An information theoretic exploratory method for learning patterns of conditional gene coexpression from microarray data

In this article, we introduce an exploratory framework for learning patterns of conditional co-expression in gene expression data. The main idea behind the proposed approach consists of estimating how the information content shared by a set of M nodes in a network (where each node is associated to an expression profile) varies upon conditioning on a set of L conditioning variables (in the simplest case represented by a separate set of expression profiles). The method is non-parametric and it is based on the concept of statistical co-information, which, unlike conventional correlation based techniques, is not restricted in scope to linear conditional dependency patterns. Moreover, such conditional co-expression relationships can potentially indicate regulatory interactions that do not manifest themselves when only pair-wise relationships are considered. A moment based approximation of the co-information measure is derived that efficiently gets around the problem of estimating high-dimensional multi-variate probability density functions from the data, a task usually not viable due to the intrinsic sample size limitations that characterize expression level measurements. By applying the proposed exploratory method, we analyzed a whole genome microarray assay of the eukaryote Saccharomices cerevisiae and were able to learn statistically significant patterns of conditional co-expression. A selection of such interactions that carry a meaningful biological interpretation are discussed.     

Structure-templated predictions of novel protein interactions from sequence information

The multitude of functions performed in the cell are largely controlled by a set of carefully orchestrated protein interactions often facilitated by specific binding of conserved domains in the interacting proteins. Interacting domains commonly exhibit distinct binding specificity to short and conserved recognition peptides called binding profiles. Although many conserved domains are known in nature, only a few have well-characterized binding profiles. Here, we describe a novel predictive method known as domain–motif interactions from structural topology (D-MIST) for elucidating the binding profiles of interacting domains. A set of domains and their corresponding binding profiles were derived from extant protein structures and protein interaction data and then used to predict novel protein interactions in yeast. A number of the predicted interactions were verified experimentally, including new interactions of the mitotic exit network, RNA polymerases, nucleotide metabolism enzymes, and the chaperone complex. These results demonstrate that new protein interactions can be predicted exclusively from sequence information.     

Integrative inference of gene-regulatory networks in Escherichia coli using information theoretic concepts and sequence analysis

Background  

Although Escherichia coli is one of the best studied model organisms, a comprehensive understanding of its gene regulation is not yet achieved. There exist many approaches to reconstruct regulatory interaction networks from gene expression experiments. Mutual information based approaches are most useful for large-scale network inference.     

Systematic interpretation of genetic interactions using protein networks

Genetic interaction analysis,in which two mutations have a combined effect not exhibited by either mutation alone, is a powerful and widespread tool for establishing functional linkages between genes. In the yeast Saccharomyces cerevisiae, ongoing screens have generated >4,800 such genetic interaction data. We demonstrate that by combining these data with information on protein-protein, prote in-DNA or metabolic networks, it is possible to uncover physical mechanisms behind many of the observed genetic effects. Using a probabilistic model, we found that 1,922 genetic interactions are significantly associated with either between- or within-pathway explanations encoded in the physical networks, covering approximately 40% of known genetic interactions. These models predict new functions for 343 proteins and suggest that between-pathway explanations are better than within-pathway explanations at interpreting genetic interactions identified in systematic screens. This study provides a road map for how genetic and physical interactions can be integrated to reveal pathway organization and function.     

Revealing biological information using data structuring and automated learning

The intermediary steps between a biological hypothesis, concretized in the input data, and meaningful results, validated using biological experiments, commonly employ bioinformatics tools. Starting with storage of the data and ending with a statistical analysis of the significance of the results, every step in a bioinformatics analysis has been intensively studied and the resulting methods and models patented. This review summarizes the bioinformatics patents that have been developed mainly for the study of genes, and points out the universal applicability of bioinformatics methods to other related studies such as RNA interference. More specifically, we overview the steps undertaken in the majority of bioinformatics analyses, highlighting, for each, various approaches that have been developed to reveal details from different perspectives. First we consider data warehousing, the first task that has to be performed efficiently, optimizing the structure of the database, in order to facilitate both the subsequent steps and the retrieval of information. Next, we review data mining, which occupies the central part of most bioinformatics analyses, presenting patents concerning differential expression, unsupervised and supervised learning. Last, we discuss how networks of interactions of genes or other players in the cell may be created, which help draw biological conclusions and have been described in several patents.     

Simultaneous recording of xylem pressure and trans-root potential in roots of intact glycophytes using a novel xylem pressure probe technique

The radial electrical potential difference between the root xylem and the bathing solution, i.e. the so-called trans-root potential, was measured in intact maize and wheat plants using a xylem pressure probe into which an Ag/AgCl electrode was incorporated. Besides other advantages (e.g. detection and removal of tip clogging; determination of the radial root resistance), the novel probe allowed placement of the electrode precisely in a single xylem vessel as indicated by the reading of sub-atmospheric or negative pressure values upon penetration. The trans-root potentials were of the order of 0 to – 70 mV and + 40 to – 20 mV for 2- to 3-week-old maize and wheat plants, respectively. Osmotic experiments performed on maize demonstrated that addition of 100 mM mannitol to the solution resulted in a decrease of xylem pressure associated with a slow, but continuous depolarization. The depolarization was reversible upon removal of the mannitol. For wheat plants it could be shown that the oscillations of the xylem pressure described recently by Schneider et al. (1997, Plant, Cell and Environment 20, 221–229) were accompanied by (rectangular, saw-tooth and/or U-shaped) oscillations in the trans-root potential (but not by corresponding changes of the membrane potential of the cortical cells measured simultaneously with conventional microelectrodes). Increase of the light intensity (up to 550 μmol m^–2 s^–1) resulted in a drop of the xylem pressure in wheat, whereas the trans-root potential showed a biphasic response: first hyperpolarization (by about 10 mV) was observed, followed by depolarization (by up to about + 40 mV). Similar light-induced biphasic (but often less pronounced) changes in the trans-root potential were also recorded for maize plants. Most interestingly, the response of the trans-root potential was always faster (by about 1–3 min) than the response of the xylem pressure upon illumination, suggesting that changes in the transpiration rate are reflected very quickly in the electrical properties of the root tissue. The impact of this and other findings on long-distance transport of solutes and water as well as on long-distance signalling is discussed.     

Template learning of cellular neural network using genetic programming

A new learning algorithm for space invariant Uncoupled Cellular Neural Network is introduced. Learning is formulated as an optimization problem. Genetic Programming has been selected for creating new knowledge because they allow the system to find new rules both near to good ones and far from them, looking for unknown good control actions. According to the lattice Cellular Neural Network architecture, Genetic Programming will be used in deriving the Cloning Template. Exploration of any stable domain is possible by the current approach. Details of the algorithm are discussed and several application results are shown.     

Simultaneous genetic mapping of multiple human minisatellite sequences using DNA fingerprinting

We have used several DNA probes which simultaneously recognize multiple loci to follow the segregation of a large number of minisatellite loci through two large reference pedigrees. The segregation data were analyzed for linkage to previously characterized marker loci using RFLP mapping data for these pedigrees from a previous study and from the Centre d'Etude du Polymorphisme Humain data bank. In this way we have mapped 31 separate minisatellite alleles of a total of 146 studied. The results of these analyses suggest that the distribution of minisatellites in the human genome is skewed toward telomeres and is highly clustered in character. A group of at least five separate minisatellites was found at 7 qter, and smaller clusters are present in several other regions. We detected a smaller than expected number of linkages, perhaps because of the clustering of minisatellite loci. The 7qter minisatellite cluster is in a region of excess male meiotic recombination, and in this respect is similar to minisatellite clusters at 16pter and in the X-Y pseudoautosomal region.     

Detecting high-order interactions of single nucleotide polymorphisms using genetic programming

MOTIVATION: Not individual single nucleotide polymorphisms (SNPs), but high-order interactions of SNPs are assumed to be responsible for complex diseases such as cancer. Therefore, one of the major goals of genetic association studies concerned with such genotype data is the identification of these high-order interactions. This search is additionally impeded by the fact that these interactions often are only explanatory for a relatively small subgroup of patients. Most of the feature selection methods proposed in the literature, unfortunately, fail at this task, since they can either only identify individual variables or interactions of a low order, or try to find rules that are explanatory for a high percentage of the observations. In this article, we present a procedure based on genetic programming and multi-valued logic that enables the identification of high-order interactions of categorical variables such as SNPs. This method called GPAS cannot only be used for feature selection, but can also be employed for discrimination. RESULTS: In an application to the genotype data from the GENICA study, an association study concerned with sporadic breast cancer, GPAS is able to identify high-order interactions of SNPs leading to a considerably increased breast cancer risk for different subsets of patients that are not found by other feature selection methods. As an application to a subset of the HapMap data shows, GPAS is not restricted to association studies comprising several 10 SNPs, but can also be employed to analyze whole-genome data. AVAILABILITY: Software can be downloaded from http://ls2-www.cs.uni-dortmund.de/~nunkesser/#Software     

Sau-PCR, a novel amplification technique for genetic fingerprinting of microorganisms

The proposed technique is based on the digestion of genomic DNA with the restriction endonuclease Sau3AI and subsequent amplification with primers whose core sequence is based on the Sau3AI recognition site. The method was tested on strains of lactic acid bacteria but could be proposed for virtually any culturable organism from which DNA can be extracted.     

Simultaneous enumeration of different bacteria using reverse sample genome probing technique

Quantitative reverse sample genome probing (RSGP) with lambdaDNA as an internal standard was used to enumerate the total numbers of Rhizobium sp. CCRC 13560, Rhizobium meliloti CCRC 13516 and Bradyrhizobium sp. CCRC 13585. K(lambda)/Kx ratios varied between the three species but also in response to the amounts of lambdaDNA or genomic DNA used in the labeling mixture or fixed upon the membrane. Comparative enumerations of pure cultures revealed higher counts using genomic probing as compared with growth-based colony forming units (CFU; 3.4+/-1.7-fold higher for R. meliloti, 6.4+/-7.8-fold higher for Rhizobium sp. and 0.34+/-0.17-fold higher for Bradyrhizobium sp.). In mixed cultures, the estimated cell numbers using genomic probing were 126+/-172-, 85+/-83- and 4.0+/-3.4-fold higher (same respective order) than the growth-based assay. By replacing the klambda/kx ratio with k'lambda/k'x (slope from signal intensity of differently diluted lambdaDNA/slope from signal intensity of differently diluted target DNAxf(x)/flambda), significant improvement in the accuracy of the estimation was achieved. The calculated cell numbers via the genomic probe technique were 0.99+/-0.13-, 1.25+/-0.23- and 0.18+/-0.11-fold higher than the respective CFUs in pure cultures of R. meliloti, Rhizobium sp. and Bradyrhizobium sp. In samples containing mixed populations, the estimated numbers from genomic probing were 1.25+/-0.51-, 45.9+/-14.8- and 0.27+/-0.07-fold higher than the CFU-derived cell count (same respective order).     

Quantitative investigations of cell-bubble interactions using a foam fractionation technique

Previous work by the authors and others has shown that suspended animal cell damage in bioreactors is caused by cell-bubble interactions, regardless whether the bubbles are from bubble entrainment or direct gas sparging. As approach to measure the adsorptivity of animal cells to bubbles, a modified batch foam fractionation technique has been developed in this work and proven to be applicable. By using this technique, the number of cells adsorbed per unit bubble surface area and the adsorption coefficients have been measured to quantify hybridoma cell-bubble interactions, and the prevetive effects of serum and Pluronic F68 on these interactions. It was demonstrated quantitatively that the hybridoma cells adhere to bubbles spontaneously and significant numbers exist in the foam, and that both the serum and Pluronic F68 provide strong prevention to these cell-bubble interactions. The results obtained provide criteria for bioreactor operation and medium formulation to prevent cell-bubble interactions and cell damage in the culture processes.Abbreviations NBCS new born calf serum - SFM serum-free medium     

Enzymatic characterization of Catalase from Bacillus anthracis and prediction of critical residues using information theoretic measure of Relative Entropy

In order to cope up with the reactive oxygen species (ROS) generated by host innate immune response, most of the intracellular organisms express Catalase for the enzymatic destruction/detoxification of hydrogen peroxide, to combat its deleterious effects. Catalase thus, scavenges ROS thereby playing a pivotal role in facilitating the survival of the pathogen within the host, and thus contributes to its pathogenesis. Bacillus anthracis harbors five isoforms of Catalase, but none of them has been studied so far. Thus, this study is the first attempt to delineate the biochemical and functional characteristics of one of the isoforms of Catalase (Cat1.4) of B. anthracis, followed by identification of residues critical for catalysis. The general strategy used, so far for mutational analysis in Catalases is structure based, i.e. the residues in the vicinity of heme were mutated to decipher the enzymatic mechanism. However, in the present study, protein sequence analysis was used for the prediction of catalytically important residues of Catalase. Essential measures were adopted to ensure the accuracy of predictions like after retrieval of well-annotated sequences from the database with EC 1.11.1.6, preprocessing was done to remove irrelevant sequences. The method used for multiple alignment of sequences, was guided by structural alignment and thereafter, an information theoretic measure, Relative Entropy was used for the critical residue prediction. By exploiting this strategy, we identified two previously known essential residues, H55 and Y338 in the active site which were demonstrated to be crucial for the activity. We also identified six novel crucial residues (Q332, Y117, H215, W257, N376 and H146) located distantly from the active site. Thus, the present study highlights the significance of this methodology to identify not only those crucial residues which lie in the active site of Catalase, but also the residues located distantly.     

Analysis and application of European genetic substructure using 300 K SNP information

European population genetic substructure was examined in a diverse set of >1,000 individuals of European descent, each genotyped with >300 K SNPs. Both STRUCTURE and principal component analyses (PCA) showed the largest division/principal component (PC) differentiated northern from southern European ancestry. A second PC further separated Italian, Spanish, and Greek individuals from those of Ashkenazi Jewish ancestry as well as distinguishing among northern European populations. In separate analyses of northern European participants other substructure relationships were discerned showing a west to east gradient. Application of this substructure information was critical in examining a real dataset in whole genome association (WGA) analyses for rheumatoid arthritis in European Americans to reduce false positive signals. In addition, two sets of European substructure ancestry informative markers (ESAIMs) were identified that provide substantial substructure information. The results provide further insight into European population genetic substructure and show that this information can be used for improving error rates in association testing of candidate genes and in replication studies of WGA scans.     

Demonstration of tubulin-glycolytic enzyme interactions using a novel electrophoretic approach

A gel electrophoretic technique was used to demonstrate an interaction with the soluble enzymes aldolase, glyceraldehydephosphate dehydrogenase, pyruvate kinase and muscle type lactate dehydrogenase to the cytoskeletal protein tubulin. It is suggested that tubulin, like actin, is a key cytoskeletal structure with which soluble proteins may associate.