Metal-organic framework-based high-performance column chip for micro gas chromatography: hybrid function for simultaneous preconcentration and separation of volatile organic compounds.

Numerous attempts have been made to replace commercial bulky gas chromatography (GC) systems with compact GC systems for monitoring volatile organic compounds in indoor air. However, recently developed compact GC systems are still too bulky in terms of user convenience, portability, and on-site analysis. Hence, an advanced miniaturization of compact GC systems is needed. Importantly, the small and high-performance gas pretreatment chip devices should be developed for compact GC systems. This paper reports the development of a metal-organic framework (MOF)-coated hybrid micro gas chromatography column chip (hybrid GC chip) capable of preconcentration and separation on harmful volatile organic compounds at low-concentration in one single chip device. The hybrid GC chip, 2 cm × 2 cm in size, was fabricated using a microelectromechanical systems process. The synthesized MOF-5 particles were coated on the inner wall of the fabricated hybrid GC chip and acted as an adsorbent and a stationary phase. The developed hybrid GC chip afforded high preconcentration factors with 1033-1237 and high separation resolutions with 3.8-13.3. The developed column showed good performance as a gas preconcentrator and separation column, and is the first device to perform both roles in one single chip device.

D-galactose Intake Alleviates Atopic Dermatitis in Mice by Modulating Intestinal Microbiota.

Atopic dermatitis (AD) is one of the most prevalent, chronic and persistent inflammatory skin diseases closely associated with intestinal microbiota. To evaluate the effect of D-galactose intake on AD, we orally administered D-galactose to BALB/c mice whose ears and skin were treated with 2,4-dinitrochlorobenzene (DNCB). D-galactose alleviated DNCB-induced AD-like phenotypes such as redness, scaling/dryness and excoriation. Ear thickness was also decreased by D-galactose administration. Histopathological analysis revealed decreased epidermal thickening, infiltration of immune cells, especially mast cells, in the dermis. Total levels of serum IgE representing the immunological response of AD were decreased by D-galactose administration. Microbiota analysis showed that D-galactose administration restored gut microbiota profiles, which were altered in AD mice, characterized by increased abundance of Bacteroidetes and decreased abundance of Firmicutes. The increased abundance of Bacteroides and the decreased abundance of Prevotella and Ruminococcus were reversed by D-galactose treatment, following improvement of AD. Our results suggest the possible use of D-galactose as a prebiotic to alleviate AD by altering gut microbiota.

Use of d-galactose to regulate biofilm growth of oral streptococci.

In the oral microbial community, commensals can compete with pathogens and reduce their colonization in the oral cavity. A substance that can inhibit harmful bacteria and enrich beneficial bacteria is required to maintain oral health. The purpose of this study was to examine the effect of d-galactose on the biofilm formation of the cariogenic bacteria Streptococcus mutans and oral commensal streptococci and to evaluate their use in solution and in paste form. Biofilms of S. mutans, Streptococcus oralis, and Streptococcus mitis were formed on saliva-coated glass slips in the absence or presence of d-galactose and evaluated by staining with 1 % crystal violet. d-Galactose significantly inhibited the biofilm formation of S. mutans at concentrations ranging from 2 µM to 200 mM but increased the biofilm formation of S. oralis and S. mitis at concentrations of 2-200 mM. d-Galactose significantly inhibited three glucosyltransferase genes, gtfB, gtfC, and gtfD. The effect of d-galactose in the form of solution and paste was evaluated using bovine teeth. Pretreatment with 100 mM d-galactose on bovine teeth resulted in significantly reduced S. mutans biofilm formation. Our results suggest that d-galactose can be a candidate substance for the development of oral hygiene products to prevent caries by inhibiting the biofilm formation of S. mutans and simultaneously increasing the biofilm formation of commensal oral streptococci.

D-Galactose as an autoinducer 2 inhibitor to control the biofilm formation of periodontopathogens.

Autoinducer 2 (AI-2) is a quorum sensing molecule to which bacteria respond to regulate various phenotypes, including virulence and biofilm formation. AI-2 plays an important role in the formation of a subgingival biofilm composed mostly of Gram-negative anaerobes, by which periodontitis is initiated. The aim of this study was to evaluate D-galactose as an inhibitor of AI-2 activity and thus of the biofilm formation of periodontopathogens. In a search for an AI-2 receptor of Fusobacterium nucleatum, D-galactose binding protein (Gbp, Gene ID FN1165) showed high sequence similarity with the ribose binding protein (RbsB), a known AI-2 receptor of Aggregatibacter actinomycetemcomitans. D-Galactose was evaluated for its inhibitory effect on the AI-2 activity of Vibrio harveyi BB152 and F. nucleatum, the major coaggregation bridge organism, which connects early colonizing commensals and late pathogenic colonizers in dental biofilms. The inhibitory effect of D-galactose on the biofilm formation of periodontopathogens was assessed by crystal violet staining and confocal laser scanning microscopy in the absence or presence of AI-2 and secreted molecules of F. nucleatum. D-Galactose significantly inhibited the AI-2 activity of V. harveyi and F. nucleatum. In addition, D-galactose markedly inhibited the biofilm formation of F. nucleatum, Porphyromonas gingivalis, and Tannerella forsythia induced by the AI-2 of F. nucleatum without affecting bacterial growth. Our results demonstrate that the Gbp may function as an AI-2 receptor and that galactose may be used for prevention of the biofilm formation of periodontopathogens by targeting AI-2 activity.

Method for identification of rigid domains and hinge residues in proteins based on exhaustive enumeration.

Many proteins undergo large-scale motions where relatively rigid domains move against each other. The identification of rigid domains, as well as the hinge residues important for their relative movements, is important for various applications including flexible docking simulations. In this work, we develop a method for protein rigid domain identification based on an exhaustive enumeration of maximal rigid domains, the rigid domains not fully contained within other domains. The computation is performed by mapping the problem to that of finding maximal cliques in a graph. A minimal set of rigid domains are then selected, which cover most of the protein with minimal overlap. In contrast to the results of existing methods that partition a protein into non-overlapping domains using approximate algorithms, the rigid domains obtained from exact enumeration naturally contain overlapping regions, which correspond to the hinges of the inter-domain bending motion. The performance of the algorithm is demonstrated on several proteins.

Differential effect of autoinducer 2 of Fusobacterium nucleatum on oral streptococci.

Autoinducer 2 (AI-2) is a quorum sensing molecule and plays an important role in dental biofilm formation, mediating interspecies communication and virulence expression of oral bacteria. Fusobacterium nucleatum connects early colonizing commensals and late colonizing periodontopathogens. F. nucleatum AI-2 and quorum sensing inhibitors (QSIs) can manipulate dental biofilm formation. In this study, we evaluated the effect of F. nucleatum AI-2 and QSIs on biofilm formation of Streptococcus gordonii and Streptococcus oralis, which are initial colonizers in dental biofilm. F. nucleatum AI-2 significantly enhanced biofilm growth of S. gordonii and attachment of F. nucleatum to preformed S. gordonii biofilms. By contrast, F. nucleatum AI-2 reduced biofilm growth of S. oralis and attachment of F. nucleatum to preformed S. oralis biofilms. The QSIs, (5Z)-4-bromo-5-(bromomethylene)-2(5H)-furanone and d-ribose, reversed the stimulatory and inhibitory effects of AI-2 on S. gordonii and S. oralis, respectively. In addition, co-culture using a two-compartment system showed that secreted molecules of F. nucleatum had the same effect on biofilm growth of the streptococci as AI-2. Our results demonstrate that early colonizing bacteria can influence the accretion of F. nucleatum, a secondary colonizer, which ultimately influences the binding of periodontopathogens.

TMA-TAB: a spreadsheet-based document for exchange of tissue microarray data based on the tissue microarray-object model.

The importance of tissue microarrays (TMA) as clinical validation tools for cDNA microarray results is increasing, whereas researchers are still suffering from TMA data management issues. After we developed a comprehensive data model for TMA data storage, exchange and analysis, TMA-OM, we focused our attention on the development of a user-friendly exchange format with high expressivity in order to promote data communication of TMA results and TMA-OM supportive database applications. We developed TMA-TAB, a spreadsheet-based data format for TMA data submission to the TMA-OM supportive TMA database system. TMA-TAB was developed by simplifying, modifying and reorganizing classes, attributes and templates of TMA-OM into five entities: experiment, block, slide, core_in_block, and core_in_slide. Five tab-delimited formats (investigation design format, block description format, slide description format, core clinicohistopathological data format, and core result data format) were made, each representing the entities of experiment, block, slide, core_in_block, and core_in_slide. We implemented TMA-TAB import and export modules on Xperanto-TMA, a TMA-OM supportive database application, to facilitate data submission. Development and implementation of TMA-TAB and TMA-OM provide a strong infrastructure for powerful and user-friendly TMA data management.

The tissue microarray object model: a data model for storage, analysis, and exchange of tissue microarray experimental data.

CONTEXT: Tissue microarray (TMA) is an array-based technology allowing the examination of hundreds of tissue samples on a single slide. To handle, exchange, and disseminate TMA data, we need standard representations of the methods used, of the data generated, and of the clinical and histopathologic information related to TMA data analysis. OBJECTIVE: To create a comprehensive data model with flexibility that supports diverse experimental designs and with expressivity and extensibility that enables an adequate and comprehensive description of new clinical and histopathologic data elements. DESIGN: We designed a tissue microarray object model (TMA-OM). Both the array information and the experimental procedure models are created by referring to the microarray gene expression object model, minimum information specification for in situ hybridization and immunohistochemistry experiments, and the TMA data exchange specifications. The clinical and histopathologic information model is created by using College of American Pathologists cancer protocols and National Cancer Institute common data elements. Microarray Gene Expression Data Ontology, the Unified Medical Language System, and the terms extracted from College of American Pathologists cancer protocols and NCI common data elements are used to create a controlled vocabulary for unambiguous annotation. RESULT: The TMA-OM consists of 111 classes in 17 packages to represent clinical and histopathologic information as well as experimental data for any type of cancer. We implemented a Web-based application for TMA-OM, supporting data export in XML format conforming to the TMA data exchange specifications or the document type definition derived from TMA-OM. CONCLUSIONS: The TMA-OM provides a comprehensive data model for storage, analysis, and exchange of TMA data and facilitates model-level integration of other biological models.

Study of protein-protein interaction using conformational space annealing.

We apply conformational space annealing (CSA), an efficient global optimization method, to the study of protein-protein interaction. The CSA is incorporated into the Tinker molecular modeling package along with a B-spline method for CAPRI Round 5 experiments. We have used an energy function for the protein-protein interaction that consists of electrostatic interaction, van der Waals interaction, and solvation energy terms represented by the occupancy desolvation method. The parameters of the AMBER94 all-atom empirical force field are used. Each energy term is calculated by precalculated grid potentials and B-spline method approximation. The ligand protein is placed inside a sphere of 50 A radius centered at an appropriate location, and the CSA rigid docking studies are carried out to find stable complexes. Up to 10 complexes are selected using the K-mean clustering method and biological information when available. These complexes are energy-minimized for further refinement by considering the flexibility of interacting proteins. The results show that the CSA method has a potential for the study of protein-protein interaction.

Prediction of protein solvent accessibility using fuzzy k-nearest neighbor method.

MOTIVATION: The solvent accessibility of amino acid residues plays an important role in tertiary structure prediction, especially in the absence of significant sequence similarity of a query protein to those with known structures. The prediction of solvent accessibility is less accurate than secondary structure prediction in spite of improvements in recent researches. The k-nearest neighbor method, a simple but powerful classification algorithm, has never been applied to the prediction of solvent accessibility, although it has been used frequently for the classification of biological and medical data. RESULTS: We applied the fuzzy k-nearest neighbor method to the solvent accessibility prediction, using PSI-BLAST profiles as feature vectors, and achieved high prediction accuracies. With leave-one-out cross-validation on the ASTRAL SCOP reference dataset constructed by sequence clustering, our method achieved 64.1% accuracy for a 3-state (buried/intermediate/exposed) prediction (thresholds of 9% for buried/intermediate and 36% for intermediate/exposed) and 86.7, 82.0, 79.0 and 78.5% accuracies for 2-state (buried/exposed) predictions (thresholds of each 0, 5, 16 and 25% for buried/exposed), respectively. Our method also showed slightly better accuracies than other methods by about 2-5% on the RS126 dataset and a benchmarking dataset with 229 proteins. AVAILABILITY: Program and datasets are available at http://biocom1.ssu.ac.kr/FKNNacc/ CONTACT: jul@ssu.ac.kr.

PPRODO: prediction of protein domain boundaries using neural networks.

Successful prediction of protein domain boundaries provides valuable information not only for the computational structure prediction of multidomain proteins but also for the experimental structure determination. Since protein sequences of multiple domains may contain much information regarding evolutionary processes such as gene-exon shuffling, this information can be detected by analyzing the position-specific scoring matrix (PSSM) generated by PSI-BLAST. We have presented a method, PPRODO (Prediction of PROtein DOmain boundaries) that predicts domain boundaries of proteins from sequence information by a neural network. The network is trained and tested using the values obtained from the PSSM generated by PSI-BLAST. A 10-fold cross-validation technique is performed to obtain the parameters of neural networks using a nonredundant set of 522 proteins containing 2 contiguous domains. PPRODO provides good and consistent results for the prediction of domain boundaries, with accuracy of about 66% using the +/-20 residue criterion. The PPRODO source code, as well as all data sets used in this work, are available from http://gene.kias.re.kr/ approximately jlee/pprodo/.