Metabolite Set Enrichment Analysis Research Articles

Metabolomics with severity of radiographic knee osteoarthritis and early phase synovitis in middle-aged women from the Iwaki Health Promotion Project: a cross-sectional study

BackgroundOsteoarthritis (OA) is one of the costliest and most disabling forms of arthritis, and it poses a major public health burden; however, its detailed etiology, pathophysiology, and metabolism remain unclear. Therefore, the purpose of this study was to investigate the key plasma metabolites and metabolic pathways, especially focusing on radiographic OA severity and synovitis, from a large sample cohort study.MethodsWe recruited 596 female volunteers who participated in the Iwaki Health Promotion Project in 2017. Standing anterior-posterior radiographs of the knee were classified by the Kellgren-Lawrence (KL) grade. Radiographic OA was defined as a KL grade of ≥ 2. Individual effusion-synovitis was scored according to the Whole-Organ Magnetic Resonance Imaging Scoring System. Blood samples were collected, and metabolites were extracted from the plasma. Metabolome analysis was performed using capillary electrophoresis time-of-flight mass spectrometry. To investigate the relationships among metabolites, the KL grade, and effusion-synovitis scores, partial least squares with rank order of groups (PLS-ROG) analyses were performed.ResultsAmong the 82 metabolites examined in this assay, PLS-ROG analysis identified 42 metabolites that correlated with OA severity. A subsequent metabolite set enrichment analysis using the significant metabolites showed the urea cycle and tricarboxylic acid cycle as key metabolic pathways. Moreover, further PLS-ROG analysis identified cystine (p = 0.009), uric acid (p = 0.024), and tyrosine (p = 0.048) as common metabolites associated with both OA severity and effusion-synovitis. Receiver operating characteristic analyses showed that cystine levels were moderately associated with radiographic OA (p < 0.001, area under the curve 0.714, odds ratio 3.7).ConclusionLarge sample metabolome analyses revealed that cystine, an amino acid associated with antioxidant activity and glutamate homeostasis, might be a potential metabolic biomarker for radiographic osteoarthritis and early phase synovitis.

Abstract 2253: Plasma metabolomic profiles associated with early and late-stage high-grade serous ovarian cancer

Abstract Background: Ovarian cancer has poor survival due to more than 60% of patients being diagnosed at advanced stage. Therefore, discovery of novel, non-invasive early detection biomarkers has high potential to improve survival. Here, we identified plasma metabolomic profiles associated with early-stage and late-stage high-grade serous ovarian cancer (HGSOC), the most common histotype. Method: We examined 197 lipid and lipid-related metabolites measured using a validated, liquid chromatography tandem mass spectrometry method (Broad Institute, Cambridge, MA) in plasma collected prior to surgery from 40 early-stage and 40 late-stage HGSOC patients and 40 population-based controls matched on age, year, and menopausal status at blood draw. We used multivariable logistic regression, adjusted for matching factors, tubal ligation, oral contraceptive use, and parity, to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) of ovarian cancer risk per 1 standard deviation increase in probit-transformed metabolite levels. We used Metabolite Set Enrichment Analysis (MSEA) to identify metabolite groups. We accounted for testing multiple correlated hypotheses using the number of effective tests (NEF) or false discovery rate (FDR). Results: Compared to controls, there were 34 metabolites associated with early-stage HGSOC and 88 metabolites associate with late-stage HGSOC (unadjusted p-value&amp;lt;0.05). All metabolites associated with risk of early-stage HGSOC and majority (n=80, 90%) of the metabolites associated with risk of late-stage HGSOC were inversely associated with ORs ranging from 0.28 to 0.48 for early-stage and 0.08 to 0.59 for late-stage HGSOC. Interestingly, the direction of association across all these metabolites were similar in both early-stage and late-stage HGSOC, with stronger effect estimates for late-stage HGSOC. After multiple testing correction, there were 9 statistically significant metabolites associated with risk of late-stage HGSOC (NEF p-value&amp;lt;0.05) whereas no individual metabolites remained associated with risk of early-stage HGSOC. MSEA revealed similar patterns of association compared to controls across the 11 lipid groups assessed in early and late-stage HGSOC cases. Metabolite groups of phosphatidylethanolamine plasmalogens and triglycerides were inversely associated with early-stage HGSOC compared to controls (FDR p-value&amp;lt;0.05). Metabolite groups of phosphatidylethanolamine plasmalogens and phosphatidylcholines were inversely, and diglycerides were positively associated with late-stage HGSOC compared to controls (FDR p-value&amp;lt;0.05). Conclusion: Our results revealed early-stage and late-stage HGSOC having similar plasma metabolomic profiles overall although there were more metabolites that were significantly associated with late-stage HGSOC than early-stage HGSOC compared to population-based controls. Citation Format: Naoko Sasamoto, Oana A. Zeleznik, Allison F. Vitonis, Daniel W. Cramer, Julian Avila-Pacheco, Clary B. Clish, Shelley S. Tworoger, Kathryn L. Terry. Plasma metabolomic profiles associated with early and late-stage high-grade serous ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2253.

Alterations in Blood Plasma Metabolome of Patients with Lesniowski-Crohn’s Disease Shortly after Surgical Treatment—Pilot Study

Lesniowski-Crohn’s disease (CD) is a type of chronic inflammatory bowel disease (IBD) of uncertain etiology. Initially, pharmacological management is undertaken; however, surgical intervention is necessary to improve life quality and relieve symptoms in most cases. Here changes are reported in blood metabolome that occurred three days after the ileo-colic region resection in the case of seven patients. Alterations are observed in levels of metabolites associated with multiple mitochondrial pathways, based on the Metabolite Set Enrichment Analysis, reflecting a high energy demand in the post-operative period. As most of these metabolites are also essential nutrients supplied from foods, we believe that our results might contribute to the discussion on perioperative nutrition’s role in enhanced recovery.

Untargeted metabolomics study and identification of potential biomarkers in the six sections of the genus Stachys L. (Lamiaceae) using HPLC-MQ-API-MS/MS.

The genus Stachys L., belonging to the family Lamiaceae, is one of the largest genera with remarkable medicinal properties. Plants of this genus produce a broad range of secondary metabolites. Due to the incomplete comprehensive assessment of chemical profiles in Stachys species, we conducted an untargeted metabolomics study and identified potential biomarkers in the six sections of Stachys with chemotaxonomic importance. Dried leaves of 17 taxa were utilized for analysis of all the constituents using HPLC-MQ-API-MS. The obtained data were processed and analyzed using multivariate statistical methods, including heatmaps, PLS-DA score plots, functional analysis of metabolic pathways, metabolite set enrichment analysis, and biomarker and network analysis. Among the 129 metabolites, 111 flavonoids and 18 non-flavonoids were recognized. The most represented flavonoids, including 41 flavones and 20 flavonols, displayed remarkable abundance. In non-flavonoid compounds, a total of six coumarins and six phenolic acids were present at high levels. In terms of approved markers in six sections, 76 chemical compounds, mainly flavonoids, coumarins, quinic acids, and cinnamic acids, were identified as potential biomarkers or chemotaxonomic indicators. Accordingly, the taxonomic complexities of some Stachys species in sections Fragilicaulis, Aucheriana, and Setifolia were properly resolved. An HPLC-MS/MS-based metabolomics approach integrated with multivariate statistical methods was employed to identify (1) valuable markers and analyze metabolic diversity and (2) predict the pharmaceutical properties of Stachys species. The obtained chemical profiles provide a new perspective for investigation of the Stachys genus.

Application of metabolite set enrichment analysis on untargeted metabolomics data prioritises relevant pathways and detects novel biomarkers for inherited metabolic disorders.

Untargeted metabolomics (UM) allows for the simultaneous measurement of hundreds of metabolites in a single analytical run. The sheer amount of data generated in UM hampers its use in patient diagnostics because manual interpretation of all features is not feasible. Here, we describe the application of a pathway‐based metabolite set enrichment analysis method to prioritise relevant biological pathways in UM data. We validate our method on a set of 55 patients with a diagnosed inherited metabolic disorder (IMD) and show that it complements feature‐based prioritisation of biomarkers by placing the features in a biological context. In addition, we find that by taking enriched pathways shared across different IMDs, we can identify common drugs and compounds that could otherwise obscure genuine disease biomarkers in an enrichment method. Finally, we demonstrate the potential of this method to identify novel candidate biomarkers for known IMDs. Our results show the added value of pathway‐based interpretation of UM data in IMD diagnostics context.

Cord Blood Metabolomics and Autism Spectrum Disorder

IntroductionAutism spectrum disorder (ASD) has increased dramatically in prevalence and incidence in recent years (1:59 in the US, CDC). With prenatal and/or genetic origins, early detection and intervention are critical to improve outcomes. Few studies have assessed perinatal biomarkers for later development of ASD. We hypothesize that cord blood plasma‐based metabolomics will differentiate infants who would later be diagnosed with ASD and typically developing (TD) controls.MethodsBanked cord blood plasma samples and clinical data from the Iowa Maternal Fetal Tissue Bank were used. Samples were analyzed via gas chromatography and mass spectrometry (GC‐MS) and relative MS signal intensity of a maximum of was quantified ratiometrically. All metabolites were normalized to internal controls (succinate) to account for effects of extraction, derivatization, and/or loading. Ratiometric levels of each metabolite were compared by t‐test between ASD and control groups. Results P&lt;0.01 are reported given the pilot nature of this small study. Metabolite set enrichment analysis (MSEA) was performed against human libraries. Significantly altered metabolites were compared to a tertiary sample of cord blood from individuals later diagnosed with attention deficit hyperactivity disorder (ADHD) to determine specificity for ASD.Results91 metabolites were detected and analyzed. Homocysteine (fold change [FC]: 1.411; p=0.017), Myristic acid (FC: 1.221; p=0.084), Pentadecanoic acid (FC: 1.256; p=0.093) were increased in ASD samples, while L‐Isoleucine (FC: 0.858; p=0.047), L‐Threonine (FC: 0.7131; p=0.068), O‐Phosphoethanolamine (FC: 0.704; p=0.070), and 2‐Hydroxybutyric acid (FC: 0.615; p=0.073) were decreased. Of these, only pentadecanoic was also significantly changed in ADHD samples (FC: 1.514; p=0.002). MSEA revealed enrichment of homocysteine degradation (p=0.017), catecholamine biosynthesis (p=0.048), threonine and 2‐Oxobutanoate degradation (p=0.068), Phosphatidylcholine Biosynthesis (p=0.070), and Phosphatidylethanolamine Biosynthesis (p=0.070) in ASD cord blood samples.ConclusionWe found that cord blood plasma metabolomics reveals intriguing and specific differences between ASD and control samples. While some of differences are supported by evidence, these alterations have not previously been extended to the prenatal period. Future work will investigate concordance with maternal blood metabolomics to determine maternal‐fetal pathways.

Presurgical blood metabolites and risk of postsurgical pelvic pain in young patients with endometriosis

To identify metabolites in presurgical blood associated with risk of persistent postsurgical pelvic pain 1 year after endometriosis surgery in adolescent and young adult patients. Prospective observational study within the Women's Health Study: From Adolescence to Adulthood, a US-based longitudinal cohort of adolescents and women enrolled from 2012-2018. Two tertiary care hospitals. Laparoscopically confirmed endometriosis patients (n = 180) with blood collected before their endometriosis surgery. Of these, 77 patients additionally provided blood samples 5 weeks to 6 months after their surgery. We measured plasma metabolites using liquid chromatography tandem mass spectrometry, and a total of 390 known metabolites were included in our analysis. None. Persistent postsurgical pelvic pain, defined as severe, life-impacting pelvic pain 1 year after endometriosis surgery. Most patients (>95%) were at stage I/II of the revised American Society for Reproductive Medicine classification. Their average age at diagnosis was 18.7 years, with 36% reporting persistent postsurgical pelvic pain. Of the 21 metabolites in presurgical blood that were associated with risk of persistent postsurgical pelvic pain, 19 metabolites, which were mainly lipid metabolites, were associated with increased risk. Only 2 metabolites-pregnenolone sulfate (odds ratio = 0.64, 95% confidence interval = 0.44-0.92) and fucose (odds ratio = 0.69, 95% confidence interval = 0.47-0.97)-were associated with decreased risk. Metabolite set enrichment analysis revealed that higher levels of lysophosphatidylethanolamines (false discovery rate = 0.01) and lysophosphatidylcholines (false discovery rate = 0.01) in presurgical blood were associated with increased risk of persistent postsurgical pelvic pain. Our results suggest that dysregulation of multiple groups of lipid metabolites may play a role in the persistence of pelvic pain postsurgery among young endometriosis patients.

Cord blood metabolomics reveals gestational metabolic disorder associated with anti-thyroid peroxidase antibodies positivity

BackgroundThyroid disease is one of the common endocrine disorders affecting the pregnant women, in which thyroid autoimmunity can alter the progress and the outcome of pregnancy. Women with euthyroid status but anti-thyroid peroxidase (anti-TPO) antibodies positivity before pregnancy are prone to subclinical gestational hypothyroidism. However, the connections between anti-TPO antibodies positivity and gestational hypothyroidism remain largely unknown. The aim of the present study is to investigate the differences of fetal metabolic profile at birth according to maternal anti-TPO status.MethodsWe performed 1H-NMR metabolomics on cord blood of a nested case control cohort of 22 pregnant women with matched thyroid hormone levels and demographic data, including 11 women with euthyroid status but anti-thyroid antibodies positivity (into the anti-TPO antibodies positivity group) and 11 matched women as controls with euthyroid status and negative anti-thyroid antibodies (into the control group).ResultsDistinct metabolic profiles were observed between the anti-TPO antibody positivity group and the nested control group, from which a total of 10 metabolites with between-group altered abundances were structurally identified. Five out of the 10 metabolites were up-regulated in the anti-TPO antibodies positivity group, including D-Glucose, L-Glutamine, 3-Hydroxybutyric acid, Myo-Inositol, Creatinine. The other 5 metabolites were down-regulated in the anti-TPO antibodies positivity group, including L-Leucine, L-Lysine, L-Glutamic acid, L-Tyrosine, and L-Phenylalanine. All the 10 metabolites have been previously reported to be correlated with hypothyroidism. Metabolite set enrichment analysis and pathway analysis suggested that amino acid metabolism pathways (especially the phenylalanine metabolism) were associated with anti-TPO antibodies positivity.ConclusionThe results of this study suggested that fetal metabolic disorder is correlated with anti-TPO antibodies positivity, representing by abundance alteration of hypothyroidism associated metabolites and the related disturbance of amino acid metabolism pathways.

Divergent Metabolic Effects of Metformin Merge to Enhance Eicosapentaenoic Acid Metabolism and Inhibit Ovarian Cancer In Vivo

Simple SummaryAlthough the anticancer effects of metformin are well studied, its effect on energy metabolism of cancer cells remains elusive. Metformin can alter the metabolism of cells either by activating AMPK or inhibiting the mitochondrial respiratory chain complex. The aim of the study was to explore the distinct metabolic profiles of ovarian cancer cell lines post-metformin treatment and to understand metformin’s metabolism-based anti-growth effect on ovarian cancer cell lines. In this study, using the untargeted metabolomics approach, we found that metformin treatment promoted omega-3 fatty acid metabolism in three different ovarian cancer cell lines, and treating ovarian xenografts with specific omega-3 fatty acids, especially EPA, reduced ovarian tumor growth. Thus, our study adds an additional potential therapeutic mechanism to the multi-potent anticancer effects of metformin.Metformin is being actively repurposed for the treatment of gynecologic malignancies including ovarian cancer. We investigated if metformin induces analogous metabolic changes across ovarian cancer cells. Functional metabolic analysis showed metformin caused an immediate and sustained decrease in oxygen consumption while increasing glycolysis across A2780, C200, and SKOV3ip cell lines. Untargeted metabolomics showed metformin to have differential effects on glycolysis and TCA cycle metabolites, while consistent increased fatty acid oxidation intermediates were observed across the three cell lines. Metabolite set enrichment analysis showed alpha-linolenic/linoleic acid metabolism as being most upregulated. Downstream mediators of the alpha-linolenic/linoleic acid metabolism, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), were abundant in all three cell lines. EPA was more effective in inhibiting SKOV3 and CaOV3 xenografts, which correlated with inhibition of inflammatory markers and indicated a role for EPA-derived specialized pro-resolving mediators such as Resolvin E1. Thus, modulation of the metabolism of omega-3 fatty acids and their anti-inflammatory signaling molecules appears to be one of the common mechanisms of metformin’s antitumor activity. The distinct metabolic signature of the tumors may indicate metformin response and aid the preclinical and clinical interpretation of metformin therapy in ovarian and other cancers.

Maternal Nutrient Restriction Disrupts Gene Expression and Metabolites Associated with Urea Cycle, Steroid Synthesis, Glucose Homeostasis, and Glucuronidation in Fetal Calf Liver.

This study aimed to understand the mechanisms underlying the effects of maternal undernutrition (MUN) on liver growth and metabolism in Japanese Black fetal calves (8.5 months in utero) using an approach that integrates metabolomics and transcriptomics. Dams were fed 60% (low-nutrition; LN) or 120% (high-nutrition; HN) of their overall nutritional requirements during gestation. We found that MUN markedly decreased the body and liver weights of the fetuses; metabolomic analysis revealed that aspartate, glycerol, alanine, gluconate 6-phosphate, and ophthalmate levels were decreased, whereas UDP-glucose, UDP-glucuronate, octanoate, and 2-hydroxybutyrate levels were decreased in the LN fetal liver (p ≤ 0.05). According to metabolite set enrichment analysis, the highly different metabolites were associated with metabolisms including the arginine and proline metabolism, nucleotide and sugar metabolism, propanoate metabolism, glutamate metabolism, porphyrin metabolism, and urea cycle. Transcriptomic and qPCR analyses revealed that MUN upregulated QRFPR and downregulated genes associated with the glucose homeostasis (G6PC, PCK1, DPP4), ketogenesis (HMGCS2), glucuronidation (UGT1A1, UGT1A6, UGT2A1), lipid metabolism (ANGPTL4, APOA5, FADS2), cholesterol and steroid homeostasis (FDPS, HSD11B1, HSD17B6), and urea cycle (CPS1, ASS1, ASL, ARG2). These metabolic pathways were extracted as relevant terms in subsequent gene ontology/pathway analyses. Collectively, these results indicate that the citrate cycle was maintained at the expense of activities of the energy metabolism, glucuronidation, steroid hormone homeostasis, and urea cycle in the liver of MUN fetuses.

Abstract P2-12-24: Exosomal metabolic signatures are associated with differential response to neoadjuvant chemotherapy in patients with breast cancer

Abstract Background: Neoadjuvant chemotherapy (NAC) is becoming the standard of care for aggressive breast cancer (BC). In patients with BC, NAC can increase eligibility for breast-conserving surgery, lower the excision volume, and assist in evaluating the disease course. NAC may shrink (residual disease, RD) or even eliminate (pathologic complete response, pCR) the tumor. Patients with RD often have a poor long-term prognosis. In contrast, pCR is associated with better long-term survival outcomes. Approximately 30% of patients with BC show pCR after NAC, while the rest exhibit RD. A critical barrier to improving NAC outcomes in patients with BC is the lack of precise molecular targets and limited understanding of the molecular mechanisms underlying differential treatment outcomes in patients with BC receiving NAC. Thus, there is an urgent clinical need to elucidate the underlying molecular regulation associated with NAC resistance. Studies have shown that cellular stress induces the release of small extracellular vesicles known as exosomes, contributing to drug resistance. In this study, we evaluated the ability of exosomal metabolic profiles to predict NAC response in BC patients. Methods: We collected blood samples of 16 patients with BC who received NAC at Grady Memorial Hospitals, USA. Further, total exosomes were isolated from plasma by an ultracentrifugation method and characterized for their concentration (number/ml), size distribution, and exosomal content. Exosome sizes were validated using nanoparticle tracking analysis. RNA, proteins, and metabolites were isolated from exosomes to perform deep multiplatform analyses (viz. miRNA seq, proteomics, and metabolomics) of exosomal cargo to identify various potential biological determinants of NAC response and evaluate their contributions to the risk of RD in patients with BC after NAC. KEGG and HMDB IDs were used for metabolomics data analyses. Various analyses viz. enriched metabolomic pathway analysis, MS peak to pathway analysis (using GSEA and Mummichog), and network explorer analysis were performed using Metaboanalyst online portal to reveal the metabolic signatures in exosomes of BC patients who exhibited pCR and RD after NAC treatment. Results: Among the 16 patients with BC who received NAC, eight patients attained pCR, and eight had RD. Interestingly, we found that patients with pCR secreted 61% fewer exosomes but had 24% larger-sized exosomes than patients with RD following NAC. Furthermore, significant differences in the loading of exosomal cargo were observed between the two groups. Based on these results, we proposed that the differential RNA, protein, and metabolite profile of exosomes might contribute to the higher drug sensitivity in patients with pCR. Our proteomics and miRNA-seq analyses of exosomes from patients with pCR and RD after NAC did not show any significant differences between the groups. However, metabolite set enrichment analysis using MetaboAnalyst revealed that compared to exosomes from patients with pCR, patients with RD were significantly enriched in various metabolic pathways. These pathways included valine, leucine, and isoleucine biosynthesis; phenylalanine metabolism; one-carbon pool folate; and inositol phosphate metabolism. Additionally, MS peak to pathway analysis revealed differential enrichment of various other metabolic pathways, particularly in patients with RD.Conclusions: These data strongly suggest that exosomal metabolic signatures are associated with differential NAC outcomes in patients with BC. We believe that in-depth studies of these metabolite signatures will give essential clues about differential NAC response in patients with BC. Citation Format: Shriya Joshi, Chakravarthy Garalapati, Shristi Bhattarai, Darshan Shimoga Chandrashekar, Sooryanarayana Varambally, Gagan Deep, Ritu Aneja. Exosomal metabolic signatures are associated with differential response to neoadjuvant chemotherapy in patients with breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-12-24.

Integrative Metabolomic Characterization Reveals the Mediating Effect of Bifidobacterium breve on Amino Acid Metabolism in a Mouse Model of Alzheimer's Disease.

Alzheimer’s disease (AD) is commonly accompanied by global alterations in metabolic profiles, resulting in cognitive impairment and neuroinflammation in the brain. Using ultraperformance liquid chromatography-mass spectrometry, we performed integrative untargeted metabolomic analysis of metabolite alterations in the serum and hippocampal tissues of amyloid-β (Aβ)-injected AD model mice and sham controls. Multivariate analysis revealed that a Bifidobacterium breve CCFM1025 intervention significantly restored the differential metabolites induced by Aβ-injection, resulting in B. breve CCFM1025 serum and hippocampal metabolomes clustering between control and model mice. Furthermore, pathway and metabolite set enrichment analysis found that these altered metabolites were predominantly linked to amino acid metabolism. Overall, the integrative metabolome analysis indicated that B. breve CCFM1025 supplementation could modulate serum and hippocampal metabolomes in the early stage of AD, with amino acids as a potential driver.

A Metabolomics-Based Screening Proposal for Colorectal Cancer.

Colorectal cancer (CRC) is a high incidence disease, characterized by high morbidity and mortality rates. Early diagnosis remains challenging because fecal occult blood screening tests have performed sub-optimally, especially due to hemorrhoidal, inflammatory, and vascular diseases, while colonoscopy is invasive and requires a medical setting to be performed. The objective of the present study was to determine if serum metabolomic profiles could be used to develop a novel screening approach for colorectal cancer. Furthermore, the study evaluated the metabolic alterations associated with the disease. Untargeted serum metabolomic profiles were collected from 100 CRC subjects, 50 healthy controls, and 50 individuals with benign colorectal disease. Different machine learning models, as well as an ensemble model based on a voting scheme, were built to discern CRC patients from CTRLs. The ensemble model correctly classified all CRC and CTRL subjects (accuracy = 100%) using a random subset of the cohort as a test set. Relevant metabolites were examined in a metabolite-set enrichment analysis, revealing differences in patients and controls primarily associated with cell glucose metabolism. These results support a potential use of the metabolomic signature as a non-invasive screening tool for CRC. Moreover, metabolic pathway analysis can provide valuable information to enhance understanding of the pathophysiological mechanisms underlying cancer. Further studies with larger cohorts, including blind trials, could potentially validate the reported results.

Plasma Metabolomics and Breast Cancer Risk over 20 Years of Follow-up among Postmenopausal Women in the Nurses' Health Study.

Metabolite profiles provide insight into biologic mechanisms contributing to breast cancer development. We explored the association between prediagnostic plasma metabolites (N = 307) and invasive breast cancer among postmenopausal women in a nested case-control study within the Nurses' Health Study (N = 1,531 matched pairs). Plasma metabolites were profiled via LC/MS-MS using samples taken ≥10 years (distant, N = 939 cases) and <10 years (proximate, N = 592 cases) before diagnosis. Multivariable conditional logistic regression was used to estimate ORs and 95% confidence intervals (CI) comparing the 90th to 10th percentile of individual metabolite level, using the number of effective tests (NEF) to account for testing multiple correlated hypotheses. Associations of metabolite groups with breast cancer were evaluated using metabolite set enrichment analysis (MSEA) and weighted gene coexpression network analysis (WGCNA), with adjustment for the FDR. No individual metabolites were significantly associated with breast cancer risk. MSEA showed negative enrichment of cholesteryl esters at the distant timepoint [normalized enrichment score (NES) = -2.26; Padj = 0.02]. Positive enrichment of triacylglycerols (TAG) with <3 double bonds was observed at both timepoints. TAGs with ≥3 double bonds were inversely associated with breast cancer at the proximate timepoint (NES = -2.91, Padj = 0.03). Cholesteryl esters measured earlier in disease etiology were inversely associated with breast cancer. TAGs with many double bonds measured closer to diagnosis were inversely associated with breast cancer risk. The discovered associations between metabolite subclasses and breast cancer risk can expand our understanding of biochemical processes involved in cancer etiology.

Metabolomics Strategy Assisted by Transcriptomics Analysis to Identify Potential Biomarkers Associated with Tuberculosis.

PurposeTo investigate the dysregulated pathways and identify reliable diagnostic biomarkers for tuberculosis using integrated analysis of metabolomics and transcriptomics.MethodsThree groups of samples, untargeted metabolomics analysis of healthy controls (HC), latent tuberculosis infection patients (LTBI), and active tuberculosis patients (TB), were analyzed using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) and ultra-high performance liquid chromatography-quantitative mass spectrometry (UHPLC-QE-MS). Both univariate and multivariate and statistical analyses were used to select differential metabolites (DMs) among group comparison, and LASSO regression analysis was employed to discover potential diagnostic biomarkers. Metabolite set enrichment analysis was performed to identify the altered metabolic pathways specifically in patients with TB. Meanwhile, a transcriptomic dataset GSEG4992 was downloaded from the GEO database to explore the differentially expressed genes (DEGs) between TB and HC identified in significantly enriched pathways. Finally, an integrative analysis of DMs and DEGs was performed to investigate the possible molecular mechanisms of TB.ResultsThirty-three specific metabolites were significantly different between TB and HC, of which 7 (5-hydroxyindoleacetic acid, isoleucyl-isoleucine, heptadecanoic acid, indole acetaldehyde, 5-ethyl-2,4-dimethyloxazole, and 2-hydroxycaproic acid, unknown 71) were chosen as combinational potential biomarkers for TB. The area under the curve (AUC) value of these biomarkers was 0.97 (95% CI: 0.92–1.00). Metabolites set enrichment analysis (MSEA) displayed that there were 3 significantly enriched pathways among all. The genes in 3 significantly enriched pathways were further analyzed, of which 9(ALDH3B1, BCAT1, BCAT2, GLYAT, GOT1, IL4I1, MIF, SDS, SDSL) were expressed differentially. The area under the curve (AUC) values of these DEGs enriched in pathways mostly were greater than 0.8. As a result, a connected network of metabolites and genes in the pathways were established, which provides insights into the credibility of selected metabolites.ConclusionThe newly identified metabolic biomarkers display a high potential to be developed into a promising tool for TB screening, diagnosis, and therapeutic effect monitoring.

Plasma Metabolomics Profiling of Metabolic Pathways Affected by Major Depressive Disorder.

Background: Major depressive disorder (MDD) is a common disease which is complicated by metabolic disorder. Although MDD has been studied relatively intensively, its metabolism is yet to be elucidated.Methods: To profile the global pathophysiological processes of MDD patients, we used metabolomics to identify differential metabolites and applied a new database Metabolite set enrichment analysis (MSEA) to discover dysfunctions of metabolic pathways of this disease. Hydrophilic metabolomics were applied to identify metabolites by profiling the plasma from 55 MDD patients and 100 sex-, gender-, BMI-matched healthy controls. The metabolites were then analyzed in MSEA in an attempt to discover different metabolic pathways. To investigate dysregulated pathways, we further divided MDD patients into two cohorts: (1) MDD patients with anxiety symptoms and (2) MDD patients without anxiety symptoms.Results: Metabolites which were hit in those pathways correlated with depressive and anxiety symptoms. Altogether, 17 metabolic pathways were enriched in MDD patients, and 23 metabolites were hit in those pathways. Three metabolic pathways were enriched in MDD patients without anxiety, including glycine and serine metabolism, arginine and proline metabolism, and phenylalanine and tyrosine metabolism. In addition, L-glutamic acid was positively correlated with the severity of depression and retardation if hit in MDD patients without anxiety symptoms.Conclusions: Different kinds of metabolic pathophysiological processes were found in MDD patients. Disorder of glycine and serine metabolism was observed in both MDD patients with anxiety and those without.

Maternal Undernutrition during Pregnancy Alters Amino Acid Metabolism and Gene Expression Associated with Energy Metabolism and Angiogenesis in Fetal Calf Muscle.

To elucidate the mechanisms underlying maternal undernutrition (MUN)-induced fetal skeletal muscle growth impairment in cattle, the longissimus thoracis muscle of Japanese Black fetal calves at 8.5 months in utero was analyzed by an integrative approach with metabolomics and transcriptomics. The pregnant cows were fed on 60% (low-nutrition, LN) or 120% (high-nutrition, HN) of their overall nutritional requirement during gestation. MUN markedly decreased the bodyweight and muscle weight of the fetus. The levels of amino acids (AAs) and arginine-related metabolites including glutamine, gamma-aminobutyric acid (GABA), and putrescine were higher in the LN group than those in the HN group. Metabolite set enrichment analysis revealed that the highly different metabolites were associated with the metabolic pathways of pyrimidine, glutathione, and AAs such as arginine and glutamate, suggesting that MUN resulted in AA accumulation rather than protein accumulation. The mRNA expression levels of energy metabolism-associated genes, such as PRKAA1, ANGPTL4, APLNR, CPT1B, NOS2, NOS3, UCP2, and glycolytic genes were lower in the LN group than in the HN group. The gene ontology/pathway analysis revealed that the downregulated genes in the LN group were associated with glucose metabolism, angiogenesis, HIF-1 signaling, PI3K-Akt signaling, pentose phosphate, and insulin signaling pathways. Thus, MUN altered the levels of AAs and expression of genes associated with energy expenditure, glucose homeostasis, and angiogenesis in the fetal muscle.

Abstract 745: Circulating plasma metabolites and breast cancer risk

Abstract Metabolite profiles reflect the integrated impact of the genome and exogenous exposures on the metabolic state and may provide insight into biologic mechanisms contributing to breast cancer development. To better understand this, we explored the association between pre-diagnostic plasma metabolites (N=307) and breast cancer among women in the Nurses' Health Study. Plasma metabolites were profiled for cases and controls via liquid chromatography tandem mass spectrometry (LC-MS) using samples taken ≥10 years prior to diagnosis (distant, N=939 cases), and &amp;lt;10 years prior to diagnosis (proximate, N=592 cases). Multivariable conditional logistic regression adjusting for standard breast cancer risk factors was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for a one standard deviation change in metabolite level, using the number of effective tests to account for testing multiple correlated hypotheses. Metabolite set enrichment analysis (MSEA) was used to group molecularly-similar metabolites based on pre-defined classes; triacylglycerides (TAGs) were further separated by double bond content (≥3 or &amp;lt;3 double bonds) (N classes=17). Weighted gene co-expression network analysis (WGCNA) identified data-driven metabolite modules based on their interconnectedness; associations between resulting module scores and breast cancer risk were examined. Multiple testing in GSEA and WGCNA was accounted for using the false discovery rate.Though no individual metabolites were significantly associated with breast cancer risk, several patterns emerged. At the distant timepoint, cholesterol esters were inversely associated with risk [e.g.: C22:5 CE OR = 0.82, 95% CI=0.75-0.9]. TAGs with ≥3 double bonds were inversely associated with breast cancer at the proximate timepoint [e.g.: C58:7 TAG OR=0.81, 95% CI=0.72-0.92]. MSEA showed negative enrichment (corresponding to an inverse association with breast cancer) of cholesterol esters at both distant (normalized enrichment score (NES)=-2.38, padj=0.02) and proximate (NES=-2.0, padj=0.02) timepoints. Though positive enrichment of TAGs with &amp;lt;3 double bonds was observed at both time points, TAGs with ≥3 double bonds were significantly inversely associated with breast cancer at the proximate time point (NES=-2.92, padj=0.02). At the proximate time point, a module characterized by essential amino acids (positively weighted) and TAGs with high number of double bonds (inversely weighted) was significantly associated with breast cancer risk (OR=1.21, 95% CI=1.06-1.37, padj=0.04). Overall, the results suggest that cholesterol esters may be inversely related to early development of breast cancer, while TAGs with many double bonds appear inversely associated with development of breast cancer closer to diagnosis. Analyses of metabolite differences between each time point and by ER status are ongoing to better understand the biologic mechanisms contributing to breast cancer risk. Citation Format: Kristen D. Brantley, Oana A. Zeleznik, Rulla Tamimi, Bernard Rosner, Julian Avila-Pacheco, Clary B. Clish, A Heather Eliassen. Circulating plasma metabolites and breast cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 745.

High Glutathione and NAD(P)H Levels, and Increased Protein Folding Capacity Are Involved in Mediating Proteasome Inhibitor-Resistance in Multiple Myeloma

Background. Proteasome inhibitors (PI) are a backbone for the treatment of multiple myeloma (MM). However, most MM patients ultimately acquire PI-resistance and die from MM. The biology of PI resistance is poorly understood and established treatment options for PI-refractory MM are lacking. We have recently generated a next-generation in vitro model for PI-refractory MM, based on single MM cell-derived subclones with specific (50-100x) resistance to either bortezomib (AMO-BTZ) or carfilzomib (AMO-CFZ). These cells consistently show downregulation of IRE1/XBP1, a hallmark of BTZ-refractory primary MM cells, and do or do not carry the PSMB5 A310G mutation. Our recent quantitative proteomic analysis suggested that the PI-resistant phenotype of AMO-BTZ and AMO-CFZ is in particular characterized by massive metabolic changes that render MM cells largely independent from proteasome activity. To verify such metabolic adaptation as a hallmark of proteasome inhibitor resistance of MM, and to identify therapeutic targets, we performed whole metabolome profiling of proteasome inhibitor-sensitive AMO-1 cells in comparison to BTZ or CFZ-resistant clones from the same cell line.Methods. Metabolomic profiling was performed using Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy (UPLC-MS/MS); data were quantified and normalized by Metabolon, Inc. Metaboanalyst v3.0 software was used for Metabolite Set Enrichment Analysis (MSEA). Reactive oxygen species (ROS) formation was determined by flow cytometry after staining the cells with H2DCFDA and exposure to 0.08% H2O2. Protein folding was determined by MERO-GFP construct, viability was determined by MTS assay.Results. Based on MSEA, PI-adapted cells showed substantial metabolic changes in glutathione metabolism, protein biosynthesis, malate/aspartate shuttle, metabolism of purines, pyrimidines, amino acids and tricarboxylic acid (TCA). Relative to AMO-1, in both AMO-BTZ and AMO-CFZ, glutathione (l-γ-Glutamyl-l-cysteinyl-glycine; GSH and GSSG) was consistently identified as the metabolite with the most significant quantitative increase (fold change (FC) relative to AMO-1 for GSH in AMO-BTZ=52.92; in AMO-CFZ=56.79; FC for GSSG in AMO-BTZ=36.92; in AMO-CFZ=54.17), while many other metabolites in glycine/serine and cysteine/methionine pathways that are associated with glutathione synthesis were decreased. Consistently, PI-adapted cells showed accumulation of nicotinamide adenine dinucleotide (phosphate) (NAD(P)+) and reduced nicotinamide adenine dinucleotide (phosphate) (NAD(P)H) (FC for NAD+ in AMO-BTZ=55.81; in AMO-CFZ=2.16; FC for NADH in AMO-BTZ=19.10; in AMO-CFZ=1.65), presumably to maintain balance between reduced and oxidized glutathione, together with changes in TCA and malate/aspartate shuttle fueling the NAD(P)H production and transport, respectively. PI-resistant cells in addition showed altered carbohydrate metabolism and a shift in availability of membrane lipids, from lysolipids to sphingomyelins, in comparison to non-adapted cells.The adaptive changes in these cells correspond well to the proteomic changes previously described, confirming that the metabolic adaptive changes of PI-refractory MM cells are in particular in the regulation of redox homeostasis and protein folding. Functional experiments confirmed that the PI-adapted cells show larger capacity to buffer ROS production and that protein re-folding after DTT challenge is more effective in PI-refractory MM, compared to PI-sensitive cells. Consequently, co-treatment of PI-adapted cells with respective proteasome inhibitors (bortezomib and carfilzomib) in the presence of the inhibitor of protein-disulfide isomerase (PDI), 16F16, re-sensitized cells to proteasome inhibition (FC of IC50 for bortezomib or carfilzomib alone vs bortezomib or carfilzomib + 16F16 in AMO-BTZ=1.26 or 1.88, respectively; in AMO-CFZ=1.25 or 2.59 respectively; in AMO-1=1.1 or 0.95, respectively.Conclusion. PI refractory MM cells show massive metabolic changes in particular in glutathione and NAD(P)H production. This is associated with increased protein folding capacity and likely results in decreased proteasome load. PI-refractory MM cells can be re-sensitized to PI treatment by reducing glutathione levels or by affecting protein folding. DisclosuresNo relevant conflicts of interest to declare.

Diet Quality, Gut Microbiome and Metabolism

ObjectivesDietary pattern is associated with circulatory and gut metabolome variation. However, it is unclear if this association is mediated by gut microbiome composition. We investigated whether the interaction between diet quality and gut microbiome influenced circulatory and gut metabolites. MethodsWe conducted a cross-sectional study among 75 healthy adults in the ABO Study. Diet quality was assessed using the Healthy Eating Index (HEI). Metabolome profiling (800 circulatory and 767 gut metabolites) was performed at Metabolon Inc. Two gut microbiome Enterotypes (1 and 2) were identified using the Partitioning Around Medoids method. Metabolite set enrichment analysis was performed using Metaboanalyst 4.0. Multivariable linear regression was conducted to test for an interaction between the gut microbiome-HEI and metabolite levels. ResultsDiet quality was significantly higher in participants with Enterotype 2, compared to those with Enterotype 1 (P = 0.01). The gut microbiome-HEI interaction (Enterotype 2 and higher HEI) was directly related to omega-3/omega-6 poly-unsaturated fatty acids (PUFAs) and acetyl/acyl derivatives of amino acids. It was inversely linked to polar lipids including 1-palmitoyl-2-linoleoyl-GPC (16:0/18:2), which demonstrated the most significant association (β = 0.008, P = 0.0009) among circulatory metabolites. Considering gut metabolome, however, the interaction directly associated with metabolites involved in DNA synthesis including thymidine 5′-monophosphate, which showed the strongest association (β = 0.041, P = 0.0007), and bile acids derivatives. It inversely associated with fatty acids and branch chain amino acids. ‘Glycine and serine metabolism’ was the only pathway that was significantly enriched by the interaction (P = 0.044). ConclusionsFuture research is warranted; however, these findings suggest that the efficacy of dietary interventions targeted at altering metabolism (the metabolism of lipids (PUFAs and polar lipids), amino acids and nucleotides) may be dependent on gut microbiome composition. Funding SourcesThe National Institutes of Health.