Development and validation of a gas chromatography/mass spectrometry method for the metabolic profiling of human colon tissue

Systematic Review: Contribution of the Gut Microbiome to the Volatile Metabolic Fingerprint of Colorectal Neoplasia

Colorectal cancer (CRC) has been associated with changes in volatile metabolic profiles in several human biological matrices. This enables its non-invasive detection, but the origin of these volatile organic compounds (VOCs) and their relation to the gut microbiome are not yet fully understood. This systematic review provides an overview of the current understanding of this topic. A systematic search using PubMed, Embase, Medline, Cochrane Library, and the Web of Science according to PRISMA guidelines resulted in seventy-one included studies. In addition, a systematic search was conducted that identified five systematic reviews from which CRC-associated gut microbiota data were extracted. The included studies analyzed VOCs in feces, urine, breath, blood, tissue, and saliva. Eight studies performed microbiota analysis in addition to VOC analysis. The most frequently reported dysregulations over all matrices included short-chain fatty acids, amino acids, proteolytic fermentation products, and products related to the tricarboxylic acid cycle and Warburg metabolism. Many of these dysregulations could be related to the shifts in CRC-associated microbiota, and thus the gut microbiota presumably contributes to the metabolic fingerprint of VOC in CRC. Future research involving VOCs analysis should include simultaneous gut microbiota analysis.

Metabolic models predict bacterial passengers in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a complex multifactorial disease. Increasing evidence suggests that the microbiome is involved in different stages of CRC initiation and progression. Beyond specific pro-oncogenic mechanisms found in pathogens, metagenomic studies indicate the existence of a microbiome signature, where particular bacterial taxa are enriched in the metagenomes of CRC patients. Here, we investigate to what extent the abundance of bacterial taxa in CRC metagenomes can be explained by the growth advantage resulting from the presence of specific CRC metabolites in the tumor microenvironment.

METHODS

We composed lists of metabolites and bacteria that are enriched on CRC samples by reviewing metabolomics experimental literature and integrating data from metagenomic case-control studies. We computationally evaluated the growth effect of CRC enriched metabolites on over 1500 genome-based metabolic models of human microbiome bacteria. We integrated the metabolomics data and the mechanistic models by using scores that quantify the response of bacterial biomass production to CRC-enriched metabolites and used these scores to rank bacteria as potential CRC passengers.

RESULTS

We found that metabolic networks of bacteria that are significantly enriched in CRC metagenomic samples either depend on metabolites that are more abundant in CRC samples or specifically benefit from these metabolites for biomass production. This suggests that metabolic alterations in the cancer environment are a major component shaping the CRC microbiome.

CONCLUSION

Here, we show with in sillico models that supplementing the intestinal environment with CRC metabolites specifically predicts the outgrowth of CRC-associated bacteria. We thus mechanistically explain why a range of CRC passenger bacteria are associated with CRC, enhancing our understanding of this disease. Our methods are applicable to other microbial communities, since it allows the systematic investigation of how shifts in the microbiome can be explained from changes in the metabolome.

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.

Metabolomic prediction of treatment outcome in pancreatic ductal adenocarcinoma patients receiving gemcitabine

PURPOSE

Resistance to gemcitabine remains a key challenge in the treatment of pancreatic ductal adenocarcinoma (PDAC), necessitating the constant search for effective strategies for a priori prediction of clinical outcome. While the existing studies focused on aberration of drug disposition genes and proteins as molecular predictors of gemcitabine treatment outcomes, the metabolic aberration associated with chemoresistance in clinical PDAC has been neglected. This exploratory study investigated the potential role of tissue metabolomics in characterizing the clinical treatment outcome of gemcitabine therapy.

METHODS

Surgically resected tumors from PDAC patients who underwent gemcitabine-based adjuvant chemotherapy (n = 25) were subjected to metabotyping using gas chromatography/time-of-flight mass spectrometry (GC/TOFMS).

RESULTS

A partial least-squares discriminant analysis (PLS-DA) model clearly distinguished patients who had favorable survival [overall survival (OS) > 24 months] from those who exhibited poorer survival (OS < 16 months) (Q ² = 0.302). Receiver-operating characteristic analysis demonstrated the robustness of the PLS-DA model with an area under the curve of 1. PLS-DA revealed 19 marker metabolites (e.g., lactic acid, proline, and pyroglutamate) that shed insights into the chemoresistance of gemcitabine in PDAC. Particularly, tissue levels of lactic acid complemented transcript expression levels of human equilibrative nucleoside transporter 1 in distinguishing patients according to their overall survival.

CONCLUSION

This work established proof-of-principle for GC/TOFMS-based global metabotyping of PDAC and laid the foundation for future discovery of metabolic biomarkers predictive of gemcitabine resistance in PDAC chemotherapy.

Impact of exercise on fecal and cecal metabolome over aging: a longitudinal study in rats

Aim: Physical exercise can reduce adverse conditions during aging, while both exercise and aging act as metabolism modifiers. The present study investigates rat fecal and cecal metabolome alterations derived from exercise during rats’ lifespan. Methods & results: Groups of rats trained life-long or for a specific period of time were under study. The training protocol consisted of swimming, 15–18 min per day, 3–5 days per week, with load of 4–0% of rat's weight. Fecal samples and cecal extracts were analyzed by targeted and untargeted metabolic profiling methods (GC–MS and LC–MS/MS). Effects of exercise and aging on the rats’ fecal and cecal metabolome were observed. Conclusion: Fecal and cecal metabolomics are a promising field to investigate exercise biochemistry and age-related alterations.

Tissue Metabonomic Phenotyping for Diagnosis and Prognosis of Human Colorectal Cancer. Sci Rep. 2016;6:20790. [PMID: 26876567 PMCID: PMC4753490 DOI: 10.1038/srep20790]

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide and prognosis based on the conventional histological grading method for CRC remains poor. To better the situation, we analyzed the metabonomic signatures of 50 human CRC tissues and their adjacent non-involved tissues (ANIT) using high-resolution magic-angle spinning (HRMAS) (1)H NMR spectroscopy together with the fatty acid compositions of these tissues using GC-FID/MS. We showed that tissue metabolic phenotypes not only discriminated CRC tissues from ANIT, but also distinguished low-grade tumor tissues (stages I-II) from the high-grade ones (stages III-IV) with high sensitivity and specificity in both cases. Metabonomic phenotypes of CRC tissues differed significantly from that of ANIT in energy metabolism, membrane biosynthesis and degradations, osmotic regulations together with the metabolism of proteins and nucleotides. Amongst all CRC tissues, the stage I tumors exhibited largest differentiations from ANIT. The combination of the differentiating metabolites showed outstanding collective power for differentiating cancer from ANIT and for distinguishing CRC tissues at different stages. These findings revealed details in the typical metabonomic phenotypes associated with CRC tissues nondestructively and demonstrated tissue metabonomic phenotyping as an important molecular pathology tool for diagnosis and prognosis of cancerous solid tumors.

Changes in urinary metabolic profiles of colorectal cancer patients enrolled in a prospective cohort study (ColoCare)

INTRODUCTION

Metabolomics is a valuable tool for biomarker screening of colorectal cancer (CRC). In this study, we profiled the urinary metabolomes of patients enrolled in a prospective patient cohort (ColoCare). We aimed to describe changes in the metabolome in the longer clinical follow-up and describe initial predictors as candidate markers with possibly prognostic significance.

METHODS

In total, 199 urine samples from CRC patients pre-surgery (n=97), 1-8 days post-surgery (n=12) and then after 6 and 12 months (n=52 and 38, respectively) were analyzed using both GC-MS and ¹H-NMR. Both datasets were analyzed separately with built in uni- and multivariate analyses of Metaboanalyst 2.0. Furthermore, adjusted linear mixed effects regression models were constructed.

RESULTS

Many concentrations of the metabolites derived from the gut microbiome were affected by CRC surgery, presumably indicating a tumor-induced shift in bacterial species. Associations of the microbial metabolites with disease stage indicate an important role of the gut microbiome in CRC.We were able to differentiate the metabolite profiles of CRC patients prior to surgery from those at any post-surgery timepoint using a multivariate model containing 20 marker metabolites (AUCROC=0.89; 95% CI:0.84-0.95).

CONCLUSION

To the best of our knowledge, this is one of the first metabolomic studies to follow CRC patients in a prospective setting with repeated urine sampling over time. We were able to confirm markers initially identified in case-control studies and pin point metabolites which may serve as candidates for prognostic biomarkers of CRC.

Evaluation of mass spectral library search algorithms implemented in commercial software

Performance of several library search algorithms (against EI mass spectral databases) implemented in commercial software products ( acd/specdb, chemstation, gc/ms solution and ms search) was estimated. Test set contained 1000 mass spectra, which were randomly selected from NIST'08 (RepLib) mass spectral database. It was shown that composite (also known as identity) algorithm implemented in ms search (NIST) software gives statistically the best results: the correct compound occupied the first position in the list of possible candidates in 81% of cases; the correct compound was within the list of top ten candidates in 98% of cases. It was found that use of presearch option can lead to rejection of the correct answer from the list of possible candidates (therefore presearch option should not be used, if possible). Overall performance of library search algorithms was estimated using receiver operating characteristic curves.

Investigation of Host-Gut Microbiota Modulation of Therapeutic Outcome

A broader understanding of factors underlying interindividual variation in pharmacotherapy is important for our pursuit of "personalized medicine." Based on knowledge gleaned from the investigation of human genetics, drug-metabolizing enzymes, and transporters, clinicians and pharmacists are able to tailor pharmacotherapies according to the genotype of patients. However, human host factors only form part of the equation that accounts for heterogeneity in therapeutic outcome. Notably, the gut microbiota possesses wide-ranging metabolic activities that expand the metabolic functions of the human host beyond that encoded by the human genome. In this review, we first illustrate the mechanisms in which gut microbes modulate pharmacokinetics and therapeutic outcome. Second, we discuss the application of metabonomics in deciphering the complex host-gut microbiota interaction in pharmacotherapy. Third, we highlight an integrative approach with particular mention of the investigation of gut microbiota using culture-based and culture-independent techniques to complement the investigation of the host-gut microbiota axes in pharmaceutical research.

Characterizing metabolic changes in human colorectal cancer

Colorectal cancer (CRC) remains a leading cause of cancer death worldwide, despite the fact that it is a curable disease when diagnosed early. The development of new screening methods to aid in early diagnosis or identify precursor lesions at risk for progressing to CRC will be vital to improving the survival rate of individuals predisposed to CRC. Metabolomics is an advancing area that has recently seen numerous applications to the field of cancer research. Altered metabolism has been studied for many years as a means to understand and characterize cancer. However, further work is required to establish standard procedures and improve our ability to identify distinct metabolomic profiles that can be used to diagnose CRC or predict disease progression. The present study demonstrates the use of direct infusion traveling wave ion mobility mass spectrometry to distinguish metabolic profiles from CRC samples and matched non-neoplastic epithelium as well as metastatic and primary tumors at different stages of disease (T1-T4). By directly infusing our samples, the analysis time was reduced significantly, thus increasing the speed and efficiency of this method compared to traditional metabolomics platforms. Partial least squares discriminant analysis was used to visualize differences between the metabolic profiles of sample types and to identify the specific m/z features that led to this differentiation. Identification of the distinct m/z features was made using the human metabolome database. We discovered alterations in fatty acid biosynthesis and oxidative, glycolytic, and polyamine pathways that distinguish tumors from non-malignant colonic epithelium as well as various stages of CRC. Although further studies are needed, our results indicate that colonic epithelial cells undergo metabolic reprogramming during their evolution to CRC, and the distinct metabolites could serve as diagnostic tools or potential targets in therapy or primary prevention. Graphical Abstract Colon tissue biopsy samples were collected from patients after which metabolites were extracted via sonication. Two-dimensional data were collected via IMS in tandem with MS (IMMS). Data were then interpreted statistically via PLS-DA. Scores plots provided a visualization of statistical separation and groupings of sample types. Loading plots allowed identification of influential ion features. Lists of these features were exported and analyzed for specific differences. Direct comparisons of the ion features led to the identification and comparative analyses of candidate biomarkers. These differences were then expressed visually in charts and tables.

Analytical protocols based on LC-MS, GC-MS and CE-MS for nontargeted metabolomics of biological tissues

Invasive, site-specific metabolite information could be better obtained from tissues. Hence, highly sensitive mass spectrometry-based metabolomics coupled with separation techniques are increasingly in demand in clinical research for tissue metabolomics application. Applying these techniques to nontargeted tissue metabolomics provides identification of distinct metabolites. These findings could help us to understand alterations at the molecular level, which can also be applied in clinical practice as screening markers for early disease diagnosis. However, tissues as solid and heterogeneous samples pose an additional analytical challenge that should be considered in obtaining broad, reproducible and representative analytical profiles. This manuscript summarizes the state of the art in tissue (human and animal) treatment (quenching, homogenization and extraction) for nontargeted metabolomics with mass spectrometry.

HMGA1 drives metabolic reprogramming of intestinal epithelium during hyperproliferation, polyposis, and colorectal carcinogenesis

Although significant progress has been made in the diagnosis and treatment of colorectal cancer (CRC), it remains a leading cause of cancer death worldwide. Early identification and removal of polyps that may progress to overt CRC is the cornerstone of CRC prevention. Expression of the High Mobility Group A1 (HMGA1) gene is significantly elevated in CRCs as compared with adjacent, nonmalignant tissues. We investigated metabolic aberrations induced by HMGA1 overexpression in small intestinal and colonic epithelium using traveling wave ion mobility mass spectrometry (TWIMMS) in a transgenic model in which murine Hmga1 was misexpressed in colonic epithelium. To determine if these Hmga1-induced metabolic alterations in mice were relevant to human colorectal carcinogenesis, we also investigated tumors from patients with CRC and matched, adjacent, nonmalignant tissues. Multivariate statistical methods and manual comparisons were used to identify metabolites specific to Hmga1 and CRC. Statistical modeling of data revealed distinct metabolic patterns in Hmga1 transgenics and human CRC samples as compared with the control tissues. We discovered that 13 metabolites were specific for Hmga1 in murine intestinal epithelium and also found in human CRC. Several of these metabolites function in fatty acid metabolism and membrane composition. Although further validation is needed, our results suggest that high levels of HMGA1 protein drive metabolic alterations that contribute to CRC pathogenesis through fatty acid synthesis. These metabolites could serve as potential biomarkers or therapeutic targets.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

Metabolomic studies of human gastric cancer: Review

Metabolomics is a field of study in systems biology that involves the identification and quantification of metabolites present in a biological system. Analyzing metabolic differences between unperturbed and perturbed networks, such as cancerous and non-cancerous samples, can provide insight into underlying disease pathology, disease prognosis and diagnosis. Despite the large number of review articles concerning metabolomics and its application in cancer research, biomarker and drug discovery, these reviews do not focus on a specific type of cancer. Metabolomics may provide biomarkers useful for identification of early stage gastric cancer, potentially addressing an important clinical need. Here, we present a short review on metabolomics as a tool for biomarker discovery in human gastric cancer, with a primary focus on its use as a predictor of anticancer drug chemosensitivity, diagnosis, prognosis, and metastasis.

Investigation of fatty acid accumulation in the engineered Saccharomyces cerevisiae under nitrogen limited culture condition

In this study, the Saccharomyces cerevisiae wild type strain and engineered strain with an overexpressed heterologous ATP-citrate lyase (acl) were cultured in medium with different carbon and nitrogen concentrations, and their fatty acid production levels were investigated. The results showed that when the S. cerevisiae engineered strain was cultivated under nitrogen limited culture condition, the yield of mono-unsaturated fatty acids showed higher than that under non-nitrogen limited condition; with the carbon concentration increased, the accumulation become more apparent, whereas in the wild type strain, no such correlation was found. Besides, the citrate level in the S. cerevisiae under nitrogen limited condition was found to be much higher than that under non-nitrogen limited condition, which indicated a relationship between the diminution of nitrogen and accumulation of citrate in the S. cerevisiae. The accumulated citrate could be further cleaved by acl to provide substrate for fatty acid synthesis.

Engineering the Saccharomyces cerevisiae β-oxidation pathway to increase medium chain fatty acid production as potential biofuel

Fatty acid-derived biofuels and biochemicals can be produced in microbes using β-oxidation pathway engineering. In this study, the β-oxidation pathway of Saccharomyces cerevisiae was engineered to accumulate a higher ratio of medium chain fatty acids (MCFAs) when cells were grown on fatty acid-rich feedstock. For this purpose, the haploid deletion strain Δpox1 was obtained, in which the sole acyl-CoA oxidase encoded by POX1 was deleted. Next, the POX2 gene from Yarrowia lipolytica, which encodes an acyl-CoA oxidase with a preference for long chain acyl-CoAs, was expressed in the Δpox1 strain. The resulting Δpox1 [pox2+] strain exhibited a growth defect because the β-oxidation pathway was blocked in peroxisomes. To unblock the β-oxidation pathway, the gene CROT, which encodes carnitine O-octanoyltransferase, was expressed in the Δpox1 [pox2+] strain to transport the accumulated medium chain acyl-coAs out of the peroxisomes. The obtained Δpox1 [pox2+, crot+] strain grew at a normal rate. The effect of these genetic modifications on fatty acid accumulation and profile was investigated when the strains were grown on oleic acids-containing medium. It was determined that the engineered strains Δpox1 [pox2+] and Δpox1 [pox2+, crot+] had increased fatty acid accumulation and an increased ratio of MCFAs. Compared to the wild-type (WT) strain, the total fatty acid production of the strains Δpox1 [pox2+] and Δpox1 [pox2+, crot+] were increased 29.5% and 15.6%, respectively. The intracellular level of MCFAs in Δpox1 [pox2+] and Δpox1 [pox2+, crot+] increased 2.26- and 1.87-fold compared to the WT strain, respectively. In addition, MCFAs in the culture medium increased 3.29-fold and 3.34-fold compared to the WT strain. These results suggested that fatty acids with an increased MCFAs ratio accumulate in the engineered strains with a modified β-oxidation pathway. Our approach exhibits great potential for transforming low value fatty acid-rich feedstock into high value fatty acid-derived products.

Non-invasive fecal metabonomic detection of colorectal cancer

Colorectal cancer (CRC) is a major cause of mortality in many developed countries. Effective screening strategies were called for to facilitate timely detection and to promote a better clinical outcome. In this study, the role of fecal metabonomics in the non-invasive detection of CRC was investigated. Gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) was utilized for the metabolic profiling of feces obtained from 11 CRC patients and 10 healthy subjects. Concurrently, matched tumor and normal mucosae surgically excised from CRC patients were profiled. CRC patients were differentiated clearly from healthy subjects based on their fecal metabonomic profiles (orthogonal partial least squares discriminant analysis [OPLS-DA], 1 predictive and 3 Y-orthogonal components, R (2)X = 0.373, R (2)Y = 0.995, Q (2) [cumulative] = 0.215). The robustness of the OPLS-DA model was demonstrated by an area of 1 under the receiver operator characteristic curve. OPLS-DA revealed fecal marker metabolites (e.g., fructose, linoleic acid, and nicotinic acid) that provided novel insights into the tumorigenesis of CRC. Interestingly, a disparate set of CRC-related metabolic aberrations occurred at the tissue level, implying the contribution of processes beyond the direct shedding of tumor cells to the fecal metabotype. In summary, this work established proof-of-principle for GC/TOFMS-based fecal metabonomic detection of CRC and offered new perspectives on the underlying mechanisms.

Review of mass spectrometry-based metabolomics in cancer research

Metabolomics, the systematic investigation of all metabolites present within a biologic system, is used in biomarker development for many human diseases, including cancer. In this review, we investigate the current role of mass spectrometry-based metabolomics in cancer research. A literature review was carried out within the databases PubMed, Embase, and Web of Knowledge. We included 106 studies reporting on 21 different types of cancer in 7 different sample types. Metabolomics in cancer research is most often used for case-control comparisons. Secondary applications include translational areas, such as patient prognosis, therapy control and tumor classification, or grading. Metabolomics is at a developmental stage with respect to epidemiology, with the majority of studies including less than 100 patients. Standardization is required especially concerning sample preparation and data analysis. In the second part of this review, we reconstructed a metabolic network of patients with cancer by quantitatively extracting all reports of altered metabolites: Alterations in energy metabolism, membrane, and fatty acid synthesis emerged, with tryptophan levels changed most frequently in various cancers. Metabolomics has the potential to evolve into a standard tool for future applications in epidemiology and translational cancer research, but further, large-scale studies including prospective validation are needed.

Metabolomic profiling in colorectal cancer: opportunities for personalized medicine

Colorectal cancer (CRC) is one of the most common types of cancer in the world, with high prevalence and mortality. Understanding the alterations of cancer metabolism and identifying reliable biomarkers would facilitate the development of novel technologies of CRC screening and early diagnosis, as well as new approaches to providing personalized medicine. Metabolomics, as an emerging molecular phenotyping approach, provides a clinical platform technology with an unprecedented amount of metabolic readout information, which is ideal for theranostic biomarker discovery. Metabolic signatures can link the unique pathophysiological states of patients to personalized health monitoring and intervention strategies. This article presents an overview of the metabolomic studies of CRC with a focus on recent advances in the biomarker discovery in serum, urine, fecal water and tissue samples for cancer diagnosis. The development and application of metabolomics towards personalized medicine, including early diagnosis, cancer staging, treatment and drug discovery are also discussed.

Global gas chromatography/time-of-flight mass spectrometry (GC/TOFMS)-based metabonomic profiling of lyophilized human feces

Gas chromatography mass spectrometry (GC/MS)-based fecal metabonomics represents a powerful systems biology approach for elucidating metabolic biomarkers of lower gastrointestinal tract (GIT) diseases. Unlike metabolic profiling of fecal water, the profiling of complete fecal material remains under-explored. Here, a gas chromatography/time-of-flight mass spectrometry (GC/TOFMS) method was developed and validated for the global metabonomic profiling of human feces. Fecal and fecal water metabotypes were also profiled and compared. Additionally, the unclear influence of blood in stool on the fecal metabotype was investigated unprecedentedly. Eighty milligram of lyophilized feces was ultrasonicated with 1mL of methanol:water (8:2) for 30min, followed by centrifugation, drying of supernatant, oximation and trimethylsilylation for 45min. Lyophilized feces demonstrated a more comprehensive metabolic coverage than fecal water, based on the number of chromatographic peaks. Principal component analysis (PCA) indicated occult blood (1mgHb/g feces) exerted a negligible effect on the fecal metabotype. Conversely, a unique metabotype related to feces spiked with gross blood (100mgHb/g feces) was revealed (PCA, R(2)X=0.837, Q(2)=0.794), confirming the potential confounding effect of gross GIT bleeding on the fecal metabotype. This pertinent finding highlights the importance of prudent interpretation of fecal metabonomic data, particularly in GIT diseases where bleeding is prevalent.

Metabolomics of colorectal cancer: past and current analytical platforms. Anal Bioanal Chem. 2013;405:5013-5030. [PMID: 23494270 DOI: 10.1007/s00216-013-6777-5]

Metabolomics is coming of age as an important area of investigation which may help reveal answers to questions left unanswered or only partially understood from proteomic or genomic approaches. Increased knowledge of the relationship of genes and proteins to smaller biomolecules (metabolites) will advance our ability to diagnose, treat, and perhaps prevent cancer and other diseases that have eluded scientists for generations. Colorectal tumors are the second leading cause of cancer mortality in the USA, and the incidence is rising. Many patients present late, after the onset of symptoms, when the tumor has spread from the primary site. Once metastases have occurred, the prognosis is significantly worse. Understanding alterations in metabolic profiles that occur with tumor onset and progression could lead to better diagnostic tests as well as uncover new approaches to treat or even prevent colorectal cancer (CRC). In this review, we explore the various analytical technologies that have been applied in CRC metabolomics research and summarize all metabolites measured in CRC and integrate them into metabolic pathways. Early studies with nuclear magnetic resonance and gas-chromatographic mass spectrometry suggest that tumor cells are characterized by aerobic glycolysis, increased purine metabolism for DNA synthesis, and protein synthesis. Liquid chromatography, capillary electrophoresis, and ion mobility, each coupled with mass spectrometry, promise to advance the field and provide new insight into metabolic pathways used by cancer cells. Studies with improved technology are needed to identify better biomarkers and targets for treatment or prevention of CRC.

MSClust: a tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data

Mass peak alignment (ion-wise alignment) has recently become a popular method for unsupervised data analysis in untargeted metabolic profiling. Here we present MSClust-a software tool for analysis GC-MS and LC-MS datasets derived from untargeted profiling. MSClust performs data reduction using unsupervised clustering and extraction of putative metabolite mass spectra from ion-wise chromatographic alignment data. The algorithm is based on the subtractive fuzzy clustering method that allows unsupervised determination of a number of metabolites in a data set and can deal with uncertain memberships of mass peaks in overlapping mass spectra. This approach is based purely on the actual information present in the data and does not require any prior metabolite knowledge. MSClust can be applied for both GC-MS and LC-MS alignment data sets. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0368-2) contains supplementary material, which is available to authorized users.

Applications of mass spectrometry to metabolomics and metabonomics: detection of biomarkers of aging and of age-related diseases

Every 5 years or so new technologies, or new combinations of old ones, seemingly burst onto the science scene and are then sought after until they reach the point of becoming commonplace. Advances in mass spectrometry instrumentation, coupled with the establishment of standardized chemical fragmentation libraries, increased computing power, novel data-analysis algorithms, new scientific applications, and commercial prospects have made mass spectrometry-based metabolomics the latest sought-after technology. This methodology affords the ability to dynamically catalogue and quantify, in parallel, femtomole quantities of cellular metabolites. The study of aging, and the diseases that accompany it, has accelerated significantly in the last decade. Mutant genes that alter the rate of aging have been found that increase lifespan by up to 10-fold in some model organisms, and substantial progress has been made in understanding fundamental alterations that occur at both the mRNA and protein level in tissues of aging organisms. The application of metabolomics to aging research is still relatively new, but has already added significant insight into the aging process. In this review we summarize these findings. We have targeted our manuscript to two audiences: mass spectrometrists interested in applying their technical knowledge to unanswered questions in the aging field, and gerontologists interested in expanding their knowledge of both mass spectrometry and the most recent advances in aging-related metabolomics.

Distinct urinary metabolic profile of human colorectal cancer

A full spectrum of metabolic aberrations that are directly linked to colorectal cancer (CRC) at early curable stages is critical for developing and deploying molecular diagnostic and therapeutic approaches that will significantly improve patient survival. We have recently reported a urinary metabonomic profiling study on CRC subjects (n = 60) and health controls (n = 63), in which a panel of urinary metabolite markers was identified. Here, we report a second urinary metabonomic study on a larger cohort of CRC (n = 101) and healthy subjects (n = 103), using gas chromatography time-of-flight mass spectrometry and ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry. Consistent with our previous findings, we observed a number of dysregulated metabolic pathways, such as glycolysis, TCA cycle, urea cycle, pyrimidine metabolism, tryptophan metabolism, polyamine metabolism, as well as gut microbial-host co-metabolism in CRC subjects. Our findings confirm distinct urinary metabolic footprints of CRC patients characterized by altered levels of metabolites derived from gut microbial-host co-metabolism. A panel of metabolite markers composed of citrate, hippurate, p-cresol, 2-aminobutyrate, myristate, putrescine, and kynurenate was selected, which was able to discriminate CRC subjects from their healthy counterparts. A receiver operating characteristic curve (ROC) analysis of these markers resulted in an area under the receiver operating characteristic curve (AUC) of 0.993 and 0.998 for the training set and the testing set, respectively. These potential metabolite markers provide a novel and promising molecular diagnostic approach for the early detection of CRC.

Metabolic profiling of HepG2 cells incubated with S(-) and R(+) enantiomers of anti-coagulating drug warfarin

Warfarin is a commonly prescribed oral anticoagulant with narrow therapeutic index. It achieves anti-coagulating effects by interfering with the vitamin K cycle. Warfarin has two enantiomers, S(-) and R(+) and undergoes stereoselective metabolism, with the S(-) enantiomer being more effective. We reported the intracellular metabolic profile in HepG2 cells incubated with S(-) and R(+) warfarin by GCMS. Chemometric method PCA was applied to analyze the individual samples. A total of 80 metabolites which belong to different categories were identified. Two batches of experiments (with and without the presence of vitamin K) were designed. In samples incubated with S(-) and R(+) warfarin, glucuronic acid showed significantly decreased in cells incubated with R(+) warfarin but not in those incubated with S(-) warfarin. It may partially explain the lower bio-activity of R(+) warfarin. And arachidonic acid showed increased in cells incubated with S(-) warfarin but not in those incubated with R(+) warfarin. In addition, a number of small molecules involved in γ-glutamyl cycle displayed ratio variations. Intracellular glutathione detection further validated the results. Taken together, our findings provided molecular evidence on a comprehensive metabolic profile on warfarin-cell interaction which may shed new lights on future improvement of warfarin therapy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0262-3) contains supplementary material, which is available to authorized users.

Prediction of gastric cancer metastasis through urinary metabolomic investigation using GC/MS

AIM: To gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic values to predict tumor metastasis.

METHODS: Human gastric cancer SGC-7901 cells were implanted into 24 severe combined immune deficiency (SCID) mice, which were randomly divided into metastasis group (n = 8), non-metastasis group (n = 8), and normal group (n = 8). Urinary metabolomic information was obtained by gas chromatography/mass spectrometry (GC/MS).

RESULTS: There were significant metabolic differences among the three groups (t test, P < 0.05). Ten selected metabolites were different between normal and cancer groups (non-metastasis and metastasis groups), and seven metabolites were also different between non-metastasis and metastasis groups. Two diagnostic models for gastric cancer and metastasis were constructed respectively by the principal component analysis (PCA). These PCA models were confirmed by corresponding receiver operating characteristic analysis (area under the curve = 1.00).

CONCLUSION: The urinary metabolomic profile is different, and the selected metabolites might be instructive to clinical diagnosis or screening metastasis for gastric cancer.

Metabolomic profiling of cellular responses to carvedilol enantiomers in vascular smooth muscle cells

Carvedilol is a non-selective β-blocker indicated in the treatment of hypertension and heart failure. Although the differential pharmacological effects of individual Carvedilol enantiomer is supported by preceding studies, the cellular response to each enantiomer is not well understood. Here we report the use of GC-MS metabolomic profiling to study the effects of Carvedilol enantiomers on vascular smooth muscle cells (A7r5) and to shed new light on molecular events underlying Carvedilol treatment. The metabolic analysis revealed alternations in the levels of 8 intracellular metabolites and 5 secreted metabolites in A7r5 cells incubated separately with S- and R-Carvedilol. Principal component analysis of the metabolite data demonstrated the characteristic metabolic signatures in S- and R-Carvedilol-treated cells. A panel of metabolites, including L-serine, L-threonine, 5-oxoproline, myristic acid, palmitic acid and inositol are closely correlated to the vascular smooth muscle contraction. Our findings reveal the differentiating metabolites for A7r5 cells incubated with individual enantiomer of Carvedilol, which opens new perspectives to employ metabolic profiling platform to study chiral drug-cell interactions and aid their incorporation into future improvement of β-blocker therapy.

Towards an unbiased metabolic profiling of protozoan parasites: optimisation of a Leishmania sampling protocol for HILIC-orbitrap analysis

Comparative metabolomics of Leishmania species requires the simultaneous identification and quantification of a large number of intracellular metabolites. Here, we describe the optimisation of a comprehensive metabolite extraction protocol for Leishmania parasites and the subsequent optimisation of the analytical approach, consisting of hydrophilic interaction liquid chromatography coupled to LTQ-orbitrap mass spectrometry. The final optimised protocol starts with a rapid quenching of parasite cells to 0 °C, followed by a triplicate washing step in phosphate-buffered saline. The intracellular metabolome of 4 × 10(7) parasites is then extracted in cold chloroform/methanol/water 20/60/20 (v/v/v) for 1 h at 4 °C, resulting in both cell disruption and comprehensive metabolite dissolution. Our developed metabolomics platform can detect approximately 20% of the predicted Leishmania metabolome in a single experiment in positive and negative ionisation mode.

Urinary metabolic profiling of colorectal carcinoma based on online affinity solid phase extraction-high performance liquid chromatography and ultra performance liquid chromatography-mass spectrometry

Colorectal carcinoma (CRC) is the third most commonly encountered cancer and fourth cause of cancer-associated death worldwide. Abundant studies have demonstrated that one of the best effective therapies for enhancing the 5-year survival rate of patients is to diagnose the disease at an early stage. Urine metabonomics is widely being utilized as an efficient platform to investigate the metabolic changes and discover the potential biomarkers of malignant diseases. In this study both ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) and online affinity solid phase extraction-high performance liquid chromatography (SPE-HPLC) were used to analyze the urinary metabolites from 34 healthy volunteers, 34 benign colorectal tumor and 50 colorectal carcinoma patients to produce comprehensive metabolic profiling data. A reliable separation between the control and disease groups as well as significantly changed metabolites were obtained from orthogonal signal correction partial least squares models which were built based on the two separate data sets from UPLC-MS and affinity SPE-HPLC, respectively. 15 metabolites, showing the metabolic disorders of CRC, were identified finally. These metabolites were found to be related to glutamine metabolism, fatty acid oxidation, nucleotide biosynthesis and protein metabolism.

Metabolomic investigation of gastric cancer tissue using gas chromatography/mass spectrometry. Anal Bioanal Chem. 2010;396:1385-1395. [PMID: 20012946 DOI: 10.1007/s00216-009-3317-4]

Gastric cancer screening or diagnosis is mainly based on endoscopy and biopsy. The aim of this study was to identify the difference of metabolomic profile between normal and malignant gastric tissue, and to further explore tumor biomarkers. Chemical derivatization together with gas chromatography/mass spectrometry (GC/MS) was utilized to obtain the metabolomic information of the malignant and non-malignant tissues of gastric mucosae in 18 gastric cancer patients. Acquired metabolomic data was analyzed using the Wilcoxon rank sum test to find the tissue metabolic biomarkers for gastric cancer. A diagnostic model for gastric cancer was constructed using principal component analysis (PCA), and was assessed with receiver-operating characteristic (ROC) curves. Results showed that 18 metabolites were detected differently between the malignant tissues and the adjacent non-malignant tissues of gastric mucosa. Five metabolites were also detected differently between the non-invasive tumors and the invasive tumors. The diagnostic model could discriminate tumors from normal mucosae with an area under the curve (AUC) value of 0.9629, and another diagnostic model constructed for clinical staging was assessed with an AUC value of 0.969. We conclude that the metabolomic profile of malignant gastric tissue was different from normal, and that the selected tissue metabolites could probably be applied for clinical diagnosis or staging for gastric cancer.

Chemometrics in metabolomics--a review in human disease diagnosis

Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided.

Combining nuclear magnetic resonance spectroscopy and mass spectrometry in biomarker discovery

Metabolic profiling of biological specimens is emerging as a promising approach for discovering specific biomarkers in the diagnosis of a number of diseases. Amongst many analytical techniques, nuclear magnetic resonance spectroscopy and mass spectrometry are the most information-rich tools that enable high-throughput and global analysis of hundreds of metabolites in a single step. Although only one of the two techniques is utilized in a majority of metabolomics applications, there is a growing interest in combining the data from the two methods to effectively unravel the mammoth complexity of biological samples. In this article, current developments in nuclear magnetic resonance, mass spectrometry and multivariate statistical analysis methods are described. While some general applications that utilize the combination of the two analytical methods are presented briefly, the emphasis is laid on the recent applications of nuclear magnetic resonance and mass spectrometry methods in the studies of hepatopancreatobiliary and gastrointestinal malignancies.