and Partial-Least Squares Discriminant Analysis and Random Forest models were trained on each dataset using k-fold cross validation and a test-train data partition for model validation. Our results show that models trained on low-field NMR can assess samples with an average accuracy >90% and an average Cohen’s kappa >80%. We demonstrate that, with untargeted machine learning algorithms trained on low-field NMR spectral data, we can overcome the limitations of low-field benchtop NMR to make accurate assessments of olive oil authenticity, and discrimination of non-EVOO edible botanical oils.

Immune cells undergo major metabolic rewiring when activated by stimuli. We have previously demonstrated that proinflammatory macrophages specifically “commit” to a metabolic state where increased aerobic glycolysis sustains fast ATP production, independently of Oxidative Phosphorylation (OXPHOS). This is associated with a “broken” mitochondrial TCA cycle and accumulation of metabolites like citrate, succinate and itaconate, important for cellular functions. We have shown by assessing enzymatic activities and through metabolomics and carbon tracing studies that induced Nitric Oxide levels are responsible for this major mitochondrial reprogramming. Importantly, in endotoxin injected mice (M1 response), we have shown alterations in the metabolic signature of the peritoneal lavage fluid, where we documented regulation of citrate, ?-KG, arginine metabolism and itaconate production. Parallelly in the plasma, we find energetic metabolites to be decreased compared to baseline, and arginine and glutamate deprived. Interestingly, in acute response both compartments upregulate similar metabolic pathways favoring glycolysis, antioxidant response and nucleotide biosynthesis for cell proliferation, while only later they reach distinct metabolic fingerprints. Our new data on M2 skewed inflammation, modeled by infection with parasite N. brasiliensis, enlarged our knowledge of changes in the niche. In this macrophage-driven condition we detect higher levels of arginase-derived metabolites, polyamines, and glycolytic intermediates in the lavage, consistent with anti-inflammatory and repair response. Moreover, via lipidomics we found that in M1 lavage, fatty acid (FA) and lipid synthesis signature is prevalent, and downregulation of beta oxidation of very long change FA is apparent, while in plasma all chain triacylglycerols (TG) and acyl carnitines accumulate, indicating preservation of oxidation but lipid macromolecule synthesis. Conversely, in M2 we show that TG are depleted in lavage after infection, suggesting utilization of FA. Our data propose that macrophage metabolically respond to the surrounding environment and at the same time, direct alterations of it, presumably to facilitate pathogen clearance.

Prebiotics are known to mitigate several metabolic diseases, including obesity. We have shown the beneficial effects of oligofructose-enriched inulin (OI), a prebiotic, in reducing body weight and improving fat metabolism in overweight and obese children. In this study, we aimed to investigate the mechanisms by which OI improved fat metabolism using a metabolomics approach. Briefly, serum and fecal samples were collected from a previous clinical trial where overweight and obese children were treated with OI (prebiotic, n=22) or maltodextrin (placebo, n=20) for 16 weeks. The samples were undergone for 1 Proton-Nuclear Magnetic Resonances (1H-NMR)-based metabolomics using Bruker 600 Hz Advanced NMR spectroscopy. Partial least square discriminant analysis identified ten significant serum metabolites that were altered in the presence of OI. Metabolites such as glucose, lysine, aspartate, glutamine, and glutamic acid were significantly downregulated in the serum of the OI group compared to the placebo. Multivariate statistics could not separate the fecal metabolites of the two treatment groups; however, we found a significant decrease in isobutyrate (p=0.019) and an increase in 1-methylhistidine (p=0.04) levels in the fecal samples of the placebo and OI groups respectively. Our data provide evidence that the regulation of specific amino acid metabolism could have played a role in reducing body fat in obese or overweight children.

The use of polypropylene microcentrifuge tubes to handle and perform lipid extractions has drastically increased in literature in recent years as an economical and convenient alternative to traditional glassware. However, even the most appropriate polypropylene labware leaches over 316 different contaminants that litter MS spectra. In this work, we investigated if the choice of labware had any consequences on lipid biomarker discovery from serum of cystic fibrosis patients when compared to serum of healthy patients.
Serum extractions of cystic fibrosis and healthy patients were performed using polypropylene Eppendorf microcentrifuge tubes or borosilicate glass centrifuge tubes with PTFE lined caps. Extracts were analyzed via reversed-phase liquid chromatography ESI MS in both ionization modes. Univariate and chemometric analyses were used to identify significantly altered serum lipids in cystic fibrosis patients. Most statistically significant lipids that could be employed as potential biomarkers were successfully identified as significant features in plasticware and glassware extractions. However, 121 significantly altered lipids were exclusively identified when glassware was employed. Furthermore, the quantitation of a small subset of the serum lipidome was adversely affected with the use of plasticware. Particularly, the introduction of lipid contaminants from polypropylene microcentrifuge tubes that are identical to human serum endogenous lipids, did not allow for the accurate quantification of the endogenous counterparts. Thus, these lipids from the pool of potential biomarkers needed to be excluded. 
With a robust exclusion list of plasticware-leached contaminants and limited exposure to high quality polypropylene plasticware (under 4 hours), polypropylene microcentrifuge tubes and polypropylene disposable micropipette tips were determined to be good labware alternatives in robust lipidomic analysis workflows and were successfully employed in a pilot disease biomarker discovery study. 

Predicting sheep pregnancy and litter size (PLS) early is crucial for sheep producers for food management, lambing rate, and animal welfare. Ultrasonography is the standard method used for determining sheep PLS, but it has limitations including requiring a veterinarian on-site and expensive equipment. Further, ultrasound can detect pregnancy positivity, but it is not very effective at detecting litter size. Biomarkers are more efficient in litter size detection in comparison to ultrasonography. Therefore, a rapid, reliable, low-cost, and portable method is needed for farmers to detect PLS in sheep. A promising method for the development of such a device is to use electrochemical detection. A key advantage of electrochemical detection is that it does not require optical transparency, allowing whole blood to be used, and that smaller amounts of blood are needed compared to other detection methods. Previous research has identified creatinine, pyruvic acid, urea, L-lactate, D-glucose, L-carnitine, L-valine and L-glutamine as biomarkers for the detection of ewe PLS.  Electrocatalytic reactions were identified for each metabolite that can generate electroactive reactants with an appropriate mediator. Our present work on electrochemical detection focuses on glucose, lactate and pyruvate oxidases as the enzymes used to detect their respective metabolites. These enzymes have been immobilized onto the working electrode of a three electrode system using drop casting to functionalize the surface. The device reads the electric current from each electrode individually and sends the signals to a separate device for reading and display in a user-friendly format. We have been successfully able to detect glucose, lactate and pyruvate in whole blood using this method in the physiological concentration ranges needed to determine PLS. We successfully detected lactate from 0-8 mM with a correlation of 0.96.  This device has potential to enhance nutrition of lambs, survivability of ewes and increase profitability and efficiency for farmers.

Aroma profiles in alcoholic beverages can vary widely due to the sugar source (i.e., malts), microorganisms, additives (hops), and interactions between these ingredients. The aroma profile of beer is comprised of a variety of volatile organic compounds (VOCs) and semi-volatile organic compounds (SVOCs) that are responsible for aroma and flavor. Due to the fermentation process, certain compounds (e.g., ethanol, ethyl acetate, isoamyl alcohol, isoamyl acetate) dominate the headspace and may obscure lesser aromas during analysis. Likewise, barrel fermentation of beers may increase the amount of some VOCs with age, further overshadowing lower abundance components. Spontaneously fermented beers, which we study here, are unique as they contain multiple species of yeast and bacteria and fermentation is carried out in barrels over a two-to-four-year period, rather than the ~14 day fermentation in stainless steel that is used for conventional beer. There is particular need to develop tools to identify metabolites in these beers due to the complexity of fermentation and flavour/aroma compounds present. Headspace sampling techniques have been demonstrated as the most effective approaches to extraction of VOCs from a variety of matrices (beer, wine, bread, etc). The most commonly employed technique for food applications is headspace solid-phase microextraction (HS-SPME). Chemical profiles can be obtained with little disturbance to the bulk sample at a relatively low cost per sample per analysis. When HS-SPME is coupled to GC×GC-TOFMS, only a small amount of representative sample ( < 5 mL) is required for a comprehensive aroma profile of beer.  Major chemical classes in beer at progressive time points were identified with automatic filtering scripts for GC×GC-TOFMS data. The optimized HS-SPME method was assessed for extraction reproducibility and chemical diversity (i.e., profiled chemical classes). Obtained aroma profiles were compared over time to determine the evolution of beer aroma throughout the aging process.

Cell surface glycans are a highly diverse class of biomolecules that are incompletely understood both in their formation and function. Understanding metabolic mechanisms that lead to their formation, and how this process is affected by different cellular states may provide important insight to their role in disease processes. We developed two LC/MS tools to enable the tracking of isotope-labeled monosaccharides through their intracellular metabolic pathways to their eventual extracellular glycans. First, we developed a method to chromatographically resolve the isomeric variants of the nucleotide-sugar precursors from which glycans are synthesized. We evaluated chromatographic separation of N-acetyl-, phosphate-, and nucleotide-sugar isomers using a hydrophilic interaction BEH amide column (Waters) in acidic, neutral, and basic pH mobile phases. Basic pH (A: water, B: 90% acetonitrile; each with 0.1% v/v ammonium hydroxide, 0.1% v/v medronic acid, and 10mM ammonium acetate) mobile phases provided superior chromatographic resolution of N-acetyl-hexose (ManNAc-6P, GalNAc-1P, GlcNAc-6P), hexose phosphate (Gal-1P, Man-6P, Man-1P), and nucleotide-hexose (UDP-Glc, UDP-gal) isomer groups.  Secondly, we sought to analyze the monosaccharide composition of extracellular glycans. Glycans were isolated by obtaining the membrane fraction by ultracentrifugation followed by endoglycosidase digestion. Then, additional ultracentrifugation pelleted the membrane fraction and the digested glycan containing supernatant was collected. Individual monosaccharides were clipped from glycans by acid hydrolysis (2M acetic acid) for N-acetylneuraminic acid or 2M trifluoroacetic acid for neutral monosaccharides. The monosaccharide mix was derivatized with 0.2M 1-phenyl-3-methyl-5-pyrazolone (PMP) and analyzed by reversed phase chromatography. Testing multiple column and mobile phase conditions revealed that a Waters T3 column with slightly basic mobile phases (0.01% ammonium hydroxide, pH=8) yielded superior chromatographic separation of isomers and peak intensity. When combined with stable isotope precursors, such as glucose, these methods will allow for deeper understanding of cellular substrate preferences and synthesis routes for individual glycan components in different physiological and disease states.

Introduction
Gut microbial metabolism of xenobiotics can have downstream impact on host biological function. Oral contraceptives have variable impact on mood and behaviour in women and we hypothesize a link between metabolism of synthetic hormones in different microbial species present in the gut microbiome. Here we present a comprehensive untargeted metabolomics workflow for analyzing alterations in microbial metabolism upon exposure to the synthetic hormones ethinyl estradiol and levonorgestrel.  
Methods
Bacteroides fragilis and Lactobacillus rhamnosus were grown anaerobically and exposed to ethinyl estradiol and/or levonorgestrel versus control. After quenching and extracting, untargeted metabolomics data was acquired using RP chromatography coupled with a Bruker Impact II QTOF using autoMSMS in positive mode. Data analysis was performed with both an open-source workflow using XCMS and GNPS, and Metaboscape, a complete untargeted software solution. After feature detection and retention time alignment, multivariate and univariate statistical analysis was performed on all bacteria and hormone conditions to identify dysregulated metabolite features. Metabolites were identified by matching experimental MS/MS data with database spectra. Heat maps were generated to compare metabolic trends between bacterial species.
Results
Both the open-source workflow and Metaboscape were able to identify amino acids as a class of compounds undergoing altered metabolism in bacterial cultures exposed to synthetic hormones. The open-source workflow took ~4 weeks to compare datasets between platforms for both statistics and fragmentation pattern matching while data was processed in a few hours using Metaboscape. Amino acids were in general downregulated in Bacteroides fragilis hormone conditions. In contrast, Lactobacillus rhamnosus hormone conditions showed an upregulated trend in amino acids. In the co-culture, there was a mediating effect in amino acid levels closer to control conditions. This suggests that supplementation of a probiotic may ameliorate the effects of synthetic hormones on synthetic hormone metabolism in the gut.

Introduction
Mucosal associated invariant T-cells are activated following recognition of bacterial antigens presented by major histocompatibility complex class I-related molecule (MR1).  Metagenomics data showed that MR1-/- knock-out (KO) mice had distinct microbiota and displayed resistance against Clostridioides difficile (CDI) colonization vs. wild type (WT) mice.  Here, LC/MS-based untargeted metabolomics was applied to evaluate changes in metabolic activities in accordance with changes in gut microbiota caused by cefoperazone (Cef) treatment. 
Methods
Adult C57Bl/6J WT and MR1–/– KO mice were given sterile drinking water or drinking water spiked with 0.5 mg/mL Cef ad libitum for five days.  Fecal pellets were collected daily and both intestinal and cecal contents were harvested at predetermined endpoints on days 0 (D0), 1 (D1), 3 (D3), and 5 (D5).  Metabolites were extracted from fecal samples and intestinal and cecal contents using methanol:water (v/v 1:3).  UPLC/QTof-MS was used to collect metabolomics data.  Data visualization, correlation analysis and statistical analysis were performed using R software.
Results
PLS-DA score plots of the metabolomic data indicate that the microbiota was relatively less disturbed by Cef treatment in KO mice, which was consistent with the metagenomics data.  The most noticeable differences in the metabolome of KO and WT mice were the increases in carbohydrates in the WT mice, but not in KO mice.  Metabolic functional biomarkers were identified through correlation analysis of gamma-aminobutyric acid (GABA) and riboflavin, which can be biosynthesized by the gut microbiota.  Correlation analysis of the metabolome in stool identified metabolites involved in the GABA pathway, the oxidative stress pathway, as well as vitamins. 
Conclusions
These detected metabolic functional biomarkers could provide complementary information to metagenomics data.  Furthermore, levels of carbohydrates in the stool might be indicators of susceptibility to Clostridioides difficile infection, although this needs further investigation.

Introduction: The vast majority of clinical samples are preserved through formalin fixation and paraffin embedding (FFPE), making them shelf stable for decades or longer.  Traditional wisdom indicates that most biological analytes are either lost or inaccessible due to the fixation process.  Methods have been widely established for the analysis of proteins from FFPE tissues, but metabolites and lipids remain elusive.  Here, we present sample preparation strategies for the detection of polar metabolites and lipids from FFPE tissue sections in both positive and negative ion mode.  Additionally, we explore the signal enhancement affects when using MALDI-2 for increased analyte ionization.
Methods: FFPE mouse spines were sectioned at 5 μm thickness. Images were collected at 50 μm resolution on a Bruker timsTOF fleX with or without the use of MALDI-2.  Images were visualized using SCiLS Lab Pro (Bruker Scientific, Billerica, MA).
Results: By using only xylene to remove paraffin from the tissue sections, robust polar metabolites signals were detected in both positive and negative ion mode with traditional MALDI.  Major species detected included several amino acids, TCA cycle metabolites, AMP, ADP, ATP, nucleotides, nucleosides, fatty acids among others.  It is anticipated that a 20-30% increase in sensitivity will be observed with the use of MALDI-2. 
While a few examples of metabolite imaging from FFPE tissue have been previously shown, MALDI imaging of lipids from FFPE tissue sections has remained elusive.  As other surface sampling techniques have successfully shown lipids retained in FFPE tissue, we sought to explore various aspects of sample preparation to image lipids in situ, including: deparaffinization, antigen retrieval, ammonium formate washing, and the use of standard and ionic matrices.  Washing with ammonium formate provided the greatest enhancement in lipid signal, while antigen retrieval provided little to no benefit.  Norhamane provided the best negative ion mode lipid signal. 

Seminal plasma, the medium produced by the male reproductive tract, contains many biomolecules related to the host metabolism and plays essential roles in sperm development and reproductivity functions. Exposure to environmentally relevant compounds, especially endocrine-disrupting chemicals, has been found to relate to reproductive disorders. Since many of these exposures are metabolized and circulated in the reproductive tract, seminal plasma is an ideal non-invasive biospecimen to study interactions between Exoposome and reproductive health.
Semen plasma (n=10) was provided to the NC-HHEAR Hub by Dr. Richard Pilsner and was extracted with methanol. The Exposome data was acquired by the Vanquish UHPLC coupled with Q Exactive™ HF-X Mass Spectrometer. We obtained 15,648 signals from seminal plasma after data preprocessing and quality control and identified (or annotated) over 100 exogenous metabolites through matching against the NC-HHEAR hub in-house experimental standard library, including environmentally relevant metabolites (phthalates, mercapturic acids, tobacco-related metabolites, phenols, and benzene metabolites), ingested food components (hippuric acid derivatives, benzaldehyde/benzoic acid metabolites, purine derivatives, tryptophan-indole metabolite, pyridinecarboxylic acids), drugs and medications (acetaminophen, ibuprofen, naproxen), and the microbiome-xenobiotic metabolites (dipeptide, sugar amide, tyrosine metabolite). Mercapturic acids, including (R,S)-N-Acetyl-S-(2-hydroxy-3-buten-1-yl)-L-cysteine and N-Acetyl-S-(2-hydroxy-3-propionamide)-L-cysteine, were significantly increased (p < 0 .1) in the low-quality sperm (LQS), which might be related to tobacco exposure. We observed differentiation of sperm metabolic profiles in the unsupervised multivariate component principle analysis (PCA) regarding different sperm quality (LQS vs.NQS) and birth outcomes (not-live birth vs. live birth). Pathway analysis indicated that fatty acid biosynthesis/metabolism, vitamin A metabolism, and histidine metabolism were associated with the differentiation of sperm quality, while vitamin A metabolism, C21-steroid hormone biosynthesis/metabolism, and arachidonic acid/Omega-3 fatty acid metabolism were perturbed with different birth outcomes.
This pilot study demonstrates the potential for using seminal plasma metabolomics to study Exposome and reproductive health. Future studies aim to expand the sample size and validate current findings.  

Chemically diverse lipid species are recognized to play a widening role in biological process and disease pathology, yet it remains challenging to distinguish lipid isomers and unambiguously reveal fine structural differences.  Contemporary mass spectrometry approaches have largely been based on collisional induced dissociation (CID) which provide only partial information on lipid structure.  Alternative techniques such as ozone-induced dissociation MS have been developed to identify double bond location, among other structural features.  In this work we evaluated the ability of a practical electron-based dissociation (ExD) cell retrofitted into an LC/Q-TOF instrument to provide unique fragmentation spectra that could distinguish phosphatidylcholine (PC) lipid class, distinguish PC sn-1/sn-2 regioisomers, and determine PC double bond location.
Phosphatidylcholine (PC) standards and plasma lipid extracts were separated with a 16-minute C18 RP-LC method.  Syringe-based infusions and LC eluents were analyzed with an Agilent 6546 LC/Q-TOF equipped with an electromagnetostatic ExD cell (e-MSion).  Electron Induced Dissociation (EID) spectra were observed from infusions of the regioisomer pair PC 18:0/18:1 and PC 18:1/18:0. Each isomer displayed a single unique EID fragment ion from sn-1 cleavage that defined the sn-2 acyl chain (18:0 or 18:1), demonstrating the ability of ExD to distinguish PC regioisomers. EID spectra additionally contained C-Type (m/z 224) and O-Type (m/z 226) fragment ions specific to PC but not SM, demonstrating the ability of ExD to distinguish lipid classes.  EID spectra also contained a series of even- and odd-electron fragment ions largely corresponding to dissociation of the acyl chain constituents (CH2 units). Comparison of PC 18:1(9)/18:1(9) and PC 18:1(6)/18:1(6) showed 2-Da shifts and diminished fragment intensities that corresponded to the positions of the acyl chain double bonds, demonstrating the ability of ExD to distinguish PC double bond location.  Standard-flow LC/Q-TOF experiments showed sufficient sensitivity to detect the unique EID lipid fragmentation patterns described above.

FooDB is one of the world's largest databases that provides compiled and comprehensive information on food, metabolites, and macro and micronutrients. FooDB currently has over 700+ food constituents and 70000+ compounds present within our database. Each of these entries  contains  information on the nomenclature, description, food source, physiological effects, health effects, nutrition, and any additional biochemical information such as Mass Spec analysis which can be interactively viewed using in-house developed tools. FooDB also contains detailed information on food components that provide its aroma, flavor, texture, and taste.
Another significant and user friendly feature of the database is its search. This includes search by food source and allows the individual to search by name, scientific name, food group, or subgroup with downloadable  supplemental images. Along with this, users can search  by chemical composition, users can now identify what you are looking for by FooDB ID, name, CAS weight, food, and structure. 
Overall allowing anyone who accesses this database in depth knowledge and overview of whatever food constituent they are searching for.The database is also only in its first version and in future will continue to expand and grow in terms of data and advanced features.

In recent years, the development of metabolomics has opened new avenues for research in the nutritional field allowing to measure the metabolites of the diet as biomarkers associated with food intake and associated diseases. Through metabolic analysis, it is possible to find out the involved metabolic pathways and to understand the relationship between some metabolites and cardiovascular risk factors in patients with type 2 diabetes. To evaluate, the metabolic profiles associated with the cardiovascular risk factors, 230 subjects of selected by the TOSCA.IT trial - were considered. This cohort of subjects was divided into two groups &quot;Healthy diet&quot; and &quot;Unhealthy diet&quot;, depending on the type of diet followed such as: high fiber intake and polyphenols, low intake of saturated fatty acids.-SFAs) Based on this consumption, by using the non-targeted LC-MS-based metabolomics approach their profiles were evaluated. It was found that the subjects of the &quot;Healthy diet group” had the highest intake of fiber and polyphenols and the lowest intake of SFAs, while the “Unhealthy diet group” consisted of subjects with the lowest consumption of fibers and polyphenols and the highest consumption of SFAs. Preliminary outcome suggests, healthy dietary pattern is associated to high plasma concentrations of phytochemicals metabolites, whereas, the unhealthy dietary pattern, with a high plasma concentrations of protein metabolites. These findings are important in diabetic patients who already have a higher risk to develop cardiovascular disease. Thanks to metabolomics is possible to increase the knowledge of disease progression and provides approaches for therapy.
This initial study has encouraged us to undertake a broader research goal. Our aim is to build a Support Decision System (SDD) tool able to automatically identify certain metabolites which directly correlate with certain dietary patterns. This tool is indented to help in prediction of patient’s dietary pattern to probable progression towards a type 2 diabetes.

Autism Spectrum Disorder (ASD) is a term used to define a series of neurodevelopmental disorders denoted by repeated behaviours that persist into adulthood and cause difficulties in communication and social interaction. The risk of developing such a condition becomes ten times higher than normal when a child is born in a family where there is already a sibling affected by the same disease. Therefore, it becomes important to carry out studies that quickly detect the presence of biomarkers that could be signs of the possible development of ASD, also considering that gastrointestinal and immune system disorders are often associated with this condition. With the same procedure already followed for the study of celiac disease (CD-GEMM), the GEMMA (Genome, Environment, Microbiome and Metabolome in Autism) project aims to evaluate the influence of external factors in the alteration of the intestinal microbiota and how the genome/metagenome interaction can cause an immune response due to heavy epigenetic and metabolomic alterations, facilitating and consequently increasing intestinal molecular traffic and contributing to the onset of ASD. GEMMA aims to carry out metabolomic analysis on blood, urine, stool and saliva samples collected from 600 subjects and coming from different parts of the world (Irish Center for Autism and neurodevelopmental Research (ICAN), Local Health Authority (ASL) in Italy and Mass General Hospital for Children (MGHFC) Lurie Centre for Autism in the USA) to describe the relationship between the gut microbiota and the host, in order to identify both the altered molecular pathways in patients with ASD and those useful for the creation of a metabolomic phenotype of the GI microbiota. This long-term study of the evolution of the microbiota and metabolomic profile will therefore help to define a rapid prognosis of ASD by identifying characteristic metabolomic biomarkers allowing for immediate and early treatment.

Glycans play essential roles in immunity, influencing ligand-immune receptor interactions, antibody functions, cellular metabolism during infection, etc. Altered glycosylation has been observed in many infectious diseases and in vaccinations. However, the roles of glycosylation in COVID-19 have not been comprehensively characterized. Using lectin microarray, a high-throughput glycomic technology, we profiled the glycan repertoires of plasma samples and autopsy tissues from COVID-19 patients and negative controls. We found an upregulation of α2,6-sialylation in the plasma and the lung tissues of severe COVID-19 patients, compared to mild COVID-19 patients and negative controls. Overactivation of proinflammatory responses is a hallmark of severe COVID-19, in which complement system activation plays an important role. We found the α2,6-sialic acid contents of certain members of the complement cascade were upregulated in the severe cases of COVID-19. In the post-mortem COVID-19 lung tissues, increased complement deposition was observed, colocalizing with elevated levels of α2,6-sialic acids, markers of poor prognosis (IL-6, etc.) and markers of fibrotic responses typically found in severe COVID-19. Moreover, we identified an increase in the enzyme for α2,6-sialylation biosynthesis (ST6GAL1) in patients who succumbed to COVID-19. Overall, our integrated glycomic and glycoproteomic analyses identified a heretofore undescribed relationship between sialylation and complement system activation in severe COVID-19, potentially informing future mechanistic investigation and therapeutic development.

Dried blood spot (DBS) metabolomics has numerous clinical applications in newborn screening, therapeutic drug monitoring, and exposomics. However, accurate metabolite quantification using DBS has been hindered due to the hematocrit effect and the unknown volume of blood spotted onto the card. Different techniques have been employed to overcome this issue but there is no agreement on the optimum normalization method for DBS metabolomics. In this study, we compared five normalization methods (hemoglobin (Hb), specific gravity (SG), protein, spot weight, potassium) to unnormalized data to differentiate between the male and female metabolome using DBS cards collected from 21 adults (10 males,11 females). The normalization performance of each method was evaluated from multiple perspectives such as (a) capability to reduce the intragroup variation (pooled median absolute deviation, the pooled estimate of variance, and pooled coefficient of variation, and principal component analysis), (b) effect on differential metabolic analysis (dendrogram, heatmap, and p-value distribution, and (c) the influence on classification accuracy (partial least square discriminant analysis, sparse partial least square discriminant analysis error rate, receiver operating curve, and random forest out of bag error rate). Our results revealed that Hb normalization outperformed all the other methods based on all the criteria. Furthermore, we observed that normalization factors between SG and Hb are significantly (p = 0.01) correlated with Pearson correlation coefficient (r2) = 0.91 and their relationship can be described as SG dilution factor = 1.0118*Hb dilution factor1.9005. Next, we validated that the Hb normalization performed better than unnormalized data in an independent cohort consisting of 18 newborn infant samples (9 males, 9 females). This is the first comparative study to standardize DBS metabolomics normalization which will serve as a robust methodological platform for future studies in multiple applications

Spinal cord injury (SCI) is a neurological condition which severely impacts motor and sensory function below the site of injury. Even with acute SCI, varying degrees of outcomes may occur. Biomarker detection to predict injury severity or outcome has important clinical application. This work employed a high-performance chemical isotope labelling LC-MS technique for untargeted metabolomics profiling of pig serum samples to compare the pre- and post-injury metabolomes.
The pigs’ spinal cords were surgically injured at different severities depending upon the height of the weight drop (10, 20, and 40 cm); an additional ‘Sham’ group did not have any injury. Serum was obtained daily for a total of 223 samples with 56, 56, 55, and 56 samples in each group, respectively. Metabolites were extracted by protein precipitation with methanol and then labelled with 4-Channel Labelling Kits (Nova Medical Testing Inc., Canada) which target amine-/phenol-, carboxyl-, hydroxyl-, and ketone-/aldehyde-containing compounds. For each channel, individual samples were labelled with 12C reagent, and a pooled sample was labelled with 13C reagent as the reference for quantification. To detect metabolites as peak pairs during LC-MS analysis, samples were mixed in a 1:1 mole ratio of individual and reference. The collected data from each channel was processed and the comprehensive, four-channel data were combined and analysed.
An average of 7452 ± 37 peak pairs (metabolites) were detected. 1096 metabolites were identified with high confidence. The PLS-DA plot visualised clear separations between SHAM and the three treatment groups. Small separations exist between the three SCI groups (R2=0.472, Q2=0.162). With a fold change criterion of > 1.2 and FDR-adjusted p-value of < 0.25, significant peak pairs determined by volcano plots between SHAM and 10 cm, 20 cm, 40 cm were 928, 1111 and 626, respectively. Biomarkers for predicting SCI and SCI severity are being discovered.

To develop a fast analytical method for the separation of isomers has been challenges over the past four decades. Chromatography (HPLC, SFC, GC, TLC) and capillary electrophoresis are commonly used techniques for isomer separation but can be costive and time consuming. In this work, a fast flow injection trapped ion mobility (TIMS) timsTOF Pro workflow was established for the separation of bile acid isomers in ESI negative mode, an established 4D-Metabolomics method (m/z 20-1300 Da; ion mobility 1/K0 0.45 – 1.45 V.s/cm2) was optimized to achieve the highest ion mobility resolution by adjusting TIMS parameters of duty cycle, ramp time, and ion mobility range etc., and data was processed in DataAnalysis 5.3 and MetaboScape® (Bruker).
Bile acids are a large family of molecules that consist of a four-ring steroid structure with various side chains, it involves a wide range of biological functions including the adsorption of dietary lipids and fat-soluble vitamins, signaling molecules with diverse endocrine and paracrine functions to regulate bile acid, lipid, and glucose metabolism, modulate temperature and energy. Bile acids are cytotoxic when present in abnormally high concentration. Deoxycholic acid (DCA, C24H40O4), chenodeoxycholic acid (CDCA, C24H40O4), isodeoxycholic acid, and (iso-DCA, C24H40O4) are bile acid isomers with same molecule formula and different hydroxy group location and orientation. Its mass spectral ion of m/z 391.2854 [M-H]- was observed and  its EIM displays baseline separated peaks with the ion mobility resolution at 166.7 (DCA), 176.7 (CDCA) and 199.2 (iso-DCA), and calculated CCS value of 200.9 (DCA), 206.2 (CDCA), and 199.2 (iso-DCA) Å2, which are in good agreement with the reported CCS reference values in ∆CCS error of 0.59% (DCA), 2.33% (CDCA) and 0.00% (iso-DCA). Similarly, lithocholic acid (LCA, C24H40O3) and isolithocholic acid (iso-LCA, C24H40O3) were also successfully separated achieving high ion mobility resolution of 206.6 (LCA) and 203.9 (iso-LCA).

Lipidome analysis provides a greater understanding of human metabolism and health due to lipids’ role in energy storage, regulation, the immune system, and other basic metabolic roles. Characterization of lipid profiles can be used to identify differences between cellular components of healthy and diseased tissue for biomarker discovery and investigation of pathological processes. Such techniques have recently been seen in plasma and serum analyses; however, it has not been widely used in tissue analyses. Because of the heterogeny of tissue, spatial information pertaining to different cellular components in samples is critical in the analysis of disease. Yet, the lower sensitivity and reduced performance of most imaging mass spectrometry techniques restrain the applications of tissue analyses for a comprehensive lipidome characterization. Here we propose a method for in-depth untargeted lipidome profiling of tissues with spatial information. Liquid chromatography-mass spectrometry was used to optimize the method of lipid extraction from sample sizes of 0.25 mm3 with masses of 0.03 mg, focusing on the development of a robust technique that enables the individual analysis of small sections of a tissue sample to ultimately construct a digital map of a sample’s lipidome through space, addressing the challenges faced with techniques such as mass spectrometry surface imaging. All aspects of the extraction procedure have been optimized, including sample size, solvent extraction volume, resuspension solvent composition, homogenization speeds and various other extraction methods. Next, lipidome analysis from individual sections of sample will be used to map and discern differences in profiles between different quadrants, across transects, and throughout grid-like sectioning of samples. The applications of this research include the ability to map cellular borders of tumors with high accuracy and the analysis of changing lipid profiles across various locations within the same organ, highlighting the value of lipidome analysis with spatial information in tissue.

Here we demonstrate feature extraction and selection from large-scale, untargeted LC-MS experiments. We focus on the core steps, which we predict to be the most routinely performed in analyses of this nature. Those are the processing of the data from the raw instrument format, to an intensity matrix of features in a given set of samples. We examine peak co-localization between already extracted features, as well as the selective generation of an in-silico library, keeping only those compounds that are observed. We show that modern tools are up to the task, but improved methods or tooling may be needed to scale further.

Gamma-aminobutyric acid (GABA) is mostly known as an inhibitory neurotransmitter in the mammalian central nervous system, but it also functions in peripheral tissues to regulate cell proliferation, differentiation, and migration. In order to better understand the function and signaling of GABA in cancer cells, we investigated the biosynthesis route of GABA. Using 13C tracers, we demonstrated that, unlike GABAergic neurons that utilize glutamate decarboxylases (GADs) for GABA synthesis, cancer cells do not rely on glutamate or glutamine for GABA production. Instead, cancer cells prefer the arginine-ornithine-putrescine pathway for GABA de novo synthesis. Ornithine, which is non-proteogenic and is often omitted from cell culture media, is actually the most preferred substrate for GABA synthesis. We found that fetal bovine serum (FBS) used in cell culture typically contains high arginase activity, and it keeps breaking down arginine to make ornithine for cell metabolism. When culturing cells with heat-inactivated FBS that is free of arginase activity, we used 2H-ornithine and 13C-arginine tracers to show that >90% GABA was made from 2H-ornithine. Taken together, our results demonstrate that cancer cells rely on extracellular ornithine to support GABA de novo synthesis.

Metabolite annotation has been a challenging issue especially in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limitations of publicly available spectral libraries, which consist of data acquired tandem mass spectrometry (MS/MS) acquired from just a fraction of known metabolites. Machine learning and deep learning methods provide the opportunity to predict molecular fingerprints based on MS/MS data. The predicted molecular fingerprints can then be used to help rank putative metabolite IDs obtained by using either the precursor mass or the formula of the unknown metabolite. This approach is particularly useful to help annotate metabolites whose corresponding MS/MS spectra are missing or cannot be matched with those in accessible spectral libraries. We investigated various machine learning and deep learning methods for molecular fingerprint prediction based on data acquired by MS/MS. Specifically, we used more than 680,000 MS/MS spectra obtained from the MoNA repository and NIST 20 representing about 36,000 compounds for training and testing different models. We demonstrated that the use of a convolutional neural network (CNN) model leads to improved fingerprint prediction accuracy and ranking of metabolite candidates compared to the previously used models such as multilayer perceptron (MLP) and support vector machine (SVM). Also, we observed improved performance when multiple CNNs are used by segmenting the spectra according to the instrument type/settings (e.g., collision energy, instrument resolution, and positive/negative ionization mode). The trained CNN model is implemented as a python package, MetFID. The package is available on GitHub for users to enter their MS/MS spectra and corresponding putative metabolite IDs to obtain ranked lists of metabolites. Better performance is achieved by MetFID in ranking putative metabolite IDs using the CASMI 2016 benchmark dataset compared to two other machine learning-based tools (CSI:FingerID and ChemDistiller).

Physical inactivity is one of the leading causes of chronic metabolic disease states including obesity. Increasing physical activity (PA) has been shown to improve cardiovascular and musculoskeletal health in elite athletes and to be associated with a distinct gut microbiota composition. However, the impact of physical activity on the gut microbiota is inconclusive for untrained individuals performing normal, light exercise in their day-to-day life. This study examined the role of PA and hand grip strength on gut microbiome composition in middle aged adults (40-65 years) with normal (18.5 – 24.9 kg/m2) and overweight (25 – 29.9 kg/m2) body mass index (BMI). PA was recorded using the International Physical Activity Questionnaire and hand grip strength was measured using a dynamometer. Serum samples were collected and assessed for lipidomics using UHPLC coupled with Q-Exactive Focus Hybrid Quadrupole-Orbitrap Mass Spectrometer and acquired data was processed using Lipid Data Analyzer (v 2.6.3). DNA was extracted from fecal samples for microbiome analysis using 16S rRNA gene sequencing. Overweight participants showed higher abundance of triacylglycerols, and lower abundance of cholesteryl esters, sphingomyelin and lyso-phosphotidylcholine lipid classes (p < 0 .05) compared to those with a  normal BMI.  Additionally, overweight participants had lower abundance of Oscillibacter (p < 0.05) genus. There was, however, higher alpha diversity (p < 0 .001) in participants with moderate-to-high PA irrespective of BMI. Furthermore, participants with high PA showed higher abundance of commensal taxa such as Actinobacteria and Proteobacteria phyla, and Collinsella and Prevotella genera (p < 0 .05). There was no difference in hand-grip strength between study groups, however, those with stronger grip strength showed higher abundance of Faecalibacterium and F.prausnitzii (p < 0 .05). Interestingly, these changes in gut microbiota were not observed in overweight participants suggesting that BMI plays a significant role in achieving improved gut microbial composition by PA.

Rapid, specific, accurate and low-cost detection of metabolites in body fluids can be achieved by using enzymatic colorimetric assays. A common example of one such immunoassay is the at-home rapid COVID test. However, the antibodies used in such immunoassays are highly expensive and difficult to synthesize. A huge range of metabolites can be detected in body fluids using recombinant enzymes, the synthesis and production of which are easier than that of antibodies. Low-cost, in-field and home-based metabolite detection kits can be developed by using enzyme catalyzed colorimetric assays. Our workflow starts with the identification of biomarkers by NMR and mass spectrometry. Once we have evidence for feasible colorimetric assay, we start optimization of user-friendly colorimetric assays. For enzymatic assays, we produce in-house enzymes to make cost-effective, robust assays. We use bacterial, yeast, insect and mammalian expression systems for protein expression. Thus far, we have developed 30 such colorimetric assays in our lab, 23 of which are enzymatic assays and 7 are based on chemical reactions. Further, we have synthesized 5 in-house enzymes in our lab. Some of which are not available commercially and others that are highly expensive. Sensitivity of our colorimetric dry reaction assays is at least 50 % higher than many other commercially available assays with better stability at room temperature. Detection of metabolites in body fluids is significantly challenging due to the presence of interfering agents. We use innovative techniques such as activated charcoal filters, ion exchange resins and polyacrylic acid to remove interference for accurate detection of metabolites. Currently, we have three active projects 1) detection of colorectal cancer in urine 2) pregnancy and litter size detection in sheep serum and 3) detection of urinary tract infection in urine. Two of our previous colorimetric assay projects are 1) chronic wasting disease in droppings of wild animals

Brassica vegetables contain a plethora of bioactive compounds which have been shown to prevent and suppress cancer and promote health. A growing body of evidence suggests that the gut microbiome plays an essential role in the generation of dietary bioactive phytochemicals from brassicas, however, the microbes at play and the microbial metabolites produced are still unclear. To investigate these gaps in knowledge, we utilized an in vitro human fecal digestion model with anaerobic incubation (24h) in the presence of in vitro digested broccoli sprouts (broc), brussels sprouts (brus), combination of the two vegetables (combo), or a negative control (NC). Untargeted metabolomics by LC-MS/MS and 16S sequencing were conducted. We identified 72 microbial ASVs in our samples, 29 of which were significantly differentially abundant between treatment groups. 11,258 metabolomic features were detected and 4,499 of which were found to be significantly different between treatment groups (q ? 0.05, fold > 2). De novo annotation was used to predict compound classes and chemical enrichment analysis revealed 45 classes of compounds to be significantly enriched by brassicas, including long-chain fatty acids, alkane sulfonic acids, cyclic depsipeptides, coumaric acids and derivatives, and peptides. Multi-block PLS-DA integrated microbiome and metabolomics data. 63 metabolomic features and 16 ASVs were found to have a variable importance greater than 0.1, the majority of which were associated with combo or NC. Long-chain fatty acids were associated with combo treatment and Clostridiales and Coriobacteriales orders. Many microbially derived metabolites from brassicas were found to be sulfur-containing, suggesting microbial sulfur metabolism could play a role in the production of these compounds. Overall, we found that brassicas can impact gut microbiome composition, identified metabolites produced during the digestion of brassicas including those produced by members of the gut microbiome, and identified relationships between specifics microbes and metabolites.

Urinary tract infections (UTIs) are one of the most common bacterial infections, afflicting approximately 150 million people worldwide annually. UTIs have been studied primarily through the lens of macromolecular mechanisms utilized by uropathogens to colonize the urinary tract. As such, there’s an extensive, growing body of literature on UTI-associated virulence factors. Meanwhile, metabolism plays a well-established, conserved role in all living beings, but pathogen metabolism during UTI is underappreciated and understudied. Organisms that are well-adapted to making use of existing urinary metabolites have asignificant competitive edge over those that cannot. Thus, uropathogens may have evolved to possess unique metabolic adaptations that enable their ability to colonize human urine. We examined the role bacterial metabolism plays in enabling urinary tract colonization through the comprehensive analysis of the metabolic activity exhibited by UTI-causing bacteria that are grown in human urine.

Isotope labeling metabolomics has helped to provide a global picture of metabolic activity and has recently gained popularity in the field of host-microbiome studies to track the metabolic contributions of various factors on the host. However, mapping of labeled isotopic peaks to metabolic pathways is still underdeveloped due to the challenge of differentiating redundant signals like adducts which get misannotated as labeled metabolites and cannot be mapped to pathways. This work aims to improve the differentiation of these adducts from putative isotopic peaks using a global optimization algorithm and further apply this to generate a network-wide map of the active biosynthetic pathways contributed by a native microbiome member in a Caenorhabditis elegans model of aging. Towards this goal, C. elegans worms at the L1 stage were fed  Chryseobacterium sp. CHNTR56 MYb120 which was previously labeled in minimal M9 media using U-13C glucose as the main carbon source. The worms were collected at both the young and late adult stages and then processed for further liquid chromatography coupled to mass spectrometry analysis. Labeled and unlabeled peaks were differentiated using statistical grouping of peaks based on differential ion intensities reducing the number of false positives detected. This was followed by isotopologue grouping based on the corresponding retention time windows and the list of peaks was then annotated using OmicsNet’s rendition (Zhou. G, et al, 2022) of the global optimization algorithm developed by Chen, L., et al, 2021, which successfully annotated the peaks and identified the previously mentioned redundant signals. This annotated list of labeled peaks was then mapped onto corresponding pathways from the KEGG database. Thus, using pathway mapping of isotopic peaks we provide a metabolome wide picture of the age dependent shifts in pathway activity of Caenorhabditis elegans in response to its microbiome, which further contributes to our overall understanding of aging. 

Currently, there are many downloadable desktop tools available to view NMR spectra, but there is a noticeable lack of options available to display this data in web-based applications. JSpectra Viewer is a lightweight, web-based Javascript application designed to display both one-dimensional and two-dimensional NMR spectra. The viewer is capable of many latest and unique   features, some of them listed as   full-zooming capability, display of peaks and assignments, display of 2D or 3D (with JSmol integration) structures and the ability to export high-quality PNG figures. The software is able to run completely on the user-side by employing several user-configurable optimization steps to maintain responsiveness without utilizing large amounts of computing resources. This stand-alone implementation means JSpectra Viewer is able to run with trivial setup and maintenance, making it a powerful candidate for use in applications such as databases and web servers. The viewer is best optimized for nmrML (XML) format, but a variety of JSON formats are currently also supported. Furthermore, while the software is primarily designed for NMR Spectra, it is fully capable of displaying IR (Infra Red spectra) and any of the 2D or 3D plots, making it an ideal future candidate for web-based spectra-viewing applications. Currently, the JSpectra Viewer is being used by several of the Wishart lab databases and servers including HMDB and NP-MRD. Further to this, we plan to continue to develop and enhance JSpectra Viewers’ capabilities in the next version.

The use of dried blood spots (DBS) is expanding in the wake of the birth of personalized medical treatment and due to an increase in global health and disease surveillance in remote populations. New ways of collecting and storing biospecimens to retain important biological information are on the rise. DBS offers a solution for convenient, at-home blood testing, enabling collection in the comfort and privacy of home. For this reason, DBS’s have gained increasing attention especially in the field of metabolomics. DBS collection utilizes dedicated paper cards, and a small volume of capillary blood. The samples are then dried onto the card, which is transported to the laboratory for analysis. DBS cards offer many advantages, including limited sample volume, less invasive testing, room temperature storage, and at-home collection. These benefits have the potential to ease experimental design constraints of longitudinal and pharmacokinetic studies which require frequent blood collections. In a recent validation study comparing DBS against the golden standard in blood collection, EDTA plasma our data showed that even though DBS had a reduction of overall metabolites coverage compared to plasma (  ̴800 vs   ̴1200 tier-1 metabolites, respectively), all major pathways and more than 95% of the metabolic sub-pathways that are routinely detected in plasma are conserved in DBS. Traditional biological signatures, including exercise biomarkers, dietary biomarkers, and even the cyclical trend of hormonal biomarkers, are equally well represented in DBS compared to plasma. The analysis of DBS for metabolomics has come a long way and this study shows DBS to be a valuable alternative to EDTA plasma and ought be considered when plasma is impractical i.e., at-home collections, remote localities, pediatrics, and IEM screening to name a few. DBS-based metabolomics provides a powerful tool for personalized medicine and an effective way of bringing metabolomics into our homes.

Theme
The analysis and structural characterization of lipids remain challenging due to the chemical structural diversity and isomeric nature of lipids.
Background
The SELECT SERIES™ Cyclic™ ion mobility has a unique multi-pass scalable capability, that increases ion mobility resolution to meet the given challenge. It provides a mobility resolution of 65W/DW with a single pass. Multi-pass IM was investigated for the separation and structural characterization of isomers from different lipid classes. Different lipid class standards were obtained from Avanti Polar Lipids with positional isomer (sn1/sn2 vs sn2/sn1), different double bond positions, cis and trans isomers, glucosyl and galactosyl ceramide isomers, phosphatidyl-mono, di, tris-phosphates and ganglioside isomers were investigated. The standard lipids were infused into the SELECT SERIES Cyclic IMS system at a flow rate of 5 µL/min. Individual standards were analyzed to determine their arrival time distribution (ATD) and then an equimolar mixture of the standards were analyzed. The results show that some of the isomers (glucosyl-galactosyl ceramides, and gangliosides) were baseline separated only after 5 passes (IMS resolution 150W/DW) with ATD of 210.53ms (GalCer) vs 214.49ms GlcCer and 41.22ms (GD1b d18:11/8:0) vs 42.58ms (GD1a d18:1/18:0). However other lipid isomers remained unresolved after even 50 passes (IMS resolution 450W/DW) mainly due to their structural similarity of the lipid isomers in the electrostatic field. In each case, to confirm the identity of the standards in the mixture, individual standards of each lipid species were infused into the cIMS and collected at different passes.
Conclusions
The correct identification of lipids is critical in understanding their biological role and importance. With its unique multi-pass cyclic ion mobility capability, it is possible to scale ion mobility resolution to separate lipid isomers. Advanced modes of operation with ion activation followed by ion mobility separation offers new insights into lipid structural characterization.

Spectral similarity (SS) metrics are used, along with a retention index (RI) score, to match gas chromatography mass spectrometry (GC-MS) query to reference spectra for metabolite identification. However, there is a lack of consensus surrounding the best-performing SS metric, and the RI score foundational assumptions are unreliable and likely violated in practice. Furthermore, features are often matched to multiple candidates, thereby requiring subjective and manual vetting characterized by low reproducibility and high uncertainty. With no established methods for false discovery rate (FDR) estimation, the unknown statistical accuracy of matches may result in incorrect scientific inference. Towards addressing these limitations, this work 1) presents an ensemble SS metric borrowing strength from existing metrics; 2) demonstrates the consequences of RI score assumption violations and provides a framework for an improved alternative; and 3) proposes an improved method for FDR estimation. 549 historical datasets of varying complexity and sample types (standard mixtures, soil, etc.) were compiled and processed using internal software. These data were used to evaluate 58  SS metrics, and the best performing among these were used within an ensemble machine learning model to generate a novel SS metric. We next evaluated the propriety of RI distributional assumptions across various metabolites and derived improved RI scores based on metabolite-specific empirical distributions. Last, we compared two FDR methods and investigated extensions to the better performing of these approaches. The median absolute estimation error (MAE) was used to determine whether these extension models improved over their baseline counterpart. Our novel SS metric improved over the best performing SS metric, and our proposed RI score framework generated scores that more accurately discriminate between true and false positives).  Relative to baseline, the best performing extension models demonstrated average reductions in the median MAE ranging from 3% to 31.8% across sample types.

The Early Career Members (ECM) network was established in 2019 with the support of the Metabolomics Association of North America (MANA). The mission of the ECM is to empower the next generation of scientists in metabolomics and its related fields in North America by supporting opportunities for mentorship, networking, training, and career development opportunities. ECM defines early career scientists as researchers who receive their latest degree within ten years.
The ECM currently has more than 350 members and is led by an elected council of nine early career scientists. Leveraging social media and virtual platforms, we promote our members’ visibility and achievements (e.g., “ECM Spotlight” on Twitter), support career development (e.g., “Career Tip Tuesday” on Twitter), and develop collaboration and networks for early career members (e.g., virtual network and workshop). The traditional virtual events organized by ECM include the Annual National Postdoc Appreciation Week, where we acknowledge postdoctoral scholars’ efforts and highlight their contributions to research and scientific discovery, and the quarterly virtual job fairs, where we bridge our members with employers from industry, government, and academia and help them explore opportunities in new jobs. In addition, the ECM also engages with the annual MANA conference organizers to implement activities that support our missions, such as organizing in-person networking events and interactive forums during the MANA conference. ECM also sponsors outstanding members to present their research in the MANA conferences by providing awards/grants, including the best poster, oral, and lightning talk presentations, and Early Career Rising Star, travel awards, and childcare grants.
The mission and objectives of the ECM network stand on the shoulders of our community. The ECM council is renewed yearly. We welcome you to join the ECM council and help promote the metabolomics field (bit.ly/3BJDV28).

Approximately 25% of women report poor mental health during their pregnancy or the postpartum period. Compared to healthy controls, poor mental health throughout pregnancy can impact fetal neurodevelopment, birth outcomes, and maternal behaviors. Given this, identifying metabolites associated with this condition may provide insight into both the identification and pathophysiology of poor mental health during pregnancy. The objectives of the present study were to assess whether metabolomics may be a sensitive tool to detect poor mental health and its relation to maternal health and birth outcomes.  Maternal serum samples were collected from 275 women at 28-32 weeks of gestation from the All Our Families (Alberta, Canada) cohort and assessed using nuclear magnetic resonance spectroscopy (1H-NMR) and inductively coupled plasma mass spectrometry (ICP-MS). Women with poor mental health scores (depression or anxiety) were age-matched (2:1) with mentally healthy pregnant controls. Metabolites were examined in relation to validated self-reported mental health questionnaires for associations with depression (Edinburgh Postnatal Depression Scale, EPDS), and anxiety (Spielberger State Anxiety Inventory, SSAI) completed in the same period. Chenomx NMR (v.9.02) was used to identify and quantify compounds based on their signature spectra; 49 metabolites were evaluated. Spearman’s correlations were used to compare EPDS and SSAI scores with metabolites, followed by simple linear regressions for the identified significant metabolites. Statistical analysis was repeated for ICP-MS data. Significant 1H-NMR metabolites were identified for depression (alanine, glucose, isopropanol, lactate, leucine, methionine, phenylalanine, and valine) and anxiety (3-hydroxybutyrate, and citrate). For ICP-MS, only mercury was found to be associated with depression, and mercury, potassium, and zinc were significant with anxiety. Although these results warrant further validation, they may serve as a predictive tool for mental health during pregnancy. Earlier identification has the potential to aid early mental health monitoring, intervention, and management to avoid harmful consequences to both mother and child.

Diabetes, which is caused by an unresponsiveness to blood glucose, is one the most prevalent pathological states in the Western world, affecting 13% of adults in the United States in 2018. The islets of Langerhans are responsible for the secretion of the hormone insulin, which ultimately leads to glucose storage within cells. Metabolomics, with lipidomics being a subcategory, can reveal much about a tissue, as it is the ultimate discipline in the omics field to reflect phenotype. Metabolomics and lipidomics of islets of Langerhans can thus provide insight into the true molecular cause of diabetes. To gain a baseline in metabolic activity in non-diabetic individuals, we first performed metabolomics experiments by employing reverse-phase liquid chromatography coupled to mass spectrometry (LC-MS) in an untargeted fashion on metabolite fractions extracted approximately from 2,000 to 2,500 islet equivalents. Second, to further characterize healthy pancreatic islets metabolically, we report the standardization of a lipidomics protocol using the same quantity of islets with the same LC-MS setup. Pathway enrichment analysis revealed purine metabolism as the primary hit in both positive and negative polarities, which is in agreement with the role of these nucleotides (i.e., ATP) in insulin secretion. Lipidomics revealed differentially expressed levels of many lipid species, including potentially ceramides and sphingosine, which have been previously measured in plasma and are important in modulating the function of pancreatic islet β-cells. Our future work will include further characterization of these lipids using enhanced LC-MS techniques such as derivatization. Our results are the staging point for better understanding of the metabolic phenotype of islets of Langerhans, both in health and disease.

Global metabolomic profiles of crab gill samples provide a way to study complex interactions between crustaceans and their environment. As crabs and other aquatic organisms use their gills to filter and breathe underwater, bacteria and pollutants in the water can easily accumulate within them. Previous studies have identified environmental exposures of aquatic organisms to pharmaceutical compounds, and symbiotic interactions between crabs and bacteria in their environment have also been demonstrated through the secondary bioactive metabolites found within them.
This study presents a sample preparation protocol for metabolite analysis of lyophilized crab gill samples from four crab species inhabiting different regions of Guadeloupe, Lesser Antilles. Sample extracts were derivatized using a two-step methoximation-trimethylsilylation protocol and analyzed by comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS). We detected compounds attributed to pharmaceuticals in the samples, including amphetamine, a psychostimulant medication, and two antimicrobial compounds: trigonelline and cycloserine. This may be indicative of environmental exposure to these pharmaceuticals through waterways; however, amphetamine production has been previously identified in crab muscles in response to their diet, trigonelline is produced naturally by various plants, and cycloserine is produced by bacteria. Therefore, these compounds may be naturally occurring in the crabs’ environment or could be derived from symbiotic interactions between the crabs and bacteria on their gills. Further analysis of crab tissue samples is facilitated using the sample preparation protocol presented herein which will provide a greater insight into the likely sources of these bioactive compounds and provide a linkage between the aquatic environment and symbiotic relationships of crustaceans with their microbiome.

Saccharomyces cerevisiae is a eukaryotic model organism that is straightforward to engineer yet complex enough to model higher eukaryotic organisms with biotechnological and biomedical relevance. The metabolism of S. cerevisiae exhibits multiple interesting metabolic phenotypes under different environmental conditions, including the Crabtree effect. In the Crabtree effect, even in the presence of oxygen, cells still undergo mostly glycolytic fermentation rather than respiration that would provide more energy for the cells. This phenotype has major applications in a variety of biotechnology applications. Extensive study of the Crabtree effect on a genetic and proteomic level has helped uncover the contribution of certain genes, but the larger impact of the respirofermentative phenotype on metabolism remains poorly understood.  Metabolomics is a powerful tool that could provide insight into the underlying metabolic mechanisms of the Crabtree effect, which may in turn enable improved metabolic engineering approaches in yeast or give insights into related topics in human metabolism. We moved towards this goal by selectively deleting individual genes of importance in central carbon metabolism, including both enzymes and transcription factors such as Pyruvate Decarboxylase and Sucrose Nonfermenting genes respectively. Metabolic profiles of these engineered knockout strains were compared to a wild-type strain, S288C, in exponential and stationary phase growth. The knockout strains exhibited a wide array of temporal metabolic profiles depending upon the deleted gene; for example, some (e.g., PDC1 knockout) had a distinct metabolic profile in its log phase as compared to the wild-type, while others (e.g., PDA1 knockout) had similar log phase metabolite profiles but distinct plateau phase profiles. Using both multivariate and univariate analyses, we were able to compare the different strains’ profiles, identify individual metabolites with statistically significant behaviors, and generate new hypotheses about respirofermentative metabolism.

Human blood samples are fundamental to many metabolomics studies, but the methods involved in collecting these samples can vary widely. The most common collection techniques used in metabolomics involve human venous or fingerstick blood draws. Whole blood is then typically separated and prepared as serum or plasma for testing. Recently, a more novel and easier collection method consisting of the self-collection of capillary whole blood from a Tasso device located on the shoulder has been introduced. As capillary and venous blood draw samples have not been compared extensively, it is difficult to determine if quantitative metabolite values should be used interchangeably between collection methods. In the present study, blood samples from 5 healthy volunteers were collected through fingerstick, Tasso and a venous blood draw. These three collection methods were then analyzed as whole blood, serum and plasma samples through quantitative liquid chromatography-tandem mass spectrometry (LC-MS/MS). The LC-MS/MS analysis measured 143 biomedically relevant metabolites (including amino acids, acylcarnitines, sugars, lipids, organic acids, etc.). This study aims to determine if these separate collection methods, capillary vs. venous, have similar metabolite profiles in various sample preparations. Preliminary data indicates a difference in metabolite profiles depending upon blood collection method and sample preparation.

Treponema pallidum is the causative agent of syphilis, a sexually transmitted disease that has been a public health problem since the 15th century and has markedly increased in recent years. Studying this pathogen is complicated by the unknown nutritional requirements of the organism—currently the microbes cannot be grown in long-term in vitro cultures without including rabbit epithelial (Sf1Ep) cells, which are evidentially contributing unknown nutrients that are essential to the growth of T. pallidum. Genomics alone is insufficient to inform which metabolites must be included in the growth medium to sustain a long-term axenic culture. To address this, we quantified the metabolic boundary fluxes of axenic cultures and co-cultures of these cells to map the flow of molecules between the host cell and the bacterium. We set up three biological replicates of control medium, Sf1Ep cells, T. pallidum, and co-cultures of these cells (Sf1Ep cells and T. pallidum) and assessed each treatment at six time points (days 1, 3, 5, 7, 9, 11). Extracellular extracts were analyzed with a Thermo Fisher Scientific Vanquish UHPLC platform with a SyncronisTM HILIC LC column and a Thermo Scientific Q ExactiveTM HF mass spectrometer. A metabolic preference assay was used to determine metabolites produced and consumed by Sf1Ep cells and T. pallidum cells.
Using this approach, we showed that four alpha-keto acids (3-methyl-2-oxobutanoic acid, 4-methyl-2-oxopentanoic acid, 3-methyl-2- oxopentanoic acid, and alpha-ketoglutarate) are transferred from the Sf1Ep cells to T. pallidum. With no known urea cycle or any other ways to dispose of nitrogenous waste, transferring amino groups between alpha-keto acids through transaminase reactions could be the way this bacterium manages excess intracellular nitrogen and maintains optimal carbon/nitrogen ratios. This emerging model will allow insights for new culture medium development as well as revealing exciting metabolic underpinnings one of humanity’s long-standing public health problems.

Fecal metabolomics has received increased attention in recent years as a platform to study the complex interactions occurring in the gut and playing a role in human health. As such, standardized fecal sample preparation and analysis protocols need to be established for reproducible metabolomics studies. Solvents have been used predominantly for metabolite extraction in fecal sample preparation. The abundance, types, and numbers of compounds detected in samples depends highly on the extraction technique or solvent used for sample preparation. However, few studies have contributed an in-depth analysis on the selection of extraction chemistry and their impact on metabolite recovery. Additionally, feces is a heterogeneous metabolite-rich matrix containing live bacteria and active enzymes resulting in a continually changing metabolite profile during and after sample collection. Therefore, it is critical to have proper control of pre-analytical conditions, including sample handling and storage, to minimize metabolite alterations and ensure reliable and meaningful results. This work presents the distinct fecal metabolomic profiles obtained using different extraction chemistries through a comparison of the metabolite families detected, as well as an evaluation of the impacts of sample handling conditions on the metabolite profile using comprehensive two-dimensional gas chromatography - time-of-flight mass spectrometry (GC×GC-TOFMS). GC×GC-TOFMS is a powerful separation and detection technique for analysis of complex samples, such as feces, allowing enhanced separation and tentative compound identification. Sample handling conditions were evaluated using two complementary preparation methods, solid-phase microextraction (SPME) and derivatization via methoximation/trimethylsilylation, to evaluate fresh, frozen, and lyophilized fecal samples with expanded coverage of the fecal metabolome. The comparison of extraction chemistries is informative for optimizing metabolite extraction, and our comprehensive comparison of the sample handling conditions provides in-depth understanding of the physicochemical changes occurring within fecal samples during storage and handling. This work contributes towards the standardization of fecal sample preparation methods for metabolomics studies.

The group of acyl-CoA metabolites carries an important role in metabolism pathways. Acyl-CoAs are part of fatty acid metabolism, and are present in ß-oxidation, fatty acid biosynthesis or modification. Both short and long chain fatty acyl-CoAs are important for homeostasis. Several studies have pointed out the importance of acyl-CoAs in the study of disorders like cancer, insulin resistance, or type 2 diabetes. Acyl-CoAs are synthetized in two steps by various acyl-CoA synthetases that connect fatty acid and cofactor coenzyme A through a thioester bond.
Analysis of acyl-CoA metabolites may be difficult due to the varying length of the fatty acyl chain resulting in their different chemical properties and presence of phosphate moieties in the molecule. This fact makes acyl-CoAs metabolites stand on the hypothetical border between metabolomics and lipidomics, where short-chain acyl-CoA are usually the target analyzed in the former and long-chain acyl-CoA in the latter. Given the importance of this metabolite group and their possible importance in understanding of various disorders an accurate method for profiling acyl-CoAs is needed. Four channel chemical isotope labeling metabolomic approach was developed by our group. Acyl-CoA metabolites can be derivatized utilizing base activated 12C/13C-dansylation for hydroxyl metabolites which allows for enhanced detection and quantification using a HPLC-MS platform. Four acyl-CoA standards were purchased and analyzed using the chemical group sub-metabolome method. Incorporation of acyl-CoA metabolites into this platform will help create a more accurate metabolomic picture of the future samples to be analysed using this approach.

Background
Diabetes is a major problem in the United States and around the world.&nbsp; There are currently millions of individuals living with diabetes worldwide and this number is predicted to rise to over 600 million by the year 2045. Type 2 diabetes mellitus (T2DM) accounts for approximately 90% of total cases. T2DM is a major cause of blindness, kidney failure, heart attacks, strokes, and lower-limb amputations. Early diagnosis is critical for medical intervention for the prevention of complications associated with T2DM or even avoidance of this severe chronic disease.
Objective
The objective of this study was to establish a methodology for the metabolic profiling of human plasma and the detection of potential biomarkers for T2DM.
Methodology
The analytical approach included effective sample extraction and automated derivatization.&nbsp; T2DM and control plasma was extracted via sonication, and then derivatized in two steps:&nbsp; 1) Methoximation and 2) silylation. Sample data was collected using EI- and CI-TOFMS technology to not only increase the total number of compounds annotated but more importantly to improve confidence in their characterization.&nbsp; Data were processed using untargeted peak find, and compounds were characterized through automated database comparisons, retention index filtering, and formula determinations using high-resolution accurate mass data.&nbsp; Differentiation of disease and control samples was accomplished using novel statistical processing software based on Fisher ratios.
Conclusion
The profiling methodology resulted in a significant increase (> 2x) in the total number of metabolites identified.&nbsp; The compounds included acids, diacids, amino acids, fatty acids, bases, monosaccharides, disaccharides, sugar phosphates, sterols, nucleosides, and others. Downstream statistical processing facilitated the identification of several T2DM candidate biomarkers including uronic acids and inositols.

Per- and polyfluoroalkyl substances (PFAS) are a group of synthetic chemicals that are known for their PBT (persistent, bioaccumulative, and toxic) properties. These toxic substances are linked to negative health outcomes such as immunotoxicity and immune system suppression within rodents and humans. Perfluorooctanoic acid (PFOA), a well-studied PFAS detectable in human and wildlife serum, has been demonstrated to suppress T-cell-dependent antibody response (TDAR) and vaccine response in humans. Environmental agents like PFOA likely influence the severity of viral infections such as the SARS-CoV-2 virus. We conducted untargeted metabolomics and lipidomics studies of serum from ferrets exposed to SARS-CoV-2 in the presence and absence of PFOA to determine how the exposure modulates the infection. We hypothesized that exposure to PFOA will alter the metabolome and negatively affect the host immune response, leading to a more severe response to COVID-19. A group of eight ferrets exposed to PFOA (10 mg/kg/d) and a group of eight control ferrets (0 mg/kg/d) were challenged with a strain of COVID-19 and evaluated over a two-week period. NMR metabolomics data were evaluated in serum across seven pre- and post-challenge timepoints. Clinical data were collected to determine the infection level (PFU/ml) and overall health of the ferrets over the time course. PFOA-exposed ferrets reached peak infectivity on day three, whereas the control ferrets reached peak infectivity on day five. In PFOA exposed samples, 17 NMR features changed significantly (p < 0 .05) over time and three features were significantly associated with the infectivity level. In control samples, 13 NMR features changed significantly (p < 0 .05) over time, and two features were significantly associated with infectivity level. Additionally, there are features significantly differentiating (p < 0 .05) the two experimental groups. These data suggest that exposure to PFOA affects the progression of SARS-CoV-2 infection and can be investigated at the metabolite level.

In metabolomics studies, the determination of confidence in metabolite identifications is ultimately made by individual researchers. After applying tolerance thresholds for e.g. mass accuracy or MS/MS library scores, researchers manually perform comparisons and annotations, accepting or rejecting the results based on arbitrary or subjective criteria.  The Chemical Analysis Working Group of the Metabolomics Standards Initiative (MSI) published in 2007 the first proposed minimum reporting standards for metabolite identification confidence, which consisted of four MSI-levels of confidence in decreasing order based on the amount and degree of orthogonality of the analytical information supporting the identification. While MSI-levels for assigning metabolite identification confidence could be further refined, neither confidence thresholds nor combining different levels of experimental and biological probabilities have been thoroughly tested. Unlike proteomics, robust workflows that result in solid false discovery rate-associated automatic structure assignments are missing in metabolomics. New methods are needed that instead focus on probability-based assessments of identification along with methods for estimating identification false discovery and that remove the subjectivity on the part of the data reporter. Here, an initial conceptual model for assigning a probability to quantify the evidence for the presence of a compound in a sample – generalizable and transferable across measurement platforms and sources of evidence – will be discussed and illustrated with representative data. The benefits of the proposed model are that it is not subjective on the part of the data reporter, it is automatable, it inherently includes a measure of ambiguity in the identification, and it is broadly applicable across different measurement platforms. The caveats of the model are that it is applicable to only known knowns (and possibly also unknown knowns) and is dependent on the size and composition of the reference library used, as well as the overall resolution of the analytical approach.

Friedreich Ataxia (FA) is a rare and often fatal autosomal recessive disease in which a mitochondrial protein, frataxin (FXN), is severely reduced in all tissues. With loss of FXN, mitochondrial metabolism is severely disrupted. Multiple therapeutic approaches are in development, but a key limitation is the lack of biomarkers reflecting the activity of FXN in a timely fashion. We predicted this dysregulated metabolism would present a unique metabolite profile in blood of FA patients versus Controls (Con). Plasma from 10 FA and 11 age and sex matched Con subjects was analyzed by targeted mass spectrometry and untargeted NMR. This combined approach yielded quantitative measurements for 540 metabolites and found 59 unique metabolites (55 from MS and 4 from NMR) that were significantly different between cohorts. Correlation-based network analysis revealed several clusters of pathway related metabolites including a cluster associated with one‑carbon (1C) metabolism composed of formate, sarcosine, hypoxanthine, and homocysteine. Receiver operator characteristics analyses demonstrated an excellent ability to discriminate between Con and FA with AUC values>0.95. These results are the first reported metabolomic analyses of human patients with FA. The metabolic perturbations, especially those related to 1C metabolism, may serve as a valuable biomarker panel of disease progression and response to therapy. The identification of dysregulated 1C metabolism may also inform the search for new therapeutic targets related to this pathway.

Biomanufacturing is a processes that relies on biological systems to produce high value molecules used in medicines.  Chinese hamster ovary (CHO) cells have long served as one of the key systems used in the production of biologics, however, modifying these cells to improve production has been challenging.  In contrast to traditional genetic manipulation, directed evolution is one way to select for host cell populations with desirable biomanufacturing properties.  Here, various physical stressors were applied to a CHO parental cell line to identify cell populations that were more tolerant to these stressors.  When subsequently tested for their biomanufacturing productivity, three lines (L1, L2, and L3) were found to have improved capacity relative to the CHO parent.  In order to understand the cellular pathways driving this improved antibody production, an untargeted LCMS-based metabolomics approach was applied.  Analysis of the polar metabolites demonstrated that the evolved cell lines displayed unique metabolic profiles not only relative to each other but also versus the parental CHO line.  Altered levels of key redox metabolites and several organic acids was a shared hallmark of the evolved lines.  In addition, altered amino acid and nucleotide metabolism was an additional discriminator between the evolved cell lines.  In the future, additional replicates, both in the producing and non-producing states, as well as multi-omic analyses including lipidomics and proteomics will be used to understand the cellular mechanisms driving this improvement in biomanufacturing capabilities.

Despite warnings, some Canadian women continue to smoke during pregnancy. Identifying circulating ions related to cigarette exposure may provide insight into the deleterious impacts that exposure may have on both mother and baby. The study's primary aim was to determine whether ions from both prior to pregnancy and pregnancy smokers were distinguishable from pregnant non-smokers. Heavy (smoking ≥1 cigarette/day) and light (smoking Maternal serum samples were collected at 28-32 weeks of gestation from the All Our Families (Alberta, Canada) cohort and assessed using ICP-MS. Both pre-pregnancy smokers and pregnancy smokers were paired with age-matched non-smokers to examine ions associated with smoking-related factors determined by self-reported questionnaires, including pre-pregnancy smoking, smoking frequency, pregnancy smoking, and household smoking status. Multivariate data analysis, including PLS-DA, revealed distinct clustering among detected ions in serum between smokers and non-smokers (p < 0 .05). VIP scores with a cut-off of 1 identified 9 discriminant ions. Smoking resulted in decreased concentrations of iodine and bromine compared to non-smokers. Phosphorous, sulphur, vanadium, titanium, copper, and isotopes of calcium were found to be increased in smoking groups. No significance was observed between heavy pre-pregnancy smokers and heavy pregnant smokers. In addition, maternal and birth-related outcomes, such as education, gestational weight gain, and drug abuse, were found to be significantly associated with smoking status. In conclusion, we identified ion associations between smokers and non-smokers. These smoking exposure biomarkers may provide future insights into its effects on maternal and birth-related outcomes. 

Metabolomics is a field that aims to comprehensively profile the metabolome through detecting as many metabolites as possible. Liquid chromatography mass spectrometry (LC-MS) has been widely applied into this field for analyses of complicated biological samples due to its superior sensitivity and specificity. However, comprehensive profiling remains challenging due to the diversity of metabolites, wide concentration range of metabolite matrix and ion suppression effects. To address these issues, the concept of multi-dimensional separation has been brought into this study. Theoretically, analytes will retain differently under different mechanisms.
In the first dimension, samples undergo a LC separation, and the eluted samples are being collected as multiple fractions within different time frames. Then, each fractionated samples will undergo a chemical derivatization process, which will significantly increase their hydrophobicity. The labeled metabolites will be later injected into a second LC dimension with a different separation mechanism. The hypothesis is we can have a much-reduced complexity for each fraction. And the hydrophobicity change and different LC mechanisms in terms of column chemistry and gradient optimization, can provide a decent orthogonality, which can be promising for improving the method performance in terms of the total detectable features.
Currently, we have chosen urine samples to demonstrate the proposed two-dimensional LS-MS method. Different LC methods and fractionation ideas are under exploring and improvement.

Metabolomics is gaining importance as an investigation tool engaged in the discovery and characterization of novel molecular mechanisms associated with physiological and pathological states of microbiome communities. Microbiome studies are inherently complex and diversified and as such, there is a need for metabolomic methods that are compatible with large sample cohorts that can properly address the many factors at play in each study. The available methods, while performing in smaller cohorts, present limitations when applied to larger cohorts. Many of these limitations come from the inherent time-dependent changes in the liquid chromatography mass spectrometry (LC-MS) system, which affect both the qualitative and quantitative performance of the instrument. In this study, we introduce a new analytical method for addressing these limitations in large-scale microbial studies. Our approach is based on quantifying microbial boundary fluxes using two multiplexed zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns, allowing for a high rate of sample throughput. By applying this method, we show that over 360 common metabolites are detected in 4.5 minutes per sample and those metabolites are quantified with a median coefficient of variation of 0.127 across 1,100 technical replicates. We applied this strategy to analyze 960 strains of Staphylococcus aureus isolated from blood stream infections. These data capture the diversity of metabolic phenotypes observed in bacterial isolates collected from patients and provide an example of how large-scale investigations can benefit from our novel analytical strategy.

Technology advancements in mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy have facilitated the application of metabolomics in genome-wide association studies (mGWAS) to study genetically influenced metabotypes (GIMs). Tens of thousands of single-nucleotide polymorphisms (SNPs) have been identified to be significantly associated with metabolite abundance in over 65 mGWAS to date. However, how to obtain mechanistic or functional insights from these associations for translational applications remains a major challenge. Here, we introduce mGWAS-EXplorer, a novel visual analytics platform linking SNPs, genes, metabolites, and diseases for functional insights. In particular, mGWAS-Explorer addresses current challenges by 1) comprehensive collection and deep annotation of SNP–metabolite associations based on data from the 65 mGWAS to date; 2) support for SNP-based, gene-based, and metabolite-based network generation to facilitate interpreting results; 3) integrating with HaploReg, VEP, KEGG, Transporter Classification Database (TCDB), Recon3D as well as common PPI databases; 4) powerful network visual analytics system facilitating interactive exploration and built-in topological and functional enrichment analysis. The application of the mGWAS-Explorer was demonstrated using COVID-19 and type 2 diabetes case studies. The mGWAS-Explorer will assist researchers in hypothesis generation for a more focused experimental design. The platform is freely available at www.mgwas.ca

Adenosine 5-triphosphate (ATP) is a complex nucleoside triphosphate, consisting of the nitrogenous base adenine, a ribose sugar, and a triphosphate chain. It is readily catalyzed to ADP, AMP and adenosine. Previous studies have found that extracellular ATP (eATP) is not only involved in airway inflammation diseases and cough, but that blocking its extracellular release has therapeutic benefit. These data support the notion that eATP may be a key driver of symptoms and pathogenesis of airway disease. Unfortunately, ATP and similar nucleotide analogues are poorly retained by traditional reversed-phase liquid-chromatography (RPLC); are unstable and subject to interconversion from enzymes, pH, and/or temperature; have multiple pKas; and are metal-sensitive analytes. Historically, ion-pairing chromatography or passivation has provided a solution to separate these challenging compounds, however they can be problematic for liquid chromatography/mass spectrometry systems. Common ATP luminescence-based assays indirectly measure ATP levels through enzymatic degradation, while lacking a simultaneous readout for its analogues. Thus, analytical techniques for these challenging anionic metabolites remains underdeveloped. Here, we present a new analytical approach that employs microfluidic-based capillary electrophoresis-mass spectrometry (CE-MS), allowing the simultaneous measurement of ATP and its analogues. ATP addition to plasma and BALF specimens collected from naïve Brown Norway rats were monitored using a bare-glass ZipChip on a 908 device coupled to a Thermo Scientific ID-X. The results show that adding EDTA to the biological fluids allows preserved detection of ATP and its analogues. In addition, when compared to a traditional coated ZipChip, the bare-glass version uniquely detects numerous challenging charged metabolites, highlighting additional potential applications. This new approach could be employed to further understand the role of eATP and help develop ATP-targeting treatments for airway diseases.

Untargeted metabolomics is a valuable tool in biology and chemistry to characterize molecules. A fast solution to identify molecules with mass spectra is by using database search. Often spectra are collected under different conditions and settings that makes comparing query spectra against database entries a challenge. Standardizing mass spectra is an important problem and has motivated other work such as PredRet to convert retention time between different chromatographic systems. In this work we translate full spectra between instrument settings and to our knowledge are the first to do so.
We propose using an MLP model to take the molecular fingerprint of a molecule, the corresponding spectra taken under the M+H ion mode for a molecule, and information on instrument setting of mass spectra to translate the spectra for the same molecule under the M-H ion mode for a specified target instrument setting. We demonstrate that our MLP model can expand a database containing positive ion mode spectra with negative ion mode translations to aid database search of negative ion mode queries. Our results showed using MLP model to translate outperforms blindly picking closest cosine similarity among positive ion mode molecule candidates.
Our MLP model produced translations that allowed database search to achieve 1.7 lower average rank than direct database search of candidates in the opposite ion mode for the NIST20 Dataset. We propose that using MLP for translation can improve database search to better take advantage of database entries in ion mode other than the ion mode of the query spectra.

Ensuring collection of high-quality LC-MS/MS data is a time-consuming hurdle in untargeted metabolomics. The use of stable isotope labeled internal standards, spiked into all samples, has become commonplace. Internal standards make it easy to distinguish failed injections or poor sample preparation, despite vast biological diversity between samples. However, digesting and interpreting internal standard results in real-time, with minimal person-hours, is a challenging hurdle.
Here we present MS-AutoQC, an interactive real-time dashboard for at-a-glance quality control of mass spectrometry data.  Within 5 minutes of sample completion the raw data is automatically copied to a Linux server, where MS-DIAL Console App quantifies each sample’s internal standards. If internal standards are not detected in a sample, an email and Slack notification are immediately sent to relevant stakeholders to determine if the analysis needs to be paused.
Furthermore, in order to detect nuanced changes such as RT shift, m/z drift, and sensitivity drops MS-AutoQC offers web-browser based visualization to ensure high-quality usable data is being generated and can be monitored from anywhere with an internet connection. The RT across samples plot allows rapid visualization of retention time shifts, and can be tailored to look at any number of internal standards or samples in parallel or separately. In a similar fashion, the ∆m/z across samples plot alerts users to drifts in mass accuracy.  Intensity across samples plot reveal instrument sensitivity perturbations. Lastly, MS-AutoQC offers a module for longitudinal comparison of instrument benchmark samples to help quantify changes in the instrument from study to study. Visualization is built atop Python libraries and code that are easy to implement and adapt to any workflow.
MS-AutoQC offers a quick, straight-forward approach to ensure collection of high-quality untargeted LC-MS/MS data. Allowing for less time extracting ion chromatograms and more time conducting experiments.

As the costs of acquiring ‘omics datasets decrease, researchers are collecting more ‘omics data: more samples, more features, more ‘omics data types, and more frequently.  ‘Omics data analysis and interpretation have become a key bottleneck for basic research and translational application. Point-and-click analytics software, for example the ‘omics platforms developed by the Xia Lab at McGill University, can alleviate this bottleneck by removing the need for programming skills or advanced bioinformatics training. However, by making analysis pipelines so accessible they run the risk of promoting ‘blind statistics’, where users are performing analyses without understanding what they are doing. Education and training should go hand-in-hand with platform development. Our hypothesis is that by leveraging existing and emerging web technologies such as social media and AI-powered bots, ‘omics software can better inform and empower researchers while they analyze their data. To test this idea, we present OmicsForum and OmicsBot as two prototype support tools for the Xia Lab ‘omics analytics platforms. OmicsForum is an interactive user forum powered by the opensource Discourse software. Users can search the more than 400 help topics that we have organized by tool for more details on data formatting, processing, statistics, and interpretation. They can register an account to comment on posts or create their own topics. OmicsBot is a chatbot powered by the open-source Rasa framework. OmicsBot will be able to recommend appropriate software for analyzing specific datasets, based on a knowledge graph of over 400 tools developed by the metabolomics community; and second, OmicsBot will integrate with MetaboAnalyst to suggest relevant topics and tips from OmicsForum while users are navigating through pipelines. Together, OmicsForum and OmicsBot work towards democratizing ‘omics analytics by empowering and educating users while they analyze their data.

The future of small molecule identification requires a shift towards a rapid, sensitive, and completely de novo method that is unbiased toward the sample under study. Otherwise, we will be constrained to elucidating only those molecules that are amenable to current, slow methods that require significant purification efforts, high sample amounts, and long experiments. We aim to integrate an ultra-high resolution ion mobility spectrometer to a cryogenically cooled ion trap infrared (IR) spectrometer, and a time-of-flight mass spectrometer, based on the pioneering work of Thomas Rizzo. Here we present the accompanying predictive computational and data processing components. We detail a flexible data processing pipeline, workflows for the prediction of cryogenic IR spectra and collision cross section (CCS), robust experimental to theoretical spectrum matching, and validation library curation. Though findings are preliminary, this multidimensional experimental and computational capability is expected to provide high throughput, unambiguous identification of molecules.

The greatest challenge of metabolomics continues to be the confident identification of metabolites. There are many computational, molecular networking and machine learning approaches for metabolite identifications based upon accurate mass and tandem mass data.  However, these have fairly poor performance for identifying the correct metabolite as the top candidate.  Thus, orthogonal data obtained and used simultaneously, such as UHPLC-MS/MS and retention time, offer greater confidence in metabolite identifications. Additional collisional cross section (CCS) matching further increases the confidence in metabolite identifications. Recently we have expanded our previous plant metabolite library to include CCS values for 135 additional authentic standard metabolites, as well as 65 additional CCS values from plant extracts, and demonstrate the use of RT, accurate mass and CCS matching to provide more confident identifications.  This was achieved through analyses of a mixture of 22 authentic standards and clearly showed the utility of CCS matching in eliminating false positives. Using the Target List library, MetaboScape software annotated 44% false positives based on Δm/z 1.5%, without any ΔRT criteria. The use of Δm/z 700) to the other orthogonal data ultimately eliminated all but 1 false positive.  Excluding ΔCCS (i.e., using the Δm/z, ΔRT, and ΔMSMS matching only) resulted in 7 false positives (24%).  When needed, ΔCCS can be effectively and confidently used in place of ΔRT as observed using different columns and gradients where all 22 standards were identified with only 35% false positives utilizing  Δm/z 1.5% and NO RT criteria. 

The extraction of cannabidiol (CBD) has become a major industry in the United States and a massive amount of left-over material known as spent hemp biomass (SHB) is produced during the process. Currently, there are no standard methods of disposal or markets for secondary product usage of SHB once CBD is extracted. Oregon is a leading producer of hemp and there is an increasing interest in utilizing SHB as a roughage source for regional livestock. However, the concern that accumulation of cannabinoids may occur in adipose tissue, a well-known reservoir for cannabinoids, has also been raised given their lipophilic nature. To address questions around food safety and cannabinoid residues in animals destined for human consumption, we evaluated a cannabinoid metabolite panel and the total lipidomics profile in fat using non-targeted high-resolution mass spectrometry analysis. Samples came from male Polypay lambs consuming SHB over 8 weeks divided into five feeding treatments: no SHB (control) or SHB at 10% (LH1) or 20% (HH1) for 4 weeks with 4 weeks withdrawal from SHB, or SHB at 10 (LH2) or 20% (HH2) for 8 weeks. Lipidomics analysis revealed no accumulation of cannabinoid metabolites in lamb fat. Most of the 1,477 lipid compounds annotated were not significantly affected by SHB supplementation. We observed the downregulation of several diacylglycerol compounds and the upregulation of several fatty acyl compounds in SHP fed animals in comparison with control. These changes did not correlate with the duration of the SHP supplementation. Our results help define the lack of cannabinoid accumulation that is occurring within ruminant livestock species and will contribute to recommendations for guidelines on safely feeding SHB to livestock.

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the 'gold standard' for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

The collection of metabolites that make up a fecal metabolome are of increased interest for population wide studies, with specific metabolites implicated in human health and disease, such as insulin resistance and Parkinson’s. To facilitate large-scale metabolomics discoveries, a standardized fecal sample collection device is required. In this work, we further evaluate our novel OMNImet™•GUT (ME-200) device as a platform agnostic solution for home sampling and transport of fecal samples for metabolomics analysis.
NMR is commonly used in metabolomics thanks to it's advantageous experimental reproducibility and quantification. We have evaluated the compatibility of OMNImet™•GUT collected samples with butanol:methanol extraction mixture prior to NMR based analysis through Prometheus (Prometheus Metabolomics). Across the five donors tested, we observed excellent concordance for 33 metabolites across one week of room temperature storage when compared to fresh samples. As expected, we observed drastic metabolite decreases in samples lacking preservation buffer, after only one day at room temperature.
In addition, building on previous work highlighting stability of SCFA at ambient storage in ME-200, we herein evaluated stabilization of fecal bile acids using LC-MS/MS. 15 bile acids, with reference standards for each, were assessed using Metabolon’s pipeline in eight adult fecal donors across two separate ME-200 collection holds, -20⁰C for two weeks and one week at room temperature. We had identified an average bias of only 3.5% for the -20⁰C held samples, with highest average bias at 15% for taurolithocholic acid, when compared to fresh extractions. Conversely, while all bile acids were successfully detected, we were only able to identify five bile acids that had a lower than 50% bias after one week at room temperature storage.
Together, these findings highlight the utility and agnostic nature of the ME-200 collection device and provide a necessary toolkit to researchers for large-scale metabolomics population studies.

To diagnose several disorders, amino acids (AAs) and acylcarnitines (ACs) are frequently measured on dried blood spots (DBS) cards. DBS has similar preparation as liquid plasma/blood. This processing includes solvent extraction, which is costly and time-consuming. In this sense, Paper-Spray Mass Spectrometry (PS-MS) is an ambient ionization method that allows the analysis of DBS samples directly, eliminating solvent extraction. In PS-MS, endogenous metabolites, such as AAs and ACs, have sensitivity issues. To improve the sensitivity of AAs and ACs on PS-MS, the factors involved in their desorption/ionization by PS-MS will be addressed and optimized. Once the factors are understood and optimized, AAs and ACs on DBS will be evaluated in blood from healthy and with metabolic syndrome Wistar male rats. This project includes the development of high-throughput analysis by using an Open License robotic platform.
The platform was evaluated with palmitoylcarnitine standard and its labeled standard palmitoylcarnitine tri-deuterated. The reliability was acceptable (< 15%), and also linearity r = 0.992 in a range of 200 - 2 000 nM.
The first part of this project is to develop a robotic platform for PS-MS analyses. The evaluation of the platform showed acceptable analytical parameters for a long chain acylcarnitine. 

Indole, and potentially other bacterial-derived tryptophan metabolites from the vaginal microbiota, is hypothesized to modulate the genital host immune response to Chlamydia trachomatis, an obligate intracellular pathogen that can severely compromise reproductive health. This modulation may have ramifications for both natural and vaccine mediated immune responses. With Chlamydia rates rising, and a vaccine needed to supplement screen and treatment programs, we set out to develop methodology to quantify tryptophan metabolites from vaginal secretions and paired serum samples. Here, we report the optimization of an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) assay for quantifying 28 tryptophan metabolites mediated through microbiota (indole pathway), indoleamine-2,3-dioxygenase (kynurenine pathway) and tryptophan hydroxylase (serotonin pathway) in human vaginal secretions collected on ophthalmic wicks and in serum. A total of 104 vaginal swabs and serum samples from 52 patients were used in this study, representing a spectrum of Chlamydia infected and uninfected women, and women with normal and dysbiotic vaginal microbiomes. An innovative sample preparation using a simple solvent protein precipitation, 96-well plates, a 12-minute chromatography gradient, and multiple reaction monitoring (MRM) mode with polarity switching give this assay a high-throughput design.  With linear standard calibrations up to 10,000 ng/mL and limits of quantitation from 0.9 to 43.3 ng/mL within 20% error, we report that this assay is suitable for wide dynamic ranges in biological samples, as would be expected in clinical patient samples such as the vaginal secretions and serum we utilized here.

PathBank is an interactive, visual database containing more than 100,000 machine-readable pathways found in model organisms such as humans, mice, E. coli, yeast, and Arabidopsis thaliana. PathBank is designed specifically to support pathway elucidation and pathway discovery in metabolomics, transcriptomics, proteomics, and systems biology. It provides detailed, fully searchable, hyperlinked diagrams of metabolic, signalling, disease, drug, and physiological pathways. Each small molecule is hyperlinked to detailed descriptions contained in the HMDB or DrugBank and each protein complex or enzyme complex is hyperlinked to UniProt. All PathBank pathways are accompanied with detailed descriptions and references, providing an overview of the pathway, condition, or processes depicted in each diagram. Each of pathbank’s pathway images, descriptions, and tables are downloadable in several formats. 
Pathbank also has a feature known as Pathwhiz which allows the users to create the detailed, visually pleasing, and biologically accurate pathway diagrams that are machine-readable and interactive. This is done using a specially designed drawing canvas to render metabolites, protein complexes, nucleic acids, membranes, subcellular structures, cells, tissues, and organs. The pathways can be generated into BioPAX, SBGN-ML, SBML, and PWML exchange formats, as well as high quality PNG, image map, and SVG images.  
Overall, anyone who accesses and uses this database can find machine-readable pathways depicting important biological processes, which can be searched, viewed, and downloaded in a variety of formats. The database can also be used to create new pathways for any biological process needed. Pathbank is in its second version and will continue to improve and develop the data.  

Triple-negative breast cancer (TNBC) patients receive chemotherapy treatment, including doxorubicin, due to the lack of targeted therapies. Drug resistance is a major cause of treatment failure in TNBC and therefore, there is a need to identify biomarkers that determine effective drug response. A pharmacometabolomics study was performed using doxorubicin sensitive and resistant TNBC patient-derived xenograft (PDX) models to detect urinary metabolic biomarkers of treatment effectiveness. Evaluation of metabolite production was assessed by directly studying tumor levels in TNBC-PDX mice and human subjects. Metabolic flux leading to biomarker production was determined using stable isotope labelled tracers in TNBC-PDX ex vivo tissue slices. Findings were validated in 12-hour urine samples from control (n=200), ER+/PR+(n=200), ER+/PR+/HER2+ (n=36), HER2+ (n=81) and TNBC (n=200) subjects. Diacetylspermine was identified as a urine metabolite that robustly changed in response to effective doxorubicin treatment, which persisted after the final dose. Urine diacetylspermine was produced by the tumor and correlated with tumor volume. Ex vivo tumor slices revealed that doxorubicin directly increases diacetylspermine production by increasing tumor spermidine/spermine N1-acetyltransferase 1 expression and activity, which was corroborated by elevated polyamine flux. In breast cancer patients, tumor diacetylspermine was elevated compared to matched non-cancerous tissue and increased in HER2+ and TNBC compared to ER+ subtypes. Urine diacetylspermine was associated with breast cancer tumor volume and poor tumor grade. This study describes a pharmacometabolomics strategy for identifying cancer metabolic biomarkers that indicate drug response. Our findings characterize urine diacetylspermine as a non-invasive biomarker of doxorubicin effectiveness in TNBC. 

The sea lamprey (Petromyzon marinus) is a jawless eel-like fish and an invasive species in the Laurentian Great Lakes. Sexually mature or spermiating males do not feed but produce and release large quantities of bile acid pheromones that modulate conspecific neuroendocrine systems and behaviors. However, pre-spermiating males produce lesser amounts of bile acids and rarely release them. To understand the metabolic changes in different maturation states, we compared the plasma and liver metabolomes of mature and juvenile male sea lampreys using both non-targeted and targeted metabolomics approaches. A data-dependent acquisition mode in the Thermo Q-Exactive Orbitrap UPLC/MS-MS with electrospray ionization in both positive and negative modes was applied for the non-targeted approach; whereas an MRM acquisition mode using the Xevo-TQS LC/MS-MS in negative ionization mode was used for targeted analysis. Metabolomics data were processed using Progenesis QI, Compound Discoverer and XCMS for alignment, peak picking, and deconvolution of the peaks. PCA and PLS-DA statistics were performed using SIMCA and Metaboanalyst to identify discriminating features, followed by fragmentation matching with extensive database search and pathway mapping. We found that the sea lamprey specific bile acids, petromyzonol sulfate and 3-ketopetromyzonol sulfate, were upregulated by thousands of folds in the liver of mature males. Plasma metabolomics profile revealed that spermiating males upregulated bile acid biosynthesis by altering amino acid metabolisms, upregulating cofactor and nucleotide metabolisms, but downregulating carbohydrate and energy metabolisms. We conclude that plasma and liver metabolomes differ significantly in pre-spermiating and spermiating sea lamprey and reflect the special metabolic demands at each life stage.

The Broad Institute Metabolomics Platform analyzes roughly 25,000 biospecimens via 45,000 LCMS injections a year. This number continues to increase as we grow in size with respect to personnel and instrumentation. A pressing need has been to develop a system to process, store, index and retrieve our data, and ensure that we are leveraging it to its full extent. For this, we have created two internal cloud-native services on GCP for processing and data storage, respectively: Nebula and Atlas. Our processing service, Nebula, consists of a serverless endpoint accessible by a web-based front end. It hosts a variety of stand alone tools to carry out our processing pipeline, which includes annotation, alignment, drift-correction, clustering, and filtering our data. Atlas, our data storage system, consists of several cloud resources to host our raw instrument files, standards database, project database, inventory, and processed datasets. These records and their metadata are linked together through an Aura Neo4j graph database. Like Nebula, Atlas uses a serverless endpoint and a web-based front end. Here, we highlight the efforts of our software engineering team, presenting the schema of Atlas, its cloud framework, the individual tools which constitute Nebula, and our overall workflow for processing a nontargeted LCMS dataset.

Fast and accurate profiling and characterization of drug metabolites play a critical role in preclinical and clinical development stages to assist lead compound optimization, screening drug candidates, find active or potentially toxic metabolites. A non-targeted trapped ion mobility (TIMS) enabled timsTOF Pro PASEF workflow was performed to investigate drug metabolites from in vitro human liver microsome (HLM) incubation experiments, when a time-series experiment was conducted by spiking HLM and Codeine, MAMA, and Tramadol into a pre-incubated NADPH regeneration system at 370C, and aliquoting 100 µL of reaction solution at 0, 5, 15, 30, 45, 60, 90 and 120 min and the reactions were stopped by adding cold acetonitrile; Samples were centrifuged at 12,000 rpm at 4 0C for 10 min and the supernatant was transferred into insert sample vial for injection; Sample analysis was performed by Elute UHPLC timsTOF Pro (Bruker) with PASEF data acquisition; Data analysis and peak findings of the investigated drug and its metabolites were performed in MetaboScape 2022b with the T-ReX ®4D algorithm applied for automatic feature extraction and alignment. Drug metabolites were postulated by utilizing BioTransformer, a knowledge and machine learning based approach to predict small molecules metabolism. All possible metabolites from enzymatic reactions of hydroxylation, N-dealkylation, N-oxidation, O-dealkylation, O-Aryl demethylation and epoxidation were investigated. Generated chemical structures for the metabolites with different metabolism locations enabled their assignment in the acquired raw data. Many isobaric metabolites exist which could be well differentiated and characterized by the retention time, isotopic pattern matching, MS/MS with in-silico MetFrag based fragmentation, especially the TIMS enabled CCS verification. In summary, TIMS enabled timsTOF Pro PASEF workflow and MetaboScape allow CCS-enabled drug metabolite profiling and characterization.

The Metabolomics Innovation Centre (TMIC) specializes in quantitative metabolomics assays for human, animal, plant and microbial samples. In the past few years, TMIC has developed and adapted several quantitative assays to expand its list of detectable metabolites and has also successfully applied the platforms to different biofluid samples by adjusting the calibration concentration ranges. These assays are based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) in multiple reaction monitoring (MRM) mode. For example, we have developed several quantitative assays capable of measuring 143 metabolites biomedically relevant metabolites (including amino acids, acylcarnitines, biogenic amines, lysolipids, organic acids, vitamins and uremic toxins), in serum and urine, etc. These assay platforms have also been expanded to include more metabolite classes as well as the number of metabolites in each class, e.g., includes up to 900 metabolites and ratios, and covers 21 chemical classes including organic acids, amino acids, nucleotides/nucleosides, ketone and keto acids, and lipids, etc.; and have been fully validated initially in urine and fecal extract samples. Most recently, these assays have also been validated in serum samples. The recovery rates of spiked serum samples with three different concentration levels are in the range of 80% to 120% with satisfactory precision values of less than 20%. These assays validated specifically for serum samples were used to successfully analyze human serum with results closely matching those reported in the literature as well as measured by our other in-house assays.

Metabolite identification and quantification are critical steps in NMR metabolomic workflow and require user-dependent data processing, significant domain expertise and are laborious and time-consuming. The purpose of this study is to increase the throughput of 1H-NMR metabolomics by introducing a neural network approach to enable user-independent and real-time metabolite quantification. In this study, a multi-layer perceptron (MLP) was trained for lipid quantification and its performance was compared to a more conventional Bayesian analysis approach of NMR data, called CRAFT.  Additionally, as data availability is limited in NMR metabolomics and neural networks require extensive amounts of data for training, we created a synthetic data generation workflow and generated 50,000 synthetic spectra from scanning individual lipid standards and scaling and combining them to produce complex synthetic lipid mixtures. The MLP was trained with spectra as input and 14 nodes corresponding to major lipid groups [i.e., triglycerides, cholesterol, phospholipids, total fatty acids, fatty acids (saturated, mono/polyunsaturated, omega-3, omega-6, omega-9, DHA, linoleic), phosphatidylethanolamine, phosphatidylcholine] as output. Lipid quantification by MLP was exceptionally fast, with 10,000 synthetic spectra quantified in ~1 second (compared to CRAFT estimate of hundreds of hours). The trained MLP was tested on 6 experimental lipid mixtures and demonstrated high accuracy with 7 lipid groups quantified showing 6%-43% less error compared to ground-truth concentrations than CRAFT. Further, MLP was able to quantify omega-6 and omega-9 fatty acids which were not able to be determined by CRAFT. Lastly, the MLP was investigated to examine spectra from a metabolomics cohort of livers from rats which ate either a chow or high-fat diet. The lipid concentrations determined by MLP and CRAFT were analyzed by two-way ANOVA and revealed similar significant differences including increased triglyceride and fatty acid levels in high-fat rats compared to chow and also in female rats compared to males. 

The Metabolomics Quality Assurance and Quality Control Consortium (mQACC) was founded on the purpose to address key quality assurance (QA) and quality control (QC) strategies in untargeted metabolomics to ensure the production and analysis of high-quality data. A defined priority of the consortium, to encourage the prioritization, development, and implementation of reference materials applicable to metabolomics research, has led to the formation of the Reference and Test Material Working Group (RTMWG). The charge of the working group involves 1) describing prototype materials that can be used for harmonizing measurements across laboratories and instrumental platforms leading to data reproducibility and study comparability, 2) education on reference material usage and 3) outreach to assess critical material needs of the metabolomics community.
Recent efforts of the RTMWG are showcased in a newly published compilation, thoroughly describing state-of-the-art reference and test materials used in current metabolomics and lipidomics practice.  We describe the principles of reference materials, index biofluid and tissue materials, catalogue commercial synthetic standard mixtures, illustrate the creation and use of reference libraries, and suggest implementation of RMs in interlaboratory studies to identify challenges within metabolite and lipid measurement workflows. At the Metabolomics Association of North America Conference, we will promote this resource as a guide for metabolomics scientists in selecting the appropriate reference materials and associated QA/QC tools available for safeguarding their untargeted metabolomics and lipidomics studies.

Background: Colorectal cancer (CRC) is the third leading cause of cancer-related deaths in men and women in the United States. This disease develops due to inherited or sporadic mutations and modification to key environmental agents including dietary factors. Moreover, it is known that CRC cells exhibit increased demand for amino acids to support cell proliferation and tumor progression.
Previously, we found an elevated level of asparagine in female patients with Stage I-III CRC suggesting that asparagine may play a role in CRC development. Existing cancer genome database revealed a strong clinical positive association between high asparagine synthetase (ASNS) expression and poorer survival outcomes. The metabolic roles of ASNS have not been well-studied. Therefore, we hypothesize that limiting asparagine production by ASNS inhibition is a viable strategy for suppressing CRC progression.
Methods: We utilized the innovative CRISPRi platform to knock out ASNS in immortalized HCT116 CRC lines. Using a 3D spheroid model, we cultured HCT116 ASNS KO and isogenic HCT116 ASNS WT cell lines in an asparagine-depleted medium. To determine the tumorigenic effects of ASNS in vivo, immunodeficient mice were inoculated with the CRISPR-edited cancer cell lines. Metabolomic analysis was performed to assess the influence of ASNS KO on CRC metabolism.
Results: ASNS CRISPR KO cells formed spheroids slowly under asparagine-depleted condition compared to their isogenic ASNS WT cells. Asparagine supplementation recovered growth in both groups. Interestingly we observed a significant reduction in tumor volume in the ASNS KO mice compared to ASNS WT. Multivariate analysis showed a distinct grouping of ASNS KO and WT tumors indicating differences in tissue metabolome.
Conclusion: Lack of ASNS suppresses tumor progression in a pre-clinical CRC model. Changes in tissue metabolome may contribute to the observed differential tumor growth and may also provide insight into the poorer outcomes observed in patients with high asparagine and ASNS expression.

We report a segmented-spectrum scan method using Orbitrap MS in chemical isotope labeling (CIL) LC-MS for improving metabolite detection efficiency. In this method, the full m/z range is divided into multiple segments with the scanning of each segment to produce multiple narrow-range spectra during the LC data acquisition. These segmented spectra are separately processed to extract the peak pair information with each peak pair arising from a differentially labeled metabolite in the analysis of a mixture of 13C- and 12C-reagent-label samples. The sub-lists of peak pairs are merged to form the final peak pair list. Various experimental conditions, including automatic gain control (AGC) values, mass resolutions, segment m/z widths, number of segments and total data acquisition time, were examined to arrive at an optimal setting in segment scan for increasing the number of detectable metabolites. The optimal method used a segment width of 120-m/z with 60k resolution for a 16-min CIL LC-MS run. Using dansyl-labeled human urine samples as an example, we demonstrated that this method could detect 5867 peak pairs or metabolites (not features), compared to 3765 peak pairs detectable in full scan, representing a 56% gain. Out of 5867 peak pairs, 5575 (95.0%) could be identified or mass-matched. Relative quantification accuracy was slightly reduced due to the inclusion of more low-abundance peak pairs in segment scan. Peak ratio measurement precision was not significantly affected by segment scan. We also showed the increase of the peak pair number detectable from 3843 in full scan to 7273 (89% gain) using the Orbitrap operated at 120k resolution with 60-m/z segment width, when multiple repeat sample injections were used. Thus, segment scan Orbitrap MS is an enabling method for detecting co-eluting metabolites in CIL LC-MS for increasing metabolomic coverage.

1)Grapes growing in a vineyard that are exposed to bushfire smoke, can result in smoke tainted wine with undesired sensory characteristics. Every year bushfires have an impact on the viticulture industry in Australia and internationally but not all bushfires are the same and the chemical changes that occur following smoke exposure of vineyards in terms of grape composition and wine composition is not entirely understood. Some diagnostic marker tests are available to industry but are based on measuring compounds that have been identified to date.
2) The project aimed to develop an innovative broad chemical screening approach, based on untargeted metabolomics, for the analysis in wine of contaminants from smoke and their grape metabolites. By combining NMR and mass spectrometry for metabolite analysis, the project aimed to identify chemical entities in wine that may have changed or are present due to smoke exposure of grapes.
3)For this study, a non-targeted metabolomics approach was used to screen 77 red wine samples that were categorised into groups ‘smoke-affected’ and ‘control’, based on defined criteria. A total of 77 wines were analysed using Liquid Chromatography High Resolution Mass Spectrometry (LC-HRMS) and Nuclear Magnetic Resonance (NMR).  The groups of wines (‘smoke-affected’ and ‘control’) could be clearly differentiated based on their chemical composition measured by LC-HRMS and NMR. Over 100 chemical entities detected by LC-HRMS were specific for the ‘smoke affected’ group and defined regions in the NMR data were important in discriminating control and smoke-affected wines.

Matrix-assisted laser desorption/ionization mass spectrometry imaging, or MALDI-MSI is a valuable tool to study molecular distributions in tissue sections in a variety of application areas, ranging from clinical research to environmental sciences, and pharmaceutical industry. More recently, laser induced post-ionization, or MALDI-2, has been coupled to MALDI-MSI, to boost ion yields enabling analysis at smaller pixel sizes. MALDI-2 also reduces ion suppression effects and results in a concomitant increase of molecular coverage in tissue analyses. Sphingolipids are a class of lipid molecules involved in the structure of eukaryotic plasma membrane. The metabolism of sphingolipids generates a wide variety of molecules including ceramids, sphingosines and sphingosine-1-phosphates (S1P). In this study, we aim to characterize the presence of these molecules in mammary tissue sections and show their spatial distribution using the Bruker timsTOF flex platform using both traditional and MALDI-2 lasers. Frozen tissues of mammary gland tumor obtained from a PyMTtransgene mouse were cryosectioned and were mounted on IntelliSlides. Two matrices were tested in both positive and negative polarity. MSI images of sphingolipids were generated and the mean spectrum of MS signals were exported using SCiLS Lab software. Sphingolipids were annotated using MetaboScape software with the LIPIDMAPS database. Imaging signals for sphingolipids of interest were evaluated by comparing the MS/MS fragmentation spectra with that of reference standards spotted onto a MALDI plate. Sphingomyelin images were obtained in positive ion mode and their abundance is very high in PyMT mammary gland tumors. Ceramide images were obtained in negative ion mode and their abundance was found to be much lower in the PyMT mammary gland tumor, compared to sphinomyelins. The results are consistent with H&E staining of the same tissue sections with ceramides found to be localized to the tumor region of the tissue and sphingomyelins localized in the adipose region of the tissue.

In recent years, significant efforts have been focussed into understanding metabolic reprograming in cancer with the hope of discerning context-specific biology that is exploitable for either for diagnosis or treatment. While some generalized phenomena such as the ‘Warburg Effect’ have been identified, the metabolic landscape of cancer is highly heterogeneous, as tumors from the same sub-type can exhibit vastly different metabolic profiles, which can influence disease progression and response to treatments.
As research begins to study how generalized treatments fail for specific patients and cancers, we need to understand what makes specific cancer lineages unique, in order to identify potential vulnerabilities with a greater level of precision. While other ‘omics fields have quantified the expression of genes, transcripts and proteins of hundreds of tissue and cell types (e.g. Human Protein Atlas), our understanding of the metabolic composition of human cells remains rudimentary, and only a limited number of highly targeted metabolite maps of cancer cell lines currently exist.
We are working on addressing this limitation by systematically mapping the metabolomes of a broad range of human cell lines including patient derived cancer cell lines (>20) using LC-MS-based metabolomics and lipidomics. Currently, the over 800 mapped and quantified metabolites have yielded valuable insights into the metabolites that are highly specific to individual brain tumor types (i.e. biomarkers) as well as cancer-selective metabolic vulnerabilities and ‘choke-points’ for specific cancer lineages, leveraging existing drugs and chemical inhibitors with wide therapeutic windows. As our Human Cell Metabolome Atlas (HCMA) continues to grow we anticipate that further discoveries will: i) improve the precision in tumor diagnosis and subtyping; ii) increase our ability to stratify patient risk; and iii) allow the development of cancer-selective treatment strategies.

One critical challenge that impacts the accurate measurement of oxylipins in biospecimens is their poor stability during sampling, storage, and transportation. Oxylipins can degrade and/or be produced due to enzymatic and non-enzymatic reactions. In vivo solid-phase microextraction (SPME) was recently introduced for quantifying and profiling oxylipins in brain tissue. These devices do not co-extract proteins from the sample, thus eliminating the enzymatic conversion of oxylipins in biological fluids and tissue samples. The aim of this study was to investigate the on-device oxylipin stability to non-enzymatic degradation during the freeze-and-thaw process and at room temperature storage for 18 days. 53 oxylipins were extracted using HLB SPME devices from standard solutions or human plasma samples. All samples were analyzed using C18 liquid chromatography-high resolution mass spectrometry. All stability results were analyzed by comparing all conditions against control samples (t=0), using the acceptance criteria of within 80-120% of t=0 samples and ANOVA statistical analysis. 5-hydroxyeicosapentaenoic acid, 13-hydroxy-docosahexaenoic acid and 11-hydroxy-docosahexaenoic acid were unstable in standard and plasma samples in the freeze-and-thaw experiment. Only 4-hydroxydocosahexaenoic acid was found unstable when comparing the time points against control in the room temperature study. This is the first time that the oxylipin stability on SPME devices has been characterized and shows how SPME can successfully improve their stability.

Background: Diagnosis of acute respiratory distress syndrome (ARDS) relies only on clinical observations. Recent studies have shown that metabolomics is useful for this diagnosis. However, many are limited to a single analytical instrument, either proton nuclear magnetic resonance spectroscopy (1H-NMR) or gas chromatography-mass spectrometry (GC-MS), and end by comparing the metabolomic profiles between ARDS and non-ARDS groups.
Objective: We aim to develop a robust bedside formula by finding a minimal metabolomic signature based on the integrated platform of NMR and GC-MS, which are highly complementary, using stability measure.
Methods: The study cohort comprised age- and sex-matched patients meeting the Berlin definition of ARDS (N=101) and ICU-ventilated control (N=29) patients. A total of 141 serum NMR and GC-MS metabolites collected as part of the Critical Care Epidemiological and Biological Tissue Resource were analyzed by Partial Least Square Discriminant Analysis and ElasticNet, with 100 resamples (each consists of 70:30 training and testing sets). The performance of the selected metabolites for ARDS diagnosis was discussed with sensitivity (Se), specificity (Sp), Area under Receiver Operating Characteristic (AUROC), fold change, stability of models over resamples, and class of chemical compounds. Metabolomic pathway analysis was also performed using the Kyoto Encyclopedia of Genes and Genomes.
Results: Seven metabolites remained as candidates for a bedside formula development after adopting stability>0.75, Se > 0.75, and Sp > 0.75. A combination of glucose (GC-MS, Decreased on ARDS, HMDB0000122, Organooxygen compounds, mVIP=1.44), 3-phenyllactic acid (GC-MS, Increased on ARDS, HMDB0000748, Phenylpropanoic acids, mVIP=1.48), isobutyrate (NMR, Increased on ARDS, HMDB0001873, Carboxylic acids, mVIP=2.25), and 3-hydroxyisovalerate (NMR, Increased on ARDS, HMDB0000754, Fatty Acyls, mVIP=2.43) showed a reasonable performance (Se=0.89, Sp=0.96, AUC=0.96) for diagnosis.
Conclusion: Using a stability-based approach is useful for selecting metabolites that are not only important but also robust for ARDS diagnosis.

With expanding knowledge of the ways organic compounds can exhibit toxic traits, environmental concerns regarding water and soil contamination have risen in the last number of years. Especially with regards to routine, forensic, metabolomic, and contract analyses, the identification of contaminants (toxic or natural) and their sources are of utmost importance. Despite many methods moving toward solvent-less extraction, a large number of certified contract laboratories that perform environmental analyses of organic compounds still use solvents as their primary method of extraction for robust and simple chemical analyses. For this reason, it is important to distinguish how to most effectively utilize a solvent extraction. 
Extraction methods using an array of solvents as well as one optimized thermal desorption (TD) method will be employed that will each provide their own benefits and drawbacks in terms of availability, cost, environmental impact, specificity, and overall extraction effectiveness. The extraction capability will be tested using a set of mixed soil and sand samples by following the ASTM E1412-19 protocol for ignitable liquid residues, the list of toxic substances managed under Canadian Environmental Protection Act, and various other literature sources to determine ions of interest for non-targeted analysis. Data will be collected by GCGC-TOFMS. Seven different solvents will be tested; hexane, methyl tert-butyl ether, toluene, ethyl acetate, dichloromethane, hexane/acetone (50:50 v:v) and t-butyl alcohol/acetone (50:50 v:v), along with the solvent-less direct thermal desorption extraction.

Biological samples are susceptible to changes in composition due to a broad range of factors, which includes handling and storage. Ideally samples should be collected and immediately frozen at -80C until sample preparation and analysis. However, often samples are stored in dry ice for shipment to other locations. This study aimed to assess and compare the stability of these samples when stored in dry ice versus at -80C for a period of 4 weeks.
Cell lines were maintained using optimum growth medium, which was renewed every two days, and harvested by trypsinization. Cell pellets were resuspended, counted using a hemocytometer, aliquoted with known cell densities (6x105 cells/mL) into sterile polypropylene tubes, washed with PBS, and frozen at -80 ºC or placed in a Styrofoam box filled with dry ice. Every few days, the stored samples were thawed, mixed with deuterated lipid standards, and extracted using dichloromethane. Chromatographic separation was performed on a UHPLC system with a Waters Acquity Premier CSH C18 column coupled to a Bruker Impact I ESI-QqTOF.
Positive and negative ionizations were used to obtain a comprehensive analysis of different lipid classes. An inhouse software was used for data processing, specifically developed for the employed application, and tailored for this dataset and instrument. Tandem-MS and mass match were used for lipid identification, along with a scoring system to increase accuracy. Identified lipids were normalized by one of the internal standards, according to structure and retention time similarity. A total of 2224 lipid species were identified, that being 1197 by tandem-MS and 1027 by mass-match. QC injections were tightly clustered in the PCA scores plot, indicating good reproducibility. No separation was observed amongst the study conditions (dry ice vs. -80C) in the PCA and PLS-DA scores plots, suggesting samples are stable for at least 4 weeks in these conditions.

Meconium is an emerging and potentially useful biosolid for disease diagnoses in infants. It is the first stool of newborns, typically passed within the first few days post-partum. Expulsion of meconium prior to birth is recognised as a sign of fetal distress, whereas delayed meconium expulsion is often a sign of cystic fibrosis. Medical practitioners relate the expulsion time relative to birth, color, and texture to identify specific pathologies. In recent studies, meconium has been analyzed to assess in utero exposures to tobacco and alcohol. Prospective biomarkers of tobacco and alcohol use were detected by liquid chromatography mass spectrometry (LC-MS) and gas chromatography flame ionization detection (GC-FID), respectively. Herein, comprehensive two-dimensional gas chromatography time of flight mass spectrometry (GC×GC-TOFMS) was used to explore the meconium metabolome. Different injection types included derivatization with liquid injection, dynamic headspace (DHS), and solid phase micro-extraction (SPME). The various sample preparation techniques were used to recover volatile and non-volatile compounds in meconium. The gold-standard for the GC×GC-TOFMS analysis of biological samples is derivatization, which has been applied to urine and stool samples previously. However, the derivatization process is time consuming and cannot recover the most volatile analytes. We utilized DHS and SPME techniques to recover volatile metabolites in meconium with minimal sample preparation. Hundreds of compounds were detected by each method, though DHS and SPME captured numerous volatile metabolites that were not detected in derivatized samples. GC×GC-TOFMS allows rapid and reliable methods for analysis of the complex metabolome of meconium. This work will inform development of clinical tools for disease diagnosis and exposure monitoring during pregnancy utilizing meconium.

Nanomaterials functionalized with biorecognition elements that interact with analytes play an important role in a biosensor design. The purpose of this study was to determine the effectiveness of two different kinds of nanoparticles in the signal improvement in an impedance-based biosensor. In the study, we were able to effectively conjugate 13 nm gold nanoparticles and 60 nm liposomes to small metabolites, including diacetylspermine, hippuric acid and creatinine, known colon cancer biomarkers. The sensing function of these conjugates was then validated by the specific attachment to an antibody-modified electrode surface for the development of an electrochemical biosensor for detecting biomarkers for colon cancer. The conjugates, as well as modified electrodes were characterized through various analytical, spectroscopic and microscopic techniques. Analysis of the impedance results suggest which kind of conjugates, gold nanoparticles or liposomes, act as a more effective signal amplifier in this biosensor. Optimizing the performance of individual components of a sensor would result in many useful advancements for the field of biosensor development.

Cytochrome P450 1B1 (CYP1B1), an environmentally inducible enzyme, has been linked to several human diseases, including cancer. CYP1B1 regulates the metabolism of both xenobiotics and endogenous substrates. Over 150 CYP1B1 mutations are associated with ocular diseases, including primary congenital glaucoma (PCG), however CYP1B1’s mechanistic role in PCG disease etiology remains unclear. Improved knowledge of CYP1B1’s tissue-specific metabolic function in the eye is needed to guide new therapeutic strategies for PCG and related disorders. Using untargeted metabolomics and a retinal endothelial cell (REC) model derived from both wild-type (WT) and the CYP1b1 knockout (KO) mouse, we identified over 50 candidate substrates of CYP1B1 potentially capable of regulating eye function, including the putative substrate arachidonic acid (AA), which strongly stimulates capillary morphogenesis in 3D REC culture, in a CYP1B1-dependent manner. To explore lipid metabolites generated by CYP1B1 in REC cells, we assessed changes in omega-3 and omega-6 polyunsaturated fatty acids (PUFA)-derived oxylipins using targeted LCMS metabolomics. We identified a small set of CYP1B1-related lipid metabolites (including HETEs and prostaglandins) that modulate capillary morphogenesis in REC cells. CYP1B1-specific metabolism of AA and related fatty acids was further validated in V79 cells engineered to overexpress human CYP1B1. Key aspects of our REC cell study were confirmed, highlighting CYP1B1’s role in regulating the formation of multiple classes of lipid modulators, including HETEs, EETs, EDPs and prostaglandins. Orphan metabolic roles of CYP1B1, were also explored in vivo using a CYP1B1-KO zebrafish model and an untargeted metabolomics approach. Significant differences in several classes of molecules, including nucleosides and amino acids were identified. Collectively, these studies reveal CYP1B1’s putative role as a CYP epoxygenase in the eye and vasculature and revealed discrete oxylipin compounds that may regulate both retinal angiogenesis and vascular wound repair processes, potentially providing new therapeutic pathways for treating glaucoma, diabetic retinopathy and cancer.

The mission of the MANA NMR Interest Group is to facilitate NMR-focused metabolomics research by providing recommendations on best practices in the field and by fostering new networking opportunities for its members. The group is comprised of 30 members and meets monthly to discuss various topics related to NMR metabolomics. It engages scientists with mutual interests in advancing and promoting the use of NMR in metabolomics inquiries. Goals are to advance and promote the inclusion of NMR in an MS dominated metabolomics world, educate and provide NMR metabolomics training resources o the broader community, solicit community contributions, advance NMR metabolomics with nontraditional workflows, build community consensus concerning experimental and computational parameters that influence the quality of NMR based metabolomics, develop best-practices for metabolite identification and quantitation, and provide recommendations for appropriate application of statistical tools to interrogate NMR and NMR-hybrid datasets.
The MANA NMR interest group strives to be highly active. Recent activities include an instructional workshop, “Wine Tasting by NMR”, an interactive forum “Introduction to MANA NMR Interest Group” at the MANA 2021 conference, and a workshop, “Frontiers in NMR Metabolomics´ at the 2022 Metabolomics Society meeting. Last year, the group completed an NMR perspective, published in Metabolites, describing the state of NMR metabolomics research. Additionally, an extensive literature survey was conducted evaluating NMR metabolomics practices in 2010 and 2020 published research. Data is under review with intent to disseminate the findings on the evolution of the field, consensus practices, and challenges that remain. During the MANA 2022 conference, the group will hold an interactive forum “Urgent Issues in NMR” addressing multiple challenges facing NMR applications. 
The group welcomes all scientists, from trainees to seasoned investigators, that are interested in NMR. If you are interested in joining MANA NMR or leading a new initiative, please contact us at nmr-group@metabolomicsna.org

The NIH Common Fund addresses emerging scientific opportunities and pressing challenges in biomedical research that are of high priority for the NIH and span across the missions of the individual NIH Institutes and Centers. The Metabolomics Program was initiated in 2012 with broader goals for increasing national capacity in metabolomics, providing training and mentoring, promoting data sharing, and supporting technology and standards development.  Building on the success of the Stage I Program, NIH has proceeded to support the Stage II Program in 2018 to further address the existing challenges in metabolite identification, overcome analysis and interpretation hurdles, transform the repository into a national resource, and promote best practices. 
The Stage II Program funded seven projects for data analysis and interpretation tools that focused on developing computational methods for quantitation, statistical assessment, and subsequent biological interpretation, thereby streamlining metabolomics workflows that were historically been viewed as arduous steps. Five compound identification development cores were also funded to develop harmonized experimental and computational approaches to enhance compound identification of the most significant, biomedically-relevant unknown metabolites. Efforts by the five CIDCs have supported the development and application of promising computational and experimental methods. The Metabolomics Workbench from the stage I CF Metabolomics program has successfully transformed into a National Metabolomics Data Repository that holds large number of metabolomics datasets and a wide array of metabolomics software and analytical tools for broad community use. The Metabolomics Consortium Coordinating Center coordinated the activities of various components of the Program, including facilitating community outreach activities. This presentation will provide an overview of the Stage II Metabolomics Program with its components and summarize its collective key accomplishments and resources developed for the community.

This work introduces a robust web server-based in silico workflow to accurately predict the CCS values of small molecules. The computational workflow is an eight-step process, utilizing the best aspects of force fields, machine learning QM, and QM methods to achieve accurate and reliable results. Accurate CCS prediction primarily depends on the molecular state (protonated/deprotonated) of a metabolite. Hence, thorough characterization of relevant molecular states yields CCS predictions within 3% of experimental values. We developed an efficient CCS computational workflow and pipeline encompassing the following steps: First, we generate the conformations of the individual metabolites using the RDKit tool. Each generated conformer is then optimized with the ASE_ANI QM machine learning model. All the optimized structures are then clustered using our in-house automated clustering code (viz. AutoGraph) to identify chemically unique conformations. Geometry optimization and Mulliken atomic charge calculation are then performed on a representative conformation of each identified cluster using QUICK, a GPU enabled, in-house developed QM engine. Finally, the CCS is computed using the HPCCS code. The unsupervised clustering method included in this workflow reduces the possibility of human bias and error in cluster selection. The QM-ML model and clustering technique makes this protocol more computationally efficient. The workflow is fully automated using the Snakemake workflow tool, making this protocol even more useful in building an in silico library of predicted CCS values and can be used to assign the structure of an unknown metabolite. Based on this workflow, a webserver is developed, namely the Portal for Open Computational Metabolomics Tools (POMICS) aims to make advanced computational methods available to the metabolomics community while highlighting existing open source software and workflows. When using pomics.org all one needs is a SMILES string to get the predicted CCS value as an output.

Pancreatic cancer has a dismal 5-year survival rate of 10% and is the third leading cause of cancer related deaths in the US. Thus, there is an urgent need to identify truly novel yet druggable protein targets for drug discovery. The cochaperone protein DNAJA1 (HSP40) is downregulated four-fold in pancreatic cancer cells, but little is known about its specific role in cancer. The impact of DNAJA1 overexpression on pancreatic ductal adenocarcinoma cells was evaluated using a combination of untargeted metabolomics, stable isotope-resolved metabolomics, confocal microscopy, flow cytometry, and cell-based assays. Differential Warburg glycolysis, an increase in redox currency, and alterations in amino acid levels were observed in both overexpression cell lines. DNAJA1 overexpression also led to mitochondrial fusion, an increase in the expression of Bcl-2 and a decrease in the phosphorylation of c-Jun (MIA PaCa-2 only), a different response to stress (H2O2, UV), a loss of structural integrity and actin fibers, and an increase in cell invasiveness (BxPC-3 only). These differences were more pronounced in BxPC-3, which contains a loss-of-function mutation in the tumor-suppressing gene SMAD4. We have also determined structures of the J-domain of DNAJA1 and of DNAJA1-107 that includes the glycine/phenylalanine rich region, identified nm-mM small-molecule binders and their binding site, and identified 11 potential protein partners, including ADP/ATP translocase 3 (ADT3). ADT3 was shown to bind DNAJA1 by NMR, which was disrupted by our small-molecule binders. Overall, our findings suggest a proto-oncogenic role for DNAJA1 in PDAC progression, suggests DNAJA1 may function synergistically with other proteins with altered activities in pancreatic cancer cell lines, and identifies DNAJA1 as a potential therapeutic target for treating pancreatic cancer.

Feature detection is critical to mass spectrometry-based analysis of biological samples. Accurate localization of maxima in multidimensional signals affects the efficacy of downstream activities – alignment, property calculation, spectral deconvolution, annotation – with errors compounding through each layer of analysis. Feature detection methods are typically configurable, with user-defined and/or data-informed parameters, such as kernel size or wavelet selection, intensity and/or signal-to-noise-based thresholds, and baseline correction, each with the potential to introduce error. Additionally, methods must be computationally efficient, as processing and memory demands increase with resolution and dimensionality. Here we present a feature detection method that leverages topological data analysis methods to determine which among collected data points are maximal. Through this process, the “persistence” of each feature is determined, indicating a measure of prominence relative to proximal signal. Notably, the approach supports N-dimensional signals, depends only on a single user-defined parameter, and is highly efficient, scaling only with the number of points considered, thus suitable for the sparse arrays typically acquired in high-dimensional mass spectrometry analyses

In Colombia, one in nine children suffer from chronic undernutrition, if this occurs during pregnancy and the first 2 years of life, it can lead to inadequate development, causing delays in growth, and lower IQ. Nutrition becomes an essential factor that facilitates the correct development of babies, with some of the key nutrients being folate, cobalamin, and choline. Humans can synthesize small amounts of choline endogenously through the phosphatidylethanolamine N-methyltransferase pathway, however, to prevent deficiencies, most of this nutrient must be acquired exogenously from the diet. Besides, gut microbiome populations have been associated only with non-beneficial choline metabolism, such as those related to the formation of trimethylamine N-oxide. This project aims to explore the microbial communities associated with beneficial choline metabolism, by making the first approach toward the ecological and chemical characterization of the gut microbiota of women from Antioquia, Colombia. Stool samples from 21 women (pregnant, breastfeeding, and control group) were lyophilized to obtain a homogeneous extraction of metabolites. Using HPLC-MS by HILIC and C18 column separation, their metabolomic profile was obtained, and then analyzed through GNPS, building classical molecular networking, doing library and manual annotation, and curation of identified metabolites. In the networks, we identified different lipids like ceramides, sphingomyelin, phosphatidylcholine, lysophosphatidylcholine, being the last two the main phospholipids found in the intestinal mucus. Tocopherol, Betaine, bile pigments such as stercobilin and urobilin, pharmaceuticals, and hormonal derivatives as diosgenin were also detected. Most chemical families were found in different abundances across treatments, specifically, lipids such as phosphatidylcholine and hormones like 4-Cholesten-3-one were found in greater abundance within the breastfeeding women group. Antihistamines were found in one volunteer who had declared consuming them. Our results suggest that the metabolomic profile of the gut microbiome is a fingerprint that varies between individuals and informs us how gut microbiome impacts diet

LC-MS data processing is a fundamental step in metabolomics, but significant challenges still exist in this area, including provenance and reproducibility. The future development of metabolomics, handling of large studies and deployment of cloud computing, also demands good computational performance and interoperability.  To address these challenges, we have developed asari, a next-generation open-source software tool for LC-MS metabolomics data processing. 
Asari is designed with a set of new algorithmic framework and data structures, and all steps are explicitly trackable. It offers substantial improvement of computational performance over current tools, and is highly scalable. Based on benchmarks on seven datasets, the performance of asarri in feature detection and quantification is at least comparable to XCMS, while 10~100 times faster than XCMS. In asari, the only parameter requiring user attention is the mass precision. This eliminates many reproducibility problems in complicated parameter setting in other tools. An interactive dashboard can be launched after data are processed, to allow users to visually inspect data and feature quality easily. 
The software is implemented as a Python package and a command line tool, freely available at https://github.com/shuzhao-li/asari.

The sum of all lipids in an organism, lipidome, is characterized by a variety of important functions at cellular level such as stabilizing the cell membrane and the formation of mediators. A change in lipid composition and concen­tration often correlates with various neurodegenerative and cardiovascular diseases. Polar phospho­lipids are major components of the cell membrane. Due to the high progress of lipidomics research in the last decade the number of biomedical applications with lipids as bio­markers has increased significantly. Quantifi­cation of lipids remains analytically challenging due to the large number of lipids and their structural diversity.
In this work, a method for polar phospho­lipids quantification in human plasma was developed using trapped ion mobility spectrometry (TIMS) after hydrophilic interaction liquid chromatography (HILIC) lipid class separation. This orthogonal, postionization separation hyphenated to mass spectrometry (MS) allows an unambiguous assignment of lipids based on mobility and retention time. For quantitative profiling of phospholipids in human plasma, an isotopically labeled internal standard per lipid class was used. Mobilogram peak area was integrated for quantification.
TIMS has the advantage of an adaptable mobility resolution according to the analytical challenge. At higher ramp times, isobaric and isomeric interferences can be identified and separated via mobility. For type-II overlap which occurs in series of lipids differing by number of double bonds due to the natural abundance of 13C, TIMS-MS is a potential alternative to ultra-high-resolution MS.
A variety of phospholipids was detected in human plasma and annotated using MetaboScape® based on accurate mass, fragmen­tation, natural isotopic distribution, and collisional cross section values. Lipid annotation could be confirmed by the lipid class-specific retention time windows and by trends in 4D Kendrick mass plots.
Therefore, this HILIC-TIMS-MS method is potentially a versatile tool for lipid quantification. The high sample throughput can be applied in clinical studies for the investigation of pathophysiological biomarkers.

Metabolic disturbances have been widely associated to the observed differences in the susceptibility to infection and the risk of severe disease in COVID-19 patients. Here, by means of untargeted metabolomics, we evaluated urinary samples from 141 individuals with or without COVID-19 symptomatology during the first pandemic wave (PCR-negative; not hospitalized (mild); hospitalized with or without supplementary oxygen or intubated (Severe)). For urinary metabolomics, urine samples were diluted with precooled water (1:1, v:v).  This analysis was performed using an ACQUITY UPLC I-Class coupled to a XEVO-G2 XS quadrupole TOF with an electrospray ionization source. Samples were analyzed in positive (ESI+) mode.  Data was acquired from m/z 50-1200 in MSE mode in which the collision energy was alternated between low energy (6 eV) and high energy (ramped from 15-40 eV). Raw MSE data was acquired in continuum mode and processed within UNIFI 1.8.1 and exported to Progenesis QI software for alignment and deconvolution using pooled QCs as reference. The data were filtered using different QC and QA procedures and exported to Metaboanalyst 5.0 for statistical analysis. PLS-DA and clustering analysis showed distinctive profiles between severe and mild/control groups. We found more than 130 metabolites in our untargeted analysis to be altered, 25 carnitines were identified and subsequently confirmed using MS/MS fragmentations (10, 30, 40 eV), parental mass and fragment 85.0284 were used as confirmatory identifications. Carnitine metabolism disorders have been widely associated with myopathies, this myopathic changes have been associated with fatigue in long term associated with COVID-19 which could be explained as a dysregulation in carnitine metabolism. In summary, we report a dysregulation in carnitines in  Severe COVID-19 that could be used as a key  in treatment or therapeutic strategies.

N1N12 – diacetylspermine (N1N12 dspm) has been reported as a promising biomarker for the diagnosis of cancer.  N1N12 dspm has been detected in urine samples of colorectal cancer patients by mass spectrometry and ELISA test. It takes 4 hours, numerous steps and a lab set up to detect N1N12 dspm by ELISA. Highly trained personnel are needed for detection of metabolites by mass spectrometry. We have developed a rapid colorimetric test for detecting N1N12 dspm in urine which can be completed in 20 minutes and can overcome many potential challenges associated with developing a portable field test. This is an enzymatic colorimetric assay which uses recombinant N1N12 - diacetylspermine oxidase. Due to its lack of commercial availability, the enzyme has been cloned, expressed, and synthesized in our lab. The reaction consists of two steps: the oxidation of N1N12 dspm by N1N12 - diacetylspermine oxidase, and the reaction between hydrogen peroxide and oxired, catalyzed by horseradish peroxidase, which produces red colored resorufin. Production of resorufin, and thus the colour, is directly proportional to the amount of N1N12 dspm present in the urine sample. The reaction components were dried with stabilizers for an easy-to-use assay. Colorimetric quantification of metabolites in urine is often impeded by interfering agents which presented a challenge for this assay. IRA-402 ion-exchange column resin was used for removing the interfering agents from urine samples. This was followed by another resin, CG120, which is used to trap N1N12 dspm. Finally, the concentrated N1N12 dspm is eluted by a 2.5 M solution of potassium chloride. The limit of quantification (LOQ) is 0.2-25 μM. Substrate specificity of the enzyme was determined against other variants of polyamines. The enzyme is highly specific for N1N12 dspm. The dry format of the assay is integrated with an automated colour sensor for field testing.

Background: The effects of novel processing technologies on food metabolome profiles remains understudied, and could help to identify how these technologies better conserve a number of favorable attributes of these food products relative to traditional processing techniques, to provide a superior product.
Objectives: To use metabolomics to study the effects of shear stress (SS) and shear stress+moderate electric field (SS+MEF) treatments on the composition of mango juice and orange.
Methods: Mango juice and orange juice were treated with either SS or SS+MEF at 3 temperatures (27°C, 40°C and 50°C) over 10 min, with samples taken at 0, 2.5, 5, 7.5, 10 min. Samples were vortexed and extracted using a bi-phasic liquid-liquid approach. The aqueous phase was separated using HILIC and C18 columns, interfaced with an Agilent 6545 quadrupole time-of-flight mass spectrometer with electrospray ionization (operated in positive mode for both HILIC and C18, and in negative mode for C18). over 50-1700 m/z, and iterative fragmentation used to produce MS2 spectra. Features which did not meet quality control cutoffs were removed, and the remaining were manually inspected. A variety of statistical approaches including PLS-DA and linear modeling were used to compare the most extreme treatments on metabolome changes. Identities of metabolites were further assessed via FooDB and METLIN.
Conclusions: A total of 681 metabolites were observed in the treated mango juice and 1047 metabolites were observed in the treated in orange juice. The greatest difference between SS and SS+MEF was observed under the most extreme treatments (i.e. at 50°C for 10 min). Higher intensities of L-methionine and butyl 3-O-caffeoylquinate were found in SS-treated mango juice relative to SS+MEF. In orange juice, the SS group had lower intensities of neolinustatin. Level 1 identities are currently being pursued. The data suggest that novel processing treatments influence compounds which influence juice flavor and protection from oxidation.

Early diagnosis of RA is hampered by suboptimal accuracy of currently available serological biomarkers. Metabolomics is a powerful tool to characterize the complex biochemical phenotypes. We applied a high-performance chemical isotope labeling (CIL) LC-MS technique for in-depth profiling of the amine/phenol-submetabolome in serum samples. We analyzed four batches of samples, namely, two separate discovery cohorts (Cohort 1 and 2), one verification cohort (Cohort 3) and one validation cohort (Cohort 4), to avoid false positives and obtain high-confidence biomarker candidates.
The first three cohorts comprised 50, 49, and 131 RA patients, respectively. Within each cohort, there were sex/age-matched healthy controls. The patient information for the Cohort 4 was totally blinded. Amine/phenol-containing metabolites in each individual sample were labeled by 12C-dansyl chloride to improve the LC-MS detection. For each cohort, a pooled sample was prepared and labeled by 13C-dansyl chloride to serve as the reference sample for relative quantification. The individual samples and the pooled sample were mixed 1:1, before an LC-QTOF-MS platform analyzed the mixtures and measured the intensity ratios of 12C/13C peak pairs. Peak pairs or metabolites were finally processed, identified and statistically analyzed.
1267 amine/phenol-containing metabolites were commonly detected across the first three sample cohorts. Among them, 151 were positively matched to our dansyl-labeling standard library, and 201 were high-confidence putatively identified by mass values and predicted retention times of dansyl-labeled metabolites. Visualized by PLS-DA, the overall amine/phenol-submetabolome demonstrated clear and consistent differences between RA and healthy controls in three cohorts, with cross-validation Q2 = 0.751, 0.796, 0.767, respectively. A six-biomarker panel was discovered, tested and verified. Using the logistic regression to build ROC curves, the AUC values (95% confidence interval) were 0.948 (0.848-1.000), 0.889 (0.779-0.980) and 0.942 (0.936-0.988) for cohort 1, 2 and 3, respectively. The diagnostic model is being further validated using the cohort 4.

Higher-throughput methodologies typically result in significant loss of species annotated due to reduced chromatographic resolution and/or an insufficient acquisition rate of the LC-MS for the increased elution concurrency. In addition, the strategy for determining in-depth structural information can involve multiple injections and methodologies. In this work, we utilized NIST 1950 human plasma and Sprague Dawley and Zucker fatty rat plasma to assess these throughput, sample volume and interpretation concerns using ZenoTOF 7600 system operated in information dependent acquisition (IDA/DDA) mode using Zeno CID and Zeno EAD IDA.

The MANA Microbiome Interest Group (MIG) formed in 2019 with a central mission of facilitating microbiome-focused metabolomics research by providing general guidance on the gold standards in the field and by fostering new networking opportunities for its members across the multidisciplinary scientific communities. Led by MANA membership, it now boasts over 80 members and meets quarterly to discuss various topics related to microbiome, metabolomics and integrated microbiome-metabolomics research.
The MIG hosts semi-monthly guest lectures from nationally and internationally recognized experts in microbiome and metabolomics research, striving for diversity, equity and inclusion among its speakers. During the height of the COVID pandemic, the MIG seminar series featured over a dozen external speakers. Moving forward, the MIG seminar series will feature stimulating lectures from its membership.
More recently, MANA MIG implemented three initiatives to further focus the activities of the interest group based on specific needs of the microbiome research community:
Informatics and Databases Initiative tasked to promote the discussion, creation, compilation and implementation of open-source specialized bioinformatics tools and biochemical databases for microbiome-metabolomics research. 
Harmonizing and Standardizing QA/QC Measures Initiative tasked to advance the on-going efforts on standardizing metabolomics methods for microbiome research through synchronizing the existing and new QA/QC procedures, assessing reasonably accessible reference materials for universal use, establishing new and more streamlined tools for data assessment/data reproducibility and compiling a  set of best-practice recommendations on quality assurance/quality control measures, interlaboratory reproducibility and data comparability.
Multi-omics Data Integration initiative tasked to familiarize, enhance and innovate new tools for bridge the gap between microbial genomics and metabolomics data. The goal is to provide new open-source tools and procedures for multi-omics data integration. 
If you are interested in joining MANA MIG or leading a new initiative please contact us at mana.microbiome@metabolomicsna.org.

Breast cancer remains one of the leading causes of death in women, with 5-year survival rates for metastatic disease hovering around 25%. Many of the most treatment resistant tumors are characterized by NRF-2 activation, which is upstream of the two-electron reductase NAD(P)H: quinone oxidoreductase-1 (NQO1). NQO1 can be targeted for bioactivation using the new agent isobutyl-deoxyniboquinone (IB-DNQ), inducing a futile cycle that produces superoxide, downstream DNA damage, and subsequent NAD+ loss due to hyperactivation of poly-ADP ribose polymerase I (PARP-I) associated with DNA repair. Cell death is mediated by a novel mechanism termed NAD+-keresis. PARP-I inhibition after NQO1 bioactivation is synergistic for cancer cell death, as preservation of the NAD+ consuming futile cycle causes very high levels of H2O2 inside the cancer cell and consequent induction of apoptosis. PARP-I inhibition has not been studied in combination with treatment with IB-DNQ, which has a superior safety profile compared to previous agents. We used liquid chromatography-mass spectrometry (LC-MS) global metabolomics to study NQO1(+) and NQO1(-) MDA-MB-231 cancer cells under IB-DNQ treatment, PARP-I inhibition with Rucaparib, and in the combination treatment (8 total study groups with controls). LC-MS identified 128 unique metabolites, but the multi-arm nature of the research design necessitated extensive development of visual representations of the data to help translate it into wisdom regarding the underlying metabolism. As in previous studies of NQO1 bioactivation, we detected large perturbations to glycolysis and TCA cycle metabolism. However, our deep analysis showed that NQO1 targeting also causes extensive changes in purine and pyrimidine metabolism. Co-treatment with Rucaparib reversed many of these effects, illuminating several metabolic control points appropriate for future investigation for drug synergy. Of all metabolites, cyclic ADP-ribose (cADPR) displayed the greatest drop in concentration on IB-DNQ treatment, suggesting it as a biomarker for treatment response.

Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is the third leading cause of mortality globally. Patients diagnosed with HCC have a dismal prognosis because the onset of symptoms often occurs in an advanced stage of the disease. In addition, conventional biomarkers exhibit suboptimal performance in diagnosing HCC in its early stages, increasing the need to identify new and more potent biomarkers. The aim of this pilot study is to discover a panel of biomarkers in serum for the detection of HCC by comparing with liver cirrhosis patients using metabolomics and lipidomics approaches. The study was carried out by analyzing serum samples from 20 HCC cases and 20 patients with liver cirrhosis using the ultra-high-performance liquid chromatography-Q Exactive mass spectrometry (UHPLC-Q?Exactive-MS). Multivariate analysis was used to identify metabolites and lipids significantly altered between HCC cases and patients with liver cirrhosis. A vast class of metabolites comprising organic acids, fatty acids, bile acids, glycerophospholipids, sphingolipids, and glycerolipids were significantly altered in the HCC vs. liver cirrhosis. Among these, fatty acids and glycerophospholipids exhibited the maximum variation. Pathway analysis revealed that fatty acid and lipid metabolism were the main affected pathways due to the progression of liver cirrhosis to HCC. Metabolites from these classes are indicated to be potential biomarkers for the detection of HCC. The completion of this study could be a crucial component in diagnosing HCC at an early stage and pave the way for effective therapy. Further large-scale cohort studies are required to validate candidate biomarkers discovered in this pilot study.

Untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics studies are typically performed under replicated experimental settings. Metabolite profiles acquired with non-identical LC-MS protocols or following extended time intervals harbor significant variation in measured retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. I present 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 different instruments, chromatography gradients, and other experimental procedures. Metabolomics features were aligned using the metabCombiner R package to generate lists of compounds detected across all batches from both experimental subsets. We applied dataset-specific normalization methods to remove inter-batch and inter-experimental variation in spectral abundances, 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 major 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 non-identically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Imaging mass spectrometry (MS) provides spatial localization of molecular species detected as ions. Thus, the type of molecule accessibly by imaging MS primarily depends on the type of ionization source used. Both, matrix-assisted laser desorption ionization (MALDI) and desorption electrospray ionization (DESI) has shown the capability of ionizing smaller molecules, such as metabolites and lipids, from tissue. DESI detects many complementary sets of molecules, such as small metabolites (e.g., lactate, adenosine, glutamine), while MALDI ionizes larger metabolites (e.g., gangliosides,  ATP, UDP). The coverage of imaged molecules can be significantly improved by using both imaging MS modalities on a single workflow.  Here, we demonstrate multimodal MALDI and DESI using a quadrupole multi reflecting-Time-of-Flight (SELECT SERIES MRT) mass spectrometer with a mass resolution of up to 200,000 FWHM and a mass accuracy Molecular annotations were obtained from an accurate mass search against publicly curated databases, such as HMDB. Images were acquired at 2, 5, and 10 HZ with both modalities at the same mass resolution and mass accuracy across a mass range of m/z 100 to 2400. Due to the orthogonal nature of the MRT, the mass accuracy and mass resolution were found to be independent of the scan speeds and imaging ion source (MALDI or DESI).  For MALDI, the effect of laser tuning (focus and attenuation) for different pixel sizes, from By using complementary imaging MS techniques, DESI and MALDI, the diversity of metabolites and lipids imaged from tissue increased painting a more comprehensive picture of the metabolome.

The lipidome of biological samples is extraordinarily complex and diverse. Although the potential of lipidomics is undeniable, its routine application requires reliable, simple, and unbiased analytical approaches with high sensitivity and reproducibility. We developed a comprehensive and robust solution for untargeted lipidomics of small amounts of fluids (4.0 to 8.0 µL of serum) and tissues (1.0 to 2.5 mg) that routinely allows the identification of 3000 to 7000 lipids. Small amounts of fluids or tissue are prepared by liquid-liquid extraction along with a mixture of 15 deuterated lipid standards with concentrations adapted for each type of sample. A comprehensive LC-MS analytical approach using optimized mobile phases ensures sensitive detection while being safe for the long-term use of delicate equipment. We process the obtained data with specialized software tailored for this application, which includes peak picking, alignment, filtering, lipid identification, normalization, and quality control. Detected features are identified by tandem-MS and mass-match with the addition of optimized filters and scores to increase identification accuracy. A class-match normalization procedure using deuterated standards from different lipid classes corrects for minor differences that may occur during sample handling. The normalized data is then employed for quality control and statistical analysis.
We routinely achieve the identification of 700-1500 lipids by tandem-MS and 3000-5500 lipids by mass-match, ranging from small fatty acids to highly hydrophobic cholesteryl esters, using less than 2.0 mg of tissue or only 6.0 µL of serum. We have successfully applied the method to over 2200 samples, including tissue, serum, plasma, cells, bacteria, plants, lipid droplets, and extracellular vesicles. This work illustrates that lipidomic profiling can be employed for comprehensive, reliable investigations of biological processes. The results we will present confirm the potential of high-quality untargeted lipidomics for routine analyses and biomarker discovery.

Metabolomic analysis of biofluids provides invaluable insights into metabolism, disease, and exposure events. We are particularly interested in using metabolomics to understand the long-term outcomes of fetal and infantile cannabis exposures. Currently, there is a lack of research exploring the implications of first- and second-hand early cannabis exposures. There are thousands of metabolites in urine and breastmilk, with a wide range of functional groups, polarities, and volatilities, making metabolomics challenging as no single analytical method can provide fully global coverage. We are employing comprehensive two-dimensional gas-chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) for the untargeted metabolomics of urine and breastmilk samples to determine how cannabis is metabolised, identify cannabis metabolites, and biomarkers of other exposure events including tobacco smoke. Derivatization is the gold standard for GC×GC-TOFMS biofluid analysis, increasing volatility and improving separation. However, derivatization is time intensive, introduces variability, and excludes highly volatile metabolites. Therefore, we have developed solid-phase micro-extraction (SPME) and dynamic headspace (DHS) sampling methods, as both require minimal sample preparation and provide detection of volatile metabolites. A Gerstel Multipurpose Sampler was used for automated SPME and DHS, coupled to a LECO Pegasus 4D GC×GC-TOFMS system for separation and detection. Two common sample preparation techniques were explored, the addition of sodium chloride prior to incubation and direct analysis of the fluid; with a protein precipitation step explored for breastmilk. While derivatization led to the detection of the most metabolites, a number of volatile metabolites, including ketones and terpenes, were only detected by SPME and DHS. Salt increased the metabolite coverage in urine with little affect on breastmilk; protein precipitation in breastmilk also increased metabolite coverage. When our new methods complement derivatization, providing near global metabolite coverage, allowing us to understand how cannabis is metabolised by pregnant women and infants, helping families make informed choices about cannabis use during pregnancy. 

Theme: Plant Metabolomics
Background: The plants emit VOCs that either attract the insects for pollination or repel them for defense. Comprehensive analysis of VOCs helps to identify the potential semiochemicals involved in plant-insect interactions. Amorphophallus titanum (corpse plant) emits foul odors during its two-day flowering period and draws pollinators that are typically attracted to carrion. However, there is limited information on the variety and abundance of VOCs produced by A. titanum, and how these VOCs change over time.
Objectives: 1) Identify the VOCs that constitute the odor profile of A. titanum. 2) Comprehensive monitoring of the presence and change in VOCs during the female and the male flowering phases.
Methods: VOCs were sampled at various time points during the A. titanum blooming using thermal desorption tubes, and analyzed using two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOF MS). The compounds were assigned putative names using spectral library match. Thermal images were taken to determine the onset and end of the flowering phases. Odor perceptions from the public were recorded. The statistical analyses were performed in R to identify the significantly different compounds between the flowering phases.
Results: A total of 422 features were detected over the entire sampling period, of which 118 features were statistically significantly different between the pre-flowering and both flowering phases. Additional 304 features were found present throughout the flowering period. In total, 45 compounds were assigned putative identifications. The female flowering phase was dominated by S-containing compounds and aldehydes, while alcohols and hydrocarbons were dominant in the male flowering phase. The GC×GC-TOF MS enabled us to identify 32 new compounds produced by A. titanum.
Conclusions: 
The A. titanum emits a wide variety of VOCs that change over the two-day flowering period.
A comprehensive list of VOCs was generated that can be tested further to identify the potential semiochemicals in A. titanum.

Background
Neutral molecules only become detectable in LC-electrospray mass spectrometry when forming adducts with buffer components. Yet, yet the mechanisms and conditions that dictate the process of forming adducts are still poorly understood. In positive mode, lipids typically generate [M+H]+ or [M+NH4]+ adduct ions, though [M+Na]+, [M+K]+ and other (more complex) species can also be abundant in MS1 precursor ion spectra. Which adducts should be used for quantification? Do these adducts appear in reproducible, robust ratios, across tissues and even across long analysis series? If all adduct species were to be considered, how should their abundances be combined? 
Methods
After a biphasic extraction with methanol/water/MTBE, we analyzed the lipid fractions of 8 different animal tissues by charged surface hybrid LC-QExactive HF+ tandem mass spectrometry. For each tissue type, 25 biological replicates were used. Data were processed in MS-DIAL v4.28 and lipids were annotated by MassBank.us libraries using MS/MS and retention time matching.    
Results
Quantitative analyses focused on diacylglycerols (DAG) because this lipid class regularly forms multiple adducts that are quantitatively significant, thus creating uncertainty in the accuracy when only one adduct is used. Since the conditions around adduct formation are generally unknown and ratio differences between adducts are largely unaccounted for, a standardized method for quantitative adduct selection is necessary. Adduct ratio variability was evaluated across individual samples, across lipid species if the same tissue type, and across lipid species annotated from different tissue types. The changes in adduct ratios for each tissue type were modeled against four potential contributing factors: carbon chain length, degree of unsaturation, retention time, and absolute peak intensity. Additionally, we found that quantitative accuracy varied between 5% and 70% for each tissue type, with respect to absolute concentrations, when using different combinations of adducts from deuterated internal standards. Resulting from this data, guidelines for proper adduct selection are proposed. 

A key challenge for elucidating disease mechanisms is to understand the topology and dynamics of relationships between individual molecules. Metabolites by their very nature consist of hundreds of correlated molecules. However, conventional approaches have focused on individual metabolites and not metabolomics as a system. Analysis of one metabolite at a time can be confounded by the correlation with other metabolites. On the other hand, the multivariable analysis, where all metabolites are in the model at the same time, if computationally feasible, may lack sufficient statistical power to assess metabolite pathways to disease and neither provide us with insights into the underlying interaction networks.
We systematically integrated polygenic factors and metabolomics and identified the metabolomic-causal network established in the principles of Mendelian randomization, which reflects the underlying interaction patterns. Using machine learning algorithms, we explored the metabolomic-causal network to identify groups of metabolites performing a function and understand the role of each individual metabolite at metabolomics system, such as intervention and prediction targets. To identify metabolites with causal effect on disease known risk factors or endpoints, we identified confounding metabolites using the metabolomic-causal network and therefore, adjusted downstream analyses, such as structural equation modeling, for the level of confounders to identify the set of metabolites with causal effect on the outcome of interest. For example, 5‐hydroxyindoleacetic acid was associated with incident heart failure using the conventional approaches (p-value=2e-3). However, through this systems approach presented above, we could identify that this association is confounded by glycine, which is indeed the metabolite with an impact on heart failure risk (p-value=3e-3). After adjusting for the effect of glycine, the effect of 5‐hydroxyindoleacetic acid on heart failure was not significant.
Our systems approach with clinically validated novel findings indicates that studying metabolomics as a system uncovers the fundamental molecular networks and serves as the basis

Metabolomic strategies play an increasingly important role in clinical studies, they help both in understanding the processes of disease development, and for identification of diagnostic markers. Machine learning (ML) has been emerging as an efficient technique in such strategies. The combination of ML methods and multi-OMICS data can be used to effectively predict disease development in an automated fashion. Our aim is to provide a predictive algorithm as an online tool where end-users can deposit the patient data and can get an accurate prediction generated for their dataset. HepatoCellular Carcinoma (HCC) is a neoplasm of the liver which is the one of leading cause of mortality, thanks to recent advancements the threat of HCC is expected to be minimal in the coming years. However, the role of Non-Alcoholic Fatty Liver Disease (NAFLD) as emerging source of HCC is of growing concern. NAFLD comprises a spectrum of pathological conditions ranging from simple steatosis (NAFL) and Non-Alcoholic SteatoHepatitis (NASH) to cirrhosis and HCC, hence its prognosis is very critical.We plan to combine data sources: HMDB 5.0 to gather more information on the metabolites involved in it, MarkerDB to fetch the biomarkers  of the metabolites and Pathbank, to decipher the role of these metabolites in oncological pathways. We investigated the metabolomic profiles of 106 NAFL patients (78 NAFL, 23 NASH and 5 NASH-HCC) analyzed by GC-MS. PLS-DA was used to reveal the separation of classes in metabolomic profiles between subjects and the metabolites that primarily contribute to class differentiation. Analysis showed a significant metabolic differentiation particularly, Glycocholic acid, Taurocholic acid and Phenylalanine, were directly related to disease progression while the opposite trend was observed for glutathione. These preliminary results support the potential use of metabolomics and ML based statistical analysis as a non-invasive tool for diagnosing NAFLD.

Background: Lipidomics assessed by data-independent acquisition (DIA) -based mass spectrometry enables the comprehensive detection of lipids and lipid fragments, creating the greatest potential to maximize the number of identified lipids. However, the resulting multiplexed fragmentation spectra lower annotation confidence and require manual analysis for identification, which has limited the adoption of this method.  
Objective: We aimed to develop a decision-tree based algorithm for lipid identification while using a method of decoy generation to estimate annotation confidence.  
Methods: Lipidomics data was acquired by reversed-phased chromatography and time-of-flight mass spectrometry in MSE DIA mode using liver samples from control and non-alcoholic fatty liver disease mice (NAFLD). Data were processed in MS-DIAL and a decision tree was created to annotate sample spectra with reference precursor and fragment ions. To calculate an empirical false discovery rate (FDR), a decoy library was generated, and a score was developed using logistic regression model to rank putative target and decoy matches.  
Results: The intensity correlation between fragments and precursors as well as the total number of missing fragments and precursors were independently predictive of target matches. These two parameters were supplied to a logistic regression model to score the target and decoy spectra matches. Beginning with TAGs, we identified 75 species and 894 molecular species at a 10% FDR cut-off. The analyses of other lipid classes are currently ongoing. 
Conclusion: A decision-tree based algorithm to systematically annotate spectra, coupled with FDR evaluation using a decoy lipid library, can improve the efficiency and confidence of lipid identification in DIA-based lipidomics analysis.

Alzheimer’s disease (AD) is the most common type of dementia and the most common neurodegenerative disorder. One of the main neuropathological hallmarks in AD is the undesirable formation of amyloid-beta (Aβ) plaques outside neuronal cells. In the present work, different in vitro assays including antioxidant, anti-inflammatory and anti-cholinergic activities of a carotenoid-enriched extract from Dunaliella salina microalgae obtained by supercritical fluid extraction are studied. Moreover, its potential neuroprotection on the human neuron-like SH-SY5Y cell model and the transgenic Caenorhabditis elegans strain CL4176 were evaluated. In parallel, comprehensive metabolomics and lipidomics studies were applied to evaluate the effects of the extract in the metabolism of the treated cells and transgenic worms. The use of advanced bioinformatics and statistical tools allowed the identification of more than 300 and 750 compounds in SH-SY5Y cells and C. elegans, respectively. In both models, several phosphatidylcholines, triacylglycerols and free fatty acids were significantly increased, while different phosphatidylglycerols were decreased in SH-SY5Y cells, and a number of phosphatidylethanolamines and lysophosphatidylethanolamines were decreased in C. elegans. These lipidomics changes along with the possible role exerted by carotenoids and other minor compounds on the cell membrane and the integration of transcriptomics data, might explain the observed neuroprotective effect of the D. salina extract.

The prevalence of plant-based diets has steadily increased in recent years. For many reasons including health, sustainability, and animal welfare, many people have opted to reduce their consumption of animal products.  Dairy milk is often being substituted for plant-based alternatives. Dairy milk is considered an essential source of nutrients and its dietary role in humans is well-established. While many studies have shown that plant based milks have potential health benefits, more research is needed to compare nutritional profiles and determine the extent to which dairy milk can be substituted by plant milk in human diets.
Milk is a source of essential dietary lipids which are important in human nutrition and health. The lipid composition of milk varies greatly between different animal or plant species. We used a comprehensive untargeted LC-MS lipidomics method to compare the lipid profiles of various types of plant and animal milks. The goal of this research is to gain insight into the nutritional similarities and differences between plant and animal milks.
Three types of dairy milk (cow, goat and human), and three types of plant milk (oat, almond, and coconut) were analyzed. Milk samples were extracted by a modified Folch extraction. Chromatographic separation was performed on a Vanquish UHPLC system (ThermoFisher) with an Acquity Premier CSH C18 column (Waters). The UHPLC system was coupled to an Impact II QTOF (Bruker) mass spectrometer. NovaMT LipidScreener was used to align chromatograms and perform identifications. Statistical analysis was performed in Metaboanalyst 5.0. The lipid profiles of dairy and plant milks were drastically different. Dairy milks had a larger concentration of triglycerides and diglycerides, while plant milks contained higher concentrations of phosphatidylethanolamines and hexosylceramides. This work will show that the substitution of dairy milk by plant-based alternatives can drastically affect the consumption of different lipid species.

Background: Traumatic brain injury (TBI) is a major cause of mortality and disability worldwide, particularly among individuals under the age of 45. It is a complex, and heterogeneous disease with a multifaceted pathophysiology that remains unknown. Metabolomics has the potential to identify diagnostic and prognostic biomarkers of TBI and characterize metabolic perturbations associated with TBI severity.
Methods:  Using 1H NMR, we metabolically profiled brain samples from mice with mild TBI (N=20), severe TBI (N=20) and compared them to sham controls (N=20) at days 0, 1 and 7 post-injury.
Results:  Following statistical analysis we identified the metabolites valine, leucine, adenine, tyrosine, histidine, and inosine to be associated with TBI severity. Metabolite set enrichment analysis highlighted purine metabolism to be the major metabolic pathway impacted by TBI. Strikingly, multi-class modelling based on metabolite concentrations was able to classify groups with high diagnostic accuracy (86%).
Conclusions: This study demonstrates the potential of metabolomics for studying TBI severity and recovery. Future studies should investigate whether: a) the biochemical pathways highlighted herein are recapitulated in clinical models of TBI and b) if the panel of central biomarkers as identified in this study have a utility in less invasive biomatrices such as serum, for objective and rapid identification of TBI severity and prognosis.