Rapid Mass Spectrometric Metabolic Profiling of Blood Sera Detects Ovarian Cancer with High Accuracy

Mitochondrial dysfunction route as a possible biomarker and therapy target for human cancer

Mitochondria are vital organelles found within living cells and have signalling, biosynthetic, and bioenergetic functions. Mitochondria play a crucial role in metabolic reprogramming, which is a characteristic of cancer cells and allows them to assure a steady supply of proteins, nucleotides, and lipids to enable rapid proliferation and development. Their dysregulated activities have been associated with the growth and metastasis of different kinds of human cancer, particularly ovarian carcinoma. In this review, we briefly demonstrated the modified mitochondrial function in cancer, including mutations in mtDNA, reactive oxygen species production, dynamics, apoptosis of cells, autophagy, and calcium excess to maintain cancer genesis, progression, and metastasis. Furthermore, the mitochondrial dysfunction pathway for some genomic, proteomic, and metabolomics modifications in ovarian cancer has been studied. Additionally, ovarian cancer has been linked to targeted therapies and biomarkers found through various alteration processes underlying mitochondrial dysfunction, notably targeting reactive oxygen species, metabolites, rewind metabolic pathways, and chemo-resistant ovarian carcinoma cells.

The role of microbial metabolites in endocrine tumorigenesis: From the mechanistic insights to potential therapeutic biomarkers

Microbial metabolites have been indicated to communicate with the host's endocrine system, regulating hormone production, immune-endocrine communications, and interactions along the gut-brain axis, eventually affecting the occurrence of endocrine cancer. Furthermore, microbiota metabolites such as short-chain fatty acids (SCFAs) have been found to affect the tumor microenvironment and boost immunity against tumors. SCFAs, including butyrate and acetate, have been demonstrated to exert anti-proliferative and anti-protective activity on pancreatic cancer cells. The employing of microbial metabolic products in conjunction with radiation and chemotherapy has shown promising outcomes in terms of reducing treatment side effects and boosting effectiveness. Certain metabolites, such as valerate and butyrate, have been made known to improve the efficiency of CAR T-cell treatment, whilst others, such as indole-derived tryptophan metabolites, have been shown to inhibit tumor immunity. This review explores the intricate interplay between microbial metabolites and endocrine tumorigenesis, spanning mechanistic insights to the discovery of potential therapeutic biomarkers.

The Study of Alanine Transaminase Activity in Tissues of Silkworm (Bombyx mori) via Direct Analysis in Real-Time (DART) Mass Spectrometry

Alanine transaminase (ALT) is an important amino acid-metabolizing enzyme in silkworm Bombyx mori L., and is mainly involved in transferring glutamate to alanine (serving as an essential precursor in silk protein synthesis) through transamination. Therefore, it is generally believed that silk protein synthesis in the silk gland and the cocoon quantity increase with the increase in ALT activity to a certain extent. Here, a novel analytical method was developed to determine the ALT activity in several key tissues of Bombyx mori L. including the posterior silk gland, midgut, fat body, middle silk gland, trachea and hemolymph, by combining the direct-analysis-in-real-time (DART) ion source with a triple-quadrupole mass spectrometer. In addition, a traditional ALT activity assay, the Reitman-Frankel method, was also used to measure ALT activity for comparison. The ALT activity results obtained via the DART-MS method are in good agreement with those obtained via the Reitman-Frankel method. However, the present DART-MS method provides a more convenient, rapid and environmentally friendly quantitative method for ALT measurement. Especially, this method can also monitor ALT activity in different tissues of Bombyx mori L. in real time.

The Female Reproductive Tract Microbiome and Cancerogenesis: A Review Story of Bacteria, Hormones, and Disease

The microbiota is the complex community of microorganisms that populate a particular environment in the human body, whereas the microbiome is defined by the entire habitat-microorganisms and their environment. The most abundant and, therefore, the most studied microbiome is that of the gastrointestinal tract. However, the microbiome of the female reproductive tract is an interesting research avenue, and this article explores its role in disease development. The vagina is the reproductive organ that hosts the largest number of bacteria, with a healthy profile represented mainly by Lactobacillus spp. On the other hand, the female upper reproductive tract (uterus, Fallopian tubes, ovaries) contains only a very small number of bacteria. Previously considered sterile, recent studies have shown the presence of a small microbiota here, but there are still debates on whether this is a physiologic or pathologic occurrence. Of particular note is that estrogen levels significantly influence the composition of the microbiota of the female reproductive tract. More and more studies show a link between the microbiome of the female reproductive tract and the development of gynecological cancers. This article reviews some of these findings.

Acidic ascites inhibits ovarian cancer cell proliferation and correlates with the metabolomic, lipidomic and inflammatory phenotype of human patients

BACKGROUND

The poor prognosis of ovarian cancer patients is strongly related to peritoneal metastasis with the production of malignant ascites. However, it remains largely unclear how ascites in the peritoneal cavity influences tumor metabolism and recurrence. This study is an explorative approach aimed at for a deeper molecular and physical-chemical characterization of malignant ascites and to investigate their effect on in vitro ovarian cancer cell proliferation.

METHODS

This study included 10 malignant ascites specimens from patients undergoing ovarian cancer resection. Ascites samples were deeply phenotyped by ¹H-NMR based metabolomics, blood-gas analyzer based gas flow analysis and flow cytomertry based a 13-plex cytokine panel. Characteristics of tumor cells were investigated in a 3D spheroid model by SEM and metabolic activity, adhesion, anti-apoptosis, migratory ability evaluated by MTT assay, adhesion assay, flowcytometry and scratch assay. The effect of different pH values was assessed by adding 10% malignant ascites to the test samples.

RESULTS

 The overall extracellular (peritoneal) environment was alkaline, with pH of ascites at stage II-III = 7.51 ± 0.16, and stage IV = 7.78 ± 0.16. Ovarian cancer spheroids grew rapidly in a slightly alkaline environment. Decreasing pH of the cell culture medium suppressed tumor features, metabolic activity, adhesion, anti-apoptosis, and migratory ability. However, 10% ascites could prevent tumor cells from being affected by acidic pH. Metabolomics analysis identified stage IV patients had significantly higher concentrations of alanine, isoleucine, phenylalanine, and glutamine than stage II-III patients, while stage II-III patients had significantly higher concentrations of 3-hydroxybutyrate. pH was positively correlated with acetate, and acetate positively correlated with lipid compounds. IL-8 was positively correlated with lipid metabolites and acetate. Glutathione and carnitine were negatively correlated with cytokines IL-6 and chemokines (IL-8 & MCP-1).

CONCLUSION

Alkaline malignant ascites facilitated ovarian cancer progression. Additionally, deep ascites phenotyping by metabolomics and cytokine investigations allows for a refined stratification of ovarian cancer patients. These findings contribute to the understanding of ascites pathology in ovarian cancer.

Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review

With a 5-year survival rate of less than 50%, ovarian high-grade serous carcinoma (HGSC) is one of the most highly aggressive gynecological malignancies affecting women today. The high mortality rate of HGSC is largely attributable to delays in diagnosis, as most patients remain undiagnosed until the late stages of -disease. There are currently no recommended screening tests for ovarian cancer and there thus remains an urgent need for new diagnostic methods, particularly those that can detect the disease at early stages when clinical intervention remains effective. While diagnostics for ovarian cancer share many of the same technical hurdles as for other cancer types, the low prevalence of the disease in the general population, coupled with a notable lack of sensitive and specific biomarkers, have made the development of a clinically useful screening strategy particularly challenging. Here, we present a detailed review of the overall landscape of ovarian cancer diagnostics, with emphasis on emerging methods that employ novel protein, genetic, epigenetic and imaging-based biomarkers and/or advanced diagnostic technologies for the noninvasive detection of HGSC, particularly in women at high risk due to germline mutations such as BRCA1/2. Lastly, we discuss the translational potential of these approaches for achieving a clinically implementable solution for screening and diagnostics of early-stage ovarian cancer as a means of ultimately improving patient outcomes in both the general and high-risk populations.

BCKDK Promotes Ovarian Cancer Proliferation and Migration by Activating the MEK/ERK Signaling Pathway

Background

Ovarian cancer (OC) is the most fatal gynecologic cancer. The branched-chain α-keto acid dehydrogenase kinase (BCKDK) plays an important role in many serious human diseases, including cancers. Its function in promoting cell proliferation and migration has been reported in various cancers. However, the biological role of BCKDK and its molecular mechanisms underlying OC initiation and progression are unclear.

Methods

First, the expression level of BCKDK in OC cell lines or tissues was determined using tissue microarray- (TMA-) based immunohistochemistry or western blotting. Then, growth curve analysis, anchorage-independent cell transformation assays, wound healing assays, cell migration assays, and tumor xenografts were used to test whether BCKDK could promote cell transformation or metastasis. Finally, the signaling pathways involved in this process were investigated by western blotting or immunoprecipitation.

Results

We found that the expression of BCKDK was upregulated in OC tissues and the high expression of BCKDK was correlated with an advanced pathological grade in patients. The ectopic overexpression of BCKDK promoted the proliferation and migration of OC cells, and the knockdown of BCKDK with shRNAs inhibited the proliferation and migration of OC ex vivo and in vivo. Moreover, BCKDK promoted OC proliferation and migration by activating MEK.

Conclusions

Our results demonstrate that BCKDK promotes OC proliferation and migration by activating the MEK/ERK signaling pathway. Targeting the BCKDK-MEK axis may provide a new therapeutic strategy for treating patients with OC.

Mitochondrial Dysfunction Pathway Alterations Offer Potential Biomarkers and Therapeutic Targets for Ovarian Cancer

The mitochondrion is a very versatile organelle that participates in some important cancer-associated biological processes, including energy metabolism, oxidative stress, mitochondrial DNA (mtDNA) mutation, cell apoptosis, mitochondria-nuclear communication, dynamics, autophagy, calcium overload, immunity, and drug resistance in ovarian cancer. Multiomics studies have found that mitochondrial dysfunction, oxidative stress, and apoptosis signaling pathways act in human ovarian cancer, which demonstrates that mitochondria play critical roles in ovarian cancer. Many molecular targeted drugs have been developed against mitochondrial dysfunction pathways in ovarian cancer, including olive leaf extract, nilotinib, salinomycin, Sambucus nigra agglutinin, tigecycline, and eupatilin. This review article focuses on the underlying biological roles of mitochondrial dysfunction in ovarian cancer progression based on omics data, potential molecular relationship between mitochondrial dysfunction and oxidative stress, and future perspectives of promising biomarkers and therapeutic targets based on the mitochondrial dysfunction pathway for ovarian cancer.

The role of bile acids in carcinogenesis

Bile acids are soluble derivatives of cholesterol produced in the liver that subsequently undergo bacterial transformation yielding a diverse array of metabolites. The bulk of bile acid synthesis takes place in the liver yielding primary bile acids; however, other tissues have also the capacity to generate bile acids (e.g. ovaries). Hepatic bile acids are then transported to bile and are subsequently released into the intestines. In the large intestine, a fraction of primary bile acids is converted to secondary bile acids by gut bacteria. The majority of the intestinal bile acids undergo reuptake and return to the liver. A small fraction of secondary and primary bile acids remains in the circulation and exert receptor-mediated and pure chemical effects (e.g. acidic bile in oesophageal cancer) on cancer cells. In this review, we assess how changes to bile acid biosynthesis, bile acid flux and local bile acid concentration modulate the behavior of different cancers. Here, we present in-depth the involvement of bile acids in oesophageal, gastric, hepatocellular, pancreatic, colorectal, breast, prostate, ovarian cancer. Previous studies often used bile acids in supraphysiological concentration, sometimes in concentrations 1000 times higher than the highest reported tissue or serum concentrations likely eliciting unspecific effects, a practice that we advocate against in this review. Furthermore, we show that, although bile acids were classically considered as pro-carcinogenic agents (e.g. oesophageal cancer), the dogma that switch, as lower concentrations of bile acids that correspond to their serum or tissue reference concentration possess anticancer activity in a subset of cancers. Differences in the response of cancers to bile acids lie in the differential expression of bile acid receptors between cancers (e.g. FXR vs. TGR5). UDCA, a bile acid that is sold as a generic medication against cholestasis or biliary surge, and its conjugates were identified with almost purely anticancer features suggesting a possibility for drug repurposing. Taken together, bile acids were considered as tumor inducers or tumor promoter molecules; nevertheless, in certain cancers, like breast cancer, bile acids in their reference concentrations may act as tumor suppressors suggesting a Janus-faced nature of bile acids in carcinogenesis.

Application of direct analysis in real-time mass spectrometry (DART-MS) in forensic science: a comprehensive review

Background

As the rate of crime is constantly increasing, the workload on the forensic analyst also piles up. The availability of a limited number of seized samples makes it crucial to directly analyze the sample, thereby preventing wastage in the prior steps of sample preparation. Due to such needs, the forensic community is consistently working on broadening the usage of direct analysis in real-time mass spectrometry (DART-MS). DART-MS is a relatively new technique for rapid mass spectral analysis. Its use for chemical analysis credits its ability to analyze the sample at atmospheric pressure.

Main body

This article gives insight into the ionization mechanisms, data analysis tools, and the use of hyphenated techniques like thermal-desorption-DART-MS, infrared-thermal-desorption-DART-MS, Joule-heating thermal-desorption-DART-MS, etc. This review summarizes the applications of DART-MS in the field of Forensic Science reported from 2005 to 2021. The applications include analysis of drugs, warfare agents, gun-shot residues, ink differentiation, and other forensically relevant samples. The paper also presents the relation between the type of DART-MS technique and the ionization mode used for a particular class of compounds.

Conclusion

The review follows that the high-resolution mass-spectrometers or low-resolution mass-spectrometers systems in the positive or negative mode were highly dependent on the type of analyte under investigation. Drugs, inks, dyes, and paints were mainly analyzed using the positive ionization mode in the HRMS technique. The examinations of fire accelerants predominantly used the positive ionization mode in the LRMS technique. Moreover, the limit of detection values obtained from the qualitative screening of street drugs were of ppb level, indicating high sensitivity of DART-MS. Considering the work done in the past years, there are potential future research needs of this technology, especially in forensic science.

Graphical Abstract

Prediction of Ovarian Cancer-Related Metabolites Based on Graph Neural Network

Ovarian cancer is one of the three most malignant tumors of the female reproductive system. At present, researchers do not know its pathogenesis, which makes the treatment effect unsatisfactory. Metabolomics is closely related to drug efficacy, safety evaluation, mechanism of action, and rational drug use. Therefore, identifying ovarian cancer-related metabolites could greatly help researchers understand the pathogenesis and develop treatment plans. However, the measurement of metabolites is inaccurate and greatly affects the environment, and biological experiment is time-consuming and costly. Therefore, researchers tend to use computational methods to identify disease-related metabolites in large scale. Since the hypothesis that similar diseases are related to similar metabolites is widely accepted, in this paper, we built both disease similarity network and metabolite similarity network and used graph convolutional network (GCN) to encode these networks. Then, support vector machine (SVM) was used to identify whether a metabolite is related to ovarian cancer. The experiment results show that the AUC and AUPR of our method are 0.92 and 0.81, respectively. Finally, we proposed an effective method to prioritize ovarian cancer-related metabolites in large scale.

Quantitative global lipidomics analysis of patients with ovarian cancer versus benign adnexal mass

Altered lipid metabolism has emerged as an important feature of ovarian cancer (OC), yet the translational potential of lipid metabolites to aid in diagnosis and triage remains unproven. We conducted a multi-level interrogation of lipid metabolic phenotypes in patients with adnexal masses, integrating quantitative lipidomics profiling of plasma and ascites with publicly-available tumor transcriptome data. Using Sciex Lipidyzer, we assessed concentrations of > 500 plasma lipids in two patient cohorts-(i) a pilot set of 100 women with OC (50) or benign tumor (50), and (ii) an independent set of 118 women with malignant (60) or benign (58) adnexal mass. 249 lipid species and several lipid classes were significantly reduced in cases versus controls in both cohorts (FDR < 0.05). 23 metabolites-triacylglycerols, phosphatidylcholines, cholesterol esters-were validated at Bonferroni significance (P < 9.16 × 10-5). Certain lipids exhibited greater alterations in early- (diacylglycerols) or late-stage (lysophospholipids) cases, and multiple lipids in plasma and ascites were positively correlated. Lipoprotein receptor gene expression differed markedly in OC versus benign tumors. Importantly, several plasma lipid species, such as DAG(16:1/18:1), improved the accuracy of CA125 in differentiating early-stage OC cases from benign controls, and conferred a 15-20% increase in specificity at 90% sensitivity in multivariate models adjusted for age and BMI. This study provides novel insight into systemic and local lipid metabolic differences between OC and benign disease, further implicating altered lipid uptake in OC biology, and advancing plasma lipid metabolites as a complementary class of circulating biomarkers for OC diagnosis and triage.

Free Amino Acid Alterations in Patients with Gynecological and Breast Cancer: A Review

Gynecological and breast cancers still remain a significant health problem worldwide. Diagnostic methods are not sensitive and specific enough to detect the disease at an early stage. During carcinogenesis and tumor progression, the cellular need for DNA and protein synthesis increases leading to changes in the levels of amino acids. An important role of amino acids in many biological pathways, including biosynthesis of proteins, nucleic acids, enzymes, etc., which serve as an energy source and maintain redox balance, has been highlighted in many research articles. The aim of this review is a detailed analysis of the literature on metabolomic studies of gynecology and breast cancers with particular emphasis on alterations in free amino acid profiles. The work includes a brief overview of the metabolomic methodology and types of biological samples used in the studies. Special attention was paid to the possible role of selected amino acids in the carcinogenesis, especially proline and amino acids related to its metabolism. There is a clear need for further research and multiple external validation studies to establish the role of amino acid profiling in diagnosing gynecological and breast cancers.

The role of the microbiome in ovarian cancer: mechanistic insights into oncobiosis and to bacterial metabolite signaling

Ovarian cancer is characterized by dysbiosis, referred to as oncobiosis in neoplastic diseases. In ovarian cancer, oncobiosis was identified in numerous compartments, including the tumor tissue itself, the upper and lower female genital tract, serum, peritoneum, and the intestines. Colonization was linked to Gram-negative bacteria with high inflammatory potential. Local inflammation probably participates in the initiation and continuation of carcinogenesis. Furthermore, local bacterial colonies in the peritoneum may facilitate metastasis formation in ovarian cancer. Vaginal infections (e.g. Neisseria gonorrhoeae or Chlamydia trachomatis) increase the risk of developing ovarian cancer. Bacterial metabolites, produced by the healthy eubiome or the oncobiome, may exert autocrine, paracrine, and hormone-like effects, as was evidenced in breast cancer or pancreas adenocarcinoma. We discuss the possible involvement of lipopolysaccharides, lysophosphatides and tryptophan metabolites, as well as, short-chain fatty acids, secondary bile acids and polyamines in the carcinogenesis of ovarian cancer. We discuss the applicability of nutrients, antibiotics, and probiotics to harness the microbiome and support ovarian cancer therapy. The oncobiome and the most likely bacterial metabolites play vital roles in mediating the effectiveness of chemotherapy. Finally, we discuss the potential of oncobiotic changes as biomarkers for the diagnosis of ovarian cancer and microbial metabolites as possible adjuvant agents in therapy.

Serum Free Amino Acid Profiling in Differential Diagnosis of Ovarian Tumors-A Comparative Study with Review of the Literature

Proper preoperative ovarian cancer (OC) diagnosis remains challenging. Serum free amino acid (SFAA) profiles were investigated to identify potential novel biomarkers of OC and assess their performance in ovarian tumor differential diagnosis. Serum samples were divided based on the histopathological result: epithelial OC (n = 38), borderline ovarian tumors (n = 6), and benign ovarian tumors (BOTs) (n = 62). SFAA profiles were evaluated using aTRAQ methodology based on high-performance liquid chromatography electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS). Levels of eleven amino acids significantly differed between OC+borderline and BOTs. The highest area under the receiver operating characteristic curve (AUC of ROC) (0.787) was obtained for histidine. Cystine and histidine were identified as best single markers for early stage OC/BOT and type I OC. For advanced stage OC, seven amino acids differed significantly between the groups and citrulline obtained the best AUC of 0.807. Between type II OC and BOTs, eight amino acids differed significantly and the highest AUC of 0.798 was achieved by histidine and citrulline (AUC of 0.778). Histidine was identified as a potential new biomarker in differential diagnosis of ovarian tumors. Adding histidine to a multimarker panel together with CA125 and HE4 improved the differential diagnosis between OC and BOTs.

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor-host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

High-throughput approaches for precision medicine in high-grade serous ovarian cancer

High-grade serous carcinoma (HGSC) is the most prevalent and aggressive subtype of ovarian cancer. The large degree of clinical heterogeneity within HGSC has justified deviations from the traditional one-size-fits-all clinical management approach. However, the majority of HGSC patients still relapse with chemo-resistant cancer and eventually succumb to their disease, evidence that further work is needed to improve patient outcomes. Advancements in high-throughput technologies have enabled novel insights into biological complexity, offering a large potential for informing precision medicine efforts. Here, we review the current landscape of clinical management for HGSC and highlight applications of high-throughput biological approaches for molecular subtyping and the discovery of putative blood-based biomarkers and novel therapeutic targets. Additionally, we present recent improvements in model systems and discuss how their intersection with high-throughput platforms and technological advancements is positioned to accelerate the realization of precision medicine in HGSC.

High-throughput quantification of drugs of abuse in biofluids via 96-solid-phase microextraction-transmission mode and direct analysis in real time mass spectrometry

RATIONALE

The workload of clinical laboratories has been steadily increasing over the last few years. High-throughput (HT) sample processing allows scientists to spend more time undertaking matters of critical thinking rather than laborious sample processing. Herein we introduce a HT 96-solid-phase microextraction (SPME) transmission mode (TM) system coupled to direct analysis in real time (DART) mass spectrometry (MS).

METHODS

Model compounds (opioids) were extracted from urine and plasma samples using a 96-SPME-TM device. A standard voltage and pressure (SVP) DART source was used for all experiments. Examination of SPME-TM performance was done using high-resolution mass spectrometry (HRMS) in full scan mode (100-500 m/z), whereas quantitation of opioids was performed using triple quadrupole MS in multiple reaction monitoring mode and by using a matrix-matched internal standard correction method.

RESULTS

Thirteen points (0.5 to 200 ng mL^-1 ) were used to establish a calibration curve. Low limits of quantitation (LOQ) were obtained (0.5 to 25 ng mL^-1 ) for matrices used. Acceptable accuracy (71.4-129.4%) and repeatability (1.1-24%) were obtained for validation levels tested (0.5, 30 and 90 ng mL^-1 ). In less than 1.5 hours, 96 samples were extracted, desorbed and processed using the 96-SPME-TM system coupled to DART-MS.

CONCLUSIONS

A rapid HT method for detection of opioids in urine and plasma samples was developed. This study demonstrated that ambient ionization mass spectrometry coupled to robust sample preparation methods such as SPME-TM can rapidly and efficiently screen/quantify target analytes in a HT context.

Deep Metabolomics of a High-Grade Serous Ovarian Cancer Triple-Knockout Mouse Model

High-grade serous carcinoma (HGSC) is the most common and deadliest ovarian cancer (OC) type, accounting for 70-80% of OC deaths. This high mortality is largely due to late diagnosis. Early detection is thus crucial to reduce mortality, yet the tumor pathogenesis of HGSC remains poorly understood, making early detection exceedingly difficult. Faithfully and reliably representing the clinical nature of human HGSC, a recently developed triple-knockout (TKO) mouse model offers a unique opportunity to examine the entire disease spectrum of HGSC. Metabolic alterations were investigated by applying ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) to serum samples collected from these mice at premalignant, early, and advanced stages of HGSC. This comprehensive analysis revealed a panel of 29 serum metabolites that distinguished mice with HGSC from controls and mice with uterine tumors with over 95% accuracy. Meanwhile, our panel could further distinguish early-stage HGSC from controls with 100% accuracy and from advanced-stage HGSC with over 90% accuracy. Important identified metabolites included phospholipids, sphingomyelins, sterols, N-acyltaurine, oligopeptides, bilirubin, 2(3)-hydroxysebacic acids, uridine, N-acetylneuraminic acid, and pyrazine derivatives. Overall, our study provides insights into dysregulated metabolism associated with HGSC development and progression, and serves as a useful guide toward early detection.

Metabolomic identification of diagnostic serum-based biomarkers for advanced stage melanoma

INTRODUCTION

Melanoma is a highly aggressive malignancy and is currently one of the fastest growing cancers worldwide. While early stage (I and II) disease is highly curable with excellent prognosis, mortality rates rise dramatically after distant spread. We sought to identify differences in the metabolome of melanoma patients to further elucidate the pathophysiology of melanoma and identify potential biomarkers to aid in earlier detection of recurrence.

METHODS

Using ¹H NMR and DI-LC-MS/MS, we profiled serum samples from 26 patients with stage III (nodal metastasis) or stage IV (distant metastasis) melanoma and compared their biochemical profiles with 46 age- and gender-matched controls.

RESULTS

We accurately quantified 181 metabolites in serum using a combination of ¹H NMR and DI-LC-MS/MS. We observed significant separation between cases and controls in the PLS-DA scores plot (permutation test p-value = 0.002). Using the concentrations of PC-aa-C40:3, DL-carnitine, octanoyl-L-carnitine, ethanol, and methylmalonyl-L-carnitine we developed a diagnostic algorithm with an AUC (95% CI) = 0.822 (0.665-0.979) with sensitivity and specificity of 100 and 56%, respectively. Furthermore, we identified arginine, proline, tryptophan, glutamine, glutamate, glutathione and ornithine metabolism to be significantly perturbed due to disease (p < 0.05).

CONCLUSION

Targeted metabolomic analysis demonstrated significant differences in metabolic profiles of advanced stage (III and IV) melanoma patients as compared to controls. These differences may represent a potential avenue for the development of multi-marker serum-based assays for earlier detection of recurrences, allow for newer, more effective targeted therapy when tumor burden is less, and further elucidate the pathophysiologic changes that occur in melanoma.

Rapid qualitative analysis of 2 flavonoids, rutin and silybin, in medical pills by direct analysis in real-time mass spectrometry (DART-MS) combined with in situ derivatization

Direct analysis in real-time mass spectrometry (DART-MS) with in situ silylation was used for the rapid analysis of the flavonoids silybin ((2R,3R)-3,5,7-trihydroxy-2-[3-(4-hydroxy-3-methoxyphenyl)-2-hydroxymethyl-2,3-dihydrobenzo[1,4]dioxin-6-yl]chroman-4-one) and rutin (quercetin-3-O-rutinoside). Three different derivatization reagents, hexamethyldisilazane/trimethylchlorosilane/pyridine (HMDS/TMCS/pyridine), N,O-bis(trimethylsilyl)acetamide/trimethylchlorosilane/N-trimethylsilyimidazole (BSA/TMCS/TMSI), and N,O-bis(trimethylsilyl)trifluoroacetamide/trimethylchlorosilane (BSTFA/TMCS), were applied. Silybin and rutin were detected with various degrees of silylation, and the formation of dimers with pyridine and imidazole was also observed. HMDS/TMCS/pyridine was the best choice for the DART-MS analysis of silybin, and BSA/TMCS/TMSI was the most effective for the detection of rutin. The effects of the DART source temperature on desorption, ionization, in-source fragmentation, dimer formation, and hydrolysis of the trimethylsilyl groups were also studied. In addition, the collision-induced dissociation properties of the derivatized silybin and rutin were explored. With our in situ silylation method, the derivatized bioactive compounds in intact medical pills could also be detected by DART-MS.

Usefulness of Amino Acid Profiling in Ovarian Cancer Screening with Special Emphasis on Their Role in Cancerogenesis

The aim of this study was to quantitate 42 serum-free amino acids, propose the biochemical explanation of their role in tumor development, and identify new ovarian cancer (OC) biomarkers for potential use in OC screening. The additional value of this work is the schematic presentation of the interrelationship between metabolites which were identified as significant for OC development and progression. The liquid chromatography-tandem mass spectrometry technique using highly-selective multiple reaction monitoring mode and labeled internal standards for each analyzed compound was applied. Performed statistical analyses showed that amino acids are potentially useful as OC biomarkers, especially as variables in multi-marker models. For the distinguishing metabolites the following metabolic pathways involved in cancer growth and development were proposed: histidine metabolism; tryptophan metabolism; arginine biosynthesis; arginine and proline metabolism; and alanine, aspartate and glutamine metabolism. The presented research identifies histidine and citrulline as potential new OC biomarkers. Furthermore, it provides evidence that amino acids are involved in metabolic pathways related to tumor growth and play an important role in cancerogenesis.

Ambient mass spectrometry in metabolomics

Since the introduction of desorption electrospray ionization (DESI) mass spectrometry (MS), ambient MS methods have seen increased use in a variety of fields from health to food science. Increasing its popularity in metabolomics, ambient MS offers limited sample preparation, rapid and direct analysis of liquids, solids, and gases, in situ and in vivo analysis, and imaging. The metabolome consists of a constantly changing collection of small (<1.5 kDa) molecules. These include endogenous molecules that are part of primary metabolism pathways, secondary metabolites with specific functions such as signaling, chemicals incorporated in the diet or resulting from environmental exposures, and metabolites associated with the microbiome. Characterization of the responsive changes of this molecule cohort is the principal goal of any metabolomics study. With adjustments to experimental parameters, metabolites with a range of chemical and physical properties can be selectively desorbed and ionized and subsequently analyzed with increased speed and sensitivity. This review covers the broad applications of a variety of ambient MS techniques in four primary fields in which metabolomics is commonly employed.

Source identification of western Oregon Douglas-fir wood cores using mass spectrometry and random forest classification

PREMISE OF THE STUDY

We investigated whether wood metabolite profiles from direct analysis in real time (time-of-flight) mass spectrometry (DART-TOFMS) could be used to determine the geographic origin of Douglas-fir wood cores originating from two regions in western Oregon, USA.

METHODS

Three annual ring mass spectra were obtained from 188 adult Douglas-fir trees, and these were analyzed using random forest models to determine whether samples could be classified to geographic origin, growth year, or growth year and geographic origin. Specific wood molecules that contributed to geographic discrimination were identified.

RESULTS

Douglas-fir mass spectra could be differentiated into two geographic classes with an accuracy between 70% and 76%. Classification models could not accurately classify sample mass spectra based on growth year. Thirty-two molecules were identified as key for classifying western Oregon Douglas-fir wood cores to geographic origin.

DISCUSSION

DART-TOFMS is capable of detecting minute but regionally informative differences in wood molecules over a small geographic scale, and these differences made it possible to predict the geographic origin of Douglas-fir wood with moderate accuracy. Studies involving DART-TOFMS, alone and in combination with other technologies, will be relevant for identifying the geographic origin of illegally harvested wood.

Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature

INTRODUCTION

Metabolomics is the emerging member of "omics" sciences advancing the understanding, diagnosis and treatment of many cancers, including ovarian cancer (OC).

OBJECTIVES

To systematically identify the metabolomic abnormalities in OC detection, and the dominant metabolic pathways associated with the observed alterations.

METHODS

An electronic literature search was performed, up to and including January 15th 2016, for studies evaluating the metabolomic profile of patients with OC compared to controls. QUADOMICS tool was used to assess the quality of the twenty-three studies included in this systematic review.

RESULTS

Biological samples utilized for metabolomic analysis include: serum/plasma (n = 13), urine (n = 4), cyst fluid (n = 3), tissue (n = 2) and ascitic fluid (n = 1). Metabolites related to cellular respiration, carbohydrate, lipid, protein and nucleotide metabolism were significantly altered in OC. Increased levels of tricarboxylic acid cycle intermediates and altered metabolites of the glycolytic pathway pointed to perturbations in cellular respiration. Alterations in lipid metabolism included enhanced fatty acid oxidation, abnormal levels of glycerolipids, sphingolipids and free fatty acids with common elevations of palmitate, oleate, and myristate. Increased levels of glutamine, glycine, cysteine and threonine were commonly reported while enhanced degradations of tryptophan, histidine and phenylalanine were found. N-acetylaspartate, a brain amino acid, was found elevated in primary and metastatic OC tissue and ovarian cyst fluid. Further, elevated levels of ketone bodies including 3-hydroxybutyrate were commonly reported. Increased levels of nucleotide metabolites and tocopherols were consistent through out the studies.

CONCLUSION

Metabolomics presents significant new opportunities for diagnostic biomarker development, elucidating previously unknown mechanisms of OC pathogenesis.

Plasma Metabolic Profiles in Women are Menopause Dependent

Menopause is an endocrinological transition that greatly affects health and disease susceptibility in middle-aged and elderly women. To gain new insights into the metabolic process of menopause, plasma metabolic profiles in 115 pre- and post-menopausal women were systematically analyzed by ultra-performance liquid chromatography/mass spectrometry in conjunction with univariate and multivariate statistical analysis. Metabolic signatures revealed considerable differences between pre- and post-menopausal women, and clear separations were observed between the groups in partial least-squares discriminant analysis score plots. In total, 28 metabolites were identified as potential metabolite markers for menopause, including up-regulated acylcarnitines, fatty acids, lysophosphatidylcholines, lysophosphatidylethanolamines, and down-regulated pregnanediol-3-glucuronide, dehydroepiandrosterone sulfate, p-hydroxyphenylacetic acid and dihydrolipoic acid. These differences highlight that significant alterations occur in fatty acid β-oxidation, phospholipid metabolism, hormone metabolism and amino acid metabolism in post-menopausal women. In conclusion, our plasma metabolomics study provides novel understanding of the metabolic profiles related to menopause, and will be useful for investigating menopause-related diseases and assessing metabolomic confounding factors.

Highly-accurate metabolomic detection of early-stage ovarian cancer

High performance mass spectrometry was employed to interrogate the serum metabolome of early-stage ovarian cancer (OC) patients and age-matched control women. The resulting spectral features were used to establish a linear support vector machine (SVM) model of sixteen diagnostic metabolites that are able to identify early-stage OC with 100% accuracy in our patient cohort. The results provide evidence for the importance of lipid and fatty acid metabolism in OC and serve as the foundation of a clinically significant diagnostic test.

Identification of novel candidate plasma metabolite biomarkers for distinguishing serous ovarian carcinoma and benign serous ovarian tumors

OBJECTIVE

Serous ovarian carcinoma (OC) represents a leading cause of cancer-related death among U.S. women. Non-invasive tools have recently emerged for discriminating benign from malignant ovarian masses, but evaluation remains ongoing, without widespread implementation. In the last decade, metabolomics has matured into a new avenue for cancer biomarker development. Here, we sought to identify novel plasma metabolite biomarkers to distinguish serous ovarian carcinoma and benign serous ovarian tumor.

METHODS

Using liquid chromatography-mass spectrometry, we conducted global and targeted metabolite profiling of plasma isolated at the time of surgery from 50 serous OC cases and 50 serous benign controls.

RESULTS

Global lipidomics analysis identified 34 metabolites (of 372 assessed) differing significantly (P<0.05) between cases and controls in both training and testing sets, with 17 candidates satisfying FDR q<0.05, and two reaching Bonferroni significance. Targeted profiling of ~150 aqueous metabolites identified a single amino acid, alanine, as differentially abundant (P<0.05). A multivariate classification model built using the top four lipid metabolites achieved an estimated AUC of 0.85 (SD=0.07) based on Monte Carlo cross validation. Evaluation of a hybrid model incorporating both CA125 and lipid metabolites was suggestive of increased classification accuracy (AUC=0.91, SD=0.05) relative to CA125 alone (AUC=0.87, SD=0.07), particularly at high fixed levels of sensitivity, without reaching significance.

CONCLUSIONS

Our results provide insight into metabolic changes potentially correlated with the presence of serous OC versus benign ovarian tumor and suggest that plasma metabolites may help differentiate these two conditions.

Metabolic profiling of epithelial ovarian cancer cell lines: evaluation of harvesting protocols for profiling using NMR spectroscopy

BACKGROUND

Metabolic profiling represents a novel technology for analyzing tumor cells. Epithelial ovarian carcinoma has a low survival rate due to the development of aggressive and chemotherapy-resistant cells. A tailored and reliable protocol is presented for profiling of chemoresistant cells using the cell line SKOV3 and a multiresistant subline SKOV3R.

RESULTS

Harvesting protocols with cold methanol or MilliQ freeze/thaw cycles were compared. Increased reproducibility using MilliQ was evidenced. Importantly, both approaches resulted in similar profiles. Compared with parental SKOV3, the SKOV3R cells showed a significantly different profile.

CONCLUSION

The MilliQ protocol is preferred owing to higher reproducibility and increased sample preparation options. The resulting metabolic profiles summarize metabolic alterations in chemoresistant cells consistent with a progressed and aggressive phenotype.

OVCAR-3 spheroid-derived cells display distinct metabolic profiles

Introduction

Recently, multicellular spheroids were isolated from a well-established epithelial ovarian cancer cell line, OVCAR-3, and were propagated in vitro. These spheroid-derived cells displayed numerous hallmarks of cancer stem cells, which are chemo- and radioresistant cells thought to be a significant cause of cancer recurrence and resultant mortality. Gene set enrichment analysis of expression data from the OVCAR-3 cells and the spheroid-derived putative cancer stem cells identified several metabolic pathways enriched in differentially expressed genes. Before this, there had been little previous knowledge or investigation of systems-scale metabolic differences between cancer cells and cancer stem cells, and no knowledge of such differences in ovarian cancer stem cells.

Methods

To determine if there were substantial metabolic changes corresponding with these transcriptional differences, we used two-dimensional gas chromatography coupled to mass spectrometry to measure the metabolite profiles of the two cell lines.

Results

These two cell lines exhibited significant metabolic differences in both intracellular and extracellular metabolite measurements. Principal components analysis, an unsupervised dimensional reduction technique, showed complete separation between the two cell types based on their metabolite profiles. Pathway analysis of intracellular metabolomics data revealed close overlap with metabolic pathways identified from gene expression data, with four out of six pathways found enriched in gene-level analysis also enriched in metabolite-level analysis. Some of those pathways contained multiple metabolites that were individually statistically significantly different between the two cell lines, with one of the most broadly and consistently different pathways, arginine and proline metabolism, suggesting an interesting hypothesis about cancerous and stem-like metabolic phenotypes in this pair of cell lines.

Conclusions

Overall, we demonstrate for the first time that metabolism in an ovarian cancer stem cell line is distinct from that of more differentiated isogenic cancer cells, supporting the potential importance of metabolism in the differences between cancer cells and cancer stem cells.

Proteome-metabolome profiling of ovarian cancer ascites reveals novel components involved in intercellular communication

Ovarian cancer ascites is a native medium for cancer cells that allows investigation of their secretome in a natural environment. This medium is of interest as a promising source of potential biomarkers, and also as a medium for cell-cell communication. The aim of this study was to elucidate specific features of the malignant ascites metabolome and proteome. In order to omit components of the systemic response to ascites formation, we compared malignant ascites with cirrhosis ascites. Metabolome analysis revealed 41 components that differed significantly between malignant and cirrhosis ascites. Most of the identified cancer-specific metabolites are known to be important signaling molecules. Proteomic analysis identified 2096 and 1855 proteins in the ovarian cancer and cirrhosis ascites, respectively; 424 proteins were specific for the malignant ascites. Functional analysis of the proteome demonstrated that the major differences between cirrhosis and malignant ascites were observed for the cluster of spliceosomal proteins. Additionally, we demonstrate that several splicing RNAs were exclusively detected in malignant ascites, where they probably existed within protein complexes. This result was confirmed in vitro using an ovarian cancer cell line. Identification of spliceosomal proteins and RNAs in an extracellular medium is of particular interest; the finding suggests that they might play a role in the communication between cancer cells. In addition, malignant ascites contains a high number of exosomes that are known to play an important role in signal transduction. Thus our study reveals the specific features of malignant ascites that are associated with its function as a medium of intercellular communication.

Feasibility of detecting prostate cancer by ultraperformance liquid chromatography-mass spectrometry serum metabolomics

Prostate cancer (PCa) is the second leading cause of cancer-related mortality in men. The prevalent diagnosis method is based on the serum prostate-specific antigen (PSA) screening test, which suffers from low specificity, overdiagnosis, and overtreatment. In this work, untargeted metabolomic profiling of age-matched serum samples from prostate cancer patients and healthy individuals was performed using ultraperformance liquid chromatography coupled to high-resolution tandem mass spectrometry (UPLC-MS/MS) and machine learning methods. A metabolite-based in vitro diagnostic multivariate index assay (IVDMIA) was developed to predict the presence of PCa in serum samples with high classification sensitivity, specificity, and accuracy. A panel of 40 metabolic spectral features was found to be differential with 92.1% sensitivity, 94.3% specificity, and 93.0% accuracy. The performance of the IVDMIA was higher than the prevalent PSA test. Within the discriminant panel, 31 metabolites were identified by MS and MS/MS, with 10 further confirmed chromatographically by standards. Numerous discriminant metabolites were mapped in the steroid hormone biosynthesis pathway. The identification of fatty acids, amino acids, lysophospholipids, and bile acids provided further insights into the metabolic alterations associated with the disease. With additional work, the results presented here show great potential toward implementation in clinical settings.

Large-scale profiling of metabolic dysregulation in ovarian cancer

Ovarian cancer is the leading cause of death in gynecologic malignancies. Profiling of endogenous metabolites has potential to identify changes caused by cancer and provide inspiring insights into cancer metabolism. To systematically investigate ovarian cancer metabolism, we performed metabolic profiling of 448 plasma samples related to epithelial ovarian cancer (EOC) based on ultra-performance liquid chromatography mass spectrometry in both positive and negative modes. These unbiased metabolomic profiles could well distinguish EOC from benign ovarian tumor (BOT) and uterine fibroid (UF). Fifty-three metabolites were identified as specific biomarkers for EOC, and this is the first report of piperine, 3-indolepropionic acid, 5-hydroxyindoleacetaldehyde and hydroxyphenyllactate as metabolic biomarkers of EOC. The AUC values of these metabolites for discriminating EOC from BOT/UF and early-stage EOC from BOT/UF were 0.9100/0.9428 and 0.8385/0.8624, respectively. Meanwhile, our metabolites were able to distinguish early-stage EOC from late-stage EOC with an AUC of 0.8801. Importantly, analysis of dysregulated metabolic pathways extends our current understanding of EOC metabolism. Metabolic pathways in EOC patients are mainly characterized by abnormal phospholipid metabolism, altered l-tryptophan catabolism, aggressive fatty acid β-oxidation and aberrant metabolism of piperidine derivatives. Together, these metabolic pathways provide a foundation to support cancer development and progression. In conclusion, our large-scale plasma metabolomics study yielded fundamental insights into dysregulated metabolism in ovarian cancer, which could facilitate clinical diagnosis, therapy, prognosis and shed new lights on ovarian cancer pathogenesis.

Metabolic profiling study of early and late recurrence of hepatocellular carcinoma based on liquid chromatography-mass spectrometry

The objectives of this pilot study were to predict early postoperative recurrence in hepatocellular carcinoma (HCC) patients based on metabolic features and to explore the related metabolic disturbances. Liquid chromatography-mass spectrometry-based metabolic profiling was performed on the plasma of 18 late recurrent and 22 early recurrent HCC patients. Metabolic differences were found to be related to amino acid, bile acid, cholesterol, fatty acid, phospholipid and carbohydrate metabolism. Bile acids, steroids and fatty acids showed significant variation in the early recurrent HCC group compared to the late recurrence group. Decreased levels of polyunsaturated eicosapentaenoic acid, docosahexaenoic acid and linolenic acid were found to be specific metabolic features for early recurrence. With the combination of methionine, GCDCA and cholesterol sulfate, 85% of the early recurrent HCCs can be predicted correctly with the corresponding area under the curve (AUC) equal to 0.95 in the training set, and 80% of the early recurrent HCCs can be predicted correctly with the corresponding AUC equal to 0.91 in the test set.

Direct Analysis in Real Time Mass Spectrometry (DART-MS) Analysis of Skin Metabolome Changes in the Ultraviolet B-Induced Mice

Ultraviolet (UV) radiation is a major environmental factor that leads to acute and chronic reactions in the human skin. UV exposure induces wrinkle formation, DNA damage, and generation of reactive oxygen species (ROS). Most mechanistic studies of skin physiology and pharmacology related with UV-irradiated skin have focused on proteins and their related gene expression or single- targeted small molecules. The present study identified and analyzed the alteration of skin metabolites following UVB irradiation and topical retinyl palmitate (RP, 5%) treatment in hairless mice using direct analysis in real time (DART) time-of-flight mass spectrometry (TOF-MS) with multivariate analysis. Under the negative ion mode, the DART ion source successfully ionized various fatty acids including palmitoleic and linolenic acid. From DART-TOF-MS fingerprints measured in positive mode, the prominent dehydrated ion peak (m/z: 369, M+H-H₂O) of cholesterol was characterized in all three groups. In positive mode, the discrimination among three groups was much clearer than that in negative mode by using multivariate analysis of orthogonal partial-least squares-discriminant analysis (OPLS-DA). DART-TOF-MS can ionize various small organic molecules in living tissues and is an efficient alternative analytical tool for acquiring full chemical fingerprints from living tissues without requiring sample preparation. DART-MS measurement of skin tissue with multivariate analysis proved to be a powerful method to discriminate between experimental groups and to find biomarkers for various experiment models in skin dermatological research.

Metabolite profiling by direct analysis in real-time mass spectrometry

Untargeted metabolite profiling is a discovery tool for the identification of metabolites associated with the responses of perturbations to biological systems, such as a disease. Direct analysis in real-time mass spectrometry (DART MS) promises to be a powerful analytical technique for high-throughput metabolome analysis of human blood sera. Here, we describe the steps involved in untargeted blood sera metabolic profiling experiments using DART MS with two different sample introduction methods: probe-mode and transmission-mode geometries. Information regarding the optimization of different DART parameters that directly affect metabolite desorption and ionization, which thus influence sensitivity, is included.

Transmission mode direct analysis in real time mass spectrometry for fast untargeted metabolic fingerprinting

RATIONALE

Untargeted metabolic fingerprinting is a discovery tool to better understand biochemical processes involved in detecting and characterizing disease states and responses to environmental stressors. Although current mass spectrometric (MS) methods are very powerful, there is a clear need for more rapid, high-throughput MS approaches for metabolomics studies.

METHODS

A rapid metabolic fingerprinting method for human blood sera that utilizes a new transmission mode direct analysis in real time (TM-DART) sampling technique coupled with quadrupole time of flight mass spectrometry (QTOFMS) is presented. In this approach, the sample is deposited directly on a stainless steel mesh that is held in the ionization region by a custom-built module. As a result, the DART plasma gas stream interacts with the sample in a flow-through fashion, which maximizes the interaction between the sample and ionizing species and minimizes variance in sample positioning.

RESULTS

The optimization of TM-DART parameters directly affecting metabolite desorption and ionization, such as sample position and ionizing gas desorption temperature, was critical in achieving high sensitivity and detecting a broad mass range of metabolites. Ramping the ionizing gas desorption temperature further enhanced analysis by adding a simple separation dimension to this ambient approach. In terms of reproducibility, TM-DART compared favorably with traditional probe mode (PM-) DART analysis, with coefficients of variation as low as 16%. The longer-lasting TM-DART signals enabled the acquisition of metabolite full scan and product ion accurate mass spectra in a single experiment, resulting in greater confidence in metabolite identification.

CONCLUSIONS

TM-DART MS proved to be a powerful analytical technique for rapid metabolome analysis of human blood sera.

Applications of metabolomics in cancer research

The first discovery of metabolic changes in cancer occurred almost a century ago. While the genetic underpinnings of cancer have dominated its study since then, altered metabolism has recently been acknowledged as a key hallmark of cancer and metabolism-focused research has received renewed attention. The emerging field of metabolomics – which attempts to profile all metabolites within a cell or biological system – is now being used to analyze cancer metabolism on a system-wide scale, painting a broad picture of the altered pathways and their interactions with each other. While a large fraction of cancer metabolomics research is focused on finding diagnostic biomarkers, metabolomics is also being used to obtain more fundamental mechanistic insight into cancer and carcinogenesis. Applications of metabolomics are also emerging in areas such as tumor staging and assessment of treatment efficacy. This review summarizes contributions that metabolomics has made in cancer research and presents the current challenges and potential future directions within the field.

What Is the Opposite of Pandora's Box? Direct Analysis, Ambient Ionization, and a New Generation of Atmospheric Pressure Ion Sources

The introduction of DART and DESI sources approximately seven years ago led to the development of a new series of atmospheric pressure ion sources referred to as "ambient ionization" sources. These fall into two major categories: spray techniques like DESI or plasma techniques like DART. The selectivity of "direct ionization," meaning analysis without chromatography and with little or no sample preparation, depends on the mass spectrometer selectivity. Although high resolution and tandem mass spectrometry are valuable tools, rapid and simple sample preparation methods can improve the utility of ambient ionization methods. The concept of ambient ionization has led to the realization that there are many more ways to form ions than might be expected. An interesting example is the use of a flint-and-steel spark source to generate ions from compounds such as phenolphthalein and Gramicidin S.

Evaluation of direct analysis in real time ionization-mass spectrometry (DART-MS) in fish metabolomics aimed to assess the response to dietary supplementation

Ambient mass spectrometry employing a direct analysis in real time (DART) ion source coupled to a medium high-resolution/accurate mass time-of-flight mass spectrometer (TOFMS) was used as a rapid tool for metabolomic fingerprinting to study the effects of supplemental feeding with cereals (triticale) on the composition of muscle metabolites of common carp (Cyprinus carpio L.). First, the sample extraction and DART-TOFMS instrumental conditions were optimized to obtain the broadest possible representation of ionizable compounds occurring in the extracts obtained from common carp muscle. To this end, a simultaneous (all-in-one) extraction procedure was developed employing water and cyclohexane mixture as the extraction solvents. Under these conditions both polar as well as non-polar metabolites were isolated within a single extraction step. Next, the metabolomic fingerprints (mass spectra) of a large set of common carp muscle extracts were acquired. Finally, the experimental data were statistically evaluated using principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). Using this approach, differentiation of common carp muscle in response to dietary supplementation (feeding with and without cereals) was feasible. Correct classification was obtained based on the assessment of polar and as well as non-polar extracts fingerprints. The current study showed that DART-TOFMS metabolomic fingerprinting represents a rapid and powerful analytical strategy enabling differentiation of common carp muscles according to feeding history by recording metabolomic fingerprints of ionizable components under the conditions of ambient MS.

Gas chromatography-mass spectrometry metabolite profiling of worker honey bee (Apis mellifera L.) hemolymph for the study of Nosema ceranae infection

Here, we are presenting a gas chromatography-mass spectrometry (GC/MS) approach for the study of infection of the worker honey bee (Apis mellifera L.) by the newly emerged obligate intracellular parasite Nosema ceranae based on metabolite profiling of hemolymph. Because of the severity of the disease, early detection is crucial for its efficient control. Results revealed that the parasite causes a general disturbance of the physiology of the honey bee affecting the mechanisms controlling the mobilization of energy reserves in infected individuals. The imposed nutritional and energetic stress to the host was depicted mainly in the decreased levels of the majority of carbohydrates and amino acids, including metabolites such as fructose, l-proline, and the cryoprotectants sorbitol and glycerol, which are implicated in various biochemical pathways. Interestingly, the level of glucose was detected at significantly higher levels in infected honey bees. Metabolomics analyses were in agreement with those of multiplex quantitative PCR analyses, indicating that it can be used as a complementary tool for the detection and the study of the physiology of the disease.

Recent advances in metabolomics in oncology

The burden of cancer is growing worldwide and with it a more desperate need for better tools to detect, diagnose and monitor the disease is required. It is well recognized that cancer cells are characterized by distinct metabolic perturbations. The metabolomics approach involves the comprehensive profiling of the full complement of low MW compounds in a biological system. By applying advanced analytical and statistical tools, the 'metabolome' is mined for biomarkers that are associated with the state of cancer. This review presents an introduction to the main analytical platforms used in metabolomics analyses, such as NMR spectroscopy and MS, as well as the statistical tools used to mine these datasets. The discussion focuses on 'state-of-the-art' investigations on the four cancer types that have received the most study by metabolomics, namely breast, prostate, colorectal and liver cancer.