Metabolite Set Enrichment Analysis Research Articles

Aging and Pathological Conditions Similarity Revealed by Meta-Analysis of Metabolomics Studies Suggests the Existence of the Health and Age-Related Metapathway

Background: The incidence of many diseases increases with age and leads to multimorbidity, characterized by the presence of multiple diseases in old age. This phenomenon is closely related to systemic metabolic changes; the most suitable way to study it is through metabolomics. The use of accumulated metabolomic data to characterize this phenomenon at the system level may provide additional insight into the nature and strength of aging–disease relationships. Methods: For this purpose, metabolic changes associated with human aging and metabolic alterations under different pathological conditions were compared. To do this, the published results of metabolomic studies on human aging were compared with data on metabolite alterations collected in the human metabolome database through metabolite set enrichment analysis (MSEA) and combinatorial analysis. Results: It was found that human aging and pathological conditions involve the set of the same metabolic pathways with a probability of 99.96%. These data show the high identity of the aging process and the development of diseases at the metabolic level and allow to identify the set of metabolic pathways reflecting age-related changes closely associated with health. Based on these pathways, a metapathway was compiled, changes in which are simultaneously associated with health and age. Conclusions: The knowledge about the strength of the convergence of aging and pathological conditions has been supplemented by the rigor evidence at the metabolome level, which also made it possible to outline the age and health-relevant place in the human metabolism.

Novel metabolomic predictors of incident colorectal cancer in men and women.

Metabolomic profiles may influence colorectal cancer (CRC) development. Few studies have performed pre-diagnostic metabolome-wide analyses with CRC risk. We conducted a nested case-control study among women (Nurses' Health Study (NHS)) and men (Health Professionals Follow-up Study (HPFS)) who provided blood between 1989 and 1995. Over 22.9 years, 684 (409 NHS, 275 HPFS) incident CRC cases occurred and were matched 1:1 to controls. Liquid chromatography-mass spectrometry (LC-MS) identified 255 plasma metabolites after quality control. Cohort-specific and combined metabolite association analyses were performed using conditional logistic regression. Metabolite set enrichment analysis (MSEA) was used to identify differential abundance in metabolite classes. Weighted Correlation Network Analysis (WGCNA) provided modules of covarying metabolites, which were tested for CRC association. MSEA identified specific acylcarnitines associated with higher CRC risk and triacylglycerols with lower CRC risk among women and men. Further, phosphatidylcholines were associated with a higher risk of CRC among men. In an analysis restricted to CRC cases diagnosed two years after blood draw, myristoleic acid (OR = 1.37; 95%CI = 1.15-1.62; FDR = 0.072) and C60:12 triacylglycerol (OR = 0.75; 95%CI = 0.64-0.88; FDR = 0.072 were associated with CRC risk in women. WGCNA identified amino acids associated with CRC in men, fatty acid esters (carnitines) with distal CRC in men, and triradylcglycerols inversely associated with CRC in women. We identified pre-diagnostic CRC-associated metabolites with distinct sex-specific profiles. These results provide insight into CRC etiopathogenesis and have implications for risk prediction strategies.

Metabolic alterations in human pulmonary artery smooth muscle cells treated with PDGF-BB.

Metabolic abnormalities are considered to play a key regulatory role in vascular remodeling of pulmonary arterial hypertension. However, to date, there is a paucity of research documenting the changes in metabolome profiles within the supernatants of pulmonary artery smooth muscle cells (PASMC) during their transition from a contractile to a synthetic phenotype. CCK-8 and Edu staining assays were used to evaluate the cell viability and proliferation of human PASMCs. IncuCyte ZOOM imaging system was used to continuously and automatically detect the migration of the PASMCs. A targeted metabolomics profiling was performed to quantitatively analyze 121 metabolites in the supernatant. Orthogonal partial least squares discriminant analysis was used to discriminate between PDGF-BB-induced PASMCs and controls. Metabolite set enrichment analysis was adapted to exploit the most disturbed metabolic pathways. Human PASMCs exhibited a transformation from contractile phenotype to synthetic phenotype after PDGF-BB induction, along with a significant increase in cell viability, proliferation, and migration. Metabolites in the supernatants of PASMCs treated with or without PDGF-BB were well profiled. Eleven metabolites were found to be significantly upregulated, whereas seven metabolites were downregulated in the supernatants of PASMCs induced by PDGF-BB compared to the vehicle-treated cells. Fourteen pathways were involved, and pyruvate metabolism pathway was ranked first with the highest enrichment impact followed by glycolysis/gluconeogenesis and pyrimidine metabolism. Significant and extensive metabolic abnormalities occurred during the phenotypic transformation of PASMCs. Disturbance of pyruvate metabolism pathway might contribute to pulmonary vascular remodeling.

LC-MS metabolomics uncovers potential biomarkers of semen cryo-injury in goats.

Semen cryopreservation acts a crucial role in enhancing breed improvement and conserving genetic resources. However, it often leads to decreased sperm activity and reduced pregnancy rates. Despite significant advancements in semen freezing techniques for goats, the precise factors and mechanisms causing cryo-injury remain unclear. In this study, we examined the motility characteristics of fresh semen versus frozen-thawed semen and investigated changes in the metabolite profiles of seminal plasma using liquid chromatograph-mass spectrometry (LC-MS). A total of 364 differentially expressed metabolites (DEMs) were identified between fresh and frozen-thawed semen samples. Among these, 185 metabolites were significantly up-regulated, while 179 were down-regulated (p<0.05). The majority of these DEMs belonged to lipids and lipid-like molecules, as well as organic acids and derivatives. The Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that these DEMs were primarily involved in pathways related to amino acid synthesis and metabolism. Additionally, metabolite set enrichment analysis (MSEA) underscored the critical role of amino acid synthesis and metabolic pathways in semen cryopreservation. Specific metabolites such as alanine, proline, phenylalanine, tryptophan, tyrosine, adenosine, citric acid, flavin adenine dinucleotide (FAD), and choline emerged as potential biomarkers for sperm cryo-injury in goats. These findings provide valuable insights into enhancing the quality of semen cryopreservation in goats, contributing to improved breeding and genetic resource conservation efforts.

Temporal changes in the proanthocyanidins to anthocyanins ratio during dormancy associate with bloom time variations in peach

Our previous research demonstrated that fall applications of ethephon, an ethylene-releasing plant growth regulator, delay bloom in peach, accompanied by changes in endogenous hormones, ROS, sugar metabolism, and transcriptomic profiles during bud dormancy phases (endodormancy and ecodormancy). In this study, floral bud tissues were collected from ethephon-treated and untreated trees at three time points (200, 600, and 1000 chilling hours, CH) during endodormancy and two points (1000 and 3000 growing degree hours, GDH) during ecodormancy. Using ultra-performance liquid chromatography time-of-flight mass spectrometry (UPLC-TOF/MS), we aimed to unravel the untargeted metabolic changes explaining ethephon-mediated bloom delay. Metabolite set-enrichment analysis (MSEA) revealed significant chemical group variations between dormancy phases, with a threefold increase in flavonoids during endodormancy and a doubling of organic and amino acids during ecodormancy. Further analysis of genes associated with the biosynthesis and transcriptional regulation of the flavonoid pathway showed that ethephon treatment upregulated genes associated with proanthocyanidin (PA) biosynthesis and downregulated genes related to anthocyanins (ACNs). We quantified PA and ACN contents in 12 peach cultivars with contrasting bloom times and chilling requirements (e.g. 727–1308 CH). Late-bloom cultivars had higher PA levels during endodormancy, while early-bloom cultivars had higher ACN levels during ecodormancy. Staining buds with 4-dimethylaminocinnamaldehyde (DMAC) dye revealed a decline in the PA/ACN ratio at later ecodormancy stages, correlating with bloom time. Integrated analysis of metabolite content and gene expression in late-bloom 'KV021779' and early-bloom 'John Boy' cultivars validated that late-blooming cultivars have higher PA levels during endodormancy, extending dormancy-release periods and resulting in later blooms.

Similarities of metabolomic disturbances in prematurity-associated obstructive lung disease to chronic obstructive pulmonary disease

Prematurity-associated lung disease (PLD) is a long-term consequence of preterm-birth. Since the underlying mechanisms of PLD remain poorly characterised, we compared the urinary metabolome between recently described spirometry phenotypes of PLD. Preterm- and term-born children aged 7–12 years, from the Respiratory Health Outcomes in Neonates (RHiNO) cohort, underwent spirometry and urine collection. The urinary metabolome was analysed by gas chromatography time-of-flight mass spectrometry. Preterm-born children were classified into phenotypes of prematurity-associated obstructive lung disease (POLD, Forced expiratory volume in 1 s (FEV1) < lower limit of normal (LLN), FEV1/Forced Vital Capacity (FVC) < LLN), prematurity-associated preserved ratio impaired spirometry (pPRISm, FEV1 < LLN, FEV1/FVC ≥ LLN) and Preterm/Term controls (FEV1 ≥ LLN). Metabolite set enrichment analysis was used to link significantly altered metabolites between the groups with metabolic pathways. Univariable and multivariable linear regression models examined associations between early and current life factors and significantly altered metabolites of interest. Urine from 197 preterm- and 94 term-born children was analysed. 23 and 25 were classified into POLD and pPRISm groups respectively. Of 242 identified metabolites, 49 metabolites were significantly altered in the POLD group compared with Preterm controls. Decreased capric acid (log2 fold change − 0.23; p = 0.003), caprylic acid (− 0.18; 0.003) and ceratinic acid (− 0.64; 0.014) in the POLD group, when compared to preterm controls, were linked with reduced β-oxidation of very long chain fatty acids (p = 0.004). Reduced alanine (log2 fold change − 0.21; p = 0.046), glutamic acid (− 0.24; 0.023), and pyroglutamic acid (− 0.17; 0.035) were linked with decreased glutathione metabolism (p = 0.008). These metabolites remained significantly associated with POLD in multivariable models adjusting for early/current life factors. The pPRISm urinary metabolome was minimally changed when compared with preterm-born controls. When compared to term-born subjects, alterations in tryptophan metabolism were implicated (p = 0.01). The urinary metabolome in POLD showed significantly altered β-oxidation of fatty acids and glutathione metabolism, implying alterations in cellular metabolism and oxidative stress. Similar findings have been noted in adults with chronic obstructive pulmonary disease. Given the similarity of findings between the POLD group and those reported for COPD, the POLD group should be considered at future risk of developing COPD.

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.