A bioinformatician's view of the metabolome

Assessment of progression of pulmonary fibrosis based on metabonomics and analysis of intestinal microbiota

The main purpose of this study was to explore the changes of biomarkers in different developmental stages of bleomycin-induced pulmonary fibrosis (PF) in rats via comprehensive pathophysiology, UPLC-QTOF/MS metabonomic technology, and 16S rRNA gene sequencing of intestinal microbiota. The rats were randomly divided into normal control and 1-, 2- and 4-week model group. The rat model of PF was established by one-time intratracheal instillation of bleomycin. The levels of inflammatory and fibrosis-related factors such as hydroxyproline (HYP), type III procollagen (COL-III), type IV collagen (COL-IV), hyaluronidase (HA), laminin (LN), interleukin (IL)-1β, IL-6, malondialdehyde (MDA) increased and superoxide dismutase (SOD) decreased as the PF cycle progressed. In the 1-, 2- and 4-week model group, 2, 19 and 18 potential metabolic biomarkers and 3, 16 and 12 potential microbial biomarkers were detected, respectively, which were significantly correlated. Glycerophospholipid metabolism pathway was observed to be an important pathway affecting PF at 1, 2 and 4 weeks; arginine and proline metabolism pathways significantly affected PF at 2 weeks. Linoleic acid metabolism pathway exhibited clear metabolic abnormalities at 2 and 4 weeks of PF, and alpha-linolenic acid metabolism pathway significantly affected PF at 4 weeks.

The effects of Clostridium butyricum on Ira rabbit growth performance, cecal microbiota and plasma metabolome

Clostridium butyricum (C. butyricum) can provide many benefits for animals’ growth performance and gut health. In this study, we investigated the effects of C. butyricum on the growth performance, cecal microbiota, and plasma metabolome in Ira rabbits. A total of 216 Ira rabbits at 32 days of age were randomly assigned to four treatments supplemented with basal diets containing 0 (CG), 200 (LC), 400 (MC), and 600 mg/kg (HC) C. butyricum for 35 days, respectively. In comparison with the CG group, C. butyricum supplementation significantly improved the average daily gain (ADG) and feed conversion rate (FCR) at 53 and 67 days of age (P < 0.05) and digestibilities of crude protein (CP) and crude fiber (CF) at 67 days of age (P < 0.05). The cellulase activity in the HC group was higher respectively by 50.14 and 90.13% at 53 and 67 days of age, than those in the CG groups (P < 0.05). Moreover, at 67 days of age, the diet supplemented with C. butyricum significantly increased the relative abundance of Verrucomicrobia at the phylum level (P < 0.05). Meanwhile, the concentrations of different metabolites, such as amino acids and purine, were significantly altered by C. butyricum (P < 0.05). In addition, 10 different genera were highly correlated with 52 different metabolites at 53-day-old and 6 different genera were highly correlated with 18 different metabolites at 67-day-old Ira rabbits. These findings indicated that the C. butyricum supplementation could significantly improve the growth performance by modifying the cecal microbiota structure and plasma metabolome of weaned Ira rabbits.

Metabolomics in clinical and forensic toxicology, sports anti-doping and veterinary residues

Metabolomics is a multidisciplinary field providing workflows for complementary approaches to conventional analytical determinations. It allows for the study of metabolically related groups of compounds or even the study of novel pathways within the biological system. The procedural stages of metabolomics; experimental design, sample preparation, analytical determinations, data processing and statistical analysis, compound identification and validation strategies are explored in this review. The selected approach will depend on the type of study being conducted. Experimental design influences the whole metabolomics workflow and thus needs to be properly assessed to ensure sufficient sample size, minimal introduced and biological variation and appropriate statistical power. Sample preparation needs to be simple, yet potentially global in order to detect as many compounds as possible. Analytical determinations need to be optimised either for the list of targeted compounds or a universal approach. Data processing and statistical analysis approaches vary widely and need to be better harmonised for review and interpretation. This includes validation strategies that are currently deficient in many presented workflows. Common compound identification approaches have been explored in this review. Metabolomics applications are discussed for clinical and forensic toxicology, human and equine sports anti-doping and veterinary residues.

Characterizing Multidevice Capillary Vibrating Sharp-Edge Spray Ionization for In-Droplet Hydrogen/Deuterium Exchange to Enhance Compound Identification

Multidevice capillary vibrating sharp-edge spray ionization (cVSSI) source parameters have been examined to determine their effects on conducting in-droplet hydrogen/deuterium exchange (HDX) experiments. Control experiments using select compounds indicate that the observed differences in mass spectral isotopic distributions obtained upon initiation of HDX result primarily from solution-phase reactions as opposed to gas-phase exchange. Preliminary studies have determined that robust HDX can only be achieved with the application of same-polarity voltage to both the analyte and the deuterium oxide reagent (D₂O) cVSSI devices. Additionally, a similar HDX reactivity dependence on the voltage applied to the D₂O device for various analytes is observed. Analyte and reagent flow experiments show that, for the multidevice cVSSI setup employed, there is a nonlinear dependence on the D₂O reagent flow rate; increasing the D₂O reagent flow by 100% results in only an ∼10-20% increase in deuterium incorporation for this setup. Instantaneous (subsecond) response times have been demonstrated in the initiation or termination of HDX, which is achieved by turning on or off the reagent cVSSI device piezoelectric transducer. The ability to distinguish isomeric species by in-droplet HDX is presented. Finally, a demonstration of a three-component cVSSI device setup to perform multiple (successive or in combination) in-droplet chemistries to enhance compound ionization and identification is presented and a hypothetical metabolomics workflow consisting of successive multidevice activation is briefly discussed.

Effects of Clostridium butyricum on growth performance, metabonomics and intestinal microbial differences of weaned piglets

Background

Weaning stress of piglets causes a huge economic loss to the pig industry. Balance and stability of the intestinal microenvironment is an effective way to reduce the occurance of stress during the weaning process. Clostridium butyricum, as a new microecological preparation, is resistant to high temperature, acid, bile salts and some antibiotics. The aim of present study is to investigate the effects of C. butyricum on the intestinal microbiota and their metabolites in weaned piglets.

Results

There was no statistical significance in the growth performance and the incidence of diarrhoea among the weaned piglets treated with C. butyricum during 0–21 days experimental period. Analysis of 16S rRNA gene sequencing results showed that the operational taxonomic units (OTUs), abundance-based coverage estimator (ACE) and Chao index of the CB group were found to be significantly increased compared with the NC group (P < 0.05). Bacteroidetes, Firmicutes and Tenericutes were the predominant bacterial phyla in the weaned piglets. A marked increase in the relative abundance of Megasphaera, Ruminococcaceae_NK4A214_group and Prevotellaceae_UCG-003, along with a decreased relative abundance of Ruminococcaceae_UCG-005 was observed in the CB group, when compared with the NC group (P < 0.05). With the addition of C. butyricum, a total of twenty-two significantly altered metabolites were obtained in the feces of piglets. The integrated pathway analysis by MetaboAnalyst indicated that arginine and proline metabolism; valine, leucine and isoleucine biosynthesis; and phenylalanine metabolism were the main three altered pathways, based on the topology. Furthermore, Spearman’s analysis revealed some altered gut microbiota genus such as Oscillospira, Ruminococcaceae_NK4A214_group, Megasphaera, Ruminococcaceae_UCG-005, Prevotella_2, Ruminococcaceae_UCG-002, Rikenellaceae_RC9_gut_group and Prevotellaceae_UCG-003 were associated with the alterations in the fecal metabolites (P < 0.05), indicating that C. butyricum presented a potential protective impact through gut microbiota. The intestinal metabolites changed by C. butyricum mainly involved the variation of citrulline, dicarboxylic acids, branched-chain amino acid and tryptophan metabolic pathways.

Conclusions

Overall, this study strengthens the idea that the dietary C. butyricum treatment can significantly alter the intestinal microbiota and metabolite profiles of the weaned piglets, and C. butyricum can offer potential benefits for the gut health.

Personalized Nutrition and -Omics

With change in global concern toward food quality over food quantity, consumer concern and choice of healthy food has become a matter of prime importance. It gave rise to concept of “personalized or precision nutrition”. The theory behind personalization of nutrition is supported by multiple factors including advances in food analytics, nutrition based diseases and public health programs, increasing use of information technology in nutrition science, concept of gene-diet interaction and growing consumer capacity or concern by better and healthy foods. The advances in “omics” tools and related analytical techniques have resulted into tremendous scope of their application in nutrition science. As a consequence, a better understanding of underlying interaction between diet and individual is expected with addressing of key challenges for successful implementation of this science. In this chapter, the above aspects are discussed to get an insight into driving factors for increasing concern in personalized nutrition.

Reduced Expression of hsa-miR-338-3p Contributes to the Development of Glioma Cells by Targeting Mitochondrial 3-Oxoacyl-ACP Synthase (OXSM) in Glioblastoma (GBM)

Background

MicroRNAs have been identified as major regulators and therapeutic targets of glioblastoma (GBM). It is thus meaningful to study the miRNAs differentially expressed (DE-miRNAs) in GBM.

Materials and Methods

We performed a meta-analysis of previously published microarray data using the R-based “metaMA” package to identify DE-miRNAs.The biological processes of the DE-miRNAs were then analyzed using FunRich. KEGG pathways of the DE-miRNAs gene targets were analyzed by mirPath V.3. Luciferase activity assay was performed to validate that OXSM is a direct target of hsa-miR338-3p. Flow cytometry was used to detect the effects of miR-338-3p on GBM cell proliferation, apoptosis and cell cycle.

Results

DE-miRNAs in blood and brain tissue from GBM were identified. “Type I interferon signaling pathway” and “VEGF and VEGFR signaling network” were the most significantly enriched biological processes shared by all GBM types. In KEGG pathway analysis, DE-miRNAs both in blood and tissue show altered fatty acid biosynthesis. Further validation shows hsa-miR-338-3p regulates fatty acid metabolism by directly targeting OXSM gene. In addition, our data revealed an accelerated cell cycle and an anti-apoptotic role for OXSM in glioma cells, which has not been reported. Finally, we confirmed that hsa-miR-338-3p inhibitor antagonized the effect of downregulation of OXSM on cell cycle and apoptosis of GBM cells.

Conclusion

We revealed that hsa-miR-338-3p, down-regulated in GBM, may affect the biogenesis and rapid proliferation of glioma cells by regulating the level of OXSM, providing new insights into understanding the pathogenesis of GBM and developing strategies to improve GBM prognosis.

Seminal Fluid Metabolomic Markers of Oligozoospermic Infertility in Humans

Infertility affects 12–15% of couples worldwide, and male factors are the cause of nearly half of all cases. Studying seminal fluid composition could lead to additional diagnostic accuracy and a better understanding of the pathophysiology of male factor infertility. Metabolomics offers a new opportunity to evaluate biomarkers and better understand pathological mechanisms. The aim of the study was to identify new markers or therapeutic targets to improve outcomes in male factor or idiopathic infertility patients. Semen samples were obtained from 29 men with a normal spermogram test, and from 18 oligozoospermic men. Samples were processed and analyzed by Nuclear Magnetic Resonance spectroscopy and, subsequently, multivariate and univariate statistical analyses. Receiving Operator Curves (ROC) and Spearman correlations were also performed. An Orthogonal Partial Least Square Discriminant Analysis supervised multivariate model was devised to compare the groups. The levels of fructose, myo-inositol, aspartate and choline were altered. Moreover, Spearman Correlation associated fructose, aspartate and myo-inositol with the total amount of spermatozoa, total motile spermatozoa, % of immotility and % of “in situ” spermatozoic motility respectively. NMR-based metabolomics allowed the identification of a specific metabolic fingerprint of the seminal fluids of patients affected by oligozoospermia.

The involvement of PybZIPa in light-induced anthocyanin accumulation via the activation of PyUFGT through binding to tandem G-boxes in its promoter

To gain insight into how anthocyanin biosynthesis is controlled by light in fruit, transcriptome and metabolome analyses were performed in the Chinese sand pear cultivar "Mantianhong" (Pyrus pyrifolia) after bagging and bag removal. We investigated transcriptional and metabolic changes and gene-metabolite correlation networks. Correlation tests of anthocyanin content and transcriptional changes revealed that 1,530 transcripts were strongly correlated with 15 anthocyanin derivatives (R ² > 0.9, P-value < 0.05), with the top 130 transcripts categorized as being associated with flavonoid metabolism, transcriptional regulation, and light signaling. The connection network revealed a new photosensitive transcription factor, PybZIPa, that might play an important role during light-induced anthocyanin accumulation. The overexpression of PybZIPa promoted anthocyanin accumulation in pear and strawberry fruit as well as tobacco leaves. Dual luciferase and Y1H assays further verified that PybZIPa directly activated the expression of PyUFGT by binding to tandem G-box motifs in the promoter, which was key to differential anthocyanin accumulation in debagged pear skin, and the number of G-box motifs affected the transcriptional activation of PyUFGT by PybZIPa. The results indicate that the light-induced anthocyanin biosynthesis regulatory mechanism in pear differs from that described in previous reports suggesting that a bZIP family member co-regulates anthocyanin biosynthesis with other transcription factors in apple and Arabidopsis. It was found that, in response to light, PybZIPa promoted anthocyanin biosynthesis by regulating important transcription factors (PyMYB114, PyMYB10, and PyBBX22) as well as structural genes (PyUFGT) via binding to G-boxes within promoters. This activation was amplified by the self-binding of PybZIPa to activate its own promoter. Overall, we demonstrate the utility of a multiomics integrative approach for discovering new functional genes and pathways underlying light-induced anthocyanin biosynthesis.

GC/MS-based urine metabolomics analysis of renal allograft recipients with acute rejection

BACKGROUND

Acute renal allograft rejection is a common complication after renal transplantation that often leads to chronic rejection and ultimate graft loss. While renal allograft biopsy remains the gold standard for diagnosis of acute rejection, the possibility of biopsy-associated complications cannot be overlooked. The development of noninvasive methods for accurate detection of acute renal allograft rejection is thus of significant clinical importance.

METHODS

Gas chromatography-mass spectrometry (GC/MS) was employed for analysis of urine metabolites in 15 renal allograft recipients with acute rejection and 15 stable renal transplant recipients. Partial least squares (PLS) regression and leave-one-out analyses were performed to ascertain whether the metabolites identified could be exploited to distinguish acute rejection from stable groups as well as their sensitivity and specificity.

RESULTS

Overall, 14 metabolites were significantly altered in the acute rejection group (11 and 3 metabolites displayed higher and lower levels, respectively) relative to the stable transplant group. Data from PLS and leave-one-out analyses revealed that the differential metabolites identified not only distinguished acute rejection from stable transplant recipients but also showed high sensitivity and specificity for diagnosis of renal allograft recipients with acute rejection.

CONCLUSION

Urine metabolites identified with GC/MS can effectively distinguish acute rejection from stable transplant recipients, supporting the potential utility of metabolome analysis in non-invasive diagnosis of acute rejection.

Biomarker Discovery in Animal Health and Disease: The Application of Post-Genomic Technologies

The causes of many important diseases in animals are complex and multifactorial, which present unique challenges. Biomarkers indicate the presence or extent of a biological process, which is directly linked to the clinical manifestations and outcome of a particular disease. Identifying biomarkers or biomarker profiles will be an important step towards disease characterization and management of disease in animals. The emergence of post-genomic technologies has led to the development of strategies aimed at identifying specific and sensitive biomarkers from the thousands of molecules present in a tissue or biological fluid. This review will summarize the current developments in biomarker discovery and will focus on the role of transcriptomics, proteomics and metabolomics in biomarker discovery for animal health and disease.

Systems Biology of Circadian Rhythms: An Outlook

The circadian system in higher organisms temporally orchestrates rhythmic changes in a vast number of genes and gene products in different organs. Complex interactions between these components, both within and among cells, ultimately lead to rhythmic behavior and physiology. Identifying the plethora of circadian targets and mapping their interactions with one another is therefore essential to comprehend the molecular mechanisms of circadian regulation. The emergence of new technology for unbiased identification of biomolecules and for mapping interactions at the genome-wide scale is offering powerful tools to decipher the regulatory networks underpinning circadian rhythms. In this review, the authors discuss the potential application of these genome-wide approaches in the study of circadian rhythms.

Application of metabonomics in research of inflammatory bowel disease

Inflammatory bowel disease (IBD), including ulcerative colitis (UC) and Crohn's disease (CD), is a chronic non-specific inflammatory disorder of the gastrointestinal tract. The etiology and pathogenesis of IBD are still not entirely understood today and are thought to be caused by the interaction of multiple factors, including environmental, genetic, infectious and immune factors. The lack of typical clinical features also leads to a difficult diagnosis of IBD. In recent years, metabonomics is becoming a very important way to find biomarkers and investigate disease mechanisms. In this paper we review the main technologies of metabonomics and their present application in IBD.

Metabolic pathway predictions for metabolomics: a molecular structure matching approach

Metabolic pathways are composed of a series of chemical reactions occurring within a cell. In each pathway, enzymes catalyze the conversion of substrates into structurally similar products. Thus, structural similarity provides a potential means for mapping newly identified biochemical compounds to known metabolic pathways. In this paper, we present TrackSM, a cheminformatics tool designed to associate a chemical compound to a known metabolic pathway based on molecular structure matching techniques. Validation experiments show that TrackSM is capable of associating 93% of tested structures to their correct KEGG pathway class and 88% to their correct individual KEGG pathway. This suggests that TrackSM may be a valuable tool to aid in associating previously unknown small molecules to known biochemical pathways and improve our ability to link metabolomics, proteomic, and genomic data sets. TrackSM is freely available at http://metabolomics.pharm.uconn.edu/?q=Software.html .

Integration of metabolomics data into metabolic networks

Metabolite levels together with their corresponding metabolic fluxes are integrative outcomes of biochemical transformations and regulatory processes and they can be used to characterize the response of biological systems to genetic and/or environmental changes. However, while changes in transcript or to some extent protein levels can usually be traced back to one or several responsible genes, changes in fluxes and particularly changes in metabolite levels do not follow such rationale and are often the outcome of complex interactions of several components. The increasing quality and coverage of metabolomics technologies have fostered the development of computational approaches for integrating metabolic read-outs with large-scale models to predict the physiological state of a system. Constraint-based approaches, relying on the stoichiometry of the considered reactions, provide a modeling framework amenable to analyses of large-scale systems and to the integration of high-throughput data. Here we review the existing approaches that integrate metabolomics data in variants of constrained-based approaches to refine model reconstructions, to constrain flux predictions in metabolic models, and to relate network structural properties to metabolite levels. Finally, we discuss the challenges and perspectives in the developments of constraint-based modeling approaches driven by metabolomics data.

Identification of biochemical differences between different forms of male infertility by nuclear magnetic resonance (NMR) spectroscopy

PURPOSE

The aim of this study was to analyze the seminal plasma of patients with idiopathic/male factor infertility and healthy controls with proven fertility by NMR spectroscopy, with a hope of establishing difference in biomarker profiles, if any, between the groups.

METHODS

A total of 103 subjects visiting the infertility clinic of Manipal University with normozoospermic parameters, oligozoospermia, asthenozoospermia, azoospermia and teratozoospermia were included. Semen characteristics were analysed by standard criteria. Seminal plasma was subjected to NMR spectroscopy at a 700 MHz (1)H frequency. The resultant data was analyzed by appropriate software.

RESULTS

The analysis revealed significant differences between the fertile control group and other forms of male infertility. Interestingly, seminal plasma profile of the idiopathic infertility group showed distinct segregation from the control population as well as other infertile groups. The difference in biomarker profiles between the idiopathic infertility and the rest of the groups combined could originate from either the up-regulation or down regulation of a several compounds, including lysine, arginine, tyrosine, citrate, proline and fructose.

CONCLUSION

Our data suggests the presence of a metabolic reason behind the origin of idiopathic infertility. (1)H NMR based metabonomic profiling based on concentration of biomarker lysine has the potential to aid in the detection and diagnosis of idiopathic infertility in an efficient manner.

Role of metabolomic analysis of biomarkers in the management of male infertility

Metabolomics is the systematic study of metabolites as small-molecule biomarkers that represent the functional phenotype in a cell, tissue or organism. Detection of crucial disturbances in the concentration of metabolites by metabolomic profiling of key biomarkers can be beneficial in the management of various medical conditions, including male-factor infertility. Recent studies have demonstrated the potential role of this rapid, noninvasive analysis in the investigation of infertile men. Differences in the concentration of oxidative stress biomarkers (-CH, -NH, -OH and ROH) have been found to be uniquely associated with semen plasma of healthy men compared with patients with idiopathic infertility, varicocele and vasectomy reversal. Furthermore, NMR spectra have shown significant differences in citrate, lactate, glycerylphosphorylcholine and glycerylphosphorylethanolamine among semen samples of men with spermatogenesis failure, obstructive azoospermia, oligoasthenoteratozoospermia and healthy donors. Evidence has also shown the value of (31)P-magnetic resonance spectroscopy in differentiating patients with testicular failure and ductal obstruction by utilizing phosphomonoester and beta-adenosine triphosphate as biomarkers. In addition, metabolomics has shown promise in assisted reproductive techniques. Recent studies involving spectroscopic measurements of follicular fluid and embryo culture media have revealed an association between biomarkers of oxidative stress and pregnancy outcome of oocytes and embryos.

Integrated analysis of transcriptomics and metabolomics profiles

BACKGROUND

Integrated analysis of transcriptomics and metabolomics data has the potential greatly to increase our understanding of metabolic networks and biological systems leading to various potential clinical applications.

OBJECTIVE

The aim is to present different applications as well as analysis tools utilized for the parallel study of gene and metabolite expressions.

METHODS

Publications dealing with integrated analysis of gene and metabolite expression data as well as publications describing tools that can be used for integrated analysis are reviewed.

RESULTS/CONCLUSION

The full benefit of integrated analysis can be achieved only if data from all utilized methods are treated equally by multidisciplinary teams. This approach can lead to advances in functional genomics with possible clinical developments in diagnostics and improved drug target selection.

Proteomics and metabolomics in renal transplantation-quo vadis?

The improvement of long-term transplant organ and patient survival remains a critical challenge following kidney transplantation. Proteomics and biochemical profiling (metabolomics) may allow for the detection of early changes in cell signal transduction regulation and biochemistry with high sensitivity and specificity. Hence, these analytical strategies hold the promise to detect and monitor disease processes and drug effects before histopathological and pathophysiological changes occur. In addition, they will identify enriched populations and enable individualized drug therapy. However, proteomics and metabolomics have not yet lived up to such high expectations. Renal transplant patients are highly complex, making it difficult to establish cause-effect relationships between surrogate markers and disease processes. Appropriate study design, adequate sample handling, storage and processing, quality and reproducibility of bioanalytical multi-analyte assays, data analysis and interpretation, mechanistic verification, and clinical qualification (=establishment of sensitivity and specificity in adequately powered prospective clinical trials) are important factors for the success of molecular marker discovery and development in renal transplantation. However, a newly developed and appropriately qualified molecular marker can only be successful if it is realistic that it can be implemented in a clinical setting. The development of combinatorial markers with supporting software tools is an attractive goal.

xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data

BACKGROUND

Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation.

RESULTS

xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites.

CONCLUSIONS

xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data

BACKGROUND

Detection of low abundance metabolites is important for de novo mapping of metabolic pathways related to diet, microbiome or environmental exposures. Multiple algorithms are available to extract m/z features from liquid chromatography-mass spectral data in a conservative manner, which tends to preclude detection of low abundance chemicals and chemicals found in small subsets of samples. The present study provides software to enhance such algorithms for feature detection, quality assessment, and annotation.

RESULTS

xMSanalyzer is a set of utilities for automated processing of metabolomics data. The utilites can be classified into four main modules to: 1) improve feature detection for replicate analyses by systematic re-extraction with multiple parameter settings and data merger to optimize the balance between sensitivity and reliability, 2) evaluate sample quality and feature consistency, 3) detect feature overlap between datasets, and 4) characterize high-resolution m/z matches to small molecule metabolites and biological pathways using multiple chemical databases. The package was tested with plasma samples and shown to more than double the number of features extracted while improving quantitative reliability of detection. MS/MS analysis of a random subset of peaks that were exclusively detected using xMSanalyzer confirmed that the optimization scheme improves detection of real metabolites.

CONCLUSIONS

xMSanalyzer is a package of utilities for data extraction, quality control assessment, detection of overlapping and unique metabolites in multiple datasets, and batch annotation of metabolites. The program was designed to integrate with existing packages such as apLCMS and XCMS, but the framework can also be used to enhance data extraction for other LC/MS data software.

Mass Spectrometry-Based Multiplexing for the Analysis of Biomarkers in Drug Development and Clinical Diagnostics- How Much is too Much?

Biomarkers, or more specifically molecular markers, can detect biochemical changes associated with disease processes and drug effects before histopathological and pathophysiological changes occur. Multiplexing technologies such as high-performance liquid chromatography/mass spectrometry (LC-MS) allow for the measurement of molecular marker patterns that confer significantly more information than the measurement of a single parameter alone. The use of multiplexing assays for drug development, and as diagnostic tools, is attractive but will require regulatory review and approval and thus requires validation following regulatory guidances. Multiplexing assays always constitute a compromise. The number of analytes that can reasonably be included in a mass spectrometry-based multiplexing assay depend on the physico-chemical properties of the analytes and their integration into a single assay in terms of extraction, HPLC separation, ionization conditions and mass spectrometry detection. Another aspect includes biomedical considerations such as the differences in physiological concentrations of analytes, the required concentration range, and how much variability is acceptable before the clinical utility of a marker is negatively affected. Regulatory considerations include validation and quality control during sample analysis. Current bioanalytical regulatory guidelines have mostly been developed for single drug compounds and are not always adequate for multiplexing molecular marker assays that often quantify endogenous compounds. Specific guidances for multiplexing assays should be developed. Even if it is possible to integrate a wide variety and large number of analytes into a multiplexing assay, it should always be taken into consideration that a set of shorter, more specialized assays, may offer a more manageable and efficient alternative.

Sponge systematics facing new challenges

Systematics is nowadays facing new challenges with the introduction of new concepts and new techniques. Compared to most other phyla, phylogenetic relationships among sponges are still largely unresolved. In the past 10 years, the classical taxonomy has been completely overturned and a review of the state of the art appears necessary. The field of taxonomy remains a prominent discipline of sponge research and studies related to sponge systematics were in greater number in the Eighth World Sponge Conference (Girona, Spain, September 2010) than in any previous world sponge conferences. To understand the state of this rapidly growing field, this chapter proposes to review studies, mainly from the past decade, in sponge taxonomy, nomenclature and phylogeny. In a first part, we analyse the reasons of the current success of this field. In a second part, we establish the current sponge systematics theoretical framework, with the use of (1) cladistics, (2) different codes of nomenclature (PhyloCode vs. Linnaean system) and (3) integrative taxonomy. Sponges are infamous for their lack of characters. However, by listing and discussing in a third part all characters available to taxonomists, we show how diverse characters are and that new ones are being used and tested, while old ones should be revisited. We then review the systematics of the four main classes of sponges (Hexactinellida, Calcispongiae, Homoscleromorpha and Demospongiae), each time focusing on current issues and case studies. We present a review of the taxonomic changes since the publication of the Systema Porifera (2002), and point to problems a sponge taxonomist is still faced with nowadays. To conclude, we make a series of proposals for the future of sponge systematics. In the light of recent studies, we establish a series of taxonomic changes that the sponge community may be ready to accept. We also propose a series of sponge new names and definitions following the PhyloCode. The issue of phantom species (potential new species revealed by molecular studies) is raised, and we show how they could be dealt with. Finally, we present a general strategy to help us succeed in building a Porifera tree along with the corresponding revised Porifera classification.

IQMNMR: Open source software using time-domain NMR data for automated identification and quantification of metabolites in batches

BACKGROUND

One of the most promising aspects of metabolomics is metabolic modeling and simulation. Central to such applications is automated high-throughput identification and quantification of metabolites. NMR spectroscopy is a reproducible, nondestructive, and nonselective method that has served as the foundation of metabolomics studies. However, the automated high-throughput identification and quantification of metabolites in NMR spectroscopy is limited by severe spectral overlap. Although numerous software programs have been developed for resolving overlapping resonances, as well as for identifying and quantifying metabolites, most of these programs are frequency-domain methods, considerably influenced by phase shifts and baseline distortions, and effective only in small-scale studies. Almost all these programs require multiple spectra for each application, and do not automatically identify and quantify metabolites in batches.

RESULTS

We created IQMNMR, an R package that integrates a relaxation algorithm, digital filter, and similarity search algorithm. It differs from existing software in that it is a time-domain method; it uses not only frequency to resolve overlapping resonances but also relaxation time constants; it requires only one NMR spectrum per application; is uninfluenced by phase shifts and baseline distortions; and most important, yields a batch of quantified metabolites.

CONCLUSIONS

IQMNMR provides a solution that can automatically identify and quantify metabolites by one-dimensional proton NMR spectroscopy. Its time-domain nature, stability against phase shifts and baseline distortions, requirement for only one NMR spectrum, and capability to output a batch of quantified metabolites are of considerable significance to metabolic modeling and simulation.IQMNMR is available at http://cran.r-project.org/web/packages/IQMNMR/.

Biomarkers of immunosuppressant organ toxicity after transplantation: status, concepts and misconceptions

INTRODUCTION

A major challenge in transplantation is improving long-term organ transplant and patient survival. Immunosuppressants protect the transplant organ from alloimmune reactions, but sometimes also exhibit limiting side effects. The key to improving long-term outcome following transplantation is the selection of the correct immunosuppressive regimen for an individual patient for minimizing toxicity while maintaining immunosuppressive efficacy.

AREAS COVERED

Proteomics and metabolomics have the potential to develop sensitive and specific diagnostic tools for monitoring early changes in cell signal transduction, regulation and biochemical pathways. Here, we review the steps required for the development of molecular markers from discovery, mechanistic and clinical qualification to regulatory approval, and present a critical discussion of the current status of molecular marker development as relevant for the management and individualization of immunosuppressive drug regimens.

EXPERT OPINION

Although metabolomics and proteomics-based studies have yielded several candidate molecular markers, most published studies are poorly designed, statistically underpowered and/or often have not gone beyond the discovery stage. Most molecular marker candidates are still at an early stage. Due to the high complexity of and the resources required for diagnostic marker development, initiatives and consortia organized and supported by funding agencies and regulatory agencies will be critical.

Metabolomics for bioactivity assessment of natural products

Natural products historically have been a rich source of lead molecules in drug discovery, based on their capability to create unique and diverse chemical structures. However, it is also true that the vast number of metabolites typically present in natural products and their huge dynamic range results in the loss of many possibly bioactive natural compounds, becoming an inextricable obstacle for drug development. Recently, new strategies which favour a holistic approach as opposed to the traditional reductionist methods used previously, have been introduced with the purpose of overcoming the bottlenecks in natural product research. This approach is based on the application of new technologies, including metabolomics, for example. Metabolomics allows a systematic study of a complex mixture such as a phytochemical preparation, which can be linked to observations obtained through biological testing systems without the need for isolating active principles. This may put drug discovery from natural products back in the limelight again. In this review paper, the description of some examples of successful metabolomics applications in several important fields related to drug discovery from natural sources aims at raising the potential of metabolomics in reducing the gap between natural products (NP) and modern drug discovery demand.

Circadian control of global gene expression patterns

An internal time-keeping mechanism has been observed in almost every organism studied from archaea to humans. This circadian clock provides a competitive advantage in fitness and survival ( 18, 30, 95, 129, 137 ). Researchers have uncovered the molecular composition of this internal clock by combining enzymology, molecular biology, genetics, and modeling approaches. However, understanding the mechanistic link between the clock and output responses has been elusive. In three model organisms, Arabidopsis thaliana, Drosophila melanogaster, and Mus musculus, whole-genome expression arrays have enabled researchers to investigate how maintaining a time-keeping mechanism connects to an adaptive advantage. Here, we review the impacts transcriptomics have had on our understanding of the clock and how this molecular clock connects with system-level circadian responses. We explore the discoveries made possible by high-throughput RNA assays, the network approaches used to investigate these large transcript datasets, and potential future directions.

Recent and potential developments in the analysis of urine: a review

Analysis of urine is a widely used diagnostic tool that traditionally measured one or, at most, a few metabolites. However, the recognition of the need for a holistic approach to metabolism led to the application of metabolomics to urine for disease diagnostics. This review looks at various aspects of urinalysis including sampling and traditional approaches before reviewing recent developments using metabolomics. Spectrometric approaches are covered briefly since there are already a number of very good reviews on NMR spectroscopy and mass spectrometry and other spectrometries are not as highly developed in their applications to metabolomics. On the other hand, there has been a recent surge in chromatographic applications dedicated to characterising the human urinary metabolome. While developments in the analysis of urine encompassing both classical approaches of urinalysis and metabolomics are covered, it must be emphasized that these approaches are not orthogonal - they both have their uses and are complementary. Regardless, the need to normalise analytical data remains an important impediment.

Organization of GC/MS and LC/MS metabolomics data into chemical libraries

Background

Metabolomics experiments involve generating and comparing small molecule (metabolite) profiles from complex mixture samples to identify those metabolites that are modulated in altered states (e.g., disease, drug treatment, toxin exposure). One non-targeted metabolomics approach attempts to identify and interrogate all small molecules in a sample using GC or LC separation followed by MS or MSⁿ detection. Analysis of the resulting large, multifaceted data sets to rapidly and accurately identify the metabolites is a challenging task that relies on the availability of chemical libraries of metabolite spectral signatures. A method for analyzing spectrometry data to identify and Quantify Individual Components in a Sample, (QUICS), enables generation of chemical library entries from known standards and, importantly, from unknown metabolites present in experimental samples but without a corresponding library entry. This method accounts for all ions in a sample spectrum, performs library matches, and allows review of the data to quality check library entries. The QUICS method identifies ions related to any given metabolite by correlating ion data across the complete set of experimental samples, thus revealing subtle spectral trends that may not be evident when viewing individual samples and are likely to be indicative of the presence of one or more otherwise obscured metabolites.

Results

LC-MS/MS or GC-MS data from 33 liver samples were analyzed simultaneously which exploited the inherent biological diversity of the samples and the largely non-covariant chemical nature of the metabolites when viewed over multiple samples. Ions were partitioned by both retention time (RT) and covariance which grouped ions from a single common underlying metabolite. This approach benefitted from using mass, time and intensity data in aggregate over the entire sample set to reject outliers and noise thereby producing higher quality chemical identities. The aggregated data was matched to reference chemical libraries to aid in identifying the ion set as a known metabolite or as a new unknown biochemical to be added to the library.

Conclusion

The QUICS methodology enabled rapid, in-depth evaluation of all possible metabolites (known and unknown) within a set of samples to identify the metabolites and, for those that did not have an entry in the reference library, to create a library entry to identify that metabolite in future studies.

Metabolomics in premature labor: a novel approach to identify patients at risk for preterm delivery

OBJECTIVE

Biomarkers for preterm labor (PTL) and delivery can be discovered through the analysis of the transcriptome (transcriptomics) and protein composition (proteomics). Characterization of the global changes in low-molecular weight compounds which constitute the 'metabolic network' of cells (metabolome) is now possible by using a 'metabolomics' approach. Metabolomic profiling has special advantages over transcriptomics and proteomics since the metabolic network is downstream from gene expression and protein synthesis, and thus more closely reflects cell activity at a functional level. This study was conducted to determine if metabolomic profiling of the amniotic fluid can identify women with spontaneous PTL at risk for preterm delivery, regardless of the presence or absence of intraamniotic infection/inflammation (IAI).

STUDY DESIGN

Two retrospective cross-sectional studies were conducted, including three groups of pregnant women with spontaneous PTL and intact membranes: (1) PTL who delivered at term; (2) PTL without IAI who delivered preterm; and (3) PTL with IAI who delivered preterm. The first was an exploratory study that included 16, 19, and 20 patients in groups 1, 2, and 3, respectively. The second study included 40, 33, and 40 patients in groups 1, 2, and 3, respectively. Amniotic fluid metabolic profiling was performed by combining chemical separation (with gas and liquid chromatography) and mass spectrometry. Compounds were identified using authentic standards. The data were analyzed using discriminant analysis for the first study and Random Forest for the second.

RESULTS

(1) In the first study, metabolomic profiling of the amniotic fluid was able to identify patients as belonging to the correct clinical group with an overall 96.3% (53/55) accuracy; 15 of 16 patients with PTL who delivered at term were correctly classified; all patients with PTL without IAI who delivered preterm neonates were correctly identified as such (19/19), while 19/20 patients with PTL and IAI were correctly classified. (2) In the second study, metabolomic profiling was able to identify patients as belonging to the correct clinical group with an accuracy of 88.5% (100/113); 39 of 40 patients with PTL who delivered at term were correctly classified; 29 of 33 patients with PTL without IAI who delivered preterm neonates were correctly classified. Among patients with PTL and IAI, 32/40 were correctly classified. The metabolites responsible for the classification of patients in different clinical groups were identified. A preliminary draft of the human amniotic fluid metabolome was generated and found to contain products of the intermediate metabolism of mammalian cells and xenobiotic compounds (e.g. bacterial products and Salicylamide).

CONCLUSION

Among patients with spontaneous PTL with intact membranes, metabolic profiling of the amniotic fluid can be used to assess the risk of preterm delivery in the presence or absence of infection/inflammation.

Structure and activity of the metal-independent fructose-1,6-bisphosphatase YK23 from Saccharomyces cerevisiae

Fructose-1,6-bisphosphatase (FBPase), a key enzyme of gluconeogenesis and photosynthetic CO(2) fixation, catalyzes the hydrolysis of fructose 1,6-bisphosphate (FBP) to produce fructose 6-phosphate, an important precursor in various biosynthetic pathways. All known FBPases are metal-dependent enzymes, which are classified into five different classes based on their amino acid sequences. Eukaryotes are known to contain only the type-I FBPases, whereas all five types exist in various combinations in prokaryotes. Here we demonstrate that the uncharacterized protein YK23 from Saccharomyces cerevisiae efficiently hydrolyzes FBP in a metal-independent reaction. YK23 is a member of the histidine phosphatase (phosphoglyceromutase) superfamily with homologues found in all organisms. The crystal structure of the YK23 apo-form was solved at 1.75-A resolution and revealed the core domain with the alpha/beta/alpha-fold covered by two small cap domains. Two liganded structures of this protein show the presence of two phosphate molecules (an inhibitor) or FBP (a substrate) bound to the active site. FBP is bound in its linear, open conformation with the cleavable C1-phosphate positioned deep in the active site. Alanine replacement mutagenesis of YK23 identified six conserved residues absolutely required for activity and suggested that His(13) and Glu(99) are the primary catalytic residues. Thus, YK23 represents the first family of metal-independent FBPases and a second FBPase family in eukaryotes.

Quantitative global studies of reactomes and metabolomes using a vectorial representation of reactions and chemical compounds

Background

Global studies of the protein repertories of organisms are providing important information on the characteristics of the protein space. Many of these studies entail classification of the protein repertory on the basis of structure and/or sequence similarities. The situation is different for metabolism. Because there is no good way of measuring similarities between chemical reactions, there is a barrier to the development of global classifications of "metabolic space" and subsequent studies comparable to those done for protein sequences and structures.

Results

In this work, we propose a vectorial representation of chemical reactions, which allows them to be compared and classified. In this representation, chemical compounds, reactions and pathways may be represented in the same vectorial space. We show that the representation of chemical compounds reflects their physicochemical properties and can be used for predictive purposes. We use the vectorial representations of reactions to perform a global classification of the reactome of the model organism E. coli.

Conclusions

We show that this unsupervised clustering results in groups of enzymes more coherent in biological terms than equivalent groupings obtained from the EC hierarchy. This hierarchical clustering produces an optimal set of 21 groups which we analyzed for their biological meaning.

LVIS553 transcriptional regulator specifically recognizes novobiocin as an effector molecule

In this study we aimed to identify small molecules with high affinity involved in the allosteric regulation of LVIS553, a MarR member from Lactobacillus brevis ATCC367. Using high throughput screening, novobiocin was found to specifically bind LVIS553 with a K(D) = 33.8 +/- 2.9 microM consistent with a biologically relevant ligand. Structure guided site-directed mutagenesis identified Lys(9) as a key residue in novobiocin recognition. The results found in vitro were correlated in vivo. An increased tolerance to the antibiotic was observed when LVIS553 and the downstream putative transport protein LVIS552 were either expressed in a low copy plasmid in L. brevis or as a single copy chromosomal insertion in Bacillus subtilis. We provide evidence that LVIS553 is involved in the specific regulation of a new mechanism of tolerance to novobiocin.

Mapping human metabolic pathways in the small molecule chemical space

The work presented here is a study of human metabolic pathways, as projected in the chemical space of the small molecules they comprise, and it is composed of three parts: a) a study of the extent of clustering and overlap of these pathways in chemical space, b) the development and assessment of a statistical model for estimating the proximity to a given pathway of any small molecule, and c) the use of the above model in estimating the proximity of marketed drugs to human metabolic pathways. The distribution, overlap, and relationships of human metabolic pathways in this space are revealed using both visual and quantitative approaches. A set of selected physicochemical and topological descriptors is used to build a classifier, whose aim is to predict metabolic class and pathway membership of any small molecule. The classifier performs well for tightly clustered, isolated pathways but is, naturally, much less accurate for strongly overlapping pathways. Finally, the extent of overlap of a set of known drugs with the human metabolome is examined, and the classifier is used to predict likely cross-interactions between drugs and the major metabolic pathways in humans.

Ultra-high performance liquid chromatography-mass spectrometry for the metabolomic analysis of urine in colorectal cancer

We report here the results of a pilot study in which ultra-high performance liquid chromatography/time-of- flight-mass spectrometry (UPLC/TOF-MS) and multivariate statistical analysis (supervised partial least squares discriminant analysis, PLS-DA) were applied for urinary metabolite profiling and data interpretation. The results of the PLS-DA indicated that the metabolic pattern as a whole was significantly different between the groups of preoperative colorectal cancer (CRC) patients, postoperative CRC patients, and healthy volunteers, respectively. The preoperative group of patients showed significantly increased levels of low-molecular weight compounds (LMC) MW 283 and MW 234 in comparison to the group of healthy volunteers group. After the operation, the levels of these two LMC significantly decreased. These preliminary results suggest that the UPLC-MS-based method coupled with pattern recognition will likely lead to procedures with the potential to be clinically applicable for the diagnosis of CRC and, consequently, to an improvement in patient prognosis.

E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs

Motivation: The IUBMB's Enzyme Nomenclature system, commonly known as the Enzyme Commission (EC) numbers, plays key roles in classifying enzymatic reactions and in linking the enzyme genes or proteins to reactions in metabolic pathways. There are numerous reactions known to be present in various pathways but without any official EC numbers, most of which have no hope to be given ones because of the lack of the published articles on enzyme assays.

Results: In this article we propose a new method to predict the potential EC numbers to given reactant pairs (substrates and products) or uncharacterized reactions, and a web-server named E-zyme as an application. This technology is based on our original biochemical transformation pattern which we call an ‘RDM pattern’, and consists of three steps: (i) graph alignment of a query reactant pair (substrates and products) for computing the query RDM pattern, (ii) multi-layered partial template matching by comparing the query RDM pattern with template patterns related with known EC numbers and (iii) weighted major voting scheme for selecting appropriate EC numbers. As the result, cross-validation experiments show that the proposed method achieves both high coverage and high prediction accuracy at a practical level, and consistently outperforms the previous method.

Availability: The E-zyme system is available at http://www.genome.jp/tools/e-zyme/

Contact:kanehisa@kuicr.kyoto-u.ac.jp

apLCMS--adaptive processing of high-resolution LC/MS data

MOTIVATION

Liquid chromatography-mass spectrometry (LC/MS) profiling is a promising approach for the quantification of metabolites from complex biological samples. Significant challenges exist in the analysis of LC/MS data, including noise reduction, feature identification/ quantification, feature alignment and computation efficiency.

RESULT

Here we present a set of algorithms for the processing of high-resolution LC/MS data. The major technical improvements include the adaptive tolerance level searching rather than hard cutoff or binning, the use of non-parametric methods to fine-tune intensity grouping, the use of run filter to better preserve weak signals and the model-based estimation of peak intensities for absolute quantification. The algorithms are implemented in an R package apLCMS, which can efficiently process large LC/ MS datasets.

AVAILABILITY

The R package apLCMS is available at www.sph.emory.edu/apLCMS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

[Recent development of metabonomics and its applications in clinical research]

In the post-genomic era, systems biology is central to the biological sciences. Functional genomics such as transcriptomics and proteomics can simultaneous determine massive gene or protein expression changes following drug treatment or other intervention. However, these changes can't be coupled directly to changes in biological function. As a result, metabonomics and its many pseudonyms (metabolomics, metabolic profiling, etc.) have exploded onto the scientific scene in the past several years. Metabonomics is a rapidly growing research area and a system approach for comprehensive and quantitative analysis of the global metabolites in a biological matrix. Analytical chemistry approach is necessary for the development of comprehensive metabonomics investigations. Fundamentally, there are two types of metabonomics approaches: mass-spectrometry (MS) based and nuclear magnetic resonance (NMR) methodologies. Metabonomics measurements provide a wealth of data information and interpretation of these data relies mainly on chemometrics approaches to perform large-scale data analysis and data visualization, such as principal and independent component analysis, multidimensional scaling, a variety of clustering techniques, and discriminant function analysis, among many others. In this review, the recent development of analytical and statistical techniques used in metabonomics is summarized. Major applications of metabonomics relevant to clinical and preclinical study are then reviewed. The applications of metabonomics in study of liver diseases, cancers and other diseases have proved useful both as an experimental tool for pathogenesis mechanism re-search and ultimately a tool for diagnosis and monitoring treatment response of these diseases. Next, the applications of metabonomics in preclinical toxicology are discussed and the role that metabonomics might do in pharmaceutical research and development is explained with special reference to the aims and achievements of the Consortium for Metabonomic Toxicology (COMET), and the concept of pharmacometabonomics as a way of predicting an individual's response to treatment is highlighted. Finally, the role of metabonomics in elucidating the function of the unknown or novel enzyme is mentioned.

An introduction to metabolic networks and their structural analysis

There has been a renewed interest for metabolism in the computational biology community, leading to an avalanche of papers coming from methodological network analysis as well as experimental and theoretical biology. This paper is meant to serve as an initial guide for both the biologists interested in formal approaches and the mathematicians or computer scientists wishing to inject more realism into their models. The paper is focused on the structural aspects of metabolism only. The literature is vast enough already, and the thread through it difficult to follow even for the more experienced worker in the field. We explain methods for acquiring data and reconstructing metabolic networks, and review the various models that have been used for their structural analysis. Several concepts such as modularity are introduced, as are the controversies that have beset the field these past few years, for instance, on whether metabolic networks are small-world or scale-free, and on which model better explains the evolution of metabolism. Clarifying the work that has been done also helps in identifying open questions and in proposing relevant future directions in the field, which we do along the paper and in the conclusion.