Biomarkers of human gastrointestinal tract regions.

Dysregulation of intestinal epithelial cell performance is associated with an array of pathologies whose onset mechanisms are incompletely understood. While whole-genomics approaches have been valuable for studying the molecular basis of several intestinal diseases, a thorough analysis of gene expression along the healthy gastrointestinal tract is still lacking. The aim of this study was to map gene expression in gastrointestinal regions of healthy human adults and to implement a procedure for microarray data analysis that would allow its use as a reference when screening for pathological deviations. We analyzed the gene expression signature of antrum, duodenum, jejunum, ileum, and transverse colon biopsies using a biostatistical method based on a multivariate and univariate approach to identify region-selective genes. One hundred sixty-six genes were found responsible for distinguishing the five regions considered. Nineteen had never been described in the GI tract, including a semaphorin probably implicated in pathogen invasion and six novel genes. Moreover, by crossing these genes with those retrieved from an existing data set of gene expression in the intestine of ulcerative colitis and Crohn's disease patients, we identified genes that might be biomarkers of Crohn's and/or ulcerative colitis in ileum and/or colon. These include CLCA4 and SLC26A2, both implicated in ion transport. This study furnishes the first map of gene expression along the healthy human gastrointestinal tract. Furthermore, the approach implemented here, and validated by retrieving known gene profiles, allowed the identification of promising new leads in both healthy and disease states.

Flavonoids for controlling starch digestion: structural requirements for inhibiting human alpha-amylase.

In this study we investigated the structural requirements for inhibition of human salivary alpha-amylase by flavonoids. Four flavonols and three flavones, out of the 19 flavonoids tested, exhibited IC50 values less than 100 microM against human salivary alpha-amylase activity. Structure-activity relationships of these inhibitors by computational ligand docking showed that the inhibitory activity of flavonols and flavones depends on (i) hydrogen bonds between the hydroxyl groups of the polyphenol ligands and the catalytic residues of the binding site and (ii) formation of a conjugated pi-system that stabilizes the interaction with the active site. Our findings show that certain naturally occurring flavonoids act as inhibitors of human alpha-amylase, which makes them promising candidates for controlling the digestion of starch and postprandial glycemia.

Functional peptides by genome reverse engineering.

Computational biology and chemistry combined with high-throughput analytical technologies contribute to reduce operational costs and foster innovation in every phase of the discovery of bioactive molecules. In order for life science industries to continue to deliver at the required market rate, new concepts need to be implemented in research and development, and new sources of bioactive molecules should be investigated. The genomic revolution provides the necessary information to generate novel bioactive peptides by the computational dissection of genomes.

Similarity networks of protein binding sites.

An increasing attention has been dedicated to the characterization of complex networks within the protein world. This work is reporting how we uncovered networked structures that reflected the structural similarities among protein binding sites. First, a 211 binding sites dataset has been compiled by removing the redundant proteins in the Protein Ligand Database (PLD) (http://www-mitchell.ch.cam.ac.uk/pld/). Using a clique detection algorithm we have performed all-against-all binding site comparisons among the 211 available ones. Within the set of nodes representing each binding site an edge was added whenever a pair of binding sites had a similarity higher than a threshold value. The generated similarity networks revealed that many nodes had few links and only few were highly connected, but due to the limited data available it was not possible to definitively prove a scale-free architecture. Within the same dataset, the binding site similarity networks were compared with the networks of sequence and fold similarity networks. In the protein world, indications were found that structure is better conserved than sequence, but on its own, sequence was better conserved than the subset of functional residues forming the binding site. Because a binding site is strongly linked with protein function, the identification of protein binding site similarity networks could accelerate the functional annotation of newly identified genes. In view of this we have discussed several potential applications of binding site similarity networks, such as the construction of novel binding site classification databases, as well as the implications for protein molecular design in general and computational chemogenomics in particular.

Putting the 'Ome' in lipid metabolism.

The recognition that altered lipid metabolism underlies many metabolic disorders challenging Western society highlights the importance of this metabolomic subset, herein referred to as the lipidome. Although comprehensive lipid analyses are not a recent concept, the novelty of a lipidomic approach lies with the application of robust statistical algorithms to highlight subtle, yet significant, changes in a population of lipid molecules. First-generation lipidomic studies have demonstrated the sensitivity of interpreting quantitative datasets with computational software; however, the innate power of comprehensive lipid profiling is often not exploited, as robust statistical models are not routinely utilized. Therefore, the current review aims to briefly describe the current technologies suitable for comprehensive lipid analysis, outline innovative mathematical models that have the ability to reveal subtle changes in metabolism, which will ameliorate our understanding of lipid biochemistry, and demonstrate the biological revelations found through lipidomic approaches and their potential implications for health management.

An integrative metabolism approach identifies stearoyl-CoA desaturase as a target for an arachidonate-enriched diet.

Epidemiological studies have correlated diets containing higher intakes of PUFA with lower rates of chronic metabolic diseases. The molecular mechanisms regulated by the consumption of PUFA were examined by using an integrative metabolism approach assaying the liver transcriptome and lipid-metabolome of mice fed a control diet, an arachidonate (AA)-enriched fungal oil, an eicosapentaenoic (EPA)/docosahexaenoic (DHA)-enriched fish oil, or a combination of the two oils. Hepatic gene transcription and fatty acid (FA) metabolism were significantly altered by diets enriched with AA, as revealed by global error assessment and singular value decomposition (SVD) analysis, respectively. SVD analysis of the lipid data, reinforced with transcriptomics, suggests that the chronic feeding of AA modulates molecular endpoints similar to those previously reported in the obesity-resistant SCD1-/- mouse, namely, genes involved in lipid oxidation/synthesis and the significant changes in FA metabolism stemming from a repressed SCD1 activity. Specifically, the total levels and FA composition of several phospholipid (PL) species were significantly changed, with phosphatidylcholine (PC) demonstrating the greatest alterations. Reduced PC levels were linked to decreased expression of enzymes in PC biosynthesis (choline kinase, -2.2-fold; glycerol-3-phosphate acyltransferase, -2.0-fold). Alterations in PL-FA composition were related to decreased expression of FA biosynthetic genes [fatty acid synthetase, -3.7-fold; stearoyl-CoA desaturase-1 (SCD1), -1.8-fold]. Lower hepatic SCD1 gene expression levels were reflected in various aspects of FA metabolism through increased concentrations of palmitic (fungal oil, +45%; combination, +106%) and stearic acids (fungal oil, +60%; combination, +63%) in PC. Importantly, an integrated approach showed that these effects were not attenuated by the addition of an EPA/DHA-enriched fish oil, thereby identifying a previously unrecognized and distinct role for AA in the regulation of hepatic lipid metabolism.

Descriptor-based protein remote homology identification.

Here, we report a novel protein sequence descriptor-based remote homology identification method, able to infer fold relationships without the explicit knowledge of structure. In a first phase, we have individually benchmarked 13 different descriptor types in fold identification experiments in a highly diverse set of protein sequences. The relevant descriptors were related to the fold class membership by using simple similarity measures in the descriptor spaces, such as the cosine angle. Our results revealed that the three best-performing sets of descriptors were the sequence-alignment-based descriptor using PSI-BLAST e-values, the descriptors based on the alignment of secondary structural elements (SSEA), and the descriptors based on the occurrence of PROSITE functional motifs. In a second phase, the three top-performing descriptors were combined to obtain a final method with improved performance, which we named DescFold. Class membership was predicted by Support Vector Machine (SVM) learning. In comparison with the individual PSI-BLAST-based descriptor, the rate of remote homology identification increased from 33.7% to 46.3%. We found out that the composite set of descriptors was able to identify the true remote homolog for nearly every sixth sequence at the 95% confidence level, or some 10% more than a single PSI-BLAST search. We have benchmarked the DescFold method against several other state-of-the-art fold recognition algorithms for the 172 LiveBench-8 targets, and we concluded that it was able to add value to the existing techniques by providing a confident hit for at least 10% of the sequences not identifiable by the previously known methods.

Global properties of biological networks.

This article discusses the most recent achievements in understanding the biological implications of the small-world and scale-free global topological properties of genetic, proteomic and metabolic networks. Most importantly, these networks are highly clustered and have small node-to-node distances. With their few very connected nodes, which are statistically unlikely to fail under random conditions, the proper functioning of these systems is maintained under external perturbations.

Exploring the sequence-structure protein landscape in the glycosyltransferase family.

To understand the molecular basis of glycosyltransferases' (GTFs) catalytic mechanism, extensive structural information is required. Here, fold recognition methods were employed to assign 3D protein shapes (folds) to the currently known GTF sequences, available in public databases such as GenBank and Swissprot. First, GTF sequences were retrieved and classified into clusters, based on sequence similarity only. Intracluster sequence similarity was chosen sufficiently high to ensure that the same fold is found within a given cluster. Then, a representative sequence from each cluster was selected to compose a subset of GTF sequences. The members of this reduced set were processed by three different fold recognition methods: 3D-PSSM, FUGUE, and GeneFold. Finally, the results from different fold recognition methods were analyzed and compared to sequence-similarity search methods (i.e., BLAST and PSI-BLAST). It was established that the folds of about 70% of all currently known GTF sequences can be confidently assigned by fold recognition methods, a value which is higher than the fold identification rate based on sequence comparison alone (48% for BLAST and 64% for PSI-BLAST). The identified folds were submitted to 3D clustering, and we found that most of the GTF sequences adopt the typical GTF A or GTF B folds. Our results indicate a lack of evidence that new GTF folds (i.e., folds other than GTF A and B) exist. Based on cases where fold identification was not possible, we suggest several sequences as the most promising targets for a structural genomics initiative focused on the GTF protein family.

Analysis of time course Omics datasets.

Over the past 20 years, Omics technologies emerged as the consensual denomination of holistic molecular profiling. These techniques enable parallel measurements of biological -omes, or "all constituents considered collectively", and utilize the latest advancements in transcriptomics, proteomics, metabolomics, imaging, and bioinformatics. The technological accomplishments in increasing the sensitivity and throughput of the analytical devices, the standardization of the protocols and the widespread availability of reagents made the capturing of static molecular portraits of biological systems a routine task. The next generation of time course molecular profiling already allows for extensive molecular snapshots to be taken along the trajectory of time evolution of the investigated biological systems. Such datasets provide the basis for application of the inverse scientific approach. It consists in the inference of scientific hypotheses and theories about the structure and dynamics of the investigated biological system without any a priori knowledge, solely relying on data analysis to unveil the underlying patterns. However, most temporal Omics data still contain a limited number of time points, taken over arbitrary time intervals, through measurements on biological processes shifted in time. The analysis of the resulting short and noisy time series data sets is a challenge. Traditional statistical methods for the study of static Omics datasets are of limited relevance and new methods are required. This chapter discusses such algorithms which enable the application of the inverse analysis approach to short Omics time series.

Minimum information about a bioactive entity (MIABE).

Bioactive molecules such as drugs, pesticides and food additives are produced in large numbers by many commercial and academic groups around the world. Enormous quantities of data are generated on the biological properties and quality of these molecules. Access to such data - both on licensed and commercially available compounds, and also on those that fail during development - is crucial for understanding how improved molecules could be developed. For example, computational analysis of aggregated data on molecules that are investigated in drug discovery programmes has led to a greater understanding of the properties of successful drugs. However, the information required to perform these analyses is rarely published, and when it is made available it is often missing crucial data or is in a format that is inappropriate for efficient data-mining. Here, we propose a solution: the definition of reporting guidelines for bioactive entities - the Minimum Information About a Bioactive Entity (MIABE) - which has been developed by representatives of pharmaceutical companies, data resource providers and academic groups.

Integration of structure-activity relationship and artificial intelligence systems to improve in silico prediction of ames test mutagenicity.

The Ames mutagenicity test in Salmonella typhimurium is a bacterial short-term in vitro assay aimed at detecting the mutagenicity caused by chemicals. Mutagenicity is considered as an early alert for carcinogenicity. After a number of decades, several (Q)SAR studies on this endpoint yielded enough evidence to make feasible the construction of reliable computational models for prediction of mutagenicity from the molecular structure of chemicals. In this study, we propose a combination of a fragment-based SAR model and an inductive database. The hybrid system was developed using a collection of 4337 chemicals (2401 mutagens and 1936 nonmutagens) and tested using 753 independent compounds (437 mutagens and 316 nonmutagens). The overall error of this system on the external test set compounds is 15% (sensitivity = 15%, specificity = 15%), which is quantitatively similar to the experimental error of Ames test data (average interlaboratory reproducibility determined by the National Toxicology Program). Moreover, each single prediction is provided with a specific confidence level. The results obtained give confidence that this system can be applied to support early and rapid evaluation of the level of mutagenicity concern.

Interaction of positional isomers of quercetin glucuronides with the transporter ABCC2 (cMOAT, MRP2).

The exporter ABCC2 (cMOAT, MRP2) is a membrane-bound protein on the apical side of enterocytes and hepatic biliary vessels that transports leukotriene C(4), glutathione, some conjugated bile salts, drugs, xenobiotics, and phytonutrients. The latter class includes quercetin, a bioactive flavonoid found in foods such as onions, apples, tea, and wine. There is no available three-dimensional (3D) structure of ABCC2. We have predicted the 3D structure by in silico modeling, showing that 3-[[3-[2-(7-chloroquinolin-2-yl)vinyl]phenyl]-(2-dimethylcarbamoylethylsulfanyl)methylsulfanyl] propionic acid (MK571) binds most tightly to the putative binding site, and then tested the computational prediction experimentally by measuring interaction with all quercetin monoglucuronides occurring in vivo (quercetin substituted with glucuronic acid at the 3-, 3'-, 4'-, and 7-hydroxyl groups). The 4'-O-beta-D-glucuronide is predicted in silico to interact most strongly and the 3-O-beta-D-glucuronide most weakly, and this prediction is supported experimentally using binding and competition assays on ABCC2-overexpressing baculovirus-infected Sf9 cells. To test the transport in situ, we examined the effect of two ABCC2 inhibitors, MK571 and cyclosporin A, on the transport into the media of quercetin glucuronides produced intracellularly by Caco2 cells. The inhibitors reduced the amount of all quercetin glucuronides in the media. The results show that the molecular model of ABCC2 agrees well with experimentally determined ABCC2-ligand interactions and, importantly, that the interaction of ABCC2 with quercetin glucuronides is dependent on the position and nature of substitution.

Global dynamics of biological systems from time-resolved omics experiments.

The emergent properties of biological systems, organized around complex networks of irregularly connected elements, limit the applications of the direct scientific method to their study. The current lack of knowledge opens new perspectives to the inverse scientific paradigm where observations are accumulated and analysed by advanced data-mining techniques to enable a better understanding and the formulation of testable hypotheses about the structure and functioning of these systems. The current technology allows for the wide application of omics analytical methods in the determination of time-resolved molecular profiles of biological samples. Here it is proposed that the theory of dynamical systems could be the natural framework for the proper analysis and interpretation of such experiments. A new method is described, based on the techniques of non-linear time series analysis, which is providing a global view on the dynamics of biological systems probed with time-resolved omics experiments.

Computational studies of ligand-receptor interactions in bitter taste receptors.

Phenylthiocarbamide tastes intensely bitter to some individuals, but others find it completely tasteless. Recently, it was suggested that phenylthiocarbamide elicits bitter taste by interacting with a human G protein-coupled receptor (hTAS2R38) encoded by the PTC gene. The phenylthiocarbamide nontaster trait was linked to three single nucleotide polymorphisms occurring in the PTC gene. Using the crystal structure of bovine rhodopsin as template, we generated the 3D structure of hTAS2R38 bitter taste receptor. We were able to map on the receptor structure the amino acids affected by the genetic polymorphisms and to propose molecular functions for two of them that explained the emergence of the nontaster trait. We used molecular docking simulations to find that phenylthiocarbamide exhibited a higher affinity for the target receptor than the structurally similar molecule 6-n-propylthiouracil, in line with recent experimental studies. A 3D model was constructed for the hTAS2R16 bitter taste receptor as well, by applying the same protocol. We found that the recently published experimental ligand binding affinity data for this receptor correlated well with the binding scores obtained from our molecular docking calculations.

Comparison of a homology model and the crystallographic structure of human 11beta-hydroxysteroid dehydrogenase type 1 (11betaHSD1) in a structure-based identification of inhibitors.

Human 11beta-hydroxysteroid dehydrogenase type 1 (11betaHSD1) catalyzes the interconversion of cortisone into active cortisol. 11betaHSD1 inhibition is a tempting target for the treatment of a host of human disorders that might benefit from blockade of glucocorticoid action, such as obesity, metabolic syndrome, and diabetes type 2. Here, we report an in silico screening study aimed at identifying new selective inhibitors of human 11betaHSD1 enzyme. In the first step, homology modeling was employed to build the 3D structure of 11betaHSD1. Further, molecular docking was used to validate the predicted model by showing that it was able to discriminate between known 11betaHSD1 inhibitors or substrates and non-inhibitors. The homology model was found to reproduce closely the crystal structure that became publicly available in the final stages of this work. Finally, we carried out structure-based virtual screening experiments on both the homology model and the crystallographic structure with a database of 114,000 natural molecules. Among these, 15 molecules were consistently selected as inhibitors based on both the model and crystal structures of the enzyme, implying a good quality for the homology model. Among these putative 11betaHSD1 inhibitors, two were flavonone derivatives that have already been shown to be potent inhibitors of the enzyme.

Polysaccharide-induced order-to-order transitions in lyotropic liquid crystals.

We report on the order-to-order transitions of lyotropic liquid crystals formed by self-assembled monogylcerides and water in the presence of polysaccharides of various molecular weights. The phase diagram of monoglyceride-water-polysaccharide systems, their morphology, and the topology of liquid crystalline structures were determined by combining optical cross-polarization, oscillatory shear rheometry, and small-angle X-ray scattering. The presence of hydrophilic mono-, oligo-, and polysaccharides in the water domains of liquid crystalline phases resulted in a general decrease of the cubic-to-hexagonal transition temperature. Provided that the sugar could fit within the water channels, the decrease was observed to be dependent on the polysaccharide concentration but independent of its molecular weight. For isotropic bicontinuous cubic phases, monomeric sugars such as glucose were reported to shrink the lattice parameter of the structure without inducing phase transitions. However, when a polymeric form of glucose was used, such as dextran, transitions from the gyroidal Ia3d cubic phase to double diamond Pn3m cubic phases were observed at well-defined molecular weights of polysaccharide. These results were interpreted in terms of size exclusions of polymer sugars by the water domains of the liquid crystal phases as well as the different topologies of water channels. Molecular dynamics simulations of polysaccharides in the water environment were performed to support these findings.

Design of virtual libraries of umami-tasting molecules.

The introduction of molecular tools in food research offers the possibility to the food industry to benefit from the experience gained in the field by pharmaceutical companies. In this work we are showing how in silico virtual screening techniques based on molecular similarity were applied for identifying novel umami-tasting compounds. The results obtained suggest that 5'-ribonucleotides and monosodium glutamate might elicit the fifth basic taste via the same molecular mechanism. New algorithms were developed and used in this work, such as the dimension reduction of data sets by singular value decomposition and the introduction of the correlation dimension as a natural dimension of a chemical space. It is shown that the representations of molecular data sets in chemical spaces possess self-similar properties, characteristic of fractal objects.

Formation of vinylogous compounds in model Maillard reaction systems.

The thermal degradation over temperature and time of selected amino acids (Asp, Gln, and Glu) in the presence of reducing sugars was investigated in low moisture model systems. Copyrolysis of glucose-Asp mixtures led to the release of acrylic acid, attaining >5 mmol/mol Asp at 230 degrees C after 5 min. Spurious amounts of 3-butenamide were detected upon heating Gln together with a carbonyl source. Apparently, intramolecular cyclization is favored to procure 2-pyrrolidinone, reaching levels >3 mmol/mol above 230 degrees C. 2-Pyrrolidinone was also formed in comparable amounts in pyrolyzed sugar-Glu mixtures, indicating that the Maillard reaction may be an important contributor to the formation of 2-pyrrolidinone in certain cooked foods. The chemical route to acrylic acid and 3-butenamide is probably analogous to that described for acrylamide recently. Evidence is also presented that acrylic acid may be an intermediate in the formation of acrylamide, and yields could be augmented by coincubation of fructose-Asp with certain amino acids such as Gln, reaching approximately 5% of the yield obtained by the Asn route. A computational study to determine the reactivity of the vinylogous products indicated a reduced ability of 3-butenamide as compared to acrylamide to form stable intermediates by Michael nucleophilic addition. Acrylamide and acrylic acid exhibited a similar theoretical reactivity potential toward nucleophiles. No information is as yet available on the occurrence of acrylic acid in cooked foods. Extensive toxicological evaluation indicates that acrylic acid is of no concern at the amounts to be expected in foods.

Soluble forms of Toll-like receptor (TLR)2 capable of modulating TLR2 signaling are present in human plasma and breast milk.

Dysregulation of the initial, innate immune response to bacterial infection may lead to septic shock and death. Toll-like receptors (TLRs) play a crucial role in this innate immune response, and yet the regulatory mechanisms controlling microbial-induced TLR triggering are still to be fully understood. We have therefore sought specific regulatory mechanisms that may modulate TLR signaling. In this study, we tested for the possible existence of a functionally active soluble form of TLR2. We demonstrated the existence of natural soluble forms of TLR2 (sTLR2), which we show to be capable of modulating cell activation. We found that blood monocytes released sTLR2 constitutively and that the kinetics of sTLR2 release increased upon cell activation. Analysis of cells expressing the human TLR2 cDNA or its c-myc-tagged version indicated that sTLR2 resulted from the posttranslational modification of the TLR2 protein in an intracellular compartment. Moreover, an intracellular pool of sTLR2 is maintained. sTLR2 was found naturally expressed in breast milk and plasma. Milk sTLR2 levels mirrored those of the TLR coreceptor soluble CD14. Depletion of sTLR2 from serum resulted in an increased cellular response to bacterial lipopeptide. Notably, serum sTLR2 was lower in tuberculosis patients. Coimmunoprecipitation experiments and computational molecular docking studies showed an interaction between sTLR2 and soluble CD14 in plasma and milk. These findings suggest the existence of a novel and specific innate immune mechanism regulating microbial-induced TLR triggering, and may lead to new therapeutics for the prevention and/or treatment of severe infectious diseases.