Extending biochemical databases by metabolomic surveys

Global omics study of Tetraselmis chuii reveals time-related metabolic adaptations upon oxidative stress

Microalgae species encounter oxidative stress in their natural environments, prompting the development of species-specific adaptation mechanisms. Understanding these mechanisms can offer valuable insights for biotechnological applications in microalgal metabolic manipulation. In this study, we investigated the response of Tetraselmis chuii, an industrially important microalga, to H2O2-induced oxidative stress. Exposure to 0.5-mM H2O2 resulted in reduced cell viability, and higher concentrations led to a drastic decline. After 1 h of exposure to H2O2, photosynthetic capacity (Qy) was negatively impacted, and this reduction intensified after 6 h of continuous stress. Global multi-omics analysis revealed that T. chuii rapidly responded to H2O2-induced oxidative stress within the first hour, causing significant changes in both transcriptomic and metabolomic profiles. Among the cellular functions negatively affected were carbon and energy flow, with photosynthesis-related PSBQ having a 2.4-fold downregulation, pyruvate kinase decreased by 1.5-fold, and urea content reduced by threefold. Prolonged exposure to H2O2 incurred a high energy cost, leading to unsuccessful attempts to enhance carbon metabolism, as depicted, for example, by the upregulation of photosystems-related PETC and PETJ by more than twofold. These findings indicate that T. chuii quickly responds to oxidative stress, but extended exposure can have detrimental effects on its cellular functions. KEY POINTS: • 0.5-mM H2O2-induced oxidative stress strongly affects T. chuii • Distinct short- and long-term adaptation mechanisms are induced • Major metabolic adaptations occur within the first hour of exposure.

Metabolic Aspects of Lentil-Fusarium Interactions

Fusarium oxysporum f. sp. lentis (Fol) is considered the most destructive disease for lentil (Lens culinaris Medik.) worldwide. Despite the extensive studies elucidating plants' metabolic response to fungal agents, there is a knowledge gap in the biochemical mechanisms governing Fol-resistance in lentil. Τhis study aimed at comparatively evaluating the metabolic response of two lentil genotypes, with contrasting phenotypes for Fol-resistance, to Fol-inoculation. Apart from gaining insights into the metabolic reprogramming in response to Fol-inoculation, the study focused on discovering novel biomarkers to improve early selection for Fol-resistance. GC-MS-mediated metabolic profiling of leaves and roots was employed to monitor changes across genotypes and treatments as well as their interaction. In total, the analysis yielded 178 quantifiable compounds, of which the vast majority belonged to the groups of carbohydrates, amino acids, polyols and organic acids. Despite the magnitude of metabolic fluctuations in response to Fol-inoculation in both genotypes under study, significant alterations were noted in the content of 18 compounds, of which 10 and 8 compounds referred to roots and shoots, respectively. Overall data underline the crucial contribution of palatinitol and L-proline in the metabolic response of roots and shoots, respectively, thus offering possibilities for their exploitation as metabolic biomarkers for Fol-resistance in lentil. To the best of our knowledge, this is the first metabolomics-based approach to unraveling the effects of Fol-inoculation on lentil's metabolome, thus providing crucial information related to key aspects of lentil-Fol interaction. Future investigations in metabolic aspects of lentil-Fol interactions will undoubtedly revolutionize the search for metabolites underlying Fol-resistance, thus paving the way towards upgrading breeding efforts to combat fusarium wilt in lentil.

Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations

BACKGROUND

Ion mobility (IM) separation capabilities are now widely available to researchers through several commercial vendors and are now being adopted into many metabolomics workflows. The added peak capacity that ion mobility offers with minimal compromise to other analytical figures-of-merit has provided real benefits to sensitivity and structural selectivity and have allowed more specific metabolite annotations to be assigned in untargeted workflows. One of the greatest promises of contemporary IM-enabled instrumentation is the capability of operating multiple analytical dimensions inline with minimal sample volumes, which has the potential to address many grand challenges currently faced in the omics fields. However, comprehensive operation of multidimensional mass spectrometry comes with its own inherent challenges that, beyond operational complexity, may not be immediately obvious to practitioners of these techniques.

AIM OF REVIEW

In this review, we outline the strengths and considerations for incorporating IM analysis in metabolomics workflows and provide a critical but forward-looking perspective on the contemporary challenges and prospects associated with interpreting IM data into chemical knowledge.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We outline a strategy for unifying IM-derived collision cross section (CCS) measurements obtained from different IM techniques and discuss the emerging field of high resolution ion mobility (HRIM) that is poised to address many of the contemporary challenges associated with ion mobility metabolomics. Whereas the LC step limits the throughput of comprehensive LC-IM-MS, the higher peak capacity of HRIM can allow fast LC gradients or rapid sample cleanup via solid-phase extraction (SPE) to be utilized, significantly improving the sample throughput.

Selenium-binding Protein 1 (SBD1): A stress response regulator in Chlamydomonas reinhardtii

Selenium-binding proteins (SBPs) represent a ubiquitous protein family implicated in various environmental stress responses, although the exact molecular and physiological role of the SBP family remains elusive. In this work, we report the identification and characterization of CrSBD1, an SBP homolog from the model microalgae Chlamydomonas reinhardtii. Growth analysis of the C. reinhardtii sbd1 mutant strain revealed that the absence of a functional CrSBD1 resulted in increased growth under mild oxidative stress conditions, although cell viability rapidly declined at higher hydrogen peroxide (H2O2) concentrations. Furthermore, a combined global transcriptomic and metabolomic analysis indicated that the sbd1 mutant exhibited a dramatic quenching of the molecular and biochemical responses upon H2O2-induced oxidative stress when compared to the wild-type. Our results indicate that CrSBD1 represents a cell regulator, which is involved in the modulation of C. reinhardtii early responses to oxidative stress. We assert that CrSBD1 acts as a member of an extensive and conserved protein-protein interaction network including Fructose-bisphosphate aldolase 3, Cysteine endopeptidase 2, and Glutaredoxin 6 proteins, as indicated by yeast two-hybrid assays.

Characterization of intestinal microbiota and serum metabolites in patients with mild hepatic encephalopathy

Mild micro-hepatic encephalopathy (MHE) is a severe complication of cirrhosis. At present, there are differences in the consistency of detection strategies and treatment directions for MHE. The characteristic changes in intestinal microbiota and serum metabolites in MHE patients and the possible relevant interaction mechanisms would inevitably affect the developmental direction of MHE. Therefore, the changes in the characteristics of intestinal microbiota and serum metabolites of MHE patients were determined, and the possible interactions between them were analyzed. Stool and serum tests were performed on both the MHE patients and healthy individuals. The 16S rRNA gene high-throughput sequencing and bioinformatics analyses were used to analyze the differences in intestinal microbiota in MHE patients. The serum metabolites were detected using liquid LC-MS/MS (liquid chromatography-mass spectrometry) technology, and the differences in the metabolic networks of blood metabolites in MHE patients were analyzed. A comprehensive bioinformatics analysis approach was adopted to identify the composition and characteristics of microbiota and serum metabolites and the possible correlation between them. The main characteristics of the structural imbalance in the intestinal microbiota of MHE patients included a decrease in the number of beneficial bacteria at the levels of phylum, class, order, family, and genus and an increase in the pathogenic bacteria, resulting in substantial changes in the relative abundances of bacteria in the intestinal microbiota. The main predicted functions that showed significant differences included chromosome, amino acid-related enzymes, methane metabolism, and arginine and proline metabolism. The detection of serum metabolites resulted in 10 different metabolites, including taurocholic acid, citrulline, d-phenyl-lactic acid, l-tyrosine, benzoate, phenylalanine, linoleic acid, eicosapedienic acid, alpha-dimorphecolic acid, and dehydroepiandrosterone. The subsequent metabolite pathways analysis showed differences in the metabolism of linoleic acid, phenyl-propane, caffeine, arginine, proline, glycine, serine, threonine, tyrosine, and pyrimidine compared to the control group. In summary, it seems that the changes in the microbiome that we have identified have resulted in corresponding changes to the serum metabolome. In turn, this may represent changes in the absorption of metabolites from the gut or reflect the changed metabolic capacity of the MHE liver or both. There were characteristic changes in the intestinal microbiota and serum metabolites in the MHE patients. There might be a related interaction mechanism between the two, which would provide evidence and direction for the detection and treatment strategies of MHE.

Approaches for completing metabolic networks through metabolite damage and repair discovery

Metabolites are prone to damage, either via enzymatic side reactions, which collectively form the underground metabolism, or via spontaneous chemical reactions. The resulting non-canonical metabolites that can be toxic, are mended by dedicated "metabolite repair enzymes." Deficiencies in the latter can cause severe disease in humans, whereas inclusion of repair enzymes in metabolically engineered systems can improve the production yield of value-added chemicals. The metabolite damage and repair loops are typically not yet included in metabolic reconstructions and it is likely that many remain to be discovered. Here, we review strategies and associated challenges for unveiling non-canonical metabolites and metabolite repair enzymes, including systematic approaches based on high-resolution mass spectrometry, metabolome-wide side-activity prediction, as well as high-throughput substrate and phenotypic screens.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

The Development of Bacteriophage Resistance in Vibrio alginolyticus Depends on a Complex Metabolic Adaptation Strategy

Lytic bacteriophages have been well documented to play a pivotal role in microbial ecology due to their complex interactions with bacterial species, especially in aquatic habitats. Although the use of phages as antimicrobial agents, known as phage therapy, in the aquatic environment has been increasing, recent research has revealed drawbacks due to the development of phage-resistant strains among Gram-negative species. Acquired phage resistance in marine Vibrios has been proven to be a very complicated process utilizing biochemical, metabolic, and molecular adaptation strategies. The results of our multi-omics approach, incorporating transcriptome and metabolome analyses of Vibrio alginolyticus phage-resistant strains, corroborate this prospect. Our results provide insights into phage-tolerant strains diminishing the expression of phage receptors ompF, lamB, and btuB. The same pattern was observed for genes encoding natural nutrient channels, such as rbsA, ptsG, tryP, livH, lysE, and hisp, meaning that the cell needs to readjust its biochemistry to achieve phage resistance. The results showed reprogramming of bacterial metabolism by transcript regulations in key-metabolic pathways, such as the tricarboxylic acid cycle (TCA) and lysine biosynthesis, as well as the content of intracellular metabolites belonging to processes that could also significantly affect the cell physiology. Finally, SNP analysis in resistant strains revealed no evidence of amino acid alterations in the studied putative bacterial phage receptors, but several SNPs were detected in genes involved in transcriptional regulation. This phenomenon appears to be a phage-specific, fine-tuned metabolic engineering, imposed by the different phage genera the bacteria have interacted with, updating the role of lytic phages in microbial marine ecology.

METLIN: A Tandem Mass Spectral Library of Standards

Untargeted mass spectrometry metabolomics studies rely on accurate databases for the identification of metabolic features. Leveraging unique fragmentation patterns as well as characteristic dissociation routes allows for structural information to be gained for specific metabolites and molecular classes, respectively. Here we describe the evolution of METLIN as a resource for small molecule analysis as well as the tools (e.g., Fragment Similarity Search and Neutral Loss Search) used to query the database and their workflows for the identification of molecular entities. Additionally, we will discuss the functionalities of isoMETLIN, a database of isotopic metabolites, and the latest addition to the METLIN family, METLIN-MRM, which facilitates the analysis of quantitative mass spectrometry data generated with triple quadrupole instrumentation.

Lysosomes, Autophagy, and Hormesis in Cell Physiology, Pathology, and Age-Related Disease

Autophagy has been strongly linked with hormesis, however, it is only relatively recently that the mechanistic basis underlying this association has begun to emerge. Lysosomal autophagy is a group of processes that degrade proteins, protein aggregates, membranes, organelles, segregated regions of cytoplasm, and even parts of the nucleus in eukaryotic cells. These degradative processes are evolutionarily very ancient and provide a survival capability for cells that are stressed or injured. Autophagy and autophagic dysfunction have been linked with many aspects of cell physiology and pathology in disease processes; and there is now intense interest in identifying various therapeutic strategies involving its regulation. The main regulatory pathway for augmented autophagy is the mechanistic target of rapamycin (mTOR) cell signaling, although other pathways can be involved, such as 5'-adenosine monophosphate-activated protein kinase. Mechanistic target of rapamycin is a key player in the many highly interconnected intracellular signaling pathways and is responsible for the control of cell growth among other processes. Inhibition of mTOR (specifically dephosphorylation of mTOR complex 1) triggers augmented autophagy and the search is on the find inhibitors that can induce hormetic responses that may be suitable for treating many diseases, including many cancers, type 2 diabetes, and age-related neurodegenerative conditions.

Chemical Discovery in the Era of Metabolomics

Untargeted metabolomics aims to quantify the complete set of metabolites within a biological system, most commonly by liquid chromatography/mass spectrometry (LC/MS). Since nearly the inception of the field, compound identification has been widely recognized as the rate-limiting step of the experimental workflow. In spite of exponential increases in the size of metabolomic databases, which now contain experimental MS/MS spectra for over a half a million reference compounds, chemical structures still cannot be confidently assigned to many signals in a typical LC/MS dataset. The purpose of this Perspective is to consider why identification rates continue to be low in untargeted metabolomics. One rationalization is that many naturally occurring metabolites detected by LC/MS are true "novel" compounds that have yet to be incorporated into metabolomic databases. An alternative possibility, however, is that research data do not provide database matches because of informatic artifacts, chemical contaminants, and signal redundancies. Increasing evidence suggests that, for at least some sample types, many unidentifiable signals in untargeted metabolomics result from the latter rather than new compounds originating from the specimen being measured. The implications of these observations on chemical discovery in untargeted metabolomics are discussed.

Current Practice in Untargeted Human Milk Metabolomics

Human milk (HM) is considered the gold standard for infant nutrition. HM contains macro- and micronutrients, as well as a range of bioactive compounds (hormones, growth factors, cell debris, etc.). The analysis of the complex and dynamic composition of HM has been a permanent challenge for researchers. The use of novel, cutting-edge techniques involving different metabolomics platforms has permitted to expand knowledge on the variable composition of HM. This review aims to present the state-of-the-art in untargeted metabolomic studies of HM, with emphasis on sampling, extraction and analysis steps. Workflows available from the literature have been critically revised and compared, including a comprehensive assessment of the achievable metabolome coverage. Based on the scientific evidence available, recommendations for future untargeted HM metabolomics studies are included.

Overview of Tandem Mass Spectral and Metabolite Databases for Metabolite Identification in Metabolomics

Liquid chromatography-mass spectrometry (LC-MS) is one of the most popular technologies in metabolomics. The large-scale and unambiguous identification of metabolite structures remains a challenging task in LC-MS based metabolomics. Tandem mass spectral databases provide experimental and in silico MS/MS spectra to facilitate the identification of both known and unknown metabolites, which has become a gold standard method in metabolomics. In addition, metabolite knowledge databases offer valuable biological and pathway information of metabolites. In this chapter, we have briefly reviewed the most common and important tandem mass spectral and metabolite databases, and illustrated how they could be used for metabolite identification.

Metabolomics: A Way Forward for Crop Improvement

Metabolomics is an emerging branch of "omics" and it involves identification and quantification of metabolites and chemical footprints of cellular regulatory processes in different biological species. The metabolome is the total metabolite pool in an organism, which can be measured to characterize genetic or environmental variations. Metabolomics plays a significant role in exploring environment-gene interactions, mutant characterization, phenotyping, identification of biomarkers, and drug discovery. Metabolomics is a promising approach to decipher various metabolic networks that are linked with biotic and abiotic stress tolerance in plants. In this context, metabolomics-assisted breeding enables efficient screening for yield and stress tolerance of crops at the metabolic level. Advanced metabolomics analytical tools, like non-destructive nuclear magnetic resonance spectroscopy (NMR), liquid chromatography mass-spectroscopy (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), and direct flow injection (DFI) mass spectrometry, have sped up metabolic profiling. Presently, integrating metabolomics with post-genomics tools has enabled efficient dissection of genetic and phenotypic association in crop plants. This review provides insight into the state-of-the-art plant metabolomics tools for crop improvement. Here, we describe the workflow of plant metabolomics research focusing on the elucidation of biotic and abiotic stress tolerance mechanisms in plants. Furthermore, the potential of metabolomics-assisted breeding for crop improvement and its future applications in speed breeding are also discussed. Mention has also been made of possible bottlenecks and future prospects of plant metabolomics.

Serum and urinary metabolomics and outcomes in cirrhosis

BACKGROUND

Cirrhosis can alter several metabolic pathways. Metabolomics could prognosticate outcomes like hepatic encephalopathy (HE), transplant, hospitalization and death.

AIM

Determine changes in serum and urine metabolomics in cirrhotics who develop outcomes.

METHODS

Cirrhotic outpatients underwent data, serum/urine collection and were followed for 90 days. Demographics, cirrhosis details and medications were collected. Metabolomics was performed on urine/serum using GC/MS with subsequent bioinformatics analyses (ChemRICH, MetaMAPP and PLS-DA). Logistic regression adjusting for covariates (demographics, alcohol etiology, prior HE, PPI, SBP prophylaxis, rifaximin/lactulose) were performed and ROC curves comparing MELD to adjusted serum & urine metabolites were created.

RESULTS

211 patients gave serum, of which 64 were hospitalized, 19 developed HE, 13 were transplanted and 11 died. 164 patients gave urine of which 56 were hospitalized, 18 developed HE, 12 were transplanted and 11 died. Metabolomics: Saturated fatty acids, amino acids and bioenergetics-related metabolites differentiated patients with/without outcomes. After regression, 232, 228, 284 and 229 serum metabolites were significant for hospitalization, HE, death and transplant. In urine 290, 284, 227 & 285 metabolites were significant for hospitalization, HE, death and transplant respectively. AUC was higher for serum metabolites vs MELD for HE (0.85 vs.0.76), death (0.99 vs.0.88), transplant (0.975 vs.0.94) and hospitalizations (0.84 vs.0.83). Similarly, urinary metabolite AUC was also higher than MELD for HE (0.87 vs.0.72), death (0.92 vs 0.86), transplant (0.99 vs.0.90) and hospitalizations (0.89 vs.0.84).

CONCLUSIONS

In this exploratory study, serum and metabolites focused on lipid, bioenergetics and amino acid metabolism are altered in cirrhotics who develop negative outcomes.

The Structure and Function of Major Plant Metabolite Modifications

Plants produce a myriad of structurally and functionally diverse metabolites that play many different roles in plant growth and development and in plant response to continually changing environmental conditions as well as abiotic and biotic stresses. This metabolic diversity is, to a large extent, due to chemical modification of the basic skeletons of metabolites. Here, we review the major known plant metabolite modifications and summarize the progress that has been achieved and the challenges we are facing in the field. We focus on discussing both technical and functional aspects in studying the influences that various modifications have on biosynthesis, degradation, transport, and storage of metabolites, as well as their bioactivity and toxicity. Finally, we discuss some emerging insights into the evolution of metabolic pathways and metabolite functionality.

Combined network analysis and machine learning allows the prediction of metabolic pathways from tomato metabolomics data

The identification and understanding of metabolic pathways is a key aspect in crop improvement and drug design. The common approach for their detection is based on gene annotation and ontology. Correlation-based network analysis, where metabolites are arranged into network formation, is used as a complentary tool. Here, we demonstrate the detection of metabolic pathways based on correlation-based network analysis combined with machine-learning techniques. Metabolites of known tomato pathways, non-tomato pathways, and random sets of metabolites were mapped as subgraphs onto metabolite correlation networks of the tomato pericarp. Network features were computed for each subgraph, generating a machine-learning model. The model predicted the presence of the β-alanine-degradation-I, tryptophan-degradation-VII-via-indole-3-pyruvate (yet unknown to plants), the β-alanine-biosynthesis-III, and the melibiose-degradation pathway, although melibiose was not part of the networks. In vivo assays validated the presence of the melibiose-degradation pathway. For the remaining pathways only some of the genes encoding regulatory enzymes were detected.

Metabolic Reprogramming in Astrocytes Distinguishes Region-Specific Neuronal Susceptibility in Huntington Mice

The basis for region-specific neuronal toxicity in Huntington disease is unknown. Here, we show that region-specific neuronal vulnerability is a substrate-driven response in astrocytes. Glucose is low in HdhQ(150/150) animals, and astrocytes in each brain region adapt by metabolically reprogramming their mitochondria to use endogenous, non-glycolytic metabolites as an alternative fuel. Each region is characterized by distinct metabolic pools, and astrocytes adapt accordingly. The vulnerable striatum is enriched in fatty acids, and mitochondria reprogram by oxidizing them as an energy source but at the cost of escalating reactive oxygen species (ROS)-induced damage. The cerebellum is replete with amino acids, which are precursors for glucose regeneration through the pentose phosphate shunt or gluconeogenesis pathways. ROS is not elevated, and this region sustains little damage. While mhtt expression imposes disease stress throughout the brain, sensitivity or resistance arises from an adaptive stress response, which is inherently region specific. Metabolic reprogramming may have relevance to other diseases.

Microbial Methylotrophic Metabolism: Recent Metabolic Modeling Efforts and Their Applications In Industrial Biotechnology

Developing methylotrophic bacteria into cell factories that meet the chemical demand of the future could be both economical and environmentally friendly. Methane is not only an abundant, low-cost resource but also a potent greenhouse gas, the capture of which could help to reduce greenhouse gas emissions. Rational strain design workflows rely on the availability of carefully combined knowledge often in the form of genome-scale metabolic models to construct high-producer organisms. In this review, the authors present the most recent genome-scale metabolic models in aerobic methylotrophy and their applications. Further, the authors present models for the study of anaerobic methanotrophy through reverse methanogenesis and suggest organisms that may be of interest for expanding one-carbon industrial biotechnology. Metabolic models of methylotrophs are scarce, yet they are important first steps toward rational strain-design in these organisms.

Chemical Similarity Enrichment Analysis (ChemRICH) as alternative to biochemical pathway mapping for metabolomic datasets

Metabolomics answers a fundamental question in biology: How does metabolism respond to genetic, environmental or phenotypic perturbations? Combining several metabolomics assays can yield datasets for more than 800 structurally identified metabolites. However, biological interpretations of metabolic regulation in these datasets are hindered by inherent limits of pathway enrichment statistics. We have developed ChemRICH, a statistical enrichment approach that is based on chemical similarity rather than sparse biochemical knowledge annotations. ChemRICH utilizes structure similarity and chemical ontologies to map all known metabolites and name metabolic modules. Unlike pathway mapping, this strategy yields study-specific, non-overlapping sets of all identified metabolites. Subsequent enrichment statistics is superior to pathway enrichments because ChemRICH sets have a self-contained size where p-values do not rely on the size of a background database. We demonstrate ChemRICH’s efficiency on a public metabolomics data set discerning the development of type 1 diabetes in a non-obese diabetic mouse model. ChemRICH is available at www.chemrich.fiehnlab.ucdavis.edu

Continued Alcohol Misuse in Human Cirrhosis is Associated with an Impaired Gut-Liver Axis

BACKGROUND

Cirrhosis and alcohol can independently affect the gut-liver axis with systemic inflammation. However, their concurrent impact in humans is unclear.

METHODS

Our aim was to determine the effect of continued alcohol misuse on the gut-liver axis in cirrhotic patients. Age- and MELD-balanced cirrhotic patients who were currently drinking (Alc) or abstinent (NAlc) and healthy controls underwent serum and stool collection. A subset underwent upper endoscopy and colonoscopy for biopsies and duodenal fluid collection. The groups were compared regarding (i) inflammation/intestinal barrier: systemic tumor necrosis factor levels, intestinal inflammatory cytokine (duodenum, ileum, sigmoid), and ileal antimicrobial peptide expression; (ii) microbiota composition: 16SrRNA sequencing of duodenal, ileal, and colonic mucosal and fecal microbiota; and (iii) microbial functionality: duodenal fluid and fecal bile acid (BA) profile (conjugation and dehydroxylation status), intestinal BA transporter (ASBT, FXR, FGF-19, SHP) expression, and stool metabolomics using gas chromatography/mass spectrometry.

RESULTS

Alc patients demonstrated a significant duodenal, ileal, and colonic mucosal and fecal dysbiosis, compared to NAlc and controls with lower autochthonous bacterial taxa. BA profile skewed toward a potentially toxic profile (higher secondary and glycine-conjugated BAs) in duodenal fluid and stool in Alc patients. Duodenal fluid demonstrated conjugated secondary BAs only in the Alc group. There was a greater expression of all ileal BA transporters in Alc patients. This group also showed higher endotoxemia, systemic and ileal inflammatory expression, and lower amino acid and bioenergetic-associated metabolites, without change in antimicrobial peptide expression.

CONCLUSIONS

Despite cirrhosis, continued alcohol misuse predisposes patients to widespread dysbiosis with alterations in microbial functionality such as a toxic BA profile, which can lead to intestinal and systemic inflammation.

Metabolite damage and repair in metabolic engineering design

The necessarily sharp focus of metabolic engineering and metabolic synthetic biology on pathways and their fluxes has tended to divert attention from the damaging enzymatic and chemical side-reactions that pathway metabolites can undergo. Although historically overlooked and underappreciated, such metabolite damage reactions are now known to occur throughout metabolism and to generate (formerly enigmatic) peaks detected in metabolomics datasets. It is also now known that metabolite damage is often countered by dedicated repair enzymes that undo or prevent it. Metabolite damage and repair are highly relevant to engineered pathway design: metabolite damage reactions can reduce flux rates and product yields, and repair enzymes can provide robust, host-independent solutions. Herein, after introducing the core principles of metabolite damage and repair, we use case histories to document how damage and repair processes affect efficient operation of engineered pathways - particularly those that are heterologous, non-natural, or cell-free. We then review how metabolite damage reactions can be predicted, how repair reactions can be prospected, and how metabolite damage and repair can be built into genome-scale metabolic models. Lastly, we propose a versatile 'plug and play' set of well-characterized metabolite repair enzymes to solve metabolite damage problems known or likely to occur in metabolic engineering and synthetic biology projects.

Differentiating signals to make biological sense - A guide through databases for MS-based non-targeted metabolomics

Metabolite identification is one of the most challenging steps in metabolomics studies and reflects one of the greatest bottlenecks in the entire workflow. The success of this step determines the success of the entire research, therefore the quality at which annotations are given requires special attention. A variety of tools and resources are available to aid metabolite identification or annotation, offering different and often complementary functionalities. In preparation for this article, almost 50 databases were reviewed, from which 17 were selected for discussion, chosen for their online ESI-MS functionality. The general characteristics and functions of each database is discussed in turn, considering the advantages and limitations of each along with recommendations for optimal use of each tool, as derived from experiences encountered at the Centre for Metabolomics and Bioanalysis (CEMBIO) in Madrid. These databases were evaluated considering their utility in non-targeted metabolomics, including aspects such as identifier assignment, structural assignment and interpretation of results.

Global open data management in metabolomics

•

The metabolome allows accessing the external influences under which an organism exists and develops in a dynamic way.

•

Recent years have seen the establishment of a global network for metabolomics data exchange.

•

Global metabolomics data exchange is leading to an exponential growth of publically available metabolomics data for re-analysis.

Chemical Biology employs chemical synthesis, analytical chemistry and other tools to study biological systems. Recent advances in both molecular biology such as next generation sequencing (NGS) have led to unprecedented insights towards the evolution of organisms’ biochemical repertoires. Because of the specific data sharing culture in Genomics, genomes from all kingdoms of life become readily available for further analysis by other researchers. While the genome expresses the potential of an organism to adapt to external influences, the Metabolome presents a molecular phenotype that allows us to asses the external influences under which an organism exists and develops in a dynamic way. Steady advancements in instrumentation towards high-throughput and highresolution methods have led to a revival of analytical chemistry methods for the measurement and analysis of the metabolome of organisms. This steady growth of metabolomics as a field is leading to a similar accumulation of big data across laboratories worldwide as can be observed in all of the other omics areas. This calls for the development of methods and technologies for handling and dealing with such large datasets, for efficiently distributing them and for enabling re-analysis. Here we describe the recently emerging ecosystem of global open-access databases and data exchange efforts between them, as well as the foundations and obstacles that enable or prevent the data sharing and reanalysis of this data.

Disease Type- and Status-Specific Alteration of CSF Metabolome Coordinated with Clinical Parameters in Inflammatory Demyelinating Diseases of CNS

Central nervous system (CNS) inflammatory demyelinating diseases (IDDs) are a group of disorders with different aetiologies, characterized by inflammatory lesions. These disorders include multiple sclerosis (MS), neuromyelitis optica spectrum disorder (NMOSD), and idiopathic transverse myelitis (ITM). Differential diagnosis of the CNS IDDs still remains challenging due to frequent overlap of clinical and radiological manifestation, leading to increased demands for new biomarker discovery. Since cerebrospinal fluid (CSF) metabolites may reflect the status of CNS tissues and provide an interfacial linkage between blood and CNS tissues, we explored multi-component biomarker for different IDDs from CSF samples using gas chromatography mass spectrometry-based metabolite profiling coupled to multiplex bioinformatics approach. We successfully constructed the single model with multiple metabolite variables in coordinated regression with clinical characteristics, expanded disability status scale, oligoclonal bands, and protein levels. The multi-composite biomarker simultaneously discriminated four different immune statuses (a total of 145 samples; 54 MS, 49 NMOSD, 30 ITM, and 12 normal controls). Furthermore, systematic characterization of transitional metabolic modulation identified relapse-associated metabolites and proposed insights into the disease network underlying type-specific metabolic dysfunctionality. The comparative analysis revealed the lipids, 1-monopalmitin and 1-monostearin were common indicative for MS, NMOSD, and ITM whereas fatty acids were specific for the relapse identified in all types of IDDs.

Rifaximin Exerts Beneficial Effects Independent of its Ability to Alter Microbiota Composition

OBJECTIVES

Rifaximin has clinical benefits in minimal hepatic encephalopathy (MHE) but the mechanism of action is unclear. The antibiotic-dependent and -independent effects of rifaximin need to be elucidated in the setting of MHE-associated microbiota. To assess the action of rifaximin on intestinal barrier, inflammatory milieu and ammonia generation independent of microbiota using rifaximin.

METHODS

Four germ-free (GF) mice groups were used (1) GF, (2) GF+rifaximin, (3) Humanized with stools from an MHE patient, and (4) Humanized+rifaximin. Mice were followed for 30 days while rifaximin was administered in chow at 100 mg/kg from days 16-30. We tested for ammonia generation (small-intestinal glutaminase, serum ammonia, and cecal glutamine/amino-acid moieties), systemic inflammation (serum IL-1β, IL-6), intestinal barrier (FITC-dextran, large-/small-intestinal expression of IL-1β, IL-6, MCP-1, e-cadherin and zonulin) along with microbiota composition (colonic and fecal multi-tagged sequencing) and function (endotoxemia, fecal bile acid deconjugation and de-hydroxylation).

RESULTS

All mice survived until day 30. In the GF setting, rifaximin decreased intestinal ammonia generation (lower serum ammonia, increased small-intestinal glutaminase, and cecal glutamine content) without changing inflammation or intestinal barrier function. Humanized microbiota increased systemic/intestinal inflammation and endotoxemia without hyperammonemia. Rifaximin therapy significantly ameliorated these inflammatory cytokines. Rifaximin also favorably impacted microbiota function (reduced endotoxin and decreased deconjugation and formation of potentially toxic secondary bile acids), but not microbial composition in humanized mice.

CONCLUSIONS

Rifaximin beneficially alters intestinal ammonia generation by regulating intestinal glutaminase expression independent of gut microbiota. MHE-associated fecal colonization results in intestinal and systemic inflammation in GF mice, which is also ameliorated with rifaximin.

A conserved phosphatase destroys toxic glycolytic side products in mammals and yeast

Metabolic enzymes are very specific. However, most of them show weak side activities toward compounds that are structurally related to their physiological substrates, thereby producing side products that may be toxic. In some cases, 'metabolite repair enzymes' eliminating side products have been identified. We show that mammalian glyceraldehyde 3-phosphate dehydrogenase and pyruvate kinase, two core glycolytic enzymes, produce 4-phosphoerythronate and 2-phospho-L-lactate, respectively. 4-Phosphoerythronate strongly inhibits an enzyme of the pentose phosphate pathway, whereas 2-phospho-L-lactate inhibits the enzyme producing the glycolytic activator fructose 2,6-bisphosphate. We discovered that a single, widely conserved enzyme, known as phosphoglycolate phosphatase (PGP) in mammals, dephosphorylates both 4-phosphoerythronate and 2-phospho-L-lactate, thereby preventing a block in the pentose phosphate pathway and glycolysis. Its yeast ortholog, Pho13, similarly dephosphorylates 4-phosphoerythronate and 2-phosphoglycolate, a side product of pyruvate kinase. Our work illustrates how metabolite repair enzymes can make up for the limited specificity of metabolic enzymes and permit high flux in central metabolic pathways.

‘Nothing of chemistry disappears in biology’: the Top 30 damage-prone endogenous metabolites

Many common metabolites are intrinsically unstable and reactive, and hence prone to chemical (i.e. non-enzymatic) damage in vivo. Although this fact is widely recognized, the purely chemical side-reactions of metabolic intermediates can be surprisingly hard to track down in the literature and are often treated in an unprioritized case-by-case way. Moreover, spontaneous chemical side-reactions tend to be overshadowed today by side-reactions mediated by promiscuous (‘sloppy’) enzymes even though chemical damage to metabolites may be even more prevalent than damage from enzyme sloppiness, has similar outcomes, and is held in check by similar biochemical repair or pre-emption mechanisms. To address these limitations and imbalances, here we draw together and systematically integrate information from the (bio)chemical literature, from cheminformatics, and from genome-scale metabolic models to objectively define a ‘Top 30’ list of damage-prone metabolites. A foundational part of this process was to derive general reaction rules for the damage chemistries involved. The criteria for a ‘Top 30’ metabolite included predicted chemical reactivity, essentiality, and occurrence in diverse organisms. We also explain how the damage chemistry reaction rules (‘operators’) are implemented in the Chemical-Damage-MINE (CD-MINE) database (minedatabase.mcs.anl.gov/#/top30) to provide a predictive tool for many additional potential metabolite damage products. Lastly, we illustrate how defining a ‘Top 30’ list can drive genomics-enabled discovery of the enzymes of previously unrecognized damage-control systems, and how applying chemical damage reaction rules can help identify previously unknown peaks in metabolomics profiles.

Systematic biomarker discovery and coordinative validation for different primary nephrotic syndromes using gas chromatography-mass spectrometry

The goal of this study is to identify systematic biomarker panel for primary nephrotic syndromes from urine samples by applying a non-target metabolite profiling, and to validate their utility in independent sampling and analysis by multiplex statistical approaches. Nephrotic syndrome (NS) is a nonspecific kidney disorder, which is mostly represented by minimal change disease (MCD), focal segmental glomerulosclerosis (FSGS), and membranous glomerulonephritis (MGN). Since urine metabolites may mirror disease-specific functional perturbations in kidney injury, we examined urine samples for distinctive metabolic changes to identify biomarkers for clinical applications. We developed unbiased multi-component covarianced models from a discovery set with 48 samples (12 healthy controls, 12 MCD, 12 FSGS, and 12 MGN). To extensively validate their diagnostic potential, new batch from 54 patients with primary NS were independently examined a year after. In the independent validation set, the model including citric acid, pyruvic acid, fructose, ethanolamine, and cysteine effectively discriminated each NS using receiver operating characteristic (ROC) analysis except MCD-MGN comparison; nonetheless an additional metabolite multi-composite greatly improved the discrimination power between MCD and MGN. Finally, we proposed the re-constructed metabolic network distinctively dysregulated by the different NSs that may deepen comprehensive understanding of the disease mechanistic, and help the enhanced identification of NS and therapeutic plans for future.

Metabolite Damage and Metabolite Damage Control in Plants

It is increasingly clear that (a) many metabolites undergo spontaneous or enzyme-catalyzed side reactions in vivo, (b) the damaged metabolites formed by these reactions can be harmful, and (c) organisms have biochemical systems that limit the buildup of damaged metabolites. These damage-control systems either return a damaged molecule to its pristine state (metabolite repair) or convert harmful molecules to harmless ones (damage preemption). Because all organisms share a core set of metabolites that suffer the same chemical and enzymatic damage reactions, certain damage-control systems are widely conserved across the kingdoms of life. Relatively few damage reactions and damage-control systems are well known. Uncovering new damage reactions and identifying the corresponding damaged metabolites, damage-control genes, and enzymes demands a coordinated mix of chemistry, metabolomics, cheminformatics, biochemistry, and comparative genomics. This review illustrates the above points using examples from plants, which are at least as prone to metabolite damage as other organisms.

Contribution of Drosophila TRPA1 to Metabolism

Transient receptor potential (TRP) cation channels are highly conserved in humans and insects. Some of these channels are expressed in internal organs and their functions remain incompletely understood. By direct knock-in of the GAL4 gene into the trpA1 locus in Drosophila, we identified the expression of this gene in the subesophageal ganglion (SOGs) region. In addition, the neurites present in the dorsal posterior region as well as the drosophila insulin-like peptide 2 (dILP2)-positive neurons send signals to the SOGs. The signal is sent to the crop, which is an enlarged organ of the esophagus and functions as a storage place for food in the digestive system. To systematically investigate the role of TRPA1 in metabolism, we applied non-targeted metabolite profiling analysis together with gas-chromatography/time-of-flight mass spectrometry, with an aim to identify a wide range of primary metabolites. We effectively captured distinctive metabolomic phenotypes and identified specific metabolic dysregulation triggered by TRPA1 mutation based on reconstructed metabolic network analysis. Primarily, the network analysis pinpointed the simultaneous down-regulation of intermediates in the methionine salvation pathway, in contrast to the synchronized up-regulation of a range of free fatty acids. The gene dosage-dependent dynamics of metabolite levels among wild-type, hetero- and homozygous mutants, and their coordinated metabolic modulation under multiple gene settings across five different genotypes confirmed the direct linkages of TRPA1 to metabolism.

Correlation-Based Network Analysis of Metabolite and Enzyme Profiles Reveals a Role of Citrate Biosynthesis in Modulating N and C Metabolism in Zea mays

To investigate the natural variability of leaf metabolism and enzymatic activity in a maize inbred population, statistical and network analyses were employed on metabolite and enzyme profiles. The test of coefficient of variation showed that sugars and amino acids displayed opposite trends in their variance within the population, consistently with their related enzymes. The overall higher CV values for metabolites as compared to the tested enzymes are indicative for their greater phenotypic plasticity. H(2) tests revealed galactinol (1) and asparagine (0.91) as the highest scorers among metabolites and nitrate reductase (0.73), NAD-glutamate dehydrogenase (0.52), and phosphoglucomutase (0.51) among enzymes. The overall low H(2) scores for metabolites and enzymes are suggestive for a great environmental impact or gene-environment interaction. Correlation-based network generation followed by community detection analysis, partitioned the network into three main communities and one dyad, (i) reflecting the different levels of phenotypic plasticity of the two molecular classes as observed for the CV values and (ii) highlighting the concerted changes between classes of chemically related metabolites. Community 1 is composed mainly of enzymes and specialized metabolites, community 2' is enriched in N-containing compounds and phosphorylated-intermediates. The third community contains mainly organic acids and sugars. Cross-community linkages are supported by aspartate, by the photorespiration amino acids glycine and serine, by the metabolically related GABA and putrescine, and by citrate. The latter displayed the strongest node-betweenness value (185.25) of all nodes highlighting its fundamental structural role in the connectivity of the network by linking between different communities and to the also strongly connected enzyme aldolase.

Gut microbiota and host metabolism in liver cirrhosis

The gut microbiota has the capacity to produce a diverse range of compounds that play a major role in regulating the activity of distal organs and the liver is strategically positioned downstream of the gut. Gut microbiota linked compounds such as short chain fatty acids, bile acids, choline metabolites, indole derivatives, vitamins, polyamines, lipids, neurotransmitters and neuroactive compounds, and hypothalamic-pituitary-adrenal axis hormones have many biological functions. This review focuses on the gut microbiota and host metabolism in liver cirrhosis. Dysbiosis in liver cirrhosis causes serious complications, such as bacteremia and hepatic encephalopathy, accompanied by small intestinal bacterial overgrowth and increased intestinal permeability. Gut dysbiosis in cirrhosis and intervention with probiotics and synbiotics in a clinical setting is reviewed and evaluated. Recent studies have revealed the relationship between gut microbiota and host metabolism in chronic metabolic liver disease, especially, non-alcoholic fatty liver disease, alcoholic liver disease, and with the gut microbiota metabolic interactions in dysbiosis related metabolic diseases such as diabetes and obesity. Recently, our understanding of the relationship between the gut and liver and how this regulates systemic metabolic changes in liver cirrhosis has increased. The serum lipid levels of phospholipids, free fatty acids, polyunsaturated fatty acids, especially, eicosapentaenoic acid, arachidonic acid, and docosahexaenoic acid have significant correlations with specific fecal flora in liver cirrhosis. Many clinical and experimental reports support the relationship between fatty acid metabolism and gut-microbiota. Various blood metabolome such as cytokines, amino acids, and vitamins are correlated with gut microbiota in probiotics-treated liver cirrhosis patients. The future evaluation of the gut-microbiota-liver metabolic network and the intervention of these relationships using probiotics, synbiotics, and prebiotics, with sufficient nutrition could aid the development of treatments and prevention for liver cirrhosis patients.

MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics

BACKGROUND

In spite of its great promise, metabolomics has proven difficult to execute in an untargeted and generalizable manner. Liquid chromatography-mass spectrometry (LC-MS) has made it possible to gather data on thousands of cellular metabolites. However, matching metabolites to their spectral features continues to be a bottleneck, meaning that much of the collected information remains uninterpreted and that new metabolites are seldom discovered in untargeted studies. These challenges require new approaches that consider compounds beyond those available in curated biochemistry databases.

DESCRIPTION

Here we present Metabolic In silico Network Expansions (MINEs), an extension of known metabolite databases to include molecules that have not been observed, but are likely to occur based on known metabolites and common biochemical reactions. We utilize an algorithm called the Biochemical Network Integrated Computational Explorer (BNICE) and expert-curated reaction rules based on the Enzyme Commission classification system to propose the novel chemical structures and reactions that comprise MINE databases. Starting from the Kyoto Encyclopedia of Genes and Genomes (KEGG) COMPOUND database, the MINE contains over 571,000 compounds, of which 93% are not present in the PubChem database. However, these MINE compounds have on average higher structural similarity to natural products than compounds from KEGG or PubChem. MINE databases were able to propose annotations for 98.6% of a set of 667 MassBank spectra, 14% more than KEGG alone and equivalent to PubChem while returning far fewer candidates per spectra than PubChem (46 vs. 1715 median candidates). Application of MINEs to LC-MS accurate mass data enabled the identity of an unknown peak to be confidently predicted.

CONCLUSIONS

MINE databases are freely accessible for non-commercial use via user-friendly web-tools at http://minedatabase.mcs.anl.gov and developer-friendly APIs. MINEs improve metabolomics peak identification as compared to general chemical databases whose results include irrelevant synthetic compounds. Furthermore, MINEs complement and expand on previous in silico generated compound databases that focus on human metabolism. We are actively developing the database; future versions of this resource will incorporate transformation rules for spontaneous chemical reactions and more advanced filtering and prioritization of candidate structures. Graphical abstractMINE database construction and access methods. The process of constructing a MINE database from the curated source databases is depicted on the left. The methods for accessing the database are shown on the right.

Enzyme complexity in intermediary metabolism

A good appraisal of the function of enzymes is essential for the understanding of inborn errors of metabolism. However, it is clear now that the 'one gene, one enzyme, one catalytic function' rule oversimplifies the actual situation. Genes often encode several related proteins, which may differ in their subcellular localisation, regulation or function. Furthermore, enzymes often show several catalytic activities. In some cases, this is because they are multifunctional, possessing two or more different active sites that catalyse different, physiologically related reactions. In enzymes with broad specificity or in multispecificity enzymes, a single type of catalytic site performs the same reaction on different physiological substrates at similar rates. Enzymes that act physiologically in only one reaction often show nonetheless substrate promiscuity: they act at low rates on compounds that resemble their physiological substrate(s), thus forming non-classical metabolites, which are in some cases eliminated by metabolite repair. In addition to their catalytic role, enzymes may have moonlighting functions, i.e. non-catalytic functions that are most often not related with their catalytic activity. Deficiency in such functions may participate in the phenotype of inborn errors of metabolism. Evolution has also made that some enzymes have lost their catalytic activity to become allosteric proteins.

N-lactoyl-amino acids are ubiquitous metabolites that originate from CNDP2-mediated reverse proteolysis of lactate and amino acids

Despite technological advances in metabolomics, large parts of the human metabolome are still unexplored. In an untargeted metabolomics screen aiming to identify substrates of the orphan transporter ATP-binding cassette subfamily C member 5 (ABCC5), we identified a class of mammalian metabolites, N-lactoyl-amino acids. Using parallel protein fractionation in conjunction with shotgun proteomics on fractions containing N-lactoyl-Phe-forming activity, we unexpectedly found that a protease, cytosolic nonspecific dipeptidase 2 (CNDP2), catalyzes their formation. N-lactoyl-amino acids are ubiquitous pseudodipeptides of lactic acid and amino acids that are rapidly formed by reverse proteolysis, a process previously considered to be negligible in vivo. The plasma levels of these metabolites strongly correlate with plasma levels of lactate and amino acid, as shown by increased levels after physical exercise and in patients with phenylketonuria who suffer from elevated Phe levels. Our approach to identify unknown metabolites and their biosynthesis has general applicability in the further exploration of the human metabolome.

Metabolomics for phytochemical discovery: development of statistical approaches using a cranberry model system

Metabolomics is the qualitative and quantitative analysis of all of the small molecules in a biological sample at a specific time and influence. Technologies for metabolomics analysis have developed rapidly as new analytical tools for chemical separations, mass spectrometry, and NMR spectroscopy have emerged. Plants have one of the largest metabolomes, and it is estimated that the average plant leaf can contain upward of 30 000 phytochemicals. In the past decade, over 1200 papers on plant metabolomics have been published. A standard metabolomics data set contains vast amounts of information and can either investigate or generate hypotheses. The key factors in using plant metabolomics data most effectively are the experimental design, authentic standard availability, extract standardization, and statistical analysis. Using cranberry (Vaccinium macrocarpon) as a model system, this review will discuss and demonstrate strategies and tools for analysis and interpretation of metabolomics data sets including eliminating false discoveries and determining significance, metabolite clustering, and logical algorithms for discovery of new metabolites and pathways. Together these metabolomics tools represent an entirely new pipeline for phytochemical discovery.

Comparison of human cell signaling pathway databases--evolution, drawbacks and challenges

Elucidating the complexities of cell signaling pathways is of immense importance to gain understanding about various biological phenomenon, such as dynamics of gene/protein expression regulation, cell fate determination, embryogenesis and disease progression. The successful completion of human genome project has also helped experimental and theoretical biologists to analyze various important pathways. To advance this study, during the past two decades, systematic collections of pathway data from experimental studies have been compiled and distributed freely by several databases, which also integrate various computational tools for further analysis. Despite significant advancements, there exist several drawbacks and challenges, such as pathway data heterogeneity, annotation, regular update and automated image reconstructions, which motivated us to perform a thorough review on popular and actively functioning 24 cell signaling databases. Based on two major characteristics, pathway information and technical details, freely accessible data from commercial and academic databases are examined to understand their evolution and enrichment. This review not only helps to identify some novel and useful features, which are not yet included in any of the databases but also highlights their current limitations and subsequently propose the reasonable solutions for future database development, which could be useful to the whole scientific community.

Plant metabolic modeling: achieving new insight into metabolism and metabolic engineering

Models are used to represent aspects of the real world for specific purposes, and mathematical models have opened up new approaches in studying the behavior and complexity of biological systems. However, modeling is often time-consuming and requires significant computational resources for data development, data analysis, and simulation. Computational modeling has been successfully applied as an aid for metabolic engineering in microorganisms. But such model-based approaches have only recently been extended to plant metabolic engineering, mainly due to greater pathway complexity in plants and their highly compartmentalized cellular structure. Recent progress in plant systems biology and bioinformatics has begun to disentangle this complexity and facilitate the creation of efficient plant metabolic models. This review highlights several aspects of plant metabolic modeling in the context of understanding, predicting and modifying complex plant metabolism. We discuss opportunities for engineering photosynthetic carbon metabolism, sucrose synthesis, and the tricarboxylic acid cycle in leaves and oil synthesis in seeds and the application of metabolic modeling to the study of plant acclimation to the environment. The aim of the review is to offer a current perspective for plant biologists without requiring specialized knowledge of bioinformatics or systems biology.

A network perspective on nitrogen metabolism from model to crop plants using integrated 'omics' approaches

Nitrogen (N), as an essential element in amino acids, nucleotides, and proteins, is a key factor in plant growth and development. Omics approaches such as metabolomics and transcriptomics have become a promising way to inspect complex network interactions in N metabolism and can be used for monitoring the uptake and regulation, translocation, and remobilization of N. In this review, the authors highlight recent progress in omics approaches, including transcript profiling using microarrays and deep sequencing, and show recent technical developments in metabolite profiling for N studies. Further, network analysis studies including network inference methods with correlations, information-theoretic measures, and a network concept to examine gene expression clusters in relation to N regulatory systems in plants are introduced, and integrating network inference methods and integrated networks using multiple omics data are discussed. Finally, this review summarizes recent omics application examples using metabolite and/or transcript profiling analysis to elucidate the regulation of N metabolism and signalling and the coordination of N and carbon metabolism in model plants (Arabidopsis and rice), crops (tomato, maize, and legumes), and trees (Populus).

Microbial metabolomics: innovation, application, insight

Most textbooks depict metabolism as a well understood housekeeping function of cells. However, organisms vary in their metabolic needs according to the specific niches they reside in and selective pressures encountered therein. Recent advances in analytical chemistry have begun to reveal an unexpected diversity in the composition, structure and regulation of metabolic networks. Here, we review key technological developments in the area of metabolism and their impact on our understanding of the fundamental roles of metabolism in cellular physiology.

Randomised clinical trial: Lactobacillus GG modulates gut microbiome, metabolome and endotoxemia in patients with cirrhosis

BACKGROUND

Safety of individual probiotic strains approved under Investigational New Drug (IND) policies in cirrhosis with minimal hepatic encephalopathy (MHE) is not clear.

AIM

The primary aim of this phase I study was to evaluate the safety, tolerability of probiotic Lactobacillus GG (LGG) compared to placebo, while secondary ones were to explore its mechanism of action using cognitive, microbiome, metabolome and endotoxin analysis in MHE patients.

METHODS

Cirrhotic patients with MHE patients were randomised 1:1 into LGG or placebo BID after being prescribed a standard diet and multi-vitamin regimen and were followed up for 8 weeks. Serum, urine and stool samples were collected at baseline and study end. Safety was assessed at Weeks 4 and 8. Endotoxin and systemic inflammation, microbiome using multi-tagged pyrosequencing, serum/urine metabolome were analysed between groups using correlation networks.

RESULTS

Thirty MHE patients (14 LGG and 16 placebo) completed the study without any differences in serious adverse events. However, self-limited diarrhoea was more frequent in LGG patients. A standard diet was maintained and LGG batches were comparable throughout. Only in the LGG-randomised group, endotoxemia and TNF-α decreased, microbiome changed (reduced Enterobacteriaceae and increased Clostridiales Incertae Sedis XIV and Lachnospiraceae relative abundance) with changes in metabolite/microbiome correlations pertaining to amino acid, vitamin and secondary BA metabolism. No change in cognition was found.

CONCLUSIONS

In this phase I study, Lactobacillus GG is safe and well-tolerated in cirrhosis and is associated with a reduction in endotoxemia and dysbiosis.

Inactivation of metabolic genes causes short- and long-range dys-regulation in Escherichia coli metabolic network

The metabolic network in E. coli can be severely affected by the inactivation of metabolic genes that are required to catabolize a nutrient (D-galactose). We hypothesized that the resulting accumulation of small molecules can yield local as well as systemic effects on the metabolic network. Analysis of metabolomics data in wild-type and D-galactose non-utilizing mutants, galT, galU and galE, reveal the large metabolic differences between the wild-type and the mutants when the strains were grown in D-galactose. Network mapping suggested that the enzymatic defects affected the metabolic modules located both at short- and long-ranges from the D-galactose metabolic module. These modules suggested alterations in glutathione, energy, nucleotide and lipid metabolism and disturbed carbon to nitrogen ratio in mutant strains. The altered modules are required for normal cell growth for the wild-type strain, explaining why the cell growth is inhibited in the mutants in the presence of D-galactose. Identification of these distance-based dys-regulations would enhance the systems level understanding of metabolic networks of microorganisms having importance in biomedical and biotechnological research.

Identification of biomarkers for apoptosis in cancer cell lines using metabolomics: tools for individualized medicine

BACKGROUND

Metabolomics is a versatile unbiased method to search for biomarkers of human disease. In particular, one approach in cancer therapy is to promote apoptosis in tumour cells; this could be improved with specific biomarkers of apoptosis for monitoring treatment. We recently observed specific metabolic patterns in apoptotic cell lines; however, in that study, apoptosis was only induced with one pro-apoptotic agent, staurosporine.

OBJECTIVE

The aim of this study was to find novel biomarkers of apoptosis by verifying our previous findings using two further pro-apoptotic agents, 5-fluorouracil and etoposide, that are commonly used in anticancer treatment.

METHODS

Metabolic parameters were assessed in HepG2 and HEK293 cells using the newborn screening assay adapted for cell culture approaches, quantifying the levels of amino acids and acylcarnitines with mass spectrometry.

RESULTS

We were able to identify apoptosis-specific changes in the metabolite profile. Moreover, the amino acids alanine and glutamate were both significantly up-regulated in apoptotic HepG2 and HEK293 cells irrespective of the apoptosis inducer.

CONCLUSION

Our observations clearly indicate the potential of metabolomics in detecting metabolic biomarkers applicable in theranostics and for monitoring drug efficacy.

In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics

Current methods of structure identification in mass-spectrometry-based nontargeted metabolomics rely on matching experimentally determined features of an unknown compound to those of candidate compounds contained in biochemical databases. A major limitation of this approach is the relatively small number of compounds currently included in these databases. If the correct structure is not present in a database, it cannot be identified, and if it cannot be identified, it cannot be included in a database. Thus, there is an urgent need to augment metabolomics databases with rationally designed biochemical structures using alternative means. Here we present the In Vivo/In Silico Metabolites Database (IIMDB), a database of in silico enzymatically synthesized metabolites, to partially address this problem. The database, which is available at http://metabolomics.pharm.uconn.edu/iimdb/, includes ~23,000 known compounds (mammalian metabolites, drugs, secondary plant metabolites, and glycerophospholipids) collected from existing biochemical databases plus more than 400,000 computationally generated human phase-I and phase-II metabolites of these known compounds. IIMDB features a user-friendly web interface and a programmer-friendly RESTful web service. Ninety-five percent of the computationally generated metabolites in IIMDB were not found in any existing database. However, 21,640 were identical to compounds already listed in PubChem, HMDB, KEGG, or HumanCyc. Furthermore, the vast majority of these in silico metabolites were scored as biological using BioSM, a software program that identifies biochemical structures in chemical structure space. These results suggest that in silico biochemical synthesis represents a viable approach for significantly augmenting biochemical databases for nontargeted metabolomics applications.

Distinct pools of non-glycolytic substrates differentiate brain regions and prime region-specific responses of mitochondria

Many hereditary diseases are characterized by region-specific toxicity, despite the fact that disease-linked proteins are generally ubiquitously expressed. The underlying basis of the region-specific vulnerability remains enigmatic. Here, we evaluate the fundamental features of mitochondrial and glucose metabolism in synaptosomes from four brain regions in basal and stressed states. Although the brain has an absolute need for glucose in vivo, we find that synaptosomes prefer to respire on non-glycolytic substrates, even when glucose is present. Moreover, glucose is metabolized differently in each brain region, resulting in region-specific “signature” pools of non-glycolytic substrates. The use of non-glycolytic resources increases and dominates during energy crisis, and triggers a marked region-specific metabolic response. We envision that disease-linked proteins confer stress on all relevant brain cells, but region-specific susceptibility stems from metabolism of non-glycolytic substrates, which limits how and to what extent neurons respond to the stress.

Metabolite proofreading, a neglected aspect of intermediary metabolism

Enzymes of intermediary metabolism are less specific than what is usually assumed: they often act on metabolites that are not their 'true' substrate, making abnormal metabolites that may be deleterious if they accumulate. Some of these abnormal metabolites are reconverted to normal metabolites by repair enzymes, which play therefore a role akin to the proofreading activities of DNA polymerases and aminoacyl-tRNA synthetases. An illustrative example of such repair enzymes is L-2-hydroxyglutarate dehydrogenase, which eliminates a metabolite abnormally made by a Krebs cycle enzyme. Mutations in L-2-hydroxyglutarate dehydrogenase lead to L-2-hydroxyglutaric aciduria, a leukoencephalopathy. Other examples are the epimerase and the ATP-dependent dehydratase that repair hydrated forms of NADH and NADPH; ethylmalonyl-CoA decarboxylase, which eliminates an abnormal metabolite formed by acetyl-CoA carboxylase, an enzyme of fatty acid synthesis; L-pipecolate oxidase, which repairs a metabolite formed by a side activity of an enzyme of L-proline biosynthesis. Metabolite proofreading enzymes are likely quite common, but most of them are still unidentified. A defect in these enzymes may account for new metabolic disorders.

Systematization of the protein sequence diversity in enzymes related to secondary metabolic pathways in plants, in the context of big data biology inspired by the KNApSAcK motorcycle database

Biology is increasingly becoming a data-intensive science with the recent progress of the omics fields, e.g. genomics, transcriptomics, proteomics and metabolomics. The species-metabolite relationship database, KNApSAcK Core, has been widely utilized and cited in metabolomics research, and chronological analysis of that research work has helped to reveal recent trends in metabolomics research. To meet the needs of these trends, the KNApSAcK database has been extended by incorporating a secondary metabolic pathway database called Motorcycle DB. We examined the enzyme sequence diversity related to secondary metabolism by means of batch-learning self-organizing maps (BL-SOMs). Initially, we constructed a map by using a big data matrix consisting of the frequencies of all possible dipeptides in the protein sequence segments of plants and bacteria. The enzyme sequence diversity of the secondary metabolic pathways was examined by identifying clusters of segments associated with certain enzyme groups in the resulting map. The extent of diversity of 15 secondary metabolic enzyme groups is discussed. Data-intensive approaches such as BL-SOM applied to big data matrices are needed for systematizing protein sequences. Handling big data has become an inevitable part of biology.