Zebrafish as a model system for biomedical studies

Zebrafish (Danio rerio) is now firmly recognized as a powerful research model for many areas of biology and medicine. Here, we review some achievements of zebrafish-based assays for modeling human diseases and for drug discovery and development. For drug discovery, zebrafish is especially valuable during the earlier stages of research as its represents a model organism to demonstrate a new treatment’s efficacy and toxicity before more costly mammalian models are used. This review considers some examples of known compounds which exhibit both physiological activity and toxicity in humans and zebrafish. The major advantages of zebrafish embryos consist in their permeability to small molecules added to their incubation medium and chorion transparency that enables the easy observation of the development. Assay of acute toxicity (LC₅₀ estimation) in embryos can also include the screening for developmental disorders as an indicator of teratogenic effects. We have used the zebrafish model for toxicity testing of new drugs based on phospholipid nanoparticles (e.g. doxorubicin). Genome organization and the pathways involved into control of signal transduction appear to be highly conserved between zebrafish and humans and therefore zebrafish may be used for modeling of human diseases. The review provides some examples of zebrafish application in this field.     

Percentile growth charts for biomedical studies using a porcine model

Increasing rates of obesity and heart disease are compromising quality of life for a growing number of people. There is much research linking adult disease with the growth and development both in utero and during the first year of life. The pig is an ideal model for studying the origins of developmental programming. The objective of this paper was to construct percentile growth curves for the pig for use in biomedical studies. The body weight (BW) of pigs was recorded from birth to 150 days of age and their crown-to-rump length was measured over the neonatal period to enable the ponderal index (PI; kg/m3) to be calculated. Data were normalised and percentile curves were constructed using Cole's lambda-mu-sigma (LMS) method for BW and PI. The construction of these percentile charts for use in biomedical research will allow a more detailed and precise tracking of growth and development of individual pigs under experimental conditions.     

A new soft-tissue simulation strategy for cranio-maxillofacial surgery using facial muscle template model

We propose a computationally efficient, bio-mechanically relevant soft-tissue simulation method for cranio-maxillofacial (CMF) surgery. Special emphasis is given to comply with the current clinical workflow. A template-based facial muscle prediction was introduced to avoid laborious segmentation from medical images. In addition, transversely isotropic mass-tensor model (MTM) was applied to realize the directional behavior of facial muscles in short computation time. Finally, sliding contact was incorporated to mimic realistic boundary condition in error-sensitive regions. Mechanical simulation result was compared with commercial finite element software. And retrospective validation study with post-operative scan of four CMF cases was performed.     

A template matching model for pattern recognition: Self-organization of templates and template matching by a disinhibitory neural network

A template matching model for pattern recognition is proposed. By following a previouslyproposed algorithm for synaptic modification (Hirai, 1980), the template of a stimulus pattern is selforganized as a spatial distribution pattern of matured synapses on the cells receiving modifiable synapses. Template matching is performed by the disinhibitory neural network cascaded beyond the neural layer composed of the cells receiving the modifiable synapses. The performance of the model has been simulated on a digital computer. After repetitive presentations of a stimulus pattern, a cell receiving the modifiable synapses comes to have the template of that pattern. And the cell in the latter layer of the disinhibitory bitory neural network that receives the disinhibitory input from that cell becomes electively sensitive to that pattern. Learning patterns are not restricted by previously learned ones. They can be subset or superset patterns of the ones previously learned. If an unknown pattern is presented to the model, no cell beyond the disinhibitory neural network will respond. However, if previously learned patterns are embedded in that pattern, the cells which have the templates of those patterns respond and are assumed to transmit the information to higher center. The computer simulation also shows that the model can organize a clean template under a noisy environment.     

LINNAEUS: A species name identification system for biomedical literature

Background  

The task of recognizing and identifying species names in biomedical literature has recently been regarded as critical for a number of applications in text and data mining, including gene name recognition, species-specific document retrieval, and semantic enrichment of biomedical articles.     

MBA: a literature mining system for extracting biomedical abbreviations

Background  

The exploding growth of the biomedical literature presents many challenges for biological researchers. One such challenge is from the use of a great deal of abbreviations. Extracting abbreviations and their definitions accurately is very helpful to biologists and also facilitates biomedical text analysis. Existing approaches fall into four broad categories: rule based, machine learning based, text alignment based and statistically based. State of the art methods either focus exclusively on acronym-type abbreviations, or could not recognize rare abbreviations. We propose a systematic method to extract abbreviations effectively. At first a scoring method is used to classify the abbreviations into acronym-type and non-acronym-type abbreviations, and then their corresponding definitions are identified by two different methods: text alignment algorithm for the former, statistical method for the latter.     

GIS: a biomedical text-mining system for gene information discovery

We present a biomedical text-mining system focused on four types of gene-related information: biological functions, associated diseases, related genes and gene-gene relations. The aim of this system is to provide researchers an easy-to-use bio-information service that will rapidly survey the rapidly burgeoning biomedical literature. AVAILABILITY: http://iir.csie.ncku.edu.tw/~yuhc/gis/     

Discovering semantic features in the literature: a foundation for building functional associations

Background  

Experimental techniques such as DNA microarray, serial analysis of gene expression (SAGE) and mass spectrometry proteomics, among others, are generating large amounts of data related to genes and proteins at different levels. As in any other experimental approach, it is necessary to analyze these data in the context of previously known information about the biological entities under study. The literature is a particularly valuable source of information for experiment validation and interpretation. Therefore, the development of automated text mining tools to assist in such interpretation is one of the main challenges in current bioinformatics research.     

The chick embryo: hatching a model for contemporary biomedical research

Animal models play a crucial role in fundamental and medical research. Progress in the fields of drug discovery, regenerative medicine and cancer research among others are heavily dependent on in vivo models to validate in vitro observations, and develop new therapeutic approaches. However, conventional rodent and large animal experiments face ethical, practical and technical issues that limit their usage. The chick embryo represents an accessible and economical in vivo model, which has long been used in developmental biology, gene expression analysis and loss/gain of function experiments. It is also an established model for tissue/cell transplantation, and because of its lack of immune system in early development, the chick embryo is increasingly recognised as a model of choice for mammalian biology with new applications for stem cell and cancer research. Here, we review novel applications of the chick embryo model, and discuss future developments of this in vivo model for biomedical research.     

Improved method for differential expression proteomics using trypsin-catalyzed 18O labeling with a correction for labeling efficiency

Quantitative strategies relying on stable isotope labeling and isotope dilution mass spectrometry have proven to be a very robust alternative to the well established gel-based techniques for the study of the dynamic proteome. Postdigestion 18O labeling is becoming very popular mainly due to the simplicity of the enzyme-catalyzed exchange reaction, the peptide handling and storage procedures, and the flexibility and versatility introduced by decoupling protein digestion from peptide labeling. Despite recent progresses, peptide quantification by postdigestion 18O labeling still involves several computational problems. In this work we analyzed the behavior of large collections of peptides when they were subjected to postdigestion labeling and concluded that this process can be explained by a universal kinetic model. On the basis of this observation, we developed an advanced quantification algorithm for this kind of labeling. Our method fits the entire isotopic envelope to parameters related with the kinetic exchange model, allowing at the same time an accurate calculation of the relative proportion of peptides in the original samples and of the specific labeling efficiency of each one of the peptides. We demonstrated that the new method eliminates artifacts produced by incomplete oxygen exchange in subsets of peptides that have a relatively low labeling efficiency and that may be considered indicative of false protein ratio deviations. Finally using a rigorous statistical analysis based on the calculation of error rates associated with false expression changes, we showed the validity of the method in the practice by detecting significant expression changes, produced by the activation of a model preparation of T cells, with only 5 microg of protein in three proteins among a pool of more than 100. By allowing a full control over potential artifacts, our method may improve automation of the procedures for relative protein quantification using this labeling strategy.     

A neural circuit model forming semantic network with exception using spike-timing-dependent plasticity of inhibitory synapses

We propose a neural circuit model forming a semantic network with exceptions using the spike-timing-dependent plasticity (STDP) of inhibitory synapses. To evaluate the proposed model, we conducted nine types of computer simulation by combining the three STDP rules for inhibitory synapses and the three spike pairing rules. The simulation results obtained with the STDP rule for inhibitory synapses by Haas et al. [Haas, J.S., Nowotny, T., Abarbanel, H.D.I., 2006, Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. J. Neurophysiol. 96, 3305–3313] are successful, whereas, the other results are unsuccessful. The results and examinations suggested that the inhibitory connection from the concept linked with an exceptional feature to the general feature is necessary for forming a semantic network with an exception.     

A model system for investigating lineshape/structure correlations in RNA site-directed spin labeling

In RNA site-directed spin labeling (SDSL) studies, structural and dynamic information at the individual RNA nucleotide level is derived from the observed electron paramagnetic resonance spectrum of a covalently attached nitroxide. A systematic approach for RNA SDSL is to establish a library that categorizes observed spectral lineshapes based on known RNA structures, thus enabling lineshape-based structure identification at any RNA site. To establish the first RNA SDSL library, selective secondary structure elements have been systematically engineered into a model RNA. Nitroxide lineshapes reporting features specific to each element were obtained utilizing a new avidin-tethering scheme for suppressing spectral effects due to uniform RNA tumbling. The data demonstrated two key features required for a SDSL library with a predicting power: (i) spectral divergence--distinctive lineshape for different elements; and (ii) spectral convergence--similar lineshape for the same element in different contexts. This sets the foundation for further RNA SDSL library development.     

Study of protein topography with flash photolytically generated nonspecific surface-labeling reagents: surface labeling of ribonuclease A

A method for nonspecifically labeling essentially all exposed residues of a protein is described. A reactive aryl nitrene is generated from N-(4-azido-2-nitrophenyl)-2-aminoethylsulfonate (NAP-Taurine), within 500 mus by flash photolysis in the presence of protein. The reactive nitrene is inserted in about 2 ms into those carbon-hydrogen bonds of the protein that are exposed to the solvent. The method is applied here to ribonuclease A to demonstrate the different degree of labeling of the native and denatured protein. On the basis of amino acid analysis, it appears that residues of the native protein that are buried in the interior of the molecule (as judged from the x-ray structure) do not react with the nitrene. However, when these residues (even nonreactive ones such as valine and proline) are exposed by denaturation of the protein, they do react with the nitrene. It is shown that native ribonuclease A retains 90% of its enzymatic activity when flashed in the absence of NAP-Taurine. This small loss in activity arises from the disruption of a limited portion of the native enzyme structure, as judged by circular dichroism, ultraviolet, and Raman spectra. The site of this limited disruption may be a portion of the enzyme surface near the Cys-26-Cys-84 disulfide bond. The utility of this surface labeling technique for studying the pathways of protein folding or unfolding is discussed.     

MaSTerClass: a case-based reasoning system for the classification of biomedical terms

MOTIVATION: The sheer volume of textually described biomedical knowledge exerts the need for natural language processing (NLP) applications in order to allow flexible and efficient access to relevant information. Specialized semantic networks (such as biomedical ontologies, terminologies or semantic lexicons) can significantly enhance these applications by supplying the necessary terminological information in a machine-readable form. With the explosive growth of bio-literature, new terms (representing newly identified concepts or variations of the existing terms) may not be explicitly described within the network and hence cannot be fully exploited by NLP applications. Linguistic and statistical clues can be used to extract many new terms from free text. The extracted terms still need to be correctly positioned relative to other terms in the network. Classification as a means of semantic typing represents the first step in updating a semantic network with new terms. RESULTS: The MaSTerClass system implements the case-based reasoning methodology for the classification of biomedical terms.     

A model of restriction endonuclease MvaI in complex with DNA: a template for interpretation of experimental data and a guide for specificity engineering

R.MvaI is a Type II restriction enzyme (REase), which specifically recognizes the pentanucleotide DNA sequence 5'-CCWGG-3' (W indicates A or T). It belongs to a family of enzymes, which recognize related sequences, including 5'-CCSGG-3' (S indicates G or C) in the case of R.BcnI, or 5'-CCNGG-3' (where N indicates any nucleoside) in the case of R.ScrFI. REases from this family hydrolyze the phosphodiester bond in the DNA between the 2nd and 3rd base in both strands, thereby generating a double strand break with 5'-protruding single nucleotides. So far, no crystal structures of REases with similar cleavage patterns have been solved. Characterization of sequence-structure-function relationships in this family would facilitate understanding of evolution of sequence specificity among REases and could aid in engineering of enzymes with new specificities. However, sequences of R.MvaI or its homologs show no significant similarity to any proteins with known structures, thus precluding straightforward comparative modeling. We used a fold recognition approach to identify a remote relationship between R.MvaI and the structure of DNA repair enzyme MutH, which belongs to the PD-(D/E)XK superfamily together with many other REases. We constructed a homology model of R.MvaI and used it to predict functionally important amino acid residues and the mode of interaction with the DNA. In particular, we predict that only one active site of R.MvaI interacts with the DNA target at a time, and the cleavage of both strands (5'-CCAGG-3' and 5'-CCTGG-3') is achieved by two independent catalytic events. The model is in good agreement with the available experimental data and will serve as a template for further analyses of R.MvaI, R.BcnI, R.ScrFI and other related enzymes.     

A model system for a fluorometric biosensor using permeabilized Zymomonas mobilis or enzymes with protein confined dinucleotides

Using permeabilized Zymomonas mobilis or glucose-fructose oxidoreductase isolated from this microorganism a model system for biosensors with a protein confined NADP(H) cofactor for the determination of glucose, fructose, gluconolactone, and sorbitol was developed. Either permeabilized microorganisms containing the oxidoreductase or the pure enzyme were confined via membrane separation in a small measuring chamber, that was integrated into a flow injection analysis system (FIA). The measuring principle was the monitoring of the NAD(P)H fluorescence, excited at 360 nm and measured at 450 nm. NADP(H), which is confined in the protein complex, was oxidized or reduced during the enzymatic reactions and the changes in the fluorescence intensity were related to the substrate concentration. The sensitivity of the system covered a range from 0.001 to 100 g/L of the analyte depending on substrate and operating conditions. The applicability of this model system for bioprocess monitoring was proved using samples from a Pseudomonas pseudoflava cultivation. (c) 1993 John Wiley & Sons, Inc.