Evidence for transcript networks composed of chimeric RNAs in human cells.

The classic organization of a gene structure has followed the Jacob and Monod bacterial gene model proposed more than 50 years ago. Since then, empirical determinations of the complexity of the transcriptomes found in yeast to human has blurred the definition and physical boundaries of genes. Using multiple analysis approaches we have characterized individual gene boundaries mapping on human chromosomes 21 and 22. Analyses of the locations of the 5' and 3' transcriptional termini of 492 protein coding genes revealed that for 85% of these genes the boundaries extend beyond the current annotated termini, most often connecting with exons of transcripts from other well annotated genes. The biological and evolutionary importance of these chimeric transcripts is underscored by (1) the non-random interconnections of genes involved, (2) the greater phylogenetic depth of the genes involved in many chimeric interactions, (3) the coordination of the expression of connected genes and (4) the close in vivo and three dimensional proximity of the genomic regions being transcribed and contributing to parts of the chimeric RNAs. The non-random nature of the connection of the genes involved suggest that chimeric transcripts should not be studied in isolation, but together, as an RNA network.

Is MDCT enough reliable for the evaluation of acute colitis including the underlying etiology? : correlation with colonoscopy and pathology : P2

Purpose: 1. To assess the diagnostic value of MDCT for acute colitis of various origin confirmed by colonoscopy and histology. 2. To evaluate the accuracy of MDCT of making the correct differential diagnosis. Methods and materials: The electronic hospital database from January 2006 to August 2008 revealed 351 patients with acute colitis of any origin wdetected by colonoscopy. In 85 out of these patients MDCT had been simultaneously performed (delay 3.1 days). Two radiologists jointly reviewed their corresponding CT features without knowledge of pathology and correlated them with the final histological diagnosis. Results: Eighty patients were finally included (46 women, mean age 63.4). Colitis was of ischemic (n = 35, 44%) or infectious (n = 15, 19%) origin. 18 patients (23%) had acute ulcerative colitis or Crohn's disease, in 10 patients (12%) another inflammatory cause and in two patients (2%) post radiation colitis was proven. MDCT was positive in 63 patients (78.9%). In 11 out of the 17 negative MDCT, the examination had been performed without large bowel distention. Ischemic colitis was responsible for 47.1% of the negative MDCT. Correct differential diagnosis was made in 32 (50.7%) out of the 63 positive MDCT. Among the different etiologies, the ischemic colitis was the most often misdiagnosed cause (n = 17, 58.6%). Conclusion: Large bowel distension is mandatory for reliable MDCT detection of acute colitis of any origin. Among the different aetiologies the ischemic cause is the most often associated with false negative MDCT findings and, in case of positive features, the most difficult to recognize as such.

Correlation of tumor-associated antigens MAGE, NY-ESO-1 and P53 expression with clinical and pathological relationships of patients with oral squamous cell carcinoma

Despite advances in the diagnosisand treatment of head and neck cancer,survival rates have not improvedover recent years. New therapeuticstrategies, including immunotherapy,are the subject of extensive research.In several types of tumors, the presenceof tumor infiltrating lymphocytes(TILs), notably CD8+ T cellsand dendritic cells, has been correlatedwith improved prognosis. Moreover,some T cells among TILs havebeen shown to kill tumor cells in vitroupon recognition of tumor-associatedantigens. Tumor associated antigensare expressed in a significant proportionof squamous cell carcinoma ofthe head and neck and apparently mayplay a role in the regulation of cancercell growth notably by inhibition ofp53 protein function in some cancers.The MAGE family CT antigens couldtherefore potentially be used as definedtargets for immunotherapy andtheir study bring new insight in tumorgrowth regulation mechanisms. Between1995 - 2005 54 patients weretreated surgically in our institution forsquamous cell carcinoma of the oralcavity. Patient and clinical data wasobtained from patient files and collectedinto a computerized database.For each patient, paraffin embeddedtumor specimens were retrieved andexpression of MAGE CT antigens,p53, NY-OESO-1 were analyzed byimmunohistochemistry. Results werethen correlated with histopathologicalparameter such as tumor depth,front invasion according to Bryne andboth, local control and disease freesurvival. MAGE-A was expressed in52% of patients. NY-ESO-1 and p53expression was found in 7% and 52%cases respectively. A higher tumordepth was significantly correlatedwith expression of MAGE-Aproteins(p = 0.03). No significant correlationcould be made between the expressionof both p53 andNY-OESO-1 andhistopathological parameters. Expressionof tumor-associated antigendid not seem to impact significantlyon patient prognosis. As does thedemonstration of p53 function inhibitionby CT antigens of MAGE family,our results suggest, that tumor associatedantigens may be implicated in tumorprogression mechanisms. Thishypothesis need further investigationto clarify the relationship betweenhost immune response and local tumorbiology.

Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science

Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates difficulties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader's own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

Heterogeneity of Ia antigen expression on myeloblastic leukaemias: correlation with stage of maturation defined by cytochemical markers.

The expression of Ia-like antigen (Ia) has been studied in 55 cases of acute myeloid leukaemia (AML) in correlation with the expression of both Sudan Black (SB) and naphthol AS-D chloroacetate esterase (NCAE) stains. Operationally the AML cases were divided into three groups using only NCAE expression on the leukaemic cells: the first group with early maturation stage (MS1) consisted of 30 cases with less than 10% NCAE positive cells (SB: 15-100%): the MS2 group of 14 cases with 10-70% NCAE positive cells (SB: 65-100%) and the MS3 group of 11 cases with 70-100% NCAE positive cells (SB: 89-100%). Ia expression was determined by complement-dependent cytotoxicity, immunofluorescence and immunoperoxidase methods. A similar high percentage (80%) of patients from both group MS1 and MS2 expressed Ia on the surface of 32-100% of the cells. Furthermore, individual comparison of all cases from these two groups showed no correlation between Ia, NCAE and SB expression. Only in the 11 cases from the MS3 group, which included nine cases of promyelocytic leukaemias, was there a correlation between very low expression of Ia antigen with the high NCAE expression. Thus, for AML with a low degree of differentiation the expression of Ia seems to be independent of conventional cytochemical markers of cell maturation.

Expression of human estrogen receptor mutants in Xenopus oocytes: correlation between transcriptional activity and ability to form protein-DNA complexes.

The human estrogen receptor (hER) is a trans-acting regulatory protein composed of a series of discrete functional domains. We have microinjected an hER expression vector (HEO) into Xenopus oocyte nuclei and demonstrate, using Western blot assay, that the hER is synthesized. When nuclear extracts from oocytes were prepared and incubated in the presence of a 2.7 kb DNA fragment comprising the 5' end of the vitellogenin gene B2, formation of estrogen-dependent complexes could be visualized by electron microscopy over the estrogen responsive element (ERE). Of crucial importance is the observation that the complex formation is inhibited by the estrogen antagonist tamoxifen, is restored by the addition of the hormone and does not take place with extracts from control oocytes injected with the expression vector lacking the sequences encoding the receptor. The presence of the biologically active hER is confirmed in co-injection experiments, in which HEO is co-introduced with a CAT reporter gene under the control of a vitellogenin promoter containing or lacking the ERE. CAT assays and primer extensions analyses reveal that both the receptor and the ERE are essential for estrogen induced stimulation of transcription. The same approach was used to analyze selective hER mutants. We find that the DNA binding domain (region C) is essential for protein--DNA complex formation at the ERE but is not sufficient by itself to activate transcription from the reporter gene. In addition to region C, both the hormone binding (region E) and amino terminal (region A/B) domains are needed for an efficient transcription activation.(ABSTRACT TRUNCATED AT 250 WORDS)

Dynamical modeling and analysis of large cellular regulatory networks.

The dynamical analysis of large biological regulatory networks requires the development of scalable methods for mathematical modeling. Following the approach initially introduced by Thomas, we formalize the interactions between the components of a network in terms of discrete variables, functions, and parameters. Model simulations result in directed graphs, called state transition graphs. We are particularly interested in reachability properties and asymptotic behaviors, which correspond to terminal strongly connected components (or "attractors") in the state transition graph. A well-known problem is the exponential increase of the size of state transition graphs with the number of network components, in particular when using the biologically realistic asynchronous updating assumption. To address this problem, we have developed several complementary methods enabling the analysis of the behavior of large and complex logical models: (i) the definition of transition priority classes to simplify the dynamics; (ii) a model reduction method preserving essential dynamical properties, (iii) a novel algorithm to compact state transition graphs and directly generate compressed representations, emphasizing relevant transient and asymptotic dynamical properties. The power of an approach combining these different methods is demonstrated by applying them to a recent multilevel logical model for the network controlling CD4+ T helper cell response to antigen presentation and to a dozen cytokines. This model accounts for the differentiation of canonical Th1 and Th2 lymphocytes, as well as of inflammatory Th17 and regulatory T cells, along with many hybrid subtypes. All these methods have been implemented into the software GINsim, which enables the definition, the analysis, and the simulation of logical regulatory graphs.

Decision-theoretic analysis of forensic sampling criteria using Bayesian decision networks

Sampling issues represent a topic of ongoing interest to the forensic science community essentially because of their crucial role in laboratory planning and working protocols. For this purpose, forensic literature described thorough (Bayesian) probabilistic sampling approaches. These are now widely implemented in practice. They allow, for instance, to obtain probability statements that parameters of interest (e.g., the proportion of a seizure of items that present particular features, such as an illegal substance) satisfy particular criteria (e.g., a threshold or an otherwise limiting value). Currently, there are many approaches that allow one to derive probability statements relating to a population proportion, but questions on how a forensic decision maker - typically a client of a forensic examination or a scientist acting on behalf of a client - ought actually to decide about a proportion or a sample size, remained largely unexplored to date. The research presented here intends to address methodology from decision theory that may help to cope usefully with the wide range of sampling issues typically encountered in forensic science applications. The procedures explored in this paper enable scientists to address a variety of concepts such as the (net) value of sample information, the (expected) value of sample information or the (expected) decision loss. All of these aspects directly relate to questions that are regularly encountered in casework. Besides probability theory and Bayesian inference, the proposed approach requires some additional elements from decision theory that may increase the efforts needed for practical implementation. In view of this challenge, the present paper will emphasise the merits of graphical modelling concepts, such as decision trees and Bayesian decision networks. These can support forensic scientists in applying the methodology in practice. How this may be achieved is illustrated with several examples. The graphical devices invoked here also serve the purpose of supporting the discussion of the similarities, differences and complementary aspects of existing Bayesian probabilistic sampling criteria and the decision-theoretic approach proposed throughout this paper.