Production of bulk chemicals via novel metabolic pathways in microorganisms

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing ‘big’ biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources.     

An algorithm for efficient identification of branched metabolic pathways

This article presents a new graph-based algorithm for identifying branched metabolic pathways in multi-genome scale metabolic data. The term branched is used to refer to metabolic pathways between compounds that consist of multiple pathways that interact biochemically. A branched pathway may produce a target compound through a combination of linear pathways that split compounds into smaller ones, work in parallel with many compounds, and join compounds into larger ones. While branched metabolic pathways predominate in metabolic networks, most previous work has focused on identifying linear metabolic pathways. The ability to automatically identify branched pathways is important in applications that require a deeper understanding of metabolism, such as metabolic engineering and drug target identification. The algorithm presented in this article utilizes explicit atom tracking to identify linear metabolic pathways and then merges them together into branched metabolic pathways. We provide results on several well-characterized metabolic pathways that demonstrate that the new merging approach can efficiently find biologically relevant branched metabolic pathways.     

Multi-Tissue Computational Modeling Analyzes Pathophysiology of Type 2 Diabetes in MKR Mice

Computational models using metabolic reconstructions for in silico simulation of metabolic disorders such as type 2 diabetes mellitus (T2DM) can provide a better understanding of disease pathophysiology and avoid high experimentation costs. There is a limited amount of computational work, using metabolic reconstructions, performed in this field for the better understanding of T2DM. In this study, a new algorithm for generating tissue-specific metabolic models is presented, along with the resulting multi-confidence level (MCL) multi-tissue model. The effect of T2DM on liver, muscle, and fat in MKR mice was first studied by microarray analysis and subsequently the changes in gene expression of frank T2DM MKR mice versus healthy mice were applied to the multi-tissue model to test the effect. Using the first multi-tissue genome-scale model of all metabolic pathways in T2DM, we found out that branched-chain amino acids'' degradation and fatty acids oxidation pathway is downregulated in T2DM MKR mice. Microarray data showed low expression of genes in MKR mice versus healthy mice in the degradation of branched-chain amino acids and fatty-acid oxidation pathways. In addition, the flux balance analysis using the MCL multi-tissue model showed that the degradation pathways of branched-chain amino acid and fatty acid oxidation were significantly downregulated in MKR mice versus healthy mice. Validation of the model was performed using data derived from the literature regarding T2DM. Microarray data was used in conjunction with the model to predict fluxes of various other metabolic pathways in the T2DM mouse model and alterations in a number of pathways were detected. The Type 2 Diabetes MCL multi-tissue model may explain the high level of branched-chain amino acids and free fatty acids in plasma of Type 2 Diabetic subjects from a metabolic fluxes perspective.     

Physiology of pyruvate metabolism in Lactococcus lactis

Lactococcus lactis, a homofermentative lactic acid bacterium, has been studied extensively over several decades to obtain sometimes conflicting concepts relating to the growth behaviour. In this review some of the data will be examined with respect to pyruvate metabolism. It will be demonstrated that the metabolic transformation of pyruvate can be predicted if the growth-limiting constraints are adequately established. In general lactate remains the major product under conditions in which sugar metabolism via a homolactic fermentation can satisfy the energy requirements necessary to assimilate anabolic substrates from the medium. In contrast, alternative pathways are involved when this energy supply becomes limiting or when the normal pathways can no longer maintain balanced carbon flux. Pyruvate occupies an important position within the metabolic network of L. lactis and the control of pyruvate distribution within the various pathways is subject to co-ordinated regulation by both gene expression mechanisms and allosteric modulation of enzyme activity.     

Immunometabolism features of metabolic deregulation and cancer

Immunometabolism is a branch dealing at the interface of immune functionalities and metabolic regulations. Considered as a bidirectional trafficking, metabolic contents and their precursors bring a considerable change in immune cells signal transductions which as a result affect the metabolic organs and states as an implication. Lipid metabolic ingredients form a major chunk of daily diet and have a proven contribution in immune cells induction, which then undergo metabolic pathway shuffling inside their ownself. Lipid metabolic states activate relevant metabolic pathways inside immune cells that in turn prime appropriate responses to outside environment in various states including lipid metabolic disorders itself and cancers as an extension. Although data on Immunometabolism are still growing, but scientific community need to adjust and readjust according to recent data on given subject. This review attempts to provide current important data on Immunometabolism and consequently its metabolic ramifications. Incumbent data on various lipid metabolic deregulations like obesity, metabolic syndrome, obese asthma and atherosclerosis are analysed. Further, metabolic repercussions on cancers and its immune modalities are also analysed.     

Categorization of metabolome in bacterial systems

Analyses of biological databases such as those of genome, proteome, metabolome etc., have given insights in organization of biological systems. However, current efforts do not utilize the complete potential of available metabolome data. In this study, metabolome of bacterial systems with reliable annotations are analyzed and a simple method is developed to categorize pathways hierarchically, using rational approach. Ninety-four bacterial systems having for each ≥ 250 annotated metabolic pathways were used to identify a set of common pathways. 42 pathways were present in all bacteria which are termed as Core/Stage I pathways. This set of pathways was used along with interacting compounds to categorize pathways in the metabolome hierarchically. In each metabolome non-interacting pathways were identified including at each stage. The case study of Escherichia coli O157, having 433 annotated pathways, shows that 378 pathways interact directly or indirectly with 41 core pathways while 14 pathways are noninteracting. These 378 pathways are distributed in Stage II (289), Stage III (75), Stage IV (13) and Stage V (1) category. The approach discussed here allows understanding of the complexity of metabolic networks. It has pointed out that core pathways could be most ancient pathways and compounds that interact with maximum pathways may be compounds with high biosynthetic potential, which can be easily identified. Further, it was shown that interactions of pathways at various stages could be one to one, one to many, many to one or many to many mappings through interacting compounds. The granularity of the method discussed being high; the impact of perturbation in a pathway on the metabolome and particularly sub networks can be studied precisely. The categorizations of metabolic pathways help in identifying choke point enzymes that are useful to identify probable drug targets. The Metabolic categorizations for 94 bacteria are available at http://115.111.37.202/mpe/.     

PathAligner

MOTIVATION: Analysis of metabolic pathways is a central topic in understanding the relationship between genotype and phenotype. The rapid accumulation of biological data provides the possibility of studying metabolic pathways at both the genomic and the metabolic levels. Retrieving metabolic pathways from current biological data sources, reconstructing metabolic pathways from rudimentary pathway components, and aligning metabolic pathways with each other are major tasks. Our motivation was to develop a conceptual framework and computational system that allows the retrieval of metabolic pathway information and the processing of alignments to reveal the similarities between metabolic pathways. RESULTS: PathAligner extracts metabolic information from biological databases via the Internet and builds metabolic pathways with data sources of genes, sequences, enzymes, metabolites etc. It provides an easy-to-use interface to retrieve, display and manipulate metabolic information. PathAligner also provides an alignment method to compare the similarity between metabolic pathways. AVAILABILITY: PathAligner is available at http://bibiserv.techfak.uni-bielefeld.de/pathaligner.     

Metabolomics in agriculture

Metabolome refers to the complete set of metabolites synthesized through a series of multiple enzymatic steps from various biochemical pathways processing the information encrypted in the plant genome. Knowledge about synthesis and regulation of various plant metabolic substances has improved substantially with availability of Omics data originating from sequencing of plant genomes. Metabolic profiling of crops is increasingly becoming popular in assessing plant phenotypes and genetic diversity. Metabolic compositional changes vividly reflect the changes occurring during plant growth, development, and in response to stress. Hence, study of plant metabolic pathways, the interconnections between them in context of systems biology is increasingly becoming popular in identification of candidate genes. The present article reviews recent developments in analysis of plant metabolomics, available bioinformatics techniques and databases employed for comparative pathway analysis, metabolic QTLs, and their application in plants.     

Regulation of respiration in plants: a role for alternative metabolic pathways

Respiratory metabolism includes the reactions of glycolysis, the tricarboxylic acid cycle and the mitochondrial electron transport chain, but is also directly linked with many other metabolic pathways such as protein and lipid biosynthesis and photosynthesis via photorespiration. Furthermore, any change in respiratory activity can impact the redox status of the cell and the production of reactive oxygen species. In this review, it is discussed how respiration is regulated and what alternative pathways are known that increase the metabolic flexibility of this vital metabolic process. By looking at the adaptive responses of respiration to hypoxia or changes in the oxygen availability of a cell, the integration of regulatory responses of various pathways is illustrated.     

Integrating gene expression and metabolic profiles

Recent advances in high throughput technologies have generated an abundance of biological information, such as gene expression, protein-protein interaction, and metabolic data. These various types of data capture different aspects of the cellular response to environmental factors. Integrating data from different measurements enhances the ability of modeling frameworks to predict cellular function more accurately and can lead to a more coherent reconstruction of the underlying regulatory network structure. Different techniques, newly developed and borrowed, have been applied for the purpose of extracting this information from experimental data. In this study, we developed a framework to integrate metabolic and gene expression profiles for a hepatocellular system. Specifically, we applied genetic algorithm and partial least square analysis to identify important genes relevant to a specific cellular function. We identified genes 1) whose expression levels quantitatively predict a metabolic function and 2) that play a part in regulating a hepatocellular function and reconstructed their role in the metabolic network. The framework 1) preprocesses the gene expression data using statistical techniques, 2) selects genes using a genetic algorithm and couples them to a partial least squares analysis to predict cellular function, and 3) reconstructs, with the assistance of a literature search, the pathways that regulate cellular function, namely intracellular triglyceride and urea synthesis. This provides a framework for identifying cellular pathways that are active as a function of the environment and in turn helps to uncover the interplay between gene and metabolic networks.     

Leveraging algal omics to reveal potential targets for augmenting TAG accumulation

Ongoing global efforts to commercialize microalgal biofuels have expedited the use of multi-omics techniques to gain insights into lipid biosynthetic pathways. Functional genomics analyses have recently been employed to complement existing sequence-level omics studies, shedding light on the dynamics of lipid synthesis and its interplay with other cellular metabolic pathways, thus revealing possible targets for metabolic engineering. Here, we review the current status of algal omics studies to reveal potential targets to augment TAG accumulation in various microalgae. This review specifically aims to examine and catalog systems level data related to stress-induced TAG accumulation in oleaginous microalgae and inform future metabolic engineering strategies to develop strains with enhanced bioproductivity, which could pave a path for sustainable green energy.     

Simple and fast alignment of metabolic pathways by exploiting local diversity

MOTIVATION: An important tool for analyzing biological networks is the ability to perform homology searches, i.e. given a pattern network one would like to be able to search for occurrences of similar (sub)networks within a set of host networks. In the context of metabolic pathways, Pinter et al. [Bioinformatics, 2005] proposed to solve this computationally hard problem by restricting it to the case where both the pattern and host networks are trees. This restriction, however, severely limits the applicability of their algorithm. RESULTS: We propose a very fast and simple algorithm for the alignment of metabolic pathways that does not restrict the topology of the host or pattern network in any way; instead, our algorithm exploits a natural property of metabolic networks that we call 'local diversity property'. Experiments on a test bed of metabolic pathways from the BioCyc database indicate that our algorithm is much faster than the restricted algorithm of Pinter et al.-the metabolic pathways of two organisms can be aligned in mere seconds-and yet has a wider range of applicability and yields new biological insights. Our ideas can likely be extended to work for the alignment of various types of biological networks other than metabolic pathways. AVAILABILITY: Our algorithm has been implemented in C++ as a user-friendly metabolic pathway alignment tool called METAPAT. The tool runs under Linux or Windows and can be downloaded at http://theinf1.informatik.uni-jena.de/metapat/     

WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction

The WIT (What Is There) (http://wit.mcs.anl.gov/WIT2/) system has been designed to support comparative analysis of sequenced genomes and to generate metabolic reconstructions based on chromosomal sequences and metabolic modules from the EMP/MPW family of databases. This system contains data derived from about 40 completed or nearly completed genomes. Sequence homologies, various ORF-clustering algorithms, relative gene positions on the chromosome and placement of gene products in metabolic pathways (metabolic reconstruction) can be used for the assignment of gene functions and for development of overviews of genomes within WIT. The integration of a large number of phylogenetically diverse genomes in WIT facilitates the understanding of the physiology of different organisms.     

Microbial degradation of halogenated aromatics: molecular mechanisms and enzymatic reactions

Halogenated aromatics are used widely in various industrial, agricultural and household applications. However, due to their stability, most of these compounds persist for a long time, leading to accumulation in the environment. Biological degradation of halogenated aromatics provides sustainable, low-cost and environmentally friendly technologies for removing these toxicants from the environment. This minireview discusses the molecular mechanisms of the enzymatic reactions for degrading halogenated aromatics which naturally occur in various microorganisms. In general, the biodegradation process (especially for aerobic degradation) can be divided into three main steps: upper, middle and lower metabolic pathways which successively convert the toxic halogenated aromatics to common metabolites in cells. The most difficult step in the degradation of halogenated aromatics is the dehalogenation step in the middle pathway. Although a variety of enzymes are involved in the degradation of halogenated aromatics, these various pathways all share the common feature of eventually generating metabolites for utilizing in the energy-producing metabolic pathways in cells. An in-depth understanding of how microbes employ various enzymes in biodegradation can lead to the development of new biotechnologies via enzyme/cell/metabolic engineering or synthetic biology for sustainable biodegradation processes.     

Metabolic pathway analysis web service (Pathway Hunter Tool at CUBIC)

MOTIVATION: Pathway Hunter Tool (PHT), is a fast, robust and user-friendly tool to analyse the shortest paths in metabolic pathways. The user can perform shortest path analysis for one or more organisms or can build virtual organisms (networks) using enzymes. Using PHT, the user can also calculate the average shortest path (Jungnickel, 2002 Graphs, Network and Algorithm. Springer-Verlag, Berlin), average alternate path and the top 10 hubs in the metabolic network. The comparative study of metabolic connectivity and observing the cross talk between metabolic pathways among various sequenced genomes is possible. RESULTS: A new algorithm for finding the biochemically valid connectivity between metabolites in a metabolic network was developed and implemented. A predefined manual assignment of side metabolites (like ATP, ADP, water, CO(2) etc.) and main metabolites is not necessary as the new concept uses chemical structure information (global and local similarity) between metabolites for identification of the shortest path.     

Error analysis of metabolic-rate measurements in mammalian-cell culture by carbon and nitrogen balances

The analysis of metabolic fluxes of large stoichiometric systems is sensitive to measurement errors in metabolic uptake and production rates. It is therefore desirable to independently test the consistency of measurement data, which is possible if at least two elemental balances can be closed. For mammalian-cell culture, closing the C balance has been hampered by problems in measuring the carbon-dioxide production rate. Here, it is shown for various sets of measurement data that the C balance can be closed by applying a method to correct for the bicarbonate buffer in the culture medium. The measurement data are subsequently subject to measurement-error analysis on the basis of the C and N balances. It is shown at 90% reliability that no gross measurement errors are present, neither in the measured production- and consumption rates, nor in the estimated in- and outgoing metabolic rates of te subnetwork, that contains the glycolysis, the pentose-phosphate, and the glutaminolysis pathways. This revised version was published online in July 2006 with corrections to the Cover Date.     

Metabolic effects of antidiabetic drugs on adipocytes and adipokine expression

Several classes of antidiabetic agents have been developed that achieve their hypoglycemic outcomes via various molecular mechanisms. Adipose tissue is a major metabolic and energy-storing tissue and plays an important role in many metabolic pathways, including insulin signaling and insulin sensitivity. Adipose tissue monitors and regulates whole body homeostasis via production and release of potent proteins, such as adipokine and adiponectin, into the circulation. Therefore, any agent that can modulate adipocyte metabolism can, in turn, affect metabolic and glucose homeostatic pathways. Antidiabetic drugs are not only recognized primarily as hypoglycemic agents but may also alter adipose tissue itself, as well as adipocyte-derived adipokine expression and secretion. In the current review, we present the major evidence concerning routinely used antidiabetic agents on adipocyte metabolism and adipokine expression.     

A survey of carbon fixation pathways through a quantitative lens

While the reductive pentose phosphate cycle is responsible for the fixation of most of the carbon in the biosphere, it has several natural substitutes. In fact, due to the characterization of three new carbon fixation pathways in the last decade, the diversity of known metabolic solutions for autotrophic growth has doubled. In this review, the different pathways are analysed and compared according to various criteria, trying to connect each of the different metabolic alternatives to suitable environments or metabolic goals. The different roles of carbon fixation are discussed; in addition to sustaining autotrophic growth it can also be used for energy conservation and as an electron sink for the recycling of reduced electron carriers. Our main focus in this review is on thermodynamic and kinetic aspects, including thermodynamically challenging reactions, the ATP requirement of each pathway, energetic constraints on carbon fixation, and factors that are expected to limit the rate of the pathways. Finally, possible metabolic structures of yet unknown carbon fixation pathways are suggested and discussed.     

Finding an “Achilles’ heel” of cancer: The role of glucose and glutamine metabolism in the survival of transformed cells

Multiple lines of evidence indicate that the process of tumorigenesis is often associated with altered metabolism of two major nutrients, glucose and glutamine. These two nutrients are engaged in multiple metabolic pathways that can be required for cell viability. The roles of glucose and glutamine in the survival of transformed cells both in vitro and in vivo have been separately evaluated in various cell systems, and glucose as the major cellular energy source has received most of the attention. At the same time, data suggests that the inclusion of glucose and glutamine into specific metabolic pathways and cellular sensitivity to the availability of either of these nutrients depends on the cell origin and the combination and nature of transforming events. Exploiting cell metabolism to develop selective cancer therapeutics requires consideration of these factors and evaluation of the requirement of glucose and glutamine metabolism for survival of different transformed cells. Here we discuss possible molecular mechanisms underlying oncogene-induced sensitivity to deprivation of these nutrients.