Protein Acetylation as an Integral Part of Metabolism in Cancer Development and Progression

Acetylation of lysine is one of the major post-translational modifications of histone and non-histone proteins of eukaryotic cells. Acetylation has been indicated as an avenue for cellular response to environmental, nutritional and behavioral factors. At the same time, aberrant protein acetylation has been related to cancer as well as many other diseases. Abnormal expression of some classes of histone deacetylases and histone acetyl transferases has been documented for the majority of cancers. These observations have led to extensive efforts in the development of inhibitors for these enzymes for the treatment of cancer as well as other diseases as well as pathogen control.Regulation of protein activities and gene expression by acetylation influences many processes relevant for cancer development, including metabolism. At the same time acetylation depends on a number of metabolic co-factors and a variety of metabolites act as inhibitors of acetylation proteins making acetylation enzymes an integral part of metabolism. Cancer metabolic phenotype is generally understood as one of the major hallmarks of cancer and thus the interplay between acetylation, anabolism and catabolism provides a very interesting forum for exploration of cancer development and for novel treatments. An ever increasing pool of publications shows relationships between the acetylation process and related enzymes with metabolites in cancerous and non-cancerous systems. In this review we are presenting previously established relationships between acetylation/deacetylation, metabolites and enzyme regulation particularly in relation to cancer development, progression and treatment

Effects of Atmospheric CO2 Level on the Metabolic Response of Resistant and Susceptible Wheat to Fusarium graminearum Infection.

Rising atmospheric CO2 concentrations and associated climate changes are thought to have contributed to the steady increase of Fusarium head blight (FHB) on wheat. However, our understanding of precisely how elevated CO2 influences the defense response of wheat against Fusarium graminearum remains limited. In this study, we evaluated the metabolic profiles of susceptible (Norm) and moderately resistant (Alsen) spring wheat in response to whole-head inoculation with two deoxynivalenol (DON)-producing F. graminearum isolates (DON+), isolates 9F1 and Gz3639, and a DON-deficient (DON−) isolate (Gzt40) at ambient (400 ppm) and elevated (800 ppm) CO2 concentrations. The effects of elevated CO2 were dependent on both the Fusarium strain and the wheat variety, but metabolic differences in the host can explain the observed changes in F. graminearum biomass and DON accumulation. The complexity of abiotic and biotic stress interactions makes it difficult to determine if the observed metabolic changes in wheat are a result of CO2-induced changes in the host, the pathogen, or a combination of both. However, the effects of elevated CO2 were not dependent on DON production. Finally, we identified several metabolic biomarkers for wheat that can reliably predict FHB resistance or susceptibility, even as atmospheric CO2 levels rise

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies

THE IMPORTANCE OF INHIBITORS FOR THE SIMULATION OF METABOLIC PROCESSES: IN SILICO Zn2+ INHIBITION OF m-ACONITASE FROM ANALYSIS OF GLYCOLYSIS AND KREBS CYCLE KINETIC MODELS

Metal ions have a major effect on the metabolic processes in cells either as inhibitors or as integral components of enzymes. The inhibition of enzymes can take place either through the inhibition of gene expression or through inhibition of protein function. A particularly interesting example of the effect of a metal ion on metabolism is the observed inhibition of Krebs cycle and alteration of energy metabolism by zinc (II) cations. In this particular case metal ion inhibition of enzyme is linked to one of the major functions of prostate cells of accumulation and excretion of citrate. Experimental results have shown that increase in concentration of zinc (II) in prostate cells effectively blocks progression of a part of the Krebs cycle leading to change in the concentration of several metabolites with largest effect in the accumulation of citrate. Based on previously reported experimental results, several distinct mechanisms for zinc (II) inhibition of Krebs cycle were proposed. In order to determine the precise mechanism of inhibition in this system, a mathematical model of glycolysis and Krebs cycle was constructed. Three different types of inhibition were analyzed, including competitive and uncompetitive inhibition as well as inhibition through the alteration of the expression level of m-aconitase. The effects of different inhibition models on metabolite concentrations were investigated as a time course simulation of the system of reactions. Several kinetic parameters in the model were optimized in order to best resemble experimental measurements. The simulation shows that only competitive inhibition leads to an agreement with experimental data.Peer reviewed: YesNRC publication: Ye

Integrated analysis of transcriptomics and metabolomics profiles

An abstract for this publication is not available.Un r\ue9sum\ue9 pour cette publication n'est pas disponible.NRC publication: Ye

Unsupervised Data Analysis Methods for Qualitative and Quantitative Metabolomics and Metabonomics

Peer reviewed: YesNRC publication: Ye

Fuzzy J-Means and VNS Methods for Clustering Genes from Microarray Data *

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Determination of Tumour Marker Genes from Gene Expression Data

Cancer classification has traditionally been based on the morphological study of tumours. However, tumours with similar histological appearances can exhibit different responses to therapy, indicating differences in tumour characteristics on the molecular level. Thus, development of a novel, reliable and precise method for classification of tumours is essential for more successful diagnosis and treatment. The high-throughput gene expression data obtained using microarray technology are currently being investigated for diagnostic applications. However, these large datasets introduce a range of challenges, making data analysis a major part of every experiment for any application, including cancer classification and diagnosis. One of the major concerns in the application of microarrays to tumour diagnostics is the fact that the expression levels of many genes are not measurably affected by carcinogenic changes in the cells. Thus, a crucial step in the application of microarrays to cancer diagnostics is the selection of diagnostic marker genes from the gene expression profiles. These molecular markers give valuable additional information for tumour diagnosis, prognosis and therapy development.Traditionnellement, la classification des cancers a toujours \ue9t\ue9 bas\ue9e sur l'\ue9tude morphologique des tumeurs. Cependant, des tumeurs dont l'aspect histologique est semblable peuvent pr\ue9senter diff\ue9rentes r\ue9ponses \ue0 la th\ue9rapie, ce qui indique qu'il existe des diff\ue9rences dans les caract\ue9ristiques des tumeurs au niveau mol\ue9culaire. Il est donc essentiel de mettre au point une nouvelle m\ue9thode fiable et pr\ue9cise permettant de classifier les tumeurs, afin de pouvoir obtenir des diagnostics et des traitements avec un meilleur taux de succ\ue8s. On \ue9tudie actuellement, pour des applications de diagnostic, les donn\ue9es d'expression g\ue9n\ue9tique \ue0 haut d\ue9bit obtenues au moyen de la technologie des micror\ue9seaux. Cependant, ces gros ensembles de donn\ue9es suscitent toute une panoplie de probl\ue8mes, de sorte que l'analyse des donn\ue9es devient une partie importante de chaque exp\ue9rience dans n'importe quelle application, y compris la classification des cancers et leur diagnostic. Un des principaux probl\ue8mes dans l'application des micror\ue9seaux au diagnostic des tumeurs r\ue9side dans le fait que les niveaux d'expression de nombreux g\ue8nes ne sont pas affect\ue9s de fa\ue7on mesurable par des changements carcinog\ue8nes dans les cellules. Le choix de g\ue8nes marqueurs, \ue0 des fins de diagnostic, dans l'ensemble des profils d'expression g\ue9n\ue9tique constitue donc une \ue9tape essentielle dans l'application des micror\ue9seaux au diagnostic du cancer. Ces marqueurs mol\ue9culaires fournissent des renseignements additionnels pr\ue9cieux pour le diagnostic des tumeurs, le pronostic et la mise au point de moyens th\ue9rapeutiques.NRC publication: Ye

CLUSTERING: UNSUPERVISED LEARNING IN LARGE SCREENING BIOLOGICAL DATA

Peer reviewed: YesNRC publication: Ye

Data analysis of Alternative Splicing Microarrays

The importance of alternative splicing in drug and biomarker discovery is best understood through several example genes. For most genes, the identification, detection and particularly quantification of isoforms in different tissues and conditions remain to be carried out. As a result, the focus in drug and biomarker development is increasingly on high-throughput studies of alternative splicing. Initial strategies for the parallel analysis of alternative splicing by microarrays have been recently published. The design specificities and goals of alternative splicing microarrays, in terms of identification and quantification of multiple mRNAs from one gene, are promoting the development of novel methods of analysis.NRC publication: Ye