Live sequence charts to model medical information

Background

Consensus exists that several bariatric surgery procedures produce a rapid improvement of glucose homeostasis in obese diabetic patients, improvement apparently uncorrelated with the degree of eventual weight loss after surgery. Several hypotheses have been suggested to account for these results: among these, the anti-incretin, the ghrelin and the lower-intestinal dumping hypotheses have been discussed in the literature. Since no clear-cut experimental results are so far available to confirm or disprove any of these hypotheses, in the present work a mathematical model of the glucose-insulin-incretin system has been built, capable of expressing these three postulated mechanisms. The model has been populated with critically evaluated parameter values from the literature, and simulations under the three scenarios have been compared.

Results

The modeling results seem to indicate that the suppression of ghrelin release is unlikely to determine major changes in short-term glucose control. The possible existence of an anti-incretin hormone would be supported if an experimental increase of GIP concentrations were evident post-surgery. Given that, on the contrary, collected evidence suggests that GIP concentrations decrease post-surgery, the lower-intestinal dumping hypothesis would seem to describe the mechanism most likely to produce the observed normalization of Type 2 Diabetes Mellitus (T2DM) after bariatric surgery.

Conclusions

The proposed model can help discriminate among competing hypotheses in a context where definitive data are not available and mechanisms are still not clear.     

Chicken genomics charts a path to the genome sequence.

In this paper, the current status of chicken genomics is reviewed. This is timely given the current intense activity centred on sequencing the complete genome of this model species. The genome project is based on a decade of map building by genetic linkage and cytogenetic methods, which are now being replaced by high-resolution radiation hybrid and bacterial artificial chromosome (BAC) contig maps. Markers for map building have generally depended on labour-intensive screening procedures, but in recent years this has changed with the availability of almost 500,000 chicken expressed sequence tags (ESTs). These resources and tools will be critical in the coming months when the chicken genome sequence is being assembled (eg cross-checked with other maps) and annotated (eg gene structures based on ESTs). The future for chicken genome and biological research is an exciting one, through the integration of these resources. For example, through the proposed chicken Ensembl database, it will be possible to solve challenging scientific questions by exploiting the power of a chicken model. One area of interest is the study of developmental mechanisms and the discovery of regulatory networks throughout the genome. Another is the study of the molecular nature of quantitative genetic variation. No other animal species have been phenotyped and selected so intensively as agricultural animals and thus there is much to be learned in basic and medical biology from this research.     

Flanking region sequence information to refine microRNA target predictions

The non-coding elements of a genome, with many of them considered as junk earlier, have now started gaining long due respectability, with microRNAs as the best current example. MicroRNAs bind preferentially to the 3′ untranslated regions (UTRs) of the target genes and negatively regulate their expression most of the time. Several microRNA:target prediction softwares have been developed based upon various assumptions and the majority of them consider the free energy of binding of a target to its microRNA and seed conservation. However, the average concordance between the predictions made by these softwares is limited and compounded by a large number of false-positive results. In this study, we describe a methodology developed by us to refine microRNA:target prediction by target prediction softwares through observations made from a comprehensive study. We incorporated the information obtained from dinucleotide content variation patterns recorded for flanking regions around the target sites using support vector machines (SVMs) trained over two different major sources of experimental data, besides other sources. We assessed the performance of our methodology with rigorous tests over four different dataset models and also compared it with a recently published refinement tool, MirTif. Our methodology attained a higher average accuracy of 0.88, average sensitivity and specificity of 0.81 and 0.94, respectively, and areas under the curves (AUCs) for all the four models scored above 0.9, suggesting better performance by our methodology and a possible role of flanking regions in microRNA targeting control. We used our methodology over genes of three different pathways — toll-like receptor (TLR), apoptosis and insulin — to finally predict the most probable targets. We also investigated their possible regulatory associations, and identified a hsa-miR-23a regulatory module.     

Deoxyribozymes that recode sequence information

Allosteric nucleic acid ligases have been used previously to transform analyte-binding into the formation of oligonucleotide templates that can be amplified and detected. We have engineered binary deoxyribozyme ligases whose two components are brought together by bridging oligonucleotide effectors. The engineered ligases can ‘read’ one sequence and then ‘write’ (by ligation) a separate, distinct sequence, which can in turn be uniquely amplified. The binary deoxyribozymes show great specificity, can discriminate against a small number of mutations in the effector, and can read and recode DNA information with high fidelity even in the presence of excess obscuring genomic DNA. In addition, the binary deoxyribozymes can read non-natural nucleotides and write natural sequence information. The binary deoxyribozyme ligases could potentially be used in a variety of applications, including the detection of single nucleotide polymorphisms in genomic DNA or the identification of short nucleic acids such as microRNAs.     

Bioinformatics applied to allergy: allergen databases, from collecting sequence information to data integration. The Allergome platform as a model

Allergens are proteins or glycoproteins that are recognized by IgE produced by the immune system of allergic individuals. Until now around 1,500 allergenic structures have been identified and this number seems not have reached a plateau after 3-4 decades of research and the advent of molecular biology. Several allergen databases are available on Internet. Different aims and philosophies lead to different products. Here we report about main feature of web sites dedicated to allergens and we describe in more details our current work on the Allergome platform. The web server Allergome (www.allergome.org) represent a free independent open resource whose goal is to provide an exhaustive repository of data related to all the IgE-binding compounds. The main purpose of Allergome is to collect a list of allergenic sources and molecules by using the widest selection criteria and sources. A further development of the Allergome platform has been represented by the Real Time Monitoring of IgE sensitization module (ReTiME) that allows uploading of raw data from both in vivo and in vitro testing, thus representing the first attempt to have IT applied to allergy data mining. More recently, a new module (RefArray) representing a tool for literature mining has been released.     

Three subsets of sequence complexity and their relevance to biopolymeric information

Genetic algorithms instruct sophisticated biological organization. Three qualitative kinds of sequence complexity exist: random (RSC), ordered (OSC), and functional (FSC). FSC alone provides algorithmic instruction. Random and Ordered Sequence Complexities lie at opposite ends of the same bi-directional sequence complexity vector. Randomness in sequence space is defined by a lack of Kolmogorov algorithmic compressibility. A sequence is compressible because it contains redundant order and patterns. Law-like cause-and-effect determinism produces highly compressible order. Such forced ordering precludes both information retention and freedom of selection so critical to algorithmic programming and control. Functional Sequence Complexity requires this added programming dimension of uncoerced selection at successive decision nodes in the string. Shannon information theory measures the relative degrees of RSC and OSC. Shannon information theory cannot measure FSC. FSC is invariably associated with all forms of complex biofunction, including biochemical pathways, cycles, positive and negative feedback regulation, and homeostatic metabolism. The algorithmic programming of FSC, not merely its aperiodicity, accounts for biological organization. No empirical evidence exists of either RSC of OSC ever having produced a single instance of sophisticated biological organization. Organization invariably manifests FSC rather than successive random events (RSC) or low-informational self-ordering phenomena (OSC).     

Probes for double helical DNA sequence information: molecular mechanics study of a proposed model

The biological activity of many molecules which bind to double helical DNA is related to the sequence specificity of their binding. The development of new substances of this type is challenging; a general solution to the problem does not exist. A new mechanism for small molecule-duplex DNA interaction termed intercalative displacement is proposed. The approach is promising for the design of new substances which will recognize sequence information on DNA. Molecular mechanics calculations in the absence of solvent and counterions predict that the proposed model is structurally and energetically closely related to the well known process of standard intercalation. The implications of these calculations for experimental studies are discussed.     

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.