Simplivariate models: ideas and first examples

One of the new expanding areas in functional genomics is metabolomics: measuring the metabolome of an organism. Data being generated in metabolomics studies are very diverse in nature depending on the design underlying the experiment. Traditionally, variation in measurements is conceptually broken down in systematic variation and noise where the latter contains, e.g. technical variation. There is increasing evidence that this distinction does not hold (or is too simple) for metabolomics data. A more useful distinction is in terms of informative and non-informative variation where informative relates to the problem being studied. In most common methods for analyzing metabolomics (or any other high-dimensional x-omics) data this distinction is ignored thereby severely hampering the results of the analysis. This leads to poorly interpretable models and may even obscure the relevant biological information. We developed a framework from first data analysis principles by explicitly formulating the problem of analyzing metabolomics data in terms of informative and non-informative parts. This framework allows for flexible interactions with the biologists involved in formulating prior knowledge of underlying structures. The basic idea is that the informative parts of the complex metabolomics data are approximated by simple components with a biological meaning, e.g. in terms of metabolic pathways or their regulation. Hence, we termed the framework 'simplivariate models' which constitutes a new way of looking at metabolomics data. The framework is given in its full generality and exemplified with two methods, IDR analysis and plaid modeling, that fit into the framework. Using this strategy of 'divide and conquer', we show that meaningful simplivariate models can be obtained using a real-life microbial metabolomics data set. For instance, one of the simple components contained all the measured intermediates of the Krebs cycle of E. coli. Moreover, these simplivariate models were able to uncover regulatory mechanisms present in the phenylalanine biosynthesis route of E. coli.     

Functional metabolomics: from biomarker discovery to metabolome reprogramming

Metabolomics is emerging as a powerful tool for studying metabolic processes, identifying crucial biomarkers responsible for metabolic characteristics and revealing metabolic mechanisms, which construct the content of discovery metabolomics. The crucial biomarkers can be used to reprogram a metabolome, leading to an aimed metabolic strategy to cope with alteration of internal and external environments, naming reprogramming metabolomics here. The striking feature on the similarity of the basic metabolic pathways and components among vastly differentspeciesmakesthe reprogrammingmetabolomics possible when the engineered metabolites play biological roles in cellular activity as a substrate of enzymes and a regulator to other molecules including proteins. The reprogramming metabolomics approach can be used to clarify metabolic mechanisms of responding to changed internal and external environmental factors and to establish a framework to develop targeted tools for dealing with the changes such as controlling and/or preventing infection with pathogens and enhancing host immunity against pathogens. This review introduces the current state and trends of discovery metabolomics and reprogramming metabolomics and highlights the importance of reprogramming metabolomics.     

The Escherichia coli proteome: past, present, and future prospects.

Proteomics has emerged as an indispensable methodology for large-scale protein analysis in functional genomics. The Escherichia coli proteome has been extensively studied and is well defined in terms of biochemical, biological, and biotechnological data. Even before the entire E. coli proteome was fully elucidated, the largest available data set had been integrated to decipher regulatory circuits and metabolic pathways, providing valuable insights into global cellular physiology and the development of metabolic and cellular engineering strategies. With the recent advent of advanced proteomic technologies, the E. coli proteome has been used for the validation of new technologies and methodologies such as sample prefractionation, protein enrichment, two-dimensional gel electrophoresis, protein detection, mass spectrometry (MS), combinatorial assays with n-dimensional chromatographies and MS, and image analysis software. These important technologies will not only provide a great amount of additional information on the E. coli proteome but also synergistically contribute to other proteomic studies. Here, we review the past development and current status of E. coli proteome research in terms of its biological, biotechnological, and methodological significance and suggest future prospects.     

Recent progress in mass spectrometry for single-cell metabolomics

Metabolites are the end products of cellular vital activities and can reflect the state of cellular to a certain extent. Rapid change of metabolites and the low abundance of signature metabolites cause difficulties in single-cell detection, which is a great challenge in single-cell metabolomics analysis. Mass spectrometry (MS) is a powerful tool that uniquely suited to detect intracellular small-molecule metabolites and has shown good application in single-cell metabolite analysis. In this mini-review, we describe three types of emerging technologies for MS-based single-cell metabolic analysis in recent years, including nano-ESI-MS based single-cell metabolomics analysis, high-throughput analysis via flow cytometry, and cellular metabolic imaging analysis. These techniques provide a large amount of single-cell metabolic data, allowing the potential of MS in single-cell metabolic analysis is gradually being explored and is of great importance in disease and life science research.     

Metabolomics assisted metabolic network modeling and network wide analysis of metabolites in microbiology

Metabolomics is the science of qualitatively and quantitatively analyzing low molecular weight metabolites occur in a given biological system. It provides valuable information to elucidate the functional roles and relations of different metabolites in a metabolic pathway. In recent years, a large amount of research on microbial metabolomics has been conducted. It has become a useful tool for achieving highly efficient synthesis of target metabolites. At the same time, many studies have been conducted over the years in order to integrate metabolomics data into metabolic network modeling, which has yielded many exciting results. Additionally, metabolomics also shows great advantages in analyzing the relationship of metabolites network wide. Integrating metabolomics data into metabolic network construction and applying it in network wide analysis of cell metabolism would further improve our ability to control cellular metabolism and optimize the design of cell factories for the overproduction of valuable biochemicals. This review will examine recent progress in the application of metabolomics approaches in metabolic network modeling and network wide analysis of microbial cell metabolism.     

Global metabolomic responses of Escherichia coli to heat stress

Microbial metabolomic analysis is essential for understanding responses of microorganisms to heat stress. To understand the comprehensive metabolic responses of Escherichia coli to continuous heat stress, we characterized the metabolomic variations induced by heat stress using NMR spectroscopy in combination with multivariate data analysis. We detected 15 amino acids, 10 nucleotides, 9 aliphatic organic acids, 7 amines, glucose and its derivative glucosylglyceric acid, and methanol in the E. coli extracts. Glucosylglyceric acid was reported for the first time in E. coli. We found that heat stress was an important factor influencing the metabolic state and growth process, mainly via suppressing energy associated metabolism, reducing nucleotide biosynthesis, altering amino acid metabolism and promoting osmotic regulation. Moreover, metabolic perturbation was aggravated during heat stress. However, a sign of recovery to control levels was observed after the removal of heat stress. These findings enhanced our understanding of the metabolic responses of E. coli to heat stress and demonstrated the effectiveness of the NMR-based metabolomics approach to study such a complex system.     

大肠杆菌细胞工厂的创建技术

大肠杆菌作为一种重要的模式工业微生物,在医药、化工、农业等方面具有广泛的应用.近30年来,多种代谢工程改造的新策略和新技术,被用于设计、构建和优化大肠杆菌化学品细胞工厂,极大地提高了生物法合成化学品的生产速率和产量.文中将从大肠杆菌途径设计、合成途径创建与优化和细胞全局优化三个方面,对大肠杆菌代谢改造起重要推动作用的技...     

From genetic footprinting to antimicrobial drug targets: examples in cofactor biosynthetic pathways

Novel drug targets are required in order to design new defenses against antibiotic-resistant pathogens. Comparative genomics provides new opportunities for finding optimal targets among previously unexplored cellular functions, based on an understanding of related biological processes in bacterial pathogens and their hosts. We describe an integrated approach to identification and prioritization of broad-spectrum drug targets. Our strategy is based on genetic footprinting in Escherichia coli followed by metabolic context analysis of essential gene orthologs in various species. Genes required for viability of E. coli in rich medium were identified on a whole-genome scale using the genetic footprinting technique. Potential target pathways were deduced from these data and compared with a panel of representative bacterial pathogens by using metabolic reconstructions from genomic data. Conserved and indispensable functions revealed by this analysis potentially represent broad-spectrum antibacterial targets. Further target prioritization involves comparison of the corresponding pathways and individual functions between pathogens and the human host. The most promising targets are validated by direct knockouts in model pathogens. The efficacy of this approach is illustrated using examples from metabolism of adenylate cofactors NAD(P), coenzyme A, and flavin adenine dinucleotide. Several drug targets within these pathways, including three distantly related adenylyltransferases (orthologs of the E. coli genes nadD, coaD, and ribF), are discussed in detail.     

Proteomic analysis of Escherichia coli associated with urinary tract infections

Escherichia coli is a major cause of urinary tract infections (UTIs) where the initial infection arises from bacteria originating in the bowel. However, significant differences are observed between the genomes of intestinal and urinary E. coli strains with the latter possessing many adaptations that promote growth in the urinary tract. To define further the adaptation of urinary E. coli isolates, the cellular proteomes of 41 E. coli strains, collected from cases of UTIs or random faecal samples, were compared by 2-D gel electrophoresis and principal component analysis. The data indicated that individual patients carried relatively homogenous E. coli populations, as defined by their cellular proteomes, but the populations were distinct between patients. For one patient, E. coli, isolated during two recurrent infections 3 months apart, were indistinguishable, indicating that for this patient the infections were possibly caused by the same bacterial population. To understand the basis of the discrimination of the bacteria, selected protein spots were identified by peptide fragment fingerprinting. The identified proteins were involved in a variety of metabolic and structural roles. The data obtained for these E. coli strains provide a basis from which to target key bacterial proteins for further investigation into E. coli pathogenesis.     

Comparative analysis of plasma metabolomics response to metabolic challenge tests in healthy subjects and influence of the FTO obesity risk allele

The measurement of metabolites during intravenous or nutritional challenges may improve the identification of novel metabolic signatures which are not detectable in the fasting state. Here, we comprehensively characterized the plasma metabolomics response to five defined challenge tests and explored their use to identify interactions with the FTO rs9939609 obesity risk genotype. Fifty-six non-diabetic male participants of the KORA S4/F4 cohort, including 25 homozygous carriers of the FTO risk allele (AA genotype) and 31 carriers of the TT genotype were recruited. Challenges comprised an oral glucose tolerance test, a standardized high-fat high-carbohydrate meal and a lipid tolerance test, as well as an intravenous glucose tolerance test and a euglycemic hyperinsulinemic clamp. Blood was sampled for biochemical and metabolomics measurement before and during the challenges. Plasma samples were analyzed using a mass spectrometry-based metabolomics approach targeting 163 metabolites. Linear mixed-effects models and cluster analysis were performed. In both genotype groups, we observed significant challenge-induced changes for all major metabolite classes (amino acids, hexose, acylcarnitines, phosphatidylcholines, lysophosphatidylcholines and sphingomyelins, with corrected p-values ranging from 0.05 to 6.7E?37), which clustered in five distinct metabolic response profiles. Our data contribute to the understanding of plasma metabolomics response to diverse metabolic challenges, including previously unreported metabolite changes in response to intravenous challenges. The FTO genotype had only minor effects on the metabolite fluxes after standardized metabolic challenges.     

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.     

Metabolic footprint analysis of recombinant Escherichia coli strains during fed-batch fermentations

Metabolic footprinting has become a valuable analytical approach for the characterization of phenotypes and the distinction of specific metabolic states resulting from environmental and/or genetic alterations. The metabolic impact of heterologous protein production in Escherichia coli cells is of particular interest, since there are numerous cellular stresses triggered during this process that limit the overall productivity, e.g. the stringent response. Because the knowledge on the metabolic responses in recombinant bioprocesses is still scarce, metabolic footprinting can provide relevant information on the intrinsic metabolic adjustments. Thus, the metabolic footprints generated by E. coli W3110 and the ΔrelA mutant strain during recombinant fed-batch fermentations at different experimental conditions were measured and interpreted. The IPTG-induction of the heterologous protein expression resulted in the rapid accumulation of inhibitors of the glyoxylate shunt in the culture broth, suggesting the clearance of this anaplerotic route to replenish the TCA intermediaries withdrawn for the additional formation of the heterologous protein. Nutritional shifts were also critical in the recombinant cellular metabolism, indicating that cells employ diverse strategies to counteract imbalances in the cellular metabolism, including the secretion of certain metabolites that are, most likely, used as a metabolic relief to survival processes.     

Prediction of microbial growth rate versus biomass yield by a metabolic network with kinetic parameters

Identifying the factors that determine microbial growth rate under various environmental and genetic conditions is a major challenge of systems biology. While current genome-scale metabolic modeling approaches enable us to successfully predict a variety of metabolic phenotypes, including maximal biomass yield, the prediction of actual growth rate is a long standing goal. This gap stems from strictly relying on data regarding reaction stoichiometry and directionality, without accounting for enzyme kinetic considerations. Here we present a novel metabolic network-based approach, MetabOlic Modeling with ENzyme kineTics (MOMENT), which predicts metabolic flux rate and growth rate by utilizing prior data on enzyme turnover rates and enzyme molecular weights, without requiring measurements of nutrient uptake rates. The method is based on an identified design principle of metabolism in which enzymes catalyzing high flux reactions across different media tend to be more efficient in terms of having higher turnover numbers. Extending upon previous attempts to utilize kinetic data in genome-scale metabolic modeling, our approach takes into account the requirement for specific enzyme concentrations for catalyzing predicted metabolic flux rates, considering isozymes, protein complexes, and multi-functional enzymes. MOMENT is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including intracellular flux rates and changes in gene expression levels under different growth rates. Most importantly, MOMENT is shown to predict growth rates of E. coli under a diverse set of media that are correlated with experimental measurements, markedly improving upon existing state-of-the art stoichiometric modeling approaches. These results support the view that a physiological bound on cellular enzyme concentrations is a key factor that determines microbial growth rate.     

Metabolomics: creating new potentials for unraveling the mechanisms in response to salt and drought stress and for the biotechnological improvement of xero-halophytes

Breeders have long been interested in understanding the biological function and mechanism of xero-halophytes and their ability for growth in drought-stricken and salinized environments. However, the mechanisms in response to stress have been difficult to unravel because their defenses require regulatory changes to the activation of multiple genes and pathways. Metabolomics is becoming a key tool in comprehensively understanding the cellular response to abiotic stress and represents an important addition to the tools currently employed in genomics-assisted selection for plant improvement. In this review, we highlight the applications of plant metabolomics in characterizing metabolic responses to salt and drought stress, and identifying metabolic quantitative trait loci (QTLs). We also discuss the potential of metabolomics as a tool to unravel stress response mechanisms, and as a viable option for the biotechnological improvement of xero-halophytes when no other genetic information such as linkage maps and QTLs are available, by combining with germplasm-regression-combined marker-trait association identification.     

Metabolomics: Creating new potentials for unraveling the mechanisms in response to salt and drought stress and for the biotechnological improvement of xero-halophytes

Breeders have long been interested in understanding the biological function and mechanism of xero-halophytes and their ability for growth in drought-stricken and salinized environments. However, the mechanisms in response to stress have been difficult to unravel because their defenses require regulatory changes to the activation of multiple genes and pathways. Metabolomics is becoming a key tool in comprehensively understanding the cellular response to abiotic stress and represents an important addition to the tools currently employed in genomics-assisted selection for plant improvement. In this review, we highlight the applications of plant metabolomics in characterizing metabolic responses to salt and drought stress, and identifying metabolic quantitative trait loci (QTLs). We also discuss the potential of metabolomics as a tool to unravel stress response mechanisms, and as a viable option for the biotechnological improvement of xero-halophytes when no other genetic information such as linkage maps and QTLs are available, by combining with germplasm-regression-combined marker-trait association identification.     

The potential of metabolomics tools in bioremediation studies

As a post-genomics tool, metabolomics is a young and vibrant field of science in its exponential growth phase. Metabolome analysis has become very popular recently, and novel techniques for acquiring and analyzing metabolomics data continue to emerge that are useful for a variety of biological studies. The bioremediation field has a lot to gain from the advances in this emerging area. Thus, this review article focuses on the potential of various experimental and conceptual approaches developed for metabolomics to be applied in bioremediation research, such as strategies for elucidation of biodegradation pathways using isotope distribution analysis and molecular connectivity analysis, the assessment of mineralization process using metabolic footprinting analysis, and the improvement of the biodegradation process via metabolic engineering. We demonstrate how the use of metabolomics tools can significantly extend and enhance the power of existing bioremediation approaches by providing a better overview of the biodegradation process.     

Glycogen is the primary source of glucose during the lag phase of E. coli proliferation

In the studies of Escherichia coli (E. coli), metabolomics analyses have mainly been performed using steady state culture. However, to analyze the dynamic changes in cellular metabolism, we performed a profiling of concentration of metabolites by using batch culture. As a first step, we focused on glucose uptake and the behavior of the first metabolite, G6P (glucose-6-phosphate). A computational formula was derived to express the glucose uptake rate by a single cell from two kinds of experimental data, extracellular glucose concentration and cell growth, being simulated by Cell Illustrator. In addition, average concentration of G6P has been measured by CE-MS. The existence of another carbon source was suggested from the computational result. After careful comparison between cell growth, G6P concentration, and the computationally obtained curve of glucose uptake rate, we predicted the consumption of glycogen in lag phase and its accumulation as an energy source in an E. coli cell for the next proliferation. We confirmed our prediction experimentally. This behavior indicates the importance of glycogen participation in the lag phase for the growth of E. coli. This article is part of a Special Issue entitled: Computational Methods for Protein Interaction and Structural Prediction.