Untargeted Liquid Chromatography-mass Spectrometry Research Articles

Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning

For the detection of food adulteration, sensitive and reproducible analytical methods are required. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a highly sensitive method that can be used to obtain analytical fingerprints consisting of a variety of different components. Since the comparability of measurements carried out with different devices and at different times is not given, specific adulterants are usually detected in targeted analyses instead of analyzing the entire fingerprint. However, this comprehensive analysis is desirable in order to stay ahead in the race against food fraudsters, who are constantly adapting their adulterations to the latest state of the art in analytics. We have developed and optimized an approach that enables the separate processing of untargeted LC‑HRMS data obtained from different devices and at different times. We demonstrate this by the successful determination of the geographical origin of honey samples using a random forest model. We then show that this approach can be applied to develop a continuously learning classification model and our final model, based on data from 835 samples, achieves a classification accuracy of 94% for 126 test samples from 6 different countries.

Elucidating of the metabolic impact of risperidone on brain microvascular endothelial cells using untargeted metabolomics-based LC-MS

Risperidone is useful for the treatment of schizophrenia symptoms; however, it also has side effects, and an overdose can be harmful. The metabolic effects of risperidone at high therapeutic doses and its metabolites have not been elucidated. Endogenous cellular metabolites may be comprehensively analyzed using untargeted metabolomics-based liquid chromatography-mass spectrometry (LC-MS), which can reveal changes in cell regulation and metabolic pathways. By identifying the metabolites and pathway changes using a nontargeted metabolomics-based LC-MS approach, we aimed to shed light on the potential toxicological effects of high-dose risperidone on brain microvascular endothelial cells (MVECs) associated with the human blood brain barrier. A total of 42 metabolites were selected as significant putative metabolites of the toxicological response of high-dose risperidone in MVECs. Six highly correlated pathways were identified, including those involving diacylglycerol, fatty acid, ceramide, glycerophospholipid, amino acid, and tricarboxylic acid metabolism. We demonstrated that methods focused on metabolomics are useful for identifying metabolites that may be used to clarify the mechanism of drug-induced toxicity.

Intestinal mycobiota dysbiosis associated inflammation activation in chronic schizophrenia

The microbiome–gut–brain axis is related to schizophrenia (SCZ). The role of intestinal mycobiota in SCZ has been under investigated. We present a half-year follow-up study involving 109 chronic SCZ patients and 77 healthy controls. Intestinal mycobiota was tested by internal transcribed spacer (ITS). Untargeted liquid chromatography-mass spectrometry (LC-MS) was used to measure fecal metabolites. Symptom severity was assessed using the Positive and Negative Syndrome Scale. Enterotype analysis showed that Candida-type patients exhibited severer positive symptoms and depression factors than Saccharomyces-type patients. Candida and its top species and operational taxonomic units (OTUs) were positively correlated with depression factors (all p=0.001). Fecal metabolites analysis showed that upregulated metabolites were associated with chronic inflammation (NF-κB pathway and T helper cell differentiation), downregulated metabolites were associated with glutamate metabolism, serotonergic and dopaminergic synapse. Procrustes analysis revealed significant correlation between intestinal mycobiota and fecal metabolites (M2=0.937, p<0.001). Metabolic module analysis showed that the top module, MEturquoise (associated with Th1 and Th2 cell differentiation), was negatively correlated with SCZ (r=-0.783, p<0.0001), positively correlated with Candida, Aspergillus, Trichosporon and Talaromyces (decreased in SCZ) and negatively correlated with Saccharomyces (increased in SCZ). We also found impairments of intestinal barrier in SCZ, characterized by increased in blood D-lactate (mucosa impairment marker) and decreased in blood mucin 2 (mucosal barrier protective protein). Serum levels of TNF-α was increased and showed stable high levels during treatment. This study suggests that mycobiota dysbiosis-related chronic inflammation and an impaired intestinal mucosal barrier are associated with chronic SCZ.

Effects of Combined Low-Dose Spironolactone Plus Vitamin E versus Vitamin E Monotherapy on Lipidomic Profile in Non-Alcoholic Fatty Liver Disease: A Post Hoc Analysis of a Randomized Controlled Trial.

Background/Objectives: Lipid dysmetabolism seems to contribute to the development and progression of nonalcoholic fatty liver disease (NAFLD). Our aim was to compare serum lipidomic profile between patients with NAFLD having received monotherapy with vitamin E (400 IU/d) and those having received combination therapy with vitamin E (400 IU/d) and low-dose spironolactone (25 mg/d) for 52 weeks. Methods: This was a post hoc study of a randomized controlled trial (NCT01147523). Serum lipidomic analysis was performed in vitamin E monotherapy group (n = 15) and spironolactone plus vitamin E combination therapy group (n = 12). We employed an untargeted liquid chromatography-mass spectrometry lipid profiling approach in positive and negative ionization mode. Results: Univariate analysis revealed 36 lipid molecules statistically different between groups in positive mode and seven molecules in negative mode. Multivariate analysis in negative mode identified six lipid molecules that remained robustly different between groups. After adjustment for potential confounders, including gender, omega-3 supplementation, leptin concentration and homeostasis model assessment-insulin resistance (HOMA-IR), four lipid molecules remained significant between groups: FA 20:5, SM 34:2;O2, SM 42:3;O2 and CE 22:6, all being higher in the combination treatment group. Conclusions: The combination of spironolactone with vitamin E led to higher circulating levels of four lipid molecules than vitamin E monotherapy, after adjustment for potential confounders. Owing to very limited relevant data, we could not support that these changes in lipid molecules may be beneficial or not for the progression of NAFLD. Thus, mechanistic studies are warranted to clarify the potential clinical significance of these findings.

Effects of Different Carbon and Nitrogen Ratios on Yield, Nutritional Value, and Amino Acid Contents of Flammulina velutipes.

The carbon-to-nitrogen (C/N) ratio in the cultivation medium significantly influences the growth rate, vigor of mycelium, yield of fruiting bodies, and their nutritional composition. Recently, agricultural and forestry wastes have been increasingly used in cultivating Flammulina velutipes. However, systematic research on how these materials affect the nutritional and functional properties of the fruiting bodies is lacking. This study investigated the effects of different C/N ratios on F. velutipes cultivation. We evaluated the agronomic traits, nutritional composition, and flavor compounds of the fruiting bodies. Our findings reveal that an optimal C/N ratio of 27:1 in the composted substrates enhances the total yield of fruiting bodies, with 25.1% soybean straw as the primary raw material. This ratio also significantly increases the levels of crude protein, total amino acids, and essential amino acids in the fruiting bodies (p < 0.05). Fruiting bodies from the high-nitrogen (HN) treatment showed the highest content of umami amino acids and equivalent umami concentration value. Additionally, we employed an untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach to analyze the metabolite profiles of fruiting bodies cultivated in high-nitrogen (HN), medium-nitrogen (MN), and low-nitrogen (LN) substrates. We found that the carbon-nitrogen ratio can affect the flavor and quality of fruiting bodies by regulating amino acid biosynthesis and metabolism and other related pathways. Our results suggest that a C/N ratio of 27:1 offers numerous benefits for the cultivation of F. velutipes with comprehensive analyses and has promising application prospects.

Assessment of Untargeted Metabolomics by Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry to Define Breast Cancer Liquid Biopsy-Based Biomarkers in Plasma Samples.

An early diagnosis of cancer is fundamental not only in regard to reducing its mortality rate but also in terms of counteracting the progression of the tumor in the initial stages. Breast cancer (BC) is the most common tumor pathology in women and the second deathliest cancer worldwide, although its survival rate is increasing thanks to improvements in screening programs. However, the most common techniques to detect a breast tumor tend to be time-consuming, unspecific or invasive. Herein, the use of untargeted hydrophilic interaction liquid chromatography-mass spectrometry analysis appears as an analytical technique with potential use for the early detection of biomarkers in liquid biopsies from BC patients. In this research, plasma samples from 134 BC patients were compared with 136 from healthy controls (HC), and multivariate statistical analyses showed a clear separation between four BC phenotypes (LA, LB, HER2, and TN) and the HC group. As a result, we identified two candidate biomarkers that discriminated between the groups under study with a VIP > 1 and an AUC of 0.958. Thus, targeting the specific aberrant metabolic pathways in future studies may allow for better molecular stratification or early detection of the disease.

Biomarker Candidates of Habitual Food Intake in a Swedish Cohort of Pregnant and Lactating Women and Their Infants.

Circulating food metabolites could improve dietary assessments by complementing traditional methods. Here, biomarker candidates of food intake were identified in plasma samples from pregnancy (gestational week 29, N = 579), delivery (mothers, N = 532; infants, N = 348), and four months postpartum (mothers, N = 477; breastfed infants, N = 193) and associated to food intake assessed with semi-quantitative food frequency questionnaires. Families from the Swedish birth cohort Nutritional impact on Immunological maturation during Childhood in relation to the Environment (NICE) were included. Samples were analyzed using untargeted liquid chromatography-mass spectrometry (LC-MS)-based metabolomics. Both exposure and outcome were standardized, and relationships were investigated using a linear regression analysis. The intake of fruits and berries and fruit juice were both positively related to proline betaine levels during pregnancy (fruits and berries, β = 0.23, FDR < 0.001; fruit juice, β = 0.27, FDR < 0.001), at delivery (fruit juice, infants: β = 0.19, FDR = 0.028), and postpartum (fruits and berries, mothers: β = 0.27, FDR < 0.001, infants: β = 0.29, FDR < 0.001; fruit juice, mothers: β = 0.37, FDR < 0.001). Lutein levels were positively related to vegetable intake during pregnancy (β = 0.23, FDR < 0.001) and delivery (mothers: β = 0.24, FDR < 0.001; newborns: β = 0.18, FDR = 0.014) and CMPF with fatty fish intake postpartum (mothers: β = 0.20, FDR < 0.001). No clear relationships were observed with the expected food sources of the remaining metabolites (acetylcarnitine, choline, indole-3-lactic acid, pipecolic acid). Our study suggests that plasma lutein could be useful as a more general food group intake biomarker for vegetables and fruits during pregnancy and delivery. Also, our results suggest the application of proline betaine as an intake biomarker of citrus fruit during gestation and lactation.

Comprehensive mass spectrometric metabolomic profiling of a chemically diverse collection of plants of the Celastraceae family

Natural products exhibit interesting structural features and significant biological activities. The discovery of new bioactive molecules is a complex process that requires high-quality metabolite profiling data to properly target the isolation of compounds of interest and enable their complete structural characterization. The same metabolite profiling data can also be used to better understand chemotaxonomic links between species. This Data Descriptor details a dataset resulting from the untargeted liquid chromatography-mass spectrometry metabolite profiling of 76 natural extracts of the Celastraceae family. The spectral annotation results and related chemical and taxonomic metadata are shared, along with proposed examples of data reuse. This data can be further studied by researchers exploring the chemical diversity of natural products. This can serve as a reference sample set for deep metabolome investigation of this chemically rich plant family.

Analysis of plasma metabolomes from 11 309 subjects in five population-based cohorts

Plasma metabolomics holds potential for precision medicine, but limited information is available to compare the performance of such methods across multiple cohorts. We compared plasma metabolite profiles after an overnight fast in 11,309 participants of five population-based Swedish cohorts (50–80 years, 52% women). Metabolite profiles were uniformly generated at a core laboratory (Metabolon Inc.) with untargeted liquid chromatography mass spectrometry and a comprehensive reference library. Analysis of a second sample obtained one year later was conducted in a subset. Of 1629 detected metabolites, 1074 (66%) were detected in all cohorts while only 10% were unique to one cohort, most of which were xenobiotics or uncharacterized. The major classes were lipids (28%), xenobiotics (22%), amino acids (14%), and uncharacterized (19%). The most abundant plasma metabolome components were the major dietary fatty acids and amino acids, glucose, lactate and creatinine. Most metabolites displayed a log-normal distribution. Temporal variability was generally similar to clinical chemistry analytes but more pronounced for xenobiotics. Extensive metabolite-metabolite correlations were observed but mainly restricted to within each class. Metabolites were broadly associated with clinical factors, particularly body mass index, sex and renal function. Collectively, our findings inform the conduct and interpretation of metabolite association and precision medicine studies.

Untargeted LC–MS metabolomics reveals the metabolic responses in the Eriocheir sinensis gills exposed to salinity and alkalinity stress

In recent years, saline–alkaline aquaculture development has become an important measure for China to expand its fishery development space to ensure food safety. Previous studies have verified that salinity and alkalinity positively influence the quality of Chinese mitten crabs (Eriocheir sinensis). However, the regulatory mechanism of E. sinensis endures saline–alkaline stress which remains obscure. This study investigated the metabolic changes in puberty-molting E. sinensis gills exposed to freshwater (FW), sodium chloride salinity of 5 ppt (SW), and carbonate alkalinity 10.00 mmol/L (AW) for 50 days using untargeted liquid chromatography-mass spectrometry metabolomics (LC–MS). A total of 5802 (positive-ion mode) and 6520 (negative-ion mode) peaks were extracted by LC–MS, respectively. A total of 188 (50 upregulated and 138 downregulated), 141 (94 upregulated and 47 downregulated), and 130 (87 upregulated and 43 downregulated) significantly regulated metabolites (SRMs) were observed in the FW–SW, FW–AW, and SW–AW treatments, respectively, wherein 42 generic SRMs were also found by Venn diagram analysis. Seven of the top 10 SRMs with the highest (variable importance in projection) VIP values were similarly identified in FW–SW and SW–AW. Integrated analysis of key metabolic pathways revealed glycerophospholipid, choline in cancer, phenylalanine, and butanoate metabolism. Overall, significant differences were observed in the metabolites and key metabolic pathways of E. sinensis gill exposed to salinity and alkalinity stress. These results will be helpful in understanding the environmental adaptability of aquatic crustaceans to saline–alkaline water.

Enhanced interactions among gut mycobiomes with the deterioration of glycemic control.

The gut mycobiome is closely linked to health and disease; however, its role in the progression of type 2 diabetes mellitus (T2DM) remains obscure. Here, a multi-omics approach was employed to explore the role of intestinal fungi in the deterioration of glycemic control. 350 participants without hypoglycemic therapies were invited for a standard oral glucose tolerance test to determine their statusof glycemic control. The gut mycobiome was identified through internal transcribed spacer sequencing, host genetics were determined by genotyping array, and plasma metabolites were measured with untargetedliquid chromatography mass spectrometry. The richness of fungi was higher, whereas its dissimilarity was markedly lower, in participants with T2DM. Moreover, the diversity and composition of fungi were closely associated with insulin sensitivity and pancreatic β-cell functions. With the exacerbation of glycemic control, the co-occurrence network among fungus taxa became increasingly complex, and the complexity of the interaction network was inversely associated with insulin sensitivity. Mendelian randomization analysis further demonstrated that the Archaeorhizomycetes class, Fusarium genus, and Neoascochyta genus were causally linked to impaired glucose metabolism. Furthermore, integrative analysis with metabolomics showed thatincreased 4-hydroxy-2-oxoglutaric acid, ketoleucine, lysophosphatidylcholine (20:3/0:0), and N-lactoyl-phenylalanine, but decreased lysophosphatidylcholine (O-18:2), functioned as key molecules linking the adverse effect of Fusarium genus on insulin sensitivity. Our study uncovers a strong association between disturbance in gut fungi and the progression of T2DM and highlights the potential of targeting the gut mycobiome for the management of T2DM. This study was supported by MOST and NSFC of China.

Classification of soybeans from different habitats based on metabolomic–transcriptomic integration

Soybeans are a significant agricultural product in China, with certain geographical locations often yielding higher quality, and thus more expensive, soybean crops. In this study, metabolomics and transcriptomics analyses were conducted on soybean samples from nine regions in Heilongjiang and Liaoning Provinces using untargeted liquid chromatography–mass spectrometry (LC–MS) and Illumina sequencing technologies. The primary objective was to devise an effective and unbiased method for determining the geographical origin of each soybean variety to mitigate potential fraudulent practices. Through multidimensional and unidimensional analyses, successful identification of differentially expressed metabolites (DEMs) and differentially expressed genes (DEGs) was achieved, yielding statistically significant outcomes. Integration of the metabolomics and transcriptomics datasets facilitated the construction of a correlation network model capable of distinguishing soybeans originating from different geographical locations, leading to the identification of significant biomarkers exemplifying noteworthy distinctions. To validate the feasibility of this method in practical applications, partial least squares discriminant analysis was employed to differentiate soybean samples from the nine regions. The results convincingly showcased the applicability and reliability of this approach in accurately pinpointing the geographical origin of soybeans. Distinguishing itself from prior research in soybean traceability, this study incorporates an integrated analysis of metabolomics and transcriptomics data, thereby unveiling biomarkers that offer a more precise differentiation of soybean traits across distinct regions, thereby bridging a critical research gap within the soybean traceability domain. This innovative dual-data integration analysis methodology is poised to enhance the accuracy of soybean traceability tools and lay a new foundation for future agricultural product identification research.

Lipidomics reveals the significance and mechanism of the cellular ceramide metabolism for rotavirus replication.

As one of the most important causative agents of severe gastroenteritis in children, piglets, and other young animals, species A rotaviruses have adversely impacted both human health and the global swine industry. Vaccines against rotaviruses (RVs) are insufficiently effective, and no specific treatment is available. To understand the relationships between porcine RV (PoRV) infection and enterocytes in terms of the cellular lipid metabolism, we performed an untargeted liquid chromatography mass spectrometry (LC-MS) lipidomics analysis of PoRV-infected IPEC-J2 cells. Herein, a total of 451 lipids (263 upregulated lipids and 188 downregulated lipids), spanning sphingolipid, glycerolipid, and glycerophospholipids, were significantly altered compared with the mock-infected group. Interestingly, almost all the ceramides among these lipids were upregulated during PoRV infection. LC-MS analysis was used to validated the lipidomics data and demonstrated that PoRV replication increased the levels of long-chain ceramides (C16-ceramide, C18-ceramide, and C24-ceramide) in cells. Furthermore, we found that these long-chain ceramides markedly inhibited PoRV infection and that their antiviral actions were exerted in the replication stage of PoRV infection. Moreover, downregulation of endogenous ceramides with the ceramide metabolic inhibitors enhanced PoRV propagation. Increasing the levels of ceramides by the addition of C6-ceramide strikingly suppressed the replication of diverse RV strains. We further found that the treatment with an apoptotic inhibitor could reverse the antiviral activity of ceramide against PoRV replication, demonstrating that ceramide restricted RV infection by inducing apoptosis. Altogether, this study revealed that ceramides played an antiviral role against RV infection, providing potential approaches for the development of antiviral therapies.IMPORTANCERotaviruses (RVs) are among the most important zoonosis viruses, which mainly infected enterocytes of the intestinal epithelium causing diarrhea in children and the young of many mammalian and avian species. Lipids play an essential role in viral infection. A comprehensive understanding of the interaction between RV and lipid metabolism in the enterocytes will be helpful to control RV infection. Here, we mapped changes in enterocyte lipids following porcine RV (PoRV) infection using an untargeted lipidomics approach. We found that PoRV infection altered the metabolism of various lipid species, especially ceramides (derivatives of the sphingosine). We further demonstrated that PoRV infection increased the accumulation of ceramides and that ceramides exerted antiviral effects on RV replication by inducing apoptosis. Our findings fill a gap in understanding the alterations of lipid metabolism in RV-infected enterocytes and highlight the antiviral effects of ceramides on RV infection, suggesting potential approaches to control RV infection.

Multi-omics analysis of the gut microbiome and metabolites associated with the psychoneurological symptom cluster in children with cancer receiving chemotherapy

BackgroundChildren with cancer receiving chemotherapy commonly report a cluster of psychoneurological symptoms (PNS), including pain, fatigue, anxiety, depression, and cognitive dysfunction. The role of the gut microbiome and its functional metabolites in PNS is rarely studied among children with cancer. This study investigated the associations between the gut microbiome–metabolome pathways and PNS in children with cancer across chemotherapy as compared to healthy children.MethodsA case–control study was conducted. Cancer cases were recruited from Children’s Healthcare of Atlanta and healthy controls were recruited via flyers. Participants reported PNS using the Pediatric Patient-Reported Outcomes Measurement Information System. Data for cases were collected pre-cycle two chemotherapy (T0) and post-chemotherapy (T1), whereas data for healthy controls were collected once. Gut microbiome and its metabolites were measured using fecal specimens. Gut microbiome profiling was performed using 16S rRNA V4 sequencing, and metabolome was performed using an untargeted liquid chromatography–mass spectrometry approach. A multi-omics network integration program analyzed microbiome–metabolome pathways of PNS.ResultsCases (n = 21) and controls (n = 14) had mean ages of 13.2 and 13.1 years. For cases at T0, PNS were significantly associated with microbial genera (e.g., Ruminococcus, Megasphaera, and Prevotella), which were linked with carnitine shuttle (p = 0.0003), fatty acid metabolism (p = 0.001) and activation (p = 0.001), and tryptophan metabolism (p = 0.008). Megasphaera, clustered with aspartate and asparagine metabolism (p = 0.034), carnitine shuttle (p = 0.002), and tryptophan (p = 0.019), was associated with PNS for cases at T1. Gut bacteria with potential probiotic functions, along with fatty acid metabolism, tryptophan, and carnitine shuttle, were more clustered in cancer cases than the control network and this linkage with PNS needs further studies.ConclusionsUsing multi-omics approaches, this study indicated specific microbiome–metabolome pathways linked with PNS in children with cancer across chemotherapy. Due to limitations such as antibiotic use in cancer cases, these findings need to be further confirmed in a larger cohort.

Accurate detection of acute sleep deprivation using a metabolomic biomarker-A machine learning approach.

Sleep deprivation enhances risk for serious injury and fatality on the roads and in workplaces. To facilitate future management of these risks through advanced detection, we developed and validated a metabolomic biomarker of sleep deprivation in healthy, young participants, across three experiments. Bi-hourly plasma samples from 2 × 40-hour extended wake protocols (for train/test models) and 1 × 40-hour protocol with an 8-hour overnight sleep interval were analyzed by untargeted liquid chromatography-mass spectrometry. Using a knowledge-based machine learning approach, five consistently important variables were used to build predictive models. Sleep deprivation (24 to 38 hours awake) was predicted accurately in classification models [versus well-rested (0 to 16 hours)] (accuracy = 94.7%/AUC 99.2%, 79.3%/AUC 89.1%) and to a lesser extent in regression (R2 = 86.1 and 47.8%) models for within- and between-participant models, respectively. Metabolites were identified for replicability/future deployment. This approach for detecting acute sleep deprivation offers potential to reduce accidents through "fitness for duty" or "post-accident analysis" assessments.

Longitudinal Lipidomic Signature of Coronary Heart Disease in American IndianPeople.

Dyslipidemia is an independent risk factor for coronary heart disease (CHD). Standard lipid panel cannot capture the complexity of the blood lipidome (ie, all molecular lipids in the blood). To date, very few large-scale epidemiological studies have assessed the full spectrum of the blood lipidome on risk of CHD, especially in a longitudinal setting. Using an untargeted liquid chromatography-mass spectrometry, we repeatedly measured 1542 lipid species from 1835 unique American Indian participants who attended 2 clinical visits (≈5.5 years apart) and followed up to 17.8 years in the Strong Heart Family Study (SHFS). We first identified baseline lipid species associated with risk of CHD, followed by replication in a European population. The model adjusted for age, sex, body mass index, smoking, hypertension, diabetes, low-density lipoprotein cholesterol, estimated glomerular filtration rate, education, and physical activity at baseline. We then examined the longitudinal association between changes in lipid species and changes in cardiovascular risk factors during follow-up. Multiple testing was controlled by the false discovery rate. We found that baseline levels of multiple lipid species (eg, phosphatidylcholines, phosphatidylethanolamines, and ceramides) were associated with the risk of CHD and improved the prediction accuracy over conventional risk factors in American Indian people. Some identified lipids in American Indian peoplewere replicated in European people. Longitudinal changes in multiple lipid species (eg, acylcarnitines, phosphatidylcholines, and triacylglycerols) were associated with changes in cardiovascular risk factors. Baseline plasma lipids and their longitudinal changes over time are associated with risk of CHD. These findings provide novel insights into the role of dyslipidemia in CHD.

Untargeted metabolomics reveals the effects of pre-analytic storage on serum metabolite profiles from healthy cats.

Untargeted metabolomics investigations have characterized metabolic disturbances associated with various diseases in domestic cats. However, the pre-analytic stability of serum metabolites in the species is unknown. Our objective was to compare serum metabolomes from healthy cats stored at -20°C for up to 12 months to samples stored at -80°C. Serum samples from 8 adult, healthy cats were stored at -20°C for 6 months, -20°C for 12 months, or -80°C for 12 months. Untargeted liquid chromatography-mass spectrometry was used to generate serum metabolite profiles containing relative abundances of 733 serum metabolites that were compared among storage conditions. Unsupervised analysis with principal component analysis and hierarchical clustering of Euclidian distances revealed separation of samples from individual cats regardless of storage condition. Linear mixed-effects models identified 75 metabolites that differed significantly among storage conditions. Intraclass correlation analysis (ICC) classified most serum metabolites as having excellent (ICC ≥ 0.9; 33%) or moderate (ICC 0.75-0.89; 33%) stability, whereas 13% had poor stability (ICC < 0.5). Biochemicals that varied significantly among storage conditions and classified with poor stability included glutathione metabolites, amino acids, gamma-glutamyl amino acids, and polyunsaturated fatty acids. The benzoate; glycine, serine and threonine; tryptophan; chemical (xenobiotics); acetylated peptide, and primary bile acid sub pathways were enriched among highly stable metabolites, whereas the monohydroxy fatty acid, polyunsaturated fatty, and monoacylglycerol sub-pathways were enriched among unstable metabolites. Our findings suggest that serum metabolome profiles are representative of the cat of origin, regardless of storage condition. However, changes in specific serum metabolites, especially glutathione, gamma-glutamyl amino acid, and fatty acid metabolites were consistent with increased sample oxidation during storage at -20°C compared with -80°C. By investigating the pre-analytic stability of serum metabolites, this investigation provides valuable insights that could aid other investigators in planning and interpreting studies of serum metabolomes in cats.

Current Practices in LC-MS Untargeted Metabolomics: A Scoping Review on the Use of Pooled Quality Control Samples.

Untargeted metabolomics is an analytical approach with numerous applications serving as an effective metabolic phenotyping platform to characterize small molecules within a biological system. Data quality can be challenging to evaluate and demonstrate in metabolomics experiments. This has driven the use of pooled quality control (QC) samples for monitoring and, if necessary, correcting for analytical variance introduced during sample preparation and data acquisition stages. Described herein is a scoping literature review detailing the use of pooled QC samples in published untargeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics studies. A literature query was performed, the list of papers was filtered, and suitable articles were randomly sampled. In total, 109 papers were each reviewed by at least five reviewers, answering predefined questions surrounding the use of pooled quality control samples. The results of the review indicate that use of pooled QC samples has been relatively widely adopted by the metabolomics community and that it is used at a similar frequency across biological taxa and sample types in both small- and large-scale studies. However, while many studies generated and analyzed pooled QC samples, relatively few reported the use of pooled QC samples to improve data quality. This demonstrates a clear opportunity for the field to more frequently utilize pooled QC samples for quality reporting, feature filtering, analytical drift correction, and metabolite annotation. Additionally, our survey approach enabled us to assess the ambiguity in the reporting of the methods used to describe the generation and use of pooled QC samples. This analysis indicates that many details of the QC framework are missing or unclear, limiting the reader's ability to determine which QC steps have been taken. Collectively, these results capture the current state of pooled QC sample usage and highlight existing strengths and deficiencies as they are applied in untargeted LC-MS metabolomics.

Abstract C097: Cancer-selective metabolic vulnerabilities in MYC-amplified medulloblastoma

Abstract Background: MYC-amplified group 3 (G3) medulloblastoma (MB) represents a clinically aggressive subgroup of MB that is defined by leptomeningeal metastases, disease recurrence and particularly poor survivorship in comparison to other MB subgroups. There is an urgent need for a more thorough molecular understanding of G3 MB to develop more effective therapeutic modalities that will improve the durability of remission. Methods: We mapped the functional G3 MB metabologenomic landscape via parallel genome-wide CRISPR-Cas9 screening and untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. The tractability of potential therapeutic targets was assessed using patient-derived orthotopic preclinical models of G3 MB. Transcriptomics, functional genomics, and metabolomics were employed to discern a mechanism whereby inhibition of DHODH (dihydroorotate dehydrogenase), which is a mitochondrial-bound enzyme that facilitates de novo pyrimidine biosynthesis, selectively impairs G3 MB tumor cell fitness. Results: Untargeted metabolomic profiling shows enrichment of de novo pyrimidine biosynthesis and depletion of salvage pyrimidine intermediates in G3 MB tumor cells in comparison to neural stem cells (NSC), which are the prospective cell-of-origin of MB. In agreement with reprogrammed nucleotide metabolism, DHODH was identified as a corresponding ‘druggable hit’ in the genetic screen. We demonstrate that genetic or pharmacological inhibition of DHODH selectively targets G3 MB brain tumor initiating cells (BTIC) while sparing normal NSC. We further show that MYC-amplified G3 MB tumors harbor subgroup-specific transcriptomic signatures that delineate enrichment of de novo pyrimidine biosynthesis, which sustains c-Myc activity via a metabolite-protein interaction. Significance: Despite clear evidence favoring an altered metabolic landscape in MYC-driven cancers, there have been few reports into the role of metabolic reprogramming in MYC-amplified G3 MB. This metabolic vulnerability is hence not exclusive to MB and has recently been identified in other primary brain malignancies; current efforts are focused on establishing relevant biomarkers that will facilitate clinical translation. Citation Format: Sheila Singh, William Gwynne, Yujin Suk, Jeremy Chan, Andrew Qualie, Laura Escudero, Kevin Brown, Chitra Venugopal, Jason Moffat, Rafael Montenegro-Burke. Cancer-selective metabolic vulnerabilities in MYC-amplified medulloblastoma [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C097.

Yeast culture repairs rumen epithelial injury by regulating microbial communities and metabolites in sheep.

This study delves into the impact of yeast culture (YC) on rumen epithelial development, microbiota, and metabolome, with the aim of investigating YC's mechanism in regulating rumen fermentation. Thirty male lambs of Hu sheep with similar age and body weight were selected and randomly divided into three groups with 10 lambs in each group. Lambs were fed a total mixed ration [TMR; rough: concentrate (R:C) ratio ≈ 30:70] to meet their nutritional needs. The experiment adopted completely randomized design (CRD). The control group (CON) was fed the basal diet with high concentrate, to which 20 g/d of YC was added in the low dose YC group (LYC) and 40 g/d of YC in the high dose YC group (HYC). The pretrial period was 14 days, and the experimental trial period was 60 days. At the end of a 60-day trial, ruminal epithelial tissues were collected for histomorphological analysis, and rumen microorganisms were analyzed by 16S rDNA sequencing and rumen metabolites by untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics techniques. The results showed that YC improved rumen papilla development and increased rumen papilla length (p < 0.05), while decreased cuticle thickness (p < 0.05). The 16S rDNA sequencing results showed that YC reduced the relative abundance of Prevotella_1 (p < 0.05), while significantly increased the relative abundance of Ruminococcaceae_UCG-005, uncultured_bacterium_f_Lachnospiraceae, and Ruminococcus_1 genus (p < 0.05). Metabolomics analysis showed that YC changed the abundance of metabolites related to amino acid metabolism, lipid metabolism and vitamin metabolism pathways in the rumen. In summary, YC might maintain rumen health under high-concentrate diet conditions by changing rumen microbiota structure and fermentation patterns, thereby affecting rumen metabolic profiles and repairing rumen epithelial injury.