Metabolomics: available results, current research projects in breast cancer, and future applications

Plasma free fatty acid levels in cervical cancer: concurrent chemoradiotherapy improves abnormal profile

Background: Epidemiology has demonstrated that plasma free fatty acids (FFAs) can prevent the development of cancer. Our study sought to evaluate the relationship between plasma (FFA) levels and cervical cancer. Methods: In recent years, metabolomics-based approaches have been recognized as an emerging tool, so we examined the plasma FFA profiles of 114 patients with cervical cancer and 151 healthy people using liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods. Results: The data results were analyzed by multifactorial binary logistic regression analysis, and it was found that palmitic acid, docosahexaenoic acid (DHA), and total ω-3 fatty acids were negatively correlated with the risk of cervical cancer; whereas tetracosanoic acid was positively correlated with the risk of cervical cancer (OR, 1.026; 95% CI, 1.013-1.040; p < 0.001). Dynamic follow-up of 40 cervical cancer patients who successfully completed CCRT revealed that most fatty acid levels tended to increase after the end of treatment, except for palmitic and stearic acid levels, which were lower than before treatment. Conclusion: Plasma FFA profiles were altered in cervical cancer patients, which may be related to abnormal fatty acid metabolism in cervical cancer. The described changes in fatty acid profiles during CCRT may be related to the good functioning of CCRT. Further studies on plasma FFA composition and its changes due to CCRT in patients with cervical cancer are warranted.

Metabolic Signatures: Pioneering the Frontier of Rectal Cancer Diagnosis and Response to Neoadjuvant Treatment with Biomarkers-A Systematic Review

Colorectal cancer (CRC) is one of the most aggressive, heterogenous, and fatal types of human cancer for which screening, and more effective therapeutic drugs are urgently needed. Early-stage detection and treatment greatly improve the 5-year survival rate. In the era of targeted therapies for all types of cancer, a complete metabolomic profile is mandatory before neoadjuvant therapy to assign the correct drugs and check the response to the treatment given. The aim of this study is to discover specific metabolic biomarkers or a sequence of metabolomic indicators that possess precise diagnostic capabilities in predicting the efficacy of neoadjuvant therapy. After searching the keywords, a total of 108 articles were identified during a timeframe of 10 years (2013-2023). Within this set, one article was excluded due to the use of non-English language. Six scientific papers were qualified for this investigation after eliminating all duplicates, publications not referring to the subject matter, open access restriction papers, and those not applicable to humans. Biomolecular analysis found a correlation between metabolomic analysis of colorectal cancer samples and poor progression-free survival rates. Biomarkers are instrumental in predicting a patient's response to specific treatments, guiding the selection of targeted therapies, and indicating resistance to certain drugs.

Metabolomics and lipidomics signature in celiac disease: a narrative review

Celiac disease (CD) is a chronic immune-mediated inflammatory disease of the small intestine caused by aberrant immune responses to consumed gluten proteins. CD is diagnosed by a combination of the patients reported symptoms, serologic and endoscopic biopsy evaluation of the small intestine; and adherence to a strict gluten-free diet (GFD) is considered the only available therapeutic approach for this disorder. Novel approaches need to be considered for finding new biomarkers to help this disorder diagnosis and finding a new alternative therapeutic method for this group of patients. Metabolomics and lipidomics are powerful tools to provide highly accurate and sensitive biomarkers. Previous studies indicated a metabolic fingerprint for CD deriving from alterations in gut microflora or intestinal permeability, malabsorption, and energy metabolism. Moreover, since CD is characterized by increased intestinal permeability and due to the importance of membrane lipid components in controlling barrier integrity, conducting lipidomics studies in this disorder is of great importance. In the current study, we tried to provide a critical overview of metabolomic and lipidomic changes in CD.

Multiomics insights on the onset, progression, and metastatic evolution of breast cancer

Breast cancer is the most common malignant neoplasm in women. Despite progress to date, 700,000 women worldwide died of this disease in 2020. Apparently, the prognostic markers currently used in the clinic are not sufficient to determine the most appropriate treatment. For this reason, great efforts have been made in recent years to identify new molecular biomarkers that will allow more precise and personalized therapeutic decisions in both primary and recurrent breast cancers. These molecular biomarkers include genetic and post-transcriptional alterations, changes in protein expression, as well as metabolic, immunological or microbial changes identified by multiple omics technologies (e.g., genomics, epigenomics, transcriptomics, proteomics, glycomics, metabolomics, lipidomics, immunomics and microbiomics). This review summarizes studies based on omics analysis that have identified new biomarkers for diagnosis, patient stratification, differentiation between stages of tumor development (initiation, progression, and metastasis/recurrence), and their relevance for treatment selection. Furthermore, this review highlights the importance of clinical trials based on multiomics studies and the need to advance in this direction in order to establish personalized therapies and prolong disease-free survival of these patients in the future.

Endocrine-disrupting compounds and metabolomic reprogramming in breast cancer

Endocrine-disrupting chemicals pose a growing threat to human health through their increasing presence in the environment and their potential interactions with the mammalian endocrine systems. Due to their structural similarity to hormones like estrogen, these chemicals can interfere with endocrine signaling, leading to many deleterious effects. Exposure to estrogenic endocrine-disrupting compounds (EDC) is a suggested risk factor for the development of breast cancer, one of the most frequently diagnosed cancers in women. However, the mechanisms through which EDCs contribute to breast cancer development remain elusive. To rapidly proliferate, cancer cells undertake distinct metabolic programs to utilize existing nutrients in the tumor microenvironment and synthesize macromolecules de novo. EDCs are known to dysregulate cell signaling pathways related to cellular metabolism, which may be an important mechanism through which they exert their cancer-promoting effects. These altered pathways can be studied via metabolomic analysis, a new advancement in -omics technologies that can interrogate molecular pathways that favor cancer development and progression. This review will summarize recent discoveries regarding EDCs and the metabolic reprogramming that they may induce to facilitate the development of breast cancer.

A Pilot Study on Biochemical Profile of Follicular Fluid in Breast Cancer Patients

Breast cancer (BC) is the most common type of cancer among women in almost all countries worldwide and is one of the oncological pathologies for which is indicated fertility preservation, a type of procedure used to help keep a person's ability to have children. Follicular fluid (FF) is a major component of oocyte microenvironment, which is involved in oocyte growth, follicular maturation, and in communication between germ and somatic cells; furthermore, it accumulates all metabolites during oocytes growth. To obtain information about changes on fertility due to cancer, we aimed at investigating potential biomarkers to discriminate between FF samples obtained from 16 BC patients and 10 healthy women undergoing in vitro fertilization treatments. An NMR-based metabolomics approach was performed to investigate the FF metabolic profiles; ELISA and western blotting assays were used to investigate protein markers of oxidative and inflammatory stress, which are processes closely related to cancer. Our results seem to suggest that FFs of BC women display some significant metabolic alterations in comparison to healthy controls, and these variations are also related with tumor staging.

Risk assessment of disease recurrence in early breast cancer: A serum metabolomic study focused on elderly patients

BACKGROUND

We previously showed that metabolomics predicts relapse in early breast cancer (eBC) patients, unselected by age. This study aims to identify a "metabolic signature" that differentiates eBC from advanced breast cancer (aBC) patients, and to investigate its potential prognostic role in an elderly population.

METHODS

Serum samples from elderly breast cancer (BC) patients enrolled in 3 onco-geriatric trials, were retrospectively analyzed via proton nuclear magnetic resonance (1H NMR) spectroscopy. Three nuclear magnetic resonance (NMR) spectra were acquired for each serum sample: NOESY1D, CPMG, Diffusion-edited. Random Forest (RF) models to predict BC relapse were built on NMR spectra, and resulting RF risk scores were evaluated by Kaplan-Meier curves.

RESULTS

Serum samples from 140 eBC patients and 27 aBC were retrieved. In the eBC cohort, median age was 76 years; 77% of patients had luminal, 10% HER2-positive and 13% triple negative (TN) BC. Forty-two percent of patients had tumors >2 cm, 43% had positive axillary nodes. Using NOESY1D spectra, the RF classifier discriminated free-from-recurrence eBC from aBC with sensitivity, specificity and accuracy of 81%, 67% and 70% respectively. We tested the NOESY1D spectra of each eBC patient on the RF models already calculated. We found that patients classified as "high risk" had higher risk of disease recurrence (hazard ratio (HR) 3.42, 95% confidence interval (CI) 1.58-7.37) than patients at low-risk.

CONCLUSIONS

This analysis suggests that a "metabolic signature", identified employing NMR fingerprinting, is able to predict the risk of disease recurrence in elderly patients with eBC independently from standard clinicopathological features.

Molecular imaging in oncology: Current impact and future directions

The authors define molecular imaging, according to the Society of Nuclear Medicine and Molecular Imaging, as the visualization, characterization, and measurement of biological processes at the molecular and cellular levels in humans and other living systems. Although practiced for many years clinically in nuclear medicine, expansion to other imaging modalities began roughly 25 years ago and has accelerated since. That acceleration derives from the continual appearance of new and highly relevant animal models of human disease, increasingly sensitive imaging devices, high-throughput methods to discover and optimize affinity agents to key cellular targets, new ways to manipulate genetic material, and expanded use of cloud computing. Greater interest by scientists in allied fields, such as chemistry, biomedical engineering, and immunology, as well as increased attention by the pharmaceutical industry, have likewise contributed to the boom in activity in recent years. Whereas researchers and clinicians have applied molecular imaging to a variety of physiologic processes and disease states, here, the authors focus on oncology, arguably where it has made its greatest impact. The main purpose of imaging in oncology is early detection to enable interception if not prevention of full-blown disease, such as the appearance of metastases. Because biochemical changes occur before changes in anatomy, molecular imaging-particularly when combined with liquid biopsy for screening purposes-promises especially early localization of disease for optimum management. Here, the authors introduce the ways and indications in which molecular imaging can be undertaken, the tools used and under development, and near-term challenges and opportunities in oncology.

Discovery of metabolomic biomarkers for discriminating platinum-sensitive and platinum-resistant ovarian cancer by using GC-MS

This study aims to determine ovarian cancer (OC) patients with platinum resistance for alternative treatment protocols by using metabolomic methodologies. Urine and serum samples of platinum-resistant and platinum-sensitive OC were analyzed using GC-MS. After data processing of GC-MS raw data, multivariate analyses were performed to interpret complex data for biologically meaningful information and to identify the biomarkers that cause differences between two groups. The biomarkers were verified after univariate, multivariate, and ROC analysis. Finally, metabolomic pathways related to group separations were specified. The results of biomarker analysis showed that 3,4-dihydroxyphenylacetic acid, 4-hydroxybutyric acid, L-threonine, D- mannose, and sorbitol metabolites were potential biomarkers in urine samples. In serum samples, L-arginine, linoleic acid, L-glutamine, and hypoxanthine were identified as important biomarkers. R2Y, Q2, AUC, sensitivity and specificity values of platinum-resistant and sensitive OC patients' urine and serum samples were 0.85, 0.545, 0.844, 91.30%, 81.08 and 0.570, 0.206, 0.743, 77.78%, 74.28%, respectively. In metabolic pathway analysis of urine samples, tyrosine metabolism and fructose and mannose metabolism were found to be statistically significant (p < 0.05) for the discrimination of the two groups. While 3,4-dihydroxyphenylacetic acid, L-tyrosine, and fumaric acid metabolites were effective in tyrosine metabolism. D-sorbitol and D-mannose metabolites were significantly important in fructose and mannose metabolism. However, seven metabolomic pathways were significant (p < 0.05) in serum samples. In terms of p-value, L-glutamine in the nitrogen metabolic pathway from the first three pathways; L-glutamine and pyroglutamic acid metabolites in D-glutamine and D-glutamate metabolism. In the arginine and proline metabolic pathway, L-arginine, L-proline, and L-ornithine metabolites differed significantly between the two groups.

Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

Metabolomics-based technologies map in vivo biochemical changes that may be used as early indicators of pathological abnormalities prior to the development of clinical symptoms in neurological conditions. Metabolomics may also reveal biochemical pathways implicated in tissue dysfunction and damage and thus assist in the development of novel targeted therapeutics for neuroinflammation and neurodegeneration. Metabolomics holds promise as a non-invasive, high-throughput and cost-effective tool for early diagnosis, follow-up and monitoring of treatment response in multiple sclerosis (MS), in combination with clinical and imaging measures. In this review, we offer evidence in support of the potential of metabolomics as a biomarker and drug discovery tool in MS. We also use pathway analysis of metabolites that are described as potential biomarkers in the literature of MS biofluids to identify the most promising molecules and upstream regulators, and show novel, still unexplored metabolic pathways, whose investigation may open novel avenues of research.

Fecal metabolomic profiles: A comparative study of patients with colorectal cancer vs adenomatous polyps

BACKGROUND

Colorectal cancer (CRC), the third most common cause of death in both males and females worldwide, shows a positive response to therapy and usually a better prognosis when detected at an early stage. However, the survival rate declines when the diagnosis is late and the tumor spreads to other organs. Currently, the measures widely used in the clinic are fecal occult blood test and evaluation of serum tumor markers, but the lack of sensitivity and specificity of these markers restricts their use for CRC diagnosis. Due to its high sensitivity and precision, colonoscopy is currently the gold-standard screening technique for CRC, but it is a costly and invasive procedure. Therefore, the implementation of custom-made methodologies including those with minimal invasiveness, protection, and reproducibility is highly desirable. With regard to other screening methods, the screening of fecal samples has several benefits, and metabolomics is a successful method to classify the metabolite shift in living systems as a reaction to pathophysiological influences, genetic modifications, and environmental factors.

AIM

To characterize the variation groups and potentially recognize some diagnostic markers, we compared with healthy controls (HCs) the fecal nuclear magnetic resonance (NMR) metabolomic profiles of patients with CRC or adenomatous polyposis (AP).

METHODS

Proton nuclear magnetic resonance spectroscopy was used in combination with multivariate and univariate statistical approaches, to define the fecal metabolic profiles of 32 CRC patients, 16 AP patients, and 38 HCs well matched in age, sex, and body mass index.

RESULTS

NMR metabolomic analyses revealed that fecal sample profiles differed among CRC patients, AP patients, and HCs, and some discriminatory metabolites including acetate, butyrate, propionate, 3-hydroxyphenylacetic acid, valine, tyrosine and leucine were identified.

CONCLUSION

In conclusion, we are confident that our data can be a forerunner for future studies on CRC management, especially the diagnosis and evaluation of the effectiveness of treatments.

A Scoping Review of Technologies and Their Applicability for Exposome-Based Risk Assessment in the Oil and Gas Industry

INTRODUCTION

Oil and gas workers have been shown to be at increased risk of chronic diseases including cancer, asthma, chronic obstructive pulmonary disease, and hearing loss, among others. Technological advances may be used to assess the external (e.g. personal sensors, smartphone apps and online platforms, exposure models) and internal exposome (e.g. physiologically based kinetic modeling (PBK), biomonitoring, omics), offering numerous possibilities for chronic disease prevention strategies and risk management measures. The objective of this study was to review the literature on these technologies, by focusing on: (i) evaluating their applicability for exposome research in the oil and gas industry, and (ii) identifying key challenges that may hamper the successful application of such technologies in the oil and gas industry.

METHOD

A scoping review was conducted by identifying peer-reviewed literature with searches in MEDLINE/PubMed and SciVerse Scopus. Two assessors trained on the search strategy screened retrieved articles on title and abstract. The inclusion criteria used for this review were: application of the aforementioned technologies at a workplace in the oil and gas industry or, application of these technologies for an exposure relevant to the oil and gas industry but in another occupational sector, English language and publication period 2005-end of 2019.

RESULTS

In total, 72 articles were included in this scoping review with most articles focused on omics and bioinformatics (N = 22), followed by biomonitoring and biomarkers (N = 20), external exposure modeling (N = 11), PBK modeling (N = 10), and personal sensors (N = 9). Several studies were identified in the oil and gas industry on the application of PBK models and biomarkers, mainly focusing on workers exposed to benzene. The application of personal sensors, new types of exposure models, and omics technology are still in their infancy with respect to the oil and gas industry. Nevertheless, applications of these technologies in other occupational sectors showed the potential for application in this sector.

DISCUSSION AND CONCLUSION

New exposome technologies offer great promise for personal monitoring of workers in the oil and gas industry, but more applied research is needed in collaboration with the industry. Current challenges hindering a successful application of such technologies include (i) the technological readiness of sensors, (ii) the availability of data, (iii) the absence of standardized and validated methods, and (iv) the need for new study designs to study the development of disease during working life.

Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics

BACKGROUND

Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks.

RESULTS

We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism.

CONCLUSIONS

In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.

A Serum Metabolomics Classifier Derived from Elderly Patients with Metastatic Colorectal Cancer Predicts Relapse in the Adjuvant Setting

Simple Summary

Around 30–40% of patients with early stage colorectal cancer (eCRC) experience relapse after surgery. Current recommendations for adjuvant therapy are based on suboptimal risk-stratification tools. In elderly patients, risk of relapse assessment is particularly important to ultimately avoid unnecessary chemotherapy-related toxicity in this frailer population. Serum metabolomics via NMR spectroscopy may improve risk stratification by identifying patients with residual micrometastases after surgery and thus at higher risk of relapse. We evaluated the serum metabolomic fingerprints of 94 elderly patients with eCRC (65 relapse free and 29 relapsed), and of 75 elderly patients with metastatic disease. Metabolomics efficiently discriminated patients with relapse-free eCRC from those with metastatic disease, correctly predicting relapse in 69% of relapsed eCRC patients. The metabolomic score was strongly and independently associated with prognosis. Our data suggest metabolomics as a valid addition to standard tools to refine risk stratification for eCRC and warrant further investigation.

Abstract

Adjuvant treatment for patients with early stage colorectal cancer (eCRC) is currently based on suboptimal risk stratification, especially for elderly patients. Metabolomics may improve the identification of patients with residual micrometastases after surgery. In this retrospective study, we hypothesized that metabolomic fingerprinting could improve risk stratification in patients with eCRC. Serum samples obtained after surgery from 94 elderly patients with eCRC (65 relapse free and 29 relapsed, after 5-years median follow up), and from 75 elderly patients with metastatic colorectal cancer (mCRC) obtained before a new line of chemotherapy, were retrospectively analyzed via proton nuclear magnetic resonance spectroscopy. The prognostic role of metabolomics in patients with eCRC was assessed using Kaplan–Meier curves. PCA-CA-kNN could discriminate the metabolomic fingerprint of patients with relapse-free eCRC and mCRC (70.0% accuracy using NOESY spectra). This model was used to classify the samples of patients with relapsed eCRC: 69% of eCRC patients with relapse were predicted as metastatic. The metabolomic classification was strongly associated with prognosis (p-value 0.0005, HR 3.64), independently of tumor stage. In conclusion, metabolomics could be an innovative tool to refine risk stratification in elderly patients with eCRC. Based on these results, a prospective trial aimed at improving risk stratification by metabolomic fingerprinting (LIBIMET) is ongoing.

Metabolomics-guided global pathway analysis reveals better insights into the metabolic alterations of breast cancer

Accurate metabolome measurements are critical for improved insights into breast cancer metabolic disturbances and enhanced exploration of novel therapeutic targets. Nevertheless, conventional functional interpretation is limited by metabolite identification capacity, which diminishes the scientific value of untargeted metabolomics analyses. In this study, we conducted a metabolomics-guided global pathway meta-analysis to investigate the metabolic alterations of breast cancer. Metabolic features were directly investigated in the pathway meta-analysis to identify breast cancer-associated metabolic processes. Conventional pathway analysis was also conducted involving identified metabolites alone. Comparison of the two strategies revealed that the global pathway meta-analysis approach could avoid the loss of functionally relevant information, relative to the conventional analysis findings. Furthermore, the pathway meta-analysis accurately captured alterations in the following components of the breast cancer metabolome: central carbon metabolism, oxidative glutamine metabolism, purine metabolism, nonessential amino acid metabolism, and glutathione metabolism. There were also substantial alterations of fatty acyl carnitine species and fatty acid β-oxidation processes. These pathways contribute to breast cancer initiation, progression, metastasis, and drug resistance. In conclusion, we suggest that global pathway analysis and the conventional approach with identified metabolites should be employed together to maximize the exploration of breast cancer's metabolic landscape.

Serum metabolite signatures of epithelial ovarian cancer based on targeted metabolomics

BACKGROUND

Epithelial ovarian cancer (EOC) is a common gynecological cancer with high mortality rates. The main objective of this study was to investigate the serum amino acid and organic acid profiles to distinguish key metabolites for screening EOC patients.

METHODS

In total, 39 patients with EOC and 31 healthy controls were selected as the training set. Serum amino acid and organic acid profiles were determined using the targeted metabolomics approach. Metabolite profiles were processed via multivariate analysis to identify potential metabolites and construct a metabolic network. Finally, a test dataset derived from 29 patients and 28 healthy controls was constructed to validate the potential metabolites.

RESULTS

Distinct amino acid and organic acid profiles were obtained between EOC and healthy control groups. Methionine, glutamine, asparagine, glutamic acid and glycolic acid were identified as potential metabolites to distinguish EOC from control samples. The areas under the curve for methionine, glutamine, asparagine, glutamic acid and glycolic acid were 0.775, 0 778, 0.955, 0.874 and 0.897, respectively, in the validation study. Metabolic network analysis of the training set indicated key roles of alanine, aspartate and glutamate metabolism as well as D-glutamine and D-glutamate metabolism in the pathogenesis of EOC.

CONCLUSIONS

Amino acid and organic acid profiles may serve as potential screening tools for EOC. Data from this study provide useful information to bridge gaps in the understanding of the amino acid and organic acid alterations associated with epithelial ovarian cancer.

NMR metabolomic profiles associated with long-term risk of prostate cancer

INTRODUCTION

Prostate cancer is a multifactorial disease whose aetiology is still not fully understood. Metabolomics, by measuring several hundred metabolites simultaneously, could enhance knowledge on the metabolic changes involved and the potential impact of external factors.

OBJECTIVES

The aim of the present study was to investigate whether pre-diagnostic plasma metabolomic profiles were associated with the risk of developing a prostate cancer within the following decade.

METHODS

A prospective nested case-control study was set up among the 5141 men participant of the SU.VI.MAX cohort, including 171 prostate cancer cases, diagnosed between 1994 and 2007, and 171 matched controls. Nuclear magnetic resonance (NMR) metabolomic profiles were established from baseline plasma samples using NOESY1D and CPMG sequences. Multivariable conditional logistic regression models were computed for each individual NMR signal and for metabolomic patterns derived using principal component analysis.

RESULTS

Men with higher fasting plasma levels of valine (odds ratio (OR) = 1.37 [1.07-1.76], p = .01), glutamine (OR = 1.30 [1.00-1.70], p = .047), creatine (OR = 1.37 [1.04-1.80], p = .02), albumin lysyl (OR = 1.48 [1.12-1.95], p = .006 and OR = 1.51 [1.13-2.02], p = .005), tyrosine (OR = 1.40 [1.06-1.85], p = .02), phenylalanine (OR = 1.39 [1.08-1.79], p = .01), histidine (OR = 1.46 [1.12-1.88], p = .004), 3-methylhistidine (OR = 1.37 [1.05-1.80], p = .02) and lower plasma level of urea (OR = .70 [.54-.92], p = .009) had a higher risk of developing a prostate cancer during the 13 years of follow-up.

CONCLUSIONS

This exploratory study highlighted associations between baseline plasma metabolomic profiles and long-term risk of developing prostate cancer. If replicated in independent cohort studies, such signatures may improve the identification of men at risk for prostate cancer well before diagnosis and the understanding of this disease.

Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers

OBJECTIVE

To characterize the serum metabolomic profile and its role in the prediction of poor ovarian response (POR).

PATIENTS

Twenty-five women with normal ovarian reserve (24-33 years, antral follicle count [AFC] ≥5, anti-Müllerian hormone [AMH] ≥1.2 ng/ml) as the control group and another twenty-five women with POR (19-35 years, AFC <5, AMH < 1.2 ng/ml) as the study group were collected in our study. The serum levels of the women in both groups were determined from their whole blood by untargeted liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analysis and cell signal pathways analysis were used to reveal the results.

RESULTS

A total of 538 different metabolites were finally identified in the two groups. Tetracosanoic acid, 2-arachidonoylglycerol, lidocaine, cortexolone, prostaglandin H2,1-naphthylamine, 5-hydroxymethyl-2-furancarboxaldehyde, 2,4-dinitrophenol, and D-erythrulose1-phosphate in POR were significantly different from control as were most important metabolites in support vector machines (p <0.05). Metabolomic profiling, together with support vector machines and pathway analysis found that the nicotinate and nicotinamide metabolism pathway, including L-aspartic acid, 6-hydroxynicotinate, maleic acid, and succinic acid semialdehyde, was identified to have significant differences in POR women compared to control women, which may be associated with ovarian reserve.

CONCLUSION

This study indicated that LC-MS-based untargeted metabolomics analysis of serum provided biological markers for women with POR. The nicotinate and nicotinamide metabolism pathway may offer new insight into the complementary prediction and therapeutic potential of POR. The functional associations of these metabolites need further investigation.

Serum biomarkers of the calcium-deficient rats identified by metabolomics based on UPLC/Q-TOF MS/MS

BACKGROUND

We previously identified the urinary biomarkers to diagnose calcium deficiency and nutritional rickets by ultra-performance liquid chromatography/quadrupole time-of-flight tandem mass spectrometry (UPLC/Q-TOF MS/MS). To find biomarkers of calcium deficiency and further confirm these biomarkers in serum, we performed serum metabolomics analysis of calcium-deficient rats.

METHODS

A calcium-deficient rat model was established with a low-calcium diet for 12 weeks. Serum metabolomics based UPLC/Q-TOF MS/MS and multivariate statistical analysis was performed to identify the alterations in metabolites associated with calcium deficiency in rats.

RESULTS

Bone mineral density, serum parathyroid hormone and alkaline phosphatase were significantly decreased in the low-calcium diet group (LCG) compared to the normal calcium diet group (NCG). Serum metabolic-profiling analysis could definitively distinguish between the LCG and NCG and identified 24 calcium-deficient biomarkers. Three metabolites (indoxyl sulfate, phosphate, and taurine) of the 24 biomarkers were found in our previous urinary metabolomics study of rats with a calcium deficiency and nutritional rickets. The areas under the curve (AUCs) of these three biomarkers were greater than 0.8, and the combination of any two biomarkers was higher than 0.95.

CONCLUSION

Dietary calcium deficiency induced the alterations of metabolites in the serum of rats, and the three identified biomarkers had relatively high diagnostic values for calcium deficiency in rats.

Identification of potential metabolite markers for colon cancer and rectal cancer using serum metabolomics

Background

To determine the metabolic characteristics of patients with colon cancer (CC) and rectal cancer (RC) using gas chromatography‐mass spectrometry (GC‐MS)‐based metabolomics.

Methods

In this study, serum samples were collected from 22 CC patients and 23 RC patients preoperatively and postoperatively and 45 healthy volunteers (HVs), and subjected to metabolomics analysis by GC‐MS. Differential metabolites in the preoperative RC and CC samples and HVs were identified as potential biomarkers and evaluated for their utilities by receiver operating characteristic analyses.

Results

The different metabolic markers between CC and RC patients were identified, which may assist in distinguishing the two types of cancers. The area under the curve (AUC) was 0.805 for combination of d‐glucose and d‐mannose for CC diagnosis, and 0.889 for combination of 2‐aminobutanoic acid, 3‐hydroxypyridine, d‐glucose, d‐mannose, isoleucine, l‐tryptophan, urea, and uric acid for RC diagnosis. The combinations of metabolite markers showed a better predictability than CEA and CA199 two commonly used protein markers for CRC diagnosis in clinical practice. Combining the metabolite markers with these two protein markers effectively improved the diagnostic accuracy with the AUC reaching 0.936 and 0.937 for CC and RC diagnosis, respectively.

Conclusions

Metabolic profiles are different in the blood samples between CC and RC patients. The study has established a panel of metabolic markers as a predictive and multiplexing signature for CC and RC diagnosis.

Using post-column infused internal standard assisted quantitative metabolomics for establishing prediction models for breast cancer detection

RATIONALE

Breast cancer is one of the most common cancers among women and its associated mortality is on the rise. Metabolomics is a potential strategy for breast cancer detection. The post-column infused internal standard (PCI-IS)-assisted liquid chromatography/tandem mass spectrometry (LC/MS/MS) method has been demonstrated as an effective strategy for quantitative metabolomics. In this study, we evaluated the performance of targeted metabolomics with the PCI-IS quantification method to identify women with breast cancer.

METHODS

We used metabolite profiling to identify 17 dysregulated metabolites in breast cancer patients. Two LC/MS/MS methods in combination with the PCI-IS strategy were developed to quantify these metabolites in plasma samples. Detection models were built through the analysis of plasma samples from 176 subjects consisting of healthy volunteers and breast cancer patients.

RESULTS

Three isotope standards were selected as the PCI-ISs for the metabolites. The accuracy was within 82.8-114.16%, except for citric acid and lactic acid at high concentration levels. The repeatability and intermediate precision were all lower than 15% relative standard deviation. We have identified several metabolites that indicate the presence of breast cancer. The area under the receiver operating characteristics (AUROC) curve, sensitivity and specificity of the linear combinations of metabolite concentrations and age with the highest AUROC were 0.940 (0.889-0.992), 88.4% and 94.2% for pre-menopausal woman, respectively, and 0.828 (0.734-0.922), 73.5% and 85.1% for post-menopausal women, respectively.

CONCLUSIONS

The targeted metabolomics with PCI-IS quantification method successfully established prediction models for breast cancer detection. Further study is essential to validate these proposed markers.

Heavy metals interfere with plasma metabolites, including lipids and amino acids, in patients with breast cancer

The aim of the present study was to examine the association between plasma heavy metals and the metabolome in patients with breast cancer (BC), and the association with cancer development. Nuclear magnetic resonance was used to determine the metabolites involved and an inductively coupled plasma mass spectrometry system was used to quantify the heavy metals in the plasma samples. It was indicated that cadmium was significantly higher in the plasma of patients with BC compared with that in the control population (~15-fold increase). Chromium, arsenic and lead were also elevated in the plasma of patients with BC by ~3.24, 2.14 and 1.52 fold, respectively. A number of small molecules, including amino acids and salts, were altered in the plasma of patients with BC compared with the control population. Another notable finding in this investigation was that plasma lipid levels were elevated in patients with BC compared with those in the control population. The findings of the present study suggest that exposure to heavy metals, including cadmium, arsenic, chromium and lead, may influence blood lipid levels and other small molecule metabolites, which in turn may be involved in BC development. Further studies surrounding urinary heavy metals and the metabolome are required to further determine the impact of metals on metabolism and on BC development.

Metabolomic tools used in marine natural product drug discovery

Introduction: The marine environment is a very promising resource for natural product research, with many of these reaching the market as new drugs, especially in the field of cancer therapy as well as the drug discovery pipeline for new antimicrobials. Exploitation for bioactive marine compounds with unique structures and novel bioactivity such as the isoquinoline alkaloid; trabectedin, the polyether macrolide; halichondrin B, and the peptide; dolastatin 10, requires the use of analytical techniques, which can generate unbiased, quantitative, and qualitative data to benefit the biodiscovery process. Metabolomics has shown to bridge this understanding and facilitate the development of new potential drugs from marine sources and particularly their microbial symbionts.Areas covered: In this review, articles on applied secondary metabolomics ranging from 1990-2018 as well as to the last quarter of 2019 were probed to investigate the impact of metabolomics on drug discovery for new antibiotics and cancer treatment.Expert opinion: The current literature review highlighted the effectiveness of metabolomics in the study of targeting biologically active secondary metabolites from marine sources for optimized discovery of potential new natural products to be made accessible to a R&D pipeline.

Evaluation of heavy metals and metabolites in the urine of patients with breast cancer

Epidemiologic studies demonstrated that the environment serves a crucial role in cancer development. Heavy metals, including arsenic (As), cadmium (cd), chromium (Cr), lead and mercury, are considered to be carcinogens or co-carcinogens. Furthermore, Cd has been detected in breast cancer (BC) tissue at high concentrations. The present study aimed to investigate the correlation between heavy metals detected in urine and urine metabolome of patients with BC, and their association with cancer development. Nuclear magnetic resonance was used to determine urine metabolites and an inductively coupled plasma mass spectrometry system was used to detect heavy metals in urine samples. The results demonstrated that Cd was markedly increased in the urine of patients with BC compared with the control population (approximately 2-fold). Cr and As were also increased in the urine of patients with BC. In addition, numerous small molecule metabolites were altered in the urine of patients with BC compared with the control population. This study also demonstrated that alterations in small molecule metabolites in the urine of patients with BC were very similar to results from a previous report. These findings indicated that environmental exposure to Cd, As, or Cr could influence the urine levels of metabolites, which may be involved in BC development. Further investigation is therefore required to examine a larger range of samples from different countries or areas in order to understand the impact of heavy metals on metabolism and BC development.

Improved workflow for mass spectrometry-based metabolomics analysis of the heart

MS-based metabolomics methods are powerful techniques to map the complex and interconnected metabolic pathways of the heart; however, normalization of metabolite abundance to sample input in heart tissues remains a technical challenge. Herein, we describe an improved GC-MS-based metabolomics workflow that uses insoluble protein-derived glutamate for the normalization of metabolites within each sample and includes normalization to protein-derived amino acids to reduce biological variation and detect small metabolic changes. Moreover, glycogen is measured within the metabolomics workflow. We applied this workflow to study heart metabolism by first comparing two different methods of heart removal: the Langendorff heart method (reverse aortic perfusion) and in situ freezing of mouse heart with a modified tissue freeze-clamp approach. We then used the in situ freezing method to study the effects of acute β-adrenergic receptor stimulation (through isoproterenol (ISO) treatment) on heart metabolism. Using our workflow and within minutes, ISO reduced the levels of metabolites involved in glycogen metabolism, glycolysis, and the Krebs cycle, but the levels of pentose phosphate pathway metabolites and of many free amino acids remained unchanged. This observation was coupled to a 6-fold increase in phosphorylated adenosine nucleotide abundance. These results support the notion that ISO acutely accelerates oxidative metabolism of glucose to meet the ATP demand required to support increased heart rate and cardiac output. In summary, our MS-based metabolomics workflow enables improved quantification of cardiac metabolites and may also be compatible with other methods such as LC or capillary electrophoresis.

Metabolomic analysis of serum may refine 21-gene expression assay risk recurrence stratification

Despite recent refinements to the 21-gene g score, allowing a better identification of patients who may derive no benefit from the addition of adjuvant chemotherapy to that of endocrine therapy, patients with early breast cancer still stand to be over-treated in the setting of clinical and/or genomic uncertainty or discordance. Here we describe and demonstrate a potential approach of further refining the OncotypeDX risk score by metabolomic analysis of serum. In a clinical dataset (N = 87), the risk of recurrence was further sub-stratified by metabolomic signature, with an effective splitting of each Oncotype risk classification. A total of seven recurrences were recorded, with metabolomic analysis accurately predicting six of these. Contrastingly, the genomic risk score of the seven recurrences ranged across all three Oncotype classifications (one recurrence occurred in the "low"-risk group, three in the "intermediate" group and three in the "high"-risk group).

Important Metabolites in Maintaining Folate Cycle, Homocysteine, and Polyamine Metabolism Associated with Ranibizumab Treatment in Cultured Human Tenon's Fibroblasts

The anti-fibrotic properties of ranibizumab have been well documented. As an antagonist to vascular endothelial growth factor (VEGF), ranibizumab works by binding and neutralizing all active VEGF-A, thus limiting progressive cell growth and proliferation. Ranibizumab application in ocular diseases has shown remarkable desired effects; however, to date, its antifibrotic mechanism is not well understood. In this study, we identified metabolic changes in ranibizumab-treated human Tenon's fibroblasts (HTFs). Cultured HTFs were treated for 48 h with 0.5 mg/mL of ranibizumab and 0.5 mg/mL control IgG antibody which serves as a negative control. Samples from each group were injected into Agilent 6520 Q-TOF liquid chromatography/mass spectrometer (LC/MS) system to establish the metabolite expression in both ranibizumab treated cells and control group. Data obtained was analyzed using Agilent Mass Hunter Qualitative Analysis software to identify the most regulated metabolite following ranibizumab treatment. At p-value < 0.01 with the cut off value of two-fold change, 31 identified metabolites were found to be significantly upregulated in ranibizumab-treated group, with six of the mostly upregulated having insignificant role in fibroblast cell cycle and wound healing regulations. Meanwhile, 121 identified metabolites that were downregulated, and seven of the mostly downregulated are significantly involved in cell cycle and proliferation. Our findings suggest that ranibizumab abrogates the tissue scarring and wound healing process by regulating the expression of metabolites associated with fibrotic activity. In particular, we found that vitamin Bs are important in maintaining normal folate cycle, nucleotide synthesis, and homocysteine and spermidine metabolism. This study provides an insight into ranibizumab's mechanism of action in HTFs from the perspective of metabolomics.

Plasma Metabolomic Signatures Associated with Long-term Breast Cancer Risk in the SU.VI.MAX Prospective Cohort

BACKGROUND

Breast cancer is a major cause of death in occidental women. The role of metabolism in breast cancer etiology remains unclear. Metabolomics may help to elucidate novel biological pathways and identify new biomarkers to predict breast cancer long before symptoms appear. The aim of this study was to investigate whether untargeted metabolomic signatures from blood draws of healthy women could contribute to better understand and predict the long-term risk of developing breast cancer.

METHODS

A nested case-control study was conducted within the SU.VI.MAX prospective cohort (13 years of follow-up) to analyze baseline plasma samples of 211 incident breast cancer cases and 211 matched controls by LC/MS. Multivariable conditional logistic regression models were computed.

RESULTS

A total of 3,565 ions were detected and 1,221 were retained for statistical analysis. A total of 73 ions were associated with breast cancer risk (P < 0.01; FDR ≤ 0.2). Notably, we observed that a lower plasma level of O-succinyl-homoserine (OR = 0.70, 95%CI = [0.55-0.89]) and higher plasma levels of valine/norvaline [1.45 (1.15-1.83)], glutamine/isoglutamine [1.33 (1.07-1.66)], 5-aminovaleric acid [1.46 (1.14-1.87)], phenylalanine [1.43 (1.14-1.78)], tryptophan [1.40 (1.10-1.79)], γ-glutamyl-threonine [1.39 (1.09-1.77)], ATBC [1.41 (1.10-1.79)], and pregnene-triol sulfate [1.38 (1.08-1.77)] were associated with an increased risk of developing breast cancer during follow-up.Conclusion: Several prediagnostic plasmatic metabolites were associated with long-term breast cancer risk and suggested a role of microbiota metabolism and environmental exposure.

IMPACT

After confirmation in other independent cohort studies, these results could help to identify healthy women at higher risk of developing breast cancer in the subsequent decade and to propose a better understanding of the complex mechanisms involved in its etiology.

Serum metabolite profiling of familial adenomatous polyposis using ultra performance liquid chromatography and tandem mass spectrometry

Familial adenomatous polyposis (FAP) is an autosomal dominantly inherited intestinal polyposis syndrome accounting for about 1% of colorectal cancers (CRC). Despite increasing researches on the molecular pathogenesis of CRC, we are still unclear about metabolic pathways and alterations probably involved in the development of CRC. To obtain new insights into the mechanisms underlying APC mutation and to elucidate the mechanisms of CRC development, we performed to identify the potential metabolites in FAP based on metabolomic strategy. Serum metabolites from FAP patients (n = 30) and healthy individuals (n = 34) were detected and qualified using Ultra Performance Liquid Chromatography and Tandem Mass Spectrometry (UPLC- MS/MS). 118 metabolites were identified with statistical tests of orthogonal partial least-squares-discriminant analysis (OPLS-DA), with the conditions of variable importance in projection (VIP) >1, p < 0.05 using the Mann-Whitney U test, and fold change (FC) ≥2 or ≤0.5. OPLS-DA model was useful for distinguishing FAP patients from healthy controls. Unique metabolic signatures were pooled in FAP patients covering tricarboxylic acid (TCA) cycle, amino acids metabolism, vitamin D, fatty acids metabolism, and bile acids (BAs) metabolism. Our results demonstrated that metabolites alterations in FAP can be helpful for further analysis of metabonomics induced by APC mutation, and these alterations might be involved in the progress of intestinal carcinogenesis.

The decrease of some serum free amino acids can predict breast cancer diagnosis and progression

This study was targeted on a metabolomic approach to compare the blood serum free amino acid profiles and concentration of confirmed breast cancer (stages I-III) patients to healthy controls in order to establish reliable biomarkers of early detection and prediction of breast cancer. The ultra-high-performance liquid chromatography coupled with mass spectrometry using positive ionization electrospray was applied for the picoline-derivatized serum free amino acids using the EZ:faastTM kit. Multivariate statistical analysis principal component analysis, partial least squares discrimination analysis and univariate analysis were applied in order to discriminate between patient groups and putative amino acid biomarkers for breast cancer. A significant decrease of amino acid concentrations between the breast cancer group and the control group was positively correlated with breast cancer progression. Arginine, Alanine, Isoleucine, Tyrosine and Tryptophan were identified as being good potential discriminants (AUROC ≥0.85) and suitable candidates to diagnose and predict the breast cancer progression.

Computational Approaches in Theranostics: Mining and Predicting Cancer Data

The ability to understand the complexity of cancer-related data has been prompted by the applications of (1) computer and data sciences, including data mining, predictive analytics, machine learning, and artificial intelligence, and (2) advances in imaging technology and probe development. Computational modelling and simulation are systematic and cost-effective tools able to identify important temporal/spatial patterns (and relationships), characterize distinct molecular features of cancer states, and address other relevant aspects, including tumor detection and heterogeneity, progression and metastasis, and drug resistance. These approaches have provided invaluable insights for improving the experimental design of therapeutic delivery systems and for increasing the translational value of the results obtained from early and preclinical studies. The big question is: Could cancer theranostics be determined and controlled in silico? This review describes the recent progress in the development of computational models and methods used to facilitate research on the molecular basis of cancer and on the respective diagnosis and optimized treatment, with particular emphasis on the design and optimization of theranostic systems. The current role of computational approaches is providing innovative, incremental, and complementary data-driven solutions for the prediction, simplification, and characterization of cancer and intrinsic mechanisms, and to promote new data-intensive, accurate diagnostics and therapeutics.

A metabolic database for biomedical studies of biopsy specimens by high-resolution magic angle spinning nuclear MR: a qualitative and quantitative tool

PURPOSE

The aim of this study is to generate a metabolic database for biomedical studies of biopsy specimens by high-resolution magic angle spinning (HRMAS) nuclear MR (NMR).

METHODS

Seventy-six metabolites, classically found in human biopsy samples, were prepared in aqueous solution at a known concentration and analyzed by HRMAS NMR. The spectra were recorded under the same conditions as the ones used for the analysis of biopsy specimens routinely performed in our hospital.

RESULTS

For each metabolite, a complete set of NMR spectra (1D ¹ H, 1D ¹ H-CPMG, 2D J-Resolved, 2D TOCSY, and 2D ¹ H-¹³ C HSQC) was recorded at 500 MHz and 277 K. All spectra were manually assigned using the information contained in the different spectra and existing databases. Experiments to measure the T₁ and the T₂ of the different protons present in the 76 metabolites were also recorded.

CONCLUSION

This new HRMAS metabolic database is a useful tool for all scientists working on human biopsy specimens, particularly in the field of oncology. It will make the identification of metabolites in biopsy specimens faster and more reliable. Additionally, the knowledge of the T₁ and T₂ values will allow to obtain a more accurate quantification of the metabolites present in biopsy specimens.

NMR metabolomic signatures reveal predictive plasma metabolites associated with long-term risk of developing breast cancer

Background

Combination of metabolomics and epidemiological approaches opens new perspectives for ground-breaking discoveries. The aim of the present study was to investigate for the first time whether plasma untargeted metabolomic profiles, established from a simple blood draw from healthy women, could contribute to predict the risk of developing breast cancer within the following decade and to better understand the aetiology of this complex disease.

Methods

A prospective nested case-control study was set up in the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, including 206 breast cancer cases diagnosed during a 13-year follow-up and 396 matched controls. Untargeted nuclear magnetic resonance (NMR) metabolomic profiles were established from baseline plasma samples. Multivariable conditional logistic regression models were computed for each individual NMR variable and for combinations of variables derived by principal component analysis.

Results

Several metabolomic variables from 1D NMR spectroscopy were associated with breast cancer risk. Women characterized by higher fasting plasma levels of valine, lysine, arginine, glutamine, creatine, creatinine and glucose, and lower plasma levels of lipoproteins, lipids, glycoproteins, acetone, glycerol-derived compounds and unsaturated lipids had a higher risk of developing breast cancer. P-values ranged from 0.00007 [odds ratio (OR)T3vsT1=0.37 (0.23-0.61) for glycerol-derived compounds] to 0.04 [ORT3vsT1=1.61 (1.02-2.55) for glutamine].

Conclusion

This study highlighted associations between baseline NMR plasma metabolomic signatures and long-term breast cancer risk. These results provide interesting insights to better understand complex mechanisms involved in breast carcinogenesis and evoke plasma metabolic disorders favourable for carcinogenesis initiation. This study may contribute to develop screening strategies for the identification of at-risk women for breast cancer well before symptoms appear.

High-Throughput Metabolomics by 1D NMR

Metabolomics deals with the whole ensemble of metabolites (the metabolome). As one of the -omic sciences, it relates to biology, physiology, pathology and medicine; but metabolites are chemical entities, small organic molecules or inorganic ions. Therefore, their proper identification and quantitation in complex biological matrices requires a solid chemical ground. With respect to for example, DNA, metabolites are much more prone to oxidation or enzymatic degradation: we can reconstruct large parts of a mammoth's genome from a small specimen, but we are unable to do the same with its metabolome, which was probably largely degraded a few hours after the animal's death. Thus, we need standard operating procedures, good chemical skills in sample preparation for storage and subsequent analysis, accurate analytical procedures, a broad knowledge of chemometrics and advanced statistical tools, and a good knowledge of at least one of the two metabolomic techniques, MS or NMR. All these skills are traditionally cultivated by chemists. Here we focus on metabolomics from the chemical standpoint and restrict ourselves to NMR. From the analytical point of view, NMR has pros and cons but does provide a peculiar holistic perspective that may speak for its future adoption as a population-wide health screening technique.

Atmo-ecometabolomics: a novel atmospheric particle chemical characterization methodology for ecological research

Aerosol particles play important roles in processes controlling the composition of the atmosphere and function of ecosystems. A better understanding of the composition of aerosol particles is beginning to be recognized as critical for ecological research to further comprehend the link between aerosols and ecosystems. While chemical characterization of aerosols has been practiced in the atmospheric science community, detailed methodology tailored to the needs of ecological research does not exist yet. In this study, we describe an efficient methodology (atmo-ecometabolomics), in step-by-step details, from the sampling to the data analyses, to characterize the chemical composition of aerosol particles, namely atmo-metabolome. This method employs mass spectrometry platforms such as liquid and gas chromatography mass spectrometries (MS) and Fourier transform ion cyclotron resonance MS (FT-ICR-MS). For methodology evaluation, we analyzed aerosol particles collected during two different seasons (spring and summer) in a low-biological-activity ecosystem. Additionally, to further validate our methodology, we analyzed aerosol particles collected in a more biologically active ecosystem during the pollination peaks of three different representative tree species. Our statistical results showed that our sampling and extraction methods are suitable for characterizing the atmo-ecometabolomes in these two distinct ecosystems with any of the analytical platforms. Datasets obtained from each mass spectrometry instrument showed overall significant differences of the atmo-ecometabolomes between spring and summer as well as between the three pollination peak periods. Furthermore, we have identified several metabolites that can be attributed to pollen and other plant-related aerosol particles. We additionally provide a basic guide of the potential use ecometabolomic techniques on different mass spectrometry platforms to accurately analyze the atmo-ecometabolomes for ecological studies. Our method represents an advanced novel approach for future studies in the impact of aerosol particle chemical compositions on ecosystem structure and function and biogeochemistry.

Follicular dynamics of glycerophospholipid and sphingolipid metabolisms in polycystic ovary syndrome patients

Polycystic ovary syndrome (PCOS) is a common heterogeneous disease, affecting up to 5-10% women at reproductive age. Although PCOS patients could produce morphologically normal metaphase II oocytes undergoing assisted reproductive techniques (ART), oocyte developmental competence and embryo development have been impaired in following in-vitro fertilization (IVF) steps. Follicular fluid (FF) provides a variety of information in oocyte environment when oocytes grow. In the present work, based on ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS), the metabolic signatures of PCOS FF have been compared with healthy women using untargeted metabolomics approach. Significant abundance differences of a series of glycerolipid, glycerophospholipids, sphingolipids, and carboxylic acids have been discovered. Among them, reduced levels of phosphatidylglycerolphosphate (PGP) and a triglyceride (TG) were highly related to the lower fertilization rate in PCOS; increased abundance of lysoPE and decreased amount of PC were significantly correlated with LH/FSH (ratio of luteinizing hormone to follicle stimulating hormone). Some metabolites, including decreased sphingolipids, glycerophospholipids, and fluctuated fatty acyls, also performed close relationship with other ART and clinical results. We concluded that dysfunctions in the metabolism of glycerolipid, glycerophospholipid, sphingolipid, and glycosphingolipid biosynthesis in PCOS patients' follicles play a non-ignorable role in declining the 2 pronuclei (PN) fertilization rate during IVF procedure.

1H nuclear magnetic resonance-based plasma metabolomics provides another perspective of response mechanisms of newborn calves upon the first colostrum feeding

The first meal of a neonatal calf after birth is crucial for survival and health. Blood IgG levels remarkably increase in neonatal calves after the first colostrum feeding. However, there is little comprehensive information on blood small-molecule metabolites in neonatal calves at that time. In this study, the changes in plasma metabolites of neonatal calves after the initial colostrum feeding were first examined with comprehensive 1H nuclear magnetic resonance (NMR). Sixteen plasma samples obtained from 8 calves before and after feeding were analyzed with 1H NMR. Multivariate analyses revealed a significant difference in metabolic profiles. After feeding, acute phase N-acetylated glycoproteins and 13 other plasma metabolites decreased, whereas 19 plasma metabolites increased. Metabolomics pathway analysis of these metabolites revealed that a global metabolic response on the first colostrum feeding was reflected by alterations of 13 metabolic pathways including lipid, carbohydrate, and amino acid metabolism in neonatal calves. These results suggested that besides meeting energy demand, a 4.0 L of high-quality colostrum feeding within 4 h after birth had a positive effect on relieving the postnatal stress in neonatal calves. This study provides another perspective of response mechanisms of newborn calves upon the first colostrum feeding.

Metabolomics in breast cancer: A decade in review

Breast cancer (BC) is a heterogeneous disease which has been characterised and stratified by many platforms such as clinicopathological risk factors, genomic assays, computer generated models, and various "-omic" technologies. Genomic, proteomic and transcriptomic analysis in breast cancer research is well established, and metabolomics, which can be considered a downstream manifestation of the former disciplines, is of growing interest. The past decade has seen significant progress made within the field of clinical metabolomic BC research, with several groups demonstrating results with significant promise in the setting of BC screening and biological characterisation, as well as future potential for prognostic metabolomic biomarkers.

Disease-related metabolites affect protein-nanoparticle interactions

Once in biological fluids, the surface of nanoparticles (NPs) is rapidly covered with a layer of biomolecules (i.e., the "protein corona") whose composition strongly determines their biological identity, regulates interactions with biological entities including cells and the immune system, and consequently directs the biological fate and pharmacokinetics of nanoparticles. We recently introduced the concept of a "personalized protein corona" which refers to the formation of different biological identities of the exact same type of NP after being exposed to extract plasmas from individuals who have various types of diseases. As different diseases have distinct metabolomic profiles and metabolites can interact with proteins, it is legitimate to hypothesize that metabolomic profiles in plasma may have the capacity to, at least partially, drive the formation of a personalized protein corona. To test this hypothesis, we employed a multi-scale approach composed of coarse-grained (CG) and all atom (AA) molecular dynamics (MD) simulations to probe the role of glucose and cholesterol (model metabolites in diabetes and hypercholesterolemia patients) in the interaction of fibrinogen protein and polystyrene NPs. Our results revealed that glucose and cholesterol had the capacity to induce substantial changes in the binding site of fibrinogen to the surface of NPs. More specifically, the simulation results demonstrated that increasing the metabolite amount could change the profiles of fibrinogen adsorption and replacement, what is known as the Vroman effect, on the NP surface. In addition, we also found out that metabolites can substantially determine the immune triggering potency of the fibrinogen-NP complex. Our proof-of-concept outcomes further emphasize the need for the development of patient-specific NPs in a disease type-specific manner for high yielding and safe clinical applications.

Urinary Metabolomic Profiling in Chronic Hepatitis B Viral Infection Using Gas Chromatography/Mass Spectrometry

Background: Chronic hepatitis B (CHB) can lead to cirrhosis and hepatocellular carcinoma. The metabolomic profiling has been shown to be associated with pathogenic mechanisms in many medical conditions including CHB. The purpose of this study was to investigate the urine metabolomic profiles in CHB patients by gas chromatography/mass spectrometry (GC/MS). Methods: Urine samples were collected from CHB patients (n = 20) and normal control subjects (n = 20). Metabolite profiles were assessed using GC/MS in conjunction with multivariate statistical analysis, in order to identify biomarker metabolites. Pathway analysis was performed by MetaboAnalyst 3.0 and KEGG database.Results: Twelve out of 377 metabolites were shown to be significantly different between the CHB and normal control groups (p < 0.05). These include palmitic acid, stearic acid, oleic acid, benzoic acid, butanoic acid, cholesterol, glycine, 3-heptanone, 4-heptanone, hexanal, 1-tetradecanol and naphthalene. Multivariate statistical analysis constructed using these expressed metabolites showed CHB patients can be discriminated from healthy controls with high sensitivity (95%) and specificity (85%). All the metabolic perturbations in this disease are associated with pathways of fatty acid, amino acid, bile acid and gut microbial metabolism. Conclusion: CHB patients have a specific urinary metabolomic profile. The abnormalities of fatty acid, amino acid, bile acid, and gut microbial metabolism lead to the development of disease progression. GC/MS-based assay is a promising tool for the metabolomic study in CHB.

Historical and contemporary stable isotope tracer approaches to studying mammalian protein metabolism

Over a century ago, Frederick Soddy provided the first evidence for the existence of isotopes; elements that occupy the same position in the periodic table are essentially chemically identical but differ in mass due to a different number of neutrons within the atomic nucleus. Allied to the discovery of isotopes was the development of some of the first forms of mass spectrometers, driven forward by the Nobel laureates JJ Thomson and FW Aston, enabling the accurate separation, identification, and quantification of the relative abundance of these isotopes. As a result, within a few years, the number of known isotopes both stable and radioactive had greatly increased and there are now over 300 stable or radioisotopes presently known. Unknown at the time, however, was the potential utility of these isotopes within biological disciplines, it was soon discovered that these stable isotopes, particularly those of carbon (13 C), nitrogen (15 N), oxygen (18 O), and hydrogen (2 H) could be chemically introduced into organic compounds, such as fatty acids, amino acids, and sugars, and used to "trace" the metabolic fate of these compounds within biological systems. From this important breakthrough, the age of the isotope tracer was born. Over the following 80 yrs, stable isotopes would become a vital tool in not only the biological sciences, but also areas as diverse as forensics, geology, and art. This progress has been almost exclusively driven through the development of new and innovative mass spectrometry equipment from IRMS to GC-MS to LC-MS, which has allowed for the accurate quantitation of isotopic abundance within samples of complex matrices. This historical review details the development of stable isotope tracers as metabolic tools, with particular reference to their use in monitoring protein metabolism, highlighting the unique array of tools that are now available for the investigation of protein metabolism in vivo at a whole body down to a single protein level. Importantly, it will detail how this development has been closely aligned to the technological development within the area of mass spectrometry. Without the dedicated development provided by these mass spectrometrists over the past century, the use of stable isotope tracers within the field of protein metabolism would not be as widely applied as it is today, this relationship will no doubt continue to flourish in the future and stable isotope tracers will maintain their importance as a tool within the biological sciences for many years to come. © 2016 The Authors. Mass Spectrometry Reviews Published by Wiley Periodicals, Inc. Mass Spec Rev.

Metabolomic analysis with 1H-NMR for non-invasive diagnosis of hepatic fibrosis degree in patients with chronic hepatitis C

BACKGROUND

The assessment of fibrosis degree in liver diseases is based on several non-invasive techniques, but none has been accurate.

AIM

This study employed proton nuclear magnetic resonance spectroscopy to identify metabolic profiles in serum and urine, specific for different fibrosis degree in chronic hepatitis C patients.

METHOD

71 plasma, 73 serum, and 578 urine samples were collected. All samples were analyzed using ¹H-NMR spectroscopy technique and three different NMR spectra were acquired for each serum/plasma sample. The data analyses were performed by partial least square regression, principal component analysis, and Monte Carlo cross-validation in a supervised methodology.

RESULTS

The cross-validation test correctly assigned each sample to its specific donor with 98.44% accuracy for urine samples and 65% for serum/plasma samples. Advanced fibrosis and cirrhosis were recognized with 71% sensitivity for CPMG plasma spectra and 69% specificity for NOESY serum spectra. Accuracy for NOESY serum spectra was 68%. Noesy spectra recognized advanced fibrosis and cirrhosis with 71% sensitivity, 30% specificity, and 50% accuracy in urine samples.

CONCLUSION

Metabolomic analysis of urine spectra using ¹H-NMR spectroscopy can recognize a specific individual profile in all patients with chronic hepatitis C. However, this method cannot yet differentiate with sufficient accuracy, patients with advanced fibrosis from patients with milder disease.

Development and Validation of a High-Throughput Mass Spectrometry Based Urine Metabolomic Test for the Detection of Colonic Adenomatous Polyps

Background: Colorectal cancer is one of the leading causes of cancer deaths worldwide. The detection and removal of the precursors to colorectal cancer, adenomatous polyps, is the key for screening. The aim of this study was to develop a clinically scalable (high throughput, low cost, and high sensitivity) mass spectrometry (MS)-based urine metabolomic test for the detection of adenomatous polyps. Methods: Prospective urine and stool samples were collected from 685 participants enrolled in a colorectal cancer screening program to undergo colonoscopy examination. Statistical analysis was performed on 69 urine metabolites measured by one-dimensional nuclear magnetic resonance spectroscopy to identify key metabolites. A targeted MS assay was then developed to quantify the key metabolites in urine. A MS-based urine metabolomic diagnostic test for adenomatous polyps was established using 67% samples (un-blinded training set) and validated using the remaining 33% samples (blinded testing set). Results: The MS-based urine metabolomic test identifies patients with colonic adenomatous polyps with an AUC of 0.692, outperforming the NMR based predictor with an AUC of 0.670. Conclusion: Here we describe a clinically scalable MS-based urine metabolomic test that identifies patients with adenomatous polyps at a higher level of sensitivity (86%) over current fecal-based tests (<18%).

Combinatorial treatment with natural compounds in prostate cancer inhibits prostate tumor growth and leads to key modulations of cancer cell metabolism

High-throughput screening of a natural compound library was performed to identify the most efficacious combinatorial treatment on prostate cancer. Ursolic acid, curcumin and resveratrol were selected for further analyses and administered in vivo via the diet, either alone or in combination, in a mouse allograft model of prostate cancer. All possible combinations of these natural compounds produced synergistic effects on tumor size and weight, as predicted in the screens. A subsequent untargeted metabolomics and metabolic flux analysis using isotopically labeled glutamine indicated that the compound combinations modulated glutamine metabolism. In addition, ASCT2 levels and STAT3, mTORC1 and AMPK activity were modulated to a greater extent by the combinations compared to the individual compounds. Overall, this approach can be useful for identifying synergistic combinations of natural compounds for chemopreventive and therapeutic interventions.