Metabolic Profiling Of Plasma Samples Research Articles

Abstract PO3-14-05: Integration of metabolomics and transcriptomics to Reveal Potential Biomarkers Associated with Treatment Response of Neoadjuvant Therapy in HER2+ Breast Cancer

Abstract Background: Neoadjuvant therapy (NAT) has become the standard treatment for early human epidermal growth factor receptor 2 positive (HER2+) breast cancer. For patients with HER2+ breast cancer, achieving a pathologic complete response (pCR) is highly indicative of their prognosis. However, not all HER2+ breast cancer patients could benefit from the NAT, about 40%-50% patients could not achieve pCR at surgery, even if they used the present standard neoadjuvant regime of TCbHP (taxane, carboplatin, trastuzumab and pertuzumab). This study was conducted to explore potential indicators associated with neoadjuvant efficacy of TCbHP in HER2+ breast cancer. Methods: Consecutive HER2+ breast cancer patients, who received and completed NAT with TCbHP regime and subsequent surgery at Chongqing University Cancer Hospital were prospectively enrolled. LC-MS and GC-MS platform-based untargeted metabolomics were performed to determine the metabolic profiles of plasma samples from these patients at different time points during treatment period. Random forest (RF) was used to establish predictive models based on pre-therapeutic metabolic traits. The potential monitors for the treatment response were obtained by time series analysis. Transcriptome analysis was performed in available samples to identify differentially expressed genes (DEGs) before treatment. qRT-pCR was used to detect DEGs in tastuzumab-sensitive and -resistant cell lines. Metabolic and transcriptomic data were integrated to explore substantially altered pathways that might be responsible for drug resistance. Results: From July 20, 2020 to May 28, 2021, a total of 40 HER2+ breast cancer patients with 120 plasma samples were eligible and recruited for this study. Of whom 21 (52.5%) patients achieved pCR and 19 (47.5%) achieved non-pCR, there are no significant difference in baseline clinicopathological features between pCR and non-pCR patients. There were significant differences in plasma metabolic profiles between non-pCR and pCR groups before and during treatment. A total of 100 differential metabolites were identified between pCR and non-pCR patients at pre-therapeutic period, which were markedly enriched in 40 metabolic pathways. Four key metabolites [sophorose, N-(2-acetamido)iminodiacetic acid, taurine and 6-hydroxy-2-aminohexanoic acid] were selected by RF analysis. The AUC value to discriminate pCR and non-PCR group to NAT of the single potential metabolite or combined panel of these metabolites were more than 0.910. 18 metabolites exhibit a potential for monitoring efficacy. Among 163 DEGs identified by RNA-seq, some genes might associate with trastuzumab resistance detected by qRT-PCR in cell lines. 39 altered pathways were found abnormally expressed at both the transcriptional and metabolic levels, including ABC transporters, protein digestion and absorption, mineral absorption, et al. Conclusion: By metabolomics analysis, we have both offered a road map of the molecular alterations underlying NAT of TCbHP regime in HER2+ breast cancer and a preliminary analysis of a unique metabolic signature that might be used to distinguish non-pCR from pCR patients. Metabolomics integrated with transcriptomics analysis could assist for gaining new insights into biochemical pathophysiology and might facilitate the development of new treatment targets for insensitive patients. Citation Format: Ningning Zhang, Yuxin Huang, Guanwen Wang, Yimei Xiang, Zhouhong Jing, Junjie Zeng, Feng Yu, Xianjun Pan, Wenqi Zhou, Xiaohua Zeng. Integration of metabolomics and transcriptomics to Reveal Potential Biomarkers Associated with Treatment Response of Neoadjuvant Therapy in HER2+ Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-14-05.

Inhibition of UBA6 by inosine augments tumour immunogenicity and responses

Anti-cancer immunity and response to immune therapy is influenced by the metabolic states of the tumours. Immune checkpoint blockade therapy (ICB) is known to involve metabolic adaptation, however, the mechanism is not fully known. Here we show, by metabolic profiling of plasma samples from melanoma-bearing mice undergoing anti-PD1 and anti-CTLA4 combination therapy, that higher levels of purine metabolites, including inosine, mark ICB sensitivity. Metabolic profiles of ICB-treated human cancers confirm the association between inosine levels and ICB sensitivity. In mouse models, inosine supplementation sensitizes tumours to ICB, even if they are intrinsically ICB resistant, by enhancing T cell-mediated cytotoxicity and hence generating an immunologically hotter microenvironment. We find that inosine directly inhibits UBA6 in tumour cells, and lower level of UBA6 makes the tumour more immunogenic and this is reflected in favourable outcome following ICB therapy in human melanomas. Transplanted mouse melanoma and breast cancer cells with genetic ablation of Uba6 show higher sensitivity to ICB than wild type tumours. Thus, we provide evidence of an inosine-regulated UBA6-dependent pathway governing tumour-intrinsic immunogenicity and hence sensitivity to immune checkpoint inhibition, which might provide targets to overcome ICB resistance.

Plasma metabolic alterations and potential biomarkers in individuals at clinical high risk for psychosis

BackgroundEarly identification and treatment of clinical high-risk for psychosis (CHRP) are critical to prevent the onset of psychosis, but there is no objective biomarker for CHR-P diagnosis. MethodsNinety medication naïve CHR-P subjects and eighty-six healthy controls (HCs) were recruited. The metabolic profiles of plasma samples were acquired using an untargeted metabolomics approach based on ultra-high-performance liquid chromatography equipped with quadrupole time-of-flight mass spectrometry. The obtained data were further mapped on the Kyoto Encyclopedia of Genes and Genomes for pathway analysis, and an ensemble learning method was applied to identify diagnostic biomarkers. Bayesian linear regression model was then used to explore predicative biomarkers of conversion to psychosis. Receiver-operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic or predicative value of potential biomarkers. ResultsA total of one hundred and four differential metabolites and forty-eight differential pathways were identified. A panel of five metabolites was found that could effectively discriminate CHR-P from HCs with area under the ROC curve of 1 in the training set (70% of the samples) and 0.997 in the testing set (30% of the samples). The biosynthesis of unsaturated fatty acids pathway perturbed most significantly in CHR-P subjects. Twenty-three CHR-P subjects converted to psychotic disorders during two-year follow-up, and increased 1-stearoyl-2-arachidonoyl-sn-glycerol in plasma was potentially associated with the higher risk of conversion to psychosis. ConclusionsThese findings demonstrate the alterations of plasma metabolic profiles in CHR-P population, which may deliver valuable biomarkers for early identification and outcome prediction of CHR-P.

A Reductive Metabolic Switch Protects Infants with Transposition of Great Arteries Undergoing Atrial Septostomy against Oxidative Stress

Transposition of the great arteries (TGA) is one of the most common cyanotic congenital heart diseases requiring neonatal surgical intervention. Parallel circulations that result in impaired cerebral oxygen delivery already in utero may lead to brain damage and long-term neurodevelopmental delay. Balloon atrial septostomy (BAS) is often employed to mix deoxygenated and oxygenated blood at the atrial level. However, BAS causes a sudden increase in arterial blood oxygenation and oxidative stress. We studied changes in oxygen saturation as well as metabolic profiles of plasma samples from nine newborn infants suffering from TGA before and until 48 h after undergoing BAS. The plasma metabolome clearly changed over time and alterations of four metabolic pathways, including the pentose phosphate pathway, were linked to changes in the cerebral tissue oxygen extraction. In contrast, no changes in levels of lipid peroxidation biomarkers over time were observed. These observations suggest that metabolic adaptations buffer the free radical burst triggered by re-oxygenation, thereby avoiding structural damage at the macromolecular level. This study enhances our understanding of the complex response of infants with TGA to changes in oxygenation induced by BAS.

Plasma metabolites changes in male heroin addicts during acute and protracted withdrawal.

Background: Heroin addiction and withdrawal have been associated with an increased risk for infectious diseases and psychological complications. However, the changes of metabolites in heroin addicts during withdrawal remain largely unknown.Methods: A total of 50 participants including 20 heroin addicts with acute abstinence stage, 15 with protracted abstinence stage and 15 healthy controls, were recruited. We performed metabolic profiling of plasma samples based on ultraperformance liquid chromatography coupled to tandem mass spectrometry to explore the potential biomarkers and mechanisms of heroin withdrawal.Results: Among the metabolites analyzed, omega-6 polyunsaturated fatty acids (linoleic acid, dihomo-gamma-linolenic acid, arachidonic acid, n-6 docosapentaenoic acid), omega-3 polyunsaturated fatty acids (docosahexaenoic acid, docosapentaenoic acid), aromatic amino acids (phenylalanine, tyrosine, tryptophan), and intermediates of the tricarboxylic acid cycle (oxoglutaric acid, isocitric acid) were significantly reduced during acute heroin withdrawal. Although majority of the metabolite changes could recover after months of withdrawal, the levels of alpha-aminobutyric acid, alloisoleucine, ketoleucine, and oxalic acid do not recover.Conclusions: In conclusion, the plasma metabolites undergo tremendous changes during heroin withdrawal. Through metabolomic analysis, we have identified links between a framework of metabolic perturbations and withdrawal stages in heroin addicts.

Investigation of pathogenesis and therapeutic targets of acute myeloid leukemia based on untargeted plasma metabolomics and network pharmacology approach

Acute myeloid leukemia (AML) is a malignant disease originating from bone marrow hematopoietic stem cells, characterized by anemia, hemorrhage, fever, and infection, with low survival rate. However, the pathogenesis of AML is not fully understood at present. In this work, an integrated approach based untargeted metabolomics and network pharmacology was adopted to elucidate the pathogenesis of AML. Metabolic profiling of plasma samples from 14 patients and 16 healthy individuals were performed based on UHPLC-MS platform. As a result, 23 metabolites were identified by using the human metabolite database based on PLS-DA (partial least squares discriminant analysis) and independent sample test. And metabolic pathways related to AML mainly included fatty acid metabolism, amino acid metabolism, energy metabolism and lipid metabolism. Meanwhile, biomarkers-targets-pathways-disease network was constructed, 75 biomarker targets and 122 disease targets were identified. Furthermore, 30 pathways were predicted, some of which were consistent with these in metabolomics. This is the first time that metabolomics and network pharmacology approach have been combined to investigate the pathogenesis and therapeutic targets of AML. ALDH, CYP2E1 and CYP3A4 were potential therapeutic targets for AML, which provide available way to elucidate the pathogenesis and treatment of AML.

Non‐invasive metastasis prognosis from plasma metabolites in stage II colorectal cancer patients: The DACHS study

Metastasis is the main cause of death from colorectal cancer (CRC). About 20% of stage II CRC patients develop metastasis during the course of disease. We performed metabolic profiling of plasma samples from non-metastasized and metachronously metastasized stage II CRC patients to assess the potential of plasma metabolites to serve as biomarkers for stratification of stage II CRC patients according to metastasis risk. We compared the metabolic profiles of plasma samples prospectively obtained prior to metastasis formation from non-metastasized vs. metachronously metastasized stage II CRC patients of the German population-based case-control multicenter DACHS study retrospectively. Plasma samples were analyzed from stage II CRC patients for whom follow-up data including the information on metachronous metastasis were available. To identify metabolites distinguishing non-metastasized from metachronously metastasized stage II CRC patients robust supervised classifications using decision trees and support vector machines were performed and verified by 10-fold cross-validation, by nested cross-validation and by traditional validation using training and test sets. We found that metabolic profiles distinguish non-metastasized from metachronously metastasized stage II CRC patients. Classification models from decision trees and support vector machines with 10-fold cross-validation gave average accuracy of 0.75 (sensitivity 0.79, specificity 0.7) and 0.82 (sensitivity 0.85, specificity 0.77), respectively, correctly predicting metachronous metastasis in stage II CRC patients. Taken together, plasma metabolic profiles distinguished non-metastasized and metachronously metastasized stage II CRC patients. The classification models consisting of few metabolites stratify non-invasively stage II CRC patients according to their risk for metachronous metastasis.

Metabolomics Study of the Biochemical Changes in the Plasma of Myocardial Infarction Patients.

Myocardial infarction (MI) is a common and multifactorial disease that has the highest morbidity and mortality in the world. Although a number of physiological, pathological, and functional parameters have been investigated, only scarce information regarding the changes of small metabolites in the plasma has been reported, and this lack of information may cause poor MI diagnosis and treatment. In the present study, we aimed to investigate the metabolic profiles of plasma samples from MI patients to identify potential disease biomarkers and to study the pathology of MI. Metabolic profiles of the plasma of 30 MI patients and 30 controls were obtained using ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry. The resulting data were processed using pattern recognition approaches, including principal component analysis and partial least squares-discriminant analysis, to identify the metabolites that differed between the groups. Significant differences in the plasma levels of the following 10 metabolites were observed in the MI patients compared with the controls: phosphatidylserine, C16-sphingosine, N-methyl arachidonic amide, N-(2-methoxyethyl) arachidonic amide, linoleamidoglycerophosphate choline, lyso-PC (C18:2), lyso-PC (C16:0), lyso-PC (C18:1), arachidonic acid, and linoleic acid. The changes in these 10 biomarkers indicated perturbations of energy metabolism, phospholipid metabolism, and fatty acid metabolism in the MI patients. These findings hold promise to advance the treatment, diagnosis, and prevention of MI.

Polyunsaturated fatty acids metabolism, purine metabolism and inosine as potential independent diagnostic biomarkers for major depressive disorder in children and adolescents

Major depressive disorder (MDD) in children and adolescents is a recurrent and disabling condition globally but its pathophysiology remains poorly elucidated and there are limited effective treatments available. We performed metabolic profiling of plasma samples based on ultra-high-performance liquid chromatography equipped with quadrupole time-offlight mass spectrometry to explore the potential biomarkers of depression in children and adolescents with MDD. We identified several perturbed pathways, including fatty acid metabolism—particularly the polyunsaturated fatty acids metabolism, and purine metabolism—that were associated with MDD in these young patients. In addition, inosine was shown as a potential independent diagnostic biomarker for MDD, achieving an area under the ROC curve of 0.999 in discriminating drug-naive MDD patients and 0.866 in discriminating drug-treated MDD from healthy controls. Moreover, we found evidence for differences in the pathophysiology of MDD in children and adolescents to that of adult MDD, specifically with tryptophan metabolism. Through metabolomic analysis, we have identified links between a framework of metabolic perturbations and the pathophysiology and diagnostic biomarker of child and adolescent MDD.

Abstract 2509: High-resolution 1H <MRS> human plasma profiling of pancreatic ductal adenocarcinoma

Abstract Early detection of pancreatic ductal carcinoma (PDAC) is critically important because by the time PDAC is detected almost 80% of patients are surgically unresectable1. While imaging with CT, MRI and ultrasound is making significant inroads in PDAC detection, the costs associated with imaging impose a barrier for routine screening. Plasma based detection of PDAC would provide a relatively inexpensive and easy method for routine screening for the purpose of early detection. Metabolic profiling of plasma samples using high resolution 1H MRS provides an opportunity to assist in the detection of PDAC. Here we characterized the plasma metabolome of normal subjects, subjects with benign pancreatic disease, and subjects with PDAC to evaluate the ability of 1H MRS to identify metabolic changes in plasma associated with PDAC. Plasma from patients with PDAC (n=4), patients with benign pancreatic disease (n=2) and from healthy control subjects (n=4) were included in this study. Final diagnosis was established by histopathological evaluation of surgical specimens. Three hundred micro liter of D2O phosphate buffer saline (NaCl 0.9% in 90% D2O) was mixed with 300 μL of plasma and transferred to 5 mm NMR tubes. High-resolution 1H MRS was performed on an Avance 750 MHz Bruker MR spectrometer. The Carr-Purcell-Meiboom-Gill (CPMG) pulse sequence with water suppression [PRESET-90°-(d-180-d)n-Aq] was performed to remove short T2 components arising due to the presence of macromolecules2. The metabolites were identified and quantified from 1H MRS spectra from the three groups. Even with a small sample size clear differences were identified in metabolites such as lactate, pyruvate, β-hydroxybutyrate, acetate and acetoacetate in plasma from PDAC subjects compared to normal subjects. The increase of lactate and pyruvate detected in plasma may reflect altered glucose metabolism occurring in PDAC. β-hydroxybutyrate, acetate and acetoacetate are involved in ketone body synthesis and may reflect altered ketone body metabolism occurring in PDAC. In a recent study, higher levels of ketone bodies and lactate were detected in the serum of rats with 7,12-dimethylben(a)anthracen (DMBA)-induced pancreatic intraepithelial neoplasia (PanIN) compared to plasma. Interestingly, with progression to PDAC several of these metabolites decreased3. Our data support further investigation of 1H MRS of human plasma to detect PDAC in combination with techniques such as circulating tumor cell phenotyping

Plasma metabolic biomarkers for discriminating individuals with alcohol use disorders from social drinkers and alcohol-naive subjects

BackgroundAlcohol use disorders (AUD) is a phase of alcohol misuse in which the drinker consumes excessive amount of alcohol and have a continuous urge to consume alcohol which may lead to various health complications. The current methods of alcohol use disorders diagnosis such as questionnaires and some biomarkers lack specificity and sensitivity. Metabolomics is a novel scientific field which may provide a novel method for the diagnosis of AUD by using a sensitive and specific technique such as nuclear magnetic resonance (NMR). MethodsA cross sectional study was conducted on three groups: individuals with alcohol use disorders (n=30), social drinkers (n=54) and alcohol-naive controls (n=60). 1H NMR-based metabolomics was used to obtain the metabolic profiles of plasma samples. Data were processed by multivariate principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) followed by univariate and multivariate logistic regressions to produce the best fit-model for discrimination between groups. ResultsThe OPLS-DA model was able to distinguish between the AUD group and the other groups with high sensitivity, specificity and accuracy of 64.29%, 98.17% and 91.24% respectively. The logistic regression model identified two biomarkers in plasma (propionic acid and acetic acid) as being significantly associated with alcohol use disorders. The reproducibility of all biomarkers was excellent (0.81–1.0). ConclusionsThe applied plasma metabolomics technique was able to differentiate the metabolites between AUD and the other groups. These metabolites are potential novel biomarkers for diagnosis of alcohol use disorders.

Strategies for individual phenotyping of linoleic and arachidonic acid metabolism using an oral glucose tolerance test.

The ability to restore homeostasis upon environmental challenges has been proposed as a measure for health. Metabolic profiling of plasma samples during the challenge response phase should offer a profound view on the flexibility of a phenotype to cope with daily stressors. Current data modeling approaches, however, struggle to extract biological descriptors from time-resolved metabolite profiles that are able to discriminate between different phenotypes. Thus, for the case of oxylipin responses in plasma upon an oral glucose tolerance test we developed a modeling approach that incorporates a priori biological pathway knowledge. The degradation pathways of arachidonic and linoleic acids were modeled using a regression model based on a pseudo-steady-state approximated model, resulting in a parameter A that summarizes the relative enzymatic activity in these pathways. Analysis of the phenotypic parameters As suggests that different phenotypes can be discriminated according to preferred relative activity of the arachidonic and linoleic pathway. Correlation analysis shows that there is little or no competition between the arachidonic and linoleic acid pathways, although they share the same enzymes.

Forecasting individual breast cancer risk using plasma metabolomics and biocontours.

Breast cancer is a major cause of death for women. To improve treatment, current oncology research focuses on discovering and validating new biomarkers for early detection of cancer; so far with limited success. Metabolic profiling of plasma samples and auxiliary lifestyle information was combined by chemometric data fusion. It was possible to create a biocontour, which we define as a complex pattern of relevant biological and phenotypic information. While single markers or known risk factors have close to no predictive value, the developed biocontour provides a forecast which, several years before diagnosis, is on par with how well most current biomarkers can diagnose current cancer. Hence, while e.g. mammography can diagnose current cancer with a sensitivity and specificity of around 75 %, the currently developed biocontour can predict that there is an increased risk that breast cancer will develop in a subject 2–5 years after the sample is taken with sensitivity and specificity well above 80 %. The model was built on data obtained in 1993–1996 and tested on persons sampled a year later in 1997. Metabolic forecasting of cancer by biocontours opens new possibilities for early prediction of individual cancer risk and thus for efficient screening. This may provide new avenues for research into disease mechanisms.Electronic supplementary materialThe online version of this article (doi:10.1007/s11306-015-0793-8) contains supplementary material, which is available to authorized users.

Pharmacometabolomic approach to predict QT prolongation in guinea pigs.

Drug-induced torsades de pointes (TdP), a life-threatening arrhythmia associated with prolongation of the QT interval, has been a significant reason for withdrawal of several medicines from the market. Prolongation of the QT interval is considered as the best biomarker for predicting the torsadogenic risk of a new chemical entity. Because of the difficulty assessing the risk for TdP during drug development, we evaluated the metabolic phenotype for predicting QT prolongation induced by sparfloxacin, and elucidated the metabolic pathway related to the QT prolongation. We performed electrocardiography analysis and liquid chromatography–mass spectroscopy-based metabolic profiling of plasma samples obtained from 15 guinea pigs after administration of sparfloxacin at doses of 33.3, 100, and 300 mg/kg. Principal component analysis and partial least squares modelling were conducted to select the metabolites that substantially contributed to the prediction of QT prolongation. QTc increased significantly with increasing dose (r = 0.93). From the PLS analysis, the key metabolites that showed the highest variable importance in the projection values (>1.5) were selected, identified, and used to determine the metabolic network. In particular, cytidine-5′-diphosphate (CDP), deoxycorticosterone, L-aspartic acid and stearic acid were found to be final metabolomic phenotypes for the prediction of QT prolongation. Metabolomic phenotypes for predicting drug-induced QT prolongation of sparfloxacin were developed and can be applied to cardiac toxicity screening of other drugs. In addition, this integrative pharmacometabolomic approach would serve as a good tool for predicting pharmacodynamic or toxicological effects caused by changes in dose.

Effect of Korean black raspberry (Rubus coreanus Miquel) fruit administration on DNA damage levels in smokers and screening biomarker investigation using 1H-NMR-based metabolic profiling

The effect of administration of Korean black raspberry (BR) to smokers was assessed by 1H nuclear magnetic resonance (NMR) coupled with multivariate statistical analysis. In this study, to investigate the effect of BR on the level of urinary 8-hydroxy-2′-deoxyguanosine (8-OHdG), biomarker for DNA damage, 38 male smokers between 20 and 30years old were treated with BR or placebo for 4weeks. After 4weeks of administration, urinary level of 8-OHdG was measured, and the metabolic profiling of plasma samples of all volunteers were established using 1H NMR-based metabolomics techniques. The volunteers of BR administered group were divided into responder group (BR-R group) and nonresponder group (BR-NR group) based on the mean decrement of urinary 8-OHdG level of placebo group (12%). There was a clear separation among weeks 0 and 4 of seven volunteers at BR-R group in principal component analysis (PCA) derived score plots. The levels of 16 metabolites including amino acids, and organic acids changed significantly between weeks 0 and 4. Then the comparative metabolic profiling of the BR-R and BR-NR groups at week 0 was performed. There was also clear separation in PCA-derived score plot. The relative levels of glycine and trimethylamine N-oxide in plasma changed significantly between the two groups, indicating that these compounds could be used as screening biomarkers to predict the 8-OHdG modulating activity of BR in smokers.

A GC–MS metabolic profiling study of plasma samples from mice on low- and high-fat diets

Metabolic profiling of biofluids, based on the quantitative analysis of the concentration profile of their free low molecular mass metabolites, has been playing increasing role employed as a means to gain understanding of the progression of metabolic disorders, including obesity. Chromatographic methods coupled with mass spectrometry have been established as a strategy for metabolic profiling. Among these, GC–MS, targeting mainly the primary metabolism intermediates, offers high sensitivity, good peak resolution and extensive databases. However, the derivatization step required for many involatile metabolites necessitates specific data validation, normalization and analysis protocols to ensure accurate and reproducible performance. In this study, the GC–MS metabolic profiles of plasma samples from mice maintained on 12- or 15-month long low (10 kcal%) or high (60 kcal%) fat diets were obtained. The profiles of the trimethylsilyl(TMS)-methoxime(MeOx) derivatives of the free polar metabolites were acquired through GC–(ion trap)MS, using [U– 13C]-glucose as the internal standard. After the application of a recently developed data correction and normalization/filtering protocol for GC–MS metabolomic datasets, the profiles of 48 out of the 77 detected metabolites were used in multivariate statistical analysis. Data mining suggested a decrease in the activity of the energy metabolism with age. In addition, the metabolic profiles indicated the presence of subpopulations with different physiology within the high- and low-fat diet mice, which correlated well with the difference in body weight among the animals and current knowledge about hyperglycemic conditions.

A metabolic profiling strategy for biomarker screening by GC-MS combined with multivariate resolution method and Monte Carlo.

A GC-MS based metabolic profiling method via a multivariate resolution method and Monte Carlo PLS-DA is proposed for screening potential biomarkers, and applied to Type 2 diabetes mellitus. The metabolic profiles of plasma samples from healthy control and Type 2 diabetes mellitus patient groups were obtained by GC-MS, and 25 compounds considered as endogenous metabolites excluding glucose were identified. With the help of a multivariate resolution method, qualitative and quantitative results of the metabolic profiles were extracted for subsequent multivariate statistical analysis. In order to select potential biomarkers, responsible for the classification of the two groups, Monte Carlo PLS-DA was introduced. The distribution of the regression coefficients of PLS-DA models corresponding to the metabolites was obtained. The levels of metabolites with all positive coefficients were considered as decreased from healthy controls to patients, and all negative coefficients were considered as increased. Univariate t-test was employed to check for metabolites whose levels changed significantly. Metabolites identified as potential biomarkers of Type 2 diabetes mellitus were ten in total, namely lactate, alanine, α-hydroxyisobutyric acid, phosphate, serine, pyroglutamic acid, palmitic acid, stearic acid, 1-monopalmitin and cholesterol. Finally, canonical correlation analysis was used to explore the correlation between the selected ten metabolites and blood glucose, which was considered to be a routine parameter reflecting the disease state. The results showed that the ten selected metabolites correlated well with blood glucose (r = 0.81, p = 0.03), and may be considered as possible biomarkers of Type 2 diabetes mellitus. The results demonstrated that the proposed method may be a useful tool to discover potential biomarkers of diseases.

Metabolomics Reveals Relationship between Plasma Inositols and Birth Weight: Possible Markers for Fetal Programming of Type 2 Diabetes

Epidemiological studies in man and with experimental animal models have shown that intrauterine growth restriction (IUGR) resulting in low birth weight is associated with higher risk of programming welfare diseases in later life. In the pig, severe IUGR occurs naturally and contribute substantially to a large intralitter variation in birth weight and may therefore be a good model for man. In the present paper the natural form of IUGR in pigs was studied close to term by nuclear magnetic resonance (NMR-)based metabolomics. The NMR-based investigations revealed different metabolic profiles of plasma samples from low-birth weight (LW) and high-birth weight (HW) piglets, respectively, and differences were assigned to levels of glucose and myo-inositol. Further studies by GC-MS revealed that LW piglets had a significant higher concentration of myoinositol and D-chiro-inositol in plasma compared to larger littermates. Myo-inositol and D-chiro-inositol have been coupled with glucose intolerance and insulin resistance in adults, and the present paper therefore suggests that IUGR is related to impaired glucose metabolism during fetal development, which may cause type 2 diabetes in adulthood.

Abstract 1768: Comprehensive mass spectrometry based metabolomic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer

Abstract Background: Poor outcome of pancreatic cancer (PDAC) necessitates development of early diagnostic method to reduce mortality. In current study, metabolic profiling of plasma samples from selected cancer patients and noncancerous controls was performed to seek novel metabolic biomarkers of PDAC. Comprehensive mass spectrometry based analytical platform were applied for detection of compounds in the plasma from early stage PDAC patients. Methods: Plasma available from the patients involved in the IRB-approved UC Davis Pancreas Registry was utilized. Five histologically proven cases of early stage pancreatic cancer without clinical evidence of distant metastasis [AJCC TNM stages: IB (2) and IIB (3)] and 5 noncancerous cases [3 chronic pancreatitis, 2 normal-based on clinical and follow-up evidences] were chosen from the Registry. Combination mass spectrometry (MS) methods, GC-TOF-, HILIC-LC-ESI-, and RP-LC-ESI-MS were applied for the plasma analysis. Data values obtained were evaluated for principle component analysis (PCA). Further feature selection and ANOVA were conducted after the PCA. Statistical significance was defined as p-value less than 0.05. Feature components with statistically significant classifiers of the PDAC were analyzed further with LTQ-Orbitrap MS for accurate mass measurement. Mass spectra of potential biomarkers thus obtained were spectrally corrected and unique elemental formula was searched against CAS database using the strategy of Explore Substances-Chemical Structure for known compounds. Mass Frontier 5.0 was used for MS/MS fragmentation modeling analysis; with parent compounds that had the best match of MS/MS fragmentation pattern were considered as the molecular structure of the potential biomarkers. Results: The candidate biomarkers included amino acids (N-methylalanine, lysine, glutamine, phenylalanine), fatty acids (arachidonic acid), lipids (lysoPC (18:2), PC (34:2), PE (26:0)), and bile acids (tauroursodeoxycholic and taurocholic acids, deoxycholylglycine, cholylglycine). Conclusion: We have demonstrated that combination of the comprehensive mass spectrometry based metabolic profiling techniques, data mining, and data analysis successfully find potent classifiers for pancreatic cancer in plasma. These metabolites upon validation, selectivity study, and broader cohort study could become clinical biomarkers for early diagnosis of pancreatic cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1768.

Comprehensive mass spectrometry based metabolic profiling of blood plasma reveals potent discriminatory classifiers of pancreatic cancer

Poor outcome of pancreatic cancer necessitates development of an early diagnostic method to reduce mortality. No reliable early diagnostic test for pancreatic cancer detection has been developed and validated to date. In the current study, metabolic profiling of plasma samples from selected cancer patients and noncancerous controls was performed to seek novel metabolic biomarkers of pancreatic cancer. A comprehensive mass spectrometry based analytical platform established at the Metabolomics Core of the UC Davis Genome Center allowed detection of multiple compounds previously unreported in plasma from pancreatic cancer patients. It was found that selective amino acids, bile acids, and polar lipids were detected with increased or decreased levels in pancreatic cancer samples compared to controls. These findings on blood plasma levels of the relevant metabolites might be very useful clinical parameters for early diagnosis of pancreatic cancer.