Metabolite Set Enrichment Analysis Research Articles

Untargeted metabolomic analysis of human milk from healthy mothers reveals drivers of metabolite variability

Understanding the human milk metabolome can help inform infant nutrition and health. Untargeted metabolomics was used to study breast milk from 31 healthy participants to assess the shared metabolites in milk from participants with various backgrounds and understand how different demographic, health, and environmental factors impact the milk metabolome. Breast milk samples were analyzed by four separate UPLC-MS/MS methods. Metabolite Set Enrichment Analysis was used to study the most and least variable metabolites. The associations between participant factors and the metabolome were assessed with redundancy analyses. Among all 31 participants and between each untargeted UPLC-MS/MS method, 731 metabolites were detected, of which 389 were shared among all participants. Of the shared metabolites, lactose was the least and lactobionate the most variable metabolite. In the biological super pathway analysis, xenobiotics were the most variable metabolites. Infant age, maternal age, number of live births, and pre-pregnancy BMI were associated with the milk metabolome. In conclusion, the most variable metabolites originate from environmental exposures while the well-conserved core metabolites are linked to cell metabolism or are crucial for infant nutrition and osmoregulation. Understanding the variability of the breast milk metabolome can help identify components that are crucial for infant nutrition, growth, and development.

Identification of Predictive Biomarkers of Lameness in Transition Dairy Cows.

The aim of this study was to identify with a high level of confidence metabolites previously identified as predictors of lameness and understand their biological relevance by carrying out pathway analyses. For the dairy cattle sector, lameness is a major challenge with a large impact on animal welfare and farm economics. Understanding metabolic alterations during the transition period associated with lameness before the appearance of clinical signs may allow its early detection and risk prevention. The annotation with high confidence of metabolite predictors of lameness and the understanding of interactions between metabolism and immunity are crucial for a better understanding of this condition. Using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with authentic standards to increase confidence in the putative annotations of metabolites previously determined as predictive for lameness in transition dairy cows, it was possible to identify cresol, valproic acid, and gluconolactone as L1, L2, and L1, respectively which are the highest levels of confidence in identification. The metabolite set enrichment analysis of biological pathways in which predictors of lameness are involved identified six significant pathways (p < 0.05). In comparison, over-representation analysis and topology analysis identified two significant pathways (p < 0.05). Overall, our LC-MS/MS analysis proved to be adequate to confidently identify metabolites in urine samples previously found to be predictive of lameness, and understand their potential biological relevance, despite the challenges of metabolite identification and pathway analysis when performing untargeted metabolomics. This approach shows potential as a reliable method to identify biomarkers that can be used in the future to predict the risk of lameness before calving. Validation with a larger cohort is required to assess the generalization of these findings.

Characterization of the volatile organic compounds and sensory properties of fermented soymilks as affected by carbohydrates and starter cultures

Soymilks were fermented by two starter cultures and with different levels of added glucose or lactose respectively. GC-MS, HS-GC-IMS and GC × GC-TOFMS were compared and the latter reported much more volatile organic compounds (VOCs). The VOCs profiles of the fermented soymilks were divided into four groups using principal component analysis and hierarchical clustering analysis. Based on the variable importance in the projection value greater than 1.0 and one way ANOVA (P < 0.05), 22 VOCs were respectively identified as the critical differential VOCs in starter culture A and B fermented soymilk, and the concentrations of the critical differential VOCs changed in different patterns in response to the addition of carbohydrates. Four significant metabolic pathways were involved via the metabolite set enrichment analysis, including the glycolysis/gluconeogenesis, pyruvate metabolism. Among critical differential VOCs, there were 11 and 13 aroma-active compounds with odor activity values greater than 1.0, for starter culture A and B fermented soymilks respectively. The deductive sensory properties of the fermented soymilks as affected by carbohydrate addition were evaluated. This study provides experimental and theoretical bases for improving flavor quality of fermented soymilks.

Metabolomic prediction of severe maternal and newborn complications in preeclampsia

IntroductionPreeclampsia (PreE) remains a major source of maternal and newborn complications. Prenatal prediction of these complications could significantly improve pregnancy management.ObjectivesUsing metabolomic analysis we investigated the prenatal prediction of maternal and newborn complications in early and late PreE and investigated the pathogenesis of such complications.MethodsSerum samples from 76 cases of PreE (36 early-onset and 40 late-onset), and 40 unaffected controls were collected. Direct Injection Liquid Chromatography–Mass Spectrometry combined with Nuclear Magnetic Resonance (NMR) spectroscopy was performed. Logistic regression analysis was used to generate models for prediction of adverse maternal and neonatal outcomes in patients with PreE. Metabolite set enrichment analysis (MSEA) was used to identify the most dysregulated metabolites and pathways in PreE.ResultsForty-three metabolites were significantly altered (p < 0.05) in PreE cases with maternal complications and 162 metabolites were altered in PreE cases with newborn adverse outcomes. The top metabolite prediction model achieved an area under the receiver operating characteristic curve (AUC) = 0.806 (0.660–0.952) for predicting adverse maternal outcomes in early-onset PreE, while the AUC for late-onset PreE was 0.843 (0.712–0.974). For the prediction of adverse newborn outcomes, regression models achieved an AUC = 0.828 (0.674–0.982) in early-onset PreE and 0.911 (0.828–0.994) in late-onset PreE. Profound alterations of lipid metabolism were associated with adverse outcomes.ConclusionPrenatal metabolomic markers achieved robust prediction, superior to conventional markers for the prediction of adverse maternal and newborn outcomes in patients with PreE. We report for the first-time the prediction and metabolomic basis of adverse maternal and newborn outcomes in patients with PreE.

Differences in metabolomic profiles between Black and White women in the U.S.: Analyses from two prospective cohorts.

There is growing interest in incorporating metabolomics into public health practice. However, Black women are under-represented in many metabolomics studies. If metabolomic profiles differ between Black and White women, this under-representation may exacerbate existing Black-White health disparities. We therefore aimed to estimate metabolomic differences between Black and White women in the U.S. We leveraged data from two prospective cohorts: the Nurses' Health Study (NHS; n = 2077) and Women's Health Initiative (WHI; n = 2128). The WHI served as the replication cohort. Plasma metabolites (n = 334) were measured via liquid chromatography-tandem mass spectrometry. Observed metabolomic differences were estimated using linear regression and metabolite set enrichment analyses. Residual metabolomic differences in a hypothetical population in which the distributions of 14 risk factors were equalized across racial groups were estimated using inverse odds ratio weighting. In the NHS, Black-White differences were observed for most metabolites (75 metabolites with observed differences |0.50| standard deviations). Black women had lower average levels than White women for most metabolites (e.g., for N6, N6-dimethlylysine, mean Black-White difference = - 0.98 standard deviations; 95% CI: - 1.11, - 0.84). In metabolite set enrichment analyses, Black women had lower levels of triglycerides, phosphatidylcholines, lysophosphatidylethanolamines, phosphatidylethanolamines, and organoheterocyclic compounds, but higher levels of phosphatidylethanolamine plasmalogens, phosphatidylcholine plasmalogens, cholesteryl esters, and carnitines. In a hypothetical population in which distributions of 14 risk factors were equalized, Black-White metabolomic differences persisted. Most results replicated in the WHI (88% of 272 metabolites available for replication). Substantial differences in metabolomic profiles exist between Black and White women. Future studies should prioritize racial representation.

Depth of Interbreed Difference in Postmortem Bovine Muscle Determined by CE-FT/MS and LC-FT/MS Metabolomics.

Japanese Brown (JBR) cattle have moderately marbled beef compared to the highly marbled beef of Japanese Black (JBL) cattle; however, their skeletal muscle properties remain poorly characterized. To unveil interbreed metabolic differences over the previous results, we explored the metabolome network changes before and after postmortem 7-day aging in the trapezius muscle of the two cattle breeds by employing a deep and high-coverage metabolomics approach. Using both capillary electrophoresis (CE) and ultra-high-performance liquid chromatography (UHPLC)-Fourier transform mass spectrometry (FT/MS), we detected 522 and 384 annotated peaks, respectively, across all muscle samples. The CE-based results showed that the cattle were clearly separated by breed and postmortem age in multivariate analyses. The metabolism related to glutathione, glycolysis, vitamin K, taurine, and arachidonic acid was enriched with differentially abundant metabolites in aged muscles, in addition to amino acid (AA) metabolisms. The LC-based results showed that the levels of bile-acid-related metabolites, such as tauroursodeoxycholic acid (TUDCA), were high in fresh JBR muscle and that acylcarnitines were enriched in aged JBR muscle, compared to JBL muscle. Postmortem aging resulted in an increase in fatty acids and a decrease in acylcarnitine in the muscles of both cattle breeds. In addition, metabolite set enrichment analysis revealed that JBR muscle was distinctive in metabolisms related to pyruvate, glycerolipid, cardiolipin, and mitochondrial energy production, whereas the metabolisms related to phosphatidylethanolamine, nucleotide triphosphate, and AAs were characteristic of JBL. This suggests that the interbreed differences in postmortem trapezius muscle are associated with carnitine/acylcarnitine transport, β-oxidation, tricarboxylic acid cycle, and mitochondrial membrane stability, in addition to energy substrate and AA metabolisms. These interbreed differences may characterize beef quality traits such as the flavor intensity and oxidative stability.

Global untargeted and individual targeted plasma metabolomics of breast cancer recurrence modified by hormone receptors.

Breast cancer is a heterogeneous and complex etiological disease. Understanding perturbations of circulating metabolites could improve prognosis. We recruited breast cancer patients from Kaohsiung Medical University (KMU) to perform untargeted (case-control design) and targeted (patient cohort) metabolomics analyses in the discovery and validation phases to evaluate interaction effects between clinical factors and plasma metabolites using multivariable Cox proportional hazards model. In the discovery phase, partial least squares-discriminant analysis (PLS-DA) showed that plasma metabolites were significantly different between recurrent and non-recurrent breast cancer patients. Metabolite set enrichment analysis (MSEA) and metabolomic pathway analysis (MetPA) showed that valine, leucine, and isoleucine degradation was the significant pathway, and volcano plot showed significant ten upregulated and two downregulated metabolites between recurrent and non-recurrent cases. Combined with receiver operating characteristic (ROC) curve and biological significance, creatine, valine, methionine, and mannose were selected for the validation phase. In this patient cohort with 41 new-recurrent vs. 248 non-recurrent breast cancer cases, followed for 720.49 person-years, compared with low level of valine, high valine level was significantly negatively associated with recurrent breast cancer (aHR: 0.36, 95% CI: 0.18-0.72, P = 0.004), especially in ER-negative and PR-negative status. There were interaction effects between valine and ER (Pinteraction = 0.006) as well as PR (Pinteraction = 0.002) on recurrent breast cancer. After Bonferroni correction, stratification effects between valine and hormone receptors were still significant. Our study revealed that plasma metabolites were significantly different between recurrent and non-recurrent patients, proposing therapeutic insights for breast cancer prognosis.

Metabolomic Approach to Identify Potential Biomarkers in KRAS-Mutant Pancreatic Cancer Cells.

Pancreatic cancer is characterized by its high mortality rate and limited treatment options, often driven by oncogenic RAS mutations. In this study, we investigated the metabolomic profiles of pancreatic cancer cells based on their KRAS genetic status. Utilizing both KRAS-wildtype BxPC3 and KRAS-mutant PANC1 cell lines, we identified 195 metabolites differentially altered by KRAS status through untargeted metabolomics. Principal component analysis and hierarchical condition trees revealed distinct separation between KRAS-wildtype and KRAS-mutant cells. Metabolite set enrichment analysis highlighted significant pathways such as homocysteine degradation and taurine and hypotaurine metabolism. Additionally, lipid enrichment analysis identified pathways including fatty acyl glycosides and sphingoid bases. Mapping of identified metabolites to KEGG pathways identified nine significant metabolic pathways associated with KRAS status, indicating diverse metabolic alterations in pancreatic cancer cells. Furthermore, we explored the impact of TRPML1 inhibition on the metabolomic profile of KRAS-mutant pancreatic cancer cells. TRPML1 inhibition using ML-SI1 significantly altered the metabolomic profile, leading to distinct separation between vehicle-treated and ML-SI1-treated PANC1 cells. Metabolite set enrichment analysis revealed enriched pathways such as arginine and proline metabolism, and mapping to KEGG pathways identified 17 significant metabolic pathways associated with TRPML1 inhibition. Interestingly, some metabolites identified in PANC1 compared to BxPC3 were oppositely regulated by TRPML1 inhibition, suggesting their potential as biomarkers for KRAS-mutant cancer cells. Overall, our findings shed light on the distinct metabolite changes induced by both KRAS status and TRPML1 inhibition in pancreatic cancer cells, providing insights into potential therapeutic targets and biomarkers for this deadly disease.

Abstract 3682: Pre-surgical blood metabolites associated with ovarian cancer prognosis

Abstract Background: Ovarian cancer has a poor prognosis with a 5-year survival less than 50%, resulting &amp;gt; 14,000 deaths in the US annually and &amp;gt; 150,000 globally. Identifying prognostic biomarkers at time of diagnosis and elucidating the biological dysregulation among those who are more likely to progress after first-line treatment could inform personalized treatment strategies. Thus, we evaluated metabolites and metabolomic profiles in pretreatment blood associated with survival in women with high-grade serous ovarian cancer, the most common and most deadly histologic subtype. Method: We examined plasma metabolites in blood samples collected prior to ovarian cancer treatment in 80 high-grade serous ovarian cancer patients who participated in the PreOperative Pelvic Mass Study, a clinic-based study enrolling women undergoing surgery for a pelvic mass. Liquid chromatography tandem mass spectrometry was used to measure 524 known metabolites. We excluded metabolites with coefficient of variation ≥ 25% (n = 104), then removed metabolites with missing in &amp;gt; 20% of the samples (n = 3). Metabolites missing in &amp;lt; 20% of the samples were imputed to be half the minimum value for that metabolite, resulting in 417 unduplicated metabolites. All metabolite values were transformed to probit scores to achieve normality. Cox proportional hazard models were used to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) adjusting for age at diagnosis, body mass index (BMI), and stage. False discovery rate (FDR) was used for multiple testing correction. Metabolite set enrichment analysis (MSEA) was used to identify metabolite classes associated with prognosis. Results: Overall, none of the individual metabolites were significantly associated with survival after multiple testing correction (FDR &amp;gt; 0.05). However, 32 metabolites were associated with survival with p&amp;lt; 0.05, with 12 metabolites being associated with worse survival (HR range: 1.38 - 1.98) and 20 metabolites being associated with better survival (HR range: 0.54 - 0.73). MSEA revealed triglycerides (normalized enrichment score (NES) = 3.14), cholesteryl esters (NES = 2.43), and phosphatidylcholines (NES = 2.30) were positively associated with risk of death (FDR &amp;lt; 0.001). Steroids and steroid derivatives (NES = -1.93), sphingomyelins (NES = -2.45), and carnitines (NES = -2.27) were negatively associated with risk of death (FDR &amp;lt; 0.001). Diglycerides (NES = 1.96), lysophosphatidylethanolamines (NES = 1.72), and phosphatidylcholine plasmalogens (NES = 1.58) were positively associated and phosphatidylethanolamines (NES = -1.93) was negatively associated with risk of death with FDR &amp;lt; 0.05. Discussion: In this study, we explored plasma metabolites associated with mortality in high-grade serous ovarian cancer patients and observed multiple classes of lipid-related metabolites being associated with worse prognosis. Citation Format: Nan Lin, Oana A. Zeleznik, Julian Avila-Pacheco, Clary B. Clish, Allison F. Vitonis, Daniel W. Cramer, Kathryn L. Terry, Naoko Sasamoto. Pre-surgical blood metabolites associated with ovarian cancer prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3682.

Abstract 3625: Metabolic phenotypes of doxorubicin-induced cardiotoxicity in breast cancer patients

Abstract Background. Doxorubicin (DOX) is a highly effective chemotherapy agent that is commonly used in combination with other chemotherapy regimens to treat a wide range of cancers, including 32% of breast cancer (BC) cases. Although DOX has greatly increased the number of long-term cancer survivors, some DOX recipients have experienced DOX-induced cardiotoxicity (DIC). Currently, there are no validated markers that can predict the early development of DIC. Cardiac troponin released by cardiomyocytes has been used in non-clinical studies as a marker of myocardial injury; however, the false positive rates are high, and the predictive value on cardiotoxicity is limited. Therefore, novel biomarkers of DIC are urgently needed to identify patients who are at an increased risk, allowing early detection of the cardiotoxicity before it causes permanent cardiac damage. Methods. Untargeted metabolomics profiling was performed for plasma samples from a cohort of 28 breast cancer patients who developed abnormal cardiac function (ABN) and those who maintained normal cardiac function (NML). Blood samples were collected before, during, and after DOX-containing chemotherapy. Using both baseline observations and pre-post changes, machine-learning techniques were used to identify metabolite markers that can be used as early indicators of DIC in BC patients. Area under the receiver operating characteristic curve was reported to evaluate the performance of significant metabolites, and metabolite set enrichment analysis (MSEA) and network analysis were used to extrapolate known associations between metabolites and diseases. Results. Significant separation of plasma metabolomics profiles between ABN and NML patients at baseline was observed. From 1,124 metabolites, abundance of 59 metabolites were found to differ between ABN and NML patients at baseline. Across all patients, levels of 78 metabolites changed between baseline and after the 1st chemotherapy cycle. Of the 6 metabolites identified in both baseline and pre-post data, ABN patients had higher levels of dicarboxylic acids suberate and sebacate derived from ω-oxidation of fatty acids at baseline than NML patients. Enrichment analysis on the metabolites from the pre-post analysis suggested myocardio injury and carnitine palmitoyltransferase deficiency. Network analysis of metabolite-disease interactions further suggests potential impact on neurological health after chemotherapy. Conclusion. The metabolomics phenotype identified is indicative of impaired beta-oxidation at baseline for ABN patients. In addition, our findings suggest neurotoxic effect from DOX, implicating the potential brain-heart axis involved behind the cardiotoxicity phenotype. Citation Format: Se-Ran Jun, Katherine Wallis, Reid D. Landes, Valentina K. Todorova, L. Joseph Su, Sam Makhoul, Ping-Ching Hsu. Metabolic phenotypes of doxorubicin-induced cardiotoxicity in breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3625.

Gallbladder microbial species and host bile acids biosynthesis linked to cholesterol gallstone comparing to pigment individuals.

Gallstones are crystalline deposits in the gallbladder that are traditionally classified as cholesterol, pigment, or mixed stones based on their composition. Microbiota and host metabolism variances among the different types of gallstones remain largely unclear. Here, the bile and gallstone microbial species spectra of 29 subjects with gallstone disease (GSD, 24 cholesterol and 5 pigment) were revealed by type IIB restriction site-associated DNA microbiome sequencing (2bRAD-M). Among them (21 subjects: 18 cholesterol and 3 pigment), plasma samples were subjected to liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics. The microbiome yielded 896 species comprising 882 bacteria, 13 fungi, and 1 archaeon. Microbial profiling revealed significant enrichment of Cutibacterium acnes and Microbacterium sp005774735 in gallstone and Agrobacterium pusense and Enterovirga sp013044135 in the bile of cholesterol GSD subjects. The metabolome revealed 2296 metabolites, in which malvidin 3-(6''-malonylglucoside), 2-Methylpropyl glucosinolate, and ergothioneine were markedly enriched in cholesterol GSD subjects. Metabolite set enrichment analysis (MSEA) demonstrated enriched bile acids biosynthesis in individuals with cholesterol GSD. Overall, the multi-omics analysis revealed that microbiota and host metabolism interaction perturbations differ depending on the disease type. Perturbed gallstone type-related microbiota may contribute to unbalanced bile acids metabolism in the gallbladder and host, representing a potential early diagnostic marker and therapeutic target for GSD.

PLA2G4A and ACHE modulate lipid profiles via glycerophospholipid metabolism in platinum-resistant gastric cancer

BackgroundBioactive lipids involved in the progression of various diseases. Nevertheless, there is still a lack of biomarkers and relative regulatory targets. The lipidomic analysis of the samples from platinum-resistant in gastric cancer patients is expected to help us further improve our understanding of it.MethodsWe employed LC–MS based untargeted lipidomic analysis to search for potential candidate biomarkers for platinum resistance in GC patients. Partial least squares discriminant analysis (PLS-DA) and variable importance in projection (VIP) analysis were used to identify differential lipids. The possible molecular mechanisms and targets were obtained by metabolite set enrichment analysis and potential gene network screened. Finally, verified them by immunohistochemical of a tissue microarray.ResultsThere were 71 differential lipid metabolites identified in GC samples between the chemotherapy-sensitivity group and the chemotherapy resistance group. According to Foldchange (FC) value, VIP value, P values (FC > 2, VIP > 1.5, p < 0.05), a total of 15 potential biomarkers were obtained, including MGDG(43:11)-H, Cer(d18:1/24:0) + HCOO, PI(18:0/18:1)-H, PE(16:1/18:1)-H, PE(36:2) + H, PE(34:2p)-H, Cer(d18:1 + hO/24:0) + HCOO, Cer(d18:1/23:0) + HCOO, PC(34:2e) + H, SM(d34:0) + H, LPC(18:2) + HCOO, PI(18:1/22:5)-H, PG(18:1/18:1)-H, Cer(d18:1/24:0) + H and PC(35:2) + H. Furthermore, we obtained five potential key targets (PLA2G4A, PLA2G3, DGKA, ACHE, and CHKA), and a metabolite-reaction-enzyme-gene interaction network was built to reveal the biological process of how they could disorder the endogenous lipid profile of platinum resistance in GC patients through the glycerophospholipid metabolism pathway. Finally, we further identified PLA2G4A and ACHE as core targets of the process by correlation analysis and tissue microarray immunohistochemical verification.ConclusionPLA2G4A and ACHE regulated endogenous lipid profile in the platinum resistance in GC patients through the glycerophospholipid metabolism pathway. The screening of lipid biomarkers will facilitate earlier precision medicine interventions for chemotherapy-resistant gastric cancer. The development of therapies targeting PLA2G4A and ACHE could enhance platinum chemotherapy effectiveness.

Tryptophan metabolism and small fibre neuropathy: a correlation study.

Small nerve fibres located in the epidermis sense pain. Dysfunction of these fibres decreases the pain threshold known as small fibre neuropathy. Diabetes mellitus is accompanied by metabolic changes other than glucose, synergistically eliciting small fibre neuropathy. These findings suggest that various metabolic changes may be involved in small fibre neuropathy. Herein, we explored the correlation between pain sensation and changes in plasma metabolites in healthy Japanese subjects. The pain threshold evaluated from the intraepidermal electrical stimulation was used to quantify pain sensation in a total of 1021 individuals in the 2017 Iwaki Health Promotion Project. Participants with a pain threshold evaluated from the intraepidermal electrical stimulation index <0.20 mA were categorized into the pain threshold evaluated from the intraepidermal electrical stimulation index-low group (n = 751); otherwise, they were categorized into the pain threshold evaluated from the intraepidermal electrical stimulation index-high group (n = 270). Metabolome analysis of plasma was conducted using capillary electrophoresis time-of-flight mass spectrometry. The metabolite set enrichment analysis revealed that the metabolism of tryptophan was significantly correlated with the pain threshold evaluated from the intraepidermal electrical stimulation index in all participants (P < 0.05). The normalized level of tryptophan was significantly decreased in participants with a high pain threshold evaluated from the intraepidermal electrical stimulation index. In addition to univariate linear regression analyses, the correlation between tryptophan concentration and the pain threshold evaluated from the intraepidermal electrical stimulation index remained significant after adjustment for multiple factors (β = -0.07615, P < 0.05). These findings indicate that specific metabolic changes are involved in the deterioration of pain thresholds. Here, we show that abnormal tryptophan metabolism is significantly correlated with an elevated pain threshold evaluated from the intraepidermal electrical stimulation index in the Japanese population. This correlation provides insight into the pathology and clinical application of small fibre neuropathy.

Strategy for Identifying a Robust Metabolomic Signature Reveals the Altered Lipid Metabolism in Pituitary Adenoma.

Despite the well-established connection between systematic metabolic abnormalities and the pathophysiology of pituitary adenoma (PA), current metabolomic studies have reported an extremely limited number of metabolites associated with PA. Moreover, there was very little consistency in the identified metabolite signatures, resulting in a lack of robust metabolic biomarkers for the diagnosis and treatment of PA. Herein, we performed a global untargeted plasma metabolomic profiling on PA and identified a highly robust metabolomic signature based on a strategy. Specifically, this strategy is unique in (1) integrating repeated random sampling and a consensus evaluation-based feature selection algorithm and (2) evaluating the consistency of metabolomic signatures among different sample groups. This strategy demonstrated superior robustness and stronger discriminative ability compared with that of other feature selection methods including Student's t-test, partial least-squares-discriminant analysis, support vector machine recursive feature elimination, and random forest recursive feature elimination. More importantly, a highly robust metabolomic signature comprising 45 PA-specific differential metabolites was identified. Moreover, metabolite set enrichment analysis of these potential metabolic biomarkers revealed altered lipid metabolism in PA. In conclusion, our findings contribute to a better understanding of the metabolic changes in PA and may have implications for the development of diagnostic and therapeutic approaches targeting lipid metabolism in PA. We believe that the proposed strategy serves as a valuable tool for screening robust, discriminating metabolic features in the field of metabolomics.

Dysregulation of acyl carnitines, pentose phosphate pathway and arginine and ornithine metabolism are associated with decline in intrinsic capacity in Chinese older adults

BackgroundIntrinsic capacity is the combination of individual physical and mental abilities, reflecting the aging degree of the older adults. However, the mechanisms and metabolic characteristics of the decline in intrinsic capacity are still unclear.AimsTo identify metabolic signatures and associated pathways of decline in intrinsic capacity based on the metabolite features.MethodsWe recruited 70 participants aged 77.19 ± 8.31 years. The five domains of intrinsic capacity were assessed by Short Physical Performance Battery (for mobility), Montreal cognition assessment (for cognition), 30-Item Geriatric Depression Scale (for psychology), self-reported hearing/visual impairment (for sensory) and Nutritional risk screening (for vitality), respectively. The serum samples of participants were analyzed by liquid chromatography-mass spectrometry-based metabolomics, followed by metabolite set enrichment analysis and metabolic pathway analysis.ResultsThere were 50 participants with a decline in intrinsic capacity in at least one of the domains. A total of 349 metabolites were identified from their serum samples. Overall, 24 differential metabolites, 5 metabolite sets and 13 pathways were associated with the decline in intrinsic capacity.DiscussionOur results indicated that decline in intrinsic capacity had unique metabolomic profiles.ConclusionThe specific change of acyl carnitines was observed to be a feature of decline in intrinsic capacity. Dysregulation of the pentose phosphate pathway and of arginine and ornithine metabolism was strongly associated with the decline in intrinsic capacity.

Abstract A029: Metabolomic profiles among African-American and White breast cancer survivors: An exploratory comparison of correlations with body weight and body composition

Abstract Objective: Obesity, a strong risk and prognostic factor for postmenopausal breast cancer, is associated with numerous disruptions to the metabolome. However, it remains unclear whether obesity-metabolite associations vary when considering direct measures of adiposity versus body weight, particularly among breast cancer survivors. This analysis aims to compare correlations of body mass index (BMI) and dual-energy x-ray absorptiometry (DXA) derived measures of adiposity (percent fat, total fat) with global serum metabolites. Methods: A cross-sectional analysis was conducted among 60 postmenopausal breast cancer survivors, including 25 African-American and 35 White women within five years of their breast cancer diagnosis who were seen for care at the University of Maryland (UM) Greenebaum Comprehensive Cancer Center (Baltimore, Maryland) and the Cancer Institute at the UM St. Joseph Medical Center (Towson, Maryland). Measured height and weight were used to calculate body mass index (kg/m2); percent fat, total fat and lean mass were quantified using DXA. Global metabolomic profiling in EDTA plasma samples was performed by liquid chromatography-tandem mass spectrometry at Metabolon, Inc. Metabolites were log transformed and partial correlations of BMI, percent fat and total fat (adjusted for age, race and fasting status) were estimated. Analyses of total fat also adjusted for lean mass. The Benjamini Hochberg procedure was used to control the false discovery rate for multiple comparisons. Results: A total of 1353 metabolites, including those with known and unknown identities, were detected. After excluding metabolites with a coefficient of variation ³ 25% or with ³ 30% of values below the limit of detection, 978 metabolites remained for analyses. The median BMI among all women (n=60) was 29.7 kg/m2 ranging from 20.7-46.7 kg/m2. Percent fat ranged from 30.5 – 54.3% with a median of 41.1% while the median total fat was 27.9 kg (17.2-62.7 kg). BMI was strongly correlated with %fat (r=0.8, p=6.6e-08) and total fat after adjustment for lean mass (r=0.6, p=5.7e-05). In analyses of all women, after adjustment for age, race and fasting status, 73 metabolites correlated with BMI, 110 with percent fat and 92 with total fat adjusted for lean mass (q-value &amp;lt;.15). Of those, 27 metabolites correlated with all three metrics while 39 metabolites were distinctly correlated with BMI only, 10 with percent fat only and 21 with total fat only. BMI, percent fat and total fat were most commonly correlated with lipids and amino acids. Conclusions: Preliminary analyses suggest characterizing obesity with metrics of body composition may provide further insight into obesity-metabolite associations among breast cancer survivors. Analyses are ongoing to assess differences in correlations by race and to examine metabolite patterns via metabolite set enrichment analyses. Citation Format: Cher M Dallal, Li Feng, Charles Ma, Katherine Tkaczuk, Stephen Thomas, Joanne F. Dorgan. Metabolomic profiles among African-American and White breast cancer survivors: An exploratory comparison of correlations with body weight and body composition [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr A029.

DOP33 Distinct perturbances in metabolic pathways associate with disease progression in patients with Inflammatory Bowel Disease

Abstract Background Patients with inflammatory bowel diseases (IBD) exhibit distinct shifts in circulating metabolite levels that have been associated with clinical disease activity and disease phenotype. However, there are limited data on the impact of metabolomic changes on disease progression in IBD. We aimed to understand the association of circulating metabolite levels and related pathways with the risk of disease progression in patients with IBD. Methods We conducted an observational cohort study using the Mount Sinai Crohn’s and Colitis Registry (MSCCR). Patients were prospectively enrolled with baseline questionnaires and biospecimens that were then linked to longitudinal electronic health record data. A total of 1,456 serum metabolites were measured in 277 patients with UC and 375 patients with CD using untargeted metabolomics. Disease progression was defined as any new steroid or biological prescriptions, IBD-related hospitalizations, or surgery. Multivariable Cox proportional hazards regression models, adjusted for age, sex, smoking, biologic use, and surgery were used to explore metabolite associations with risk of disease progression. Metabolite set enrichment analysis (MSEA) was performed to identify underlying metabolic pathways associated with disease progression. Results During a median 2-year follow-up time (IQR 0.3-5.1 years), 375 patients (57.5%) had a disease progression event. Among CD, 151 metabolites significantly correlated with the composite outcome (FDR&amp;lt;0.1). Of these, 81 (53.6%) were linked to higher risk, mainly enriched in amino acids, purine, and pyrimidine metabolism, fibrinogen cleavage peptides, and bile acid pathways. Additionally, 70 (46.4%) metabolites associated with lower risk with enrichment in fatty acid metabolism, steroid biosynthesis, histidine, tryptophan, and antioxidant pathways. In CD, aspartate metabolism was most significantly associated with increased risk and linoleic metabolism with decreased risk. In UC, 84 metabolites were significantly associated with disease progression (FDR&amp;lt;0.1), with 29 (34.5%) associated with increased and 55 (65.5%) with decreased risk. Metabolic pathways linked to higher risk were sphingolipid, benzoate, hydrogen sulfide, polyamine, and tyrosine metabolism, while protective metabolites were enriched in branched-chain amino acid (BCAA), steroid biosynthesis, histidine, and phenylalanine pathways. In UC, sphingolipid metabolism was prominently associated with increased risk and BCAA metabolism with decreased risk. Conclusion Specific metabolites and metabolic pathways are associated with risk of disease progression in patients with IBD, highlighting potential prognostic metabolite biomarkers and pathways that may be important in modulating risk of disease progression.

Abnormal functional connectivity within the prefrontal cortex is associated with multiple plasma lipid species in major depressive disorder

BackgroundAbnormalities in functional connectivity (FC) in major depressive disorder (MDD) have been widely reported. Analysis of the relationship between FC and plasma lipid profiles would be meaningful in the exploration of pathophysiological mechanisms and helpful for the identification of biomarkers for MDD. MethodsPatients with MDD (n = 49) and healthy controls (HC, n = 87) were recruited. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected for FC construction. The plasma lipid profiles were acquired using ultra-performance liquid chromatography (UPLC) and mass spectrometry (MS) analysis and clustered as co-expression modules. The differential FC and lipid modules between HCs and patients with MDD were identified, and then the association between FC and lipid co-expression modules was analyzed using correlation analysis. The modules associated molecular function was explored using metabolite set enrichment analysis (MSEA). ResultsMDD-associated FC and lipid co-expression modules were identified. One module was associated with FC values between the right orbital part of the middle frontal gyrus and the opercular part of the left inferior frontal gyrus, which was enriched in lipid sets of diacylglycerols and fatty alcohols; another module was associated with FC values between the right middle frontal gyrus and the right anterior cingulate and paracingulate gyri, which was enriched in lipid sets of glycerophosphocholines and glycerophosphoethanolamines. ConclusionOur results indicated that abnormal FC in the prefrontal cortex is associated with multiple plasma lipid species, which may provide novel clues for exploring the pathophysiology of MDD.

Testosterone regulates thymic remodeling by altering metabolic reprogramming in male rats

The thymus is an energy-consuming organ, and its metabolism changes with atrophy. Testosterone regulates thymus remodeling (atrophy and regeneration). However, the characteristics of the energy metabolism during testosterone-mediated thymic atrophy and regeneration remain unclear. In this study, we demonstrated that testosterone ablation (implemented by immunocastration and surgical castration) induced global metabolic changes in the thymus. Kyoto Encyclopedia of Genes and Genomes pathway enrichment for differential metabolites and metabolite set enrichment analysis for total metabolites revealed that testosterone ablation affected thymic glycolysis, glutamate metabolism, and fatty acid β-oxidation. Testosterone ablation-induced thymic regeneration was accompanied by attenuated glycolysis and glutamate metabolism and changed fatty acid composition and content. Testosterone supplementation in immunocastrated and surgically castrated rats enhanced glutaminolysis, reduced the level of unsaturated fatty acids, enhanced the β-oxidation of unsaturated fatty acids in the mitochondria, boosted the tricarboxylic acid (TCA) cycle, and accelerated thymic atrophy. Overall, these results imply that metabolic reprogramming is directly related to thymic remodeling.

Perturbation of lipid metabolism in 3T3-L1 at different stages of preadipocyte differentiation and new insights into the association between changed metabolites and adipogenesis promoted by TBBPA or TBBPS

Tetrabromobisphenol A (TBBPA) and tetrabromobisphenol S (TBBPS) are widely distributed brominated flame retardants. While TBBPA has been demonstrated to stimulate adipogenesis, TBBPS is also under suspicion for potentially inducing comparable effects. In this study, we conducted a non-targeted metabolomics to examine the metabolic changes in 3T3-L1 cells exposed to an environmentally relevant dose of TBBPA or TBBPS. Our findings revealed that 0.1 µM of both TBBPA and TBBPS promoted the adipogenesis of 3T3-L1 preadipocytes. Multivariate analysis showed significant increases in glycerophospholipids, sphingolipids, and steroids relative levels in 3T3-L1 cells exposed to TBBPA or TBBPS at the final stage of preadipocyte differentiation. Metabolites set composed of glycerophospholipids was found to be highly effective predictors of adipogenesis in 3T3-L1 cells exposed to TBBPA or TBBPS (revealed from the receiver operating characteristic curve with an area under curve > 0.90). The results from metabolite set enrichment analysis suggested both TBBPA and TBBPS exposures significantly perturbed steroid biosynthesis in adipocytes. Moreover, TBBPS additionally disrupted the sphingolipid metabolism in the adipocytes. Our study presents new insights into the obesogenic effects of TBBPS and provides valuable information about the metabolites associated with adipogenesis induced by TBBPA or TBBPS.