Sugars as signal molecules in plant seed development.

Higher plants as sessile organisms react very flexible to environmental changes and stresses and use metabolites like glucose, sucrose and nitrate not only as nutrients but also as signals as part of their life strategies. The role of metabolites as signal molecules has attracted considerable interest during recent years. Data reviewed here for developing plant seeds suggest a trigger function of especially sugars also in development in that metabolic regulatory control can override developmental regulation, i.e., the developmental programme only continues normally if a certain metabolic state is sensed at a given time point in a given cell or tissue. Several experimental strategies have provided mainly correlative evidence that certain sugar levels and/or the resulting changes in osmotic values are necessary within defined tissues or cells to maintain a distinct stage of differentiation or to proceed with the developmental programme. In young legume seeds, but certainly also in other tissues, a high hexose (probably mainly glucose) level seems to maintain the capacity of cells to divide whereas - later in seed development - a certain sucrose level is necessary to induce storage-associated cell differentiation. A major determinant of embryo hexose levels in young legume seeds is an apoplastic invertase preferentially expressed in the inner cell layers of the seed coat. The enzyme cleaves the incoming photoassimilate sucrose into glucose and fructose. During development the tissue harbouring the invertase is degraded in a very specific spatial and temporal pattern as part of the developmental programme and is thus creating steep glucose gradients within the cotyledons. These gradients can be measured at nearly cellular resolution and were found to be correlated positively with cell division rate and negatively with cell differentiation and storage activities. A hexose and a sucrose transporter accumulating only in the epidermal cell layer of the cotyledons seem to be essential in creating and maintaining these gradients. To gain further insights into the role of metabolites, especially sugars, as triggers of developmental processes we foremost have to identify receptor molecules already characterised in yeast, and to describe and understand the signal transduction networks involved.     

Predictive macroscopic modeling of cell growth,metabolism and monoclonal antibody production: Case study of a CHO fed-batch production

We describe a systematic approach to establish predictive models of CHO cell growth, cell metabolism and monoclonal antibody (mAb) formation during biopharmaceutical production. The prediction is based on a combination of an empirical metabolic model connecting extracellular metabolic fluxes with cellular growth and product formation with mixed Monod-inhibition type kinetics that we generalized to every possible external metabolite. We describe the maximum specific growth rate as a function of the integral viable cell density (IVCD). Moreover, we also take into account the accumulation of metabolites in intracellular pools that can influence cell growth. This is possible even without identification and quantification of these metabolites as illustrated with fed-batch cultures of Chinese Hamster Ovary (CHO) cells producing a mAb. The impact of cysteine and tryptophan on cell growth and cell productivity was assessed, and the resulting macroscopic model was successfully used to predict the impact of new, untested feeding strategies on cell growth and mAb production. This model combining piecewise linear relationships between metabolic rates, growth rate and production rate together with Monod-inhibition type models for cell growth did well in predicting cell culture performance in fed-batch cultures even outside the range of experimental data used for establishing the model. It could therefore also successfully be applied for in silico prediction of optimal operating conditions.     

Métabolomique et imagerie TEP-FDG des cancers du sein

Cancer metabolism is an essential aspect of tumorogenesis, as cancer cells have increased energy requirements in comparison to normal cells. Metabolomic techniques can provide quantitative data for a large number of small molecules in tissues and enable the analysis of multiple intricate metabolic pathways. Positron emission tomography (PET) using ¹⁸F-Fluorodeoxyglucose (FDG) enables in vivo analysis of glycolysis and is widely used in oncology. High tumor FDG uptake is a prognostic factor in breast cancer and has been associated with tumor aggressively. Seventy breast cancer samples obtained from untreated patients who had underwent FDG-PET imagery were analyzed through an untargeted metabolomic approach using liquid chromatography-mass spectroscopy (LC-MS) to study possible correlations between metabolomic data and FDG uptake. Tumors were split into two groups depending on avidity for FDG as measured with PET. The Compound Discoverer 4.0 software enabled identification of 854 metabolites. PLSDA based models predicted FDG uptake with an accuracy ranging from 0,73 to 0,77. Selected metabolites varied depending on the use of scaling or log transformation. Metabolites correlated with tumor FDG uptake were, among others, glutathione, amino-acids such as glutamate, proline or tyrosine, L-acetyl-carnitine, metabolites from the kynurenine pathway such as L-kynurenine or formyl-kynurenine and polyamines such as N1,N12-diacetylspermine or N1-acetylspermine. These metabolites have been previously shown to reflect cancer aggressivity. The correlation between the glycolytic pathway activation and tumor FDG uptake could not be directly assessed but indirect signs showed a higher glycolytic activity in tumours presenting a higher FDG uptake. Studying new metabolites identified through this process could enable a better understanding of tumor metabolism and identification of new biomarkers.     

VisANT: an online visualization and analysis tool for biological interaction data

Background  

New techniques for determining relationships between biomolecules of all types – genes, proteins, noncoding DNA, metabolites and small molecules – are now making a substantial contribution to the widely discussed explosion of facts about the cell. The data generated by these techniques promote a picture of the cell as an interconnected information network, with molecular components linked with one another in topologies that can encode and represent many features of cellular function. This networked view of biology brings the potential for systematic understanding of living molecular systems.     

An ultra-fast metabolite prediction algorithm

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy.     

Hub and switches: endocannabinoid signalling in midbrain dopamine neurons

The last decade has provided a wealth of experimental data on the role played by lipids belonging to the endocannabinoid family in several facets of physiopathology of dopamine neurons. We currently suggest that these molecules, being intimately connected with diverse metabolic and signalling pathways, might differently affect various functions of dopamine neurons through activation not only of surface receptors, but also of nuclear receptors. It is now emerging how dopamine neurons can regulate their constituent biomolecules to compensate for changes in either internal functions or external conditions. Consequently, dopamine neurons use these lipid molecules as metabolic and homeostatic signal detectors, which can dynamically impact cell function and fitness. Because dysfunctions of the dopamine system underlie diverse neuropsychiatric disorders, including schizophrenia and drug addiction, the importance of better understanding the correlation between an unbalanced endocannabinoid signal and the dopamine system is even greater. Particularly, because dopamine neurons are critical in controlling incentive-motivated behaviours, the involvement of endocannabinoid molecules in fine-tuning dopamine cell activity opened new avenues in both understanding and treating drug addiction. Here, we review recent advances that have shed new light on the understanding of differential roles of endocannabinoids and their cognate molecules in the regulation of the reward circuit, and discuss their anti-addicting properties, particularly with a focus on their potential engagement in the prevention of relapse.     

The significance of amino acids and amino acid-derived molecules in plant responses and adaptation to heavy metal stress

Plants exposed to heavy metals accumulate an array of metabolites, some to high millimolar concentrations. This review deals with N-containing metabolites frequently preferentially synthesized under heavy metal stress such as Cd, Cu, Ni, and Zn. Special focus is given to proline, but certain other amino acids and oligopeptides, as well as betaine, polyamines, and nicotianamine are also addressed. Particularly for proline a large body of data suggests significant beneficial functions under metal stress. In general, the molecules have three major functions, namely metal binding, antioxidant defence, and signalling. Strong correlative and mechanistic experimental evidence, including work with transgenic plants and algae, has been provided that indicates the involvement of metal-induced proline in metal stress defence. Histidine, other amino acids and particularly phytochelatins and glutathione play a role in metal binding, while polyamines function as signalling molecules and antioxidants. Their accumulation needs to be considered as active response and not as consequence of metabolic dys-regulation.     

Aggregation risk prediction for antibodies and its application to biotherapeutic development

Aggregation is a common problem affecting biopharmaceutical development that can have a significant effect on the quality of the product, as well as the safety to patients, particularly because of the increased risk of immune reactions. Here, we describe a new high-throughput screening algorithm developed to classify antibody molecules based on their propensity to aggregate. The tool, constructed and validated on experimental aggregation data for over 500 antibodies, is able to discern molecules with a high aggregation propensity as defined by experimental criteria relevant to bioprocessing and manufacturing of these molecules. Furthermore, we show how this tool can be combined with other computational approaches during early drug development to select molecules with reduced risk of aggregation and optimal developability properties.     

Aggregation risk prediction for antibodies and its application to biotherapeutic development

Aggregation is a common problem affecting biopharmaceutical development that can have a significant effect on the quality of the product, as well as the safety to patients, particularly because of the increased risk of immune reactions. Here, we describe a new high-throughput screening algorithm developed to classify antibody molecules based on their propensity to aggregate. The tool, constructed and validated on experimental aggregation data for over 500 antibodies, is able to discern molecules with a high aggregation propensity as defined by experimental criteria relevant to bioprocessing and manufacturing of these molecules. Furthermore, we show how this tool can be combined with other computational approaches during early drug development to select molecules with reduced risk of aggregation and optimal developability properties.     

Rat C6 glioma as experimental model system for the study of glioblastoma growth and invasion

Infiltration of the central nervous system by neoplastic cells in patients with glioblastoma multiforme (GBM) leads to neurological dysfunction and eventually to death. The elucidation of the mechanisms underlying the aggressive nature of GBM aims at improving radio-, chemo- and gene therapy. This review is focused on the use of rat C6 glioma as an experimental model system for GBM and provides an overview of the experimental data published in the literature using this cell line in elucidating the mechanism of tumor growth, angiogenesis and invasion, and in the design and evaluation of anticancer therapies. Understanding the stages of malignant brain tumor progression requires a series of experimental approaches with a varying degree of complexity. Implantation of malignant cells into animal brain tissue closely resembles in vivo tumor growth and has the advantage over simplified models that inflammatory and vascular mechanisms are activated. However, the complexity of these models makes it difficult to identify the individual processes involved in sustained tumor growth, angiogenesis and invasion. In cell culture models, the effect of growth factors, extracellular matrix components, proteases and adhesion molecules can be investigated. The secretion of tumor-derived factors into the medium can also be analyzed when simplified models are used. This review is a compilation of experimental data focused on the characterization of tumor-related processes and on the evaluation of new therapies for the treatment of malignant glial neoplasms using rat C6 glioma as a model system.     

Toxicology and genetic toxicology in the new era of "toxicogenomics": impact of "-omics" technologies.

The unprecedented advances in molecular biology during the last two decades have resulted in a dramatic increase in knowledge about gene structure and function, an immense database of genetic sequence information, and an impressive set of efficient new technologies for monitoring genetic sequences, genetic variation, and global functional gene expression. These advances have led to a new sub-discipline of toxicology: "toxicogenomics". We define toxicogenomics as "the study of the relationship between the structure and activity of the genome (the cellular complement of genes) and the adverse biological effects of exogenous agents". This broad definition encompasses most of the variations in the current usage of this term, and in its broadest sense includes studies of the cellular products controlled by the genome (messenger RNAs, proteins, metabolites, etc.). The new "global" methods of measuring families of cellular molecules, such as RNA, proteins, and intermediary metabolites have been termed "-omic" technologies, based on their ability to characterize all, or most, members of a family of molecules in a single analysis. With these new tools, we can now obtain complete assessments of the functional activity of biochemical pathways, and of the structural genetic (sequence) differences among individuals and species, that were previously unattainable. These powerful new methods of high-throughput and multi-endpoint analysis include gene expression arrays that will soon permit the simultaneous measurement of the expression of all human genes on a single "chip". Likewise, there are powerful new methods for protein analysis (proteomics: the study of the complement of proteins in the cell) and for analysis of cellular small molecules (metabonomics: the study of the cellular metabolites formed and degraded under genetic control). This will likely be extended in the near future to other important classes of biomolecules such as lipids, carbohydrates, etc. These assays provide a general capability for global assessment of many classes of cellular molecules, providing new approaches to assessing functional cellular alterations. These new methods have already facilitated significant advances in our understanding of the molecular responses to cell and tissue damage, and of perturbations in functional cellular systems.As a result of this rapidly changing scientific environment, regulatory and industrial toxicology practice is poised to undergo dramatic change during the next decade. These advances present exciting opportunities for improved methods of identifying and evaluating potential human and environmental toxicants, and of monitoring the effects of exposures to these toxicants. These advances also present distinct challenges. For example, the significance of specific changes and the performance characteristics of new methods must be fully understood to avoid misinterpretation of data that could lead to inappropriate conclusions about the toxicity of a chemical or a mechanism of action. We discuss the likely impact of these advances on the fields of general and genetic toxicology, and risk assessment. We anticipate that these new technologies will (1) lead to new families of biomarkers that permit characterization and efficient monitoring of cellular perturbations, (2) provide an increased understanding of the influence of genetic variation on toxicological outcomes, and (3) allow definition of environmental causes of genetic alterations and their relationship to human disease. The broad application of these new approaches will likely erase the current distinctions among the fields of toxicology, pathology, genetic toxicology, and molecular genetics. Instead, a new integrated approach will likely emerge that involves a comprehensive understanding of genetic control of cellular functions, and of cellular responses to alterations in normal molecular structure and function.     

A simple molecular mathematical model of mammalian hibernation

A simple model of the dynamics of the body temperature of a hibernating mammal is presented. Our model provides a good match to experimental data, showing the interruption of low-temperature torpor bouts with periodic interbout arousals (IBAs). In this paper we present a mathematical model of the molecules that participate in the initiation, regulation, and maintenance of the hibernating state. This model can be used to describe the role of regulatory molecules, signal transducers, downstream target enzymes, structural proteins, or metabolites. Because many of the biochemical mechanisms are unknown, this is a preliminary and largely phenomenological model that we hope will inspire further investigation.     

Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks

The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. Constraint based methods, such as Flux Balance Analysis (FBA) and Thermodynamics-based flux analysis (TFA), are commonly used to estimate the flow of metabolites through genome-wide metabolic networks, making it possible to identify the ranges of flux values that are consistent with the studied physiological and thermodynamic conditions. However, unless key intracellular fluxes and metabolite concentrations are known, constraint-based models lead to underdetermined problem formulations. This lack of information propagates as uncertainty in the estimation of fluxes and basic reaction properties such as the determination of reaction directionalities. Therefore, knowledge of which metabolites, if measured, would contribute the most to reducing this uncertainty can significantly improve our ability to define the internal state of the cell. In the present work we combine constraint based modeling, Design of Experiments (DoE) and Global Sensitivity Analysis (GSA) into the Thermodynamics-based Metabolite Sensitivity Analysis (TMSA) method. TMSA ranks metabolites comprising a metabolic network based on their ability to constrain the gamut of possible solutions to a limited, thermodynamically consistent set of internal states. TMSA is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements.     

Misidentification of prostamides as prostaglandins

Prostaglandins and endogenous cannabinoid metabolites share the same lipid backbone with differing polar head groups at exactly the position through which a large molecule is attached to provide antigenicity and thus raise antisera. Hence, we hypothesized that antisera raised against prostaglandins linked to a large molecule such as BSA at the carboxyl functional group would also recognize endogenous cannabinoid metabolites and lead to highly misleading interpretations of data. We found major cross-reactivity of commercial antisera raised to prostaglandins with endocannabinoid metabolites. Furthermore, in a well-characterized cell line (WISH) or primary amnion tissue explants, endocannabinoid treatment led to increased production of endocannabinoid metabolites as opposed to primary prostaglandins. This was apparent only after separation of products by thin-layer chromatography, because they measured as prostaglandins by radioimmunoassay. These findings have major implications for our interpretation of data in situations in which these prostaglandin-like molecules are formed, and they stress the need for chromatographic or spectrometric confirmation of prostaglandin production detected by antibody-based methods.     

Secondary metabolites as endogenous effectors of microbial cytodifferentiation

The present survey covers the regulatory role of microbial secondary metabolites and related compounds as endogenous signals of cell differentiation of the producing organisms. Several antibiotics have been shown to exert autoregulatory effects on differentiation-associated functions. The mechanisms of self-protection of the producing cells against the autotoxicity of secondary metabolites are discussed in terms of an integral part of the modulation of signal strength. As a further topic, the review deals with the hormone-like interference of particular metabolites with differentiating cells. Conclusive discussion concerns the potential use of microbial signal molecules either as tools for directed manipulations of product syntheses or as pharmaceutics.     

Negative mode nanostructure-initiator mass spectrometry for detection of phosphorylated metabolites

The chemical complexity of the metabolome requires the development of new detection methods to enlarge the range of compounds detectable in a biological sample. Recently, a novel matrix-free laser desorption/ionization method called nanostructure-initiator mass spectrometry (NIMS) [Northen et al., Nature 449(7165):1033–1036, 2007] was reported. Here we investigate NIMS in negative ion mode for the detection of endogenous metabolites, namely small phosphorylated molecules. 3-Aminopropyldimethylethoxysilane was found to be suitable as initiator for the analytes studied and a limit of detection in the tens of femtomoles was reached. The detection of different endogenous cell metabolites in a yeast cell extract is demonstrated.     

BioMagResBank (BMRB) as a partner in the Worldwide Protein Data Bank (wwPDB): new policies affecting biomolecular NMR depositions

We describe the role of the BioMagResBank (BMRB) within the Worldwide Protein Data Bank (wwPDB) and recent policies affecting the deposition of biomolecular NMR data. All PDB depositions of structures based on NMR data must now be accompanied by experimental restraints. A scheme has been devised that allows depositors to specify a representative structure and to define residues within that structure found experimentally to be largely unstructured. The BMRB now accepts coordinate sets representing three-dimensional structural models based on experimental NMR data of molecules of biological interest that fall outside the guidelines of the Protein Data Bank (i.e., the molecule is a peptide with 23 or fewer residues, a polynucleotide with 3 or fewer residues, a polysaccharide with 3 or fewer sugar residues, or a natural product), provided that the coordinates are accompanied by representation of the covalent structure of the molecule (atom connectivity), assigned NMR chemical shifts, and the structural restraints used in generating model. The BMRB now contains an archive of NMR data for metabolites and other small molecules found in biological systems.     

Network-based functional modeling of genomics, transcriptomics and metabolism in bacteria

Molecular entities present in a cell (mRNA, proteins, metabolites,…) do not act in isolation, but rather in cooperation with each other to define an organisms form and function. Their concerted action can be viewed as networks of interacting entities that are active under certain conditions within the cell or upon certain environmental signals. A main challenge in systems biology is to model these networks, or in other words studying which entities interact to form cellular systems or accomplish similar functions. On the contrary, viewing a single entity or an experimental dataset in the light of an interaction network can reveal previous unknown insights in biological processes. In this review we give an overview of how integrated networks can be reconstructed from multiple omics data and how they can subsequently be used for network-based modeling of cellular function in bacteria.     

Stable, resealable pores formed in sea urchin eggs by electric discharge (electroporation) permit substrate loading for assay of enzymes in vivo.

We describe a simple electroporation procedure for loading suspensions of unfertilized sea urchin eggs with impermeant small molecules under conditions that allow close to 90% successful fertilization and development. Poration is carried out in a low-Ca2+ medium that mimicks the intracellular milieu. The induced pores remain open for several minutes in this medium, allowing loading of the cells; resealing is achieved by adding back millimolar calcium ions to the medium. While the pores are open, an influx of exogenous molecules and efflux of endogenous metabolites takes place, and the eggs can lose up to 40% of their ATP content and still survive. Introduced metabolites are utilized by the cells, e.g., introduced 3H-thymidine is incorporated into DNA. This procedure will be useful for loading impermeant substrates into eggs, permitting in vivo assessment of metabolism, and also for introducing other interesting impermeant molecules, such as inhibitors, fluorescent indicators, etc. Though the details may differ, the principle of electroporation in an intracellular-like medium may prove to be useful for loading other cell types with minimal loss of viability.