Cross-reactions vs co-sensitization evaluated by in silico motifs and in vitro IgE microarray testing

Using an in silico allergen clustering method, we have recently shown that allergen extracts are highly cross-reactive. Here we used serological data from a multi-array IgE test based on recombinant or highly purified natural allergens to evaluate whether co-reactions are true cross-reactions or co-sensitizations by allergens with the same motifs.

Besnoitia besnoiti protein disulfide isomerase (BbPDI): molecular characterization, expression and in silico modelling

Besnoitia besnoiti is an apicomplexan parasite responsible for bovine besnoitiosis, a disease with a high prevalence in tropical and subtropical regions and re-emerging in Europe. Despite the great economical losses associated with besnoitiosis, this disease has been underestimated and poorly studied, and neither an effective therapy nor an efficacious vaccine is available. Protein disulfide isomerase (PDI) is an essential enzyme for the acquisition of the correct three-dimensional structure of proteins. Current evidence suggests that in Neosporacaninum and Toxoplasmagondii, which are closely related to B. besnoiti, PDI play an important role in host cell invasion, is a relevant target for the host immune response, and represents a promising drug target and/or vaccine candidate. In this work, we present the nucleotide sequence of the B. besnoiti PDI gene. BbPDI belongs to the thioredoxin-like superfamily (cluster 00388) and is included in the PDI_a family (cluster defined cd02961) and the PDI_a_PDI_a'_c subfamily (cd02995). A 3D theoretical model was built by comparative homology using Swiss-Model server, using as a template the crystallographic deduced model of Tapasin-ERp57 (PDB code 3F8U chain C). Analysis of the phylogenetic tree for PDI within the phylum apicomplexa reinforces the close relationship among B. besnoiti, N. caninum and T. gondii. When subjected to a PDI-assay based on the polymerisation of reduced insulin, recombinant BbPDI expressed in E. coli exhibited enzymatic activity, which was inhibited by bacitracin. Antiserum directed against recombinant BbPDI reacted with PDI in Western blots and by immunofluorescence with B. besnoiti tachyzoites and bradyzoites.

Engineering Tocopherol Selectivity in alpha-TTP: A Combined In Vitro/In Silico Study

"We present a combined in vitro/in silico study to determine the molecular origin of the selectivity of a-tocopherol transfer" "protein (a-TTP) towards a-tocopherol. Molecular dynamics simulations combined to free energy perturbation calculations predict a binding free energy for a-tocopherol to a-TTP 8.26+2.13 kcal mol{1 lower than that of c-tocopherol. Our calculations show that c-tocopherol binds to a-TTP in a significantly distorted geometry as compared to that of the natural ligand. Variations in the hydration of the binding pocket and in the protein structure are found as well. We propose a mutation, A156L, which significantly modifies the selectivity properties of a-TTP towards the two tocopherols. In particular, our simulations predict that A156L binds preferentially to c-tocopherol, with striking structural similarities to the wild-type- a-tocopherol complex. The affinity properties are confirmed by differential scanning fluorimetry as well as in vitro competitive binding assays. Our data indicate that residue A156 is at a critical position for determination of the selectivity of a-TTP. The engineering of TTP mutants with modulating binding properties can have potential impact at industrial level for easier purification of single tocopherols from vitamin E mixtures coming from natural oils or synthetic processes. Moreover," "the identification of a c-tocopherol selective TTP offers the possibility to challenge the hypotheses for the evolutionary development of a mechanism for a-tocopherol selection in omnivorous animals."

Vitellogenin synthesis in primary cultures of fish liver cells as endpoint for in vitro screening of the (anti)estrogenic activity of chemical substances

Concern over possible adverse effects of endocrine-disrupting compounds on fish has caused the development of appropriate testing methods. In vitro screening assays may provide initial information on endocrine activities of a test compound and thereby may direct and optimize subsequent testing. Induction of vitellogenin (VTG) is used as a biomarker of exposure of fish to estrogen-active substances. Since VTG induction can be measured not only in vivo but also in fish hepatocytes in vitro, the use of VTG induction response in isolated fish liver cells has been suggested as in vitro screen for identifying estrogenic-active substances. The main advantages of the hepatocyte VTG assay are considered its ability to detect effects of estrogenic metabolites, since hepatocytes in vitro remain metabolically competent, and its ability to detect both estrogenic and anti-estrogenic effects. In this article, we critically review the current knowledge on the VTG response of cultured fish hepatocytes to (anti)estrogenic substances. In particular, we discuss the sensitivity, specificity, and variability of the VTG hepatocyte assay. In addition, we review the available data on culture factors influencing basal and induced VTG production, the response to natural and synthetic estrogens as well as to xenoestrogens, the detection of indirect estrogens, and the sources of assay variability. The VTG induction in cultured fish hepatocytes is clearly influenced by culture conditions (medium composition, temperature, etc.) and culture system (hepatocyte monolayers, aggregates, liver slices, etc.). The currently available database on estrogen-mediated VTG induction in cultured teleost hepatocytes is too small to support conclusive statements on whether there exist systematic differences of the VTG response between in vitro culture systems, VTG analytical methods or fish species. The VTG hepatocyte assay detects sensitively natural and synthetic estrogens, whereas the response to xenoestrogens appears to be more variable. The detection of weak estrogens can be critical due to the overshadow with cytotoxic concentrations. Moreover, the VTG hepatocyte assay is able to detect antiestrogens as well as indirect estrogens, i.e substances which require metabolic activation to induce an estrogenic response. Nevertheless, more chemicals need to be analysed to corroborate this statement. It will be necessary to establish standardized protocols to minimize assay variability, and to develop a set of pass-fail criteria as well as cut-offs for designating positive and negative responses.

Polymorphism and chromosomal location of the MC4R (melanocortin-4 receptor) gene in the dog and red fox

The melanocortin-4 receptor (MC4R) is expressed in the hypothalamus and regulates energy intake and body weight. In silico screening of the canine chromosome 1 sequence and a comparison with the porcine MC4R sequence by BLAST were performed. The nucleotide sequence of the whole coding region and 3'- and 5'-flanking regions of the dog (1214 bp) and red fox (1177 bp) MC4R gene was established and high conservation of the nucleotide sequences was revealed (99%). Five sets of PCR primers were designed and a search for polymorphism was performed by the SSCP technique in a group of 31 dogs representing nineteen breeds and 35 farm red foxes. Sequencing of DNA fragments, representing the identified SSCP patterns, revealed three single nucleotide polymorphisms (including a missense one) in dogs and four silent SNPs in red foxes. An average SNP frequency was approx. 1/400 bp in the dog and 1/300 bp in the red fox. We mapped the MC4R gene by FISH to the canine chromosome 1 (CFA1q1.1) and to the red fox chromosome 5 (VVU5p1.2).

Proteins in silico-modeling and sampling

Proteins are linear chain molecules made out of amino acids. Only when they fold to their native states, they become functional. This dissertation aims to model the solvent (environment) effect and to develop & implement enhanced sampling methods that enable a reliable study of the protein folding problem in silico. We have developed an enhanced solvation model based on the solution to the Poisson-Boltzmann equation in order to describe the solvent effect. Following the quantum mechanical Polarizable Continuum Model (PCM), we decomposed net solvation free energy into three physical terms– Polarization, Dispersion and Cavitation. All the terms were implemented, analyzed and parametrized individually to obtain a high level of accuracy. In order to describe the thermodynamics of proteins, their conformational space needs to be sampled thoroughly. Simulations of proteins are hampered by slow relaxation due to their rugged free-energy landscape, with the barriers between minima being higher than the thermal energy at physiological temperatures. In order to overcome this problem a number of approaches have been proposed of which replica exchange method (REM) is the most popular. In this dissertation we describe a new variant of canonical replica exchange method in the context of molecular dynamic simulation. The advantage of this new method is the easily tunable high acceptance rate for the replica exchange. We call our method Microcanonical Replica Exchange Molecular Dynamic (MREMD). We have described the theoretical frame work, comment on its actual implementation, and its application to Trp-cage mini-protein in implicit solvent. We have been able to correctly predict the folding thermodynamics of this protein using our approach.

Modeling of human P450 oxidoreductase structure by in silico mutagenesis and MD simulation

P450 oxidoreductase (POR) is the obligate electron donor for microsomal cytochrome P450s and mutations in POR cause several metabolic disorders. We have modeled the structure of human P450 oxidoreductase by in silico amino acid replacements in the rat POR crystal structure. The rat POR has 94% homology with human POR and 38 amino acids were replaced to make its sequence identical to human POR. Several rounds of molecular dynamic simulations refined the model and removed structural clashes from side chain alterations of replaced amino acids. This approach has the advantage of keeping the cofactor contacts and structural features of the core enzyme intact which could not be achieved by homology based approaches. The final model from our approach was of high quality and compared well with experimentally determined structures of other PORs. This model will be used for analyzing the structural implications of mutations and polymorphisms in human POR.

Predictive oncology: a review of multidisciplinary, multiscale in silico modeling linking phenotype, morphology and growth.

Empirical evidence and theoretical studies suggest that the phenotype, i.e., cellular- and molecular-scale dynamics, including proliferation rate and adhesiveness due to microenvironmental factors and gene expression that govern tumor growth and invasiveness, also determine gross tumor-scale morphology. It has been difficult to quantify the relative effect of these links on disease progression and prognosis using conventional clinical and experimental methods and observables. As a result, successful individualized treatment of highly malignant and invasive cancers, such as glioblastoma, via surgical resection and chemotherapy cannot be offered and outcomes are generally poor. What is needed is a deterministic, quantifiable method to enable understanding of the connections between phenotype and tumor morphology. Here, we critically assess advantages and disadvantages of recent computational modeling efforts (e.g., continuum, discrete, and cellular automata models) that have pursued this understanding. Based on this assessment, we review a multiscale, i.e., from the molecular to the gross tumor scale, mathematical and computational "first-principle" approach based on mass conservation and other physical laws, such as employed in reaction-diffusion systems. Model variables describe known characteristics of tumor behavior, and parameters and functional relationships across scales are informed from in vitro, in vivo and ex vivo biology. We review the feasibility of this methodology that, once coupled to tumor imaging and tumor biopsy or cell culture data, should enable prediction of tumor growth and therapy outcome through quantification of the relation between the underlying dynamics and morphological characteristics. In particular, morphologic stability analysis of this mathematical model reveals that tumor cell patterning at the tumor-host interface is regulated by cell proliferation, adhesion and other phenotypic characteristics: histopathology information of tumor boundary can be inputted to the mathematical model and used as a phenotype-diagnostic tool to predict collective and individual tumor cell invasion of surrounding tissue. This approach further provides a means to deterministically test effects of novel and hypothetical therapy strategies on tumor behavior.

The performance of human papillomavirus high-risk DNA testing in the screening and diagnostic settings.

OBJECTIVE: We sought to evaluate the performance of the human papillomavirus high-risk DNA test in patients 30 years and older. MATERIALS AND METHODS: Screening (n=835) and diagnosis (n=518) groups were defined based on prior Papanicolaou smear results as part of a clinical trial for cervical cancer detection. We compared the Hybrid Capture II (HCII) test result with the worst histologic report. We used cervical intraepithelial neoplasia (CIN) 2/3 or worse as the reference of disease. We calculated sensitivities, specificities, positive and negative likelihood ratios (LR+ and LR-), receiver operating characteristic (ROC) curves, and areas under the ROC curves for the HCII test. We also considered alternative strategies, including Papanicolaou smear, a combination of Papanicolaou smear and the HCII test, a sequence of Papanicolaou smear followed by the HCII test, and a sequence of the HCII test followed by Papanicolaou smear. RESULTS: For the screening group, the sensitivity was 0.69 and the specificity was 0.93; the area under the ROC curve was 0.81. The LR+ and LR- were 10.24 and 0.34, respectively. For the diagnosis group, the sensitivity was 0.88 and the specificity was 0.78; the area under the ROC curve was 0.83. The LR+ and LR- were 4.06 and 0.14, respectively. Sequential testing showed little or no improvement over the combination testing. CONCLUSIONS: The HCII test in the screening group had a greater LR+ for the detection of CIN 2/3 or worse. HCII testing may be an additional screening tool for cervical cancer in women 30 years and older.

DISTRIBUTION OF NEONATAL HYPOTHYROIDISM IN TEXAS (SCREENING)

The State of Texas began mandatory screening for neonatal hypothyroidism in February 1980. The data from the first three years of the program's operation were compiled and incidence rates were calculated. Incidence rates include summary rates for the Texas population as well as specific rates by sex, race, geographic area, and month of the year.^ Differences in incidence rates were studied to determine whether these differences may be attributed to bias in data collection, or bias due to differences in blood thyroxine levels associated with sex, race, or geographic location.^ An attempt was made to definitively identify the type of neonatal hypothyroidism for each case from Harris County. Incidence rates were used to study relationships between specific diagnoses and race and sex. ^

Properties of plant food allergens: In silico studies

International Comparative Medicine Symposium. Allergology. Revisión de métodos computacionales para determinar propiedades de alergenos alimentarios de plantas

In silico identification and characterisation of 17 polymorphic anonymous non-coding sequence markers (ANMs) for red grouse (Lagopus lagopus scotica)

Peer reviewed

In silico detection of control signals: mRNA 3′-end-processing sequences in diverse species

We have investigated mRNA 3′-end-processing signals in each of six eukaryotic species (yeast, rice, arabidopsis, fruitfly, mouse, and human) through the analysis of more than 20,000 3′-expressed sequence tags. The use and conservation of the canonical AAUAAA element vary widely among the six species and are especially weak in plants and yeast. Even in the animal species, the AAUAAA signal does not appear to be as universal as indicated by previous studies. The abundance of single-base variants of AAUAAA correlates with their measured processing efficiencies. As found previously, the plant polyadenylation signals are more similar to those of yeast than to those of animals, with both common content and arrangement of the signal elements. In all species examined, the complete polyadenylation signal appears to consist of an aggregate of multiple elements. In light of these and previous results, we present a broadened concept of 3′-end-processing signals in which no single exact sequence element is universally required for processing. Rather, the total efficiency is a function of all elements and, importantly, an inefficient word in one element can be compensated for by strong words in other elements. These complex patterns indicate that effective tools to identify 3′-end-processing signals will require more than consensus sequence identification.

The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities

The Escherichia coli MG1655 genome has been completely sequenced. The annotated sequence, biochemical information, and other information were used to reconstruct the E. coli metabolic map. The stoichiometric coefficients for each metabolic enzyme in the E. coli metabolic map were assembled to construct a genome-specific stoichiometric matrix. The E. coli stoichiometric matrix was used to define the system's characteristics and the capabilities of E. coli metabolism. The effects of gene deletions in the central metabolic pathways on the ability of the in silico metabolic network to support growth were assessed, and the in silico predictions were compared with experimental observations. It was shown that based on stoichiometric and capacity constraints the in silico analysis was able to qualitatively predict the growth potential of mutant strains in 86% of the cases examined. Herein, it is demonstrated that the synthesis of in silico metabolic genotypes based on genomic, biochemical, and strain-specific information is possible, and that systems analysis methods are available to analyze and interpret the metabolic phenotype.