Navigating the Human Metabolome for Biomarker Identification and Design of Pharmaceutical Molecules

Metabolomics is a rapidly evolving discipline that involves the systematic study of endogenous small molecules that characterize the metabolic pathways of biological systems. The study of metabolism at a global level has the potential to contribute significantly to biomedical research, clinical medical practice, as well as drug discovery. In this paper, we present the most up-to-date metabolite and metabolic pathway resources, and we summarize the statistical, and machine-learning tools used for the analysis of data from clinical metabolomics. Through specific applications on cancer, diabetes, neurological and other diseases, we demonstrate how these tools can facilitate diagnosis and identification of potential biomarkers for use within disease diagnosis. Additionally, we discuss the increasing importance of the integration of metabolomics data in drug discovery. On a case-study based on the Human Metabolome Database (HMDB) and the Chinese Natural Product Database (CNPD), we demonstrate the close relatedness of the two data sets of compounds, and we further illustrate how structural similarity with human metabolites could assist in the design of novel pharmaceuticals and the elucidation of the molecular mechanisms of medicinal plants

Integrated Text Mining and Chemoinformatics Analysis Associates Diet to Health Benefit at Molecular Level.

Awareness that disease susceptibility is not only dependent on genetic make up, but can be affected by lifestyle decisions, has brought more attention to the role of diet. However, food is often treated as a black box, or the focus is limited to few, well-studied compounds, such as polyphenols, lipids and nutrients. In this work, we applied text mining and Naïve Bayes classification to assemble the knowledge space of food-phytochemical and food-disease associations, where we distinguish between disease prevention/amelioration and disease progression. We subsequently searched for frequently occurring phytochemical-disease pairs and we identified 20,654 phytochemicals from 16,102 plants associated to 1,592 human disease phenotypes. We selected colon cancer as a case study and analyzed our results in three directions; i) one stop legacy knowledge-shop for the effect of food on disease, ii) discovery of novel bioactive compounds with drug-like properties, and iii) discovery of novel health benefits from foods. This works represents a systematized approach to the association of food with health effect, and provides the phytochemical layer of information for nutritional systems biology research

NutriChem: a systems chemical biology resource to explore the medicinal value of plant-based foods

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Exploring mechanisms of diet-colon cancer associations through candidate molecular interaction networks

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Identification of biomarkers for genotyping Aspergilli using non-linear methods for clustering and classification

<p>Abstract</p> <p>Background</p> <p>In the present investigation, we have used an exhaustive metabolite profiling approach to search for biomarkers in recombinant <it>Aspergillus nidulans </it>(mutants that produce the 6- methyl salicylic acid polyketide molecule) for application in metabolic engineering.</p> <p>Results</p> <p>More than 450 metabolites were detected and subsequently used in the analysis. Our approach consists of two analytical steps of the metabolic profiling data, an initial non-linear unsupervised analysis with Self-Organizing Maps (SOM) to identify similarities and differences among the metabolic profiles of the studied strains, followed by a second, supervised analysis for training a classifier based on the selected biomarkers. Our analysis identified seven putative biomarkers that were able to cluster the samples according to their genotype. A Support Vector Machine was subsequently employed to construct a predictive model based on the seven biomarkers, capable of distinguishing correctly 14 out of the 16 samples of the different <it>A. nidulans </it>strains.</p> <p>Conclusion</p> <p>Our study demonstrates that it is possible to use metabolite profiling for the classification of filamentous fungi as well as for the identification of metabolic engineering targets and draws the attention towards the development of a common database for storage of metabolomics data.</p

Multiple nucleophilic elbows leading to multiple active sites in a single module esterase from Sorangium cellulosum

The catalytic residues in carbohydrate esterase enzyme families constitute a highly conserved triad: serine, histidine and aspartic acid. This catalytic triad is generally located in a very sharp turn of the protein backbone structure, called the nucleophilic elbow and identified by the consensus sequence GXSXG. An esterase from Sorangium cellulosum Soce56 that contains five nucleophilic elbows was cloned and expressed in Escherichia coli and the function of each nucleophilic elbowed site was characterized. In order to elucidate the function of each nucleophilic elbow, site directed mutagenesis was used to generate variants with deactivated nucleophilic elbows and the functional promiscuity was analyzed. In silico analysis together with enzymological characterization interestingly showed that each nucleophilic elbow formed a local active site with varied substrate specificities and affinities. To our knowledge, this is the first report presenting the role of multiple nucleophilic elbows in the catalytic promiscuity of an esterase. Further structural analysis at protein unit level indicates the new evolutionary trajectories in emerging promiscuous esterases. NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Structural Biology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Structural Biology, 2015. http://dx.doi.org/10.1016/j.jsb.2015.04.00

Complete Genome Sequence of Escherichia coli Strain WG5

Escherichia colistrain WG5 is a widely used host for phage detection, in-cluding somatic coliphages employed as standard ISO method 10705-1 (2000). Here,we present the complete genome sequence of a commercialE. coliWG5 strain

Yeast Biological Networks Unfold the Interplay of Antioxidants, Genome and Phenotype, and Reveal a Novel Regulator of the Oxidative Stress Response

Background Identifying causative biological networks associated with relevant phenotypes is essential in the field of systems biology. We used ferulic acid (FA) as a model antioxidant to characterize the global expression programs triggered by this small molecule and decipher the transcriptional network controlling the phenotypic adaptation of the yeast Saccharomyces cerevisiae. Methodology/Principal Findings By employing a strict cut off value during gene expression data analysis, 106 genes were found to be involved in the cell response to FA, independent of aerobic or anaerobic conditions. Network analysis of the system guided us to a key target node, the FMP43 protein, that when deleted resulted in marked acceleration of cellular growth (~15% in both minimal and rich media). To extend our findings to human cells and identify proteins that could serve as drug targets, we replaced the yeast FMP43 protein with its human ortholog BRP44 in the genetic background of the yeast strain Δfmp43. The conservation of the two proteins was phenotypically evident, with BRP44 restoring the normal specific growth rate of the wild type. We also applied homology modeling to predict the 3D structure of the FMP43 and BRP44 proteins. The binding sites in the homology models of FMP43 and BRP44 were computationally predicted, and further docking studies were performed using FA as the ligand. The docking studies demonstrated the affinity of FA towards both FMP43 and BRP44. Conclusions This study proposes a hypothesis on the mechanisms yeast employs to respond to antioxidant molecules, while demonstrating how phenome and metabolome yeast data can serve as biomarkers for nutraceutical discovery and development. Additionally, we provide evidence for a putative therapeutic target, revealed by replacing the FMP43 protein with its human ortholog BRP44, a brain protein, and functionally characterizing the relevant mutant strain