Identifying gnostic predictors of the vaccine response.

PubMed

Haining, W Nicholas; Pulendran, Bali

2012-06-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis of their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of vaccine prediction. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. Copyright © 2012 Elsevier Ltd. All rights reserved.

Action Unit Models of Facial Expression of Emotion in the Presence of Speech

PubMed Central

Shah, Miraj; Cooper, David G.; Cao, Houwei; Gur, Ruben C.; Nenkova, Ani; Verma, Ragini

2014-01-01

Automatic recognition of emotion using facial expressions in the presence of speech poses a unique challenge because talking reveals clues for the affective state of the speaker but distorts the canonical expression of emotion on the face. We introduce a corpus of acted emotion expression where speech is either present (talking) or absent (silent). The corpus is uniquely suited for analysis of the interplay between the two conditions. We use a multimodal decision level fusion classifier to combine models of emotion from talking and silent faces as well as from audio to recognize five basic emotions: anger, disgust, fear, happy and sad. Our results strongly indicate that emotion prediction in the presence of speech from action unit facial features is less accurate when the person is talking. Modeling talking and silent expressions separately and fusing the two models greatly improves accuracy of prediction in the talking setting. The advantages are most pronounced when silent and talking face models are fused with predictions from audio features. In this multi-modal prediction both the combination of modalities and the separate models of talking and silent facial expression of emotion contribute to the improvement. PMID:25525561

Sex differences in Portuguese lonely hearts advertisements.

PubMed

Neto, Félix

2005-10-01

Advertisements from "Lonely Hearts" columns in the major daily Portuguese newspaper (Jornal de Notícias) were used to test hypotheses about the mate preferences of men and women. A total of 484 advertisements were coded for demographic descriptors and offers of and appeals for attractiveness, financial security, sincerity, expressiveness, and instrumentality, e.g., intelligence and ambition. Some results supported social exchange and evolutionary predictions: men sought younger women and offered security; women sought older men with status and resources. However, other results challenged such predictions: attractiveness and expressiveness did not differ by sex.

Temporal Expression-based Analysis of Metabolism

PubMed Central

Segrè, Daniel

2012-01-01

Metabolic flux is frequently rerouted through cellular metabolism in response to dynamic changes in the intra- and extra-cellular environment. Capturing the mechanisms underlying these metabolic transitions in quantitative and predictive models is a prominent challenge in systems biology. Progress in this regard has been made by integrating high-throughput gene expression data into genome-scale stoichiometric models of metabolism. Here, we extend previous approaches to perform a Temporal Expression-based Analysis of Metabolism (TEAM). We apply TEAM to understanding the complex metabolic dynamics of the respiratorily versatile bacterium Shewanella oneidensis grown under aerobic, lactate-limited conditions. TEAM predicts temporal metabolic flux distributions using time-series gene expression data. Increased predictive power is achieved by supplementing these data with a large reference compendium of gene expression, which allows us to take into account the unique character of the distribution of expression of each individual gene. We further propose a straightforward method for studying the sensitivity of TEAM to changes in its fundamental free threshold parameter θ, and reveal that discrete zones of distinct metabolic behavior arise as this parameter is changed. By comparing the qualitative characteristics of these zones to additional experimental data, we are able to constrain the range of θ to a small, well-defined interval. In parallel, the sensitivity analysis reveals the inherently difficult nature of dynamic metabolic flux modeling: small errors early in the simulation propagate to relatively large changes later in the simulation. We expect that handling such “history-dependent” sensitivities will be a major challenge in the future development of dynamic metabolic-modeling techniques. PMID:23209390

CLIC, a tool for expanding biological pathways based on co-expression across thousands of datasets

PubMed Central

Li, Yang; Liu, Jun S.; Mootha, Vamsi K.

2017-01-01

In recent years, there has been a huge rise in the number of publicly available transcriptional profiling datasets. These massive compendia comprise billions of measurements and provide a special opportunity to predict the function of unstudied genes based on co-expression to well-studied pathways. Such analyses can be very challenging, however, since biological pathways are modular and may exhibit co-expression only in specific contexts. To overcome these challenges we introduce CLIC, CLustering by Inferred Co-expression. CLIC accepts as input a pathway consisting of two or more genes. It then uses a Bayesian partition model to simultaneously partition the input gene set into coherent co-expressed modules (CEMs), while assigning the posterior probability for each dataset in support of each CEM. CLIC then expands each CEM by scanning the transcriptome for additional co-expressed genes, quantified by an integrated log-likelihood ratio (LLR) score weighted for each dataset. As a byproduct, CLIC automatically learns the conditions (datasets) within which a CEM is operative. We implemented CLIC using a compendium of 1774 mouse microarray datasets (28628 microarrays) or 1887 human microarray datasets (45158 microarrays). CLIC analysis reveals that of 910 canonical biological pathways, 30% consist of strongly co-expressed gene modules for which new members are predicted. For example, CLIC predicts a functional connection between protein C7orf55 (FMC1) and the mitochondrial ATP synthase complex that we have experimentally validated. CLIC is freely available at www.gene-clic.org. We anticipate that CLIC will be valuable both for revealing new components of biological pathways as well as the conditions in which they are active. PMID:28719601

Creating and validating cis-regulatory maps of tissue-specific gene expression regulation

PubMed Central

O'Connor, Timothy R.; Bailey, Timothy L.

2014-01-01

Predicting which genomic regions control the transcription of a given gene is a challenge. We present a novel computational approach for creating and validating maps that associate genomic regions (cis-regulatory modules–CRMs) with genes. The method infers regulatory relationships that explain gene expression observed in a test tissue using widely available genomic data for ‘other’ tissues. To predict the regulatory targets of a CRM, we use cross-tissue correlation between histone modifications present at the CRM and expression at genes within 1 Mbp of it. To validate cis-regulatory maps, we show that they yield more accurate models of gene expression than carefully constructed control maps. These gene expression models predict observed gene expression from transcription factor binding in the CRMs linked to that gene. We show that our maps are able to identify long-range regulatory interactions and improve substantially over maps linking genes and CRMs based on either the control maps or a ‘nearest neighbor’ heuristic. Our results also show that it is essential to include CRMs predicted in multiple tissues during map-building, that H3K27ac is the most informative histone modification, and that CAGE is the most informative measure of gene expression for creating cis-regulatory maps. PMID:25200088

Inter-species prediction of protein phosphorylation in the sbv IMPROVER species translation challenge

PubMed Central

Biehl, Michael; Sadowski, Peter; Bhanot, Gyan; Bilal, Erhan; Dayarian, Adel; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Zeller, Michael D.; Hormoz, Sahand

2015-01-01

Motivation: Animal models are widely used in biomedical research for reasons ranging from practical to ethical. An important issue is whether rodent models are predictive of human biology. This has been addressed recently in the framework of a series of challenges designed by the systems biology verification for Industrial Methodology for Process Verification in Research (sbv IMPROVER) initiative. In particular, one of the sub-challenges was devoted to the prediction of protein phosphorylation responses in human bronchial epithelial cells, exposed to a number of different chemical stimuli, given the responses in rat bronchial epithelial cells. Participating teams were asked to make inter-species predictions on the basis of available training examples, comprising transcriptomics and phosphoproteomics data. Results: Here, the two best performing teams present their data-driven approaches and computational methods. In addition, post hoc analyses of the datasets and challenge results were performed by the participants and challenge organizers. The challenge outcome indicates that successful prediction of protein phosphorylation status in human based on rat phosphorylation levels is feasible. However, within the limitations of the computational tools used, the inclusion of gene expression data does not improve the prediction quality. The post hoc analysis of time-specific measurements sheds light on the signaling pathways in both species. Availability and implementation: A detailed description of the dataset, challenge design and outcome is available at www.sbvimprover.com. The code used by team IGB is provided under http://github.com/uci-igb/improver2013. Implementations of the algorithms applied by team AMG are available at http://bhanot.biomaps.rutgers.edu/wiki/AMG-sc2-code.zip. Contact: meikelbiehl@gmail.com PMID:24994890

Sig2GRN: a software tool linking signaling pathway with gene regulatory network for dynamic simulation.

PubMed

Zhang, Fan; Liu, Runsheng; Zheng, Jie

2016-12-23

Linking computational models of signaling pathways to predicted cellular responses such as gene expression regulation is a major challenge in computational systems biology. In this work, we present Sig2GRN, a Cytoscape plugin that is able to simulate time-course gene expression data given the user-defined external stimuli to the signaling pathways. A generalized logical model is used in modeling the upstream signaling pathways. Then a Boolean model and a thermodynamics-based model are employed to predict the downstream changes in gene expression based on the simulated dynamics of transcription factors in signaling pathways. Our empirical case studies show that the simulation of Sig2GRN can predict changes in gene expression patterns induced by DNA damage signals and drug treatments. As a software tool for modeling cellular dynamics, Sig2GRN can facilitate studies in systems biology by hypotheses generation and wet-lab experimental design. http://histone.scse.ntu.edu.sg/Sig2GRN/.

Identifying gnostic predictors of the vaccine response

PubMed Central

Haining, W. Nicholas; Pulendran, Bali

2012-01-01

Molecular predictors of the response to vaccination could transform vaccine development. They would allow larger numbers of vaccine candidates to be rapidly screened, shortening the development time for new vaccines. Gene-expression based predictors of vaccine response have shown early promise. However, a limitation of gene-expression based predictors is that they often fail to reveal the mechanistic basis for their ability to classify response. Linking predictive signatures to the function of their component genes would advance basic understanding of vaccine immunity and also improve the robustness of outcome classification. New analytic tools now allow more biological meaning to be extracted from predictive signatures. Functional genomic approaches to perturb gene expression in mammalian cells permit the function of predictive genes to be surveyed in highly parallel experiments. The challenge for vaccinologists is therefore to use these tools to embed mechanistic insights into predictors of vaccine response. PMID:22633886

Prediction of gene expression in embryonic structures of Drosophila melanogaster.

PubMed

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-07-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms.

Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster

PubMed Central

Samsonova, Anastasia A; Niranjan, Mahesan; Russell, Steven; Brazma, Alvis

2007-01-01

Understanding how sets of genes are coordinately regulated in space and time to generate the diversity of cell types that characterise complex metazoans is a major challenge in modern biology. The use of high-throughput approaches, such as large-scale in situ hybridisation and genome-wide expression profiling via DNA microarrays, is beginning to provide insights into the complexities of development. However, in many organisms the collection and annotation of comprehensive in situ localisation data is a difficult and time-consuming task. Here, we present a widely applicable computational approach, integrating developmental time-course microarray data with annotated in situ hybridisation studies, that facilitates the de novo prediction of tissue-specific expression for genes that have no in vivo gene expression localisation data available. Using a classification approach, trained with data from microarray and in situ hybridisation studies of gene expression during Drosophila embryonic development, we made a set of predictions on the tissue-specific expression of Drosophila genes that have not been systematically characterised by in situ hybridisation experiments. The reliability of our predictions is confirmed by literature-derived annotations in FlyBase, by overrepresentation of Gene Ontology biological process annotations, and, in a selected set, by detailed gene-specific studies from the literature. Our novel organism-independent method will be of considerable utility in enriching the annotation of gene function and expression in complex multicellular organisms. PMID:17658945

Semi-supervised prediction of gene regulatory networks using machine learning algorithms.

PubMed

Patel, Nihir; Wang, Jason T L

2015-10-01

Use of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this article, we propose semi-supervised methods for GRN prediction by utilizing two machine learning algorithms, namely, support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabelled data for training. We investigated inductive and transductive learning approaches, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabelled data. We then applied our semi-supervised methods to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of our methods using the expression data. Our analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.

The relation between intrapersonal and interpersonal staff behaviour towards clients with ID and challenging behaviour: a validation study of the Staff-Client Interactive Behaviour Inventory.

PubMed

Willems, A P A M; Embregts, P J C M; Stams, G J J M; Moonen, X M H

2010-01-01

Interpersonal staff behaviour is one of the instigating factors associated with challenging behaviour in clients with intellectual disabilities (ID). There are several studies focusing on the influence of intrapersonal staff characteristics - such as beliefs, attributions and emotional reactions - on staff behaviour. Little is known, however, about interpersonal staff behaviour itself. This study describes the development and validation of the Staff-Client Interactive Behaviour Inventory (SCIBI), measuring both intrapersonal and interpersonal staff behaviour in response to challenging behaviour in clients with ID. A total of 292 staff members, employed in residential and community services, completed the SCIBI for 34 clients with ID and challenging behaviour. Confirmatory factor analysis of a seven-factor model - with assertive control, hostile, friendly and support-seeking interpersonal behaviour; proactive thinking; self-reflection; and critical expressed emotion as reliable factors - showed an exact fit to the data, indicating construct validity and reliability of the SCIBI. A series of multilevel regression analyses showed higher age of the client to be negatively associated with assertive control. Job experience, level of education, type and sex of staff predicted interpersonal behaviour. Also, intrapersonal staff behaviour, including critical expressed emotion, proactive thinking and self-reflection, predicted interpersonal behaviour. The SCIBI can be used to identify staff intrapersonal and interpersonal behaviour towards clients with ID and challenging behaviour. Results obtained with the SCIBI can provide new directions for individual client treatment plans and staff training programmes.

Development of an objective gene expression panel as an alternative to self-reported symptom scores in human influenza challenge trials.

PubMed

Muller, Julius; Parizotto, Eneida; Antrobus, Richard; Francis, James; Bunce, Campbell; Stranks, Amanda; Nichols, Marshall; McClain, Micah; Hill, Adrian V S; Ramasamy, Adaikalavan; Gilbert, Sarah C

2017-06-08

Influenza challenge trials are important for vaccine efficacy testing. Currently, disease severity is determined by self-reported scores to a list of symptoms which can be highly subjective. A more objective measure would allow for improved data analysis. Twenty-one volunteers participated in an influenza challenge trial. We calculated the daily sum of scores (DSS) for a list of 16 influenza symptoms. Whole blood collected at baseline and 24, 48, 72 and 96 h post challenge was profiled on Illumina HT12v4 microarrays. Changes in gene expression most strongly correlated with DSS were selected to train a Random Forest model and tested on two independent test sets consisting of 41 individuals profiled on a different microarray platform and 33 volunteers assayed by qRT-PCR. 1456 probes are significantly associated with DSS at 1% false discovery rate. We selected 19 genes with the largest fold change to train a random forest model. We observed good concordance between predicted and actual scores in the first test set (r = 0.57; RMSE = -16.1%) with the greatest agreement achieved on samples collected approximately 72 h post challenge. Therefore, we assayed samples collected at baseline and 72 h post challenge in the second test set by qRT-PCR and observed good concordance (r = 0.81; RMSE = -36.1%). We developed a 19-gene qRT-PCR panel to predict DSS, validated on two independent datasets. A transcriptomics based panel could provide a more objective measure of symptom scoring in future influenza challenge studies. Trial registration Samples were obtained from a clinical trial with the ClinicalTrials.gov Identifier: NCT02014870, first registered on December 5, 2013.

Interleukin-27 is a novel candidate diagnostic biomarker for bacterial infection in critically ill children.

PubMed

Wong, Hector R; Cvijanovich, Natalie Z; Hall, Mark; Allen, Geoffrey L; Thomas, Neal J; Freishtat, Robert J; Anas, Nick; Meyer, Keith; Checchia, Paul A; Lin, Richard; Bigham, Michael T; Sen, Anita; Nowak, Jeffrey; Quasney, Michael; Henricksen, Jared W; Chopra, Arun; Banschbach, Sharon; Beckman, Eileen; Harmon, Kelli; Lahni, Patrick; Shanley, Thomas P

2012-10-29

Differentiating between sterile inflammation and bacterial infection in critically ill patients with fever and other signs of the systemic inflammatory response syndrome (SIRS) remains a clinical challenge. The objective of our study was to mine an existing genome-wide expression database for the discovery of candidate diagnostic biomarkers to predict the presence of bacterial infection in critically ill children. Genome-wide expression data were compared between patients with SIRS having negative bacterial cultures (n = 21) and patients with sepsis having positive bacterial cultures (n = 60). Differentially expressed genes were subjected to a leave-one-out cross-validation (LOOCV) procedure to predict SIRS or sepsis classes. Serum concentrations of interleukin-27 (IL-27) and procalcitonin (PCT) were compared between 101 patients with SIRS and 130 patients with sepsis. All data represent the first 24 hours of meeting criteria for either SIRS or sepsis. Two hundred twenty one gene probes were differentially regulated between patients with SIRS and patients with sepsis. The LOOCV procedure correctly predicted 86% of the SIRS and sepsis classes, and Epstein-Barr virus-induced gene 3 (EBI3) had the highest predictive strength. Computer-assisted image analyses of gene-expression mosaics were able to predict infection with a specificity of 90% and a positive predictive value of 94%. Because EBI3 is a subunit of the heterodimeric cytokine, IL-27, we tested the ability of serum IL-27 protein concentrations to predict infection. At a cut-point value of ≥5 ng/ml, serum IL-27 protein concentrations predicted infection with a specificity and a positive predictive value of >90%, and the overall performance of IL-27 was generally better than that of PCT. A decision tree combining IL-27 and PCT improved overall predictive capacity compared with that of either biomarker alone. Genome-wide expression analysis has provided the foundation for the identification of IL-27 as a novel candidate diagnostic biomarker for predicting bacterial infection in critically ill children. Additional studies will be required to test further the diagnostic performance of IL-27. The microarray data reported in this article have been deposited in the Gene Expression Omnibus under accession number GSE4607.

Macromolecular Expression and Function: A New Paradigm for NASA Risk Assessment

NASA Technical Reports Server (NTRS)

Richmond, Robert

2003-01-01

Predicting risks in humans of either acute effects such as bone loss or muscle wasting, or late effects such as cancer, is challenging. To an approximation, this is because uncertainties of exposure to stress factors or toxic agents and the uniformity of processing subsequent damage at the cellular level within a complex set of biological variables degrade the confidence of predicting pathologic outcome. A cellular biodosimeter that simultaneously reports 1) the type of damage due to that exposure, 2) the quantity of damage incurred by that exposure, and 3) the dataset used to assess risk of developing pathologic outcome caused by that exposure would therefore be useful for predicting ultimate risks faced by an individual, such as an astronaut. It is suggested that such a biodosimeter can be based upon analyses of gene-expression and protein expression whereby large datasets of cellular response to damage are obtained and analyzed for expression-profiles correlated with established end points and molecular markers predictive for risks being assessed. The usefulness of multiparametric cellular biodosimeters could be realized by quantitatively profiling these datasets using techniques of bioinformatics. Such an approach contributes to the foundation of molecular epidemiology as a new scientific discipline, and represents a new paradigm of risk assessment.

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

PubMed

Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik

2017-01-01

Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

Progress and challenges in bioinformatics approaches for enhancer identification

PubMed Central

Kleftogiannis, Dimitrios; Kalnis, Panos

2016-01-01

Enhancers are cis-acting DNA elements that play critical roles in distal regulation of gene expression. Identifying enhancers is an important step for understanding distinct gene expression programs that may reflect normal and pathogenic cellular conditions. Experimental identification of enhancers is constrained by the set of conditions used in the experiment. This requires multiple experiments to identify enhancers, as they can be active under specific cellular conditions but not in different cell types/tissues or cellular states. This has opened prospects for computational prediction methods that can be used for high-throughput identification of putative enhancers to complement experimental approaches. Potential functions and properties of predicted enhancers have been catalogued and summarized in several enhancer-oriented databases. Because the current methods for the computational prediction of enhancers produce significantly different enhancer predictions, it will be beneficial for the research community to have an overview of the strategies and solutions developed in this field. In this review, we focus on the identification and analysis of enhancers by bioinformatics approaches. First, we describe a general framework for computational identification of enhancers, present relevant data types and discuss possible computational solutions. Next, we cover over 30 existing computational enhancer identification methods that were developed since 2000. Our review highlights advantages, limitations and potentials, while suggesting pragmatic guidelines for development of more efficient computational enhancer prediction methods. Finally, we discuss challenges and open problems of this topic, which require further consideration. PMID:26634919

Biological interpretation of genome-wide association studies using predicted gene functions.

PubMed

Pers, Tune H; Karjalainen, Juha M; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R; Yang, Jian; Lui, Julian C; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S N; Hirschhorn, Joel N; Franke, Lude

2015-01-19

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes.

Inter-species pathway perturbation prediction via data-driven detection of functional homology.

PubMed

Hafemeister, Christoph; Romero, Roberto; Bilal, Erhan; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Bonneau, Richard; Tarca, Adi L

2015-02-15

Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli. We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli. Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h. christoph.hafemeister@nyu.edu or atarca@med.wayne.edu. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

Individual Differences in Typical Reappraisal Use Predict Amygdala and Prefrontal Responses

PubMed Central

Drabant, Emily M.; McRae, Kateri; Manuck, Stephen B.; Hariri, Ahmad R.; Gross, James J.

2010-01-01

Background Participants who are instructed to use reappraisal to downregulate negative emotion show decreased amygdala responses and increased prefrontal responses. However, it is not known whether individual differences in the tendency to use reappraisal manifests in similar neural responses when individuals are spontaneously confronted with negative situations. Such spontaneous emotion regulation might play an important role in normal and pathological responses to the emotional challenges of everyday life. Methods Fifty-six healthy women completed a blood oxygenation-level dependent functional magnetic resonance imaging challenge paradigm involving the perceptual processing of emotionally negative facial expressions. Participants also completed measures of typical emotion regulation use, trait anxiety, and neuroticism. Results Greater use of reappraisal in everyday life was related to decreased amygdala activity and increased prefrontal and parietal activity during the processing of negative emotional facial expressions. These associations were not attributable to variation in trait anxiety, neuroticism, or the use of another common form of emotion regulation, namely suppression. Conclusions These findings suggest that, like instructed reappraisal, individual differences in reappraisal use are associated with decreased activation in ventral emotion generative regions and increased activation in prefrontal control regions in response to negative stimuli. Such individual differences in emotion regulation might predict successful coping with emotional challenges as well as the onset of affective disorders. PMID:18930182

The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

PubMed Central

Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A.; Kim, Sungjoon; Wilson, Christopher J.; Lehár, Joseph; Kryukov, Gregory V.; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F.; Monahan, John E.; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A.; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H.; Cheng, Jill; Yu, Guoying K.; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D.; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C.; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P.; Gabriel, Stacey B.; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E.; Weber, Barbara L.; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L.; Meyerson, Matthew; Golub, Todd R.; Morrissey, Michael P.; Sellers, William R.; Schlegel, Robert; Garraway, Levi A.

2012-01-01

The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available1. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens2. PMID:22460905

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

PubMed

Barretina, Jordi; Caponigro, Giordano; Stransky, Nicolas; Venkatesan, Kavitha; Margolin, Adam A; Kim, Sungjoon; Wilson, Christopher J; Lehár, Joseph; Kryukov, Gregory V; Sonkin, Dmitriy; Reddy, Anupama; Liu, Manway; Murray, Lauren; Berger, Michael F; Monahan, John E; Morais, Paula; Meltzer, Jodi; Korejwa, Adam; Jané-Valbuena, Judit; Mapa, Felipa A; Thibault, Joseph; Bric-Furlong, Eva; Raman, Pichai; Shipway, Aaron; Engels, Ingo H; Cheng, Jill; Yu, Guoying K; Yu, Jianjun; Aspesi, Peter; de Silva, Melanie; Jagtap, Kalpana; Jones, Michael D; Wang, Li; Hatton, Charles; Palescandolo, Emanuele; Gupta, Supriya; Mahan, Scott; Sougnez, Carrie; Onofrio, Robert C; Liefeld, Ted; MacConaill, Laura; Winckler, Wendy; Reich, Michael; Li, Nanxin; Mesirov, Jill P; Gabriel, Stacey B; Getz, Gad; Ardlie, Kristin; Chan, Vivien; Myer, Vic E; Weber, Barbara L; Porter, Jeff; Warmuth, Markus; Finan, Peter; Harris, Jennifer L; Meyerson, Matthew; Golub, Todd R; Morrissey, Michael P; Sellers, William R; Schlegel, Robert; Garraway, Levi A

2012-03-28

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

Customizing chemotherapy for colon cancer: the potential of gene expression profiling.

PubMed

Mariadason, John M; Arango, Diego; Augenlicht, Leonard H

2004-06-01

The value of gene expression profiling, or microarray analysis, for the classification and prognosis of multiple forms of cancer is now clearly established. For colon cancer, expression profiling can readily discriminate between normal and tumor tissue, and to some extent between tumors of different histopathological stage and prognosis. While a definitive in vivo study demonstrating the potential of this methodology for predicting response to chemotherapy is presently lacking, the ability of microarrays to distinguish other subtleties of colon cancer phenotype, as well as recent in vitro proof-of-principle experiments utilizing colon cancer cell lines, illustrate the potential of this methodology for predicting the probability of response to specific chemotherapeutic agents. This review discusses some of the recent advances in the use of microarray analysis for understanding and distinguishing colon cancer subtypes, and attempts to identify challenges that need to be overcome in order to achieve the goal of using gene expression profiling for customizing chemotherapy in colon cancer.

GLUT1 expression in pediatric adrenocortical tumors: a promising candidate to predict clinical behavior.

PubMed

Pinheiro, Céline; Granja, Sara; Longatto-Filho, Adhemar; Faria, André M; Fragoso, Maria C B V; Lovisolo, Silvana M; Bonatelli, Murilo; Costa, Ricardo F A; Lerário, Antonio M; Almeida, Madson Q; Baltazar, Fátima; Zerbini, Maria C N

2017-09-08

Discrimination between benign and malignant tumors is a challenging process in pediatric adrenocortical tumors. New insights in the metabolic profile of pediatric adrenocortical tumors may contribute to this distinction, predict prognosis, as well as identify new molecular targets for therapy. The aim of this work is to characterize the expression of the metabolism-related proteins MCT1, MCT2, MCT4, CD147, CD44, GLUT1 and CAIX in a series of pediatric adrenocortical tumors. A total of 50 pediatric patients presenting adrenocortical tumors, including 41 clinically benign and 9 clinically malignant tumors, were included. Protein expression was evaluated using immunohistochemistry in samples arranged in tissue microarrays. The immunohistochemical analysis showed a significant increase in plasma membrane expression of GLUT1 in malignant lesions, when compared to benign lesions ( p =0.004), being the expression of this protein associated with shorter overall and disease-free survival ( p =0.004 and p =0.001, respectively). Although significant differences were not observed for proteins other than GLUT1, MCT1, MCT4 and CD147 were highly expressed in pediatric adrenocortical neoplasias (around 90%). GLUT1 expression was differentially expressed in pediatric adrenocortical tumors, with higher expression in clinically malignant tumors, and associated with shorter survival, suggesting a metabolic remodeling towards a hyperglycolytic phenotype in this malignancy.

Biological interpretation of genome-wide association studies using predicted gene functions

PubMed Central

Pers, Tune H.; Karjalainen, Juha M.; Chan, Yingleong; Westra, Harm-Jan; Wood, Andrew R.; Yang, Jian; Lui, Julian C.; Vedantam, Sailaja; Gustafsson, Stefan; Esko, Tonu; Frayling, Tim; Speliotes, Elizabeth K.; Boehnke, Michael; Raychaudhuri, Soumya; Fehrmann, Rudolf S.N.; Hirschhorn, Joel N.; Franke, Lude

2015-01-01

The main challenge for gaining biological insights from genetic associations is identifying which genes and pathways explain the associations. Here we present DEPICT, an integrative tool that employs predicted gene functions to systematically prioritize the most likely causal genes at associated loci, highlight enriched pathways and identify tissues/cell types where genes from associated loci are highly expressed. DEPICT is not limited to genes with established functions and prioritizes relevant gene sets for many phenotypes. PMID:25597830

A Survey of Computational Intelligence Techniques in Protein Function Prediction

PubMed Central

Tiwari, Arvind Kumar; Srivastava, Rajeev

2014-01-01

During the past, there was a massive growth of knowledge of unknown proteins with the advancement of high throughput microarray technologies. Protein function prediction is the most challenging problem in bioinformatics. In the past, the homology based approaches were used to predict the protein function, but they failed when a new protein was different from the previous one. Therefore, to alleviate the problems associated with homology based traditional approaches, numerous computational intelligence techniques have been proposed in the recent past. This paper presents a state-of-the-art comprehensive review of various computational intelligence techniques for protein function predictions using sequence, structure, protein-protein interaction network, and gene expression data used in wide areas of applications such as prediction of DNA and RNA binding sites, subcellular localization, enzyme functions, signal peptides, catalytic residues, nuclear/G-protein coupled receptors, membrane proteins, and pathway analysis from gene expression datasets. This paper also summarizes the result obtained by many researchers to solve these problems by using computational intelligence techniques with appropriate datasets to improve the prediction performance. The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction. PMID:25574395

Dietary challenges differentially affect activity and sleep/wake behavior in mus musculus: Isolating independent associations with diet/energy balance and body weight.

PubMed

Perron, Isaac J; Keenan, Brendan T; Chellappa, Karthikeyani; Lahens, Nicholas F; Yohn, Nicole L; Shockley, Keith R; Pack, Allan I; Veasey, Sigrid C

2018-01-01

Associated with numerous metabolic and behavioral abnormalities, obesity is classified by metrics reliant on body weight (such as body mass index). However, overnutrition is the common cause of obesity, and may independently contribute to these obesity-related abnormalities. Here, we use dietary challenges to parse apart the relative influence of diet and/or energy balance from body weight on various metabolic and behavioral outcomes. Seventy male mice (mus musculus) were subjected to the diet switch feeding paradigm, generating groups with various body weights and energetic imbalances. Spontaneous activity patterns, blood metabolite levels, and unbiased gene expression of the nutrient-sensing ventral hypothalamus (using RNA-sequencing) were measured, and these metrics were compared using standardized multivariate linear regression models. Spontaneous activity patterns were negatively related to body weight (p<0.0001) but not diet/energy balance (p = 0.63). Both body weight and diet/energy balance predicted circulating glucose and insulin levels, while body weight alone predicted plasma leptin levels. Regarding gene expression within the ventral hypothalamus, only two genes responded to diet/energy balance (neuropeptide y [npy] and agouti-related peptide [agrp]), while others were related only to body weight. Collectively, these results demonstrate that individual components of obesity-specifically obesogenic diets/energy imbalance and elevated body mass-can have independent effects on metabolic and behavioral outcomes. This work highlights the shortcomings of using body mass-based indices to assess metabolic health, and identifies novel associations between blood biomarkers, neural gene expression, and animal behavior following dietary challenges.

Understanding Transcription Factor Regulation by Integrating Gene Expression and DNase I Hypersensitive Sites.

PubMed

Wang, Guohua; Wang, Fang; Huang, Qian; Li, Yu; Liu, Yunlong; Wang, Yadong

2015-01-01

Transcription factors are proteins that bind to DNA sequences to regulate gene transcription. The transcription factor binding sites are short DNA sequences (5-20 bp long) specifically bound by one or more transcription factors. The identification of transcription factor binding sites and prediction of their function continue to be challenging problems in computational biology. In this study, by integrating the DNase I hypersensitive sites with known position weight matrices in the TRANSFAC database, the transcription factor binding sites in gene regulatory region are identified. Based on the global gene expression patterns in cervical cancer HeLaS3 cell and HelaS3-ifnα4h cell (interferon treatment on HeLaS3 cell for 4 hours), we present a model-based computational approach to predict a set of transcription factors that potentially cause such differential gene expression. Significantly, 6 out 10 predicted functional factors, including IRF, IRF-2, IRF-9, IRF-1 and IRF-3, ICSBP, belong to interferon regulatory factor family and upregulate the gene expression levels responding to the interferon treatment. Another factor, ISGF-3, is also a transcriptional activator induced by interferon alpha. Using the different transcription factor binding sites selected criteria, the prediction result of our model is consistent. Our model demonstrated the potential to computationally identify the functional transcription factors in gene regulation.

Interleukin-27 is a novel candidate diagnostic biomarker for bacterial infection in critically ill children

PubMed Central

2012-01-01

Introduction Differentiating between sterile inflammation and bacterial infection in critically ill patients with fever and other signs of the systemic inflammatory response syndrome (SIRS) remains a clinical challenge. The objective of our study was to mine an existing genome-wide expression database for the discovery of candidate diagnostic biomarkers to predict the presence of bacterial infection in critically ill children. Methods Genome-wide expression data were compared between patients with SIRS having negative bacterial cultures (n = 21) and patients with sepsis having positive bacterial cultures (n = 60). Differentially expressed genes were subjected to a leave-one-out cross-validation (LOOCV) procedure to predict SIRS or sepsis classes. Serum concentrations of interleukin-27 (IL-27) and procalcitonin (PCT) were compared between 101 patients with SIRS and 130 patients with sepsis. All data represent the first 24 hours of meeting criteria for either SIRS or sepsis. Results Two hundred twenty one gene probes were differentially regulated between patients with SIRS and patients with sepsis. The LOOCV procedure correctly predicted 86% of the SIRS and sepsis classes, and Epstein-Barr virus-induced gene 3 (EBI3) had the highest predictive strength. Computer-assisted image analyses of gene-expression mosaics were able to predict infection with a specificity of 90% and a positive predictive value of 94%. Because EBI3 is a subunit of the heterodimeric cytokine, IL-27, we tested the ability of serum IL-27 protein concentrations to predict infection. At a cut-point value of ≥5 ng/ml, serum IL-27 protein concentrations predicted infection with a specificity and a positive predictive value of >90%, and the overall performance of IL-27 was generally better than that of PCT. A decision tree combining IL-27 and PCT improved overall predictive capacity compared with that of either biomarker alone. Conclusions Genome-wide expression analysis has provided the foundation for the identification of IL-27 as a novel candidate diagnostic biomarker for predicting bacterial infection in critically ill children. Additional studies will be required to test further the diagnostic performance of IL-27. The microarray data reported in this article have been deposited in the Gene Expression Omnibus under accession number GSE4607. PMID:23107287

Transcription Factor Binding Site Enrichment Analysis in Co-Expression Modules in Celiac Disease

PubMed Central

Romero-Garmendia, Irati; Jauregi-Miguel, Amaia; Plaza-Izurieta, Leticia; Cros, Marie-Pierre; Legarda, Maria; Irastorza, Iñaki; Herceg, Zdenko; Fernandez-Jimenez, Nora

2018-01-01

The aim of this study was to construct celiac co-expression patterns at a whole genome level and to identify transcription factors (TFs) that could drive the gliadin-related changes in coordination of gene expression observed in celiac disease (CD). Differential co-expression modules were identified in the acute and chronic responses to gliadin using expression data from a previous microarray study in duodenal biopsies. Transcription factor binding site (TFBS) and Gene Ontology (GO) annotation enrichment analyses were performed in differentially co-expressed genes (DCGs) and selection of candidate regulators was performed. Expression of candidates was measured in clinical samples and the activation of the TFs was further characterized in C2BBe1 cells upon gliadin challenge. Enrichment analyses of the DCGs identified 10 TFs and five were selected for further investigation. Expression changes related to active CD were detected in four TFs, as well as in several of their in silico predicted targets. The activation of TFs was further characterized in C2BBe1 cells upon gliadin challenge, and an increase in nuclear translocation of CAMP Responsive Element Binding Protein 1 (CREB1) and IFN regulatory factor-1 (IRF1) in response to gliadin was observed. Using transcriptome-wide co-expression analyses we are able to propose novel genes involved in CD pathogenesis that respond upon gliadin stimulation, also in non-celiac models. PMID:29748492

Transcription Factor Binding Site Enrichment Analysis in Co-Expression Modules in Celiac Disease.

PubMed

Romero-Garmendia, Irati; Garcia-Etxebarria, Koldo; Hernandez-Vargas, Hector; Santin, Izortze; Jauregi-Miguel, Amaia; Plaza-Izurieta, Leticia; Cros, Marie-Pierre; Legarda, Maria; Irastorza, Iñaki; Herceg, Zdenko; Fernandez-Jimenez, Nora; Bilbao, Jose Ramon

2018-05-10

The aim of this study was to construct celiac co-expression patterns at a whole genome level and to identify transcription factors (TFs) that could drive the gliadin-related changes in coordination of gene expression observed in celiac disease (CD). Differential co-expression modules were identified in the acute and chronic responses to gliadin using expression data from a previous microarray study in duodenal biopsies. Transcription factor binding site (TFBS) and Gene Ontology (GO) annotation enrichment analyses were performed in differentially co-expressed genes (DCGs) and selection of candidate regulators was performed. Expression of candidates was measured in clinical samples and the activation of the TFs was further characterized in C2BBe1 cells upon gliadin challenge. Enrichment analyses of the DCGs identified 10 TFs and five were selected for further investigation. Expression changes related to active CD were detected in four TFs, as well as in several of their in silico predicted targets. The activation of TFs was further characterized in C2BBe1 cells upon gliadin challenge, and an increase in nuclear translocation of CAMP Responsive Element Binding Protein 1 (CREB1) and IFN regulatory factor-1 (IRF1) in response to gliadin was observed. Using transcriptome-wide co-expression analyses we are able to propose novel genes involved in CD pathogenesis that respond upon gliadin stimulation, also in non-celiac models.

An Automated Pipeline for Engineering Many-Enzyme Pathways: Computational Sequence Design, Pathway Expression-Flux Mapping, and Scalable Pathway Optimization.

PubMed

Halper, Sean M; Cetnar, Daniel P; Salis, Howard M

2018-01-01

Engineering many-enzyme metabolic pathways suffers from the design curse of dimensionality. There are an astronomical number of synonymous DNA sequence choices, though relatively few will express an evolutionary robust, maximally productive pathway without metabolic bottlenecks. To solve this challenge, we have developed an integrated, automated computational-experimental pipeline that identifies a pathway's optimal DNA sequence without high-throughput screening or many cycles of design-build-test. The first step applies our Operon Calculator algorithm to design a host-specific evolutionary robust bacterial operon sequence with maximally tunable enzyme expression levels. The second step applies our RBS Library Calculator algorithm to systematically vary enzyme expression levels with the smallest-sized library. After characterizing a small number of constructed pathway variants, measurements are supplied to our Pathway Map Calculator algorithm, which then parameterizes a kinetic metabolic model that ultimately predicts the pathway's optimal enzyme expression levels and DNA sequences. Altogether, our algorithms provide the ability to efficiently map the pathway's sequence-expression-activity space and predict DNA sequences with desired metabolic fluxes. Here, we provide a step-by-step guide to applying the Pathway Optimization Pipeline on a desired multi-enzyme pathway in a bacterial host.

Detecting circular RNAs: bioinformatic and experimental challenges

PubMed Central

Szabo, Linda; Salzman, Julia

2017-01-01

The pervasive expression of circular RNAs (circRNAs) is a recently discovered feature of gene expression in highly diverged eukaryotes. Numerous algorithms that are used to detect genome-wide circRNA expression from RNA sequencing (RNA-seq) data have been developed in the past few years, but there is little overlap in their predictions and no clear gold-standard method to assess the accuracy of these algorithms. We review sources of experimental and bioinformatic biases that complicate the accurate discovery of circRNAs and discuss statistical approaches to address these biases. We conclude with a discussion of the current experimental progress on the topic. PMID:27739534

Molecular identification and expression analysis of a natural killer cell enhancing factor (NKEF) from rock bream Oplegnathus fasciatus and the biological activity of its recombinant protein

PubMed Central

Kim, Ju-Won; Choi, Hye-Sung; Kwon, Mun-Gyeong; Park, Myoung-Ae; Hwang, Jee-Youn; Kim, Do-Hyung; Park, Chan-Il

2011-01-01

Natural killer cell enhancing factor (NKEF) belongs to the defined peroxiredoxin (Prx) family. Rock bream NKEF cDNA was identified by expressed sequence tag (EST) analysis of rock bream liver that was stimulated with the LPS. The full-length RbNKEF cDNA (1062 bp) contained an open reading frame (ORF) of 594 bp encoding 198 amino acids. RbNKEF was significantly expressed in the gill, liver, and intestine. mRNA expression of NKEF in the head kidney was examined under viral and bacterial challenge via real-time RT-PCR. Experimental challenge of rock bream with Edwardsiella tarda, Streptococcus iniae, and RSIV resulted in significant increases in RbNKEF mRNA in the head kidney. To obtain a recombinant NKEF, the RbNKEF ORF was expressed in Escherichia coli BL21 (DE3), and the purified soluble protein exhibited a single band corresponding to the predicted molecular mass. When kidney leucocytes were treated with a high concentration of rRbNKEF (10 μg/mL), they exhibited significantly enhanced cell proliferation and viability under oxidative stress. PMID:24371552

Prediction of essential proteins based on gene expression programming.

PubMed

Zhong, Jiancheng; Wang, Jianxin; Peng, Wei; Zhang, Zhen; Pan, Yi

2013-01-01

Essential proteins are indispensable for cell survive. Identifying essential proteins is very important for improving our understanding the way of a cell working. There are various types of features related to the essentiality of proteins. Many methods have been proposed to combine some of them to predict essential proteins. However, it is still a big challenge for designing an effective method to predict them by integrating different features, and explaining how these selected features decide the essentiality of protein. Gene expression programming (GEP) is a learning algorithm and what it learns specifically is about relationships between variables in sets of data and then builds models to explain these relationships. In this work, we propose a GEP-based method to predict essential protein by combing some biological features and topological features. We carry out experiments on S. cerevisiae data. The experimental results show that the our method achieves better prediction performance than those methods using individual features. Moreover, our method outperforms some machine learning methods and performs as well as a method which is obtained by combining the outputs of eight machine learning methods. The accuracy of predicting essential proteins can been improved by using GEP method to combine some topological features and biological features.

A prior-based integrative framework for functional transcriptional regulatory network inference

PubMed Central

Siahpirani, Alireza F.

2017-01-01

Abstract Transcriptional regulatory networks specify regulatory proteins controlling the context-specific expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we first develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-specific motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identifies core TFs whose targets are predictable from expression. Multiple reasons make the identification of targets of other TFs difficult, including network architecture and insufficient variation of TF mRNA level. Finally, we demonstrate the utility of our inference algorithm to infer stress-specific regulatory networks and for regulator prioritization. PMID:27794550

BICD1 expression, as a potential biomarker for prognosis and predicting response to therapy in patients with glioblastomas

PubMed Central

Huang, Shang-Pen; Chang, Yu-Chan; Low, Qie Hua; Wu, Alexander T.H.; Chen, Chi-Long; Lin, Yuan-Feng; Hsiao, Michael

2017-01-01

There is variation in the survival and therapeutic outcome of patients with glioblastomas (GBMs). Therapy resistance is an important challenge in the treatment of GBM patients. The aim of this study was to identify Temozolomide (TMZ) related genes and confirm their clinical relevance. The TMZ-related genes were discovered by analysis of the gene-expression profiling in our cell-based microarray. Their clinical relevance was verified by in silico meta-analysis of the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) datasets. Our results demonstrated that BICD1 expression could predict both prognosis and response to therapy in GBM patients. First, high BICD1 expression was correlated with poor prognosis in the TCGA GBM cohort (n=523) and in the CGGA glioma cohort (n=220). Second, high BICD1 expression predicted poor outcome in patients with TMZ treatment (n=301) and radiation therapy (n=405). Third, multivariable Cox regression analysis confirmed BICD1 expression as an independent factor affecting the prognosis and therapeutic response of TMZ and radiation in GBM patients. Additionally, age, MGMT and BICD1 expression were combinedly utilized to stratify GBM patients into more distinct risk groups, which may provide better outcome assessment. Finally, we observed a strong correlation between BICD1 expression and epithelial-mesenchymal transition (EMT) in GBMs, and proposed a possible mechanism of BICD1-associated survival or therapeutic resistance in GBMs accordingly. In conclusion, our study suggests that high BICD1 expression may result in worse prognosis and could be a predictor of poor response to TMZ and radiation therapies in GBM patients. PMID:29371945

Predicting survival times for neuroblastoma patients using RNA-seq expression profiles.

PubMed

Grimes, Tyler; Walker, Alejandro R; Datta, Susmita; Datta, Somnath

2018-05-30

Neuroblastoma is the most common tumor of early childhood and is notorious for its high variability in clinical presentation. Accurate prognosis has remained a challenge for many patients. In this study, expression profiles from RNA-sequencing are used to predict survival times directly. Several models are investigated using various annotation levels of expression profiles (genes, transcripts, and introns), and an ensemble predictor is proposed as a heuristic for combining these different profiles. The use of RNA-seq data is shown to improve accuracy in comparison to using clinical data alone for predicting overall survival times. Furthermore, clinically high-risk patients can be subclassified based on their predicted overall survival times. In this effort, the best performing model was the elastic net using both transcripts and introns together. This model separated patients into two groups with 2-year overall survival rates of 0.40±0.11 (n=22) versus 0.80±0.05 (n=68). The ensemble approach gave similar results, with groups 0.42±0.10 (n=25) versus 0.82±0.05 (n=65). This suggests that the ensemble is able to effectively combine the individual RNA-seq datasets. Using predicted survival times based on RNA-seq data can provide improved prognosis by subclassifying clinically high-risk neuroblastoma patients. This article was reviewed by Subharup Guha and Isabel Nepomuceno.

Long Non-Coding RNAs (lncRNAs) of Sea Cucumber: Large-Scale Prediction, Expression Profiling, Non-Coding Network Construction, and lncRNA-microRNA-Gene Interaction Analysis of lncRNAs in Apostichopus japonicus and Holothuria glaberrima During LPS Challenge and Radial Organ Complex Regeneration.

PubMed

Mu, Chuang; Wang, Ruijia; Li, Tianqi; Li, Yuqiang; Tian, Meilin; Jiao, Wenqian; Huang, Xiaoting; Zhang, Lingling; Hu, Xiaoli; Wang, Shi; Bao, Zhenmin

2016-08-01

Long non-coding RNA (lncRNA) structurally resembles mRNA but cannot be translated into protein. Although the systematic identification and characterization of lncRNAs have been increasingly reported in model species, information concerning non-model species is still lacking. Here, we report the first systematic identification and characterization of lncRNAs in two sea cucumber species: (1) Apostichopus japonicus during lipopolysaccharide (LPS) challenge and in heathy tissues and (2) Holothuria glaberrima during radial organ complex regeneration, using RNA-seq datasets and bioinformatics analysis. We identified A. japonicus and H. glaberrima lncRNAs that were differentially expressed during LPS challenge and radial organ complex regeneration, respectively. Notably, the predicted lncRNA-microRNA-gene trinities revealed that, in addition to targeting protein-coding transcripts, miRNAs might also target lncRNAs, thereby participating in a potential novel layer of regulatory interactions among non-coding RNA classes in echinoderms. Furthermore, the constructed coding-non-coding network implied the potential involvement of lncRNA-gene interactions during the regulation of several important genes (e.g., Toll-like receptor 1 [TLR1] and transglutaminase-1 [TGM1]) in response to LPS challenge and radial organ complex regeneration in sea cucumbers. Overall, this pioneer systematic identification, annotation, and characterization of lncRNAs in echinoderm pave the way for similar studies and future genetic, genomic, and evolutionary research in non-model species.

Building a genome analysis pipeline to predict disease risk and prevent disease.

PubMed

Bromberg, Y

2013-11-01

Reduced costs and increased speed and accuracy of sequencing can bring the genome-based evaluation of individual disease risk to the bedside. While past efforts have identified a number of actionable mutations, the bulk of genetic risk remains hidden in sequence data. The biggest challenge facing genomic medicine today is the development of new techniques to predict the specifics of a given human phenome (set of all expressed phenotypes) encoded by each individual variome (full set of genome variants) in the context of the given environment. Numerous tools exist for the computational identification of the functional effects of a single variant. However, the pipelines taking advantage of full genomic, exomic, transcriptomic (and other) sequences have only recently become a reality. This review looks at the building of methodologies for predicting "variome"-defined disease risk. It also discusses some of the challenges for incorporating such a pipeline into everyday medical practice. © 2013. Published by Elsevier Ltd. All rights reserved.

Stock price change rate prediction by utilizing social network activities.

PubMed

Deng, Shangkun; Mitsubuchi, Takashi; Sakurai, Akito

2014-01-01

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques.

Stock Price Change Rate Prediction by Utilizing Social Network Activities

PubMed Central

Mitsubuchi, Takashi; Sakurai, Akito

2014-01-01

Predicting stock price change rates for providing valuable information to investors is a challenging task. Individual participants may express their opinions in social network service (SNS) before or after their transactions in the market; we hypothesize that stock price change rate is better predicted by a function of social network service activities and technical indicators than by a function of just stock market activities. The hypothesis is tested by accuracy of predictions as well as performance of simulated trading because success or failure of prediction is better measured by profits or losses the investors gain or suffer. In this paper, we propose a hybrid model that combines multiple kernel learning (MKL) and genetic algorithm (GA). MKL is adopted to optimize the stock price change rate prediction models that are expressed in a multiple kernel linear function of different types of features extracted from different sources. GA is used to optimize the trading rules used in the simulated trading by fusing the return predictions and values of three well-known overbought and oversold technical indicators. Accumulated return and Sharpe ratio were used to test the goodness of performance of the simulated trading. Experimental results show that our proposed model performed better than other models including ones using state of the art techniques. PMID:24790586

Development and Validation of a Scale to Measure an Adaptive Culture Profile Using Student Affairs Divisions in Higher Education

ERIC Educational Resources Information Center

Fowler, Tammy Lynne

2013-01-01

The landscape of higher education in the United States shifts and moves in response to environmental challenges often hard to predict or measure. A joint taskforce of the American College Personnel Association and the National Association of Student Personnel Administrators taskforce expressed the concern that no other time in history has the…

Interdisciplinary Team Science in Cell Biology.

PubMed

Horwitz, Rick

2016-11-01

The cell is complex. With its multitude of components, spatial-temporal character, and gene expression diversity, it is challenging to comprehend the cell as an integrated system and to develop models that predict its behaviors. I suggest an approach to address this issue, involving system level data analysis, large scale team science, and philanthropy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Network regularised Cox regression and multiplex network models to predict disease comorbidities and survival of cancer.

PubMed

Xu, Haoming; Moni, Mohammad Ali; Liò, Pietro

2015-12-01

In cancer genomics, gene expression levels provide important molecular signatures for all types of cancer, and this could be very useful for predicting the survival of cancer patients. However, the main challenge of gene expression data analysis is high dimensionality, and microarray is characterised by few number of samples with large number of genes. To overcome this problem, a variety of penalised Cox proportional hazard models have been proposed. We introduce a novel network regularised Cox proportional hazard model and a novel multiplex network model to measure the disease comorbidities and to predict survival of the cancer patient. Our methods are applied to analyse seven microarray cancer gene expression datasets: breast cancer, ovarian cancer, lung cancer, liver cancer, renal cancer and osteosarcoma. Firstly, we applied a principal component analysis to reduce the dimensionality of original gene expression data. Secondly, we applied a network regularised Cox regression model on the reduced gene expression datasets. By using normalised mutual information method and multiplex network model, we predict the comorbidities for the liver cancer based on the integration of diverse set of omics and clinical data, and we find the diseasome associations (disease-gene association) among different cancers based on the identified common significant genes. Finally, we evaluated the precision of the approach with respect to the accuracy of survival prediction using ROC curves. We report that colon cancer, liver cancer and renal cancer share the CXCL5 gene, and breast cancer, ovarian cancer and renal cancer share the CCND2 gene. Our methods are useful to predict survival of the patient and disease comorbidities more accurately and helpful for improvement of the care of patients with comorbidity. Software in Matlab and R is available on our GitHub page: https://github.com/ssnhcom/NetworkRegularisedCox.git. Copyright © 2015. Published by Elsevier Ltd.

Looking for trouble: revenge-planning and preattentive vigilance for angry facial expressions.

PubMed

Crowe, Sarah E; Wilkowski, Benjamin M

2013-08-01

Revenge-planning refers to individual differences in the tendency to actively seek out hostile confrontations with others. Building on past theory, we hypothesized that revenge-planning would be related to preattentive vigilance for angry facial expressions. By being vigilant for such expressions, individuals could more readily notice and prepare to confront social challenges. We conducted 2 studies to test this prediction. Across studies, results indicated that participants high in revenge-planning had significantly longer color-naming latencies for masked angry expressions presented in a subliminal Stroop task, regardless of whether the expression was presented inside or outside participants' attentional focus. This phenomenon was specific to revenge-planning and did not extend to the related construct of angry rumination. Such results suggest that preattentive vigilance for angry expressions supports a confrontational social style in which a person actively seeks out hostile social encounters. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Characteristics of the interferon regulatory factor 5 (IRF5) and its expression in response to LCDV and poly I:C challenges in Japanese flounder, Paralichthys olivaceus.

PubMed

Hu, Guo-Bin; Lou, Hui-Min; Dong, Xian-Zhi; Liu, Qiu-Ming; Zhang, Shi-Cui

2012-10-01

Interferon regulatory factor 5 (IRF5) has been identified as a key transcriptional mediator regulating expression of both type I interferons (IFNs) and proinflammatory cytokines. In this study, the cDNA and genomic sequences of IRF5 were isolated from Japanese flounder, Paralichthys olivaceus. The gene of Japanese flounder (Jf)IRF5 is 7326 bp long, contains 9 exons and 8 introns and encodes a putative protein of 472 amino acids. The predicted protein sequence shares 61.1-81.9% identity to fish IRF5 and possesses a DNA-binding domain (DBD), a middle region (MR), an IRF association domain (IAD), a virus activated domain (VAD) and two nuclear localization signals (NLSs) conserved in all known IRF5s. Phylogenetic analysis clustered it into the teleost IRF5 subgroup within vertebrate IRF5 group. JfIRF5 mRNA was constitutively expressed in all tissues examined, with higher levels observed in the gills and head kidney. Gene expression of JfIRF5 was analyzed over a 7-day time course in the gills, head kidney, spleen and muscle of Japanese flounders challenged with lymphocystis disease virus (LCDV) and polyinosinic:polycytidylic acid (poly I:C). The data showed that JfIRF5 expression was slightly up-regulated by LCDV, but its induction time was clearly moved up; in contrast, the induction upon poly I:C challenge started not earlier than day 2 post-injection and was stronger and more persistent with a later peak time in all four organs. The late and long-lasting inductive expression of JfIRF5 following poly I:C challenge suggests that it might be an interferon stimulated gene (ISG), the induction of which is driven by poly I:C-induced type I IFNs. Copyright © 2012 Elsevier Ltd. All rights reserved.

Estimating replicate time shifts using Gaussian process regression

PubMed Central

Liu, Qiang; Andersen, Bogi; Smyth, Padhraic; Ihler, Alexander

2010-01-01

Motivation: Time-course gene expression datasets provide important insights into dynamic aspects of biological processes, such as circadian rhythms, cell cycle and organ development. In a typical microarray time-course experiment, measurements are obtained at each time point from multiple replicate samples. Accurately recovering the gene expression patterns from experimental observations is made challenging by both measurement noise and variation among replicates' rates of development. Prior work on this topic has focused on inference of expression patterns assuming that the replicate times are synchronized. We develop a statistical approach that simultaneously infers both (i) the underlying (hidden) expression profile for each gene, as well as (ii) the biological time for each individual replicate. Our approach is based on Gaussian process regression (GPR) combined with a probabilistic model that accounts for uncertainty about the biological development time of each replicate. Results: We apply GPR with uncertain measurement times to a microarray dataset of mRNA expression for the hair-growth cycle in mouse back skin, predicting both profile shapes and biological times for each replicate. The predicted time shifts show high consistency with independently obtained morphological estimates of relative development. We also show that the method systematically reduces prediction error on out-of-sample data, significantly reducing the mean squared error in a cross-validation study. Availability: Matlab code for GPR with uncertain time shifts is available at http://sli.ics.uci.edu/Code/GPRTimeshift/ Contact: ihler@ics.uci.edu PMID:20147305

Compassionate love buffers stress-reactive mothers from fight-or-flight parenting.

PubMed

Miller, Jonas G; Kahle, Sarah; Lopez, Monica; Hastings, Paul D

2015-01-01

The links among mothers' compassionate love for their child, autonomic nervous system activity, and parenting behavior during less and more challenging mother-child interactions were examined. Mothers expressed and reported less negative affect when they exhibited autonomic patterns of increased parasympathetic dominance (high parasympathetic and low sympathetic activation) or autonomic coactivation (high parasympathetic and high sympathetic activation) during the less challenging interaction and autonomic coactivation during the more challenging interaction. Compassionate love predicted less reported and observed negativity in mothers who showed increased sympathetic nervous system dominance (high sympathetic and low parasympathetic activation). Compassionate love appeared to help mothers, and particularly those who experienced strong physiological arousal during difficult parenting situations, establish positive socialization contexts for their children and avoid stress-induced adverse parenting.

Utility of Gene Expression and Ex vivo Steroid Production in a 96 h Assay for Predicting Impacts of Endocrine Active Chemicals on Fish Reproduction.

EPA Science Inventory

Development of efficient test methods that can generate reliable data to inform risk assessment is an on-going challenge in the field of ecotoxicology. In the present study we evaluated whether a 96 h in vivo assay focused on a small number of quantitative real-time polymerase ch...

Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection.

PubMed

Dong, Zuoli; Zhang, Naiqian; Li, Chun; Wang, Haiyun; Fang, Yun; Wang, Jun; Zheng, Xiaoqi

2015-06-30

An enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel. Recently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP). Our model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80% accuracy for 10 drugs, ≥75% accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively. These results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

Transcriptional network inference from functional similarity and expression data: a global supervised approach.

PubMed

Ambroise, Jérôme; Robert, Annie; Macq, Benoit; Gala, Jean-Luc

2012-01-06

An important challenge in system biology is the inference of biological networks from postgenomic data. Among these biological networks, a gene transcriptional regulatory network focuses on interactions existing between transcription factors (TFs) and and their corresponding target genes. A large number of reverse engineering algorithms were proposed to infer such networks from gene expression profiles, but most current methods have relatively low predictive performances. In this paper, we introduce the novel TNIFSED method (Transcriptional Network Inference from Functional Similarity and Expression Data), that infers a transcriptional network from the integration of correlations and partial correlations of gene expression profiles and gene functional similarities through a supervised classifier. In the current work, TNIFSED was applied to predict the transcriptional network in Escherichia coli and in Saccharomyces cerevisiae, using datasets of 445 and 170 affymetrix arrays, respectively. Using the area under the curve of the receiver operating characteristics and the F-measure as indicators, we showed the predictive performance of TNIFSED to be better than unsupervised state-of-the-art methods. TNIFSED performed slightly worse than the supervised SIRENE algorithm for the target genes identification of the TF having a wide range of yet identified target genes but better for TF having only few identified target genes. Our results indicate that TNIFSED is complementary to the SIRENE algorithm, and particularly suitable to discover target genes of "orphan" TFs.

Combining Evidence of Preferential Gene-Tissue Relationships from Multiple Sources

PubMed Central

Guo, Jing; Hammar, Mårten; Öberg, Lisa; Padmanabhuni, Shanmukha S.; Bjäreland, Marcus; Dalevi, Daniel

2013-01-01

An important challenge in drug discovery and disease prognosis is to predict genes that are preferentially expressed in one or a few tissues, i.e. showing a considerably higher expression in one tissue(s) compared to the others. Although several data sources and methods have been published explicitly for this purpose, they often disagree and it is not evident how to retrieve these genes and how to distinguish true biological findings from those that are due to choice-of-method and/or experimental settings. In this work we have developed a computational approach that combines results from multiple methods and datasets with the aim to eliminate method/study-specific biases and to improve the predictability of preferentially expressed human genes. A rule-based score is used to merge and assign support to the results. Five sets of genes with known tissue specificity were used for parameter pruning and cross-validation. In total we identify 3434 tissue-specific genes. We compare the genes of highest scores with the public databases: PaGenBase (microarray), TiGER (EST) and HPA (protein expression data). The results have 85% overlap to PaGenBase, 71% to TiGER and only 28% to HPA. 99% of our predictions have support from at least one of these databases. Our approach also performs better than any of the databases on identifying drug targets and biomarkers with known tissue-specificity. PMID:23950964

Comprehensive red blood cell and platelet antigen prediction from whole genome sequencing: proof of principle

PubMed Central

Westhoff, Connie M.; Uy, Jon Michael; Aguad, Maria; Smeland‐Wagman, Robin; Kaufman, Richard M.; Rehm, Heidi L.; Green, Robert C.; Silberstein, Leslie E.

2015-01-01

BACKGROUND There are 346 serologically defined red blood cell (RBC) antigens and 33 serologically defined platelet (PLT) antigens, most of which have known genetic changes in 45 RBC or six PLT genes that correlate with antigen expression. Polymorphic sites associated with antigen expression in the primary literature and reference databases are annotated according to nucleotide positions in cDNA. This makes antigen prediction from next‐generation sequencing data challenging, since it uses genomic coordinates. STUDY DESIGN AND METHODS The conventional cDNA reference sequences for all known RBC and PLT genes that correlate with antigen expression were aligned to the human reference genome. The alignments allowed conversion of conventional cDNA nucleotide positions to the corresponding genomic coordinates. RBC and PLT antigen prediction was then performed using the human reference genome and whole genome sequencing (WGS) data with serologic confirmation. RESULTS Some major differences and alignment issues were found when attempting to convert the conventional cDNA to human reference genome sequences for the following genes: ABO, A4GALT, RHD, RHCE, FUT3, ACKR1 (previously DARC), ACHE, FUT2, CR1, GCNT2, and RHAG. However, it was possible to create usable alignments, which facilitated the prediction of all RBC and PLT antigens with a known molecular basis from WGS data. Traditional serologic typing for 18 RBC antigens were in agreement with the WGS‐based antigen predictions, providing proof of principle for this approach. CONCLUSION Detailed mapping of conventional cDNA annotated RBC and PLT alleles can enable accurate prediction of RBC and PLT antigens from whole genomic sequencing data. PMID:26634332

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress

PubMed Central

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies. PMID:29765399

A Seasonal Time-Series Model Based on Gene Expression Programming for Predicting Financial Distress.

PubMed

Cheng, Ching-Hsue; Chan, Chia-Pang; Yang, Jun-He

2018-01-01

The issue of financial distress prediction plays an important and challenging research topic in the financial field. Currently, there have been many methods for predicting firm bankruptcy and financial crisis, including the artificial intelligence and the traditional statistical methods, and the past studies have shown that the prediction result of the artificial intelligence method is better than the traditional statistical method. Financial statements are quarterly reports; hence, the financial crisis of companies is seasonal time-series data, and the attribute data affecting the financial distress of companies is nonlinear and nonstationary time-series data with fluctuations. Therefore, this study employed the nonlinear attribute selection method to build a nonlinear financial distress prediction model: that is, this paper proposed a novel seasonal time-series gene expression programming model for predicting the financial distress of companies. The proposed model has several advantages including the following: (i) the proposed model is different from the previous models lacking the concept of time series; (ii) the proposed integrated attribute selection method can find the core attributes and reduce high dimensional data; and (iii) the proposed model can generate the rules and mathematical formulas of financial distress for providing references to the investors and decision makers. The result shows that the proposed method is better than the listing classifiers under three criteria; hence, the proposed model has competitive advantages in predicting the financial distress of companies.

Gene-Expression Signature Predicts Postoperative Recurrence in Stage I Non-Small Cell Lung Cancer Patients

PubMed Central

Lu, Yan; Wang, Liang; Liu, Pengyuan; Yang, Ping; You, Ming

2012-01-01

About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. The purpose of this study is to develop and validate a novel gene-expression signature that can predict tumor recurrence of stage I NSCLC patients. Cox proportional hazards regression analysis was performed to identify recurrence-related genes and a partial Cox regression model was used to generate a gene signature of recurrence in the training dataset −142 stage I lung adenocarcinomas without adjunctive therapy from the Director's Challenge Consortium. Four independent validation datasets, including GSE5843, GSE8894, and two other datasets provided by Mayo Clinic and Washington University, were used to assess the prediction accuracy by calculating the correlation between risk score estimated from gene expression and real recurrence-free survival time and AUC of time-dependent ROC analysis. Pathway-based survival analyses were also performed. 104 probesets correlated with recurrence in the training dataset. They are enriched in cell adhesion, apoptosis and regulation of cell proliferation. A 51-gene expression signature was identified to distinguish patients likely to develop tumor recurrence (Dxy = −0.83, P<1e-16) and this signature was validated in four independent datasets with AUC >85%. Multiple pathways including leukocyte transendothelial migration and cell adhesion were highly correlated with recurrence-free survival. The gene signature is highly predictive of recurrence in stage I NSCLC patients, which has important prognostic and therapeutic implications for the future management of these patients. PMID:22292069

Computational Modeling and Simulation of Developmental ...

EPA Pesticide Factsheets

Developmental and Reproductive Toxicity (DART) testing is important for assessing the potential consequences of drug and chemical exposure on human health and well-being. Complexity of pregnancy and the reproductive cycle makes DART testing challenging and costly for traditional (animal-based) methods. A compendium of in vitro data from ToxCast/Tox21 high-throughput screening (HTS) programs is available for predictive toxicology. ‘Predictive DART’ will require an integrative strategy that mobilizes HTS data into in silico models that capture the relevant embryology. This lecture addresses progress on EPA's 'virtual embryo'. The question of how tissues and organs are shaped during development is crucial for understanding (and predicting) human birth defects. While ToxCast HTS data may predict developmental toxicity with reasonable accuracy, mechanistic models are still necessary to capture the relevant biology. Subtle microscopic changes induced chemically may amplify to an adverse outcome but coarse changes may override lesion propagation in any complex adaptive system. Modeling system dynamics in a developing tissue is a multiscale problem that challenges our ability to predict toxicity from in vitro profiling data (ToxCast/Tox21). (DISCLAIMER: The views expressed in this presentation are those of the presenter and do not necessarily reflect the views or policies of the US EPA). This was an invited seminar presentation to the National Institute for Public H

A new method for enhancer prediction based on deep belief network.

PubMed

Bu, Hongda; Gan, Yanglan; Wang, Yang; Zhou, Shuigeng; Guan, Jihong

2017-10-16

Studies have shown that enhancers are significant regulatory elements to play crucial roles in gene expression regulation. Since enhancers are unrelated to the orientation and distance to their target genes, it is a challenging mission for scholars and researchers to accurately predicting distal enhancers. In the past years, with the high-throughout ChiP-seq technologies development, several computational techniques emerge to predict enhancers using epigenetic or genomic features. Nevertheless, the inconsistency of computational models across different cell-lines and the unsatisfactory prediction performance call for further research in this area. Here, we propose a new Deep Belief Network (DBN) based computational method for enhancer prediction, which is called EnhancerDBN. This method combines diverse features, composed of DNA sequence compositional features, DNA methylation and histone modifications. Our computational results indicate that 1) EnhancerDBN outperforms 13 existing methods in prediction, and 2) GC content and DNA methylation can serve as relevant features for enhancer prediction. Deep learning is effective in boosting the performance of enhancer prediction.

Longitudinal prediction of language emergence in infants at high and low risk for autism spectrum disorder.

PubMed

Edmunds, Sarah R; Ibañez, Lisa V; Warren, Zachary; Messinger, Daniel S; Stone, Wendy L

2017-02-01

This study used a prospective longitudinal design to examine the early developmental pathways that underlie language growth in infants at high risk (n = 50) and low risk (n = 34) for autism spectrum disorder in the first 18 months of life. While motor imitation and responding to joint attention (RJA) have both been found to predict expressive language in children with autism spectrum disorder and those with typical development, the longitudinal relation between these capacities has not yet been identified. As hypothesized, results revealed that 15-month RJA mediated the association between 12-month motor imitation and 18-month expressive vocabulary, even after controlling for earlier levels of RJA and vocabulary. These results provide new information about the developmental sequencing of skills relevant to language growth that may inform future intervention efforts for children at risk for language delay or other developmental challenges.

Transcriptional Activation of c3 and hsp70 as Part of the Immune Response of Acropora millepora to Bacterial Challenges

PubMed Central

Brown, Tanya; Bourne, David; Rodriguez-Lanetty, Mauricio

2013-01-01

The impact of disease outbreaks on coral physiology represents an increasing concern for the fitness and resilience of reef ecosystems. Predicting the tolerance of corals to disease relies on an understanding of the coral immune response to pathogenic interactions. This study explored the transcriptional response of two putative immune genes (c3 and c-type lectin) and one stress response gene (hsp70) in the reef building coral, Acropora millepora challenged for 48 hours with bacterial strains, Vibrio coralliilyticus and Alteromonas sp. at concentrations of 106 cells ml-1. Coral fragments challenged with V. coralliilyticus appeared healthy while fragments challenged with Alteromonas sp. showed signs of tissue lesions after 48 hr. Coral-associated bacterial community profiles assessed using denaturing gradient gel electrophoresis changed after challenge by both bacterial strains with the Alteromonas sp. treatment demonstrating the greatest community shift. Transcriptional profiles of c3 and hsp70 increased at 24 hours and correlated with disease signs in the Alteromonas sp. treatment. The expression of hsp70 also showed a significant increase in V. coralliilyticus inoculated corals at 24 h suggesting that even in the absence of disease signs, the microbial inoculum activated a stress response in the coral. C-type lectin did not show a response to any of the bacterial treatments. Increase in gene expression of c3 and hsp70 in corals showing signs of disease indicates their potential involvement in immune and stress response to microbial challenges. PMID:23861754

Compassionate Love Buffers Stress-Reactive Mothers From Fight-or-Flight Parenting

PubMed Central

Miller, Jonas G.; Kahle, Sarah; Lopez, Monica; Hastings, Paul D.

2015-01-01

The links among mothers’ compassionate love for their child, autonomic nervous system activity, and parenting behavior during less and more challenging mother–child interactions were examined. Mothers expressed and reported less negative affect when they exhibited autonomic patterns of increased parasympathetic dominance (high parasympathetic and low sympathetic activation) or autonomic coactivation (high parasympathetic and high sympathetic activation) during the less challenging interaction and autonomic coactivation during the more challenging interaction. Compassionate love predicted less reported and observed negativity in mothers who showed increased sympathetic nervous system dominance (high sympathetic and low parasympathetic activation). Compassionate love appeared to help mothers, and particularly those who experienced strong physiological arousal during difficult parenting situations, establish positive socialization contexts for their children and avoid stress-induced adverse parenting. PMID:25329554

Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages.

PubMed

Reddy, Anupama; Growney, Joseph D; Wilson, Nick S; Emery, Caroline M; Johnson, Jennifer A; Ward, Rebecca; Monaco, Kelli A; Korn, Joshua; Monahan, John E; Stump, Mark D; Mapa, Felipa A; Wilson, Christopher J; Steiger, Janine; Ledell, Jebediah; Rickles, Richard J; Myer, Vic E; Ettenberg, Seth A; Schlegel, Robert; Sellers, William R; Huet, Heather A; Lehár, Joseph

2015-01-01

Death Receptor 5 (DR5) agonists demonstrate anti-tumor activity in preclinical models but have yet to demonstrate robust clinical responses. A key limitation may be the lack of patient selection strategies to identify those most likely to respond to treatment. To overcome this limitation, we screened a DR5 agonist Nanobody across >600 cell lines representing 21 tumor lineages and assessed molecular features associated with response. High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges. To address the translational challenge of establishing thresholds of gene expression, we developed a classifier based on ratios of genes that predicted response across lineages. The ratio classifier outperformed the DR5+Casp8 classifier, as well as standard approaches for feature selection and classification using genes, instead of ratios. This classifier was independently validated using 11 primary patient-derived pancreatic xenograft models showing perfect predictions as well as a striking linearity between prediction probability and anti-tumor response. A network analysis of the genes in the ratio classifier captured important biological relationships mediating drug response, specifically identifying key positive and negative regulators of DR5 mediated apoptosis, including DR5, CASP8, BID, cFLIP, XIAP and PEA15. Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo). Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response.

Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages

PubMed Central

Reddy, Anupama; Growney, Joseph D.; Wilson, Nick S.; Emery, Caroline M.; Johnson, Jennifer A.; Ward, Rebecca; Monaco, Kelli A.; Korn, Joshua; Monahan, John E.; Stump, Mark D.; Mapa, Felipa A.; Wilson, Christopher J.; Steiger, Janine; Ledell, Jebediah; Rickles, Richard J.; Myer, Vic E.; Ettenberg, Seth A.; Schlegel, Robert; Sellers, William R.

2015-01-01

Death Receptor 5 (DR5) agonists demonstrate anti-tumor activity in preclinical models but have yet to demonstrate robust clinical responses. A key limitation may be the lack of patient selection strategies to identify those most likely to respond to treatment. To overcome this limitation, we screened a DR5 agonist Nanobody across >600 cell lines representing 21 tumor lineages and assessed molecular features associated with response. High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges. To address the translational challenge of establishing thresholds of gene expression, we developed a classifier based on ratios of genes that predicted response across lineages. The ratio classifier outperformed the DR5+Casp8 classifier, as well as standard approaches for feature selection and classification using genes, instead of ratios. This classifier was independently validated using 11 primary patient-derived pancreatic xenograft models showing perfect predictions as well as a striking linearity between prediction probability and anti-tumor response. A network analysis of the genes in the ratio classifier captured important biological relationships mediating drug response, specifically identifying key positive and negative regulators of DR5 mediated apoptosis, including DR5, CASP8, BID, cFLIP, XIAP and PEA15. Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo). Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response. PMID:26378449

Study on predictive role of AR and EGFR family genes with response to neoadjuvant chemotherapy in locally advanced breast cancer in Indian women.

PubMed

Singh, L C; Chakraborty, Anurupa; Mishra, Ashwani K; Devi, Thoudam Regina; Sugandhi, Nidhi; Chintamani, Chintamani; Bhatnagar, Dinesh; Kapur, Sujala; Saxena, Sunita

2012-06-01

Locally advanced breast cancer (LABC) remains a clinical challenge as the majority of patients with this diagnosis develop distant metastases despite appropriate therapy. We analyzed expression of steroid and growth hormone receptor genes as well as gene associated with metabolism of chemotherapeutic drugs in locally advanced breast cancer before and after neoadjuvant chemotherapy (NACT) to study whether there is a change in gene expression induced by chemotherapy and whether such changes are associated with tumor response or non-response. Fifty patients were included with locally advanced breast cancer treated with cyclophosphamide, adriamycin, 5-fluorouracil (CAF)-based neoadjuvant chemotherapy before surgery. Total RNA was extracted from 50 match samples of pre- and post-NACT tumor tissues. RNA expression levels of epidermal growth factor receptor family genes including EGFR, ERBB2, ERBB3, androgen receptor (AR), and multidrug-resistance gene 1 (MDR1) were determined by quantitative real-time reverse transcriptase-polymerase chain reaction. Responders show significantly high levels of pre-NACT AR gene expression (P = 0.016), which reduces following NACT (P = 0.008), and hence can serve as a useful tool for the prediction of the success of neoadjuvant chemotherapy in individual cancer patients with locally advanced breast carcinoma. Moreover, a significant post-therapeutic increase in the expression levels of EGFR and MDR1 gene in responders (P = 0.026 and P < 0.001) as well as in non-responders (P = 0.055, P = 0.001) suggests that expression of these genes changes during therapy but they do not have any impact on tumor response, whereas a post-therapeutic reduction was observed in AR in responders. This indicates an independent predictive role of AR with response to NACT.

Predictive Value of Tertiary Lymphoid Structures Assessed by High Endothelial Venule Counts in the Neoadjuvant Setting of Triple-Negative Breast Cancer

PubMed Central

Song, In Hye; Heo, Sun-Hee; Bang, Won Seon; Park, Hye Seon; Park, In Ah; Kim, Young-Ae; Park, Suk Young; Roh, Jin; Gong, Gyungyub; Lee, Hee Jin

2017-01-01

Purpose The tertiary lymphoid structure (TLS) is an important source of tumor-infiltrating lymphocytes (TILs), which have a strong prognostic and predictive value in triple-negative breast cancer (TNBC). A previous study reported that the levels of CXCL13 mRNA expression were associated with TLSs, but measuring the gene expression is challenging in routine practice. Therefore, this study evaluated the MECA79-positive high endothelial venule (HEV) densities and their association with the histopathologically assessed TLSs in biopsy samples. In addition, the relationship of TLSs with the CXCL13 transcript levels and clinical outcomes were examined. Materials and Methods A total of 108 TNBC patients treated with neoadjuvant chemotherapy (NAC) were studied. The amounts of TILs and TLSs were measured histopathologically using hematoxylin and eosin–stained slides. The HEV densities and TIL subpopulations were measured by immunohistochemistry for MECA79, CD3, CD8, and CD20. CXCL13mRNA expression levels using a NanoString assay (NanoString Technologies). Results The mean number of HEVs in pre-NAC biopsies was 12 (range, 0 to 72). The amounts of TILs and TLSs, HEV density, and CXCL13 expression showed robust correlations with each other. A lower pre-NAC clinical T stage, higher TIL and TLS levels, a higher HEV density, CD20-positive cell density, and CXCL13 expression were significant predictors of a pathologic complete response (pCR). Higher CD8-positive cell density and levels of CXCL13 expression were significantly associated with a better disease-free survival rate. Conclusion MECA79-positive HEV density in pre-NAC biopsies is an objective and quantitative surrogate marker of TLS and might be a valuable tool for predicting pCR of TNBC in routine pathology practice. PMID:27488875

Molecular cloning, characterization and expression analysis of PPAR gamma in the orange-spotted grouper (Epinephelus coioides) after the Vibrio alginolyticus challenge.

PubMed

Luo, Shengwei; Huang, Youhua; Xie, Fuxing; Huang, Xiaohong; Liu, Yuan; Wang, Weina; Qin, Qiwei

2015-04-01

PPAR gamma was a key nuclear receptor, playing an important role in the immune defense and the anti-inflammatory mechanism. In this study, the full-length PPAR gamma (EcPPAR gamma) was obtained, containing a 5'UTR of 133 bp, an ORF of 1602 bp and a 3'UTR of 26 bp besides the poly (A) tail. The EcPPAR gamma gene encoded a protein of 533 amino acids with an estimated molecular mass of 60.02 KDa and a predicted isoelectric point (pI) of 6.26. The deduced amino acid sequence showed that EcPPAR gamma consisted of the conserved residues and the domains known to be critical for the PPAR gamma function. The quantitative real-time PCR analysis revealed that EcPPAR gamma transcript was expressed in all the examined tissue, while the strong expression was observed in intestine, followed by the expression in liver, gill, spleen heart, kidney and muscle. Vibrio challenge could stimulate the inflammatory response in grouper and induce a sharp increase of pro-inflammatory cytokines expression, lipid peroxidation and DNA damage, while the up-regulation of vibrio-induced inflammation could also increase the non-specific immune defense. The groupers challenged with Vibrio alginolyticus showed a sharp increase of EcPPAR gamma transcript in immune tissues. Subcellular localization analysis revealed that EcPPAR gamma was distributed in the nucleus. Furthermore, overexpression of EcPPAR gamma could down-regulated the expression of IL1b, IL6, TNF1 and TNF2. In addition, the administration of PPAR gamma antagonist, GW9662, could up-regulate the expression of pro-inflammatory genes, including IL1b, IL6, TNF1 and TNF2. Together, these results indicated that EcPPAR gamma serving as a negative regulator of pro-inflammatory cytokines may play an important role in the immune defense against vibrio-induced inflammation in grouper. Copyright © 2015 Elsevier Ltd. All rights reserved.

Predicting Viral Infection From High-Dimensional Biomarker Trajectories

PubMed Central

Chen, Minhua; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S.; Lucas, Joseph; Dunson, David; Carin, Lawrence

2013-01-01

There is often interest in predicting an individual’s latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent factors, which vary dynamically starting at the latent initiation time of infection. Using a nonparametric cure rate model for the latent initiation times, we allow selection of the genes in the viral response pathway, variability among individuals in infection times, and a subset of individuals who are not infected. As we demonstrate using held-out data, this statistical framework allows accurate predictions of infected individuals in advance of the development of clinical symptoms, without labeled data and even when the number of biomarkers vastly exceeds the number of individuals under study. Biological interpretation of several of the inferred pathways (factors) is provided. PMID:23704802

An Optimized Transient Dual Luciferase Assay for Quantifying MicroRNA Directed Repression of Targeted Sequences

PubMed Central

Moyle, Richard L.; Carvalhais, Lilia C.; Pretorius, Lara-Simone; Nowak, Ekaterina; Subramaniam, Gayathery; Dalton-Morgan, Jessica; Schenk, Peer M.

2017-01-01

Studies investigating the action of small RNAs on computationally predicted target genes require some form of experimental validation. Classical molecular methods of validating microRNA action on target genes are laborious, while approaches that tag predicted target sequences to qualitative reporter genes encounter technical limitations. The aim of this study was to address the challenge of experimentally validating large numbers of computationally predicted microRNA-target transcript interactions using an optimized, quantitative, cost-effective, and scalable approach. The presented method combines transient expression via agroinfiltration of Nicotiana benthamiana leaves with a quantitative dual luciferase reporter system, where firefly luciferase is used to report the microRNA-target sequence interaction and Renilla luciferase is used as an internal standard to normalize expression between replicates. We report the appropriate concentration of N. benthamiana leaf extracts and dilution factor to apply in order to avoid inhibition of firefly LUC activity. Furthermore, the optimal ratio of microRNA precursor expression construct to reporter construct and duration of the incubation period post-agroinfiltration were determined. The optimized dual luciferase assay provides an efficient, repeatable and scalable method to validate and quantify microRNA action on predicted target sequences. The optimized assay was used to validate five predicted targets of rice microRNA miR529b, with as few as six technical replicates. The assay can be extended to assess other small RNA-target sequence interactions, including assessing the functionality of an artificial miRNA or an RNAi construct on a targeted sequence. PMID:28979287

How distinct is the coding of face identity and expression? Evidence for some common dimensions in face space.

PubMed

Rhodes, Gillian; Pond, Stephen; Burton, Nichola; Kloth, Nadine; Jeffery, Linda; Bell, Jason; Ewing, Louise; Calder, Andrew J; Palermo, Romina

2015-09-01

Traditional models of face perception emphasize distinct routes for processing face identity and expression. These models have been highly influential in guiding neural and behavioural research on the mechanisms of face perception. However, it is becoming clear that specialised brain areas for coding identity and expression may respond to both attributes and that identity and expression perception can interact. Here we use perceptual aftereffects to demonstrate the existence of dimensions in perceptual face space that code both identity and expression, further challenging the traditional view. Specifically, we find a significant positive association between face identity aftereffects and expression aftereffects, which dissociates from other face (gaze) and non-face (tilt) aftereffects. Importantly, individual variation in the adaptive calibration of these common dimensions significantly predicts ability to recognize both identity and expression. These results highlight the role of common dimensions in our ability to recognize identity and expression, and show why the high-level visual processing of these attributes is not entirely distinct. Copyright © 2015 Elsevier B.V. All rights reserved.

[Prediction of the molecular response to pertubations from single cell measurements].

PubMed

Remacle, Françoise; Levine, Raphael D

2014-12-01

The response of protein signalization networks to perturbations is analysed from single cell measurements. This experimental approach allows characterizing the fluctuations in protein expression levels from cell to cell. The analysis is based on an information theoretic approach grounded in thermodynamics leading to a quantitative version of Le Chatelier principle which allows to predict the molecular response. Two systems are investigated: human macrophages subjected to lipopolysaccharide challenge, analogous to the immune response against Gram-negative bacteria and the response of the proteins involved in the mTOR signalizing network of GBM cancer cells to changes in partial oxygen pressure. © 2014 médecine/sciences – Inserm.

Impact of higher order diagrams on phase equilibrium calculations for small molecules using lattice cluster theory

NASA Astrophysics Data System (ADS)

Zimmermann, Patrick; Walowski, Christoph; Enders, Sabine

2018-03-01

The Lattice Cluster Theory (LCT) provides a powerful tool to predict thermodynamic properties of large molecules (e.g., polymers) of different molecular architectures. When the pure-component parameters of a certain compound have been derived by adjustment to experimental data and the number of atoms is held constant within the molecule so that only the architecture is changed, the LCT is capable of predicting the properties of isomers without further parameter adjustment just based on the incorporation of molecular architecture. Trying to predict the thermodynamic properties of smaller molecules, one might face some challenges, which are addressed in this contribution. After factoring out the mean field term of the partition function, the LCT poses an expression that involves corrections to the mean field depending on molecular architecture, resulting in the free energy formally being expressed as a double series expansion in lattice coordination number z and interaction energy ɛ ˜ . In the process of deriving all contributing sub-structures within a molecule, some parts have been neglected to this point due to the double series expansion being truncated after the order ɛ˜ 2z-2. We consider the neglected parts that are of the order z-3 and reformulate the expression for the free energy within the LCT to achieve a higher predictive capability of the theory when it comes to small isomers and compressible systems. The modified version was successfully applied for phase equilibrium calculations of binary mixtures composed of linear and branched alkanes.

RWEN: Response-Weighted Elastic Net For Prediction of Chemosensitivity of Cancer Cell Lines. | Office of Cancer Genomics

Cancer.gov

Motivation: In recent years there have been several efforts to generate sensitivity profiles of collections of genomically characterized cell lines to panels of candidate therapeutic compounds. These data provide the basis for the development of in silico models of sensitivity based on cellular, genetic, or expression biomarkers of cancer cells. However, a remaining challenge is an efficient way to identify accurate sets of biomarkers to validate.

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

DOE PAGES

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.; ...

2016-11-24

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. A multitude of technologies, abstractions, and interpretive frameworks have emerged to answer the challenges presented by genome function and regulatory network inference. Here, we propose a new approach for producing biologically meaningful clusters of coexpressed genes, called Atomic Regulons (ARs), based on expression data, gene context, and functional relationships. We demonstrate this new approach by computing ARs for Escherichia coli, which we compare with the coexpressed gene clusters predicted by two prevalent existing methods: hierarchical clustering and k-meansmore » clustering. We test the consistency of ARs predicted by all methods against expected interactions predicted by the Context Likelihood of Relatedness (CLR) mutual information based method, finding that the ARs produced by our approach show better agreement with CLR interactions. We then apply our method to compute ARs for four other genomes: Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus. We compare the AR clusters from all genomes to study the similarity of coexpression among a phylogenetically diverse set of species, identifying subsystems that show remarkable similarity over wide phylogenetic distances. We also study the sensitivity of our method for computing ARs to the expression data used in the computation, showing that our new approach requires less data than competing approaches to converge to a near final configuration of ARs. We go on to use our sensitivity analysis to identify the specific experiments that lead most rapidly to the final set of ARs for E. coli. As a result, this analysis produces insights into improving the design of gene expression experiments.« less

Distinguishing between the Permeability Relationships with Absorption and Metabolism To Improve BCS and BDDCS Predictions in Early Drug Discovery

PubMed Central

2015-01-01

The biopharmaceutics classification system (BCS) and biopharmaceutics drug distribution classification system (BDDCS) are complementary classification systems that can improve, simplify, and accelerate drug discovery, development, and regulatory processes. Drug permeability has been widely accepted as a screening tool for determining intestinal absorption via the BCS during the drug development and regulatory approval processes. Currently, predicting clinically significant drug interactions during drug development is a known challenge for industry and regulatory agencies. The BDDCS, a modification of BCS that utilizes drug metabolism instead of intestinal permeability, predicts drug disposition and potential drug–drug interactions in the intestine, the liver, and most recently the brain. Although correlations between BCS and BDDCS have been observed with drug permeability rates, discrepancies have been noted in drug classifications between the two systems utilizing different permeability models, which are accepted as surrogate models for demonstrating human intestinal permeability by the FDA. Here, we recommend the most applicable permeability models for improving the prediction of BCS and BDDCS classifications. We demonstrate that the passive transcellular permeability rate, characterized by means of permeability models that are deficient in transporter expression and paracellular junctions (e.g., PAMPA and Caco-2), will most accurately predict BDDCS metabolism. These systems will inaccurately predict BCS classifications for drugs that particularly are substrates of highly expressed intestinal transporters. Moreover, in this latter case, a system more representative of complete human intestinal permeability is needed to accurately predict BCS absorption. PMID:24628254

Distinguishing between the permeability relationships with absorption and metabolism to improve BCS and BDDCS predictions in early drug discovery.

PubMed

Larregieu, Caroline A; Benet, Leslie Z

2014-04-07

The biopharmaceutics classification system (BCS) and biopharmaceutics drug distribution classification system (BDDCS) are complementary classification systems that can improve, simplify, and accelerate drug discovery, development, and regulatory processes. Drug permeability has been widely accepted as a screening tool for determining intestinal absorption via the BCS during the drug development and regulatory approval processes. Currently, predicting clinically significant drug interactions during drug development is a known challenge for industry and regulatory agencies. The BDDCS, a modification of BCS that utilizes drug metabolism instead of intestinal permeability, predicts drug disposition and potential drug-drug interactions in the intestine, the liver, and most recently the brain. Although correlations between BCS and BDDCS have been observed with drug permeability rates, discrepancies have been noted in drug classifications between the two systems utilizing different permeability models, which are accepted as surrogate models for demonstrating human intestinal permeability by the FDA. Here, we recommend the most applicable permeability models for improving the prediction of BCS and BDDCS classifications. We demonstrate that the passive transcellular permeability rate, characterized by means of permeability models that are deficient in transporter expression and paracellular junctions (e.g., PAMPA and Caco-2), will most accurately predict BDDCS metabolism. These systems will inaccurately predict BCS classifications for drugs that particularly are substrates of highly expressed intestinal transporters. Moreover, in this latter case, a system more representative of complete human intestinal permeability is needed to accurately predict BCS absorption.

αIIbβ3 variants defined by next-generation sequencing: Predicting variants likely to cause Glanzmann thrombasthenia

PubMed Central

Buitrago, Lorena; Rendon, Augusto; Liang, Yupu; Simeoni, Ilenia; Negri, Ana; Filizola, Marta; Ouwehand, Willem H.; Coller, Barry S.; Alessi, Marie-Christine; Ballmaier, Matthias; Bariana, Tadbir; Bellissimo, Daniel; Bertoli, Marta; Bray, Paul; Bury, Loredana; Carrell, Robin; Cattaneo, Marco; Collins, Peter; French, Deborah; Favier, Remi; Freson, Kathleen; Furie, Bruce; Germeshausen, Manuela; Ghevaert, Cedric; Gomez, Keith; Goodeve, Anne; Gresele, Paolo; Guerrero, Jose; Hampshire, Dan J.; Hadinnapola, Charaka; Heemskerk, Johan; Henskens, Yvonne; Hill, Marian; Hogg, Nancy; Johnsen, Jill; Kahr, Walter; Kerr, Ron; Kunishima, Shinji; Laffan, Michael; Natwani, Amit; Neerman-Arbez, Marguerite; Nurden, Paquita; Nurden, Alan; Ormiston, Mark; Othman, Maha; Ouwehand, Willem; Perry, David; Vilk, Shoshana Ravel; Reitsma, Pieter; Rondina, Matthew; Simeoni, Ilenia; Smethurst, Peter; Stephens, Jonathan; Stevenson, William; Szkotak, Artur; Turro, Ernest; Van Geet, Christel; Vries, Minka; Ward, June; Waye, John; Westbury, Sarah; Whiteheart, Sidney; Wilcox, David; Zhang, Bi

2015-01-01

Next-generation sequencing is transforming our understanding of human genetic variation but assessing the functional impact of novel variants presents challenges. We analyzed missense variants in the integrin αIIbβ3 receptor subunit genes ITGA2B and ITGB3 identified by whole-exome or -genome sequencing in the ThromboGenomics project, comprising ∼32,000 alleles from 16,108 individuals. We analyzed the results in comparison with 111 missense variants in these genes previously reported as being associated with Glanzmann thrombasthenia (GT), 20 associated with alloimmune thrombocytopenia, and 5 associated with aniso/macrothrombocytopenia. We identified 114 novel missense variants in ITGA2B (affecting ∼11% of the amino acids) and 68 novel missense variants in ITGB3 (affecting ∼9% of the amino acids). Of the variants, 96% had minor allele frequencies (MAF) < 0.1%, indicating their rarity. Based on sequence conservation, MAF, and location on a complete model of αIIbβ3, we selected three novel variants that affect amino acids previously associated with GT for expression in HEK293 cells. αIIb P176H and β3 C547G severely reduced αIIbβ3 expression, whereas αIIb P943A partially reduced αIIbβ3 expression and had no effect on fibrinogen binding. We used receiver operating characteristic curves of combined annotation-dependent depletion, Polyphen 2-HDIV, and sorting intolerant from tolerant to estimate the percentage of novel variants likely to be deleterious. At optimal cut-off values, which had 69–98% sensitivity in detecting GT mutations, between 27% and 71% of the novel αIIb or β3 missense variants were predicted to be deleterious. Our data have implications for understanding the evolutionary pressure on αIIbβ3 and highlight the challenges in predicting the clinical significance of novel missense variants. PMID:25827233

Polymer physics predicts the effects of structural variants on chromatin architecture.

PubMed

Bianco, Simona; Lupiáñez, Darío G; Chiariello, Andrea M; Annunziatella, Carlo; Kraft, Katerina; Schöpflin, Robert; Wittler, Lars; Andrey, Guillaume; Vingron, Martin; Pombo, Ana; Mundlos, Stefan; Nicodemi, Mario

2018-05-01

Structural variants (SVs) can result in changes in gene expression due to abnormal chromatin folding and cause disease. However, the prediction of such effects remains a challenge. Here we present a polymer-physics-based approach (PRISMR) to model 3D chromatin folding and to predict enhancer-promoter contacts. PRISMR predicts higher-order chromatin structure from genome-wide chromosome conformation capture (Hi-C) data. Using the EPHA4 locus as a model, the effects of pathogenic SVs are predicted in silico and compared to Hi-C data generated from mouse limb buds and patient-derived fibroblasts. PRISMR deconvolves the folding complexity of the EPHA4 locus and identifies SV-induced ectopic contacts and alterations of 3D genome organization in homozygous or heterozygous states. We show that SVs can reconfigure topologically associating domains, thereby producing extensive rewiring of regulatory interactions and causing disease by gene misexpression. PRISMR can be used to predict interactions in silico, thereby providing a tool for analyzing the disease-causing potential of SVs.

Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing.

PubMed

Ghadie, Mohamed Ali; Lambourne, Luke; Vidal, Marc; Xia, Yu

2017-08-01

Alternative splicing is known to remodel protein-protein interaction networks ("interactomes"), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing.

Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing

PubMed Central

Lambourne, Luke; Vidal, Marc

2017-01-01

Alternative splicing is known to remodel protein-protein interaction networks (“interactomes”), yet large-scale determination of isoform-specific interactions remains challenging. We present a domain-based method to predict the isoform interactome from the reference interactome. First, we construct the domain-resolved reference interactome by mapping known domain-domain interactions onto experimentally-determined interactions between reference proteins. Then, we construct the isoform interactome by predicting that an isoform loses an interaction if it loses the domain mediating the interaction. Our prediction framework is of high-quality when assessed by experimental data. The predicted human isoform interactome reveals extensive network remodeling by alternative splicing. Protein pairs interacting with different isoforms of the same gene tend to be more divergent in biological function, tissue expression, and disease phenotype than protein pairs interacting with the same isoforms. Our prediction method complements experimental efforts, and demonstrates that integrating structural domain information with interactomes provides insights into the functional impact of alternative splicing. PMID:28846689

Predictors of chemotherapy efficacy in non-small-cell lung cancer: a challenging landscape.

PubMed

Olaussen, K A; Postel-Vinay, S

2016-11-01

Conventional cytotoxic chemotherapy (CCC) is the backbone of non-small-cell lung cancer (NSCLC) treatment since decades and still represents a key element of the therapeutic armamentarium. Contrary to molecularly targeted therapies and immune therapies, for which predictive biomarkers of activity have been actively looked for and developed in parallel to the drug development process ('companion biomarkers'), no patient selection biomarker is currently available for CCC, precluding customizing treatment. We reviewed preclinical and clinical studies that assessed potential predictive biomarkers of CCC used in NSCLC (platinum, antimetabolites, topoisomerase inhibitors, and spindle poisons). Biomarker evaluation method, analytical validity, and robustness are described and challenged for each biomarker. The best-validated predictive biomarkers for efficacy are currently ERCC1, RRM1, and TS for platinum agents, gemcitabine and pemetrexed, respectively. Other potential biomarkers include hENT1 for gemcitabine, class III β-tubulin for spindle poisons, TOP2A expression and CEP17 duplication (mostly studied for predicting anthracyclines efficacy) whose applicability concerning etoposide would deserve further evaluation. However, none of these biomarkers has till now been validated prospectively in an appropriately designed and powered randomised trial, and none of them is currently ready for implementation in routine clinical practice. The search for predictive biomarkers to CCC has been proven challenging. If a plethora of biomarkers have been evaluated either in the preclinical or in the clinical setting, none of them is ready for clinical implementation yet. Considering that most mechanisms of resistance or sensitivity to CCC are multifactorial, a combinatorial approach might be relevant and further efforts are required. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.

PubMed

Technow, Frank; Messina, Carlos D; Totir, L Radu; Cooper, Mark

2015-01-01

Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics.

Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation

PubMed Central

Technow, Frank; Messina, Carlos D.; Totir, L. Radu; Cooper, Mark

2015-01-01

Genomic selection, enabled by whole genome prediction (WGP) methods, is revolutionizing plant breeding. Existing WGP methods have been shown to deliver accurate predictions in the most common settings, such as prediction of across environment performance for traits with additive gene effects. However, prediction of traits with non-additive gene effects and prediction of genotype by environment interaction (G×E), continues to be challenging. Previous attempts to increase prediction accuracy for these particularly difficult tasks employed prediction methods that are purely statistical in nature. Augmenting the statistical methods with biological knowledge has been largely overlooked thus far. Crop growth models (CGMs) attempt to represent the impact of functional relationships between plant physiology and the environment in the formation of yield and similar output traits of interest. Thus, they can explain the impact of G×E and certain types of non-additive gene effects on the expressed phenotype. Approximate Bayesian computation (ABC), a novel and powerful computational procedure, allows the incorporation of CGMs directly into the estimation of whole genome marker effects in WGP. Here we provide a proof of concept study for this novel approach and demonstrate its use with synthetic data sets. We show that this novel approach can be considerably more accurate than the benchmark WGP method GBLUP in predicting performance in environments represented in the estimation set as well as in previously unobserved environments for traits determined by non-additive gene effects. We conclude that this proof of concept demonstrates that using ABC for incorporating biological knowledge in the form of CGMs into WGP is a very promising and novel approach to improving prediction accuracy for some of the most challenging scenarios in plant breeding and applied genetics. PMID:26121133

Transcriptional regulation of mammalian selenoprotein expression

PubMed Central

Stoytcheva, Zoia R.; Berry, Marla J.

2009-01-01

Background Selenoproteins contain the twenty-first amino acid, selenocysteine, and are involved in cellular defenses against oxidative damage, important metabolic and developmental pathways, and responses to environmental challenges. Elucidating the mechanisms regulating selenoprotein expression at the transcriptional level is key to understanding how these mechanisms are called into play to respond to the changing environment. Methods This review summarizes published studies on transcriptional regulation of selenoprotein genes, focused primarily on genes whose encoded protein functions are at least partially understood. This is followed by in silico analysis of predicted regulatory elements in selenoprotein genes, including those in the aforementioned category as well as the genes whose functions are not known. Results Our findings reveal regulatory pathways common to many selenoprotein genes, including several involved in stress-responses. In addition, tissue-specific regulatory factors are implicated in regulating many selenoprotein genes. Conclusions These studies provide new insights into how selenoprotein genes respond to environmental and other challenges, and the roles these proteins play in allowing cells to adapt to these changes. General Significance Elucidating the regulatory mechanisms affecting selenoprotein expression is essential for understanding their roles in human diseases, and for developing diagnostic and potential therapeutic approaches to address dysregulation of members of this gene family. PMID:19465084

Integration of somatic mutation, expression and functional data reveals potential driver genes predictive of breast cancer survival.

PubMed

Suo, Chen; Hrydziuszko, Olga; Lee, Donghwan; Pramana, Setia; Saputra, Dhany; Joshi, Himanshu; Calza, Stefano; Pawitan, Yudi

2015-08-15

Genome and transcriptome analyses can be used to explore cancers comprehensively, and it is increasingly common to have multiple omics data measured from each individual. Furthermore, there are rich functional data such as predicted impact of mutations on protein coding and gene/protein networks. However, integration of the complex information across the different omics and functional data is still challenging. Clinical validation, particularly based on patient outcomes such as survival, is important for assessing the relevance of the integrated information and for comparing different procedures. An analysis pipeline is built for integrating genomic and transcriptomic alterations from whole-exome and RNA sequence data and functional data from protein function prediction and gene interaction networks. The method accumulates evidence for the functional implications of mutated potential driver genes found within and across patients. A driver-gene score (DGscore) is developed to capture the cumulative effect of such genes. To contribute to the score, a gene has to be frequently mutated, with high or moderate mutational impact at protein level, exhibiting an extreme expression and functionally linked to many differentially expressed neighbors in the functional gene network. The pipeline is applied to 60 matched tumor and normal samples of the same patient from The Cancer Genome Atlas breast-cancer project. In clinical validation, patients with high DGscores have worse survival than those with low scores (P = 0.001). Furthermore, the DGscore outperforms the established expression-based signatures MammaPrint and PAM50 in predicting patient survival. In conclusion, integration of mutation, expression and functional data allows identification of clinically relevant potential driver genes in cancer. The documented pipeline including annotated sample scripts can be found in http://fafner.meb.ki.se/biostatwiki/driver-genes/. yudi.pawitan@ki.se Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Expression profiles of miRNAs from bovine mammary glands in response to Streptococcus agalactiae-induced mastitis.

PubMed

Pu, Junhua; Li, Rui; Zhang, Chenglong; Chen, Dan; Liao, Xiangxiang; Zhu, Yihui; Geng, Xiaohan; Ji, Dejun; Mao, Yongjiang; Gong, Yunchen; Yang, Zhangping

2017-08-01

This study aimed to describe the expression profiles of microRNAs (miRNAs) from mammary gland tissues collected from dairy cows with Streptococcus agalactiae-induced mastitis and to identify differentially expressed miRNAs related to mastitis. The mammary glands of Chinese Holstein cows were challenged with Streptococcus agalactiae to induce mastitis. Small RNAs were isolated from the mammary tissues of the test and control groups and then sequenced using the Solexa sequencing technology to construct two small RNA libraries. Potential target genes of these differentially expressed miRNAs were predicted using the RNAhybrid software, and KEGG pathways associated with these genes were analysed. A total of 18 555 913 and 20 847 000 effective reads were obtained from the test and control groups, respectively. In total, 373 known and 399 novel miRNAs were detected in the test group, and 358 known and 232 novel miRNAs were uncovered in the control group. A total of 35 differentially expressed miRNAs were identified in the test group compared to the control group, including 10 up-regulated miRNAs and 25 down-regulated miRNAs. Of these miRNAs, miR-223 exhibited the highest degree of up-regulation with an approximately 3-fold increase in expression, whereas miR-26a exhibited the most decreased expression level (more than 2-fold). The RNAhybrid software predicted 18 801 genes as potential targets of these 35 miRNAs. Furthermore, several immune response and signal transduction pathways, including the RIG-I-like receptor signalling pathway, cytosolic DNA sensing pathway and Notch signal pathway, were enriched in these predicted targets. In summary, this study provided experimental evidence for the mechanism underlying the regulation of bovine mastitis by miRNAs and showed that miRNAs might be involved in signal pathways during S. agalactiae-induced mastitis.

A distinct adipose tissue gene expression response to caloric restriction predicts 6-mo weight maintenance in obese subjects.

PubMed

Mutch, David M; Pers, Tune H; Temanni, M Ramzi; Pelloux, Veronique; Marquez-Quiñones, Adriana; Holst, Claus; Martinez, J Alfredo; Babalis, Dimitris; van Baak, Marleen A; Handjieva-Darlenska, Teodora; Walker, Celia G; Astrup, Arne; Saris, Wim H M; Langin, Dominique; Viguerie, Nathalie; Zucker, Jean-Daniel; Clément, Karine

2011-12-01

Weight loss has been shown to reduce risk factors associated with cardiovascular disease and diabetes; however, successful maintenance of weight loss continues to pose a challenge. The present study was designed to assess whether changes in subcutaneous adipose tissue (scAT) gene expression during a low-calorie diet (LCD) could be used to differentiate and predict subjects who experience successful short-term weight maintenance from subjects who experience weight regain. Forty white women followed a dietary protocol consisting of an 8-wk LCD phase followed by a 6-mo weight-maintenance phase. Participants were classified as weight maintainers (WMs; 0-10% weight regain) and weight regainers (WRs; 50-100% weight regain) by considering changes in body weight during the 2 phases. Anthropometric measurements, bioclinical variables, and scAT gene expression were studied in all individuals before and after the LCD. Energy intake was estimated by using 3-d dietary records. No differences in body weight and fasting insulin were observed between WMs and WRs at baseline or after the LCD period. The LCD resulted in significant decreases in body weight and in several plasma variables in both groups. WMs experienced a significant reduction in insulin secretion in response to an oral-glucose-tolerance test after the LCD; in contrast, no changes in insulin secretion were observed in WRs after the LCD. An ANOVA of scAT gene expression showed that genes regulating fatty acid metabolism, citric acid cycle, oxidative phosphorylation, and apoptosis were regulated differently by the LCD in WM and WR subjects. This study suggests that LCD-induced changes in insulin secretion and scAT gene expression may have the potential to predict successful short-term weight maintenance. This trial was registered at clinicaltrials.gov as NCT00390637.

Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data

PubMed Central

2017-01-01

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr. PMID:28821014

Experimental Assessment of Splicing Variants Using Expression Minigenes and Comparison with In Silico Predictions

PubMed Central

Sharma, Neeraj; Sosnay, Patrick R.; Ramalho, Anabela S.; Douville, Christopher; Franca, Arianna; Gottschalk, Laura B.; Park, Jeenah; Lee, Melissa; Vecchio-Pagan, Briana; Raraigh, Karen S.; Amaral, Margarida D.; Karchin, Rachel; Cutting, Garry R.

2015-01-01

Assessment of the functional consequences of variants near splice sites is a major challenge in the diagnostic laboratory. To address this issue, we created expression minigenes (EMGs) to determine the RNA and protein products generated by splice site variants (n = 10) implicated in cystic fibrosis (CF). Experimental results were compared with the splicing predictions of eight in silico tools. EMGs containing the full-length Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) coding sequence and flanking intron sequences generated wild-type transcript and fully processed protein in Human Embryonic Kidney (HEK293) and CF bronchial epithelial (CFBE41o-) cells. Quantification of variant induced aberrant mRNA isoforms was concordant using fragment analysis and pyrosequencing. The splicing patterns of c.1585−1G>A and c.2657+5G>A were comparable to those reported in primary cells from individuals bearing these variants. Bioinformatics predictions were consistent with experimental results for 9/10 variants (MES), 8/10 variants (NNSplice), and 7/10 variants (SSAT and Sroogle). Programs that estimate the consequences of mis-splicing predicted 11/16 (HSF and ASSEDA) and 10/16 (Fsplice and SplicePort) experimentally observed mRNA isoforms. EMGs provide a robust experimental approach for clinical interpretation of splice site variants and refinement of in silico tools. PMID:25066652

Robust diagnosis of non-Hodgkin lymphoma phenotypes validated on gene expression data from different laboratories.

PubMed

Bhanot, Gyan; Alexe, Gabriela; Levine, Arnold J; Stolovitzky, Gustavo

2005-01-01

A major challenge in cancer diagnosis from microarray data is the need for robust, accurate, classification models which are independent of the analysis techniques used and can combine data from different laboratories. We propose such a classification scheme originally developed for phenotype identification from mass spectrometry data. The method uses a robust multivariate gene selection procedure and combines the results of several machine learning tools trained on raw and pattern data to produce an accurate meta-classifier. We illustrate and validate our method by applying it to gene expression datasets: the oligonucleotide HuGeneFL microarray dataset of Shipp et al. (www.genome.wi.mit.du/MPR/lymphoma) and the Hu95Av2 Affymetrix dataset (DallaFavera's laboratory, Columbia University). Our pattern-based meta-classification technique achieves higher predictive accuracies than each of the individual classifiers , is robust against data perturbations and provides subsets of related predictive genes. Our techniques predict that combinations of some genes in the p53 pathway are highly predictive of phenotype. In particular, we find that in 80% of DLBCL cases the mRNA level of at least one of the three genes p53, PLK1 and CDK2 is elevated, while in 80% of FL cases, the mRNA level of at most one of them is elevated.

The Prediction of Drug-Disease Correlation Based on Gene Expression Data.

PubMed

Cui, Hui; Zhang, Menghuan; Yang, Qingmin; Li, Xiangyi; Liebman, Michael; Yu, Ying; Xie, Lu

2018-01-01

The explosive growth of high-throughput experimental methods and resulting data yields both opportunity and challenge for selecting the correct drug to treat both a specific patient and their individual disease. Ideally, it would be useful and efficient if computational approaches could be applied to help achieve optimal drug-patient-disease matching but current efforts have met with limited success. Current approaches have primarily utilized the measureable effect of a specific drug on target tissue or cell lines to identify the potential biological effect of such treatment. While these efforts have met with some level of success, there exists much opportunity for improvement. This specifically follows the observation that, for many diseases in light of actual patient response, there is increasing need for treatment with combinations of drugs rather than single drug therapies. Only a few previous studies have yielded computational approaches for predicting the synergy of drug combinations by analyzing high-throughput molecular datasets. However, these computational approaches focused on the characteristics of the drug itself, without fully accounting for disease factors. Here, we propose an algorithm to specifically predict synergistic effects of drug combinations on various diseases, by integrating the data characteristics of disease-related gene expression profiles with drug-treated gene expression profiles. We have demonstrated utility through its application to transcriptome data, including microarray and RNASeq data, and the drug-disease prediction results were validated using existing publications and drug databases. It is also applicable to other quantitative profiling data such as proteomics data. We also provide an interactive web interface to allow our Prediction of Drug-Disease method to be readily applied to user data. While our studies represent a preliminary exploration of this critical problem, we believe that the algorithm can provide the basis for further refinement towards addressing a large clinical need.

GenoCAD Plant Grammar to Design Plant Expression Vectors for Promoter Analysis.

PubMed

Coll, Anna; Wilson, Mandy L; Gruden, Kristina; Peccoud, Jean

2016-01-01

With the rapid advances in prediction tools for discovery of new promoters and their cis-elements, there is a need to improve plant expression methodologies in order to facilitate a high-throughput functional validation of these promoters in planta. The promoter-reporter analysis is an indispensible approach for characterization of plant promoters. It requires the design of complex plant expression vectors, which can be challenging. Here, we describe the use of a plant grammar implemented in GenoCAD that will allow the users to quickly design constructs for promoter analysis experiments but also for other in planta functional studies. The GenoCAD plant grammar includes a library of plant biological parts organized in structural categories to facilitate their use and management and a set of rules that guides the process of assembling these biological parts into large constructs.

Transcriptomic Analysis of Carboxylic Acid Challenge in Escherichia coli: Beyond Membrane Damage

PubMed Central

Royce, Liam A.; Boggess, Erin; Fu, Yao; Liu, Ping; Shanks, Jacqueline V.; Dickerson, Julie; Jarboe, Laura R.

2014-01-01

Carboxylic acids are an attractive biorenewable chemical. Enormous progress has been made in engineering microbes for production of these compounds though titers remain lower than desired. Here we used transcriptome analysis of Escherichia coli during exogenous challenge with octanoic acid (C8) at pH 7.0 to probe mechanisms of toxicity. This analysis highlights the intracellular acidification and membrane damage caused by C8 challenge. Network component analysis identified transcription factors with altered activity including GadE, the activator of the glutamate-dependent acid resistance system (AR2) and Lrp, the amino acid biosynthesis regulator. The intracellular acidification was quantified during exogenous challenge, but was not observed in a carboxylic acid producing strain, though this may be due to lower titers than those used in our exogenous challenge studies. We developed a framework for predicting the proton motive force during adaptation to strong inorganic acids and carboxylic acids. This model predicts that inorganic acid challenge is mitigated by cation accumulation, but that carboxylic acid challenge inverts the proton motive force and requires anion accumulation. Utilization of native acid resistance systems was not useful in terms of supporting growth or alleviating intracellular acidification. AR2 was found to be non-functional, possibly due to membrane damage. We proposed that interaction of Lrp and C8 resulted in repression of amino acid biosynthesis. However, this hypothesis was not supported by perturbation of lrp expression or amino acid supplementation. E. coli strains were also engineered for altered cyclopropane fatty acid content in the membrane, which had a dramatic effect on membrane properties, though C8 tolerance was not increased. We conclude that achieving higher production titers requires circumventing the membrane damage. As higher titers are achieved, acidification may become problematic. PMID:24586888

Protein-DNA binding dynamics predict transcriptional response to nutrients in archaea.

PubMed

Todor, Horia; Sharma, Kriti; Pittman, Adrianne M C; Schmid, Amy K

2013-10-01

Organisms across all three domains of life use gene regulatory networks (GRNs) to integrate varied stimuli into coherent transcriptional responses to environmental pressures. However, inferring GRN topology and regulatory causality remains a central challenge in systems biology. Previous work characterized TrmB as a global metabolic transcription factor in archaeal extremophiles. However, it remains unclear how TrmB dynamically regulates its ∼100 metabolic enzyme-coding gene targets. Using a dynamic perturbation approach, we elucidate the topology of the TrmB metabolic GRN in the model archaeon Halobacterium salinarum. Clustering of dynamic gene expression patterns reveals that TrmB functions alone to regulate central metabolic enzyme-coding genes but cooperates with various regulators to control peripheral metabolic pathways. Using a dynamical model, we predict gene expression patterns for some TrmB-dependent promoters and infer secondary regulators for others. Our data suggest feed-forward gene regulatory topology for cobalamin biosynthesis. In contrast, purine biosynthesis appears to require TrmB-independent regulators. We conclude that TrmB is an important component for mediating metabolic modularity, integrating nutrient status and regulating gene expression dynamics alone and in concert with secondary regulators.

Inferring evolution of gene duplicates using probabilistic models and nonparametric belief propagation.

PubMed

Zeng, Jia; Hannenhalli, Sridhar

2013-01-01

Gene duplication, followed by functional evolution of duplicate genes, is a primary engine of evolutionary innovation. In turn, gene expression evolution is a critical component of overall functional evolution of paralogs. Inferring evolutionary history of gene expression among paralogs is therefore a problem of considerable interest. It also represents significant challenges. The standard approaches of evolutionary reconstruction assume that at an internal node of the duplication tree, the two duplicates evolve independently. However, because of various selection pressures functional evolution of the two paralogs may be coupled. The coupling of paralog evolution corresponds to three major fates of gene duplicates: subfunctionalization (SF), conserved function (CF) or neofunctionalization (NF). Quantitative analysis of these fates is of great interest and clearly influences evolutionary inference of expression. These two interrelated problems of inferring gene expression and evolutionary fates of gene duplicates have not been studied together previously and motivate the present study. Here we propose a novel probabilistic framework and algorithm to simultaneously infer (i) ancestral gene expression and (ii) the likely fate (SF, NF, CF) at each duplication event during the evolution of gene family. Using tissue-specific gene expression data, we develop a nonparametric belief propagation (NBP) algorithm to predict the ancestral expression level as a proxy for function, and describe a novel probabilistic model that relates the predicted and known expression levels to the possible evolutionary fates. We validate our model using simulation and then apply it to a genome-wide set of gene duplicates in human. Our results suggest that SF tends to be more frequent at the earlier stage of gene family expansion, while NF occurs more frequently later on.

Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data.

PubMed

Ryall, Karen A; Shin, Jimin; Yoo, Minjae; Hinz, Trista K; Kim, Jihye; Kang, Jaewoo; Heasley, Lynn E; Tan, Aik Choon

2015-12-01

Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret. We developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055. KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/. aikchoon.tan@ucdenver.edu. Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

OptSSeq: High-throughput sequencing readout of growth enrichment defines optimal gene expression elements for homoethanologenesis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghosh, Indro Neil; Landick, Robert

The optimization of synthetic pathways is a central challenge in metabolic engineering. OptSSeq (Optimization by Selection and Sequencing) is one approach to this challenge. OptSSeq couples selection of optimal enzyme expression levels linked to cell growth rate with high-throughput sequencing to track enrichment of gene expression elements (promoters and ribosomebinding sites) from a combinatorial library. OptSSeq yields information on both optimal and suboptimal enzyme levels, and helps identify constraints that limit maximal product formation. Here we report a proof-of-concept implementation of OptSSeq using homoethanologenesis, a two-step pathway consisting of pyruvate decarboxylase (Pdc) and alcohol dehydrogenase (Adh) that converts pyruvate tomore » ethanol and is naturally optimized in the bacterium Zymomonas mobilis. We used OptSSeq to determine optimal gene expression elements and enzyme levels for Z. mobilis Pdc, AdhA, and AdhB expressed in Escherichia coli. By varying both expression signals and gene order, we identified an optimal solution using only Pdc and AdhB. We resolved current uncertainty about the functions of the Fe 2+-dependent AdhB and Zn 2+- dependent AdhA by showing that AdhB is preferred over AdhA for rapid growth in both E. coli and Z. mobilis. Finally, by comparing predictions of growth-linked metabolic flux to enzyme synthesis costs, we established that optimal E. coli homoethanologenesis was achieved by our best pdc-adhB expression cassette and that the remaining constraints lie in the E. coli metabolic network or inefficient Pdc or AdhB function in E. coli. Furthermore, OptSSeq is a general tool for synthetic biology to tune enzyme levels in any pathway whose optimal function can be linked to cell growth or survival.« less

OptSSeq: High-throughput sequencing readout of growth enrichment defines optimal gene expression elements for homoethanologenesis

DOE PAGES

Ghosh, Indro Neil; Landick, Robert

2016-07-16

The optimization of synthetic pathways is a central challenge in metabolic engineering. OptSSeq (Optimization by Selection and Sequencing) is one approach to this challenge. OptSSeq couples selection of optimal enzyme expression levels linked to cell growth rate with high-throughput sequencing to track enrichment of gene expression elements (promoters and ribosomebinding sites) from a combinatorial library. OptSSeq yields information on both optimal and suboptimal enzyme levels, and helps identify constraints that limit maximal product formation. Here we report a proof-of-concept implementation of OptSSeq using homoethanologenesis, a two-step pathway consisting of pyruvate decarboxylase (Pdc) and alcohol dehydrogenase (Adh) that converts pyruvate tomore » ethanol and is naturally optimized in the bacterium Zymomonas mobilis. We used OptSSeq to determine optimal gene expression elements and enzyme levels for Z. mobilis Pdc, AdhA, and AdhB expressed in Escherichia coli. By varying both expression signals and gene order, we identified an optimal solution using only Pdc and AdhB. We resolved current uncertainty about the functions of the Fe 2+-dependent AdhB and Zn 2+- dependent AdhA by showing that AdhB is preferred over AdhA for rapid growth in both E. coli and Z. mobilis. Finally, by comparing predictions of growth-linked metabolic flux to enzyme synthesis costs, we established that optimal E. coli homoethanologenesis was achieved by our best pdc-adhB expression cassette and that the remaining constraints lie in the E. coli metabolic network or inefficient Pdc or AdhB function in E. coli. Furthermore, OptSSeq is a general tool for synthetic biology to tune enzyme levels in any pathway whose optimal function can be linked to cell growth or survival.« less

Myocyte enhancer factor 2D provides a cross-talk between chronic inflammation and lung cancer.

PubMed

Zhu, Hai-Xing; Shi, Lin; Zhang, Yong; Zhu, Yi-Chun; Bai, Chun-Xue; Wang, Xiang-Dong; Zhou, Jie-Bai

2017-03-24

Lung cancer is the leading cause of cancer-related morbidity and mortality worldwide. Patients with chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD), are exposed to a higher risk of developing lung cancer. Chronic inflammation may play an important role in the lung carcinogenesis among those patients. The present study aimed at identifying candidate biomarker predicting lung cancer risk among patients with chronic respiratory diseases. We applied clinical bioinformatics tools to analyze different gene profile datasets with a special focus on screening the potential biomarker during chronic inflammation-lung cancer transition. Then we adopted an in vitro model based on LPS-challenged A549 cells to validate the biomarker through RNA-sequencing, quantitative real time polymerase chain reaction, and western blot analysis. Bioinformatics analyses of the 16 enrolled GSE datasets from Gene Expression Omnibus online database showed myocyte enhancer factor 2D (MEF2D) level significantly increased in COPD patients coexisting non-small-cell lung carcinoma (NSCLC). Inflammation challenge increased MEF2D expression in NSCLC cell line A549, associated with the severity of inflammation. Extracellular signal-regulated protein kinase inhibition could reverse the up-regulation of MEF2D in inflammation-activated A549. MEF2D played a critical role in NSCLC cell bio-behaviors, including proliferation, differentiation, and movement. Inflammatory conditions led to increased MEF2D expression, which might further contribute to the development of lung cancer through influencing cancer microenvironment and cell bio-behaviors. MEF2D might be a potential biomarker during chronic inflammation-lung cancer transition, predicting the risk of lung cancer among patients with chronic respiratory diseases.

Differential Expression of Inflammatory Cytokines and Stress Genes in Male and Female Mice in Response to a Lipopolysaccharide Challenge

PubMed Central

Everhardt Queen, Ashleigh; Moerdyk-Schauwecker, Megan; McKee, Leslie M.; Leamy, Larry J.

2016-01-01

Background Sex plays a key role in an individual’s immune response against pathogenic challenges such that females fare better when infected with certain pathogens. It is thought that sex hormones impact gene expression in immune cells and lead to sexually dimorphic responses to pathogens. We predicted that, in the presence of E. coli gram-negative lipopolysaccharide (LPS), there would be a sexually dimorphic response in proinflammatory cytokine production and acute phase stress gene expression and that these responses might vary among different mouse strains and times in a pattern opposite to that of body temperature associated with LPS-induced shock. Materials and Methods Interleukin-6 (IL-6), macrophage inflammatory protein-Iβ (MIP-1β), tumor necrosis factor alpha (TNF-α) and interleukin-1β (IL-1β) as well as beta-fibrinogen (Fgb) and metallothionein-1 (Mt-1) mRNA expression were measured at four time points (0, 2, 4 and 7 hours) after injection of E. coli LPS in mice from three inbred strains. Results Statistical analysis using analyses of variance (ANOVAs) showed that the levels of the all six traits changed over time, generally peaking at 2 hours after LPS injection. Mt-1, Fgb, and IL-6 showed differences among strains, although these were time-specific. Sexual dimorphism was seen for Fgb and IL6, and was most pronounced at the latest time period (7 hours) where male levels exceeded those for females. Trends for all six cytokine/gene expression traits were negatively correlated with those for body temperatures. Discussion The higher levels of expression of Fgb and IL6 in males compared with females are consistent with the greater vulnerability of males to infection and subsequent inflammation. Temperature appears to be a useful proxy for mortality in endotoxic shock, but sexual dimorphism in cytokine and stress gene expression levels may persist after an LPS challenge even if temperatures in the two sexes are similar and have begun to stabilize. PMID:27120355

Different factors influence self-reports and third-party reports of anger by adults with intellectual disabilities.

PubMed

Rose, John; Willner, Paul; Shead, Jennifer; Jahoda, Andrew; Gillespie, David; Townson, Julia; Lammie, Claire; Woodgate, Christopher; Stenfert Kroese, Biza; Felce, David; MacMahon, Pamela; Rose, Nikki; Stimpson, Aimee; Nuttall, Jacqueline; Hood, Kerenza

2013-09-01

Many people with intellectual disabilities display high levels of anger, and cognitive-behavioural anger management interventions are used routinely. However, for these methods to be used optimally, a better understanding is needed of different forms of anger assessment. The aim of this study was to investigate the relationship of a range of measures to self- and carer reports of anger expression, including instruments used to assess mental health and challenging behaviour. Adults with intellectual disabilities, who had been identified as having problems with anger control, their key-workers and home carers all rated the service users' trait anger, using parallel versions of the same instrument (the Provocation Inventory). In addition, service users completed a battery of mental health assessments (the Glasgow Depression Scale, Glasgow Anxiety Scale and Rosenberg Self-Esteem Scale), and both groups of carers completed a battery of challenging behaviour measures (the Hyperactivity and Irritability domains of the Aberrant Behavior Checklist and the Modified Overt Anger Scale). Participants had high levels of mental health problems (depression: 34%; anxiety: 73%) and severe challenging behaviour (26%). Hierarchical linear regression analysis was used to explore the extent to which anger ratings by the three groups of respondents were predicted by demographic factors, mental health measures and challenging behaviour measures. Older service users rated themselves as less angry and were also rated as less angry by home carers, but not by key-workers. More intellectually able service users were rated as more angry by both sets of carers, but not by the service users themselves. Significantly, mental health status (but not challenging behaviour) predicted service users' self-ratings of anger, whereas challenging behaviour (but not mental health status) predicted carers' ratings of service users' anger. Service users and their carers appear to use different information when rating the service users' anger. Service users' self-ratings reflect their internal emotional state and mental health, as reflected by their ratings of anxiety and depression, whereas staff rate service users' anger on the basis of overt behaviours, as measured by challenging behaviour scales. © 2013 John Wiley & Sons Ltd.

Comparison of alternative approaches for analysing multi-level RNA-seq data

PubMed Central

Mohorianu, Irina; Bretman, Amanda; Smith, Damian T.; Fowler, Emily K.; Dalmay, Tamas

2017-01-01

RNA sequencing (RNA-seq) is widely used for RNA quantification in the environmental, biological and medical sciences. It enables the description of genome-wide patterns of expression and the identification of regulatory interactions and networks. The aim of RNA-seq data analyses is to achieve rigorous quantification of genes/transcripts to allow a reliable prediction of differential expression (DE), despite variation in levels of noise and inherent biases in sequencing data. This can be especially challenging for datasets in which gene expression differences are subtle, as in the behavioural transcriptomics test dataset from D. melanogaster that we used here. We investigated the power of existing approaches for quality checking mRNA-seq data and explored additional, quantitative quality checks. To accommodate nested, multi-level experimental designs, we incorporated sample layout into our analyses. We employed a subsampling without replacement-based normalization and an identification of DE that accounted for the hierarchy and amplitude of effect sizes within samples, then evaluated the resulting differential expression call in comparison to existing approaches. In a final step to test for broader applicability, we applied our approaches to a published set of H. sapiens mRNA-seq samples, The dataset-tailored methods improved sample comparability and delivered a robust prediction of subtle gene expression changes. The proposed approaches have the potential to improve key steps in the analysis of RNA-seq data by incorporating the structure and characteristics of biological experiments. PMID:28792517

Whole-body transcriptome of selectively bred, resistant-, control-, and susceptible-line rainbow trout following experimental challenge with Flavobacterium psychrophilum

PubMed Central

Marancik, David; Gao, Guangtu; Paneru, Bam; Ma, Hao; Hernandez, Alvaro G.; Salem, Mohamed; Yao, Jianbo; Palti, Yniv; Wiens, Gregory D.

2014-01-01

Genetic improvement for enhanced disease resistance in fish is an increasingly utilized approach to mitigate endemic infectious disease in aquaculture. In domesticated salmonid populations, large phenotypic variation in disease resistance has been identified but the genetic basis for altered responsiveness remains unclear. We previously reported three generations of selection and phenotypic validation of a bacterial cold water disease (BCWD) resistant line of rainbow trout, designated ARS-Fp-R. This line has higher survival after infection by either standardized laboratory challenge or natural challenge as compared to two reference lines, designated ARS-Fp-C (control) and ARS-Fp-S (susceptible). In this study, we utilized 1.1 g fry from the three genetic lines and performed RNA-seq to measure transcript abundance from the whole body of naive and Flavobacterium psychrophilum infected fish at day 1 (early time-point) and at day 5 post-challenge (onset of mortality). Sequences from 24 libraries were mapped onto the rainbow trout genome reference transcriptome of 46,585 predicted protein coding mRNAs that included 2633 putative immune-relevant gene transcripts. A total of 1884 genes (4.0% genome) exhibited differential transcript abundance between infected and mock-challenged fish (FDR < 0.05) that included chemokines, complement components, tnf receptor superfamily members, interleukins, nod-like receptor family members, and genes involved in metabolism and wound healing. The largest number of differentially expressed genes occurred on day 5 post-infection between naive and challenged ARS-Fp-S line fish correlating with high bacterial load. After excluding the effect of infection, we identified 21 differentially expressed genes between the three genetic lines. In summary, these data indicate global transcriptome differences between genetic lines of naive animals as well as differentially regulated transcriptional responses to infection. PMID:25620978

[99mTc] HYNIC-hEGF, a potential agent for imaging of EGF receptors in vivo: preparation and pre-clinical evaluation.

PubMed

Babaei, Mohammad Hossein; Almqvist, Ylva; Orlova, Anna; Shafii, Mohammad; Kairemo, Kalevi; Tolmachev, Vladimir

2005-06-01

Expression of epidermal growth factor receptors (EGFR) has prognostic and predictive value in many kinds of tumors. Imaging of expression of EGFR in vivo may give valuable diagnostic information. The epidermal growth factor (EGF), a natural ligand, is a possible candidate for the targeting of EGFR. The present study describes a method for preparation of (99m)Tc-EGF via the hydrazinopyridine-3-carboxylic acid (HYNIC) conjugation using tricine and ethylenediamine-N,N'-diacetic acid (EDDA) as co-ligands. Both conjugates bound EGFR expressing cells with nanomolar affinity, and demonstrated good intracellular retention. The complex with EDDA demonstrated much higher stability in blood serum and during cysteine challenge. Biodistribution of (99m)Tc-EDDA-HYNIC-EGF in normal mice demonstrated fast blood clearance of conjugate, and its ability to bind EGFR in vivo. (99m)Tc-EDDA-HYNIC-EGF is a promising candidate for visualization of EGFR expression in vivo.

In-the-wild facial expression recognition in extreme poses

NASA Astrophysics Data System (ADS)

Yang, Fei; Zhang, Qian; Zheng, Chi; Qiu, Guoping

2018-04-01

In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

PubMed Central

Kim, Yunee; Jeon, Jouhyun; Mejia, Salvador; Yao, Cindy Q; Ignatchenko, Vladimir; Nyalwidhe, Julius O; Gramolini, Anthony O; Lance, Raymond S; Troyer, Dean A; Drake, Richard R; Boutros, Paul C; Semmes, O. John; Kislinger, Thomas

2016-01-01

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers. PMID:27350604

NanoString, a novel digital color-coded barcode technology: current and future applications in molecular diagnostics.

PubMed

Tsang, Hin-Fung; Xue, Vivian Weiwen; Koh, Su-Pin; Chiu, Ya-Ming; Ng, Lawrence Po-Wah; Wong, Sze-Chuen Cesar

2017-01-01

Formalin-fixed, paraffin-embedded (FFPE) tissue sample is a gold mine of resources for molecular diagnosis and retrospective clinical studies. Although molecular technologies have expanded the range of mutations identified in FFPE samples, the applications of existing technologies are limited by the low nucleic acids yield and poor extraction quality. As a result, the routine clinical applications of molecular diagnosis using FFPE samples has been associated with many practical challenges. NanoString technologies utilize a novel digital color-coded barcode technology based on direct multiplexed measurement of gene expression and offer high levels of precision and sensitivity. Each color-coded barcode is attached to a single target-specific probe corresponding to a single gene which can be individually counted without amplification. Therefore, NanoString is especially useful for measuring gene expression in degraded clinical specimens. Areas covered: This article describes the applications of NanoString technologies in molecular diagnostics and challenges associated with its applications and the future development. Expert commentary: Although NanoString technology is still in the early stages of clinical use, it is expected that NanoString-based cancer expression panels would play more important roles in the future in classifying cancer patients and in predicting the response to therapy for better personal therapeutic care.

Gene Expression Differences in Peripheral Blood of Parkinson’s Disease Patients with Distinct Progression Profiles

PubMed Central

Soreq, Lilach; Lobo, Patrícia P.; Mestre, Tiago; Coelho, Miguel; Rosa, Mário M.; Gonçalves, Nilza; Wales, Pauline; Mendes, Tiago; Gerhardt, Ellen; Fahlbusch, Christiane; Bonifati, Vincenzo; Bonin, Michael; Miltenberger-Miltényi, Gabriel; Borovecki, Fran; Soreq, Hermona; Ferreira, Joaquim J.; F. Outeiro, Tiago

2016-01-01

The prognosis of neurodegenerative disorders is clinically challenging due to the inexistence of established biomarkers for predicting disease progression. Here, we performed an exploratory cross-sectional, case-control study aimed at determining whether gene expression differences in peripheral blood may be used as a signature of Parkinson’s disease (PD) progression, thereby shedding light into potential molecular mechanisms underlying disease development. We compared transcriptional profiles in the blood from 34 PD patients who developed postural instability within ten years with those of 33 patients who did not develop postural instability within this time frame. Our study identified >200 differentially expressed genes between the two groups. The expression of several of the genes identified was previously found deregulated in animal models of PD and in PD patients. Relevant genes were selected for validation by real-time PCR in a subset of patients. The genes validated were linked to nucleic acid metabolism, mitochondria, immune response and intracellular-transport. Interestingly, we also found deregulation of these genes in a dopaminergic cell model of PD, a simple paradigm that can now be used to further dissect the role of these molecular players on dopaminergic cell loss. Altogether, our study provides preliminary evidence that expression changes in specific groups of genes and pathways, detected in peripheral blood samples, may be correlated with differential PD progression. Our exploratory study suggests that peripheral gene expression profiling may prove valuable for assisting in prediction of PD prognosis, and identifies novel culprits possibly involved in dopaminergic cell death. Given the exploratory nature of our study, further investigations using independent, well-characterized cohorts will be essential in order to validate our candidates as predictors of PD prognosis and to definitively confirm the value of gene expression analysis in aiding patient stratification and therapeutic intervention. PMID:27322389

A quantitative validated model reveals two phases of transcriptional regulation for the gap gene giant in Drosophila.

PubMed

Hoermann, Astrid; Cicin-Sain, Damjan; Jaeger, Johannes

2016-03-15

Understanding eukaryotic transcriptional regulation and its role in development and pattern formation is one of the big challenges in biology today. Most attempts at tackling this problem either focus on the molecular details of transcription factor binding, or aim at genome-wide prediction of expression patterns from sequence through bioinformatics and mathematical modelling. Here we bridge the gap between these two complementary approaches by providing an integrative model of cis-regulatory elements governing the expression of the gap gene giant (gt) in the blastoderm embryo of Drosophila melanogaster. We use a reverse-engineering method, where mathematical models are fit to quantitative spatio-temporal reporter gene expression data to infer the regulatory mechanisms underlying gt expression in its anterior and posterior domains. These models are validated through prediction of gene expression in mutant backgrounds. A detailed analysis of our data and models reveals that gt is regulated by domain-specific CREs at early stages, while a late element drives expression in both the anterior and the posterior domains. Initial gt expression depends exclusively on inputs from maternal factors. Later, gap gene cross-repression and gt auto-activation become increasingly important. We show that auto-regulation creates a positive feedback, which mediates the transition from early to late stages of regulation. We confirm the existence and role of gt auto-activation through targeted mutagenesis of Gt transcription factor binding sites. In summary, our analysis provides a comprehensive picture of spatio-temporal gene regulation by different interacting enhancer elements for an important developmental regulator. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

Expression analysis of HSP70 in the testis of Octopus tankahkeei under thermal stress.

PubMed

Long, Ling-Li; Han, Ying-Li; Sheng, Zhang; Du, Chen; Wang, You-Fa; Zhu, Jun-Quan

2015-09-01

The gene encoding heat shock protein 70 (HSP70) was identified in Octopus tankahkeei by homologous cloning and rapid amplification of cDNA ends (RACE). The full-length cDNA (2471 bp) consists of a 5'-untranslated region (UTR) (89 bp), a 3'-UTR (426 bp), and an open reading frame (1956 bp) that encodes 651 amino acid residues with a predicted molecular mass of 71.8 kDa and an isoelectric point of 5.34. Based on the amino acid sequence analysis and multiple sequence alignment, this cDNA is a member of cytoplasmic hsp70 subfamily of the hsp70 family and was designated as ot-hsp70. Tissue expression analysis showed that HSP70 expression is highest in the testes when all examined organs were compared. Immunohistochemistry analysis, together with hematoxylin-eosin staining, revealed that the HSP70 protein was expressed in all spermatogenic cells, but not in fibroblasts. In addition, O. tankahkeei were heat challenged by exposure to 32 °C seawater for 2 h, then returned to 13 °C for various recovery time (0-24 h). Relative expression of ot-hsp70 mRNA in the testes was measured at different time points post-challenge by quantitative real-time PCR. A clear time-dependent mRNA expression of ot-hsp70 after thermal stress indicates that the HSP70 gene is inducible. Ultrastructural changes of the heat-stressed testis were observed by transmission electron microscopy. We suggest that HSP70 plays an important role in spermatogenesis and testis protection against thermal stress in O. tankahkeei. Copyright © 2015 Elsevier Inc. All rights reserved.

Induction of Strain-Transcending Immunity against Plasmodium chabaudi adami Malaria with a Multiepitope DNA Vaccine

PubMed Central

Scorza, T.; Grubb, K.; Smooker, P.; Rainczuk, A.; Proll, D.; Spithill, T. W.

2005-01-01

A major goal of current malaria vaccine programs is to develop multivalent vaccines that will protect humans against the many heterologous malaria strains that circulate in endemic areas. We describe a multiepitope DNA vaccine, derived from a genomic Plasmodium chabaudi adami DS DNA expression library of 30,000 plasmids, which induces strain-transcending immunity in mice against challenge with P. c. adami DK. Segregation of this library and DNA sequence analysis identified vaccine subpools encoding open reading frames (ORFs)/peptides of >9 amino acids [aa] (the V9+ pool, 303 plasmids) and >50 aa (V50+ pool, 56 plasmids), respectively. The V9+ and V50+ plasmid vaccine subpools significantly cross-protected mice against heterologous P. c. adami DK challenge, and protection correlated with the induction of both specific gamma interferon production by splenic cells and opsonizing antibodies. Bioinformatic analysis showed that 22 of the V50+ ORFs were polypeptides conserved among three or more Plasmodium spp., 13 of which are predicted hypothetical proteins. Twenty-nine of these ORFs are orthologues of predicted Plasmodium falciparum sequences known to be expressed in the blood stage, suggesting that this vaccine pool encodes multiple blood-stage antigens. The results have implications for malaria vaccine design by providing proof-of-principle that significant strain-transcending immunity can be induced using multiepitope blood-stage DNA vaccines and suggest that both cellular responses and opsonizing antibodies are necessary for optimal protection against P. c. adami. PMID:15845504

Analysis of Bioactive Amino Acids from Fish Hydrolysates with a New Bioinformatic Intelligent System Approach.

PubMed

Elaziz, Mohamed Abd; Hemdan, Ahmed Monem; Hassanien, AboulElla; Oliva, Diego; Xiong, Shengwu

2017-09-07

The current economics of the fish protein industry demand rapid, accurate and expressive prediction algorithms at every step of protein production especially with the challenge of global climate change. This help to predict and analyze functional and nutritional quality then consequently control food allergies in hyper allergic patients. As, it is quite expensive and time-consuming to know these concentrations by the lab experimental tests, especially to conduct large-scale projects. Therefore, this paper introduced a new intelligent algorithm using adaptive neuro-fuzzy inference system based on whale optimization algorithm. This algorithm is used to predict the concentration levels of bioactive amino acids in fish protein hydrolysates at different times during the year. The whale optimization algorithm is used to determine the optimal parameters in adaptive neuro-fuzzy inference system. The results of proposed algorithm are compared with others and it is indicated the higher performance of the proposed algorithm.

Prediction of novel pre-microRNAs with high accuracy through boosting and SVM.

PubMed

Zhang, Yuanwei; Yang, Yifan; Zhang, Huan; Jiang, Xiaohua; Xu, Bo; Xue, Yu; Cao, Yunxia; Zhai, Qian; Zhai, Yong; Xu, Mingqing; Cooke, Howard J; Shi, Qinghua

2011-05-15

High-throughput deep-sequencing technology has generated an unprecedented number of expressed short sequence reads, presenting not only an opportunity but also a challenge for prediction of novel microRNAs. To verify the existence of candidate microRNAs, we have to show that these short sequences can be processed from candidate pre-microRNAs. However, it is laborious and time consuming to verify these using existing experimental techniques. Therefore, here, we describe a new method, miRD, which is constructed using two feature selection strategies based on support vector machines (SVMs) and boosting method. It is a high-efficiency tool for novel pre-microRNA prediction with accuracy up to 94.0% among different species. miRD is implemented in PHP/PERL+MySQL+R and can be freely accessed at http://mcg.ustc.edu.cn/rpg/mird/mird.php.

Integrated analysis of drug-induced gene expression profiles predicts novel hERG inhibitors.

PubMed

Babcock, Joseph J; Du, Fang; Xu, Kaiping; Wheelan, Sarah J; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays.

Integrated Analysis of Drug-Induced Gene Expression Profiles Predicts Novel hERG Inhibitors

PubMed Central

Babcock, Joseph J.; Du, Fang; Xu, Kaiping; Wheelan, Sarah J.; Li, Min

2013-01-01

Growing evidence suggests that drugs interact with diverse molecular targets mediating both therapeutic and toxic effects. Prediction of these complex interactions from chemical structures alone remains challenging, as compounds with different structures may possess similar toxicity profiles. In contrast, predictions based on systems-level measurements of drug effect may reveal pharmacologic similarities not evident from structure or known therapeutic indications. Here we utilized drug-induced transcriptional responses in the Connectivity Map (CMap) to discover such similarities among diverse antagonists of the human ether-à-go-go related (hERG) potassium channel, a common target of promiscuous inhibition by small molecules. Analysis of transcriptional profiles generated in three independent cell lines revealed clusters enriched for hERG inhibitors annotated using a database of experimental measurements (hERGcentral) and clinical indications. As a validation, we experimentally identified novel hERG inhibitors among the unannotated drugs in these enriched clusters, suggesting transcriptional responses may serve as predictive surrogates of cardiotoxicity complementing existing functional assays. PMID:23936032

A gene network model accounting for development and evolution of mammalian teeth

PubMed Central

Salazar-Ciudad, Isaac; Jernvall, Jukka

2002-01-01

Generation of morphological diversity remains a challenge for evolutionary biologists because it is unclear how an ultimately finite number of genes involved in initial pattern formation integrates with morphogenesis. Ideally, models used to search for the simplest developmental principles on how genes produce form should account for both developmental process and evolutionary change. Here we present a model reproducing the morphology of mammalian teeth by integrating experimental data on gene interactions and growth into a morphodynamic mechanism in which developing morphology has a causal role in patterning. The model predicts the course of tooth-shape development in different mammalian species and also reproduces key transitions in evolution. Furthermore, we reproduce the known expression patterns of several genes involved in tooth development and their dynamics over developmental time. Large morphological effects frequently can be achieved by small changes, according to this model, and similar morphologies can be produced by different changes. This finding may be consistent with why predicting the morphological outcomes of molecular experiments is challenging. Nevertheless, models incorporating morphology and gene activity show promise for linking genotypes to phenotypes. PMID:12048258

A Predictive Model of the Oxygen and Heme Regulatory Network in Yeast

PubMed Central

Kundaje, Anshul; Xin, Xiantong; Lan, Changgui; Lianoglou, Steve; Zhou, Mei; Zhang, Li; Leslie, Christina

2008-01-01

Deciphering gene regulatory mechanisms through the analysis of high-throughput expression data is a challenging computational problem. Previous computational studies have used large expression datasets in order to resolve fine patterns of coexpression, producing clusters or modules of potentially coregulated genes. These methods typically examine promoter sequence information, such as DNA motifs or transcription factor occupancy data, in a separate step after clustering. We needed an alternative and more integrative approach to study the oxygen regulatory network in Saccharomyces cerevisiae using a small dataset of perturbation experiments. Mechanisms of oxygen sensing and regulation underlie many physiological and pathological processes, and only a handful of oxygen regulators have been identified in previous studies. We used a new machine learning algorithm called MEDUSA to uncover detailed information about the oxygen regulatory network using genome-wide expression changes in response to perturbations in the levels of oxygen, heme, Hap1, and Co2+. MEDUSA integrates mRNA expression, promoter sequence, and ChIP-chip occupancy data to learn a model that accurately predicts the differential expression of target genes in held-out data. We used a novel margin-based score to extract significant condition-specific regulators and assemble a global map of the oxygen sensing and regulatory network. This network includes both known oxygen and heme regulators, such as Hap1, Mga2, Hap4, and Upc2, as well as many new candidate regulators. MEDUSA also identified many DNA motifs that are consistent with previous experimentally identified transcription factor binding sites. Because MEDUSA's regulatory program associates regulators to target genes through their promoter sequences, we directly tested the predicted regulators for OLE1, a gene specifically induced under hypoxia, by experimental analysis of the activity of its promoter. In each case, deletion of the candidate regulator resulted in the predicted effect on promoter activity, confirming that several novel regulators identified by MEDUSA are indeed involved in oxygen regulation. MEDUSA can reveal important information from a small dataset and generate testable hypotheses for further experimental analysis. Supplemental data are included. PMID:19008939

Computing and Applying Atomic Regulons to Understand Gene Expression and Regulation

PubMed Central

Faria, José P.; Davis, James J.; Edirisinghe, Janaka N.; Taylor, Ronald C.; Weisenhorn, Pamela; Olson, Robert D.; Stevens, Rick L.; Rocha, Miguel; Rocha, Isabel; Best, Aaron A.; DeJongh, Matthew; Tintle, Nathan L.; Parrello, Bruce; Overbeek, Ross; Henry, Christopher S.

2016-01-01

Understanding gene function and regulation is essential for the interpretation, prediction, and ultimate design of cell responses to changes in the environment. An important step toward meeting the challenge of understanding gene function and regulation is the identification of sets of genes that are always co-expressed. These gene sets, Atomic Regulons (ARs), represent fundamental units of function within a cell and could be used to associate genes of unknown function with cellular processes and to enable rational genetic engineering of cellular systems. Here, we describe an approach for inferring ARs that leverages large-scale expression data sets, gene context, and functional relationships among genes. We computed ARs for Escherichia coli based on 907 gene expression experiments and compared our results with gene clusters produced by two prevalent data-driven methods: Hierarchical clustering and k-means clustering. We compared ARs and purely data-driven gene clusters to the curated set of regulatory interactions for E. coli found in RegulonDB, showing that ARs are more consistent with gold standard regulons than are data-driven gene clusters. We further examined the consistency of ARs and data-driven gene clusters in the context of gene interactions predicted by Context Likelihood of Relatedness (CLR) analysis, finding that the ARs show better agreement with CLR predicted interactions. We determined the impact of increasing amounts of expression data on AR construction and find that while more data improve ARs, it is not necessary to use the full set of gene expression experiments available for E. coli to produce high quality ARs. In order to explore the conservation of co-regulated gene sets across different organisms, we computed ARs for Shewanella oneidensis, Pseudomonas aeruginosa, Thermus thermophilus, and Staphylococcus aureus, each of which represents increasing degrees of phylogenetic distance from E. coli. Comparison of the organism-specific ARs showed that the consistency of AR gene membership correlates with phylogenetic distance, but there is clear variability in the regulatory networks of closely related organisms. As large scale expression data sets become increasingly common for model and non-model organisms, comparative analyses of atomic regulons will provide valuable insights into fundamental regulatory modules used across the bacterial domain. PMID:27933038

Transcriptomic correlates of neuron electrophysiological diversity

PubMed Central

Li, Brenna; Crichlow, Cindy-Lee; Mancarci, B. Ogan; Pavlidis, Paul

2017-01-01

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating pooled- and single-cell transcriptomics with intracellular electrophysiology. Using neuroinformatics methods, we compiled a brain-wide dataset of 34 neuron types with paired gene expression and intrinsic electrophysiological features from publically accessible sources, the largest such collection to date. We identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 physiological parameters. We next trained statistical models to infer cellular features from multivariate gene expression patterns. Such models were predictive of gene-electrophysiological relationships in an independent collection of 12 visual cortex cell types from the Allen Institute, suggesting that these correlations might reflect general principles relating expression patterns to phenotypic diversity across very different cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. Our work highlights the promise and inherent challenges in using cell type-specific transcriptomics to understand the mechanistic origins of neuronal diversity. PMID:29069078

Low-rank regularization for learning gene expression programs.

PubMed

Ye, Guibo; Tang, Mengfan; Cai, Jian-Feng; Nie, Qing; Xie, Xiaohui

2013-01-01

Learning gene expression programs directly from a set of observations is challenging due to the complexity of gene regulation, high noise of experimental measurements, and insufficient number of experimental measurements. Imposing additional constraints with strong and biologically motivated regularizations is critical in developing reliable and effective algorithms for inferring gene expression programs. Here we propose a new form of regulation that constrains the number of independent connectivity patterns between regulators and targets, motivated by the modular design of gene regulatory programs and the belief that the total number of independent regulatory modules should be small. We formulate a multi-target linear regression framework to incorporate this type of regulation, in which the number of independent connectivity patterns is expressed as the rank of the connectivity matrix between regulators and targets. We then generalize the linear framework to nonlinear cases, and prove that the generalized low-rank regularization model is still convex. Efficient algorithms are derived to solve both the linear and nonlinear low-rank regularized problems. Finally, we test the algorithms on three gene expression datasets, and show that the low-rank regularization improves the accuracy of gene expression prediction in these three datasets.

Interpretation of Genomic Data Questions and Answers

PubMed Central

Simon, Richard

2008-01-01

Using a question and answer format we describe important aspects of using genomic technologies in cancer research. The main challenges are not managing the mass of data, but rather the design, analysis and accurate reporting of studies that result in increased biological knowledge and medical utility. Many analysis issues address the use of expression microarrays but are also applicable to other whole genome assays. Microarray based clinical investigations have generated both unrealistic hyperbole and excessive skepticism. Genomic technologies are tremendously powerful and will play instrumental roles in elucidating the mechanisms of oncogenesis and in devlopingan era of predictive medicine in which treatments are tailored to individual tumors. Achieving these goals involves challenges in re-thinking many paradigms for the conduct of basic and clinical cancer research and for the organization of interdisciplinary collaboration. PMID:18582627

Transcription Factor Map Alignment of Promoter Regions

PubMed Central

Blanco, Enrique; Messeguer, Xavier; Smith, Temple F; Guigó, Roderic

2006-01-01

We address the problem of comparing and characterizing the promoter regions of genes with similar expression patterns. This remains a challenging problem in sequence analysis, because often the promoter regions of co-expressed genes do not show discernible sequence conservation. In our approach, thus, we have not directly compared the nucleotide sequence of promoters. Instead, we have obtained predictions of transcription factor binding sites, annotated the predicted sites with the labels of the corresponding binding factors, and aligned the resulting sequences of labels—to which we refer here as transcription factor maps (TF-maps). To obtain the global pairwise alignment of two TF-maps, we have adapted an algorithm initially developed to align restriction enzyme maps. We have optimized the parameters of the algorithm in a small, but well-curated, collection of human–mouse orthologous gene pairs. Results in this dataset, as well as in an independent much larger dataset from the CISRED database, indicate that TF-map alignments are able to uncover conserved regulatory elements, which cannot be detected by the typical sequence alignments. PMID:16733547

Predicting enhancer activity and variant impact using gkm-SVM.

PubMed

Beer, Michael A

2017-09-01

We participated in the Critical Assessment of Genome Interpretation eQTL challenge to further test computational models of regulatory variant impact and their association with human disease. Our prediction model is based on a discriminative gapped-kmer SVM (gkm-SVM) trained on genome-wide chromatin accessibility data in the cell type of interest. The comparisons with massively parallel reporter assays (MPRA) in lymphoblasts show that gkm-SVM is among the most accurate prediction models even though all other models used the MPRA data for model training, and gkm-SVM did not. In addition, we compare gkm-SVM with other MPRA datasets and show that gkm-SVM is a reliable predictor of expression and that deltaSVM is a reliable predictor of variant impact in K562 cells and mouse retina. We further show that DHS (DNase-I hypersensitive sites) and ATAC-seq (assay for transposase-accessible chromatin using sequencing) data are equally predictive substrates for training gkm-SVM, and that DHS regions flanked by H3K27Ac and H3K4me1 marks are more predictive than DHS regions alone. © 2017 Wiley Periodicals, Inc.

Genomic Features That Predict Allelic Imbalance in Humans Suggest Patterns of Constraint on Gene Expression Variation

PubMed Central

Fédrigo, Olivier; Haygood, Ralph; Mukherjee, Sayan; Wray, Gregory A.

2009-01-01

Variation in gene expression is an important contributor to phenotypic diversity within and between species. Although this variation often has a genetic component, identification of the genetic variants driving this relationship remains challenging. In particular, measurements of gene expression usually do not reveal whether the genetic basis for any observed variation lies in cis or in trans to the gene, a distinction that has direct relevance to the physical location of the underlying genetic variant, and which may also impact its evolutionary trajectory. Allelic imbalance measurements identify cis-acting genetic effects by assaying the relative contribution of the two alleles of a cis-regulatory region to gene expression within individuals. Identification of patterns that predict commonly imbalanced genes could therefore serve as a useful tool and also shed light on the evolution of cis-regulatory variation itself. Here, we show that sequence motifs, polymorphism levels, and divergence levels around a gene can be used to predict commonly imbalanced genes in a human data set. Reduction of this feature set to four factors revealed that only one factor significantly differentiated between commonly imbalanced and nonimbalanced genes. We demonstrate that these results are consistent between the original data set and a second published data set in humans obtained using different technical and statistical methods. Finally, we show that variation in the single allelic imbalance-associated factor is partially explained by the density of genes in the region of a target gene (allelic imbalance is less probable for genes in gene-dense regions), and, to a lesser extent, the evenness of expression of the gene across tissues and the magnitude of negative selection on putative regulatory regions of the gene. These results suggest that the genomic distribution of functional cis-regulatory variants in the human genome is nonrandom, perhaps due to local differences in evolutionary constraint. PMID:19506001

Venezuelan Equine Encephalitis Virus Replicon Particle Vaccine Protects Nonhuman Primates from Intramuscular and Aerosol Challenge with Ebolavirus

PubMed Central

Herbert, Andrew S.; Kuehne, Ana I.; Barth, James F.; Ortiz, Ramon A.; Nichols, Donald K.; Zak, Samantha E.; Stonier, Spencer W.; Muhammad, Majidat A.; Bakken, Russell R.; Prugar, Laura I.; Olinger, Gene G.; Groebner, Jennifer L.; Lee, John S.; Pratt, William D.; Custer, Max; Kamrud, Kurt I.; Smith, Jonathan F.; Hart, Mary Kate

2013-01-01

There are no vaccines or therapeutics currently approved for the prevention or treatment of ebolavirus infection. Previously, a replicon vaccine based on Venezuelan equine encephalitis virus (VEEV) demonstrated protective efficacy against Marburg virus in nonhuman primates. Here, we report the protective efficacy of Sudan virus (SUDV)- and Ebola virus (EBOV)-specific VEEV replicon particle (VRP) vaccines in nonhuman primates. VRP vaccines were developed to express the glycoprotein (GP) of either SUDV or EBOV. A single intramuscular vaccination of cynomolgus macaques with VRP expressing SUDV GP provided complete protection against intramuscular challenge with SUDV. Vaccination against SUDV and subsequent survival of SUDV challenge did not fully protect cynomolgus macaques against intramuscular EBOV back-challenge. However, a single simultaneous intramuscular vaccination with VRP expressing SUDV GP combined with VRP expressing EBOV GP did provide complete protection against intramuscular challenge with either SUDV or EBOV in cynomolgus macaques. Finally, intramuscular vaccination with VRP expressing SUDV GP completely protected cynomolgus macaques when challenged with aerosolized SUDV, although complete protection against aerosol challenge required two vaccinations with this vaccine. PMID:23408633

Venezuelan equine encephalitis virus replicon particle vaccine protects nonhuman primates from intramuscular and aerosol challenge with ebolavirus.

PubMed

Herbert, Andrew S; Kuehne, Ana I; Barth, James F; Ortiz, Ramon A; Nichols, Donald K; Zak, Samantha E; Stonier, Spencer W; Muhammad, Majidat A; Bakken, Russell R; Prugar, Laura I; Olinger, Gene G; Groebner, Jennifer L; Lee, John S; Pratt, William D; Custer, Max; Kamrud, Kurt I; Smith, Jonathan F; Hart, Mary Kate; Dye, John M

2013-05-01

There are no vaccines or therapeutics currently approved for the prevention or treatment of ebolavirus infection. Previously, a replicon vaccine based on Venezuelan equine encephalitis virus (VEEV) demonstrated protective efficacy against Marburg virus in nonhuman primates. Here, we report the protective efficacy of Sudan virus (SUDV)- and Ebola virus (EBOV)-specific VEEV replicon particle (VRP) vaccines in nonhuman primates. VRP vaccines were developed to express the glycoprotein (GP) of either SUDV or EBOV. A single intramuscular vaccination of cynomolgus macaques with VRP expressing SUDV GP provided complete protection against intramuscular challenge with SUDV. Vaccination against SUDV and subsequent survival of SUDV challenge did not fully protect cynomolgus macaques against intramuscular EBOV back-challenge. However, a single simultaneous intramuscular vaccination with VRP expressing SUDV GP combined with VRP expressing EBOV GP did provide complete protection against intramuscular challenge with either SUDV or EBOV in cynomolgus macaques. Finally, intramuscular vaccination with VRP expressing SUDV GP completely protected cynomolgus macaques when challenged with aerosolized SUDV, although complete protection against aerosol challenge required two vaccinations with this vaccine.

Smad4/Fascin index is highly prognostic in patients with diffuse type EBV-associated gastric cancer.

PubMed

Son, Byoung Kwan; Kim, Dong-Hoon; Min, Kyueng-Whan; Kim, Eun-Kyung; Kwon, Mi Jung

2018-04-01

Gastric cancer is a heterogeneous disorder for which predicting clinical outcomes is challenging, although various biomarkers have been suggested. The Smad4 and Fascin proteins are known prognostic indicators of different types of malignancy. Smad4 primarily functions as a key regulator of tumor suppression, whereas Fascin exhibits oncogenic function by enhancing tumor infiltration. A combined marker based on these opposing roles may improve prognostic accuracy in gastric cancer. Smad4 and Fascin expression was assessed in tissue microarrays obtained from 285 primary gastric adenocarcinoma, 201 normal tissue, and 51 metastatic adenocarcinoma samples. A Smad4/Fascin index based on the relative expression of each protein was divided into low- and high-expression groups using receiver operating characteristic curves. We compared normal tissue, primary adenocarcinoma, and metastatic adenocarcinoma in Smad4 and Fascin expression and the differences in clinicopathological findings between low Smad4/Fascin and high Smad4/Fascin expression in gastric adenocarcinoma. High Smad4/Fascin expression was significantly associated with worse outcomes, such as old age, advanced T and N category, large tumor size, high histological grade, lymphatic and vascular invasion, and presence of Epstein-Barr virus (EBV) (all p < 0.05). Univariate and multivariate analyses revealed a significant relationship between disease-free or overall survival and Smad4/Fascin index in diffuse-type or EBV-associated gastric cancer (all p < 0.05). A dual marker system using Smad4 and Fascin may be a reliable indicator for predicting clinical outcomes in patients with diffuse-type or EBV-associated gastric cancer. Copyright © 2018 Elsevier GmbH. All rights reserved.

The role of distress intolerance for panic and nicotine withdrawal symptoms during a biological challenge.

PubMed

Farris, Samantha G; Zvolensky, Michael J; Otto, Michael W; Leyro, Teresa M

2015-07-01

Distress intolerance is linked to the maintenance of panic disorder and cigarette smoking, and may underlie both problems. Smokers (n = 54; 40.7% panic disorder) were recruited for an experimental study; half were randomly assigned to 12-hour nicotine deprivation and half smoked as usual. The current investigation consisted of secondary, exploratory analyses from this larger experimental study. Four distress intolerance indices were examined as predictors of anxious responding to an emotional elicitation task (10% carbon dioxide (CO2)-enriched air challenge); anxious responding was in turn examined as a predictor of post-challenge panic and nicotine withdrawal symptoms. The Distress Tolerance Scale (DTS) was significantly negatively associated with anxious responding to the challenge (β = -0.41, p = 0.017). The DTS was negatively associated with post-challenge increases nicotine withdrawal symptoms indirectly through the effect of anxious responding to the challenge (b = -0.485, CI95% (-1.095, -0.033)). This same indirect effect was found for post-challenge severity of panic symptoms (b = -0.515, CI95% (-0.888, -0.208)). The DTS was directly predictive of post-challenge increases nicotine withdrawal symptoms, in the opposite direction (β = 0.37, p = 0.009), but not panic symptom severity. Anxious responding in response to stressful experiences may explain the impact of perceived distress intolerance on panic and nicotine withdrawal symptom expression. © The Author(s) 2015.

Sildenafil decreases rat tracheal hyperresponsiveness to carbachol and changes canonical transient receptor potential gene expression after antigen challenge.

PubMed

Sousa, C T; Brito, T S; Lima, F J B; Siqueira, R J B; Magalhães, P J C; Lima, A A M; Santos, A A; Havt, A

2011-06-01

Inhibition of type-5 phosphodiesterase by sildenafil decreases capacitative Ca2+ entry mediated by transient receptor potential proteins (TRPs) in the pulmonary artery. These families of channels, especially the canonical TRP (TRPC) subfamily, may be involved in the development of bronchial hyperresponsiveness, a hallmark of asthma. In the present study, we evaluated i) the effects of sildenafil on tracheal rings of rats subjected to antigen challenge, ii) whether the extent of TRPC gene expression may be modified by antigen challenge, and iii) whether inhibition of type-5 phosphodiesterase (PDE5) may alter TRPC gene expression after antigen challenge. Sildenafil (0.1 µM to 0.6 mM) fully relaxed carbachol-induced contractions in isolated tracheal rings prepared from naive male Wistar rats (250-300 g) by activating the NO-cGMP-K+ channel pathway. Rats sensitized to antigen by intraperitoneal injections of ovalbumin were subjected to antigen challenge by ovalbumin inhalation, and their tracheal rings were used to study the effects of sildenafil, which more effectively inhibited contractions induced by either carbachol (10 µM) or extracellular Ca2+ restoration after thapsigargin (1 µM) treatment. Antigen challenge increased the expression of the TRPC1 and TRPC4 genes but not the expression of the TRPC5 and TRPC6 genes. Applied before the antigen challenge, sildenafil increased the gene expression, which was evaluated by RT-PCR, of TRPC1 and TRPC6, decreased TRPC5 expression, and was inert against TRPC4. Thus, we conclude that PDE5 inhibition is involved in the development of an airway hyperresponsive phenotype in rats after antigen challenge by altering TRPC gene expression.

An integrated approach to infer dynamic protein-gene interactions - A case study of the human P53 protein.

PubMed

Wang, Junbai; Wu, Qianqian; Hu, Xiaohua Tony; Tian, Tianhai

2016-11-01

Investigating the dynamics of genetic regulatory networks through high throughput experimental data, such as microarray gene expression profiles, is a very important but challenging task. One of the major hindrances in building detailed mathematical models for genetic regulation is the large number of unknown model parameters. To tackle this challenge, a new integrated method is proposed by combining a top-down approach and a bottom-up approach. First, the top-down approach uses probabilistic graphical models to predict the network structure of DNA repair pathway that is regulated by the p53 protein. Two networks are predicted, namely a network of eight genes with eight inferred interactions and an extended network of 21 genes with 17 interactions. Then, the bottom-up approach using differential equation models is developed to study the detailed genetic regulations based on either a fully connected regulatory network or a gene network obtained by the top-down approach. Model simulation error, parameter identifiability and robustness property are used as criteria to select the optimal network. Simulation results together with permutation tests of input gene network structures indicate that the prediction accuracy and robustness property of the two predicted networks using the top-down approach are better than those of the corresponding fully connected networks. In particular, the proposed approach reduces computational cost significantly for inferring model parameters. Overall, the new integrated method is a promising approach for investigating the dynamics of genetic regulation. Copyright © 2016 Elsevier Inc. All rights reserved.

Two challenges in embedded systems design: predictability and robustness.

PubMed

Henzinger, Thomas A

2008-10-28

I discuss two main challenges in embedded systems design: the challenge to build predictable systems, and that to build robust systems. I suggest how predictability can be formalized as a form of determinism, and robustness as a form of continuity.

Differential expression pattern of protein markers for predicting chemosensitivity of dexamethasone-based chemotherapy of B cell acute lymphoblastic leukemia.

PubMed

Dehghan-Nayeri, Nasrin; Eshghi, Peyman; Pour, Kourosh Goudarzi; Rezaei-Tavirani, Mostafa; Omrani, Mir Davood; Gharehbaghian, Ahmad

2017-07-01

Dexamethasone is considered as a direct chemotherapeutic agent in the treatment of pediatric acute lymphoblastic leukemia (ALL). Beside the advantages of the drug, some problems arising from the dose-related side effects are challenging issues during the treatment. Accordingly, the classification of patients to dexamethasone sensitive and resistance groups can help to select optimizing the therapeutic dose with the lowest adverse effects particularly in sensitive cases. For this purpose, we investigated inhibited proliferation and induced cytotoxicity in NALM-6 cells, as sensitive cells, after dexamethasone treatment. In addition, comparative protein expression analysis using the 2DE-MALDI-TOF MS technique was performed to identify the specific altered proteins. In addition, we evaluated mRNA expression levels of the identified proteins in bone-marrow samples from pediatric ALL patients using the real-time q-PCR method. Eventually, proteomic analysis revealed a combination of biomarkers, including capping proteins (CAPZA1 and CAPZB), chloride channel (CLIC1), purine nucleoside phosphorylase (PNP), and proteasome activator (PSME1), in response to the dexamethasone treatment. In addition, our results indicated low expression of identified proteins at both the mRNA and protein expression levels after drug treatment. Moreover, quantitative real-time PCR data analysis indicated that independent of the molecular subtypes of the leukemia, CAPZA1, CAPZB, CLIC1, and PNP expression levels were lower in ALL samples than normal samples, although PSME1 expression level was higher in ALL samples than normal samples. Furthermore, the expression level of all proteins (except PSME1) was different between high-risk and standard-risk patients that suggesting the prognostic value of them. In conclusion, our study suggests a panel of biomarkers comprising CAPZA1, CAPZB, CLIC1, PNP, and PSME1 as early diagnosis and treatment evaluation markers that may differentiate cancer cells which are presumably to benefit from dexamethasone-based chemotherapy and may facilitate the prediction of clinical outcome.

PTEN genomic deletion predicts prostate cancer recurrence and is associated with low AR expression and transcriptional activity

PubMed Central

2012-01-01

Background Prostate cancer (PCa), a leading cause of cancer death in North American men, displays a broad range of clinical outcome from relatively indolent to lethal metastatic disease. Several genomic alterations have been identified in PCa which may serve as predictors of progression. PTEN, (10q23.3), is a negative regulator of the phosphatidylinositol 3-kinase (PIK3)/AKT survival pathway and a tumor suppressor frequently deleted in PCa. The androgen receptor (AR) signalling pathway is known to play an important role in PCa and its blockade constitutes a commonly used treatment modality. In this study, we assessed the deletion status of PTEN along with AR expression levels in 43 primary PCa specimens with clinical follow-up. Methods Fluorescence In Situ Hybridization (FISH) was done on formalin fixed paraffin embedded (FFPE) PCa samples to examine the deletion status of PTEN. AR expression levels were determined using immunohistochemistry (IHC). Results Using FISH, we found 18 cases of PTEN deletion. Kaplan-Meier analysis showed an association with disease recurrence (P=0.03). Concurrently, IHC staining for AR found significantly lower levels of AR expression within those tumors deleted for PTEN (P<0.05). To validate these observations we interrogated a copy number alteration and gene expression profiling dataset of 64 PCa samples, 17 of which were PTEN deleted. We confirmed the predictive value of PTEN deletion in disease recurrence (P=0.03). PTEN deletion was also linked to diminished expression of PTEN (P<0.01) and AR (P=0.02). Furthermore, gene set enrichment analysis revealed a diminished expression of genes downstream of AR signalling in PTEN deleted tumors. Conclusions Altogether, our data suggest that PTEN deleted tumors expressing low levels of AR may represent a worse prognostic subset of PCa establishing a challenge for therapeutic management. PMID:23171135

Altered expression of HER-2 and the mismatch repair genes MLH1 and MSH2 predicts the outcome of T1 high-grade bladder cancer.

PubMed

Sanguedolce, Francesca; Cormio, Antonella; Massenio, Paolo; Pedicillo, Maria C; Cagiano, Simona; Fortunato, Francesca; Calò, Beppe; Di Fino, Giuseppe; Carrieri, Giuseppe; Bufo, Pantaleo; Cormio, Luigi

2018-04-01

The identification of factors predicting the outcome of stage T1 high-grade bladder cancer (BC) is a major clinical issue. We performed immunohistochemistry to assess the role of human epidermal growth factor receptor-2 (HER-2) and microsatellite instability (MSI) factors MutL homologue 1 (MLH1) and MutS homologue 2 (MSH2) in predicting recurrence and progression of T1 high-grade BCs having undergone transurethral resection of bladder tumor (TURBT) alone or TURBT + intravesical instillations of bacillus Calmette-Guerin (BCG). HER-2 overexpression was a significant predictor of disease-free survival (DFS) in the overall as well as in the two patients' population; as for progression-free survival (PFS), it was significant in the overall but not in the two patients' population. MLH1 was an independent predictor of PFS only in patients treated with BCG and MSH2 failed to predict DFS and PFS in all populations. Most importantly, the higher the number of altered markers the lowers the DFS and PFS. In multivariate Cox proportional-hazards regression analysis, the number of altered molecular markers and BCG treatment were significant predictors (p = 0.0004 and 0.0283, respectively) of DFS, whereas the number of altered molecular markers was the only significant predictor (p = 0.0054) of PFS. Altered expression of the proto-oncogene HER-2 and the two molecular markers of genetic instability MLH1 and MSH2 predicted T1 high-grade BC outcome with the higher the number of altered markers the lower the DFS and PFS. These findings provide grounds for further testing them in predicting the outcome of this challenging disease.

Nanospecific Inhibition of Pyoverdine Siderophore Production in Pseudomonas Chlororaphis O6 by CuO Nanoparticles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dimkpa, Christian O.; McLean, Joan E.; Britt, David W.

2012-03-01

As traditional antibiotics become less effective against a growing number of pathogens, engineered nanoparticles (NPs) are becoming more widely applied as biocides. NPs of Ag, ZnO, and CuO exhibit dose-dependent antimicrobial activity; however, information is scant on the impact of sublethal levels of NPs on bacteria. In this paper, we evaluated the effect of a sublethal concentration (200 mg/L) of commercial CuO NPs on the expression of genes involved in the production of the fluorescent siderophore, pyoverdine (PVD) in the plant-beneficial bacterium Pseudomonas chlororaphis O6. PVDs are important in microbe-microbe and microbe-plant interactions, and are a virulence factor in pathogenicmore » pseudomonads. Cells challenged with the NPs had reduced amounts of PVD in their periplasm and the external medium. The NPs impaired the expression of genes involved in transport of the PVD precursor through the plasmamembrane, PVD maturation in the periplasm, and export through the outer membrane. Also, expression from one of three predicted Fe-PVD receptors was reduced by the NPs. As these effects were not observed for cells challenged with copper ions, this is a nanoparticlespecific phenomenon mediating cellular reprogramming in bacteria, affecting secondary metabolism and thus associated critical microbial processes. The regulation of bacterial genes and secondary metabolites by sublethal doses of a common metal oxide NP has strong environmental and medical implications.« less

A full-length cDNA infectious clone of North American type 1 porcine reproductive and respiratory syndrome virus: expression of green fluorescent protein in the Nsp2 region.

PubMed

Fang, Ying; Rowland, Raymond R R; Roof, Michael; Lunney, Joan K; Christopher-Hennings, Jane; Nelson, Eric A

2006-12-01

The recent emergence of a unique group of North American type 1 porcine reproductive and respiratory syndrome virus (PRRSV) in the United States presents new disease control problems for a swine industry that has already been impacted seriously by North American type 2 PRRSV. In this study, a full-length cDNA infectious clone was generated from a low-virulence North American type 1 PRRSV isolate, SD01-08. In vitro studies demonstrated that the cloned virus maintained growth properties similar to those of the parental virus. Virological, pathological, and immunological observations from animals challenged with cloned viruses were similar to those from animals challenged with the parental virus and a modified live virus vaccine. To further explore the potential use as a viral backbone for expressing foreign genes, the green fluorescent protein (GFP) was inserted into a unique deletion site located at amino acid positions 348 and 349 of the predicted Nsp2 region in the virus, and expression of the Nsp2-GFP fusion protein was visualized by fluorescent microscopy. The availability of this North American type 1 infectious clone provides an important research tool for further study of the basic viral biology and pathogenic mechanisms of this group of type 1 PRRSV in the United States.

A Full-Length cDNA Infectious Clone of North American Type 1 Porcine Reproductive and Respiratory Syndrome Virus: Expression of Green Fluorescent Protein in the Nsp2 Region▿

PubMed Central

Fang, Ying; Rowland, Raymond R. R.; Roof, Michael; Lunney, Joan K.; Christopher-Hennings, Jane; Nelson, Eric A.

2006-01-01

The recent emergence of a unique group of North American type 1 porcine reproductive and respiratory syndrome virus (PRRSV) in the United States presents new disease control problems for a swine industry that has already been impacted seriously by North American type 2 PRRSV. In this study, a full-length cDNA infectious clone was generated from a low-virulence North American type 1 PRRSV isolate, SD01-08. In vitro studies demonstrated that the cloned virus maintained growth properties similar to those of the parental virus. Virological, pathological, and immunological observations from animals challenged with cloned viruses were similar to those from animals challenged with the parental virus and a modified live virus vaccine. To further explore the potential use as a viral backbone for expressing foreign genes, the green fluorescent protein (GFP) was inserted into a unique deletion site located at amino acid positions 348 and 349 of the predicted Nsp2 region in the virus, and expression of the Nsp2-GFP fusion protein was visualized by fluorescent microscopy. The availability of this North American type 1 infectious clone provides an important research tool for further study of the basic viral biology and pathogenic mechanisms of this group of type 1 PRRSV in the United States. PMID:16971421

Transcriptional profiling reveals the expression of novel genes in response to various stimuli in the human dermatophyte Trichophyton rubrum

PubMed Central

2010-01-01

Background Cutaneous mycoses are common human infections among healthy and immunocompromised hosts, and the anthropophilic fungus Trichophyton rubrum is the most prevalent microorganism isolated from such clinical cases worldwide. The aim of this study was to determine the transcriptional profile of T. rubrum exposed to various stimuli in order to obtain insights into the responses of this pathogen to different environmental challenges. Therefore, we generated an expressed sequence tag (EST) collection by constructing one cDNA library and nine suppression subtractive hybridization libraries. Results The 1388 unigenes identified in this study were functionally classified based on the Munich Information Center for Protein Sequences (MIPS) categories. The identified proteins were involved in transcriptional regulation, cellular defense and stress, protein degradation, signaling, transport, and secretion, among other functions. Analysis of these unigenes revealed 575 T. rubrum sequences that had not been previously deposited in public databases. Conclusion In this study, we identified novel T. rubrum genes that will be useful for ORF prediction in genome sequencing and facilitating functional genome analysis. Annotation of these expressed genes revealed metabolic adaptations of T. rubrum to carbon sources, ambient pH shifts, and various antifungal drugs used in medical practice. Furthermore, challenging T. rubrum with cytotoxic drugs and ambient pH shifts extended our understanding of the molecular events possibly involved in the infectious process and resistance to antifungal drugs. PMID:20144196

The prognostic value of a seven-microRNA classifier as a novel biomarker for the prediction and detection of recurrence in glioma patients.

PubMed

Chen, Wanghao; Yu, Qiang; Chen, Bo; Lu, Xingyu; Li, Qiaoyu

2016-08-16

Glioma is often diagnosed at a later stage, and the high risk of recurrence remains a major challenge. We hypothesized that the microRNA expression profile may serve as a biomarker for the prognosis and prediction of glioblastoma recurrence. We defined microRNAs that were associated with good and poor prognosis in 300 specimens of glioblastoma from the Cancer Genome Atlas. By analyzing microarray gene expression data and clinical information from three random groups, we identified 7 microRNAs that have prognostic and prognostic accuracy: microRNA-124a, microRNA-129, microRNA-139, microRNA-15b, microRNA-21, microRNA-218 and microRNA-7. The differential expression of these miRNAs was verified using an independent set of glioma samples from the Affiliated People's Hospital of Jiangsu University. We used the log-rank test and the Kaplan-Meier method to estimate correlations between the miRNA signature and disease-free survival/overall survival. Using the LASSO model, we observed a uniform significant difference in disease-free survival and overall survival between patients with high-risk and low-risk miRNA signature scores. Furthermore, the prognostic capability of the seven-miRNA signature was demonstrated by receiver operator characteristic curve analysis. A Circos plot was generated to examine the network of genes and pathways predicted to be targeted by the seven-miRNA signature. The seven-miRNA-based classifier should be useful in the stratification and individualized management of patients with glioma.

Characterization of four heat-shock protein genes from Nile tilapia (Oreochromis niloticus) and demonstration of the inducible transcriptional activity of Hsp70 promoter.

PubMed

Zhang, Lili; Sun, Chengfei; Ye, Xing; Zou, Shuming; Lu, Maixin; Liu, Zhigang; Tian, Yuanyuan

2014-02-01

Heat-shock proteins (Hsps), known as stress proteins and extrinsic chaperones, play important roles in the folding, translocation, and refolding/degradation of proteins. In this study, we identified four Hsps in Nile tilapia (Oreochromis niloticus), which display conserved Hsp characteristics in their predicted amino acid sequences. Further analyses on the structures, homology, and phylogenetics revealed that the four Hsps belong to Hsp70 family. One of them does not contain introns and is named Hsp70, while all the other three contain introns and are named Hsc70-1, Hsc70-2, and Hsc70-3. Expressions of the four Hsp proteins were observed in all examined tissues. Six hours after infection of Streptococcus agalactiae in Nile tilapia, the expression of Hsp70 was significantly increased in the liver, head kidney, spleen and gill, while Hsc70s' expression was unchanged in all examined tissues except the head kidney that showed significantly reduced expression of both Hsc70-2 and Hsc70-3. These results suggest that Hsp70 may participate in the defense against S. agalactiae infection. We then isolated the promoter of Hsp70 gene and inserted it into the donor plasmid of Tgf2 transposon system containing green fluorescent protein (GFP) gene. The plasmid was microinjected into zebrafish embryos, where the expression of GFP was induced by heat shock, S. agalactiae immersion challenge, indicating that the isolated Hsp70 promoter has transcriptional activity and is inducible by both heat shock and bacterial challenge. This promoter may facilitate the future construction of disease-resistant transgenic fish. The work also contributes to the further study of immune response of tilapia after bacterial infection.

Lactobacillus acidophilus modulates inflammatory activity by regulating the TLR4 and NF-κB expression in porcine peripheral blood mononuclear cells after lipopolysaccharide challenge.

PubMed

Lee, Sang In; Kim, Hyun Soo; Koo, Jin Mo; Kim, In Ho

2016-02-28

A total of forty weaned pigs ((Landrace × Yorkshire) × Duroc) were used to evaluate the effects of Lactobacillus acidophilus on inflammatory activity after lipopolysaccharide (LPS) challenge. Experimental treatments were as follows: (T1) control diet+saline challenge; (T2) control diet with 0·1% L. acidophilus+saline challenge; (T3) control diet+LPS challenge; and (T4) control diet with 0·1% L. acidophilus+LPS challenge. On d-14, piglets were challenged with saline (T1 and T2) or LPS (T3 and T4). Blood samples were obtained at 0, 2, 4, 6 and 12 h after being challenged and analysed for immune cell cytokine production and gene expression pattern. The L. acidophilus treatment increased the average daily weight gain (ADWG) and average daily feed intake (ADFI) compared with the control diet. With the control diet, the LPS challenge (T3) increased the number of immune cells and expression of TNF-α and IL-6 compared with the saline challenge (T1). Whereas with the saline challenge L. acidophilus treatment (T2) increased the number of leucocytes and CD4 compared with the control diet (T1), with the LPS challenge L. acidophilus treatment (T4) decreased the number of leucocytes, lymphocytes, CD4+ and CD8+ and expression of TNF-α and IL-6 compared with the control diet (T3). L. acidophilus treatment decreased the expression of TRL4 and NF-κB in peripheral blood mononuclear cells (PBMC) after LPS challenge, which leads to inhibition of TNF-α, IFN-γ, IL-6, IL-8 and IL1B1 and to induction of IL-4 and IL-10. We suggested that L. acidophilus improved ADWG and ADFI and protected against LPS-induced inflammatory responses by regulating TLR4 and NF-κB expression in porcine PBMC.

Global gene expression and systems biology analysis of bovine monocyte-derived macrophages in response to in vitro challenge with Mycobacterium bovis.

PubMed

Magee, David A; Taraktsoglou, Maria; Killick, Kate E; Nalpas, Nicolas C; Browne, John A; Park, Stephen D E; Conlon, Kevin M; Lynn, David J; Hokamp, Karsten; Gordon, Stephen V; Gormley, Eamonn; MacHugh, David E

2012-01-01

Mycobacterium bovis, the causative agent of bovine tuberculosis, is a major cause of mortality in global cattle populations. Macrophages are among the first cell types to encounter M. bovis following exposure and the response elicited by these cells is pivotal in determining the outcome of infection. Here, a functional genomics approach was undertaken to investigate global gene expression profiles in bovine monocyte-derived macrophages (MDM) purified from seven age-matched non-related females, in response to in vitro challenge with M. bovis (multiplicity of infection 2:1). Total cellular RNA was extracted from non-challenged control and M. bovis-challenged MDM for all animals at intervals of 2 hours, 6 hours and 24 hours post-challenge and prepared for global gene expression analysis using the Affymetrix® GeneChip® Bovine Genome Array. Comparison of M. bovis-challenged MDM gene expression profiles with those from the non-challenged MDM controls at each time point identified 3,064 differentially expressed genes 2 hours post-challenge, with 4,451 and 5,267 differentially expressed genes detected at the 6 hour and 24 hour time points, respectively (adjusted P-value threshold ≤ 0.05). Notably, the number of downregulated genes exceeded the number of upregulated genes in the M. bovis-challenged MDM across all time points; however, the fold-change in expression for the upregulated genes was markedly higher than that for the downregulated genes. Systems analysis revealed enrichment for genes involved in: (1) the inflammatory response; (2) cell signalling pathways, including Toll-like receptors and intracellular pathogen recognition receptors; and (3) apoptosis. The increased number of downregulated genes is consistent with previous studies showing that M. bovis infection is associated with the repression of host gene expression. The results also support roles for MyD88-independent signalling and intracellular PRRs in mediating the host response to M. bovis.

Transcriptome Profiling of Shewanella oneidensis Gene Expression following Exposure to Acidic and Alkaline pH†

PubMed Central

Leaphart, Adam B.; Thompson, Dorothea K.; Huang, Katherine; Alm, Eric; Wan, Xiu-Feng; Arkin, Adam; Brown, Steven D.; Wu, Liyou; Yan, Tingfen; Liu, Xueduan; Wickham, Gene S.; Zhou, Jizhong

2006-01-01

The molecular response of Shewanella oneidensis MR-1 to variations in extracellular pH was investigated based on genomewide gene expression profiling. Microarray analysis revealed that cells elicited both general and specific transcriptome responses when challenged with environmental acid (pH 4) or base (pH 10) conditions over a 60-min period. Global responses included the differential expression of genes functionally linked to amino acid metabolism, transcriptional regulation and signal transduction, transport, cell membrane structure, and oxidative stress protection. Response to acid stress included the elevated expression of genes encoding glycogen biosynthetic enzymes, phosphate transporters, and the RNA polymerase sigma-38 factor (rpoS), whereas the molecular response to alkaline pH was characterized by upregulation of nhaA and nhaR, which are predicted to encode an Na+/H+ antiporter and transcriptional activator, respectively, as well as sulfate transport and sulfur metabolism genes. Collectively, these results suggest that S. oneidensis modulates multiple transporters, cell envelope components, and pathways of amino acid consumption and central intermediary metabolism as part of its transcriptome response to changing external pH conditions. PMID:16452448

Predicting Ki67% expression from DCE-MR images of breast tumors using textural kinetic features in tumor habitats

NASA Astrophysics Data System (ADS)

Chaudhury, Baishali; Zhou, Mu; Farhidzadeh, Hamidreza; Goldgof, Dmitry B.; Hall, Lawrence O.; Gatenby, Robert A.; Gillies, Robert J.; Weinfurtner, Robert J.; Drukteinis, Jennifer S.

2016-03-01

The use of Ki67% expression, a cell proliferation marker, as a predictive and prognostic factor has been widely studied in the literature. Yet its usefulness is limited due to inconsistent cut off scores for Ki67% expression, subjective differences in its assessment in various studies, and spatial variation in expression, which makes it difficult to reproduce as a reliable independent prognostic factor. Previous studies have shown that there are significant spatial variations in Ki67% expression, which may limit its clinical prognostic utility after core biopsy. These variations are most evident when examining the periphery of the tumor vs. the core. To date, prediction of Ki67% expression from quantitative image analysis of DCE-MRI is very limited. This work presents a novel computer aided diagnosis framework to use textural kinetics to (i) predict the ratio of periphery Ki67% expression to core Ki67% expression, and (ii) predict Ki67% expression from individual tumor habitats. The pilot cohort consists of T1 weighted fat saturated DCE-MR images from 17 patients. Support vector regression with a radial basis function was used for predicting the Ki67% expression and ratios. The initial results show that texture features from individual tumor habitats are more predictive of the Ki67% expression ratio and spatial Ki67% expression than features from the whole tumor. The Ki67% expression ratio could be predicted with a root mean square error (RMSE) of 1.67%. Quantitative image analysis of DCE-MRI using textural kinetic habitats, has the potential to be used as a non-invasive method for predicting Ki67 percentage and ratio, thus more accurately reporting high KI-67 expression for patient prognosis.

Systematic Analysis of Challenge-Driven Improvements in Molecular Prognostic Models for Breast Cancer

PubMed Central

Margolin, Adam A.; Bilal, Erhan; Huang, Erich; Norman, Thea C.; Ottestad, Lars; Mecham, Brigham H.; Sauerwine, Ben; Kellen, Michael R.; Mangravite, Lara M.; Furia, Matthew D.; Vollan, Hans Kristian Moen; Rueda, Oscar M.; Guinney, Justin; Deflaux, Nicole A.; Hoff, Bruce; Schildwachter, Xavier; Russnes, Hege G.; Park, Daehoon; Vang, Veronica O.; Pirtle, Tyler; Youseff, Lamia; Citro, Craig; Curtis, Christina; Kristensen, Vessela N.; Hellerstein, Joseph; Friend, Stephen H.; Stolovitzky, Gustavo; Aparicio, Samuel; Caldas, Carlos; Børresen-Dale, Anne-Lise

2013-01-01

Although molecular prognostics in breast cancer are among the most successful examples of translating genomic analysis to clinical applications, optimal approaches to breast cancer clinical risk prediction remain controversial. The Sage Bionetworks–DREAM Breast Cancer Prognosis Challenge (BCC) is a crowdsourced research study for breast cancer prognostic modeling using genome-scale data. The BCC provided a community of data analysts with a common platform for data access and blinded evaluation of model accuracy in predicting breast cancer survival on the basis of gene expression data, copy number data, and clinical covariates. This approach offered the opportunity to assess whether a crowdsourced community Challenge would generate models of breast cancer prognosis commensurate with or exceeding current best-in-class approaches. The BCC comprised multiple rounds of blinded evaluations on held-out portions of data on 1981 patients, resulting in more than 1400 models submitted as open source code. Participants then retrained their models on the full data set of 1981 samples and submitted up to five models for validation in a newly generated data set of 184 breast cancer patients. Analysis of the BCC results suggests that the best-performing modeling strategy outperformed previously reported methods in blinded evaluations; model performance was consistent across several independent evaluations; and aggregating community-developed models achieved performance on par with the best-performing individual models. PMID:23596205

EST-PAC a web package for EST annotation and protein sequence prediction

PubMed Central

Strahm, Yvan; Powell, David; Lefèvre, Christophe

2006-01-01

With the decreasing cost of DNA sequencing technology and the vast diversity of biological resources, researchers increasingly face the basic challenge of annotating a larger number of expressed sequences tags (EST) from a variety of species. This typically consists of a series of repetitive tasks, which should be automated and easy to use. The results of these annotation tasks need to be stored and organized in a consistent way. All these operations should be self-installing, platform independent, easy to customize and amenable to using distributed bioinformatics resources available on the Internet. In order to address these issues, we present EST-PAC a web oriented multi-platform software package for expressed sequences tag (EST) annotation. EST-PAC provides a solution for the administration of EST and protein sequence annotations accessible through a web interface. Three aspects of EST annotation are automated: 1) searching local or remote biological databases for sequence similarities using Blast services, 2) predicting protein coding sequence from EST data and, 3) annotating predicted protein sequences with functional domain predictions. In practice, EST-PAC integrates the BLASTALL suite, EST-Scan2 and HMMER in a relational database system accessible through a simple web interface. EST-PAC also takes advantage of the relational database to allow consistent storage, powerful queries of results and, management of the annotation process. The system allows users to customize annotation strategies and provides an open-source data-management environment for research and education in bioinformatics. PMID:17147782

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells

PubMed Central

Trevino, Victor; Cassese, Alberto; Nagy, Zsuzsanna; Zhuang, Xiaodong; Herbert, John; Antzack, Philipp; Clarke, Kim; Davies, Nicholas; Rahman, Ayesha; Campbell, Moray J.; Bicknell, Roy; Vannucci, Marina; Falciani, Francesco

2016-01-01

Abstract The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems. PMID:27124473

A Network Biology Approach Identifies Molecular Cross-Talk between Normal Prostate Epithelial and Prostate Carcinoma Cells.

PubMed

Trevino, Victor; Cassese, Alberto; Nagy, Zsuzsanna; Zhuang, Xiaodong; Herbert, John; Antczak, Philipp; Clarke, Kim; Davies, Nicholas; Rahman, Ayesha; Campbell, Moray J; Guindani, Michele; Bicknell, Roy; Vannucci, Marina; Falciani, Francesco

2016-04-01

The advent of functional genomics has enabled the genome-wide characterization of the molecular state of cells and tissues, virtually at every level of biological organization. The difficulty in organizing and mining this unprecedented amount of information has stimulated the development of computational methods designed to infer the underlying structure of regulatory networks from observational data. These important developments had a profound impact in biological sciences since they triggered the development of a novel data-driven investigative approach. In cancer research, this strategy has been particularly successful. It has contributed to the identification of novel biomarkers, to a better characterization of disease heterogeneity and to a more in depth understanding of cancer pathophysiology. However, so far these approaches have not explicitly addressed the challenge of identifying networks representing the interaction of different cell types in a complex tissue. Since these interactions represent an essential part of the biology of both diseased and healthy tissues, it is of paramount importance that this challenge is addressed. Here we report the definition of a network reverse engineering strategy designed to infer directional signals linking adjacent cell types within a complex tissue. The application of this inference strategy to prostate cancer genome-wide expression profiling data validated the approach and revealed that normal epithelial cells exert an anti-tumour activity on prostate carcinoma cells. Moreover, by using a Bayesian hierarchical model integrating genetics and gene expression data and combining this with survival analysis, we show that the expression of putative cell communication genes related to focal adhesion and secretion is affected by epistatic gene copy number variation and it is predictive of patient survival. Ultimately, this study represents a generalizable approach to the challenge of deciphering cell communication networks in a wide spectrum of biological systems.

Molecular Characterization and Expression of α-Globin and β-Globin Genes in the Euryhaline Flounder (Platichthys flesus)

PubMed Central

Lu, Weiqun; Mayolle, Aurelie; Cui, Guoqiang; Luo, Lei; Balment, Richard J.

2011-01-01

In order to understand the possible role of globin genes in fish salinity adaptation, we report the molecular characterization and expression of all four subunits of haemoglobin, and their response to salinity challenge in flounder. The entire open reading frames of α1-globin and α2-globin genes were 432 and 435 bp long, respectively, whereas the β1-globin and β2-globin genes were both 447 bp. Although the head kidney (pronephros) is the predicted major site of haematopoiesis, real-time PCR revealed that expression of α-globin and β-globin in kidney (mesonephros) was 1.5 times higher than in head kidney. Notably, the α1-globin and β1-globin mRNA expression was higher than α2-globin and β2-globin in kidney. Expression levels of all four globin subunits were higher in freshwater- (FW-) than in seawater- (SW-)adapted fish kidney. If globins do play a role in salinity adaptation, this is likely to be more important in combating the hemodilution faced by fish in FW than the dehydration and salt loading which occur in SW. PMID:21969841

Genetic Modifiers and Oligogenic Inheritance

PubMed Central

Kousi, Maria; Katsanis, Nicholas

2015-01-01

Despite remarkable progress in the identification of mutations that drive genetic disorders, progress in understanding the effect of genetic background on the penetrance and expressivity of causal alleles has been modest, in part because of the methodological challenges in identifying genetic modifiers. Nonetheless, the progressive discovery of modifier alleles has improved both our interpretative ability and our analytical tools to dissect such phenomena. In this review, we analyze the genetic properties and behaviors of modifiers as derived from studies in patient populations and model organisms and we highlight conceptual and technological tools used to overcome some of the challenges inherent in modifier mapping and cloning. Finally, we discuss how the identification of these modifiers has facilitated the elucidation of biological pathways and holds the potential to improve the clinical predictive value of primary causal mutations and to develop novel drug targets. PMID:26033081

Evaluating the physiological reserves of older patients with cancer: the value of potential biomarkers of aging?

PubMed

Pallis, Athanasios G; Hatse, Sigrid; Brouwers, Barbara; Pawelec, Graham; Falandry, Claire; Wedding, Ulrich; Lago, Lissandra Dal; Repetto, Lazzaro; Ring, Alistair; Wildiers, Hans

2014-04-01

Aging of an individual entails a progressive decline of functional reserves and loss of homeostasis that eventually lead to mortality. This process is highly individualized and is influenced by multiple genetic, epigenetic and environmental factors. This individualization and the diversity of factors influencing aging result in a significant heterogeneity among people with the same chronological age, representing a major challenge in daily oncology practice. Thus, many factors other than mere chronological age will contribute to treatment tolerance and outcome in the older patients with cancer. Clinical/comprehensive geriatric assessment can provide information on the general health status of individuals, but is far from perfect as a prognostic/predictive tool for individual patients. On the other hand, aging can also be assessed in terms of biological changes in certain tissues like the blood compartment which result from adaptive alterations due to past history of exposures, as well as intrinsic aging processes. There are major signs of 'aging' in lymphocytes (e.g. lymphocyte subset distribution, telomere length, p16INK4A expression), and also in (inflammatory) cytokine expression and gene expression patterns. These result from a combination of the above two processes, overlaying genetic predispositions which contribute significantly to the aging phenotype. These potential "aging biomarkers" might provide additional prognostic/predictive information supplementing clinical evaluation. The purpose of the current paper is to describe the most relevant potential "aging biomarkers" (markers that indicate the biological functional age of patients) which focus on the biological background, the (limited) available clinical data, and technical challenges. Despite their great potential interest, there is a need for much more (validated) clinical data before these biomarkers could be used in a routine clinical setting. This manuscript tries to provide a guideline on how these markers can be integrated in future research aimed at providing such data. © 2013.

Heterologous Expression of Plant Cell Wall Degrading Enzymes for Effective Production of Cellulosic Biofuels

PubMed Central

Jung, Sang-Kyu; Parisutham, Vinuselvi; Jeong, Seong Hun; Lee, Sung Kuk

2012-01-01

A major technical challenge in the cost-effective production of cellulosic biofuel is the need to lower the cost of plant cell wall degrading enzymes (PCDE), which is required for the production of sugars from biomass. Several competitive, low-cost technologies have been developed to produce PCDE in different host organisms such as Escherichia coli, Zymomonas mobilis, and plant. Selection of an ideal host organism is very important, because each host organism has its own unique features. Synthetic biology-aided tools enable heterologous expression of PCDE in recombinant E. coli or Z. mobilis and allow successful consolidated bioprocessing (CBP) in these microorganisms. In-planta expression provides an opportunity to simplify the process of enzyme production and plant biomass processing and leads to self-deconstruction of plant cell walls. Although the future of currently available technologies is difficult to predict, a complete and viable platform will most likely be available through the integration of the existing approaches with the development of breakthrough technologies. PMID:22911272

Stochastic models for inferring genetic regulation from microarray gene expression data.

PubMed

Tian, Tianhai

2010-03-01

Microarray expression profiles are inherently noisy and many different sources of variation exist in microarray experiments. It is still a significant challenge to develop stochastic models to realize noise in microarray expression profiles, which has profound influence on the reverse engineering of genetic regulation. Using the target genes of the tumour suppressor gene p53 as the test problem, we developed stochastic differential equation models and established the relationship between the noise strength of stochastic models and parameters of an error model for describing the distribution of the microarray measurements. Numerical results indicate that the simulated variance from stochastic models with a stochastic degradation process can be represented by a monomial in terms of the hybridization intensity and the order of the monomial depends on the type of stochastic process. The developed stochastic models with multiple stochastic processes generated simulations whose variance is consistent with the prediction of the error model. This work also established a general method to develop stochastic models from experimental information. 2009 Elsevier Ireland Ltd. All rights reserved.

Network-based de-noising improves prediction from microarray data.

PubMed

Kato, Tsuyoshi; Murata, Yukio; Miura, Koh; Asai, Kiyoshi; Horton, Paul B; Koji, Tsuda; Fujibuchi, Wataru

2006-03-20

Prediction of human cell response to anti-cancer drugs (compounds) from microarray data is a challenging problem, due to the noise properties of microarrays as well as the high variance of living cell responses to drugs. Hence there is a strong need for more practical and robust methods than standard methods for real-value prediction. We devised an extended version of the off-subspace noise-reduction (de-noising) method to incorporate heterogeneous network data such as sequence similarity or protein-protein interactions into a single framework. Using that method, we first de-noise the gene expression data for training and test data and also the drug-response data for training data. Then we predict the unknown responses of each drug from the de-noised input data. For ascertaining whether de-noising improves prediction or not, we carry out 12-fold cross-validation for assessment of the prediction performance. We use the Pearson's correlation coefficient between the true and predicted response values as the prediction performance. De-noising improves the prediction performance for 65% of drugs. Furthermore, we found that this noise reduction method is robust and effective even when a large amount of artificial noise is added to the input data. We found that our extended off-subspace noise-reduction method combining heterogeneous biological data is successful and quite useful to improve prediction of human cell cancer drug responses from microarray data.

Accounting for isotopic clustering in Fourier transform mass spectrometry data analysis for clinical diagnostic studies.

PubMed

Kakourou, Alexia; Vach, Werner; Nicolardi, Simone; van der Burgt, Yuri; Mertens, Bart

2016-10-01

Mass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.

Dynamic gene expression changes precede dioxin-induced liver pathogenesis in medaka fish.

PubMed

Volz, David C; Hinton, David E; Law, J McHugh; Kullman, Seth W

2006-02-01

A major challenge for environmental genomics is linking gene expression to cellular toxicity and morphological alteration. Herein, we address complexities related to hepatic gene expression responses after a single injection of the aryl hydrocarbon receptor (AHR) agonist 2,3,7,8-tetrachlorodibenzo-p-dioxin (dioxin) and illustrate an initial stress response followed by cytologic and adaptive changes in the teleost fish medaka. Using a custom 175-gene array, we find that overall hepatic gene expression and histological changes are strongly dependent on dose and time. The most pronounced dioxin-induced gene expression changes occurred early and preceded morphologic alteration in the liver. Following a systematic search for putative Ah response elements (AHREs) (5'-CACGCA-3') within 2000 bp upstream of the predicted transcriptional start site, the majority (87%) of genes screened in this study did not contain an AHRE, suggesting that gene expression was not solely dependent on AHRE-mediated transcription. Moreover, in the highest dosage, we observed gene expression changes associated with adaptation that persisted for almost two weeks, including induction of a gene putatively identified as ependymin that may function in hepatic injury repair. These data suggest that the cellular response to dioxin involves both AHRE- and non-AHRE-mediated transcription, and that coupling gene expression profiling with analysis of morphologic pathogenesis is essential for establishing temporal relationships between transcriptional changes, toxicity, and adaptation to hepatic injury.

Molecular characterization of kappa class glutathione S-transferase from the disk abalone (Haliotis discus discus) and changes in expression following immune and stress challenges.

PubMed

Sandamalika, W M Gayashani; Priyathilaka, Thanthrige Thiunuwan; Liyanage, D S; Lee, Sukkyoung; Lim, Han-Kyu; Lee, Jehee

2018-06-01

Glutathione S-transferase (GST; EC 2.5.1.18) isoenzymes represent a complex group of proteins that are involved in phase II detoxification in several organisms. In this study, GST kappa (GSTκ) from the disk abalone (Haliotis discus discus; AbGSTκ) was characterized at both the transcriptional and functional levels to determine its potential capacity to perform as a detoxification agent under conditions of different stress. The predicted AbGSTκ protein consists of 227 amino acids, with a predicted molecular weight of 25.6 kDa and a theoretical isoelectric point (pI) of 7.78. In silico analysis reveals that AbGSTκ is a disulfide bond formation protein A (DsbA), consisting of a thioredoxin domain, GSH binding sites (G-sites), and a catalytic residue. In contrast, no hydrophobic ligand binding site (H-site), or signal peptides, were detected. AbGSTκ showed the highest sequence identity with the orthologue from pufferfish (Takifugu obscurus) (60.0%). In a phylogenetic tree, AbGSTκ clustered closely together with other fish GSTκs, and was evolutionarily distanced from other cytosolic GSTs. The predicted three-dimensional structure clearly demonstrates that the dimer adopts a butterfly-like shape. A tissue distribution analysis revealed that GSTκ was highly expressed in the digestive tract, suggesting it has detoxification ability. Depending on the tissue and time, AbGSTκ showed different expression patterns, and levels of expression, following challenge of the abalone with immune stimulants. Enzyme kinetics of the purified recombinant proteins demonstrated its conjugating ability using 1-Chloro-2,4-dinitrobenzene (CDNB) and glutathione (GSH) as substrates, and suggested it has a low affinity for both substrates. The optimum temperature and pH for the rAbGSTκ GSH: CDNB conjugating activity were found to be 35 °C and pH 8, respectively indicating that the abalone is well adapted to a wide range of environmental conditions. Cibacron blue (100 μM) was capable of completely inhibiting rAbGSTκ (100%) with an IC 50 (half maximal inhibitory concentration) of 0.05 μM. A disk diffusion assay revealed that rAbGSTκ could significantly protect cells from H 2 O 2 , CdCl 2, and ZnCl 2 . Altogether, this current study suggests that AbGSTκ is involved in detoxification and immunological host defense mechanisms and allows abalones to overcome stresses in order for them to have an increased chance of survival. Copyright © 2018. Published by Elsevier Ltd.

Exceptions to the rule: case studies in the prediction of pathogenicity for genetic variants in hereditary cancer genes.

PubMed

Rosenthal, E T; Bowles, K R; Pruss, D; van Kan, A; Vail, P J; McElroy, H; Wenstrup, R J

2015-12-01

Based on current consensus guidelines and standard practice, many genetic variants detected in clinical testing are classified as disease causing based on their predicted impact on the normal expression or function of the gene in the absence of additional data. However, our laboratory has identified a subset of such variants in hereditary cancer genes for which compelling contradictory evidence emerged after the initial evaluation following the first observation of the variant. Three representative examples of variants in BRCA1, BRCA2 and MSH2 that are predicted to disrupt splicing, prematurely truncate the protein, or remove the start codon were evaluated for pathogenicity by analyzing clinical data with multiple classification algorithms. Available clinical data for all three variants contradicts the expected pathogenic classification. These variants illustrate potential pitfalls associated with standard approaches to variant classification as well as the challenges associated with monitoring data, updating classifications, and reporting potentially contradictory interpretations to the clinicians responsible for translating test outcomes to appropriate clinical action. It is important to address these challenges now as the model for clinical testing moves toward the use of large multi-gene panels and whole exome/genome analysis, which will dramatically increase the number of genetic variants identified. © 2015 The Authors. Clinical Genetics published by John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

EffectorP: predicting fungal effector proteins from secretomes using machine learning.

PubMed

Sperschneider, Jana; Gardiner, Donald M; Dodds, Peter N; Tini, Francesco; Covarelli, Lorenzo; Singh, Karam B; Manners, John M; Taylor, Jennifer M

2016-04-01

Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N-terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine-rich, which suffers from poor accuracy. We present EffectorP which pioneers the application of machine learning to fungal effector prediction. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EffectorP is powerful when combined with in planta expression data for predicting high-priority effector candidates. EffectorP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EffectorP is available at http://effectorp.csiro.au. © 2015 CSIRO New Phytologist © 2015 New Phytologist Trust.

eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes.

PubMed

Chang, Zheng; Wang, Zhenjia; Ashby, Cody; Zhou, Chuan; Li, Guojun; Zhang, Shuzhong; Huang, Xiuzhen

2014-01-01

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice.

eMBI: Boosting Gene Expression-based Clustering for Cancer Subtypes

PubMed Central

Chang, Zheng; Wang, Zhenjia; Ashby, Cody; Zhou, Chuan; Li, Guojun; Zhang, Shuzhong; Huang, Xiuzhen

2014-01-01

Identifying clinically relevant subtypes of a cancer using gene expression data is a challenging and important problem in medicine, and is a necessary premise to provide specific and efficient treatments for patients of different subtypes. Matrix factorization provides a solution by finding checker-board patterns in the matrices of gene expression data. In the context of gene expression profiles of cancer patients, these checkerboard patterns correspond to genes that are up- or down-regulated in patients with particular cancer subtypes. Recently, a new matrix factorization framework for biclustering called Maximum Block Improvement (MBI) is proposed; however, it still suffers several problems when applied to cancer gene expression data analysis. In this study, we developed many effective strategies to improve MBI and designed a new program called enhanced MBI (eMBI), which is more effective and efficient to identify cancer subtypes. Our tests on several gene expression profiling datasets of cancer patients consistently indicate that eMBI achieves significant improvements in comparison with MBI, in terms of cancer subtype prediction accuracy, robustness, and running time. In addition, the performance of eMBI is much better than another widely used matrix factorization method called nonnegative matrix factorization (NMF) and the method of hierarchical clustering, which is often the first choice of clinical analysts in practice. PMID:25374455

The increase in the expression and hypomethylation of MUC4 gene with the progression of pancreatic ductal adenocarcinoma.

PubMed

Zhu, Yi; Zhang, Jing-jing; Zhu, Rong; Zhu, Yan; Liang, Wen-biao; Gao, Wen-tao; Yu, Jun-bo; Xu, Ze-kuan; Miao, Yi

2011-12-01

The MUC4 gene could have a key role in the progression of pancreatic cancer, but the quantitative measurement of its expression in clinical tissue samples remains a challenge. The correlations between MUC4 promoter methylation status in vivo and either pancreatic cancer progression or MUC4 mRNA expression need to be demonstrated. We used the techniques of quantitative real-time PCR and DNA methylation-specific PCR combined microdissection to precisely detect MUC4 expression and promoter methylation status in 116 microdissected foci from 57 patients with pancreatic ductal adenocarcinoma. Both mRNA expression and hypomethylation frequency increased from normal to precancerous lesions to pancreatic cancer. Multivariate Cox regression analysis showed that high-level MUC4 expression (P = 0.008) and tumor-node-metastasis staging (P = 0.038) were significant independent risk factors for predicting the prognosis of 57 patients. The MUC4 mRNA expression was not significantly correlated with promoter methylation status in 30 foci of pancreatic ductal adenocarcinoma. These results suggest that high mRNA expression and hypomethylation of the MUC4 gene could be involved in carcinogenesis and in the malignant development of pancreatic ductal adenocarcinoma. The MUC4 mRNA expression may become a new prognostic marker for pancreatic cancer. Microdissection-based quantitative real-time PCR and methylation-specific PCR contribute to the quantitative detection of MUC4 expression in clinical samples and reflect the epigenetic regulatory mechanisms of MUC4 in vivo.

Can we customize chemotherapy? Individualizing cytotoxic regimens in advanced non-small-cell lung cancer.

PubMed

Rosell, Rafael; Manegold, Christian; Moran, Teresa; Garrido, Pilar; Blanco, Remei; Lianes, Pilar; Stahel, Rolf; Trigo, Jose Manuel; Wei, Jia; Taron, Miquel

2008-03-01

Metastatic non-small-cell lung cancer remains a fatal disease with a median survival of < 1 year. A critical challenge is to develop predictive markers for customizing platinum-based treatment. The first studies focused on the excision repair cross-complementing 1 (ERCC1) gene in this difficult task. Several layers of evidence indicate that ERCC1 mRNA expression could be a predictive marker for cisplatin alone or in combination with certain drugs such as etoposide, gemcitabine, and 5-fluorouracil but not in combination with antimicrotubule drugs. Several retrospective studies demonstrated an impressive survival advantage for gemcitabine plus cisplatin but not for other combinations in tumors with low ERCC1 expression. A customized phase III ERCC1-based trial met the primary endpoint of improvement in response but not in survival, leading us to hypothesize that docetaxel might not be the most appropriate partner for cisplatin in the presence of low ERCC1 levels or for gemcitabine in the presence of high ERCC1 levels. A phase II study demonstrated the feasibility of combining carboplatin, gemcitabine, docetaxel, and vinorelbine according to ERCC1 and ribonucleotide reductase subunit M1 expression levels. These findings highlight the importance of continual learning, and decision-making strategies for customizing treatment should reflect the limitations of our knowledge. Copyright © 2008 Elsevier Inc. All rights reserved.

The balanced ideological antipathy model: explaining the effects of ideological attitudes on inter-group antipathy across the political spectrum.

PubMed

Crawford, Jarret T; Mallinas, Stephanie R; Furman, Bryan J

2015-12-01

We introduce the balanced ideological antipathy (BIA) model, which challenges assumptions that right-wing authoritarianism (RWA) and social dominance orientation (SDO) predict inter-group antipathy per se. Rather, the effects of RWA and SDO on antipathy should depend on the target's political orientation and political objectives, the specific components of RWA, and the type of antipathy expressed. Consistent with the model, two studies (N = 585) showed that the Traditionalism component of RWA positively and negatively predicted both political intolerance and prejudice toward tradition-threatening and -reaffirming groups, respectively, whereas SDO positively and negatively predicted prejudice (and to some extent political intolerance) toward hierarchy-attenuating and -enhancing groups, respectively. Critically, the Conservatism component of RWA positively predicted political intolerance (but not prejudice) toward each type of target group, suggesting it captures the anti-democratic impulse at the heart of authoritarianism. Recommendations for future research on the relationship between ideological attitudes and inter-group antipathy are discussed. © 2015 by the Society for Personality and Social Psychology, Inc.

Microglia Transcriptome Changes in a Model of Depressive Behavior after Immune Challenge

PubMed Central

Gonzalez-Pena, Dianelys; Nixon, Scott E.; O’Connor, Jason C.; Southey, Bruce R.; Lawson, Marcus A.; McCusker, Robert H.; Borras, Tania; Machuca, Debbie; Hernandez, Alvaro G.; Dantzer, Robert; Kelley, Keith W.; Rodriguez-Zas, Sandra L.

2016-01-01

Depression symptoms following immune response to a challenge have been reported after the recovery from sickness. A RNA-Seq study of the dysregulation of the microglia transcriptome in a model of inflammation-associated depressive behavior was undertaken. The transcriptome of microglia from mice at day 7 after Bacille Calmette Guérin (BCG) challenge was compared to that from unchallenged Control mice and to the transcriptome from peripheral macrophages from the same mice. Among the 562 and 3,851 genes differentially expressed between BCG-challenged and Control mice in microglia and macrophages respectively, 353 genes overlapped between these cells types. Among the most differentially expressed genes in the microglia, serum amyloid A3 (Saa3) and cell adhesion molecule 3 (Cadm3) were over-expressed and coiled-coil domain containing 162 (Ccdc162) and titin-cap (Tcap) were under-expressed in BCG-challenged relative to Control. Many of the differentially expressed genes between BCG-challenged and Control mice were associated with neurological disorders encompassing depression symptoms. Across cell types, S100 calcium binding protein A9 (S100A9), interleukin 1 beta (Il1b) and kynurenine 3-monooxygenase (Kmo) were differentially expressed between challenged and control mice. Immune response, chemotaxis, and chemokine activity were among the functional categories enriched by the differentially expressed genes. Functional categories enriched among the 9,117 genes differentially expressed between cell types included leukocyte regulation and activation, chemokine and cytokine activities, MAP kinase activity, and apoptosis. More than 200 genes exhibited alternative splicing events between cell types including WNK lysine deficient protein kinase 1 (Wnk1) and microtubule-actin crosslinking factor 1(Macf1). Network visualization revealed the capability of microglia to exhibit transcriptome dysregulation in response to immune challenge still after resolution of sickness symptoms, albeit lower than that observed in macrophages. The persistent transcriptome dysregulation in the microglia shared patterns with neurological disorders indicating that the associated persistent depressive symptoms share a common transcriptome basis. PMID:26959683

Microglia Transcriptome Changes in a Model of Depressive Behavior after Immune Challenge.

PubMed

Gonzalez-Pena, Dianelys; Nixon, Scott E; O'Connor, Jason C; Southey, Bruce R; Lawson, Marcus A; McCusker, Robert H; Borras, Tania; Machuca, Debbie; Hernandez, Alvaro G; Dantzer, Robert; Kelley, Keith W; Rodriguez-Zas, Sandra L

2016-01-01

Depression symptoms following immune response to a challenge have been reported after the recovery from sickness. A RNA-Seq study of the dysregulation of the microglia transcriptome in a model of inflammation-associated depressive behavior was undertaken. The transcriptome of microglia from mice at day 7 after Bacille Calmette Guérin (BCG) challenge was compared to that from unchallenged Control mice and to the transcriptome from peripheral macrophages from the same mice. Among the 562 and 3,851 genes differentially expressed between BCG-challenged and Control mice in microglia and macrophages respectively, 353 genes overlapped between these cells types. Among the most differentially expressed genes in the microglia, serum amyloid A3 (Saa3) and cell adhesion molecule 3 (Cadm3) were over-expressed and coiled-coil domain containing 162 (Ccdc162) and titin-cap (Tcap) were under-expressed in BCG-challenged relative to Control. Many of the differentially expressed genes between BCG-challenged and Control mice were associated with neurological disorders encompassing depression symptoms. Across cell types, S100 calcium binding protein A9 (S100A9), interleukin 1 beta (Il1b) and kynurenine 3-monooxygenase (Kmo) were differentially expressed between challenged and control mice. Immune response, chemotaxis, and chemokine activity were among the functional categories enriched by the differentially expressed genes. Functional categories enriched among the 9,117 genes differentially expressed between cell types included leukocyte regulation and activation, chemokine and cytokine activities, MAP kinase activity, and apoptosis. More than 200 genes exhibited alternative splicing events between cell types including WNK lysine deficient protein kinase 1 (Wnk1) and microtubule-actin crosslinking factor 1(Macf1). Network visualization revealed the capability of microglia to exhibit transcriptome dysregulation in response to immune challenge still after resolution of sickness symptoms, albeit lower than that observed in macrophages. The persistent transcriptome dysregulation in the microglia shared patterns with neurological disorders indicating that the associated persistent depressive symptoms share a common transcriptome basis.

Biodosimetry as a New Paradigm for Determination of Radiation Risks and Risk-Mitigation in Astronauts Exposed to Space Radiation

NASA Technical Reports Server (NTRS)

Richmond, Robert; Cruz, Angela; Bors, Karen

2004-01-01

Predicting risk of cancer in astronauts exposed to space radiation is challenging partly because uncertainties of absorption of dose and the processing of dose-related damage at the cellular level degrade the confidence of predicting the expression of cancer. Cellular biodosimeters that simultaneously report: 1) the quantity of absorbed dose after exposure to ionizing radiation, 2) the quality of radiation delivering that dose, and 3) the macromolecular profiles related to malignant transformation in cells absorbing that dose would therefore be useful. An approach to such a multiparametric biodosimeter will be reported, This is the demonstration of two dose-responsive field-effects of enhanced protein-expression. In one case, expression of keratin 18 (K18) in cultures of human mammary epithelial cells (HMEC) irradiated with cesium-137 gamma-rays is enhanced following exposure of log phase cells to relatively low doses of 30 to 90 cGy. K18 has been reported by a marker for tumor staging and for apoptosis. In the second case, expression of connexin 43 (Cx43) is increased in irradiated stationary phase cultures of HMEC, indicating enhanced formation of gap junctions. Gap junctions have been reported to be involved in bystander effects following irradiation. It is a biodosimeter for assessing radiogenic damage. It is suggested further that such biomolecular dosimetry may introduce a new paradigm for assessing cancer risk and risk-mitigation in individuals, a requirement for managing radiation health in astronauts during extended missions in space. This new paradigm is built upon the statistical power provided by the use of functional genomics and proteomics represented in combined gene- and protein-expression assays.

Preoperative Molecular Markers in Thyroid Nodules.

PubMed

Sahli, Zeyad T; Smith, Philip W; Umbricht, Christopher B; Zeiger, Martha A

2018-01-01

The need for distinguishing benign from malignant thyroid nodules has led to the pursuit of differentiating molecular markers. The most common molecular tests in clinical use are Afirma ® Gene Expression Classifier (GEC) and Thyroseq ® V2. Despite the rapidly developing field of molecular markers, several limitations exist. These challenges include the recent introduction of the histopathological diagnosis "Non-Invasive Follicular Thyroid neoplasm with Papillary-like nuclear features", the correlation of genetic mutations within both benign and malignant pathologic diagnoses, the lack of follow-up of molecular marker negative nodules, and the cost-effectiveness of molecular markers. In this manuscript, we review the current published literature surrounding the diagnostic value of Afirma ® GEC and Thyroseq ® V2. Among Afirma ® GEC studies, sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) ranged from 75 to 100%, 5 to 53%, 13 to 100%, and 20 to 100%, respectively. Among Thyroseq ® V2 studies, Se, Sp, PPV, and NPV ranged from 40 to 100%, 56 to 93%, 13 to 90%, and 48 to 97%, respectively. We also discuss current challenges to Afirma ® GEC and Thyroseq ® V2 utility and clinical application, and preview the future directions of these rapidly developing technologies.

Person-centered Therapeutics

PubMed Central

Cloninger, C. Robert; Cloninger, Kevin M.

2015-01-01

A clinician’s effectiveness in treatment depends substantially on his or her attitude toward -- and understanding of -- the patient as a person endowed with self-awareness and the will to direct his or her own future. The assessment of personality in the therapeutic encounter is a crucial foundation for forming an effective working alliance with shared goals. Helping a person to reflect on their personality provides a mirror image of their strengths and weaknesses in adapting to life’s many challenges. The Temperament and Character Inventory (TCI) provides an effective way to describe personality thoroughly and to predict both the positive and negative aspects of health. Strengths and weaknesses in TCI personality traits allow strong predictions of individual differences of all aspects of well-being. Diverse therapeutic techniques, such as diet, exercise, mood self-regulation, meditation, or acts of kindness, influence health and personality development in ways that are largely indistinguishable from one another or from effective allopathic treatments. Hence the development of well-being appears to be the result of activating a synergistic set of mechanisms of well-being, which are expressed as fuller functioning, plasticity, and virtue in adapting to life’s challenges PMID:26052429

HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease.

PubMed

Ward, Lucas D; Kellis, Manolis

2016-01-04

More than 90% of common variants associated with complex traits do not affect proteins directly, but instead the circuits that control gene expression. This has increased the urgency of understanding the regulatory genome as a key component for translating genetic results into mechanistic insights and ultimately therapeutics. To address this challenge, we developed HaploReg (http://compbio.mit.edu/HaploReg) to aid the functional dissection of genome-wide association study (GWAS) results, the prediction of putative causal variants in haplotype blocks, the prediction of likely cell types of action, and the prediction of candidate target genes by systematic mining of comparative, epigenomic and regulatory annotations. Since first launching the website in 2011, we have greatly expanded HaploReg, increasing the number of chromatin state maps to 127 reference epigenomes from ENCODE 2012 and Roadmap Epigenomics, incorporating regulator binding data, expanding regulatory motif disruption annotations, and integrating expression quantitative trait locus (eQTL) variants and their tissue-specific target genes from GTEx, Geuvadis, and other recent studies. We present these updates as HaploReg v4, and illustrate a use case of HaploReg for attention deficit hyperactivity disorder (ADHD)-associated SNPs with putative brain regulatory mechanisms. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

A Method to Predict the Structure and Stability of RNA/RNA Complexes.

PubMed

Xu, Xiaojun; Chen, Shi-Jie

2016-01-01

RNA/RNA interactions are essential for genomic RNA dimerization and regulation of gene expression. Intermolecular loop-loop base pairing is a widespread and functionally important tertiary structure motif in RNA machinery. However, computational prediction of intermolecular loop-loop base pairing is challenged by the entropy and free energy calculation due to the conformational constraint and the intermolecular interactions. In this chapter, we describe a recently developed statistical mechanics-based method for the prediction of RNA/RNA complex structures and stabilities. The method is based on the virtual bond RNA folding model (Vfold). The main emphasis in the method is placed on the evaluation of the entropy and free energy for the loops, especially tertiary kissing loops. The method also uses recursive partition function calculations and two-step screening algorithm for large, complicated structures of RNA/RNA complexes. As case studies, we use the HIV-1 Mal dimer and the siRNA/HIV-1 mutant (T4) to illustrate the method.

An approach for reduction of false predictions in reverse engineering of gene regulatory networks.

PubMed

Khan, Abhinandan; Saha, Goutam; Pal, Rajat Kumar

2018-05-14

A gene regulatory network discloses the regulatory interactions amongst genes, at a particular condition of the human body. The accurate reconstruction of such networks from time-series genetic expression data using computational tools offers a stiff challenge for contemporary computer scientists. This is crucial to facilitate the understanding of the proper functioning of a living organism. Unfortunately, the computational methods produce many false predictions along with the correct predictions, which is unwanted. Investigations in the domain focus on the identification of as many correct regulations as possible in the reverse engineering of gene regulatory networks to make it more reliable and biologically relevant. One way to achieve this is to reduce the number of incorrect predictions in the reconstructed networks. In the present investigation, we have proposed a novel scheme to decrease the number of false predictions by suitably combining several metaheuristic techniques. We have implemented the same using a dataset ensemble approach (i.e. combining multiple datasets) also. We have employed the proposed methodology on real-world experimental datasets of the SOS DNA Repair network of Escherichia coli and the IMRA network of Saccharomyces cerevisiae. Subsequently, we have experimented upon somewhat larger, in silico networks, namely, DREAM3 and DREAM4 Challenge networks, and 15-gene and 20-gene networks extracted from the GeneNetWeaver database. To study the effect of multiple datasets on the quality of the inferred networks, we have used four datasets in each experiment. The obtained results are encouraging enough as the proposed methodology can reduce the number of false predictions significantly, without using any supplementary prior biological information for larger gene regulatory networks. It is also observed that if a small amount of prior biological information is incorporated here, the results improve further w.r.t. the prediction of true positives. Copyright © 2018 Elsevier Ltd. All rights reserved.

Using physics-based pose predictions and free energy perturbation calculations to predict binding poses and relative binding affinities for FXR ligands in the D3R Grand Challenge 2

NASA Astrophysics Data System (ADS)

Athanasiou, Christina; Vasilakaki, Sofia; Dellis, Dimitris; Cournia, Zoe

2018-01-01

Computer-aided drug design has become an integral part of drug discovery and development in the pharmaceutical and biotechnology industry, and is nowadays extensively used in the lead identification and lead optimization phases. The drug design data resource (D3R) organizes challenges against blinded experimental data to prospectively test computational methodologies as an opportunity for improved methods and algorithms to emerge. We participated in Grand Challenge 2 to predict the crystallographic poses of 36 Farnesoid X Receptor (FXR)-bound ligands and the relative binding affinities for two designated subsets of 18 and 15 FXR-bound ligands. Here, we present our methodology for pose and affinity predictions and its evaluation after the release of the experimental data. For predicting the crystallographic poses, we used docking and physics-based pose prediction methods guided by the binding poses of native ligands. For FXR ligands with known chemotypes in the PDB, we accurately predicted their binding modes, while for those with unknown chemotypes the predictions were more challenging. Our group ranked #1st (based on the median RMSD) out of 46 groups, which submitted complete entries for the binding pose prediction challenge. For the relative binding affinity prediction challenge, we performed free energy perturbation (FEP) calculations coupled with molecular dynamics (MD) simulations. FEP/MD calculations displayed a high success rate in identifying compounds with better or worse binding affinity than the reference (parent) compound. Our studies suggest that when ligands with chemical precedent are available in the literature, binding pose predictions using docking and physics-based methods are reliable; however, predictions are challenging for ligands with completely unknown chemotypes. We also show that FEP/MD calculations hold predictive value and can nowadays be used in a high throughput mode in a lead optimization project provided that crystal structures of sufficiently high quality are available.

Sex-specific lean body mass predictive equations are accurate in the obese paediatric population

PubMed Central

Jackson, Lanier B.; Henshaw, Melissa H.; Carter, Janet; Chowdhury, Shahryar M.

2015-01-01

Background The clinical assessment of lean body mass (LBM) is challenging in obese children. A sex-specific predictive equation for LBM derived from anthropometric data was recently validated in children. Aim The purpose of this study was to independently validate these predictive equations in the obese paediatric population. Subjects and methods Obese subjects aged 4–21 were analysed retrospectively. Predicted LBM (LBMp) was calculated using equations previously developed in children. Measured LBM (LBMm) was derived from dual-energy x-ray absorptiometry. Agreement was expressed as [(LBMm-LBMp)/LBMm] with 95% limits of agreement. Results Of 310 enrolled patients, 195 (63%) were females. The mean age was 11.8 ± 3.4 years and mean BMI Z-score was 2.3 ± 0.4. The average difference between LBMm and LBMp was −0.6% (−17.0%, 15.8%). Pearson’s correlation revealed a strong linear relationship between LBMm and LBMp (r=0.97, p<0.01). Conclusion This study validates the use of these clinically-derived sex-specific LBM predictive equations in the obese paediatric population. Future studies should use these equations to improve the ability to accurately classify LBM in obese children. PMID:26287383

omniClassifier: a Desktop Grid Computing System for Big Data Prediction Modeling

PubMed Central

Phan, John H.; Kothari, Sonal; Wang, May D.

2016-01-01

Robust prediction models are important for numerous science, engineering, and biomedical applications. However, best-practice procedures for optimizing prediction models can be computationally complex, especially when choosing models from among hundreds or thousands of parameter choices. Computational complexity has further increased with the growth of data in these fields, concurrent with the era of “Big Data”. Grid computing is a potential solution to the computational challenges of Big Data. Desktop grid computing, which uses idle CPU cycles of commodity desktop machines, coupled with commercial cloud computing resources can enable research labs to gain easier and more cost effective access to vast computing resources. We have developed omniClassifier, a multi-purpose prediction modeling application that provides researchers with a tool for conducting machine learning research within the guidelines of recommended best-practices. omniClassifier is implemented as a desktop grid computing system using the Berkeley Open Infrastructure for Network Computing (BOINC) middleware. In addition to describing implementation details, we use various gene expression datasets to demonstrate the potential scalability of omniClassifier for efficient and robust Big Data prediction modeling. A prototype of omniClassifier can be accessed at http://omniclassifier.bme.gatech.edu/. PMID:27532062

Temporal fluctuations after a quantum quench: Many-particle dephasing

NASA Astrophysics Data System (ADS)

Marquardt, Florian; Kiendl, Thomas

After a quantum quench, the expectation values of observables continue to fluctuate in time. In the thermodynamic limit, one expects such fluctuations to decrease to zero, in order for standard statistical physics to hold. However, it is a challenge to determine analytically how the fluctuations decay as a function of system size. So far, there have been analytical predictions for integrable models (which are, naturally, somewhat special), analytical bounds for arbitrary systems, and numerical results for moderate-size systems. We have discovered a dynamical regime where the decrease of fluctuations is driven by many-particle dephasing, instead of a redistribution of occupation numbers. On the basis of this insight, we are able to provide exact analytical expressions for a model with weak integrability breaking (transverse Ising chain with additional terms). These predictions explicitly show how fluctuations are exponentially suppressed with system size.

The Eighth Industrial Fluids Properties Simulation Challenge

PubMed Central

Schultz, Nathan E.; Ahmad, Riaz; Brennan, John K.; Frankel, Kevin A.; Moore, Jonathan D.; Moore, Joshua D.; Mountain, Raymond D.; Ross, Richard B.; Thommes, Matthias; Shen, Vincent K.; Siderius, Daniel W.; Smith, Kenneth D.

2016-01-01

The goal of the eighth industrial fluid properties simulation challenge was to test the ability of molecular simulation methods to predict the adsorption of organic adsorbates in activated carbon materials. In particular, the eighth challenge focused on the adsorption of perfluorohexane in the activated carbon BAM-109. Entrants were challenged to predict the adsorption in the carbon at 273 K and relative pressures of 0.1, 0.3, and 0.6. The predictions were judged by comparison to a benchmark set of experimentally determined values. Overall good agreement and consistency were found between the predictions of most entrants. PMID:27840542

A Meta-Analysis of Multiple Matched Copy Number and Transcriptomics Data Sets for Inferring Gene Regulatory Relationships

PubMed Central

Newton, Richard; Wernisch, Lorenz

2014-01-01

Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments. PMID:25148247

Systematic Proteomic Approach to Characterize the Impacts of ...

EPA Pesticide Factsheets

Chemical interactions have posed a big challenge in toxicity characterization and human health risk assessment of environmental mixtures. To characterize the impacts of chemical interactions on protein and cytotoxicity responses to environmental mixtures, we established a systems biology approach integrating proteomics, bioinformatics, statistics, and computational toxicology to measure expression or phosphorylation levels of 21 critical toxicity pathway regulators and 445 downstream proteins in human BEAS-28 cells treated with 4 concentrations of nickel, 2 concentrations each of cadmium and chromium, as well as 12 defined binary and 8 defined ternary mixtures of these metals in vitro. Multivariate statistical analysis and mathematical modeling of the metal-mediated proteomic response patterns showed a high correlation between changes in protein expression or phosphorylation and cellular toxic responses to both individual metals and metal mixtures. Of the identified correlated proteins, only a small set of proteins including HIF-1a is likely to be responsible for selective cytotoxic responses to different metals and metals mixtures. Furthermore, support vector machine learning was utilized to computationally predict protein responses to uncharacterized metal mixtures using experimentally generated protein response profiles corresponding to known metal mixtures. This study provides a novel proteomic approach for characterization and prediction of toxicities of

The Spike-and-Slab Lasso Generalized Linear Models for Prediction and Associated Genes Detection.

PubMed

Tang, Zaixiang; Shen, Yueping; Zhang, Xinyan; Yi, Nengjun

2017-01-01

Large-scale "omics" data have been increasingly used as an important resource for prognostic prediction of diseases and detection of associated genes. However, there are considerable challenges in analyzing high-dimensional molecular data, including the large number of potential molecular predictors, limited number of samples, and small effect of each predictor. We propose new Bayesian hierarchical generalized linear models, called spike-and-slab lasso GLMs, for prognostic prediction and detection of associated genes using large-scale molecular data. The proposed model employs a spike-and-slab mixture double-exponential prior for coefficients that can induce weak shrinkage on large coefficients, and strong shrinkage on irrelevant coefficients. We have developed a fast and stable algorithm to fit large-scale hierarchal GLMs by incorporating expectation-maximization (EM) steps into the fast cyclic coordinate descent algorithm. The proposed approach integrates nice features of two popular methods, i.e., penalized lasso and Bayesian spike-and-slab variable selection. The performance of the proposed method is assessed via extensive simulation studies. The results show that the proposed approach can provide not only more accurate estimates of the parameters, but also better prediction. We demonstrate the proposed procedure on two cancer data sets: a well-known breast cancer data set consisting of 295 tumors, and expression data of 4919 genes; and the ovarian cancer data set from TCGA with 362 tumors, and expression data of 5336 genes. Our analyses show that the proposed procedure can generate powerful models for predicting outcomes and detecting associated genes. The methods have been implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/). Copyright © 2017 by the Genetics Society of America.

The impact of rare variation on gene expression across tissues.

PubMed

Li, Xin; Kim, Yungil; Tsang, Emily K; Davis, Joe R; Damani, Farhan N; Chiang, Colby; Hess, Gaelen T; Zappala, Zachary; Strober, Benjamin J; Scott, Alexandra J; Li, Amy; Ganna, Andrea; Bassik, Michael C; Merker, Jason D; Hall, Ira M; Battle, Alexis; Montgomery, Stephen B

2017-10-11

Rare genetic variants are abundant in humans and are expected to contribute to individual disease risk. While genetic association studies have successfully identified common genetic variants associated with susceptibility, these studies are not practical for identifying rare variants. Efforts to distinguish pathogenic variants from benign rare variants have leveraged the genetic code to identify deleterious protein-coding alleles, but no analogous code exists for non-coding variants. Therefore, ascertaining which rare variants have phenotypic effects remains a major challenge. Rare non-coding variants have been associated with extreme gene expression in studies using single tissues, but their effects across tissues are unknown. Here we identify gene expression outliers, or individuals showing extreme expression levels for a particular gene, across 44 human tissues by using combined analyses of whole genomes and multi-tissue RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project v6p release. We find that 58% of underexpression and 28% of overexpression outliers have nearby conserved rare variants compared to 8% of non-outliers. Additionally, we developed RIVER (RNA-informed variant effect on regulation), a Bayesian statistical model that incorporates expression data to predict a regulatory effect for rare variants with higher accuracy than models using genomic annotations alone. Overall, we demonstrate that rare variants contribute to large gene expression changes across tissues and provide an integrative method for interpretation of rare variants in individual genomes.

Prognostic stratification of gliomatosis cerebri by IDH1 R132H and INA expression.

PubMed

Desestret, Virginie; Ciccarino, Pietro; Ducray, François; Crinière, Emmanuelle; Boisselier, Blandine; Labussière, Marianne; Polivka, Marc; Idbaih, Ahmed; Kaloshi, Gentian; von Deimling, Andreas; Hoang-Xuan, Khe; Delattre, Jean-Yves; Mokhtari, Karima; Sanson, Marc

2011-11-01

Gliomatosis cerebri (GC) constitutes a heterogeneous group of conditions involving diffuse neoplastic glial cell infiltration of the brain. Management is difficult and an obvious challenge is to identify prognostic factors. Alpha-internexin (INA) expression, which is closely related to the 1p19q codeletion, is a strong prognostic marker in oligodendroglial tumors. Similarly, the R132H isocitrate dehydrogenase 1 IDH1 mutation, which can now be detected by use of a specific antibody, predicts better outcome in gliomas. In a retrospective series of 40 GC treated with up-front chemotherapy, we analyzed IDH1(R132H) mutant protein and INA immunohistochemical expression and correlated it with outcome; 17/40 GC expressed IDH1(R132H) and 10/40 GC expressed INA. IDH1(R132H) staining was strongly related to progression-free survival (42.3 vs. 15.5 months for positive IDH1(R132H) vs. negative tumors; P < 0.0001) and overall survival (73.9 vs. 23.6 months; P < 0.0001). This effect was independent of grade, histologic subtype, and INA expression (P < 0.001). Combined expression of IDH1(R132H) and INA was strongly associated with response to chemotherapy (100% vs. 36%; P = 0.003). These data strongly suggest that INA and IDH1(R132H) mutant protein immunohistochemical analysis is of a great prognostic value in biopsied GC.

Insulin-like growth factor II mRNA-binding protein 3 (IMP3) is a marker that predicts presence of invasion in papillary biliary tumors.

PubMed

Sasaki, Motoko; Sato, Yasunori

2017-04-01

Biliary tumors showing intraductal papillary growth (Pap-BTs) include intraductal papillary neoplasm of the bile duct (IPNB) and papillary cholangiocarcinoma (CC). A differential diagnosis between IPNB and papillary CC currently remains challenging. The aim of the present study is to identify histological features and immunohistochemical markers of malignant potential such as tumor invasion in Pap-BTs. Subjects comprised 37 patients with Pap-BT (intrahepatic and perihilar [proximal], 27: 17 noninvasive and 10 invasive; distal, 10: all invasive). We examined histological features and the expression of p53, enhancer of zeste homolog 2, insulin-like growth factor II mRNA-binding protein 3 (IMP3), and DNA methyltransferase-1 in the intraductal area in Pap-BTs. Noninvasive Pap-BT was characterized by the presence of a low-grade dysplastic area, edematous stroma, and the absence of necrosis. The expression of p53, enhancer of zeste homolog 2, IMP3, and DNA methyltransferase-1 was significantly weaker in noninvasive Pap-BTs than in invasive Pap-BTs (P<.01). Diffuse cytoplasmic IMP3 expression was absent in noninvasive Pap-BTs. IMP3 showed the greatest specificity to predict a presence of invasion. A heatmap demonstrated that proximal noninvasive Pap-BTs and distal Pap-BTs may be completely different. In bile duct biopsies, the expression of IMP3 was the most precise predictor of invasion in Pap-BTs. In conclusion, Pap-BTs may be separated into 3 subgroups: (1) proximal noninvasive Pap-BT, corresponding to IPNB; (2) distal invasive Pap-BT, corresponding to papillary CC; and (3) the remaining Pap-BT including IPNB with associated adenocarcinomas, based on histological and immunohistochemical features. IMP3 may be a useful marker for predicting invasion in Pap-BT. Copyright © 2017 Elsevier Inc. All rights reserved.

Synthetic and systems biology for microbial production of commodity chemicals.

PubMed

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J; Keasling, Jay D; Martín, Héctor García

2016-01-01

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.

Synthetic and systems biology for microbial production of commodity chemicals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J.

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges startmore » at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.« less

Synthetic and systems biology for microbial production of commodity chemicals

DOE PAGES

Chubukov, Victor; Mukhopadhyay, Aindrila; Petzold, Christopher J.; ...

2016-04-07

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges startmore » at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.« less

Characterization of microRNAs in Mud Crab Scylla paramamosain under Vibrio parahaemolyticus Infection

PubMed Central

Li, Chuanbiao; Zhang, Zhao; Zhou, Lizhen; Wang, Shijia; Wang, Shuqi; Zhang, Yueling; Wen, Xiaobo

2013-01-01

Background Infection of bacterial Vibrio parahaemolyticus is common in mud crab farms. However, the mechanisms of the crab’s response to pathogenic V. parahaemolyticus infection are not fully understood. MicroRNAs (miRNAs) are a class of small noncoding RNAs that function as regulators of gene expression and play essential roles in various biological processes. To understand the underlying mechanisms of the molecular immune response of the crab to the pathogens, high-throughput Illumina/Solexa deep sequencing technology was used to investigate the expression profiles of miRNAs in S . paramamosain under V. parahaemolyticus infection. Methodology/Principal Findings Two mixed RNA pools of 7 tissues (intestine, heart, liver, gill, brain, muscle and blood) were obtained from V. parahaemolyticus infected crabs and the control groups, respectively. By aligning the sequencing data with known miRNAs, we characterized 421 miRNA families, and 133 conserved miRNA families in mud crab S . paramamosain were either identical or very similar to existing miRNAs in miRBase. Stem-loop qRT-PCRs were used to scan the expression levels of four randomly chosen differentially expressed miRNAs and tissue distribution. Eight novel potential miRNAs were confirmed by qRT-PCR analysis and the precursors of these novel miRNAs were verified by PCR amplification, cloning and sequencing in S . paramamosain . 161 miRNAs (106 of which up-regulated and 55 down-regulated) were significantly differentially expressed during the challenge and the potential targets of these differentially expressed miRNAs were predicted. Furthermore, we demonstrated evolutionary conservation of mud crab miRNAs in the animal evolution process. Conclusions/Significance In this study, a large number of miRNAs were identified in S . paramamosain when challenged with V. parahaemolyticus, some of which were differentially expressed. The results show that miRNAs might play some important roles in regulating gene expression in mud crab under V. parahaemolyticus infection, providing a basis for further investigation of miRNA-modulating networks in innate immunity of mud crab. PMID:24023678

Molecular Characteristics of High-Dose Melphalan Associated Oral Mucositis in Patients with Multiple Myeloma: A Gene Expression Study on Human Mucosa

PubMed Central

Bødker, Julie Støve; Christensen, Heidi Søgaard; Johansen, Preben; Nielsen, Søren; Christiansen, Ilse; Bergmann, Olav Jonas; Bøgsted, Martin; Dybkær, Karen; Vyberg, Mogens; Johnsen, Hans Erik

2017-01-01

Background Toxicity of the oral and gastrointestinal mucosa induced by high-dose melphalan is a clinical challenge with no documented prophylactic interventions or predictive tests. The aim of this study was to describe molecular changes in human oral mucosa and to identify biomarkers correlated with the grade of clinical mucositis. Methods and Findings Ten patients with multiple myeloma (MM) were included. For each patient, we acquired three buccal biopsies, one before, one at 2 days, and one at 20 days after high-dose melphalan administration. We also acquired buccal biopsies from 10 healthy individuals that served as controls. We analyzed the biopsies for global gene expression and performed an immunohistochemical analysis to determine HLA-DRB5 expression. We evaluated associations between clinical mucositis and gene expression profiles. Compared to gene expression levels before and 20 days after therapy, at two days after melphalan treatment, we found gene regulation in the p53 and TNF pathways (MDM2, INPPD5, TIGAR), which favored anti-apoptotic defense, and upregulation of immunoregulatory genes (TREM2, LAMP3) in mucosal dendritic cells. This upregulation was independent of clinical mucositis. HLA-DRB1 and HLA-DRB5 (surface receptors on dendritic cells) were expressed at low levels in all patients with MM, in the subgroup of patients with ulcerative mucositis (UM), and in controls; in contrast, the subgroup with low-grade mucositis (NM) displayed 5–6 fold increases in HLA-DRB1 and HLA-DRB5 expression in the first two biopsies, independent of melphalan treatment. Moreover, different splice variants of HLA-DRB1 were expressed in the UM and NM subgroups. Conclusions Our results revealed that, among patients with MM, immunoregulatory genes and genes involved in defense against apoptosis were affected immediately after melphalan administration, independent of the presence of clinical mucositis. Furthermore, our results suggested that the expression levels of HLA-DRB1 and HLA-DRB5 may serve as potential predictive biomarkers for mucositis severity. PMID:28052121

Novel Modeling of Combinatorial miRNA Targeting Identifies SNP with Potential Role in Bone Density

PubMed Central

Coronnello, Claudia; Hartmaier, Ryan; Arora, Arshi; Huleihel, Luai; Pandit, Kusum V.; Bais, Abha S.; Butterworth, Michael; Kaminski, Naftali; Stormo, Gary D.; Oesterreich, Steffi; Benos, Panayiotis V.

2012-01-01

MicroRNAs (miRNAs) are post-transcriptional regulators that bind to their target mRNAs through base complementarity. Predicting miRNA targets is a challenging task and various studies showed that existing algorithms suffer from high number of false predictions and low to moderate overlap in their predictions. Until recently, very few algorithms considered the dynamic nature of the interactions, including the effect of less specific interactions, the miRNA expression level, and the effect of combinatorial miRNA binding. Addressing these issues can result in a more accurate miRNA:mRNA modeling with many applications, including efficient miRNA-related SNP evaluation. We present a novel thermodynamic model based on the Fermi-Dirac equation that incorporates miRNA expression in the prediction of target occupancy and we show that it improves the performance of two popular single miRNA target finders. Modeling combinatorial miRNA targeting is a natural extension of this model. Two other algorithms show improved prediction efficiency when combinatorial binding models were considered. ComiR (Combinatorial miRNA targeting), a novel algorithm we developed, incorporates the improved predictions of the four target finders into a single probabilistic score using ensemble learning. Combining target scores of multiple miRNAs using ComiR improves predictions over the naïve method for target combination. ComiR scoring scheme can be used for identification of SNPs affecting miRNA binding. As proof of principle, ComiR identified rs17737058 as disruptive to the miR-488-5p:NCOA1 interaction, which we confirmed in vitro. We also found rs17737058 to be significantly associated with decreased bone mineral density (BMD) in two independent cohorts indicating that the miR-488-5p/NCOA1 regulatory axis is likely critical in maintaining BMD in women. With increasing availability of comprehensive high-throughput datasets from patients ComiR is expected to become an essential tool for miRNA-related studies. PMID:23284279

Real time expression of ACC oxidase and PR-protein genes mediated by Methylobacterium spp. in tomato plants challenged with Xanthomonas campestris pv. vesicatoria.

PubMed

Yim, W J; Kim, K Y; Lee, Y W; Sundaram, S P; Lee, Y; Sa, T M

2014-07-15

Biotic stress like pathogenic infection increases ethylene biosynthesis in plants and ethylene inhibitors are known to alleviate the severity of plant disease incidence. This study aimed to reduce the bacterial spot disease incidence in tomato plants caused by Xanthomonas campestris pv. vesicatoria (XCV) by modulating stress ethylene with 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity of Methylobacterium strains. Under greenhouse condition, Methylobacterium strains inoculated and pathogen challenged tomato plants had low ethylene emission compared to pathogen infected ones. ACC accumulation and ACC oxidase (ACO) activity with ACO related gene expression increased in XCV infected tomato plants over Methylobacterium strains inoculated plants. Among the Methylobacterium spp., CBMB12 resulted lowest ACO related gene expression (1.46 Normalized Fold Expression), whereas CBMB20 had high gene expression (3.42 Normalized Fold Expression) in pathogen challenged tomato. But a significant increase in ACO gene expression (7.09 Normalized Fold Expression) was observed in the bacterial pathogen infected plants. In contrast, Methylobacterium strains enhanced β-1,3-glucanase and phenylalanine ammonia-lyase (PAL) enzyme activities in pathogen challenged tomato plants. The respective increase in β-1,3-glucanase related gene expressions due to CBMB12, CBMB15, and CBMB20 strains were 66.3, 25.5 and 10.4% higher over pathogen infected plants. Similarly, PAL gene expression was high with 0.67 and 0.30 Normalized Fold Expression, in pathogen challenged tomato plants inoculated with CBMB12 and CBMB15 strains. The results suggest that ethylene is a crucial factor in bacterial spot disease incidence and that methylobacteria with ACC deaminase activity can reduce the disease severity with ultimate pathogenesis-related protein increase in tomato. Copyright © 2014 Elsevier GmbH. All rights reserved.

Performance of multiple docking and refinement methods in the pose prediction D3R prospective Grand Challenge 2016

NASA Astrophysics Data System (ADS)

Fradera, Xavier; Verras, Andreas; Hu, Yuan; Wang, Deping; Wang, Hongwu; Fells, James I.; Armacost, Kira A.; Crespo, Alejandro; Sherborne, Brad; Wang, Huijun; Peng, Zhengwei; Gao, Ying-Duo

2018-01-01

We describe the performance of multiple pose prediction methods for the D3R 2016 Grand Challenge. The pose prediction challenge includes 36 ligands, which represent 4 chemotypes and some miscellaneous structures against the FXR ligand binding domain. In this study we use a mix of fully automated methods as well as human-guided methods with considerations of both the challenge data and publicly available data. The methods include ensemble docking, colony entropy pose prediction, target selection by molecular similarity, molecular dynamics guided pose refinement, and pose selection by visual inspection. We evaluated the success of our predictions by method, chemotype, and relevance of publicly available data. For the overall data set, ensemble docking, visual inspection, and molecular dynamics guided pose prediction performed the best with overall mean RMSDs of 2.4, 2.2, and 2.2 Å respectively. For several individual challenge molecules, the best performing method is evaluated in light of that particular ligand. We also describe the protein, ligand, and public information data preparations that are typical of our binding mode prediction workflow.

Gaining insights from social media language: Methodologies and challenges.

PubMed

Kern, Margaret L; Park, Gregory; Eichstaedt, Johannes C; Schwartz, H Andrew; Sap, Maarten; Smith, Laura K; Ungar, Lyle H

2016-12-01

Language data available through social media provide opportunities to study people at an unprecedented scale. However, little guidance is available to psychologists who want to enter this area of research. Drawing on tools and techniques developed in natural language processing, we first introduce psychologists to social media language research, identifying descriptive and predictive analyses that language data allow. Second, we describe how raw language data can be accessed and quantified for inclusion in subsequent analyses, exploring personality as expressed on Facebook to illustrate. Third, we highlight challenges and issues to be considered, including accessing and processing the data, interpreting effects, and ethical issues. Social media has become a valuable part of social life, and there is much we can learn by bringing together the tools of computer science with the theories and insights of psychology. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Large-scale investigation of the parameters in response to Eimeria maxima challenge in broilers.

PubMed

Hamzic, E; Bed'Hom, B; Juin, H; Hawken, R; Abrahamsen, M S; Elsen, J M; Servin, B; Pinard-van der Laan, M H; Demeure, O

2015-04-01

Coccidiosis, a parasitic disease of the intestinal tract caused by members of the genera Eimeria and Isospora, is one of the most common and costly diseases in chicken. The aims of this study were to assess the effect of the challenge and level of variability of measured parameters in chickens during the challenge with Eimeria maxima. Furthermore, this study aimed to investigate which parameters are the most relevant indicators of the health status. Finally, the study also aimed to estimate accuracy of prediction for traits that cannot be measured on large scale (such as intestinal lesion score and fecal oocyst count) using parameters that can easily be measured on all animals. The study was performed in 2 parts: a pilot challenge on 240 animals followed by a large-scale challenge on 2,024 animals. In both experiments, animals were challenged with 50,000 Eimeria maxima oocysts at 16 d of age. In the pilot challenge, all animals were measured for BW gain, plasma coloration, hematocrit, and rectal temperature and, in addition, a subset of 48 animals was measured for oocyst count and the intestinal lesion score. All animals from the second challenge were measured for BW gain, plasma coloration, and hematocrit whereas a subset of 184 animals was measured for intestinal lesion score, fecal oocyst count, blood parameters, and plasma protein content and composition. Most of the parameters measured were significantly affected by the challenge. Lesion scores for duodenum and jejunum (P < 0.001), oocyst count (P < 0.05), plasma coloration for the optical density values between 450 and 490 nm (P < 0.001), albumin (P < 0.001), α1-globulin (P < 0.01), α2-globulin (P < 0.001), α3-globulin (P < 0.01), and β2-globulin (P < 0.001) were the most strongly affected parameters and expressed the greatest levels of variation. Plasma protein profiles proved to be a new, reliable parameter for measuring response to Eimeria maxima. Prediction of intestinal lesion score and fecal oocyst count using the other parameters measured was not very precise (R2 < 0.7). The study was successfully performed in real raising conditions on a large scale. Finally, we observed a high variability in response to the challenge, suggesting that broilers' response to Eimeria maxima has a strong genetic determinism, which may be improved by genetic selection.

Divergence of Iron Metabolism in Wild Malaysian Yeast

PubMed Central

Lee, Hana N.; Mostovoy, Yulia; Hsu, Tiffany Y.; Chang, Amanda H.; Brem, Rachel B.

2013-01-01

Comparative genomic studies have reported widespread variation in levels of gene expression within and between species. Using these data to infer organism-level trait divergence has proven to be a key challenge in the field. We have used a wild Malaysian population of S. cerevisiae as a test bed in the search to predict and validate trait differences based on observations of regulatory variation. Malaysian yeast, when cultured in standard medium, activated regulatory programs that protect cells from the toxic effects of high iron. Malaysian yeast also showed a hyperactive regulatory response during culture in the presence of excess iron and had a unique growth defect in conditions of high iron. Molecular validation experiments pinpointed the iron metabolism factors AFT1, CCC1, and YAP5 as contributors to these molecular and cellular phenotypes; in genome-scale sequence analyses, a suite of iron toxicity response genes showed evidence for rapid protein evolution in Malaysian yeast. Our findings support a model in which iron metabolism has diverged in Malaysian yeast as a consequence of a change in selective pressure, with Malaysian alleles shifting the dynamic range of iron response to low-iron concentrations and weakening resistance to extreme iron toxicity. By dissecting the iron scarcity specialist behavior of Malaysian yeast, our work highlights the power of expression divergence as a signpost for biologically and evolutionarily relevant variation at the organismal level. Interpreting the phenotypic relevance of gene expression variation is one of the primary challenges of modern genomics. PMID:24142925

Divergence of iron metabolism in wild Malaysian yeast.

PubMed

Lee, Hana N; Mostovoy, Yulia; Hsu, Tiffany Y; Chang, Amanda H; Brem, Rachel B

2013-12-09

Comparative genomic studies have reported widespread variation in levels of gene expression within and between species. Using these data to infer organism-level trait divergence has proven to be a key challenge in the field. We have used a wild Malaysian population of S. cerevisiae as a test bed in the search to predict and validate trait differences based on observations of regulatory variation. Malaysian yeast, when cultured in standard medium, activated regulatory programs that protect cells from the toxic effects of high iron. Malaysian yeast also showed a hyperactive regulatory response during culture in the presence of excess iron and had a unique growth defect in conditions of high iron. Molecular validation experiments pinpointed the iron metabolism factors AFT1, CCC1, and YAP5 as contributors to these molecular and cellular phenotypes; in genome-scale sequence analyses, a suite of iron toxicity response genes showed evidence for rapid protein evolution in Malaysian yeast. Our findings support a model in which iron metabolism has diverged in Malaysian yeast as a consequence of a change in selective pressure, with Malaysian alleles shifting the dynamic range of iron response to low-iron concentrations and weakening resistance to extreme iron toxicity. By dissecting the iron scarcity specialist behavior of Malaysian yeast, our work highlights the power of expression divergence as a signpost for biologically and evolutionarily relevant variation at the organismal level. Interpreting the phenotypic relevance of gene expression variation is one of the primary challenges of modern genomics.

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

PubMed

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

Assessing Anger Expression: Construct Validity of Three Emotion Expression-Related Measures

PubMed Central

Jasinski, Matthew J.; Lumley, Mark A.; Latsch, Deborah V.; Schuster, Erik; Kinner, Ellen; Burns, John W.

2016-01-01

Self-report measures of emotional expression are common, but their validity to predict objective emotional expression, particularly of anger, is unclear. We tested the validity of the Anger Expression Inventory (AEI; Spielberger et al., 1985)), Emotional Approach Coping Scale (EAC; Stanton, Kirk, Cameron & Danoff-Burg, 2000), and Toronto Alexithymia Scale-20 (TAS-20; Bagby, Taylor, & Parker, 1994) to predict objective anger expression in 95 adults with chronic back pain. Participants attempted to solve a difficult computer maze by following the directions of a confederate who treated them rudely and unjustly. Participants then expressed their feelings for 4 minutes. Blinded raters coded the videos for anger expression, and a software program analyzed expression transcripts for anger-related words. Analyses related each questionnaire to anger expression. The AEI anger-out scale predicted greater anger expression, as expected, but AEI anger-in did not. The EAC emotional processing scale predicted less anger expression, but the EAC emotional expression scale was unrelated to anger expression. Finally, the TAS-20 predicted greater anger expression. Findings support the validity of the AEI anger-out scale but raise questions about the other measures. The assessment of emotional expression by self-report is complex and perhaps confounded by general emotional experience, the specificity or generality of the emotion(s) assessed, and self-awareness limitations. Performance-based or clinician-rated measures of emotion expression are needed. PMID:27248355

Dynamics of Immune System Gene Expression upon Bacterial Challenge and Wounding in a Social Insect (Bombus terrestris)

PubMed Central

Erler, Silvio; Popp, Mario; Lattorff, H. Michael G.

2011-01-01

The innate immune system which helps individuals to combat pathogens comprises a set of genes representing four immune system pathways (Toll, Imd, JNK and JAK/STAT). There is a lack of immune genes in social insects (e.g. honeybees) when compared to Diptera. Potentially, this might be compensated by an advanced system of social immunity (synergistic action of several individuals). The bumble bee, Bombus terrestris, is a primitively eusocial species with an annual life cycle and colonies headed by a single queen. We used this key pollinator to study the temporal dynamics of immune system gene expression in response to wounding and bacterial challenge. Antimicrobial peptides (AMP) (abaecin, defensin 1, hymenoptaecin) were strongly up-regulated by wounding and bacterial challenge, the latter showing a higher impact on the gene expression level. Sterile wounding down-regulated TEP A, an effector gene of the JAK/STAT pathway, and bacterial infection influenced genes of the Imd (relish) and JNK pathway (basket). Relish was up-regulated within the first hour after bacterial challenge, but decreased strongly afterwards. AMP expression following wounding and bacterial challenge correlates with the expression pattern of relish whereas correlated expression with dorsal was absent. Although expression of AMPs was high, continuous bacterial growth was observed throughout the experiment. Here we demonstrate for the first time the temporal dynamics of immune system gene expression in a social insect. Wounding and bacterial challenge affected the innate immune system significantly. Induction of AMP expression due to wounding might comprise a pre-adaptation to accompanying bacterial infections. Compared with solitary species this social insect exhibits reduced immune system efficiency, as bacterial growth could not be inhibited. A negative feedback loop regulating the Imd-pathway is suggested. AMPs, the end product of the Imd-pathway, inhibited the up-regulation of the transcription factor relish, which is necessary for effector gene expression. PMID:21479237

Immunohistochemical expression of p16 in lipoblastomas.

PubMed

Cappellesso, Rocco; d'Amore, Emanuele S G; Dall'Igna, Patrizia; Guzzardo, Vincenza; Vassarotto, Elisa; Rugge, Massimo; Alaggio, Rita

2016-01-01

Lipoblastoma (LB) is a rare benign adipocytic tumor of childhood occasionally showing histological similarities to myxoid liposarcoma (ML) or well-differentiated liposarcoma (WDL). p16 immunohistochemistry has proved to be useful in distinguishing various types of liposarcomas, in particular WDL from lipoma, with higher sensitivity and specificity than MDM2 and CDK4 immunohistochemistry. In this study, we reported the histologic features of a series of 30 LB with emphasis on the potential diagnostic pitfalls and investigated the immunohistochemical expression of p16. Moreover, p16 immunostaining was performed in 16 liposarcomas (11 WDL and 5 ML), 16 lipomas, and 16 cases of liponecrosis in order to evaluate its usefulness in the differential diagnosis of challenging lesions occurring in older children. Overall, p16 immunostaining was positive in 3 LBs and in 12 out of 16 liposarcomas (10 WDL and 2 ML), with a sensitivity of 75%, a specificity of 90%, a positive predictive value of 80%, and a negative predictive value of 87%. All lipomas were p16 negative, whereas 5 liponecroses were positive. Accounting altogether the benign lesions versus liposarcomas, p16 showed a sensitivity of 75%, a specificity of 87%, a positive predictive value of 60%, and a negative predictive value of 93%. Our data suggest that a negative p16 immunostaining may be helpful in excluding a liposarcoma when occurring in unusual clinical contexts, such as in adolescence or late recurrence. However, such finding should be interpreted with caution since also some liposarcomas lack p16 and occasional LBs are positive. Copyright © 2015 Elsevier Inc. All rights reserved.

Pediatric acute lymphoblastic leukemia.

PubMed

Carroll, William L; Bhojwani, Deepa; Min, Dong-Joon; Raetz, Elizabeth; Relling, Mary; Davies, Stella; Downing, James R; Willman, Cheryl L; Reed, John C

2003-01-01

The outcome for children with acute lymphoblastic leukemia (ALL) has improved dramatically with current therapy resulting in an event free survival exceeding 75% for most patients. However significant challenges remain including developing better methods to predict which patients can be cured with less toxic treatment and which ones will benefit from augmented therapy. In addition, 25% of patients fail therapy and novel treatments that are focused on undermining specifically the leukemic process are needed urgently. In Section I, Dr. Carroll reviews current approaches to risk classification and proposes a system that incorporates well-established clinical parameters, genetic lesions of the blast as well as early response parameters. He then provides an overview of emerging technologies in genomics and proteomics and how they might lead to more rational, biologically based classification systems. In Section II, Drs. Mary Relling and Stella Davies describe emerging findings that relate to host features that influence outcome, the role of inherited germline variation. They highlight technical breakthroughs in assessing germline differences among patients. Polymorphisms of drug metabolizing genes have been shown to influence toxicity and the best example is the gene thiopurine methyltransferase (TPMT) a key enzyme in the metabolism of 6-mercaptopurine. Polymorphisms are associated with decreased activity that is also associated with increased toxicity. The role of polymorphisms in other genes whose products play an important role in drug metabolism as well as cytokine genes are discussed. In Sections III and IV, Drs. James Downing and Cheryl Willman review their findings using gene expression profiling to classify ALL. Both authors outline challenges in applying this methodology to analysis of clinical samples. Dr. Willman describes her laboratory's examination of infant leukemia and precursor B-ALL where unsupervised approaches have led to the identification of inherent biologic groups not predicted by conventional morphologic, immunophenotypic and cytogenetic variables. Dr. Downing describes his results from a pediatric ALL expression database using over 327 diagnostic samples, with 80% of the dataset consisting of samples from patients treated on a single institutional protocol. Seven distinct leukemia subtypes were identified representing known leukemia subtypes including: BCR-ABL, E2A-PBX1, TEL-AML1, rearrangements in the MLL gene, hyperdiploid karyotype (i.e., > 50 chromosomes), and T-ALL as well as a new leukemia subtype. A subset of genes have been identified whose expression appears to be predictive of outcome but independent verification is needed before this type of analysis can be integrated into treatment assignment. Chemotherapeutic agents kill cancer cells by activating apoptosis, or programmed cell death. In Section V, Dr. John Reed describes major apoptotic pathways and the specific role of key proteins in this response. The expression level of some of these proteins, such as BCL2, BAX, and caspase 3, has been shown to be predictive of ultimate outcome in hematopoietic tumors. New therapeutic approaches that modulate the apoptotic pathway are now available and Dr. Reed highlights those that may be applicable to the treatment of childhood ALL.

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.

PubMed

Bannan, Caitlin C; Burley, Kalistyn H; Chiu, Michael; Shirts, Michael R; Gilson, Michael K; Mobley, David L

2016-11-01

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty-how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided.

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge

PubMed Central

Bannan, Caitlin C.; Burley, Kalistyn H.; Chiu, Michael; Shirts, Michael R.; Gilson, Michael K.; Mobley, David L.

2016-01-01

In the recent SAMPL5 challenge, participants submitted predictions for cyclohexane/water distribution coefficients for a set of 53 small molecules. Distribution coefficients (log D) replace the hydration free energies that were a central part of the past five SAMPL challenges. A wide variety of computational methods were represented by the 76 submissions from 18 participating groups. Here, we analyze submissions by a variety of error metrics and provide details for a number of reference calculations we performed. As in the SAMPL4 challenge, we assessed the ability of participants to evaluate not just their statistical uncertainty, but their model uncertainty – how well they can predict the magnitude of their model or force field error for specific predictions. Unfortunately, this remains an area where prediction and analysis need improvement. In SAMPL4 the top performing submissions achieved a root-mean-squared error (RMSE) around 1.5 kcal/mol. If we anticipate accuracy in log D predictions to be similar to the hydration free energy predictions in SAMPL4, the expected error here would be around 1.54 log units. Only a few submissions had an RMSE below 2.5 log units in their predicted log D values. However, distribution coefficients introduced complexities not present in past SAMPL challenges, including tautomer enumeration, that are likely to be important in predicting biomolecular properties of interest to drug discovery, therefore some decrease in accuracy would be expected. Overall, the SAMPL5 distribution coefficient challenge provided great insight into the importance of modeling a variety of physical effects. We believe these types of measurements will be a promising source of data for future blind challenges, especially in view of the relatively straightforward nature of the experiments and the level of insight provided. PMID:27677750

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

Inducible Defenses Stay Up Late: Temporal Patterns of Immune Gene Expression in Tenebrio molitor

PubMed Central

Johnston, Paul R; Makarova, Olga; Rolff, Jens

2014-01-01

The course of microbial infection in insects is shaped by a two-stage process of immune defense. Constitutive defenses, such as engulfment and melanization, act immediately and are followed by inducible defenses, archetypically the production of antimicrobial peptides, which eliminate or suppress the remaining microbes. By applying RNAseq across a 7-day time course, we sought to characterize the long-lasting immune response to bacterial challenge in the mealworm beetle Tenebrio molitor, a model for the biochemistry of insect immunity and persistent bacterial infection. By annotating a hybrid de novo assembly of RNAseq data, we were able to identify putative orthologs for the majority of components of the conserved insect immune system. Compared with Tribolium castaneum, the most closely related species with a reference genome sequence and a manually curated immune system annotation, the T. molitor immune gene count was lower, with lineage-specific expansions of genes encoding serine proteases and their countervailing inhibitors accounting for the majority of the deficit. Quantitative mapping of RNAseq reads to the reference assembly showed that expression of genes with predicted functions in cellular immunity, wound healing, melanization, and the production of reactive oxygen species was transiently induced immediately after immune challenge. In contrast, expression of genes encoding antimicrobial peptides or components of the Toll signaling pathway and iron sequestration response remained elevated for at least 7 days. Numerous genes involved in metabolism and nutrient storage were repressed, indicating a possible cost of immune induction. Strikingly, the expression of almost all antibacterial peptides followed the same pattern of long-lasting induction, regardless of their spectra of activity, signaling possible interactive roles in vivo. PMID:24318927

Inducible defenses stay up late: temporal patterns of immune gene expression in Tenebrio molitor.

PubMed

Johnston, Paul R; Makarova, Olga; Rolff, Jens

2013-12-06

The course of microbial infection in insects is shaped by a two-stage process of immune defense. Constitutive defenses, such as engulfment and melanization, act immediately and are followed by inducible defenses, archetypically the production of antimicrobial peptides, which eliminate or suppress the remaining microbes. By applying RNAseq across a 7-day time course, we sought to characterize the long-lasting immune response to bacterial challenge in the mealworm beetle Tenebrio molitor, a model for the biochemistry of insect immunity and persistent bacterial infection. By annotating a hybrid de novo assembly of RNAseq data, we were able to identify putative orthologs for the majority of components of the conserved insect immune system. Compared with Tribolium castaneum, the most closely related species with a reference genome sequence and a manually curated immune system annotation, the T. molitor immune gene count was lower, with lineage-specific expansions of genes encoding serine proteases and their countervailing inhibitors accounting for the majority of the deficit. Quantitative mapping of RNAseq reads to the reference assembly showed that expression of genes with predicted functions in cellular immunity, wound healing, melanization, and the production of reactive oxygen species was transiently induced immediately after immune challenge. In contrast, expression of genes encoding antimicrobial peptides or components of the Toll signaling pathway and iron sequestration response remained elevated for at least 7 days. Numerous genes involved in metabolism and nutrient storage were repressed, indicating a possible cost of immune induction. Strikingly, the expression of almost all antibacterial peptides followed the same pattern of long-lasting induction, regardless of their spectra of activity, signaling possible interactive roles in vivo. Copyright © 2014 Johnston et al.

"Was it something I said?" "No, it was something you posted!" A study of the spiral of silence theory in social media contexts.

PubMed

Gearhart, Sherice; Zhang, Weiwu

2015-04-01

New media technologies make it necessary for scholars to reassess mass communication theories developed among legacy media. One such theory is the spiral of silence theory originally proposed by Noelle-Neumann in the 1970s. Increasing diversity of media content, selectivity, social networking site (SNS) interactivity, and the potential for anonymity have posed various challenges to its theoretical assumptions. While application of the spiral of silence in SNS contexts has been theorized, its empirical testing is scarce. To fill this void, the Pew 2012 Search, Social Networks, and Politics survey is used to test the theory. Results reveal that encountering agreeable political content predicts speaking out, while encountering disagreeable postings stifles opinion expression, supporting the spiral of silence theory in the SNS environment. However, certain uses of SNSs and psychological factors demonstrate a liberating effect on opinion expression.

Oleamide derivatives are prototypical anti-metastasis drugs that act by inhibiting Connexin 26.

PubMed

Nojima, Hiroshi; Ohba, Yusuke; Kita, Yasuyuki

2007-09-01

Despite considerable research, metastasis remains a major challenge in the clinical management of cancer. Recent reports show that abnormally augmented expression of Cx26 is responsible for the enhanced spontaneous metastasis of mouse BL6 melanoma cells. The function of Cx26 appears to be responsible for this phenotype since exogenous expression of a dominant-negative form of Cx26 and oleamide derivatives called MI-18 and MI-22 that specifically inhibit Cx26-mediated gap junction-mediated intercellular communications (GJIC) prevent the spontaneous metastasis of BL6 cells. As expected from their structural similarity to oleic acid (the major component of olive oil), both MI-18 and MI-22 are safe drugs; nonetheless, they are potent inhibitors of the spontaneous metastasis of BL6 mouse melanoma cells. Thus, they are a novel prototype of an anti-metastasis drug that has minimal side effects. While the primary tumors do not necessarily show strong Cx26-immunostaining signals, pronounced Cx26 expression is detected in the highly invasive tumor regions; it is also more frequently observed in metastasized tumors. Thus, Cx26 expression may be useful as a prognostic tool that can predict the existence of highly metastatic cancer cells in clinical samples.

Transcriptome analysis of a wild bird reveals physiological responses to the urban environment

PubMed Central

Watson, Hannah; Videvall, Elin; Andersson, Martin N.; Isaksson, Caroline

2017-01-01

Identifying the molecular basis of environmentally induced phenotypic variation presents exciting opportunities for furthering our understanding of how ecological processes and the environment can shape the phenotype. Urban and rural environments present free-living organisms with different challenges and opportunities, which have marked consequences for the phenotype, yet little is known about responses at the molecular level. We characterised transcriptomes from an urban and a rural population of great tits Parus major, demonstrating striking differences in gene expression profiles in both blood and liver tissues. Differentially expressed genes had functions related to immune and inflammatory responses, detoxification, protection against oxidative stress, lipid metabolism, and regulation of gene expression. Many genes linked to stress responses were expressed at higher levels in the urban birds, in accordance with our prediction that urban animals are exposed to greater environmental stress. This is one of the first studies to reveal transcriptional differences between urban- and rural-dwelling animals and suggests an important role for epigenetics in mediating environmentally induced physiological variation. The study provides valuable resources for developing further in-depth studies of the mechanisms driving phenotypic variation in the urban context at larger spatial and temporal scales. PMID:28290496

Prediction of gene expression with cis-SNPs using mixed models and regularization methods.

PubMed

Zeng, Ping; Zhou, Xiang; Huang, Shuiping

2017-05-11

It has been shown that gene expression in human tissues is heritable, thus predicting gene expression using only SNPs becomes possible. The prediction of gene expression can offer important implications on the genetic architecture of individual functional associated SNPs and further interpretations of the molecular basis underlying human diseases. We compared three types of methods for predicting gene expression using only cis-SNPs, including the polygenic model, i.e. linear mixed model (LMM), two sparse models, i.e. Lasso and elastic net (ENET), and the hybrid of LMM and sparse model, i.e. Bayesian sparse linear mixed model (BSLMM). The three kinds of prediction methods have very different assumptions of underlying genetic architectures. These methods were evaluated using simulations under various scenarios, and were applied to the Geuvadis gene expression data. The simulations showed that these four prediction methods (i.e. Lasso, ENET, LMM and BSLMM) behaved best when their respective modeling assumptions were satisfied, but BSLMM had a robust performance across a range of scenarios. According to R 2 of these models in the Geuvadis data, the four methods performed quite similarly. We did not observe any clustering or enrichment of predictive genes (defined as genes with R 2  ≥ 0.05) across the chromosomes, and also did not see there was any clear relationship between the proportion of the predictive genes and the proportion of genes in each chromosome. However, an interesting finding in the Geuvadis data was that highly predictive genes (e.g. R 2  ≥ 0.30) may have sparse genetic architectures since Lasso, ENET and BSLMM outperformed LMM for these genes; and this observation was validated in another gene expression data. We further showed that the predictive genes were enriched in approximately independent LD blocks. Gene expression can be predicted with only cis-SNPs using well-developed prediction models and these predictive genes were enriched in some approximately independent LD blocks. The prediction of gene expression can shed some light on the functional interpretation for identified SNPs in GWASs.

HLA-G Haplotypes Are Differentially Associated with Asthmatic Features.

PubMed

Ribeyre, Camille; Carlini, Federico; René, Céline; Jordier, François; Picard, Christophe; Chiaroni, Jacques; Abi-Rached, Laurent; Gouret, Philippe; Marin, Grégory; Molinari, Nicolas; Chanez, Pascal; Paganini, Julien; Gras, Delphine; Di Cristofaro, Julie

2018-01-01

Human leukocyte antigen (HLA)-G, a HLA class Ib molecule, interacts with receptors on lymphocytes such as T cells, B cells, and natural killer cells to influence immune responses. Unlike classical HLA molecules, HLA-G expression is not found on all somatic cells, but restricted to tissue sites, including human bronchial epithelium cells (HBEC). Individual variation in HLA-G expression is linked to its genetic polymorphism and has been associated with many pathological situations such as asthma, which is characterized by epithelium abnormalities and inflammatory cell activation. Studies reported both higher and equivalent soluble HLA-G (sHLA-G) expression in different cohorts of asthmatic patients. In particular, we recently described impaired local expression of HLA-G and abnormal profiles for alternatively spliced isoforms in HBEC from asthmatic patients. sHLA-G dosage is challenging because of its many levels of polymorphism (dimerization, association with β2-microglobulin, and alternative splicing), thus many clinical studies focused on HLA-G single-nucleotide polymorphisms as predictive biomarkers, but few analyzed HLA-G haplotypes. Here, we aimed to characterize HLA-G haplotypes and describe their association with asthmatic clinical features and sHLA-G peripheral expression and to describe variations in transcription factor (TF) binding sites and alternative splicing sites. HLA - G haplotypes were differentially distributed in 330 healthy and 580 asthmatic individuals. Furthermore, HLA-G haplotypes were associated with asthmatic clinical features showed. However, we did not confirm an association between sHLA-G and genetic, biological, or clinical parameters. HLA-G haplotypes were phylogenetically split into distinct groups, with each group displaying particular variations in TF binding or RNA splicing sites that could reflect differential HLA-G qualitative or quantitative expression, with tissue-dependent specificities. Our results, based on a multicenter cohort, thus support the pertinence of HLA-G haplotypes as predictive genetic markers for asthma.

HLA-G Haplotypes Are Differentially Associated with Asthmatic Features

PubMed Central

Ribeyre, Camille; Carlini, Federico; René, Céline; Jordier, François; Picard, Christophe; Chiaroni, Jacques; Abi-Rached, Laurent; Gouret, Philippe; Marin, Grégory; Molinari, Nicolas; Chanez, Pascal; Paganini, Julien; Gras, Delphine; Di Cristofaro, Julie

2018-01-01

Human leukocyte antigen (HLA)-G, a HLA class Ib molecule, interacts with receptors on lymphocytes such as T cells, B cells, and natural killer cells to influence immune responses. Unlike classical HLA molecules, HLA-G expression is not found on all somatic cells, but restricted to tissue sites, including human bronchial epithelium cells (HBEC). Individual variation in HLA-G expression is linked to its genetic polymorphism and has been associated with many pathological situations such as asthma, which is characterized by epithelium abnormalities and inflammatory cell activation. Studies reported both higher and equivalent soluble HLA-G (sHLA-G) expression in different cohorts of asthmatic patients. In particular, we recently described impaired local expression of HLA-G and abnormal profiles for alternatively spliced isoforms in HBEC from asthmatic patients. sHLA-G dosage is challenging because of its many levels of polymorphism (dimerization, association with β2-microglobulin, and alternative splicing), thus many clinical studies focused on HLA-G single-nucleotide polymorphisms as predictive biomarkers, but few analyzed HLA-G haplotypes. Here, we aimed to characterize HLA-G haplotypes and describe their association with asthmatic clinical features and sHLA-G peripheral expression and to describe variations in transcription factor (TF) binding sites and alternative splicing sites. HLA-G haplotypes were differentially distributed in 330 healthy and 580 asthmatic individuals. Furthermore, HLA-G haplotypes were associated with asthmatic clinical features showed. However, we did not confirm an association between sHLA-G and genetic, biological, or clinical parameters. HLA-G haplotypes were phylogenetically split into distinct groups, with each group displaying particular variations in TF binding or RNA splicing sites that could reflect differential HLA-G qualitative or quantitative expression, with tissue-dependent specificities. Our results, based on a multicenter cohort, thus support the pertinence of HLA-G haplotypes as predictive genetic markers for asthma. PMID:29527207

Expression Profiles of TGF-β and TLR Pathways in Porphyromonas gingivalis and Prevotella intermedia Challenged Osteoblasts.

PubMed

Aydin, Kubra; Ekinci, Fatma Yesim; Korachi, May

2015-04-01

The presence of certain oral pathogens at implant sites can hinder the osseointegration process. However, it is unclear how and by what microorganisms it happens. This study investigated whether the presence of oral pathogens of Porphyromonas gingivalis and Prevotella intermedia individually, play a role in the failure of bone formation by determining the expression profiles of Transforming Growth Factor Beta (TGF-β/Bone Morphogenic Protein (BMP) and Toll-Like Receptor (TLR) pathways in challenged osteoblasts. Cell viability of P. gingivalis and P. intermedia challenged osteoblasts were determined by WST assay. Changes in osteoblast morphology and inhibition of mineralization were observed by Scanning Electron Microscopy (SEM) and Von Kossa staining, respectively. Expression of TGF-β and TLR pathway genes on challenged cells were identified by RT profiler array. Both P. gingivalis and P. intermedia challenges resulted in reduced viability and mineralization of osteoblasts. Viability was reduced to 56.8% (P. gingivalis) and 52.75% (P. intermedia) at 1000 multiplicity. Amongst 48 genes examined, expressions of BMPER, SMAD1, IL8 and NFRKB were found to be highly upregulated by both bacterial challenges (Fold Change > 4). P. gingivalis and P. intermedia could play a role in implant failure by changing the expression profiles of genes related to bone formation and resorption.

Kidney Transplant Rejection and Tissue Injury by Gene Profiling of Biopsies and Peripheral Blood Lymphocytes

PubMed Central

Flechner, Stuart M.; Kurian, Sunil M.; Head, Steven R.; Sharp, Starlette M.; Whisenant, Thomas C.; Zhang, Jie; Chismar, Jeffrey D.; Horvath, Steve; Mondala, Tony; Gilmartin, Timothy; Cook, Daniel J.; Kay, Steven A.; Walker, John R.; Salomon, Daniel R.

2007-01-01

A major challenge for kidney transplantation is balancing the need for immunosuppression to prevent rejection, while minimizing drug-induced toxicities. We used DNA microarrays (HG-U95Av2 GeneChips, Affymetrix) to determine gene expression profiles for kidney biopsies and peripheral blood lymphocytes (PBLs) in transplant patients including normal donor kidneys, well-functioning transplants without rejection, kidneys undergoing acute rejection, and transplants with renal dysfunction without rejection. We developed a data analysis schema based on expression signal determination, class comparison and prediction, hierarchical clustering, statistical power analysis and real-time quantitative PCR validation. We identified distinct gene expression signatures for both biopsies and PBLs that correlated significantly with each of the different classes of transplant patients. This is the most complete report to date using commercial arrays to identify unique expression signatures in transplant biopsies distinguishing acute rejection, acute dysfunction without rejection and well-functioning transplants with no rejection history. We demonstrate for the first time the successful application of high density DNA chip analysis of PBL as a diagnostic tool for transplantation. The significance of these results, if validated in a multicenter prospective trial, would be the establishment of a metric based on gene expression signatures for monitoring the immune status and immunosuppression of transplanted patients. PMID:15307835

A Biodosimeter for Multiparametric Determination of Radiation Dose, Radiation Quality, and Radiation Risk

NASA Technical Reports Server (NTRS)

Richmond, Robert; Cruz, Angela; Jansen, Heather; Bors, Karen

2003-01-01

Predicting risk of human cancer following exposure of an individual or a population to ionizing radiation is challenging. To an approximation, this is because uncertainties of uniform absorption of dose and the uniform processing of dose-related damage at the cellular level within a complex set of biological variables degrade the confidence of predicting the delayed expression of cancer as a relatively rare event. Cellular biodosimeters that simultaneously report: 1) the quantity of absorbed dose after exposure to ionizing radiation, 2) the quality of radiation delivering that dose, and 3) the risk of developing cancer by the cells absorbing that dose would therefore be useful. An approach to such a multiparametric biodosimeter will be reported. This is the demonstration of a dose responsive field effect of enhanced expression of keratin 18 (K18) in cultures of human mammary epithelial cells irradiated with cesium-1 37 gamma-rays. Dose response of enhanced K18 expression was experimentally extended over a range of 30 to 90 cGy for cells evaluated at mid-log phase. K18 has been reported to be a marker for tumor staging and for apoptosis, and thereby serves as an example of a potential marker for cancer risk, where the reality of such predictive value would require additional experimental development. Since observed radiogenic increase in expression of K18 is a field effect, ie., chronically present in all cells of the irradiated population, it may be hypothesized that K18 expression in specific cells absorbing particulate irradiation, such as the high-LET-producing atomic nuclei of space radiation, will report on both the single-cell distributions of those particles amongst cells within the exposed population, and that the relatively high dose per cell delivered by densely ionizing tracks of those intersecting particles will lead to cell-specific high-expression levels of K18, thereby providing analytical end points that may be used to resolve both the quantity and the quality of the radiation dose absorbed by individual cells. The principal value of this reported potential multiparametric cellular biodosimeter is suggested to be that it justifies a search for similar but more robust radiogenic assays. That is, K18 is only one radiation dose-sensitive expressed protein, whereas analytical techniques of genomics and proteomics can be used to simultaneously analyze multiple gene and protein expressions resulting from radiation-dose absorption. The potential usefulness of multiparametric cellular biodosimeters will be best realized from quantitatively profiling these multiple markers using these modern techniques.

A 17-gene assay to predict prostate cancer aggressiveness in the context of Gleason grade heterogeneity, tumor multifocality, and biopsy undersampling.

PubMed

Klein, Eric A; Cooperberg, Matthew R; Magi-Galluzzi, Cristina; Simko, Jeffry P; Falzarano, Sara M; Maddala, Tara; Chan, June M; Li, Jianbo; Cowan, Janet E; Tsiatis, Athanasios C; Cherbavaz, Diana B; Pelham, Robert J; Tenggara-Hunter, Imelda; Baehner, Frederick L; Knezevic, Dejan; Febbo, Phillip G; Shak, Steven; Kattan, Michael W; Lee, Mark; Carroll, Peter R

2014-09-01

Prostate tumor heterogeneity and biopsy undersampling pose challenges to accurate, individualized risk assessment for men with localized disease. To identify and validate a biopsy-based gene expression signature that predicts clinical recurrence, prostate cancer (PCa) death, and adverse pathology. Gene expression was quantified by reverse transcription-polymerase chain reaction for three studies-a discovery prostatectomy study (n=441), a biopsy study (n=167), and a prospectively designed, independent clinical validation study (n=395)-testing retrospectively collected needle biopsies from contemporary (1997-2011) patients with low to intermediate clinical risk who were candidates for active surveillance (AS). The main outcome measures defining aggressive PCa were clinical recurrence, PCa death, and adverse pathology at prostatectomy. Cox proportional hazards regression models were used to evaluate the association between gene expression and time to event end points. Results from the prostatectomy and biopsy studies were used to develop and lock a multigene-expression-based signature, called the Genomic Prostate Score (GPS); in the validation study, logistic regression was used to test the association between the GPS and pathologic stage and grade at prostatectomy. Decision-curve analysis and risk profiles were used together with clinical and pathologic characteristics to evaluate clinical utility. Of the 732 candidate genes analyzed, 288 (39%) were found to predict clinical recurrence despite heterogeneity and multifocality, and 198 (27%) were predictive of aggressive disease after adjustment for prostate-specific antigen, Gleason score, and clinical stage. Further analysis identified 17 genes representing multiple biological pathways that were combined into the GPS algorithm. In the validation study, GPS predicted high-grade (odds ratio [OR] per 20 GPS units: 2.3; 95% confidence interval [CI], 1.5-3.7; p<0.001) and high-stage (OR per 20 GPS units: 1.9; 95% CI, 1.3-3.0; p=0.003) at surgical pathology. GPS predicted high-grade and/or high-stage disease after controlling for established clinical factors (p<0.005) such as an OR of 2.1 (95% CI, 1.4-3.2) when adjusting for Cancer of the Prostate Risk Assessment score. A limitation of the validation study was the inclusion of men with low-volume intermediate-risk PCa (Gleason score 3+4), for whom some providers would not consider AS. Genes representing multiple biological pathways discriminate PCa aggressiveness in biopsy tissue despite tumor heterogeneity, multifocality, and limited sampling at time of biopsy. The biopsy-based 17-gene GPS improves prediction of the presence or absence of adverse pathology and may help men with PCa make more informed decisions between AS and immediate treatment. Prostate cancer (PCa) is often present in multiple locations within the prostate and has variable characteristics. We identified genes with expression associated with aggressive PCa to develop a biopsy-based, multigene signature, the Genomic Prostate Score (GPS). GPS was validated for its ability to predict men who have high-grade or high-stage PCa at diagnosis and may help men diagnosed with PCa decide between active surveillance and immediate definitive treatment. Copyright © 2014 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Brain coordination dynamics: True and false faces of phase synchrony and metastability

PubMed Central

Tognoli, Emmanuelle; Kelso, J.A. Scott

2009-01-01

Understanding the coordination of multiple parts in a complex system such as the brain is a fundamental challenge. We present a theoretical model of cortical coordination dynamics that shows how brain areas may cooperate (integration) and at the same time retain their functional specificity (segregation). This model expresses a range of desirable properties that the brain is known to exhibit, including self-organization, multi-functionality, metastability and switching. Empirically, the model motivates a thorough investigation of collective phase relationships among brain oscillations in neurophysiological data. The most serious obstacle to interpreting coupled oscillations as genuine evidence of inter-areal coordination in the brain stems from volume conduction of electrical fields. Spurious coupling due to volume conduction gives rise to zero-lag (inphase) and antiphase synchronization whose magnitude and persistence obscure the subtle expression of real synchrony. Through forward modeling and the help of a novel colorimetric method, we show how true synchronization can be deciphered from continuous EEG patterns. Developing empirical efforts along the lines of continuous EEG analysis constitutes a major response to the challenge of understanding how different brain areas work together. Key predictions of cortical coordination dynamics can now be tested thereby revealing the essential modus operandi of the intact living brain. PMID:18938209

Characterization of leptospiral proteins that afford partial protection in hamsters against lethal challenge with Leptospira interrogans.

PubMed

Atzingen, Marina V; Gonçales, Amane P; de Morais, Zenaide M; Araújo, Eduardo R; De Brito, Thales; Vasconcellos, Silvio A; Nascimento, Ana L T O

2010-09-01

Leptospirosis is a worldwide zoonosis caused by pathogenic Leptospira. The whole-genome sequence of Leptospira interrogans serovar Copenhageni together with bioinformatic tools allow us to search for novel antigen candidates suitable for improved vaccines against leptospirosis. This study focused on three genes encoding conserved hypothetical proteins predicted to be exported to the outer membrane. The genes were amplified by PCR from six predominant pathogenic serovars in Brazil. The genes were cloned and expressed in Escherichia coli strain BL21-SI using the expression vector pDEST17. The recombinant proteins tagged with N-terminal 6xHis were purified by metal-charged chromatography. The proteins were recognized by antibodies present in sera from hamsters that were experimentally infected. Immunization of hamsters followed by challenge with a lethal dose of a virulent strain of Leptospira showed that the recombinant protein rLIC12730 afforded statistically significant protection to animals (44 %), followed by rLIC10494 (40 %) and rLIC12922 (30 %). Immunization with these proteins produced an increase in antibody titres during subsequent boosters, suggesting the involvement of a T-helper 2 response. Although more studies are needed, these data suggest that rLIC12730 and rLIC10494 are promising candidates for a multivalent vaccine for the prevention of leptospirosis.

Seven-year-olds' aggressive choices in a computer game can be predicted in infancy.

PubMed

Hay, Dale F; Johansen, Mark K; Daly, Peter; Hashmi, Salim; Robinson, Charlotte; Collishaw, Stephan; van Goozen, Stephanie

2018-05-01

Concerns about the relationship between computer games and children's aggression have been expressed for decades, but it is not yet clear whether the content of such games evokes aggression or a prior history of aggression promotes children's interest in aggressive games. Two hundred and sixty-six 7-year-old children from a nationally representative longitudinal sample in the UK played a novel computer game (CAMGAME) in which the child's avatar encountered a series of social challenges that might evoke aggressive, prosocial or neutral behaviour. Aggressive choices during the game were predicted by well-known risk factors for aggressive conduct problems and the children's own early angry aggressiveness as infants. These findings suggest that children who are predisposed to aggression bring those tendencies to virtual as well as real environments. © 2017 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

HITS-CLIP yields genome-wide insights into brain alternative RNA processing

NASA Astrophysics Data System (ADS)

Licatalosi, Donny D.; Mele, Aldo; Fak, John J.; Ule, Jernej; Kayikci, Melis; Chi, Sung Wook; Clark, Tyson A.; Schweitzer, Anthony C.; Blume, John E.; Wang, Xuning; Darnell, Jennifer C.; Darnell, Robert B.

2008-11-01

Protein-RNA interactions have critical roles in all aspects of gene expression. However, applying biochemical methods to understand such interactions in living tissues has been challenging. Here we develop a genome-wide means of mapping protein-RNA binding sites in vivo, by high-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP). HITS-CLIP analysis of the neuron-specific splicing factor Nova revealed extremely reproducible RNA-binding maps in multiple mouse brains. These maps provide genome-wide in vivo biochemical footprints confirming the previous prediction that the position of Nova binding determines the outcome of alternative splicing; moreover, they are sufficiently powerful to predict Nova action de novo. HITS-CLIP revealed a large number of Nova-RNA interactions in 3' untranslated regions, leading to the discovery that Nova regulates alternative polyadenylation in the brain. HITS-CLIP, therefore, provides a robust, unbiased means to identify functional protein-RNA interactions in vivo.

Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

PubMed Central

Mannakee, Brian K.; Gutenkunst, Ryan N.

2016-01-01

The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265

Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy.

PubMed

Brogden, Kim A; Parashar, Deepak; Hallier, Andrea R; Braun, Terry; Qian, Fang; Rizvi, Naiyer A; Bossler, Aaron D; Milhem, Mohammed M; Chan, Timothy A; Abbasi, Taher; Vali, Shireen

2018-02-27

Programmed Death Ligand 1 (PD-L1) is a co-stimulatory and immune checkpoint protein. PD-L1 expression in non-small cell lung cancers (NSCLC) is a hallmark of adaptive resistance and its expression is often used to predict the outcome of Programmed Death 1 (PD-1) and PD-L1 immunotherapy treatments. However, clinical benefits do not occur in all patients and new approaches are needed to assist in selecting patients for PD-1 or PD-L1 immunotherapies. Here, we hypothesized that patient tumor cell genomics influenced cell signaling and expression of PD-L1, chemokines, and immunosuppressive molecules and these profiles could be used to predict patient clinical responses. We used a recent dataset from NSCLC patients treated with pembrolizumab. Deleterious gene mutational profiles in patient exomes were identified and annotated into a cancer network to create NSCLC patient-specific predictive computational simulation models. Validation checks were performed on the cancer network, simulation model predictions, and PD-1 match rates between patient-specific predicted and clinical responses. Expression profiles of these 24 chemokines and immunosuppressive molecules were used to identify patients who would or would not respond to PD-1 immunotherapy. PD-L1 expression alone was not sufficient to predict which patients would or would not respond to PD-1 immunotherapy. Adding chemokine and immunosuppressive molecule expression profiles allowed patient models to achieve a greater than 85.0% predictive correlation among predicted and reported patient clinical responses. Our results suggested that chemokine and immunosuppressive molecule expression profiles can be used to accurately predict clinical responses thus differentiating among patients who would and would not benefit from PD-1 or PD-L1 immunotherapies.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens

PubMed Central

Chasman, Deborah; Walters, Kevin B.; Lopes, Tiago J. S.; Eisfeld, Amie J.; Kawaoka, Yoshihiro; Roy, Sushmita

2016-01-01

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection. PMID:27403523

Bleomycin induces molecular changes directly relevant to idiopathic pulmonary fibrosis: a model for "active" disease.

PubMed

Peng, Ruoqi; Sridhar, Sriram; Tyagi, Gaurav; Phillips, Jonathan E; Garrido, Rosario; Harris, Paul; Burns, Lisa; Renteria, Lorena; Woods, John; Chen, Leena; Allard, John; Ravindran, Palanikumar; Bitter, Hans; Liang, Zhenmin; Hogaboam, Cory M; Kitson, Chris; Budd, David C; Fine, Jay S; Bauer, Carla M T; Stevenson, Christopher S

2013-01-01

The preclinical model of bleomycin-induced lung fibrosis, used to investigate mechanisms related to idiopathic pulmonary fibrosis (IPF), has incorrectly predicted efficacy for several candidate compounds suggesting that it may be of limited value. As an attempt to improve the predictive nature of this model, integrative bioinformatic approaches were used to compare molecular alterations in the lungs of bleomycin-treated mice and patients with IPF. Using gene set enrichment analysis we show for the first time that genes differentially expressed during the fibrotic phase of the single challenge bleomycin model were significantly enriched in the expression profiles of IPF patients. The genes that contributed most to the enrichment were largely involved in mitosis, growth factor, and matrix signaling. Interestingly, these same mitotic processes were increased in the expression profiles of fibroblasts isolated from rapidly progressing, but not slowly progressing, IPF patients relative to control subjects. The data also indicated that TGFβ was not the sole mediator responsible for the changes observed in this model since the ALK-5 inhibitor SB525334 effectively attenuated some but not all of the fibrosis associated with this model. Although some would suggest that repetitive bleomycin injuries may more effectively model IPF-like changes, our data do not support this conclusion. Together, these data highlight that a single bleomycin instillation effectively replicates several of the specific pathogenic molecular changes associated with IPF, and may be best used as a model for patients with active disease.

Expression of host defense peptides in the intestine of Eimeria-challenged chickens.

PubMed

Su, S; Dwyer, D M; Miska, K B; Fetterer, R H; Jenkins, M C; Wong, E A

2017-07-01

Avian coccidiosis is caused by the intracellular protozoan Eimeria, which produces intestinal lesions leading to weight gain depression. Current control methods include vaccination and anticoccidial drugs. An alternative approach involves modulating the immune system. The objective of this study was to profile the expression of host defense peptides such as avian beta-defensins (AvBDs) and liver expressed antimicrobial peptide 2 (LEAP2), which are part of the innate immune system. The mRNA expression of AvBD family members 1, 6, 8, 10, 11, 12, and 13 and LEAP2 was examined in chickens challenged with either E. acervulina, E. maxima, or E. tenella. The duodenum, jejunum, ileum, and ceca were collected 7 d post challenge. In study 1, E. acervulina challenge resulted in down-regulation of AvBD1, AvBD6, AvBD10, AvBD11, AvBD12, and AvBD13 in the duodenum. E. maxima challenge caused down-regulation of AvBD6, AvBD10, and AvBD11 in the duodenum, down-regulation of AvBD10 in the jejunum, but up-regulation of AvBD8 and AvBD13 in the ceca. E. tenella challenge showed no change in AvBD expression in any tissue. In study 2, which involved challenge with only E. maxima, there was down-regulation of AvBD1 in the ileum, AvBD11 in the jejunum and ileum, and LEAP2 in all 3 segments of the small intestine. The expression of LEAP2 was further examined by in situ hybridization in the jejunum of chickens from study 2. LEAP2 mRNA was expressed similarly in the enterocytes lining the villi, but not in the crypts of control and Eimeria challenged chickens. The lengths of the villi in the Eimeria challenged chickens were less than those in the control chickens, which may in part account for the observed down-regulation of LEAP2 mRNA quantified by PCR. Overall, the AvBD response to Eimeria challenge was not consistent; whereas LEAP2 was consistently down-regulated, which suggests that LEAP2 plays an important role in modulating an Eimeria infection. Published by Oxford University Press on behalf of Poultry Science Association 2017.

A recombinant fowl adenovirus expressing the S1 gene of infectious bronchitis virus protects against challenge with infectious bronchitis virus.

PubMed

Johnson, Michael A; Pooley, Catherine; Ignjatovic, Jagoda; Tyack, Scott G

2003-06-20

The spike peplomer S1 subunit sequence from avian infectious bronchitis virus (IBV) Vic S strain was expressed in a plasmid under the control of the fowl adenovirus (FAV) major late promoter (MLP). Two recombinants were constructed in FAV serotype 8 (FAV 8) by inserting the expression cassette between the SnaBI and XbaI restriction enzyme sites (clone DA3) or between the SpeI sites (clone CA6-20). Expression of the S1 gene in the recombinants was confirmed by reverse transcription-polymerase chain reaction (RT-PCR) by 20h post-infection. Commercial broiler chickens were orally vaccinated at day 0 or day 6 post-hatch and challenged at day 35 post-hatch. FAV antibody ELISA confirmed that maternal antibody directed against inclusion body hepatitis (serotype 8) had decayed in control birds and that FAV specific serum IgG responses were produced in vaccinated birds at the time of challenge. Further, an S1 specific antibody response was detected prior to challenge. Birds were challenged with either Vic S (serotype B) or N1/62 (serotype C) strains of IBV. The tracheas of challenged birds were analyzed by RT-PCR and re-isolation of virus. In birds vaccinated at day 6, 90-100% protection at the trachea was induced against either homologous or heterologous challenge. The construction of a recombinant FAV expressing S1 of IBV demonstrates the potential of an alternative vaccination strategy against IBV.

Cognitive Modeling of Individual Variation in Reference Production and Comprehension

PubMed Central

Hendriks, Petra

2016-01-01

A challenge for most theoretical and computational accounts of linguistic reference is the observation that language users vary considerably in their referential choices. Part of the variation observed among and within language users and across tasks may be explained from variation in the cognitive resources available to speakers and listeners. This paper presents a computational model of reference production and comprehension developed within the cognitive architecture ACT-R. Through simulations with this ACT-R model, it is investigated how cognitive constraints interact with linguistic constraints and features of the linguistic discourse in speakers’ production and listeners’ comprehension of referring expressions in specific tasks, and how this interaction may give rise to variation in referential choice. The ACT-R model of reference explains and predicts variation among language users in their referential choices as a result of individual and task-related differences in processing speed and working memory capacity. Because of limitations in their cognitive capacities, speakers sometimes underspecify or overspecify their referring expressions, and listeners sometimes choose incorrect referents or are overly liberal in their interpretation of referring expressions. PMID:27092101

Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria

PubMed Central

Hui, Sheng; Silverman, Josh M; Chen, Stephen S; Erickson, David W; Basan, Markus; Wang, Jilong; Hwa, Terence; Williamson, James R

2015-01-01

A central aim of cell biology was to understand the strategy of gene expression in response to the environment. Here, we study gene expression response to metabolic challenges in exponentially growing Escherichia coli using mass spectrometry. Despite enormous complexity in the details of the underlying regulatory network, we find that the proteome partitions into several coarse-grained sectors, with each sector's total mass abundance exhibiting positive or negative linear relations with the growth rate. The growth rate-dependent components of the proteome fractions comprise about half of the proteome by mass, and their mutual dependencies can be characterized by a simple flux model involving only two effective parameters. The success and apparent generality of this model arises from tight coordination between proteome partition and metabolism, suggesting a principle for resource allocation in proteome economy of the cell. This strategy of global gene regulation should serve as a basis for future studies on gene expression and constructing synthetic biological circuits. Coarse graining may be an effective approach to derive predictive phenomenological models for other ‘omics’ studies. PMID:25678603

Multi-omics facilitated variable selection in Cox-regression model for cancer prognosis prediction.

PubMed

Liu, Cong; Wang, Xujun; Genchev, Georgi Z; Lu, Hui

2017-07-15

New developments in high-throughput genomic technologies have enabled the measurement of diverse types of omics biomarkers in a cost-efficient and clinically-feasible manner. Developing computational methods and tools for analysis and translation of such genomic data into clinically-relevant information is an ongoing and active area of investigation. For example, several studies have utilized an unsupervised learning framework to cluster patients by integrating omics data. Despite such recent advances, predicting cancer prognosis using integrated omics biomarkers remains a challenge. There is also a shortage of computational tools for predicting cancer prognosis by using supervised learning methods. The current standard approach is to fit a Cox regression model by concatenating the different types of omics data in a linear manner, while penalty could be added for feature selection. A more powerful approach, however, would be to incorporate data by considering relationships among omics datatypes. Here we developed two methods: a SKI-Cox method and a wLASSO-Cox method to incorporate the association among different types of omics data. Both methods fit the Cox proportional hazards model and predict a risk score based on mRNA expression profiles. SKI-Cox borrows the information generated by these additional types of omics data to guide variable selection, while wLASSO-Cox incorporates this information as a penalty factor during model fitting. We show that SKI-Cox and wLASSO-Cox models select more true variables than a LASSO-Cox model in simulation studies. We assess the performance of SKI-Cox and wLASSO-Cox using TCGA glioblastoma multiforme and lung adenocarcinoma data. In each case, mRNA expression, methylation, and copy number variation data are integrated to predict the overall survival time of cancer patients. Our methods achieve better performance in predicting patients' survival in glioblastoma and lung adenocarcinoma. Copyright © 2017. Published by Elsevier Inc.

Molecular cloning, biochemical characterization, and expression analysis of two glutathione S-transferase paralogs from the big-belly seahorse (Hippocampus abdominalis).

PubMed

Tharuka, M D Neranjan; Bathige, S D N K; Lee, Jehee

2017-12-01

Glutathione S-transferases (GSTs, EC 2.5.1.18) are important Phase II detoxifying enzymes that catalyze hydrophobic, electrophilic xenobiotic substance with the conjugation of reduced glutathione (GSH). In this study, GSTμ and GSTρ paralogs of GST in the big belly seahorse (Hippocampus abdominalis; HaGSTρ, HaGSTμ) were biochemically, molecularly, functionally characterized to determine their detoxification range and protective capacities upon different pathogenic stresses. HaGSTρ and HaGSTμ are composed of coding sequences of 681bp and 654bp, which encode proteins 225 and 217 amino acids, with predicted molecular masses of 26.06kDa and 25.74kDa respectively. Sequence analysis revealed that both HaGSTs comprise the characteristic GSH-binding site in the thioredoxin-like N-terminal domain and substrate binding site in the C-terminal domain. The recombinant HaGSTρ and HaGSTμ proteins catalyzed the model GST substrate 1-chloro-2, 4-dinitrobenzene (CDNB). Enzyme kinetic analysis revealed different K m and V max values for each rHaGST, suggesting that they have different conjugation rates. The optimum conditions (pH, temperature) and inhibitory assays of each protein demonstrated different optimal ranges. However, HaGSTμ was highly expressed in the ovary and gill, whereas HaGSTρ was highly expressed in the gill and pouch. mRNA expression of HaGSTρ and HaGSTμ was significantly elevated upon lipopolysaccharide, Poly (I:C), and Edwardsiella tarda challenges in liver and in blood cells as well as with Streptococcus iniae challenge in blood cells. From these collective experimental results, we propose that HaGSTρ and HaGSTμ are effective in detoxifying xenobiotic toxic agents, and importantly, their mRNA expression could be stimulated by immunological stress signals in the aquatic environment. Copyright © 2017 Elsevier Inc. All rights reserved.

Fluorometric In Situ Monitoring of an Escherichia coli Cell Factory with Cytosolic Expression of Human Glycosyltransferase GalNAcT2: Prospects and Limitations

PubMed Central

Schwab, Karen; Lauber, Jennifer; Hesse, Friedemann

2016-01-01

The glycosyltransferase HisDapGalNAcT2 is the key protein of the Escherichia coli (E. coli) SHuffle® T7 cell factory which was genetically engineered to allow glycosylation of a protein substrate in vivo. The specific activity of the glycosyltransferase requires time-intensive analytics, but is a critical process parameter. Therefore, it has to be monitored closely. This study evaluates fluorometric in situ monitoring as option to access this critical process parameter during complex E. coli fermentations. Partial least square regression (PLS) models were built based on the fluorometric data recorded during the EnPresso® B fermentations. Capable models for the prediction of glucose and acetate concentrations were built for these fermentations with rout mean squared errors for prediction (RMSEP) of 0.19 g·L−1 and 0.08 g·L−1, as well as for the prediction of the optical density (RMSEP 0.24). In situ monitoring of soluble enzyme to cell dry weight ratios (RMSEP 5.5 × 10−4 µg w/w) and specific activity of the glycosyltransferase (RMSEP 33.5 pmol·min−1·µg−1) proved to be challenging, since HisDapGalNAcT2 had to be extracted from the cells and purified. However, fluorescence spectroscopy, in combination with PLS modeling, proved to be feasible for in situ monitoring of complex expression systems. PMID:28952595

Integrative gene network construction to analyze cancer recurrence using semi-supervised learning.

PubMed

Park, Chihyun; Ahn, Jaegyoon; Kim, Hyunjin; Park, Sanghyun

2014-01-01

The prognosis of cancer recurrence is an important research area in bioinformatics and is challenging due to the small sample sizes compared to the vast number of genes. There have been several attempts to predict cancer recurrence. Most studies employed a supervised approach, which uses only a few labeled samples. Semi-supervised learning can be a great alternative to solve this problem. There have been few attempts based on manifold assumptions to reveal the detailed roles of identified cancer genes in recurrence. In order to predict cancer recurrence, we proposed a novel semi-supervised learning algorithm based on a graph regularization approach. We transformed the gene expression data into a graph structure for semi-supervised learning and integrated protein interaction data with the gene expression data to select functionally-related gene pairs. Then, we predicted the recurrence of cancer by applying a regularization approach to the constructed graph containing both labeled and unlabeled nodes. The average improvement rate of accuracy for three different cancer datasets was 24.9% compared to existing supervised and semi-supervised methods. We performed functional enrichment on the gene networks used for learning. We identified that those gene networks are significantly associated with cancer-recurrence-related biological functions. Our algorithm was developed with standard C++ and is available in Linux and MS Windows formats in the STL library. The executable program is freely available at: http://embio.yonsei.ac.kr/~Park/ssl.php.

Decoding the usefulness of non-coding RNAs as breast cancer markers.

PubMed

Amorim, Maria; Salta, Sofia; Henrique, Rui; Jerónimo, Carmen

2016-09-15

Although important advances in the management of breast cancer (BC) have been recently accomplished, it still constitutes the leading cause of cancer death in women worldwide. BC is a heterogeneous and complex disease, making clinical prediction of outcome a very challenging task. In recent years, gene expression profiling emerged as a tool to assist in clinical decision, enabling the identification of genetic signatures that better predict prognosis and response to therapy. Nevertheless, translation to routine practice has been limited by economical and technical reasons and, thus, novel biomarkers, especially those requiring non-invasive or minimally invasive collection procedures, while retaining high sensitivity and specificity might represent a significant development in this field. An increasing amount of evidence demonstrates that non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), are aberrantly expressed in several cancers, including BC. miRNAs are of particular interest as new, easily accessible, cost-effective and non-invasive tools for precise management of BC patients because they circulate in bodily fluids (e.g., serum and plasma) in a very stable manner, enabling BC assessment and monitoring through liquid biopsies. This review focus on how ncRNAs have the potential to answer present clinical needs in the personalized management of patients with BC and comprehensively describes the state of the art on the role of ncRNAs in the diagnosis, prognosis and prediction of response to therapy in BC.

Electronic coarse graining enhances the predictive power of molecular simulation allowing challenges in water physics to be addressed

NASA Astrophysics Data System (ADS)

Cipcigan, Flaviu S.; Sokhan, Vlad P.; Crain, Jason; Martyna, Glenn J.

2016-12-01

One key factor that limits the predictive power of molecular dynamics simulations is the accuracy and transferability of the input force field. Force fields are challenged by heterogeneous environments, where electronic responses give rise to biologically important forces such as many-body polarisation and dispersion. The importance of polarisation in the condensed phase was recognised early on, as described by Cochran in 1959 [Philosophical Magazine 4 (1959) 1082-1086] [32]. Currently in molecular simulation, dispersion forces are treated at the two-body level and in the dipole limit, although the importance of three-body terms in the condensed phase was demonstrated by Barker in the 1980s [Phys. Rev. Lett. 57 (1986) 230-233] [72]. One approach for treating both polarisation and dispersion on an equal basis is to coarse grain the electrons surrounding a molecular moiety to a single quantum harmonic oscillator (cf. Hirschfelder, Curtiss and Bird 1954 [The Molecular Theory of Gases and Liquids (1954)] [37]). The approach, when solved in strong coupling beyond the dipole limit, gives a description of long-range forces that includes two- and many-body terms to all orders. In the last decade, the tools necessary to implement the strong coupling limit have been developed, culminating in a transferable model of water with excellent predictive power across the phase diagram. Transferability arises since the environment automatically identifies the important long range interactions, rather than the modeller through a limited set of expressions. Here, we discuss the role of electronic coarse-graining in predictive multiscale materials modelling and describe the first implementation of the method in a general purpose molecular dynamics software: QDO_MD.

Characterization of the glyoxalase 1 gene TcGLX1 in the metal hyperaccumulator plant Thlaspi caerulescens.

PubMed

Tuomainen, Marjo; Ahonen, Viivi; Kärenlampi, Sirpa O; Schat, Henk; Paasela, Tanja; Svanys, Algirdas; Tuohimetsä, Saara; Peräniemi, Sirpa; Tervahauta, Arja

2011-06-01

Stress tolerance is currently one of the major research topics in plant biology because of the challenges posed by changing climate and increasing demand to grow crop plants in marginal soils. Increased Zn tolerance and accumulation has been reported in tobacco expressing the glyoxalase 1-encoding gene from Brassica juncea. Previous studies in our laboratory showed some Zn tolerance-correlated differences in the levels of glyoxalase 1-like protein among accessions of Zn hyperaccumulator Thlaspi caerulescens. We have now isolated the corresponding gene (named here TcGLX1), including ca. 570 bp of core and proximal promoter region. The predicted protein contains three glyoxalase 1 motifs and several putative sites for post-translational modification. In silico analysis predicted a number of cis-acting elements related to stress. The expression of TcGLX1 was not responsive to Zn. There was no correlation between the levels of TcGLX1 expression and the degrees of Zn tolerance or accumulation among T. caerulescens accessions nor was there co-segregation of TcGLX1 expression with Zn tolerance or Zn accumulation among F3 lines derived from crosses between plants from accessions with contrasting phenotypes for these properties. No phenotype was observed in an A. thaliana T-DNA insertion line for the closest A. thaliana homolog of TcGLX1, ATGLX1. These results suggest that glyoxalase 1 or at least the particular isoform studied here is not a major determinant of Zn tolerance in the Zn hyperaccumulator plant T. caerulescens. In addition, ATGLX1 is not essential for normal Zn tolerance in the non-tolerant, non-accumulator plant A. thaliana. Possible explanations for the apparent discrepancy between this and previous studies are discussed.

Gene-Specific DNA Methylation Changes Predict Remission in Patients with ANCA-Associated Vasculitis

PubMed Central

Jones, Britta E.; Yang, Jiajin; Muthigi, Akhil; Hogan, Susan L.; Hu, Yichun; Starmer, Joshua; Henderson, Candace D.; Poulton, Caroline J.; Brant, Elizabeth J.; Pendergraft, William F.; Jennette, J. Charles; Falk, Ronald J.

2017-01-01

ANCA-associated vasculitis is an autoimmune condition characterized by vascular inflammation and organ damage. Pharmacologically induced remission of this condition is complicated by relapses. Potential triggers of relapse are immunologic challenges and environmental insults, both of which associate with changes in epigenetic silencing modifications. Altered histone modifications implicated in gene silencing associate with aberrant autoantigen expression. To establish a link between DNA methylation, a model epigenetic gene silencing modification, and autoantigen gene expression and disease status in ANCA-associated vasculitis, we measured gene-specific DNA methylation of the autoantigen genes myeloperoxidase (MPO) and proteinase 3 (PRTN3) in leukocytes of patients with ANCA-associated vasculitis observed longitudinally (n=82) and of healthy controls (n=32). Patients with active disease demonstrated hypomethylation of MPO and PRTN3 and increased expression of the autoantigens; in remission, DNA methylation generally increased. Longitudinal analysis revealed that patients with ANCA-associated vasculitis could be divided into two groups, on the basis of whether DNA methylation increased or decreased from active disease to remission. In patients with increased DNA methylation, MPO and PRTN3 expression correlated with DNA methylation. Kaplan–Meier estimate of relapse revealed patients with increased DNA methylation at the PRTN3 promoter had a significantly greater probability of a relapse-free period (P<0.001), independent of ANCA serotype. Patients with decreased DNA methylation at the PRTN3 promoter had a greater risk of relapse (hazard ratio, 4.55; 95% confidence interval, 2.09 to 9.91). Thus, changes in the DNA methylation status of the PRTN3 promoter may predict the likelihood of stable remission and explain autoantigen gene regulation. PMID:27821628

Fathers' challenging parenting behavior prevents social anxiety development in their 4-year-old children: a longitudinal observational study.

PubMed

Majdandžić, Mirjana; Möller, Eline L; de Vente, Wieke; Bögels, Susan M; van den Boom, Dymphna C

2014-02-01

Recent models on parenting propose different roles for fathers and mothers in the development of child anxiety. Specifically, it is suggested that fathers' challenging parenting behavior, in which the child is playfully encouraged to push her limits, buffers against child anxiety. In this longitudinal study, we explored whether the effect of challenging parenting on children's social anxiety differed between fathers and mothers. Fathers and mothers from 94 families were separately observed with their two children (44 % girls), aged 2 and 4 years at Time 1, in three structured situations involving one puzzle task and two games. Overinvolved and challenging parenting behavior were coded. Child social anxiety was measured by observing the child's response to a stranger at Time 1, and half a year later at Time 2, and by parental ratings. In line with predictions, father's challenging parenting behavior predicted less subsequent observed social anxiety of the 4-year-old child. Mothers' challenging behavior, however, predicted more observed social anxiety of the 4-year-old. Parents' overinvolvement at Time 1 did not predict change in observed social anxiety of the 4-year-old child. For the 2-year-old child, maternal and paternal parenting behavior did not predict subsequent social anxiety, but early social anxiety marginally did. Parent-rated social anxiety was predicted by previous parental ratings of social anxiety, and not by parenting behavior. Challenging parenting behavior appears to have favorable effects on observed 4-year-old's social anxiety when displayed by the father. Challenging parenting behavior emerges as an important focus for future research and interventions.

Expression Profiles of TGF-β and TLR Pathways in Porphyromonas gingivalis and Prevotella intermedia Challenged Osteoblasts

PubMed Central

Aydin, Kubra; Ekinci, Fatma Yesim; Korachi, May

2015-01-01

Background: The presence of certain oral pathogens at implant sites can hinder the osseointegration process. However, it is unclear how and by what microorganisms it happens. Objectives: This study investigated whether the presence of oral pathogens of Porphyromonas gingivalis and Prevotella intermedia individually, play a role in the failure of bone formation by determining the expression profiles of Transforming Growth Factor Beta (TGF-β/Bone Morphogenic Protein (BMP) and Toll-Like Receptor (TLR) pathways in challenged osteoblasts. Materials and Methods: Cell viability of P. gingivalis and P. intermedia challenged osteoblasts were determined by WST assay. Changes in osteoblast morphology and inhibition of mineralization were observed by Scanning Electron Microscopy (SEM) and Von Kossa staining, respectively. Expression of TGF-β and TLR pathway genes on challenged cells were identified by RT profiler array. Both P. gingivalis and P. intermedia challenges resulted in reduced viability and mineralization of osteoblasts. Results: Viability was reduced to 56.8% (P. gingivalis) and 52.75% (P. intermedia) at 1000 multiplicity. Amongst 48 genes examined, expressions of BMPER, SMAD1, IL8 and NFRKB were found to be highly upregulated by both bacterial challenges (Fold Change > 4). Conclusions: P. gingivalis and P. intermedia could play a role in implant failure by changing the expression profiles of genes related to bone formation and resorption. PMID:26034550

Macaques can predict social outcomes from facial expressions.

PubMed

Waller, Bridget M; Whitehouse, Jamie; Micheletta, Jérôme

2016-09-01

There is widespread acceptance that facial expressions are useful in social interactions, but empirical demonstration of their adaptive function has remained elusive. Here, we investigated whether macaques can use the facial expressions of others to predict the future outcomes of social interaction. Crested macaques (Macaca nigra) were shown an approach between two unknown individuals on a touchscreen and were required to choose between one of two potential social outcomes. The facial expressions of the actors were manipulated in the last frame of the video. One subject reached the experimental stage and accurately predicted different social outcomes depending on which facial expressions the actors displayed. The bared-teeth display (homologue of the human smile) was most strongly associated with predicted friendly outcomes. Contrary to our predictions, screams and threat faces were not associated more with conflict outcomes. Overall, therefore, the presence of any facial expression (compared to neutral) caused the subject to choose friendly outcomes more than negative outcomes. Facial expression in general, therefore, indicated a reduced likelihood of social conflict. The findings dispute traditional theories that view expressions only as indicators of present emotion and instead suggest that expressions form part of complex social interactions where individuals think beyond the present.

Self vs. Other Focus: Predicting Professionalism Remediation of Emergency Medicine Residents.

PubMed

Thaxton, Robert E; Jones, Woodson S; Hafferty, Fred W; April, Carolyn W; April, Michael D

2018-01-01

Unprofessionalism is a major reason for resident dismissal from training. Because of the high stakes involved, residents and educators alike would benefit from information predicting whether they might experience challenges related to this competency. Our objective was to correlate the outcome of professionalism-related remedial actions during residency with the predictor variable of resident response to a standardized interview question: "Why is Medicine important to you?" We conducted a professional development quality improvement (QI) initiative to improve resident education and mentorship by achieving a better understanding of each resident's reasons for valuing a career in medicine. This initiative entailed an interview administered to each resident beginning emergency medicine training at San Antonio Military Medical Center during 2006-2013. The interviews uniformly began with the standardized question "Why is Medicine important to you?" The residency program director documented a free-text summary of each response to this question, the accuracy of which was confirmed by the resident. We analyzed the text of each resident's response after a review of the QI data suggested an association between responses and professionalism actions (retrospective cohort design). Two associate investigators blinded to all interview data, remedial actions, and resident identities categorized each text response as either self-focused (e.g., "I enjoy the challenge") or other-focused (e.g., "I enjoy helping patients"). Additional de-identified data collected included demographics, and expressed personal importance of politics and religion. The primary outcome was a Clinical Competency Committee professionalism remedial action. Of 114 physicians starting residency during 2006-2013, 106 (93.0%) completed the interview. There was good inter-rater reliability in associate investigator categorization of resident responses as either self-focused or other-focused (kappa coefficient 0.85). Thirteen of 50 residents (26.0%) expressed self-focus versus three of 54 (5.4%) residents expressed other-focus experienced professionalism remedial actions (p<0.01). This association held in a logistic regression model controlling for measured confounders (p=0.02). Self-focused responses to the question "Why is Medicine important to you?" correlated with professionalism remedial actions during residency.

Fast prediction of RNA-RNA interaction using heuristic algorithm.

PubMed

Montaseri, Soheila

2015-01-01

Interaction between two RNA molecules plays a crucial role in many medical and biological processes such as gene expression regulation. In this process, an RNA molecule prohibits the translation of another RNA molecule by establishing stable interactions with it. Some algorithms have been formed to predict the structure of the RNA-RNA interaction. High computational time is a common challenge in most of the presented algorithms. In this context, a heuristic method is introduced to accurately predict the interaction between two RNAs based on minimum free energy (MFE). This algorithm uses a few dot matrices for finding the secondary structure of each RNA and binding sites between two RNAs. Furthermore, a parallel version of this method is presented. We describe the algorithm's concurrency and parallelism for a multicore chip. The proposed algorithm has been performed on some datasets including CopA-CopT, R1inv-R2inv, Tar-Tar*, DIS-DIS, and IncRNA54-RepZ in Escherichia coli bacteria. The method has high validity and efficiency, and it is run in low computational time in comparison to other approaches.

ChIP-seq Accurately Predicts Tissue-Specific Activity of Enhancers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Visel, Axel; Blow, Matthew J.; Li, Zirong

2009-02-01

A major yet unresolved quest in decoding the human genome is the identification of the regulatory sequences that control the spatial and temporal expression of genes. Distant-acting transcriptional enhancers are particularly challenging to uncover since they are scattered amongst the vast non-coding portion of the genome. Evolutionary sequence constraint can facilitate the discovery of enhancers, but fails to predict when and where they are active in vivo. Here, we performed chromatin immunoprecipitation with the enhancer-associated protein p300, followed by massively-parallel sequencing, to map several thousand in vivo binding sites of p300 in mouse embryonic forebrain, midbrain, and limb tissue. Wemore » tested 86 of these sequences in a transgenic mouse assay, which in nearly all cases revealed reproducible enhancer activity in those tissues predicted by p300 binding. Our results indicate that in vivo mapping of p300 binding is a highly accurate means for identifying enhancers and their associated activities and suggest that such datasets will be useful to study the role of tissue-specific enhancers in human biology and disease on a genome-wide scale.« less

Model of brain activation predicts the neural collective influence map of the brain

PubMed Central

Morone, Flaviano; Roth, Kevin; Min, Byungjoon; Makse, Hernán A.

2017-01-01

Efficient complex systems have a modular structure, but modularity does not guarantee robustness, because efficiency also requires an ingenious interplay of the interacting modular components. The human brain is the elemental paradigm of an efficient robust modular system interconnected as a network of networks (NoN). Understanding the emergence of robustness in such modular architectures from the interconnections of its parts is a longstanding challenge that has concerned many scientists. Current models of dependencies in NoN inspired by the power grid express interactions among modules with fragile couplings that amplify even small shocks, thus preventing functionality. Therefore, we introduce a model of NoN to shape the pattern of brain activations to form a modular environment that is robust. The model predicts the map of neural collective influencers (NCIs) in the brain, through the optimization of the influence of the minimal set of essential nodes responsible for broadcasting information to the whole-brain NoN. Our results suggest intervention protocols to control brain activity by targeting influential neural nodes predicted by network theory. PMID:28351973

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states

PubMed Central

Jang, Sumin; Choubey, Sandeep; Furchtgott, Leon; Zou, Ling-Nan; Doyle, Adele; Menon, Vilas; Loew, Ethan B; Krostag, Anne-Rachel; Martinez, Refugio A; Madisen, Linda; Levi, Boaz P; Ramanathan, Sharad

2017-01-01

The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Our study provides a framework to infer the dynamics of differentiation from single cell transcriptomics data and to build predictive models of the gene regulatory networks that drive the sequence of cell fate decisions during development. DOI: http://dx.doi.org/10.7554/eLife.20487.001 PMID:28296635

Nitric oxide mediates antimicrobial peptide gene expression by activating eicosanoid signaling

PubMed Central

Sadekuzzaman, Md.

2018-01-01

Nitric oxide (NO) mediates both cellular and humoral immune responses in insects. Its mediation of cellular immune responses uses eicosanoids as a downstream signal. However, the cross-talk with two immune mediators was not known in humoral immune responses. This study focuses on cross-talk between two immune mediators in inducing gene expression of anti-microbial peptides (AMPs) of a lepidopteran insect, Spodoptera exigua. Up-regulation of eight AMPs was observed in S. exigua against bacterial challenge. However, the AMP induction was suppressed by injection of an NO synthase inhibitor, L-NAME, while little expressional change was observed on injecting its enantiomer, D-NAME. The functional association between NO biosynthesis and AMP gene expression was further supported by RNA interference (RNAi) against NO synthase (SeNOS), which suppressed AMP gene expression under the immune challenge. The AMP induction was also mimicked by NO alone because injecting an NO analog, SNAP, without bacterial challenge significantly induced the AMP gene expression. Interestingly, an eicosanoid biosynthesis inhibitor, dexamethasone (DEX), suppressed the NO induction of AMP expression. The inhibitory activity of DEX was reversed by the addition of arachidonic acid, a precursor of eicosanoid biosynthesis. AMP expression of S. exigua was also controlled by the Toll/IMD signal pathway. The RNAi of Toll receptors or Relish suppressed AMP gene expression by suppressing NO levels and subsequently reducing PLA2 enzyme activity. These results suggest that eicosanoids are a downstream signal of NO mediation of AMP expression against bacterial challenge. PMID:29466449

Promoting advance care planning among community-based older adults: A randomized controlled trial.

PubMed

Bravo, Gina; Trottier, Lise; Arcand, Marcel; Boire-Lavigne, Anne-Marie; Blanchette, Danièle; Dubois, Marie-France; Guay, Maryse; Lane, Julie; Hottin, Paule; Bellemare, Suzanne

2016-11-01

To test an intervention designed to motivate older adults in documenting their healthcare preferences in advance, and to guide proxies in making hypothetical decisions that match those of the older adult. The trial involved 235 older adults, of which half were assisted in communicating their wishes to their proxy. Hypothetical vignettes were used at baseline and twice after the intervention to elicit older adults' preferences and assess their proxy's ability to predict them. By the end of the trial, 80% of older adults allocated to the experimental group had documented their wishes. Changes over time in mean accuracy scores did not differ between groups for any hypothetical situations, except when limiting the sample to dyads that were highly discordant at baseline. The intervention motivated a large proportion of older adults to express their preferences but had little effect on proxies' ability to predict them. Educational tools developed for this study will assist healthcare providers in helping older adults to record their wishes in advance. Clients must be informed of the challenge of making substitute decisions and of the need to discuss the amount of leeway the proxy should have in interpreting expressed wishes. Copyright © 2016 The Author(s). Published by Elsevier Ireland Ltd.. All rights reserved.

Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics

PubMed Central

Welch, Stephen M.; White, Jeffrey W.; Thorp, Kelly R.; Bello, Nora M.

2018-01-01

Ecophysiological crop models encode intra-species behaviors using parameters that are presumed to summarize genotypic properties of individual lines or cultivars. These genotype-specific parameters (GSP’s) can be interpreted as quantitative traits that can be mapped or otherwise analyzed, as are more conventional traits. The goal of this study was to investigate the estimation of parameters controlling maize anthesis date with the CERES-Maize model, based on 5,266 maize lines from 11 plantings at locations across the eastern United States. High performance computing was used to develop a database of 356 million simulated anthesis dates in response to four CERES-Maize model parameters. Although the resulting estimates showed high predictive value (R2 = 0.94), three issues presented serious challenges for use of GSP’s as traits. First (expressivity), the model was unable to express the observed data for 168 to 3,339 lines (depending on the combination of site-years), many of which ended up sharing the same parameter value irrespective of genetics. Second, for 2,254 lines, the model reproduced the data, but multiple parameter sets were equally effective (equifinality). Third, parameter values were highly dependent (p<10−6919) on the sets of environments used to estimate them (instability), calling in to question the assumption that they represent fundamental genetic traits. The issues of expressivity, equifinality and instability must be addressed before the genetic mapping of GSP’s becomes a robust means to help solve the genotype-to-phenotype problem in crops. PMID:29672629

The second Sandia Fracture Challenge. Predictions of ductile failure under quasi-static and moderate-rate dynamic loading

DOE PAGES

Boyce, B. L.; Kramer, S. L. B.; Bosiljevac, T. R.; ...

2016-03-14

Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Instead of evaluating the predictions of a single simulation approach, the Sandia Fracture Challenge relied on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive modelsmore » to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in ~ 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. In addition, shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.« less

The second Sandia Fracture Challenge. Predictions of ductile failure under quasi-static and moderate-rate dynamic loading

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boyce, B. L.; Kramer, S. L. B.; Bosiljevac, T. R.

Ductile failure of structural metals is relevant to a wide range of engineering scenarios. Computational methods are employed to anticipate the critical conditions of failure, yet they sometimes provide inaccurate and misleading predictions. Challenge scenarios, such as the one presented in the current work, provide an opportunity to assess the blind, quantitative predictive ability of simulation methods against a previously unseen failure problem. Instead of evaluating the predictions of a single simulation approach, the Sandia Fracture Challenge relied on numerous volunteer teams with expertise in computational mechanics to apply a broad range of computational methods, numerical algorithms, and constitutive modelsmore » to the challenge. This exercise is intended to evaluate the state of health of technologies available for failure prediction. In the first Sandia Fracture Challenge, a wide range of issues were raised in ductile failure modeling, including a lack of consistency in failure models, the importance of shear calibration data, and difficulties in quantifying the uncertainty of prediction [see Boyce et al. (Int J Fract 186:5–68, 2014) for details of these observations]. This second Sandia Fracture Challenge investigated the ductile rupture of a Ti–6Al–4V sheet under both quasi-static and modest-rate dynamic loading (failure in ~ 0.1 s). Like the previous challenge, the sheet had an unusual arrangement of notches and holes that added geometric complexity and fostered a competition between tensile- and shear-dominated failure modes. The teams were asked to predict the fracture path and quantitative far-field failure metrics such as the peak force and displacement to cause crack initiation. Fourteen teams contributed blind predictions, and the experimental outcomes were quantified in three independent test labs. In addition, shortcomings were revealed in this second challenge such as inconsistency in the application of appropriate boundary conditions, need for a thermomechanical treatment of the heat generation in the dynamic loading condition, and further difficulties in model calibration based on limited real-world engineering data. As with the prior challenge, this work not only documents the ‘state-of-the-art’ in computational failure prediction of ductile tearing scenarios, but also provides a detailed dataset for non-blind assessment of alternative methods.« less

Cancer survival classification using integrated data sets and intermediate information.

PubMed

Kim, Shinuk; Park, Taesung; Kon, Mark

2014-09-01

Although numerous studies related to cancer survival have been published, increasing the prediction accuracy of survival classes still remains a challenge. Integration of different data sets, such as microRNA (miRNA) and mRNA, might increase the accuracy of survival class prediction. Therefore, we suggested a machine learning (ML) approach to integrate different data sets, and developed a novel method based on feature selection with Cox proportional hazard regression model (FSCOX) to improve the prediction of cancer survival time. FSCOX provides us with intermediate survival information, which is usually discarded when separating survival into 2 groups (short- and long-term), and allows us to perform survival analysis. We used an ML-based protocol for feature selection, integrating information from miRNA and mRNA expression profiles at the feature level. To predict survival phenotypes, we used the following classifiers, first, existing ML methods, support vector machine (SVM) and random forest (RF), second, a new median-based classifier using FSCOX (FSCOX_median), and third, an SVM classifier using FSCOX (FSCOX_SVM). We compared these methods using 3 types of cancer tissue data sets: (i) miRNA expression, (ii) mRNA expression, and (iii) combined miRNA and mRNA expression. The latter data set included features selected either from the combined miRNA/mRNA profile or independently from miRNAs and mRNAs profiles (IFS). In the ovarian data set, the accuracy of survival classification using the combined miRNA/mRNA profiles with IFS was 75% using RF, 86.36% using SVM, 84.09% using FSCOX_median, and 88.64% using FSCOX_SVM with a balanced 22 short-term and 22 long-term survivor data set. These accuracies are higher than those using miRNA alone (70.45%, RF; 75%, SVM; 75%, FSCOX_median; and 75%, FSCOX_SVM) or mRNA alone (65.91%, RF; 63.64%, SVM; 72.73%, FSCOX_median; and 70.45%, FSCOX_SVM). Similarly in the glioblastoma multiforme data, the accuracy of miRNA/mRNA using IFS was 75.51% (RF), 87.76% (SVM) 85.71% (FSCOX_median), 85.71% (FSCOX_SVM). These results are higher than the results of using miRNA expression and mRNA expression alone. In addition we predict 16 hsa-miR-23b and hsa-miR-27b target genes in ovarian cancer data sets, obtained by SVM-based feature selection through integration of sequence information and gene expression profiles. Among the approaches used, the integrated miRNA and mRNA data set yielded better results than the individual data sets. The best performance was achieved using the FSCOX_SVM method with independent feature selection, which uses intermediate survival information between short-term and long-term survival time and the combination of the 2 different data sets. The results obtained using the combined data set suggest that there are some strong interactions between miRNA and mRNA features that are not detectable in the individual analyses. Copyright © 2014 Elsevier B.V. All rights reserved.

Individual differences in the recognition of facial expressions: an event-related potentials study.

PubMed

Tamamiya, Yoshiyuki; Hiraki, Kazuo

2013-01-01

Previous studies have shown that early posterior components of event-related potentials (ERPs) are modulated by facial expressions. The goal of the current study was to investigate individual differences in the recognition of facial expressions by examining the relationship between ERP components and the discrimination of facial expressions. Pictures of 3 facial expressions (angry, happy, and neutral) were presented to 36 young adults during ERP recording. Participants were asked to respond with a button press as soon as they recognized the expression depicted. A multiple regression analysis, where ERP components were set as predictor variables, assessed hits and reaction times in response to the facial expressions as dependent variables. The N170 amplitudes significantly predicted for accuracy of angry and happy expressions, and the N170 latencies were predictive for accuracy of neutral expressions. The P2 amplitudes significantly predicted reaction time. The P2 latencies significantly predicted reaction times only for neutral faces. These results suggest that individual differences in the recognition of facial expressions emerge from early components in visual processing.

Gene Expression Profiling Predicts the Development of Oral Cancer

PubMed Central

Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li

2011-01-01

Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. PMID:21292635

Challenges facing developers of CAD/CAM models that seek to predict human working postures

NASA Astrophysics Data System (ADS)

Wiker, Steven F.

2005-11-01

This paper outlines the need for development of human posture prediction models for Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) design applications in product, facility and work design. Challenges facing developers of posture prediction algorithms are presented and discussed.

Dexamethasone Stimulated Gene Expression in Peripheral Blood is a Sensitive Marker for Glucocorticoid Receptor Resistance in Depressed Patients

PubMed Central

Menke, Andreas; Arloth, Janine; Pütz, Benno; Weber, Peter; Klengel, Torsten; Mehta, Divya; Gonik, Mariya; Rex-Haffner, Monika; Rubel, Jennifer; Uhr, Manfred; Lucae, Susanne; Deussing, Jan M; Müller-Myhsok, Bertram; Holsboer, Florian; Binder, Elisabeth B

2012-01-01

Although gene expression profiles in peripheral blood in major depression are not likely to identify genes directly involved in the pathomechanism of affective disorders, they may serve as biomarkers for this disorder. As previous studies using baseline gene expression profiles have provided mixed results, our approach was to use an in vivo dexamethasone challenge test and to compare glucocorticoid receptor (GR)-mediated changes in gene expression between depressed patients and healthy controls. Whole genome gene expression data (baseline and following GR-stimulation with 1.5 mg dexamethasone p.o.) from two independent cohorts were analyzed to identify gene expression pattern that would predict case and control status using a training (N=18 cases/18 controls) and a test cohort (N=11/13). Dexamethasone led to reproducible regulation of 2670 genes in controls and 1151 transcripts in cases. Several genes, including FKBP5 and DUSP1, previously associated with the pathophysiology of major depression, were found to be reliable markers of GR-activation. Using random forest analyses for classification, GR-stimulated gene expression outperformed baseline gene expression as a classifier for case and control status with a correct classification of 79.1 vs 41.6% in the test cohort. GR-stimulated gene expression performed best in dexamethasone non-suppressor patients (88.7% correctly classified with 100% sensitivity), but also correctly classified 77.3% of the suppressor patients (76.7% sensitivity), when using a refined set of 19 genes. Our study suggests that in vivo stimulated gene expression in peripheral blood cells could be a promising molecular marker of altered GR-functioning, an important component of the underlying pathology, in patients suffering from depressive episodes. PMID:22237309

Expression and Purification of Soluble STAT5b/STAT3 Proteins for SH2 Domain Binding Assay.

PubMed

Asai, Akira; Takakuma, Kazuyuki

2017-01-01

When a large hydrophobic full-length protein is expressed in bacteria, it is often challenging to obtain recombinant proteins in the soluble fraction. One way to overcome this challenge is expression of deletion mutants that have improved solubility while maintaining biological activity. In this chapter, we describe a protocol for expression of truncated forms of STAT5b and STAT3 proteins that are soluble and retain SH2-mediated activity for phospho-Tyr peptide recognition.

Experimental hookworm infection and gluten microchallenge promote tolerance in celiac disease.

PubMed

Croese, John; Giacomin, Paul; Navarro, Severine; Clouston, Andrew; McCann, Leisa; Dougall, Annette; Ferreira, Ivana; Susianto, Atik; O'Rourke, Peter; Howlett, Mariko; McCarthy, James; Engwerda, Christian; Jones, Dianne; Loukas, Alex

2015-02-01

Celiac disease (CeD) is a common gluten-sensitive autoimmune enteropathy. A gluten-free diet is an effective treatment, but compliance is demanding; hence, new treatment strategies for CeD are required. Parasitic helminths hold promise for treating inflammatory disorders, so we examined the influence of experimental hookworm infection on the predicted outcomes of escalating gluten challenges in CeD subjects. A 52-week study was conducted involving 12 adults with diet-managed CeD. Subjects were inoculated with 20 Necator americanus larvae, and escalating gluten challenges consumed as pasta were subsequently administered: (1) 10 to 50 mg for 12 weeks (microchallenge); (2) 25 mg daily + 1 g twice weekly for 12 weeks (GC-1g); and (3) 3 g daily (60-75 straws of spaghetti) for 2 weeks (GC-3g). Symptomatic, serologic, and histological outcomes evaluated gluten toxicity. Regulatory and inflammatory T cell populations in blood and mucosa were examined. Two gluten-intolerant subjects were withdrawn after microchallenge. Ten completed GC-1g, 8 of whom enrolled in and completed GC-3g. median villous height-to-crypt depth ratios (2.60-2.63; P = .98) did not decrease as predicted after GC-1g, and the mean IgA-tissue transglutaminase titers declined, contrary to the predicted rise after GC-3g. quality of life scores improved (46.3-40.6; P = .05); celiac symptom indices (24.3-24.3; P = .53), intra-epithelial lymphocyte percentages (32.5-35.0; P = .47), and Marsh scores were unchanged by gluten challenge. Intestinal T cells expressing IFNγ were reduced following hookworm infection (23.9%-11.5%; P = .04), with corresponding increases in CD4(+) Foxp3(+) regulatory T cells (0.19%-1.12%; P = .001). Necator americanus and gluten microchallenge promoted tolerance and stabilized or improved all tested indices of gluten toxicity in CeD subjects. Copyright © 2014 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

PubMed Central

Memarian, Negar; Torre, Jared B.; Haltom, Kate E.; Stanton, Annette L.

2017-01-01

Abstract Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. PMID:28992270

Skin prick testing predicts peanut challenge outcome in previously allergic or sensitized children with low serum peanut-specific IgE antibody concentration.

PubMed

Nolan, Richard C; Richmond, Peter; Prescott, Susan L; Mallon, Dominic F; Gong, Grace; Franzmann, Annkathrin M; Naidoo, Rama; Loh, Richard K S

2007-05-01

Peanut allergy is transient in some children but it is not clear whether quantitating peanut-specific IgE by Skin Prick Test (SPT) adds additional information to fluorescent-enzyme immunoassay (FEIA) in discriminating between allergic and tolerant children. To investigate whether SPT with a commercial extract or fresh foods adds additional predictive information for peanut challenge in children with a low FEIA (<10 k UA/L) who were previously sensitized, or allergic to peanuts. Children from a hospital-based allergy service who were previously sensitized or allergic to peanuts were invited to undergo a peanut challenge unless they had a serum peanut-specific IgE>10 k UA/L, a previous severe reaction, or a recent reaction to peanuts (within two years). SPT with a commercial extract, raw and roasted saline soaked peanuts was performed immediately prior to open challenge in hospital with increasing quantity of peanuts until total of 26.7 g of peanut was consumed. A positive challenge consisted of an objective IgE mediated reaction occurring during the observation period. 54 children (median age of 6.3 years) were admitted for a challenge. Nineteen challenges were positive, 27 negative, five were indeterminate and three did not proceed after SPT. Commercial and fresh food extracts provided similar diagnostic information. A wheal diameter of >or=7 mm of the commercial extract predicted an allergic outcome with specificity 97%, positive predictive value 93% and sensitivity 83%. There was a tendency for an increase in SPT wheal since initial diagnosis in children who remained allergic to peanuts while it decreased in those with a negative challenge. The outcome of a peanut challenge in peanut sensitized or previously allergic children with a low FEIA can be predicted by SPT. In this cohort, not challenging children with a SPT wheal of >or=7 mm would have avoided 15 of 18 positive challenges and denied a challenge to one out of 27 tolerant children.

The RAPIDD ebola forecasting challenge: Synthesis and lessons learnt.

PubMed

Viboud, Cécile; Sun, Kaiyuan; Gaffey, Robert; Ajelli, Marco; Fumanelli, Laura; Merler, Stefano; Zhang, Qian; Chowell, Gerardo; Simonsen, Lone; Vespignani, Alessandro

2018-03-01

Infectious disease forecasting is gaining traction in the public health community; however, limited systematic comparisons of model performance exist. Here we present the results of a synthetic forecasting challenge inspired by the West African Ebola crisis in 2014-2015 and involving 16 international academic teams and US government agencies, and compare the predictive performance of 8 independent modeling approaches. Challenge participants were invited to predict 140 epidemiological targets across 5 different time points of 4 synthetic Ebola outbreaks, each involving different levels of interventions and "fog of war" in outbreak data made available for predictions. Prediction targets included 1-4 week-ahead case incidences, outbreak size, peak timing, and several natural history parameters. With respect to weekly case incidence targets, ensemble predictions based on a Bayesian average of the 8 participating models outperformed any individual model and did substantially better than a null auto-regressive model. There was no relationship between model complexity and prediction accuracy; however, the top performing models for short-term weekly incidence were reactive models with few parameters, fitted to a short and recent part of the outbreak. Individual model outputs and ensemble predictions improved with data accuracy and availability; by the second time point, just before the peak of the epidemic, estimates of final size were within 20% of the target. The 4th challenge scenario - mirroring an uncontrolled Ebola outbreak with substantial data reporting noise - was poorly predicted by all modeling teams. Overall, this synthetic forecasting challenge provided a deep understanding of model performance under controlled data and epidemiological conditions. We recommend such "peace time" forecasting challenges as key elements to improve coordination and inspire collaboration between modeling groups ahead of the next pandemic threat, and to assess model forecasting accuracy for a variety of known and hypothetical pathogens. Published by Elsevier B.V.

Identification of highly expressed host microRNAs that respond to white spot syndrome virus infection in the Pacific white shrimp Litopenaeus vannamei (Penaeidae).

PubMed

Zeng, D G; Chen, X L; Xie, D X; Zhao, Y Z; Yang, Q; Wang, H; Li, Y M; Chen, X H

2015-05-11

MicroRNAs (miRNAs) are known to play an important role in regulating both adaptive and innate immunity. Pacific white shrimp (Litopenaeus vannamei) is the most widely farmed crustacean species in the world. However, little is known about the role miRNAs play in shrimp immunity. To understand the impact of viral infection on miRNA expression in shrimp, we used high-throughput sequencing technology to sequence two small RNA libraries prepared from L. vannamei under normal and white spot syndrome virus (WSSV) challenged conditions. Approximately 19,312,189 and 39,763,551 raw reads corresponding to 17,414,787 and 28,633,379 high-quality mappable reads were obtained from the two libraries, respectively. Twelve conserved miRNAs and one novel miRNA that were highly expressed (>100 RPM) in L. vannamei were identified. Of the identified miRNAs, 8 were differentially expressed in response to the virus infection, of which 1 was upregulated and 7 were downregulated. The prediction of miRNA targets showed that the target genes of the differentially expressed miRNAs were related to immunity, apoptosis, and development functions. Our study provides the first characterization of L. vannamei miRNAs in response to WSSV infection, which will help to reveal the roles of miRNAs in the antiviral mechanisms of shrimp.

Quantifying the Effect of DNA Packaging on Gene Expression Level

NASA Astrophysics Data System (ADS)

Kim, Harold

2010-10-01

Gene expression, the process by which the genetic code comes alive in the form of proteins, is one of the most important biological processes in living cells, and begins when transcription factors bind to specific DNA sequences in the promoter region upstream of a gene. The relationship between gene expression output and transcription factor input which is termed the gene regulation function is specific to each promoter, and predicting this gene regulation function from the locations of transcription factor binding sites is one of the challenges in biology. In eukaryotic organisms (for example, animals, plants, fungi etc), DNA is highly compacted into nucleosomes, 147-bp segments of DNA tightly wrapped around histone protein core, and therefore, the accessibility of transcription factor binding sites depends on their locations with respect to nucleosomes - sites inside nucleosomes are less accessible than those outside nucleosomes. To understand how transcription factor binding sites contribute to gene expression in a quantitative manner, we obtain gene regulation functions of promoters with various configurations of transcription factor binding sites by using fluorescent protein reporters to measure transcription factor input and gene expression output in single yeast cells. In this talk, I will show that the affinity of a transcription factor binding site inside and outside the nucleosome controls different aspects of the gene regulation function, and explain this finding based on a mass-action kinetic model that includes competition between nucleosomes and transcription factors.

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation

EPA Science Inventory

Building gene expression signatures indicative of transcription factor activation to predict AOP modulation Adverse outcome pathways (AOPs) are a framework for predicting quantitative relationships between molecular initiatin...

Predicting features of breast cancer with gene expression patterns.

PubMed

Lu, Xuesong; Lu, Xin; Wang, Zhigang C; Iglehart, J Dirk; Zhang, Xuegong; Richardson, Andrea L

2008-03-01

Data from gene expression arrays hold an enormous amount of biological information. We sought to determine if global gene expression in primary breast cancers contained information about biologic, histologic, and anatomic features of the disease in individual patients. Microarray data from the tumors of 129 patients were analyzed for the ability to predict biomarkers [estrogen receptor (ER) and HER2], histologic features [grade and lymphatic-vascular invasion (LVI)], and stage parameters (tumor size and lymph node metastasis). Multiple statistical predictors were used and the prediction accuracy was determined by cross-validation error rate; multidimensional scaling (MDS) allowed visualization of the predicted states under study. Models built from gene expression data accurately predict ER and HER2 status, and divide tumor grade into high-grade and low-grade clusters; intermediate-grade tumors are not a unique group. In contrast, gene expression data is inaccurate at predicting tumor size, lymph node status or LVI. The best model for prediction of nodal status included tumor size, LVI status and pathologically defined tumor subtype (based on combinations of ER, HER2, and grade); the addition of microarray-based prediction to this model failed to improve the prediction accuracy. Global gene expression supports a binary division of ER, HER2, and grade, clearly separating tumors into two categories; intermediate values for these bio-indicators do not define intermediate tumor subsets. Results are consistent with a model of regional metastasis that depends on inherent biologic differences in metastatic propensity between breast cancer subtypes, upon which time and chance then operate.

Application of a fuzzy neural network model in predicting polycyclic aromatic hydrocarbon-mediated perturbations of the Cyp1b1 transcriptional regulatory network in mouse skin

DOE Office of Scientific and Technical Information (OSTI.GOV)

Larkin, Andrew; Department of Statistics, Oregon State University; Superfund Research Center, Oregon State University

2013-03-01

Polycyclic aromatic hydrocarbons (PAHs) are present in the environment as complex mixtures with components that have diverse carcinogenic potencies and mostly unknown interactive effects. Non-additive PAH interactions have been observed in regulation of cytochrome P450 (CYP) gene expression in the CYP1 family. To better understand and predict biological effects of complex mixtures, such as environmental PAHs, an 11 gene input-1 gene output fuzzy neural network (FNN) was developed for predicting PAH-mediated perturbations of dermal Cyp1b1 transcription in mice. Input values were generalized using fuzzy logic into low, medium, and high fuzzy subsets, and sorted using k-means clustering to create Mamdanimore » logic functions for predicting Cyp1b1 mRNA expression. Model testing was performed with data from microarray analysis of skin samples from FVB/N mice treated with toluene (vehicle control), dibenzo[def,p]chrysene (DBC), benzo[a]pyrene (BaP), or 1 of 3 combinations of diesel particulate extract (DPE), coal tar extract (CTE) and cigarette smoke condensate (CSC) using leave-one-out cross-validation. Predictions were within 1 log{sub 2} fold change unit of microarray data, with the exception of the DBC treatment group, where the unexpected down-regulation of Cyp1b1 expression was predicted but did not reach statistical significance on the microarrays. Adding CTE to DPE was predicted to increase Cyp1b1 expression, whereas adding CSC to CTE and DPE was predicted to have no effect, in agreement with microarray results. The aryl hydrocarbon receptor repressor (Ahrr) was determined to be the most significant input variable for model predictions using back-propagation and normalization of FNN weights. - Highlights: ► Tested a model to predict PAH mixture-mediated changes in Cyp1b1 expression ► Quantitative predictions in agreement with microarrays for Cyp1b1 induction ► Unexpected difference in expression between DBC and other treatments predicted ► Model predictions for combining PAH mixtures in agreement with microarrays ► Predictions highly dependent on aryl hydrocarbon receptor repressor expression.« less

Contrasting diets reveal metabolic plasticity in the tree-killing beetle, Anoplophora glabripennis (Cerambycidae: Lamiinae)

NASA Astrophysics Data System (ADS)

Mason, Charles J.; Scully, Erin D.; Geib, Scott M.; Hoover, Kelli

2016-09-01

Wood-feeding insects encounter challenging diets containing low protein quantities, recalcitrant carbohydrate sources, and plant defensive compounds. The Asian longhorned beetle (Anoplophora glabripennis) is a wood-feeding insect that attacks and kills a diversity of hardwood tree species. We compared gene expression of midguts collected from larvae feeding in a preferred tree, sugar maple, to those consuming a nutrient-rich artificial diet, to identify genes putatively involved in host plant utilization. Anoplophora glabripennis larvae exhibited differential expression of ~3600 genes in response to different diets. Genes with predicted capacity for plant and microbial carbohydrate usage, detoxification, nutrient recycling, and immune-related genes relevant for facilitating interactions with microbial symbionts were upregulated in wood-feeding larvae compared to larvae feeding in artificial diet. Upregulation of genes involved in protein degradation and synthesis was also observed, suggesting that proteins incur more rapid turnover in insects consuming wood. Additionally, wood-feeding individuals exhibited elevated expression of several mitochondrial cytochrome C oxidase genes, suggesting increased aerobic respiration compared to diet-fed larvae. These results indicate that A. glabripennis modulates digestive and basal gene expression when larvae are feeding in a nutrient-poor, yet suitable host plant compared to a tractable and nutrient-rich diet that is free of plant defensive compounds.

Neural correlates of combinatorial semantic processing of literal and figurative noun noun compound words.

PubMed

Forgács, Bálint; Bohrn, Isabel; Baudewig, Jürgen; Hofmann, Markus J; Pléh, Csaba; Jacobs, Arthur M

2012-11-15

The right hemisphere's role in language comprehension is supported by results from several neuropsychology and neuroimaging studies. Special interest surrounds right temporoparietal structures, which are thought to be involved in processing novel metaphorical expressions, primarily due to the coarse semantic coding of concepts. In this event related fMRI experiment we aimed at assessing the extent of semantic distance processing in the comprehension of figurative meaning to clarify the role of the right hemisphere. Four categories of German noun noun compound words were presented in a semantic decision task: a) conventional metaphors; b) novel metaphors; c) conventional literal, and; d) novel literal expressions, controlled for length, frequency, imageability, arousal, and emotional valence. Conventional literal and metaphorical compounds increased BOLD signal change in right temporoparietal regions, suggesting combinatorial semantic processing, in line with the coarse semantic coding theory, but at odds with the graded salience hypothesis. Both novel literal and novel metaphorical expressions increased activity in left inferior frontal areas, presumably as a result of phonetic, morphosyntactic, and semantic unification processes, challenging predictions regarding right hemispheric involvement in processing unusual meanings. Meanwhile, both conventional and novel metaphorical expressions induced BOLD signal change in left hemispherical regions, suggesting that even novel metaphor processing involves more than linking semantically distant concepts. Copyright © 2012 Elsevier Inc. All rights reserved.

Immune signatures of protective spleen memory CD8 T cells.

PubMed

Brinza, Lilia; Djebali, Sophia; Tomkowiak, Martine; Mafille, Julien; Loiseau, Céline; Jouve, Pierre-Emmanuel; de Bernard, Simon; Buffat, Laurent; Lina, Bruno; Ottmann, Michèle; Rosa-Calatrava, Manuel; Schicklin, Stéphane; Bonnefoy, Nathalie; Lauvau, Grégoire; Grau, Morgan; Wencker, Mélanie; Arpin, Christophe; Walzer, Thierry; Leverrier, Yann; Marvel, Jacqueline

2016-11-24

Memory CD8 T lymphocyte populations are remarkably heterogeneous and differ in their ability to protect the host. In order to identify the whole range of qualities uniquely associated with protective memory cells we compared the gene expression signatures of two qualities of memory CD8 T cells sharing the same antigenic-specificity: protective (Influenza-induced, Flu-TM) and non-protective (peptide-induced, TIM) spleen memory CD8 T cells. Although Flu-TM and TIM express classical phenotypic memory markers and are polyfunctional, only Flu-TM protects against a lethal viral challenge. Protective memory CD8 T cells express a unique set of genes involved in migration and survival that correlate with their unique capacity to rapidly migrate within the infected lung parenchyma in response to influenza infection. We also enlighten a new set of poised genes expressed by protective cells that is strongly enriched in cytokines and chemokines such as Ccl1, Ccl9 and Gm-csf. CCL1 and GM-CSF genes are also poised in human memory CD8 T cells. These immune signatures are also induced by two other pathogens (vaccinia virus and Listeria monocytogenes). The immune signatures associated with immune protection were identified on circulating cells, i.e. those that are easily accessible for immuno-monitoring and could help predict vaccines efficacy.

Characterization of the c-type lysozyme gene family in Anopheles gambiae.

PubMed

Li, Bin; Calvo, Eric; Marinotti, Osvaldo; James, Anthony A; Paskewitz, Susan M

2005-11-07

Seven new c-type lysozyme genes were found using the Anopheles gambiae genome sequence, increasing to eight the total number of genes in this family identified in this species. The eight lysozymes in An. gambiae have considerable variation in gene structure and expression patterns. Lys c-6 has the most unusual primary amino acid structure as the predicted protein consists of five lysozyme-like domains. Transcript abundance of each c-type lysozyme was determined by semiquantitative RT-PCR. Lys c-1, c-6 and c-7 are expressed constitutively in all developmental stages from egg to adult. Lys c-2 and c-4 also are found in all stages, but with relatively much higher levels in adults. Conversely, Lys c-3 and c-8 transcripts are highest in larvae. Lys c-1, c-6 and c-7 transcripts are found in nearly all the adult tissue samples examined while Lys c-2 and Lys c-4 are more restricted in their expression. Lys c-1 and c-2 transcripts are clearly immune responsive and are increased significantly 6-12 h post challenge with bacteria. The functional adaptive changes that may have evolved during the expansion of this gene family are briefly discussed in terms of the expression patterns, gene and protein structures.

Recommendations for sexual expression management in long-term care: a qualitative needs assessment

PubMed Central

Syme, Maggie L.; Lichtenberg, Peter; Moye, Jennifer

2017-01-01

Aims To conduct a qualitative needs assessment of Directors of Nursing regarding challenges and recommendations for addressing sexual expression and consent. Background Sexual expression management among long-term care residents is a complex issue for nursing home staff. Little guidance is available for those wanting to follow a person-centred approach. Policies and procedures are needed, and must be usable across long-term care settings. Design Qualitative design for in-depth exploration. Methods Semi-structured interviews were conducted with 20 Directors of Nursing in the spring and summer of 2013, representing a range of regions, facility sizes and resident populations. Interview questions prompted them to identify recommendations that address challenges to improving sexual expression management in long-term care settings. Results Comparative thematic analysis resulted in several codes, which were grouped into eight overall categories. Recommendation categories that addressed key challenges included: address the issue, make environmental changes, identify staff expertise, provide education and training, assess sexuality initially and recurrently, establish policies/procedures for sexual expression management, develop assessment tools for sexual expression and consent, and clarify legal issues. The recommendation to develop national guidelines was observed across categories. Discussion Directors of Nursing report several challenges to sexual expression management in their facilities, and perceive their current methods to be ad hoc. A proactive approach to policy and procedure development is needed. PMID:27188413

Using natural products for drug discovery: the impact of the genomics era.

PubMed

Zhang, Mingzi M; Qiao, Yuan; Ang, Ee Lui; Zhao, Huimin

2017-05-01

Evolutionarily selected over billions of years for their interactions with biomolecules, natural products have been and continue to be a major source of pharmaceuticals. In the 1990s, pharmaceutical companies scaled down their natural product discovery programs in favor of synthetic chemical libraries due to major challenges such as high rediscovery rates, challenging isolation, and low production titers. Propelled by advances in DNA sequencing and synthetic biology technologies, insights into microbial secondary metabolism provided have inspired a number of strategies to address these challenges. Areas covered: This review highlights the importance of genomics and metagenomics in natural product discovery, and provides an overview of the technical and conceptual advances that offer unprecedented access to molecules encoded by biosynthetic gene clusters. Expert opinion: Genomics and metagenomics revealed nature's remarkable biosynthetic potential and her vast chemical inventory that we can now prioritize and systematically mine for novel chemical scaffolds with desirable bioactivities. Coupled with synthetic biology and genome engineering technologies, significant progress has been made in identifying and predicting the chemical output of biosynthetic gene clusters, as well as in optimizing cluster expression in native and heterologous host systems for the production of pharmaceutically relevant metabolites and their derivatives.

Using natural products for drug discovery: the impact of the genomics era

PubMed Central

Zhang, Mingzi M; Qiao, Yuan; Ang, Ee Lui; Zhao, Huimin

2017-01-01

Introduction Evolutionarily selected over billions of years for their interactions with biomolecules, natural products have been and continue to be a major source of pharmaceuticals. In the 1990s, pharmaceutical companies scaled down their natural product discovery programs in favor of synthetic chemical libraries due to major challenges such as high rediscovery rates, challenging isolation, and low production titers. Propelled by advances in DNA sequencing and synthetic biology technologies, insights into microbial secondary metabolism provided have inspired a number of strategies to address these challenges. Areas covered This review highlights the importance of genomics and metagenomics in natural product discovery, and provides an overview of the technical and conceptual advances that offer unprecedented access to molecules encoded by biosynthetic gene clusters. Expert opinion Genomics and metagenomics revealed nature’s remarkable biosynthetic potential and her vast chemical inventory that we can now prioritize and systematically mine for novel chemical scaffolds with desirable bioactivities. Coupled with synthetic biology and genome engineering technologies, significant progress has been made in identifying and predicting the chemical output of biosynthetic gene clusters, as well as in optimizing cluster expression in native and heterologous host systems for the production of pharmaceutically relevant metabolites and their derivatives. PMID:28277838

Molecular characterisation and expression analysis of the cathepsin H gene from rock bream (Oplegnathus fasciatus).

PubMed

Kim, Ju-Won; Park, Chan-Il; Hwang, Seong Don; Jeong, Ji-Min; Kim, Ki-Hyuk; Kim, Do-Hyung; Shim, Sang Hee

2013-07-01

Cathepsins are lysosomal cysteine proteases belonging to the papain family, whose members play important roles in normal metabolism for the maintenance of cellular homeostasis. Rock bream (Oplegnathus fasciatus) cathepsin H (RbCTSH) cDNAs were identified by expressed sequence tag analysis of a lipopolysaccharide-stimulated rock bream liver cDNA library. The full-length RbCTSH cDNA (1326 bp) contained an open reading frame of 978 bp encoding 325 amino acids. The presence of an ERFNIN-like motif was predicted in the propeptide region of RbCTSH. Furthermore, multiple alignments showed that the EPQNCSAT region was well conserved among other cathepsin H sequences. Phylogenetic analysis revealed that RbCTSH is most closely related to Nile tilapia cathepsin H. RbCTSH was expressed significantly in the intestine, spleen, head kidney and stomach. RbCTSH mRNA expression was also examined in several tissues under conditions of bacterial and viral challenge. All examined tissues of fish infected with Edwardsiella tarda, Streptococcus iniae and red sea bream iridovirus (RSIV) showed significant increases in RbCTSH expression compared to the control. In the kidney and spleen, RbCTSH mRNA expression was upregulated markedly following infection with bacterial pathogens. These findings indicate that RbCTSH plays an important role in the innate immune response of rock bream. Furthermore, these results provide important information for the identification of other cathepsin H genes in various fish species. Copyright © 2013 Elsevier Ltd. All rights reserved.

Associations between transcriptional changes and protein phenotypes provide insights into immune regulation in corals.

PubMed

Fuess, Lauren E; Pinzόn C, Jorge H; Weil, Ernesto; Mydlarz, Laura D

2016-09-01

Disease outbreaks in marine ecosystems have driven worldwide declines of numerous taxa, including corals. Some corals, such as Orbicella faveolata, are particularly susceptible to disease. To explore the mechanisms contributing to susceptibility, colonies of O. faveolata were exposed to immune challenge with lipopolysaccharides. RNA sequencing and protein activity assays were used to characterize the response of corals to immune challenge. Differential expression analyses identified 17 immune-related transcripts that varied in expression post-immune challenge. Network analyses revealed several groups of transcripts correlated to immune protein activity. Several transcripts, which were annotated as positive regulators of immunity were included in these groups, and some were downregulated following immune challenge. Correlations between expression of these transcripts and protein activity results further supported the role of these transcripts in positive regulation of immunity. The observed pattern of gene expression and protein activity may elucidate the processes contributing to the disease susceptibility of species like O. faveolata. Copyright © 2016 Elsevier Ltd. All rights reserved.

Challenges in Rotorcraft Acoustic Flight Prediction and Validation

NASA Technical Reports Server (NTRS)

Boyd, D. Douglas, Jr.

2003-01-01

Challenges associated with rotorcraft acoustic flight prediction and validation are examined. First, an outline of a state-of-the-art rotorcraft aeroacoustic prediction methodology is presented. Components including rotorcraft aeromechanics, high resolution reconstruction, and rotorcraft acoustic prediction arc discussed. Next, to illustrate challenges and issues involved, a case study is presented in which an analysis of flight data from a specific XV-15 tiltrotor acoustic flight test is discussed in detail. Issues related to validation of methodologies using flight test data are discussed. Primary flight parameters such as velocity, altitude, and attitude are discussed and compared for repeated flight conditions. Other measured steady state flight conditions are examined for consistency and steadiness. A representative example prediction is presented and suggestions are made for future research.

Proteomic Analysis of Apis cerana and Apis mellifera Larvae Fed with Heterospecific Royal Jelly and by CSBV Challenge

PubMed Central

Huang, Xiu; Han, Richou

2014-01-01

Chinese honeybee Apis cerana (Ac) is one of the major Asian honeybee species for local apiculture. However, Ac is frequently damaged by Chinese sacbrood virus (CSBV), whereas Apis mellifera (Am) is usually resistant to it. Heterospecific royal jelly (RJ) breeding in two honeybee species may result in morphological and genetic modification. Nevertheless, knowledge on the resistant mechanism of Am to this deadly disease is still unknown. In the present study, heterospecific RJ breeding was conducted to determine the effects of food change on the larval mortality after CSBV infection at early larval stage. 2-DE and MALDI-TOF/TOF MS proteomic technology was employed to unravel the molecular event of the bees under heterospecific RJ breeding and CSBV challenge. The change of Ac larval food from RJC to RJM could enhance the bee resistance to CSBV. The mortality rate of Ac larvae after CSBV infection was much higher when the larvae were fed with RJC compared with the larvae fed with RJM. There were 101 proteins with altered expressions after heterospecific RJ breeding and viral infection. In Ac larvae, 6 differential expression proteins were identified from heterospecific RJ breeding only, 21 differential expression proteins from CSBV challenge only and 7 differential expression proteins from heterospecific RJ breeding plus CSBV challenge. In Am larvae, 17 differential expression proteins were identified from heterospecific RJ breeding only, 26 differential expression proteins from CSBV challenge only and 24 differential expression proteins from heterospecific RJ breeding plus CSBV challenge. The RJM may protect Ac larvae from CSBV infection, probably by activating the genes in energy metabolism pathways, antioxidation and ubiquitin-proteasome system. The present results, for the first time, comprehensively descript the molecular events of the viral infection of Ac and Am after heterospecific RJ breeding and are potentially useful for establishing CSBV resistant populations of Ac for apiculture. PMID:25102167

Applicability of a gene expression based prediction method to SD and Wistar rats: an example of CARCINOscreen®.

PubMed

Matsumoto, Hiroshi; Saito, Fumiyo; Takeyoshi, Masahiro

2015-12-01

Recently, the development of several gene expression-based prediction methods has been attempted in the fields of toxicology. CARCINOscreen® is a gene expression-based screening method to predict carcinogenicity of chemicals which target the liver with high accuracy. In this study, we investigated the applicability of the gene expression-based screening method to SD and Wistar rats by using CARCINOscreen®, originally developed with F344 rats, with two carcinogens, 2,4-diaminotoluen and thioacetamide, and two non-carcinogens, 2,6-diaminotoluen and sodium benzoate. After the 28-day repeated dose test was conducted with each chemical in SD and Wistar rats, microarray analysis was performed using total RNA extracted from each liver. Obtained gene expression data were applied to CARCINOscreen®. Predictive scores obtained by the CARCINOscreen® for known carcinogens were > 2 in all strains of rats, while non-carcinogens gave prediction scores below 0.5. These results suggested that the gene expression based screening method, CARCINOscreen®, can be applied to SD and Wistar rats, widely used strains in toxicological studies, by setting of an appropriate boundary line of prediction score to classify the chemicals into carcinogens and non-carcinogens.

Effect of yeast-derived products and distillers dried grains with solubles (DDGS) on growth performance and local innate immune response of broiler chickens challenged with Clostridium perfringens.

PubMed

Alizadeh, M; Rogiewicz, A; McMillan, E; Rodriguez-Lecompte, J C; Patterson, R; Slominski, B A

2016-06-01

This study evaluated the effect of yeast-derived products on growth performance, gut lesion score, intestinal population of Clostridium perfringens, and local innate immunity of broiler chickens challenged with C. perfringens. One-day-old broiler chickens were randomly assigned to eight dietary treatments providing six replicate pens of 55 birds each per treatment. Dietary treatments consisted of Control diets without and with C. perfringens challenge, and diets containing bacitracin methylene disalicylate (BMD, 55 g/tonne), nucleotides (150 g/tonne), yeast cell wall (YCW, 300 g/tonne), and a commercial product Maxi-Gen Plus (1 kg/tonne) fed to chickens challenged with C. perfringens. Diets containing 10% distillers dried grains with solubles without and with C. perfringens challenge were also used. Birds were orally challenged with C. perfringens (10(8) colony-forming units (cfu)/bird) on day 14. On day 21, intestinal samples were collected for gene expression analysis. Pathogen challenge significantly (P < 0.05) impaired feed intake, body weight gain, and feed conversion ratio (FCR) shortly after the challenge (14-21 days). Increased C. perfringens counts and intestinal lesion scores were observed for challenged birds except the BMD-containing diet. Over the entire trial (1-35 days), no difference in growth performance was observed except the BMD diet which improved FCR over the Control, challenged group. Birds receiving nucleotides showed increased expression of toll-like receptors and cytokines interleukin (IL)-4 and IL-18 compared to the Control, challenged group. Expression of macrophage mannose receptor and IL-18 was upregulated in birds receiving YCW. Increased expression of cytokines and receptors involved in innate immunity in broilers receiving nucleotides and YCW suggests the immunomodulatory properties of these products under pathogen challenge conditions.

The Discovery of Novel Biomarkers Improves Breast Cancer Intrinsic Subtype Prediction and Reconciles the Labels in the METABRIC Data Set

PubMed Central

Milioli, Heloisa Helena; Vimieiro, Renato; Riveros, Carlos; Tishchenko, Inna; Berretta, Regina; Moscato, Pablo

2015-01-01

Background The prediction of breast cancer intrinsic subtypes has been introduced as a valuable strategy to determine patient diagnosis and prognosis, and therapy response. The PAM50 method, based on the expression levels of 50 genes, uses a single sample predictor model to assign subtype labels to samples. Intrinsic errors reported within this assay demonstrate the challenge of identifying and understanding the breast cancer groups. In this study, we aim to: a) identify novel biomarkers for subtype individuation by exploring the competence of a newly proposed method named CM1 score, and b) apply an ensemble learning, as opposed to the use of a single classifier, for sample subtype assignment. The overarching objective is to improve class prediction. Methods and Findings The microarray transcriptome data sets used in this study are: the METABRIC breast cancer data recorded for over 2000 patients, and the public integrated source from ROCK database with 1570 samples. We first computed the CM1 score to identify the probes with highly discriminative patterns of expression across samples of each intrinsic subtype. We further assessed the ability of 42 selected probes on assigning correct subtype labels using 24 different classifiers from the Weka software suite. For comparison, the same method was applied on the list of 50 genes from the PAM50 method. Conclusions The CM1 score portrayed 30 novel biomarkers for predicting breast cancer subtypes, with the confirmation of the role of 12 well-established genes. Intrinsic subtypes assigned using the CM1 list and the ensemble of classifiers are more consistent and homogeneous than the original PAM50 labels. The new subtypes show accurate distributions of current clinical markers ER, PR and HER2, and survival curves in the METABRIC and ROCK data sets. Remarkably, the paradoxical attribution of the original labels reinforces the limitations of employing a single sample classifiers to predict breast cancer intrinsic subtypes. PMID:26132585

78 FR 70303 - Announcement of Requirements and Registration for the Predict the Influenza Season Challenge

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-25

... public. Mathematical and statistical models can be useful in predicting the timing and impact of the... applying any mathematical, statistical, or other approach to predictive modeling. This challenge will... Services (HHS) region level(s) in the United States by developing mathematical and statistical models that...

Correlation of in vitro challenge testing with consumer use testing for cosmetic products.

PubMed Central

Brannan, D K; Dille, J C; Kaufman, D J

1987-01-01

An in vitro microbial challenge test has been developed to predict the likelihood of consumer contamination of cosmetic products. The challenge test involved inoculating product at four concentrations (30, 50, 70, and 100%) with microorganisms known to contaminate cosmetics. Elimination of these microorganisms at each concentration was followed over a 28-day period. The test was used to classify products as poorly preserved, marginally preserved, or well preserved. Consumer use testing was then used to determine whether the test predicted the risk of actual consumer contamination. Products classified by the challenge test as poorly preserved returned 46 to 90% contaminated after use. Products classified by the challenge test as well preserved returned with no contamination. Marginally preserved products returned with 0 to 21% of the used units contaminated. As a result, the challenge test described can be accurately used to predict the risk of consumer contamination of cosmetic products. PMID:3662517

Peritrophin-like protein from Litopenaeus vannamei (LvPT) involved in white spot syndrome virus (WSSV) infection in digestive tract challenged with reverse gavage

NASA Astrophysics Data System (ADS)

Xie, Shijun; Li, Fuhua; Zhang, Xiaojun; Zhang, Jiquan; Xiang, Jianhai

2017-11-01

The peritrophic membrane plays an important role in the defense system of the arthropod gut. The digestive tract is considered one of the major tissues targeted by white spot syndrome virus (WSSV) in shrimp. In this study, the nucleotide sequence encoding peritrophin-like protein of Litopenaeus vannamei (LvPT) was amplified from a yeast two-hybrid library of L. vannamei. The epitope peptide of LvPT was predicted with the GenScript OptimumAntigen™ design tool. An anti-LvPT polyclonal antibody was produced and shown to specifically bind a band at 27 kDa, identified as LvPT. The LvPT protein was expressed and its concentration determined. LvPT dsRNA (4 μg per shrimp) was used to inhibit LvPT expression in shrimp, and a WSSV challenge experiment was then performed with reverse gavage. The pleopods, stomachs, and guts were collected from the shrimp at 0, 24, 48, and 72 h post-infection (hpi). Viral load quantification showed that the levels of WSSV were significantly lower in the pleopods, stomachs, and guts of shrimp after LvPT dsRNA interference than in those of the controls at 48 and 72 hpi. Our results imply that LvPT plays an important role during WSSV infection of the digestive tract.

Validation of housekeeping genes as internal controls for studying biomarkers of endocrine-disrupting chemicals in disk abalone by real-time PCR.

PubMed

Wan, Qiang; Whang, Ilson; Choi, Cheol Young; Lee, Jae-Seong; Lee, Jehee

2011-04-01

Our experiments were designed to identify suitable housekeeping genes (HKGs) in disk abalone as internal controls to quantify biomarker expression following endocrine disrupting chemicals (EDCs). Relative expression levels of twelve candidate HKGs were examined by real-time reverse transcription PCR (qRT-PCR) in gill and hepatopancreas of abalone following a 7-day challenge with either tributyltin chloride (TBT) or 17β-estradiol (E2). The expression levels of several conventional HKGs, such as 18s rRNA, glyceraldehyde-3-phosphate dehydrogenase and β-actin, were significantly altered by the challenges, indicating that they might not be suitable internal controls. Instead, the geNorm analysis pinpointed ribosomal protein L-5/ elongation factor 1 and ribosomal protein L-5/ succinate dehydrogenase as the most stable HKGs under TBT and E2 challenges, respectively. Moreover, these three HKGs also showed the highest stabilities overall amongst different tissues, genders and EDC challenges. The expression of a biomarker gene, cytochrome P450 4B (CYP4), was also investigated and exhibited a significant increase after the challenges. Importantly, when unsuitable HKGs were used for normalization, the influence of two EDCs on CYP4 expression was imprecisely overestimated or underestimated, which strongly emphasized the importance of selecting appropriately validated HKGs as internal controls in biomarker studies. Copyright © 2010 Elsevier Inc. All rights reserved.

Prognostic impact of alternative splicing-derived hMENA isoforms in resected, node-negative, non-small-cell lung cancer

PubMed Central

Sperduti, Isabella; Iapicca, Pierluigi; Visca, Paolo; Alessandrini, Gabriele; Antoniani, Barbara; Pilotto, Sara; Ludovini, Vienna; Vannucci, Jacopo; Bellezza, Guido; Sidoni, Angelo; Tortora, Giampaolo; Radisky, Derek C.; Crinò, Lucio; Cognetti, Francesco; Facciolo, Francesco; Mottolese, Marcella

2014-01-01

Risk assessment and treatment choice remain a challenge in early non-small-cell lung cancer (NSCLC). Alternative splicing is an emerging source for diagnostic, prognostic and therapeutic tools. Here, we investigated the prognostic value of the actin cytoskeleton regulator hMENA and its isoforms, hMENA11a and hMENAΔv6, in early NSCLC. The epithelial hMENA11a isoform was expressed in NSCLC lines expressing E-CADHERIN and was alternatively expressed with hMENAΔv6. Enforced expression of hMENAΔv6 or hMENA11a increased or decreased the invasive ability of A549 cells, respectively. hMENA isoform expression was evaluated in 248 node-negative NSCLC. High pan-hMENA and low hMENA11a were the only independent predictors of shorter disease-free and cancer-specific survival, and low hMENA11a was an independent predictor of shorter overall survival, at multivariate analysis. Patients with low pan-hMENA/high hMENA11a expression fared significantly better (P≤0.0015) than any other subgroup. Such hybrid variable was incorporated with T-size and number of resected lymph nodes into a 3-class-risk stratification model, which strikingly discriminated between different risks of relapse, cancer-related death, and death. The model was externally validated in an independent dataset of 133 patients. Relative expression of hMENA splice isoforms is a powerful prognostic factor in early NSCLC, complementing clinical parameters to accurately predict individual patient risk. PMID:25373410

Integrated genomic analysis of recurrence-associated small non-coding RNAs in oesophageal cancer.

PubMed

Jang, Hee-Jin; Lee, Hyun-Sung; Burt, Bryan M; Lee, Geon Kook; Yoon, Kyong-Ah; Park, Yun-Yong; Sohn, Bo Hwa; Kim, Sang Bae; Kim, Moon Soo; Lee, Jong Mog; Joo, Jungnam; Kim, Sang Cheol; Yun, Ju Sik; Na, Kook Joo; Choi, Yoon-La; Park, Jong-Lyul; Kim, Seon-Young; Lee, Yong Sun; Han, Leng; Liang, Han; Mak, Duncan; Burks, Jared K; Zo, Jae Ill; Sugarbaker, David J; Shim, Young Mog; Lee, Ju-Seog

2017-02-01

Oesophageal squamous cell carcinoma (ESCC) is a heterogeneous disease with variable outcomes that are challenging to predict. A better understanding of the biology of ESCC recurrence is needed to improve patient care. Our goal was to identify small non-coding RNAs (sncRNAs) that could predict the likelihood of recurrence after surgical resection and to uncover potential molecular mechanisms that dictate clinical heterogeneity. We developed a robust prediction model for recurrence based on the analysis of the expression profile data of sncRNAs from 108 fresh frozen ESCC specimens as a discovery set and assessment of the associations between sncRNAs and recurrence-free survival (RFS). We also evaluated the mechanistic and therapeutic implications of sncRNA obtained through integrated analysis from multiple datasets. We developed a risk assessment score (RAS) for recurrence with three sncRNAs (microRNA (miR)-223, miR-1269a and nc886) whose expression was significantly associated with RFS in the discovery cohort (n=108). RAS was validated in an independent cohort of 512 patients. In multivariable analysis, RAS was an independent predictor of recurrence (HR, 2.27; 95% CI, 1.26 to 4.09; p=0.007). This signature implies the expression of ΔNp63 and multiple alterations of driver genes like PIK3CA. We suggested therapeutic potentials of immune checkpoint inhibitors in low-risk patients, and Polo-like kinase inhibitors, mammalian target of rapamycin (mTOR) inhibitors, and histone deacetylase inhibitors in high-risk patients. We developed an easy-to-use prognostic model with three sncRNAs as robust prognostic markers for postoperative recurrence of ESCC. We anticipate that such a stratified and systematic, tumour-specific biological approach will potentially contribute to significant improvement in ESCC treatment. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

Designing deep sequencing experiments: detecting structural variation and estimating transcript abundance.

PubMed

Bashir, Ali; Bansal, Vikas; Bafna, Vineet

2010-06-18

Massively parallel DNA sequencing technologies have enabled the sequencing of several individual human genomes. These technologies are also being used in novel ways for mRNA expression profiling, genome-wide discovery of transcription-factor binding sites, small RNA discovery, etc. The multitude of sequencing platforms, each with their unique characteristics, pose a number of design challenges, regarding the technology to be used and the depth of sequencing required for a particular sequencing application. Here we describe a number of analytical and empirical results to address design questions for two applications: detection of structural variations from paired-end sequencing and estimating mRNA transcript abundance. For structural variation, our results provide explicit trade-offs between the detection and resolution of rearrangement breakpoints, and the optimal mix of paired-read insert lengths. Specifically, we prove that optimal detection and resolution of breakpoints is achieved using a mix of exactly two insert library lengths. Furthermore, we derive explicit formulae to determine these insert length combinations, enabling a 15% improvement in breakpoint detection at the same experimental cost. On empirical short read data, these predictions show good concordance with Illumina 200 bp and 2 Kbp insert length libraries. For transcriptome sequencing, we determine the sequencing depth needed to detect rare transcripts from a small pilot study. With only 1 Million reads, we derive corrections that enable almost perfect prediction of the underlying expression probability distribution, and use this to predict the sequencing depth required to detect low expressed genes with greater than 95% probability. Together, our results form a generic framework for many design considerations related to high-throughput sequencing. We provide software tools http://bix.ucsd.edu/projects/NGS-DesignTools to derive platform independent guidelines for designing sequencing experiments (amount of sequencing, choice of insert length, mix of libraries) for novel applications of next generation sequencing.

Radiation Risk Assessment of the Individual Astronaut: A Complement to Radiation Interests at the NIH

NASA Technical Reports Server (NTRS)

Richmond, Robert C.

2004-01-01

Predicting human risks following exposure to space radiation is uncertain in part because of unpredictable distribution of high-LET and low-dose-derived damage amongst cells in tissues, unknown synergistic effects of microgravity upon gene- and protein-expression, and inadequately modeled processing of radiation-induced damage within cells to produce rare and late-appearing malignant cancers. Furthermore, estimation of risks of radiogenic outcome within small numbers of astronauts is not possible using classic epidemiologic study. It therefore seems useful to develop strategies of risk-assessment based upon large datasets acquired from correlated biological models useful for resolving radiogenic risk-assessment for irradiated individuals. In this regard, it is suggested that sensitive cellular biodosimeters that simultaneously report 1) the quantity of absorbed dose after exposure to ionizing radiation, 2) the quality of radiation delivering that dose, and 3) the biomolecular risk of malignant transformation be developed in order to resolve these NASA-specific challenges. Multiparametric cellular biodosimeters could be developed using analyses of gene-expression and protein-expression whereby large datasets of cellular response to radiation-induced damage are analyzed for markers predictive for acute response as well as cancer-risk. A new paradigm is accordingly addressed wherein genomic and proteomic datasets are registered and interrogated in order to provide statistically significant dose-dependent risk estimation in individual astronauts. This evaluation of the individual for assessment of radiogenic outcomes connects to NIH program in that such a paradigm also supports assignment of a given patient to a specific therapy, the diagnosis of response of that patient to therapy, and the prediction of risks accumulated by that patient during therapy - such as risks incurred by scatter and neutrons produced during high-energy Intensity-Modulated Radiation Therapy. Value of assessment of radiogenic outcome for individuals exposed to radiation is suggested to be common to both NASA and NIH.

Dynamic transcriptional signatures and network responses for clinical symptoms in influenza-infected human subjects using systems biology approaches.

PubMed

Linel, Patrice; Wu, Shuang; Deng, Nan; Wu, Hulin

2014-10-01

Recent studies demonstrate that human blood transcriptional signatures may be used to support diagnosis and clinical decisions for acute respiratory viral infections such as influenza. In this article, we propose to use a newly developed systems biology approach for time course gene expression data to identify significant dynamically response genes and dynamic gene network responses to viral infection. We illustrate the methodological pipeline by reanalyzing the time course gene expression data from a study with healthy human subjects challenged by live influenza virus. We observed clear differences in the number of significant dynamic response genes (DRGs) between the symptomatic and asymptomatic subjects and also identified DRG signatures for symptomatic subjects with influenza infection. The 505 common DRGs shared by the symptomatic subjects have high consistency with the signature genes for predicting viral infection identified in previous works. The temporal response patterns and network response features were carefully analyzed and investigated.

Pretend Play: Antecedent of Adult Creativity.

PubMed

Russ, Sandra W

2016-01-01

This article reviews the theoretical and empirical literature in the area of pretend play as a predictor of adult creativity. There is strong evidence that processes expressed in pretend play are associated with measures of creativity, especially with divergent thinking. There is some evidence from longitudinal studies that this association is stable over time. Converging evidence suggests that cognitive and affective processes in pretend play are involved in adult creative production. However, there is a lack of consensus in the field as to whether engaging in pretend play actually facilitates creative thinking. In addition, many other variables (opportunity, tolerance for failure, motivation, work ethic, etc.) determine whether children with creative potential are actually creative in adulthood. In spite of the many methodological challenges in conducting research in the play area, it is important to continue investigating specific processes expressed in play and their developmental trajectories. Large samples in multisite studies would be ideal in investigating the ability of specific play processes to predict these creative processes and creative productivity in adulthood. © 2016 Wiley Periodicals, Inc.

Gene Expression and Proteome Analysis as Sources of Biomarkers in Basal Cell Carcinoma

PubMed Central

Ghita, Mihaela Adriana; Voiculescu, Suzana; Rosca, Adrian E.; Moraru, Liliana; Greabu, Maria

2016-01-01

Basal cell carcinoma (BCC) is the world's leading skin cancer in terms of frequency at the moment and its incidence continues to rise each year, leading to profound negative psychosocial and economic consequences. UV exposure is the most important environmental factor in the development of BCC in genetically predisposed individuals, this being reflected by the anatomical distribution of lesions mainly on sun-exposed skin areas. Early diagnosis and prompt management are of crucial importance in order to prevent local tissue destruction and subsequent disfigurement. Although various noninvasive or minimal invasive techniques have demonstrated their utility in increasing diagnostic accuracy of BCC and progress has been made in its treatment options, recurrent, aggressive, and metastatic variants of BCC still pose significant challenge for the healthcare system. Analysis of gene expression and proteomic profiling of tumor cells and of tumoral microenvironment in various tissues strongly suggests that certain molecules involved in skin cancer pathogenic pathways might represent novel predictive and prognostic biomarkers in BCC. PMID:27578920

Functional analysis and transcriptional output of the Göttingen minipig genome.

PubMed

Heckel, Tobias; Schmucki, Roland; Berrera, Marco; Ringshandl, Stephan; Badi, Laura; Steiner, Guido; Ravon, Morgane; Küng, Erich; Kuhn, Bernd; Kratochwil, Nicole A; Schmitt, Georg; Kiialainen, Anna; Nowaczyk, Corinne; Daff, Hamina; Khan, Azinwi Phina; Lekolool, Isaac; Pelle, Roger; Okoth, Edward; Bishop, Richard; Daubenberger, Claudia; Ebeling, Martin; Certa, Ulrich

2015-11-14

In the past decade the Göttingen minipig has gained increasing recognition as animal model in pharmaceutical and safety research because it recapitulates many aspects of human physiology and metabolism. Genome-based comparison of drug targets together with quantitative tissue expression analysis allows rational prediction of pharmacology and cross-reactivity of human drugs in animal models thereby improving drug attrition which is an important challenge in the process of drug development. Here we present a new chromosome level based version of the Göttingen minipig genome together with a comparative transcriptional analysis of tissues with pharmaceutical relevance as basis for translational research. We relied on mapping and assembly of WGS (whole-genome-shotgun sequencing) derived reads to the reference genome of the Duroc pig and predict 19,228 human orthologous protein-coding genes. Genome-based prediction of the sequence of human drug targets enables the prediction of drug cross-reactivity based on conservation of binding sites. We further support the finding that the genome of Sus scrofa contains about ten-times less pseudogenized genes compared to other vertebrates. Among the functional human orthologs of these minipig pseudogenes we found HEPN1, a putative tumor suppressor gene. The genomes of Sus scrofa, the Tibetan boar, the African Bushpig, and the Warthog show sequence conservation of all inactivating HEPN1 mutations suggesting disruption before the evolutionary split of these pig species. We identify 133 Sus scrofa specific, conserved long non-coding RNAs (lncRNAs) in the minipig genome and show that these transcripts are highly conserved in the African pigs and the Tibetan boar suggesting functional significance. Using a new minipig specific microarray we show high conservation of gene expression signatures in 13 tissues with biomedical relevance between humans and adult minipigs. We underline this relationship for minipig and human liver where we could demonstrate similar expression levels for most phase I drug-metabolizing enzymes. Higher expression levels and metabolic activities were found for FMO1, AKR/CRs and for phase II drug metabolizing enzymes in minipig as compared to human. The variability of gene expression in equivalent human and minipig tissues is considerably higher in minipig organs, which is important for study design in case a human target belongs to this variable category in the minipig. The first analysis of gene expression in multiple tissues during development from young to adult shows that the majority of transcriptional programs are concluded four weeks after birth. This finding is in line with the advanced state of human postnatal organ development at comparative age categories and further supports the minipig as model for pediatric drug safety studies. Genome based assessment of sequence conservation combined with gene expression data in several tissues improves the translational value of the minipig for human drug development. The genome and gene expression data presented here are important resources for researchers using the minipig as model for biomedical research or commercial breeding. Potential impact of our data for comparative genomics, translational research, and experimental medicine are discussed.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

PubMed

Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

2017-09-01

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.

A deep auto-encoder model for gene expression prediction.

PubMed

Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

2017-11-17

Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

A novel method for prediction of dynamic smiling expressions after orthodontic treatment: a case report.

PubMed

Dai, Fanfan; Li, Yangjing; Chen, Gui; Chen, Si; Xu, Tianmin

2016-02-01

Smile esthetics has become increasingly important for orthodontic patients, thus prediction of post-treatment smile is necessary for a perfect treatment plan. In this study, with a combination of three-dimensional craniofacial data from the cone beam computed tomography and color-encoded structured light system, a novel method for smile prediction was proposed based on facial expression transfer, in which dynamic facial expression was interpreted as a matrix of facial depth changes. Data extracted from the pre-treatment smile expression record were applied to the post-treatment static model to realize expression transfer. Therefore smile esthetics of the patient after treatment could be evaluated in pre-treatment planning procedure. The positive and negative mean values of error for prediction accuracy were 0.9 and - 1.1 mm respectively, with the standard deviation of ± 1.5 mm, which is clinically acceptable. Further studies would be conducted to reduce the prediction error from both the static and dynamic sides as well as to explore automatically combined prediction from the two sides.

Prediction of cardioembolic, arterial and lacunar causes of cryptogenic stroke by gene expression and infarct location

PubMed Central

Jickling, Glen C; Stamova, Boryana; Ander, Bradley P; Zhan, Xinhua; Liu, Dazhi; Sison, Shara-Mae; Verro, Piero; Sharp, Frank R

2012-01-01

Background and Purpose The cause of ischemic stroke remains unclear, or cryptogenic, in as many as 35% of stroke patients. Not knowing the cause of stroke restricts optimal implementation of prevention therapy and limits stroke research. We demonstrate how gene expression profiles in blood can be used in conjunction with a measure of infarct location on neuroimaging to predict a probable cause in cryptogenic stroke. Methods The cause of cryptogenic stroke was predicted using previously described profiles of differentially expressed genes characteristic of patients with cardioembolic, arterial and lacunar stroke. RNA was isolated from peripheral blood of 131 cryptogenic strokes and compared to profiles derived from 149 strokes of known cause. Each sample was run on Affymetrix U133 Plus2.0 microarrays. Cause of cryptogenic stroke was predicted using gene expression in blood and infarct location. Results Cryptogenic strokes were predicted to be 58% cardioembolic, 18% arterial, 12% lacunar and 12% unclear etiology. Cryptogenic stroke of predicted cardioembolic etiology had more prior myocardial infarction and higher CHA2DS2-VASc scores compared to stroke of predicted arterial etiology. Predicted lacunar strokes had higher systolic and diastolic blood pressures and lower NIHSS compared to predicted arterial and cardioembolic strokes. Cryptogenic strokes of unclear predicted etiology were less likely to have a prior TIA or ischemic stroke. Conclusions Gene expression in conjunction with a measure of infarct location can predict a probable cause in cryptogenic strokes. Predicted groups require further evaluation to determine whether relevant clinical, imaging, or therapeutic differences exist for each group. PMID:22627989

A statistical approach to identify, monitor, and manage incomplete curated data sets.

PubMed

Howe, Douglas G

2018-04-02

Many biological knowledge bases gather data through expert curation of published literature. High data volume, selective partial curation, delays in access, and publication of data prior to the ability to curate it can result in incomplete curation of published data. Knowing which data sets are incomplete and how incomplete they are remains a challenge. Awareness that a data set may be incomplete is important for proper interpretation, to avoiding flawed hypothesis generation, and can justify further exploration of published literature for additional relevant data. Computational methods to assess data set completeness are needed. One such method is presented here. In this work, a multivariate linear regression model was used to identify genes in the Zebrafish Information Network (ZFIN) Database having incomplete curated gene expression data sets. Starting with 36,655 gene records from ZFIN, data aggregation, cleansing, and filtering reduced the set to 9870 gene records suitable for training and testing the model to predict the number of expression experiments per gene. Feature engineering and selection identified the following predictive variables: the number of journal publications; the number of journal publications already attributed for gene expression annotation; the percent of journal publications already attributed for expression data; the gene symbol; and the number of transgenic constructs associated with each gene. Twenty-five percent of the gene records (2483 genes) were used to train the model. The remaining 7387 genes were used to test the model. One hundred and twenty-two and 165 of the 7387 tested genes were identified as missing expression annotations based on their residuals being outside the model lower or upper 95% confidence interval respectively. The model had precision of 0.97 and recall of 0.71 at the negative 95% confidence interval and precision of 0.76 and recall of 0.73 at the positive 95% confidence interval. This method can be used to identify data sets that are incompletely curated, as demonstrated using the gene expression data set from ZFIN. This information can help both database resources and data consumers gauge when it may be useful to look further for published data to augment the existing expertly curated information.

Challenges in Archetypes Terminology Binding Using SNOMED-CT Compositional Grammar: The Norwegian Patient Summary Case.

PubMed

Marco-Ruiz, Luis; Pedersen, Rune

2017-01-01

In order to cover the requirements for interoperability in the Norwegian context, we studied the terminology binding of archetypes to terminology expressions created with the SNOMED-CT compositional grammar. As a result we identified important challenges categorized as technical, expressivity, human, and models mismatch.

Predictive Power Estimation Algorithm (PPEA) - A New Algorithm to Reduce Overfitting for Genomic Biomarker Discovery

PubMed Central

Liu, Jiangang; Jolly, Robert A.; Smith, Aaron T.; Searfoss, George H.; Goldstein, Keith M.; Uversky, Vladimir N.; Dunker, Keith; Li, Shuyu; Thomas, Craig E.; Wei, Tao

2011-01-01

Toxicogenomics promises to aid in predicting adverse effects, understanding the mechanisms of drug action or toxicity, and uncovering unexpected or secondary pharmacology. However, modeling adverse effects using high dimensional and high noise genomic data is prone to over-fitting. Models constructed from such data sets often consist of a large number of genes with no obvious functional relevance to the biological effect the model intends to predict that can make it challenging to interpret the modeling results. To address these issues, we developed a novel algorithm, Predictive Power Estimation Algorithm (PPEA), which estimates the predictive power of each individual transcript through an iterative two-way bootstrapping procedure. By repeatedly enforcing that the sample number is larger than the transcript number, in each iteration of modeling and testing, PPEA reduces the potential risk of overfitting. We show with three different cases studies that: (1) PPEA can quickly derive a reliable rank order of predictive power of individual transcripts in a relatively small number of iterations, (2) the top ranked transcripts tend to be functionally related to the phenotype they are intended to predict, (3) using only the most predictive top ranked transcripts greatly facilitates development of multiplex assay such as qRT-PCR as a biomarker, and (4) more importantly, we were able to demonstrate that a small number of genes identified from the top-ranked transcripts are highly predictive of phenotype as their expression changes distinguished adverse from nonadverse effects of compounds in completely independent tests. Thus, we believe that the PPEA model effectively addresses the over-fitting problem and can be used to facilitate genomic biomarker discovery for predictive toxicology and drug responses. PMID:21935387

Sensory Drive, Color, and Color Vision.

PubMed

Price, Trevor D

2017-08-01

Colors often appear to differ in arbitrary ways among related species. However, a fraction of color diversity may be explained because some signals are more easily perceived in one environment rather than another. Models show that not only signals but also the perception of signals should regularly evolve in response to different environments, whether these primarily involve detection of conspecifics or detection of predators and prey. Thus, a deeper understanding of how perception of color correlates with environmental attributes should help generate more predictive models of color divergence. Here, I briefly review our understanding of color vision in vertebrates. Then I focus on opsin spectral tuning and opsin expression, two traits involved in color perception that have become amenable to study. I ask how opsin tuning is correlated with ecological differences, notably the light environment, and how this potentially affects perception of conspecific colors. Although opsin tuning appears to evolve slowly, opsin expression levels are more evolutionarily labile but have been difficult to connect to color perception. The challenge going forward will be to identify how physiological differences involved in color vision, such as opsin expression levels, translate into perceptual differences, the selection pressures that have driven those differences, and ultimately how this may drive evolution of conspecific colors.

Does the ability to express different emotions predict different indices of physical health? A skill-based study of physical symptoms and heart rate variability.

PubMed

Tuck, Natalie L; Adams, Kathryn S; Consedine, Nathan S

2017-09-01

The outward expression of emotion has been frequently associated with better health outcomes, whereas suppressing emotion is thought to contribute to worse physical health. However, work has typically focused on trait expressive tendencies and the possibility that individual differences in the ability to express specific emotions may also be associated with health has not been widely tested. A cross-sectional study of community dwelling adults. One hundred and twenty-eight participants aged 18-88 years completed questionnaires assessing demographics and health status, before attending a testing session in which resting heart rate variability (HRV) was assessed. Participants then completed a performance-based test of expressive regulatory skill in which they were instructed to enhance and suppress their emotional expressions while they watched film clips validated to elicit amusement, sadness, and anger. Participants rated subjective emotional experience before and after each clip, and their degree of expressivity was scored using FACS-based Noldus FaceReader. Missing data resulted in a final sample size of 117. Linear regressions controlling for age, sex, diagnoses, and trait emotion revealed that greater ability to enhance sad expressions was associated with higher HRV while the ability to enhance expressions of joy was associated with lower symptom interference. In parallel models, the ability to flexibly regulate (both enhance and suppress) expressions of joy and sadness was also associated with lower symptom interference. Findings suggest that the ability to regulate expressions of both sadness and joy is associated with health indices even when controlling for trait affect and potential confounds. The present findings offer early evidence that individual differences in the ability to regulate the outward expression of emotion may be relevant to health and suggest that expressive regulatory skills offer a novel avenue for research and intervention. Statement of contribution What is already known on this subject The tendency to outwardly express felt emotion generally predicts better health, whereas expressive suppression typically predicts worse health outcomes. Most work has been based on trait assessments; however, the ability to regulate the expression of felt emotion can be objectively assessed using performance-based tests. Prior work in mental health suggests that the ability to flexibly up- and downregulate the expression of emotion predicts better outcomes. What does this study add The first evidence that the ability to flexibly regulate expressions predicts indices of health. Skill in both expressing and suppressing facial expressions predicts better reported health. Skills with different emotions differentially predict symptom interference and cardiac vagal tone. © 2017 The British Psychological Society.

Detecting paralinguistic events in audio stream using context in features and probabilistic decisions☆

PubMed Central

Gupta, Rahul; Audhkhasi, Kartik; Lee, Sungbok; Narayanan, Shrikanth

2017-01-01

Non-verbal communication involves encoding, transmission and decoding of non-lexical cues and is realized using vocal (e.g. prosody) or visual (e.g. gaze, body language) channels during conversation. These cues perform the function of maintaining conversational flow, expressing emotions, and marking personality and interpersonal attitude. In particular, non-verbal cues in speech such as paralanguage and non-verbal vocal events (e.g. laughters, sighs, cries) are used to nuance meaning and convey emotions, mood and attitude. For instance, laughters are associated with affective expressions while fillers (e.g. um, ah, um) are used to hold floor during a conversation. In this paper we present an automatic non-verbal vocal events detection system focusing on the detect of laughter and fillers. We extend our system presented during Interspeech 2013 Social Signals Sub-challenge (that was the winning entry in the challenge) for frame-wise event detection and test several schemes for incorporating local context during detection. Specifically, we incorporate context at two separate levels in our system: (i) the raw frame-wise features and, (ii) the output decisions. Furthermore, our system processes the output probabilities based on a few heuristic rules in order to reduce erroneous frame-based predictions. Our overall system achieves an Area Under the Receiver Operating Characteristics curve of 95.3% for detecting laughters and 90.4% for fillers on the test set drawn from the data specifications of the Interspeech 2013 Social Signals Sub-challenge. We perform further analysis to understand the interrelation between the features and obtained results. Specifically, we conduct a feature sensitivity analysis and correlate it with each feature's stand alone performance. The observations suggest that the trained system is more sensitive to a feature carrying higher discriminability with implications towards a better system design. PMID:28713197

Genome-wide patterns of promoter sharing and co-expression in bovine skeletal muscle.

PubMed

Gu, Quan; Nagaraj, Shivashankar H; Hudson, Nicholas J; Dalrymple, Brian P; Reverter, Antonio

2011-01-12

Gene regulation by transcription factors (TF) is species, tissue and time specific. To better understand how the genetic code controls gene expression in bovine muscle we associated gene expression data from developing Longissimus thoracis et lumborum skeletal muscle with bovine promoter sequence information. We created a highly conserved genome-wide promoter landscape comprising 87,408 interactions relating 333 TFs with their 9,242 predicted target genes (TGs). We discovered that the complete set of predicted TGs share an average of 2.75 predicted TF binding sites (TFBSs) and that the average co-expression between a TF and its predicted TGs is higher than the average co-expression between the same TF and all genes. Conversely, pairs of TFs sharing predicted TGs showed a co-expression correlation higher that pairs of TFs not sharing TGs. Finally, we exploited the co-occurrence of predicted TFBS in the context of muscle-derived functionally-coherent modules including cell cycle, mitochondria, immune system, fat metabolism, muscle/glycolysis, and ribosome. Our findings enabled us to reverse engineer a regulatory network of core processes, and correctly identified the involvement of E2F1, GATA2 and NFKB1 in the regulation of cell cycle, fat, and muscle/glycolysis, respectively. The pivotal implication of our research is two-fold: (1) there exists a robust genome-wide expression signal between TFs and their predicted TGs in cattle muscle consistent with the extent of promoter sharing; and (2) this signal can be exploited to recover the cellular mechanisms underpinning transcription regulation of muscle structure and development in bovine. Our study represents the first genome-wide report linking tissue specific co-expression to co-regulation in a non-model vertebrate.

Sequence features of viral and human Internal Ribosome Entry Sites predictive of their activity

PubMed Central

Elias-Kirma, Shani; Nir, Ronit; Segal, Eran

2017-01-01

Translation of mRNAs through Internal Ribosome Entry Sites (IRESs) has emerged as a prominent mechanism of cellular and viral initiation. It supports cap-independent translation of select cellular genes under normal conditions, and in conditions when cap-dependent translation is inhibited. IRES structure and sequence are believed to be involved in this process. However due to the small number of IRESs known, there have been no systematic investigations of the determinants of IRES activity. With the recent discovery of thousands of novel IRESs in human and viruses, the next challenge is to decipher the sequence determinants of IRES activity. We present the first in-depth computational analysis of a large body of IRESs, exploring RNA sequence features predictive of IRES activity. We identified predictive k-mer features resembling IRES trans-acting factor (ITAF) binding motifs across human and viral IRESs, and found that their effect on expression depends on their sequence, number and position. Our results also suggest that the architecture of retroviral IRESs differs from that of other viruses, presumably due to their exposure to the nuclear environment. Finally, we measured IRES activity of synthetically designed sequences to confirm our prediction of increasing activity as a function of the number of short IRES elements. PMID:28922394

BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone.

PubMed

Yang, Bite; Liu, Feng; Ren, Chao; Ouyang, Zhangyi; Xie, Ziwei; Bo, Xiaochen; Shu, Wenjie

2017-07-01

Enhancer elements are noncoding stretches of DNA that play key roles in controlling gene expression programmes. Despite major efforts to develop accurate enhancer prediction methods, identifying enhancer sequences continues to be a challenge in the annotation of mammalian genomes. One of the major issues is the lack of large, sufficiently comprehensive and experimentally validated enhancers for humans or other species. Thus, the development of computational methods based on limited experimentally validated enhancers and deciphering the transcriptional regulatory code encoded in the enhancer sequences is urgent. We present a deep-learning-based hybrid architecture, BiRen, which predicts enhancers using the DNA sequence alone. Our results demonstrate that BiRen can learn common enhancer patterns directly from the DNA sequence and exhibits superior accuracy, robustness and generalizability in enhancer prediction relative to other state-of-the-art enhancer predictors based on sequence characteristics. Our BiRen will enable researchers to acquire a deeper understanding of the regulatory code of enhancer sequences. Our BiRen method can be freely accessed at https://github.com/wenjiegroup/BiRen . shuwj@bmi.ac.cn or boxc@bmi.ac.cn. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Functional analysis of rare variants in mismatch repair proteins augments results from computation-based predictive methods

PubMed Central

Arora, Sanjeevani; Huwe, Peter J.; Sikder, Rahmat; Shah, Manali; Browne, Amanda J.; Lesh, Randy; Nicolas, Emmanuelle; Deshpande, Sanat; Hall, Michael J.; Dunbrack, Roland L.; Golemis, Erica A.

2017-01-01

ABSTRACT The cancer-predisposing Lynch Syndrome (LS) arises from germline mutations in DNA mismatch repair (MMR) genes, predominantly MLH1, MSH2, MSH6, and PMS2. A major challenge for clinical diagnosis of LS is the frequent identification of variants of uncertain significance (VUS) in these genes, as it is often difficult to determine variant pathogenicity, particularly for missense variants. Generic programs such as SIFT and PolyPhen-2, and MMR gene-specific programs such as PON-MMR and MAPP-MMR, are often used to predict deleterious or neutral effects of VUS in MMR genes. We evaluated the performance of multiple predictive programs in the context of functional biologic data for 15 VUS in MLH1, MSH2, and PMS2. Using cell line models, we characterized VUS predicted to range from neutral to pathogenic on mRNA and protein expression, basal cellular viability, viability following treatment with a panel of DNA-damaging agents, and functionality in DNA damage response (DDR) signaling, benchmarking to wild-type MMR proteins. Our results suggest that the MMR gene-specific classifiers do not always align with the experimental phenotypes related to DDR. Our study highlights the importance of complementary experimental and computational assessment to develop future predictors for the assessment of VUS. PMID:28494185

Molecular mechanisms of system responses to novel stimuli are predictable from public data

PubMed Central

Danziger, Samuel A.; Ratushny, Alexander V.; Smith, Jennifer J.; Saleem, Ramsey A.; Wan, Yakun; Arens, Christina E.; Armstrong, Abraham M.; Sitko, Katherine; Chen, Wei-Ming; Chiang, Jung-Hsien; Reiss, David J.; Baliga, Nitin S.; Aitchison, John D.

2014-01-01

Systems scale models provide the foundation for an effective iterative cycle between hypothesis generation, experiment and model refinement. Such models also enable predictions facilitating the understanding of biological complexity and the control of biological systems. Here, we demonstrate the reconstruction of a globally predictive gene regulatory model from public data: a model that can drive rational experiment design and reveal new regulatory mechanisms underlying responses to novel environments. Specifically, using ∼1500 publically available genome-wide transcriptome data sets from Saccharomyces cerevisiae, we have reconstructed an environment and gene regulatory influence network that accurately predicts regulatory mechanisms and gene expression changes on exposure of cells to completely novel environments. Focusing on transcriptional networks that induce peroxisomes biogenesis, the model-guided experiments allow us to expand a core regulatory network to include novel transcriptional influences and linkage across signaling and transcription. Thus, the approach and model provides a multi-scalar picture of gene dynamics and are powerful resources for exploiting extant data to rationally guide experimentation. The techniques outlined here are generally applicable to any biological system, which is especially important when experimental systems are challenging and samples are difficult and expensive to obtain—a common problem in laboratory animal and human studies. PMID:24185701

Predicting colloid transport through saturated porous media: A critical review

NASA Astrophysics Data System (ADS)

Molnar, Ian L.; Johnson, William P.; Gerhard, Jason I.; Willson, Clinton S.; O'Carroll, Denis M.

2015-09-01

Understanding and predicting colloid transport and retention in water-saturated porous media is important for the protection of human and ecological health. Early applications of colloid transport research before the 1990s included the removal of pathogens in granular drinking water filters. Since then, interest has expanded significantly to include such areas as source zone protection of drinking water systems and injection of nanometals for contaminated site remediation. This review summarizes predictive tools for colloid transport from the pore to field scales. First, we review experimental breakthrough and retention of colloids under favorable and unfavorable colloid/collector interactions (i.e., no significant and significant colloid-surface repulsion, respectively). Second, we review the continuum-scale modeling strategies used to describe observed transport behavior. Third, we review the following two components of colloid filtration theory: (i) mechanistic force/torque balance models of pore-scale colloid trajectories and (ii) approximating correlation equations used to predict colloid retention. The successes and limitations of these approaches for favorable conditions are summarized, as are recent developments to predict colloid retention under the unfavorable conditions particularly relevant to environmental applications. Fourth, we summarize the influences of physical and chemical heterogeneities on colloid transport and avenues for their prediction. Fifth, we review the upscaling of mechanistic model results to rate constants for use in continuum models of colloid behavior at the column and field scales. Overall, this paper clarifies the foundation for existing knowledge of colloid transport and retention, features recent advances in the field, critically assesses where existing approaches are successful and the limits of their application, and highlights outstanding challenges and future research opportunities. These challenges and opportunities include improving mechanistic descriptions, and subsequent correlation equations, for nanoparticle (i.e., Brownian particle) transport through soil, developing mechanistic descriptions of colloid retention in so-called "unfavorable" conditions via methods such as the "discrete heterogeneity" approach, and employing imaging techniques such as X-ray tomography to develop realistic expressions for grain topology and mineral distribution that can aid the development of these mechanistic approaches.

Evolutionary conserved mechanisms pervade structure and transcriptional modulation of allograft inflammatory factor-1 from sea anemone Anemonia viridis.

PubMed

Cuttitta, Angela; Ragusa, Maria Antonietta; Costa, Salvatore; Bennici, Carmelo; Colombo, Paolo; Mazzola, Salvatore; Gianguzza, Fabrizio; Nicosia, Aldo

2017-08-01

Gene family encoding allograft inflammatory factor-1 (AIF-1) is well conserved among organisms; however, there is limited knowledge in lower organisms. In this study, the first AIF-1 homologue from cnidarians was identified and characterised in the sea anemone Anemonia viridis. The full-length cDNA of AvAIF-1 was of 913 bp with a 5' -untranslated region (UTR) of 148 bp, a 3'-UTR of 315 and an open reading frame (ORF) of 450 bp encoding a polypeptide with149 amino acid residues and predicted molecular weight of about 17 kDa. The predicted protein possesses evolutionary conserved EF hand Ca 2+ binding motifs, post-transcriptional modification sites and a 3D structure which can be superimposed with human members of AIF-1 family. The AvAIF-1 transcript was constitutively expressed in all tested tissues of unchallenged sea anemone, suggesting that AvAIF-1 could serve as a general protective factor under normal physiological conditions. Moreover, we profiled the transcriptional activation of AvAIF-1 after challenges with different abiotic/biotic stresses showing induction by warming conditions, heavy metals exposure and immune stimulation. Thus, mechanisms associated to inflammation and immune challenges up-regulated AvAIF-1 mRNA levels. Our results suggest its involvement in the inflammatory processes and immune response of A. viridis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparison of two free-energy expressions and their implications in surface enrichment

NASA Astrophysics Data System (ADS)

Jerry, Rocco A.; Nauman, E. Bruce

1993-08-01

We compare two free-energy expressions, developed by Cohen and Muthukumar [J. Chem. Phys. 90, 5749 (1989)] and by Jerry and Nauman [J. Colloid Interface Sci. 154, 122 (1992)], in terms of their predictions concerning surface enrichment. We show that a term must be added to the former expression so that it may predict the correct dependence of the surface composition on the bulk. The latter expression does predict the correct dependence. We have also derived the quadratic surface-energy contribution from a finite (nonzero) range interaction model.

The skin barrier function gene SPINK5 is associated with challenge-proven IgE-mediated food allergy in infants.

PubMed

Ashley, S E; Tan, H-T T; Vuillermin, P; Dharmage, S C; Tang, M L K; Koplin, J; Gurrin, L C; Lowe, A; Lodge, C; Ponsonby, A-L; Molloy, J; Martin, P; Matheson, M C; Saffery, R; Allen, K J; Ellis, J A; Martino, D

2017-09-01

A defective skin barrier is hypothesized to be an important route of sensitization to dietary antigens and may lead to food allergy in some children. Missense mutations in the serine peptidase inhibitor Kazal type 5 (SPINK5) skin barrier gene have previously been associated with allergic conditions. To determine whether genetic variants in and around SPINK5 are associated with IgE-mediated food allergy. We genotyped 71 "tag" single nucleotide polymorphisms (tag-SNPs) within a region spanning ~263 kb including SPINK5 (~61 kb) in n=722 (n=367 food-allergic, n=199 food-sensitized-tolerant and n=156 non-food-allergic controls) 12-month-old infants (discovery sample) phenotyped for food allergy with the gold standard oral food challenge. Transepidermal water loss (TEWL) measures were collected at 12 months from a subset (n=150) of these individuals. SNPs were tested for association with food allergy using the Cochran-Mantel-Haenszel test adjusting for ancestry strata. Association analyses were replicated in an independent sample group derived from four paediatric cohorts, total n=533 (n=203 food-allergic, n=330 non-food-allergic), mean age 2.5 years, with food allergy defined by either clinical history of reactivity, 95% positive predictive value (PPV) or challenge, corrected for ancestry by principal components. SPINK5 variant rs9325071 (A⟶G) was associated with challenge-proven food allergy in the discovery sample (P=.001, OR=2.95, CI=1.49-5.83). This association was further supported by replication (P=.007, OR=1.58, CI=1.13-2.20) and by meta-analysis (P=.0004, OR=1.65). Variant rs9325071 is associated with decreased SPINK5 gene expression in the skin in publicly available genotype-tissue expression data, and we generated preliminary evidence for association of this SNP with elevated TEWL also. We report, for the first time, association between SPINK5 variant rs9325071 and challenge-proven IgE-mediated food allergy. © 2017 EAACI and John Wiley and Sons A/S. Published by John Wiley and Sons Ltd.

Identification of a novel nematotoxic protein by challenging the model mushroom Coprinopsis cinerea with a fungivorous nematode

DOE PAGES

Plaza, David Fernando; Schmieder, Stefanie Sofia; Lipzen, Anna; ...

2015-11-19

The dung of herbivores, the natural habitat of the model mushroom Coprinopsis cinerea, is a nutrient-rich but also very competitive environment for a saprophytic fungus. Previously we showed that C. cinerea expresses constitutive, tissue-specific armories against antagonists such as animal predators and bacterial competitors. In order to dissect the inducible armories against such antagonists, we sequenced the poly(A)-positive transcriptome of C. cinerea vegetative mycelium upon challenge with fungivorous and bacterivorous nematodes, Gram-negative and Gram-positive bacteria and mechanical damage. As a response to the fungivorous nematode Aphelenchus avenae, C. cinerea was found to specifically induce the transcription of several genes encodingmore » previously characterized nematotoxic lectins. In addition, a previously not characterized gene encoding a cytoplasmic protein with several predicted Ricin B-fold domains, was found to be strongly up-regulated under this condition. Functional analysis of the recombinant protein revealed a high toxicity towards the bacterivorous nematode Caenorhabditis elegans. Challenge of the mycelium with A. avenae also lead to the induction of several genes encoding putative antibacterial proteins. Some of these genes were also induced upon challenge of the mycelium with the bacteria Escherichia coli and Bacillus subtilis. Lastly, these results suggest that fungi have the ability to induce specific innate defense responses similar to plants and animals.« less

Contribution of bioinformatics prediction in microRNA-based cancer therapeutics.

PubMed

Banwait, Jasjit K; Bastola, Dhundy R

2015-01-01

Despite enormous efforts, cancer remains one of the most lethal diseases in the world. With the advancement of high throughput technologies massive amounts of cancer data can be accessed and analyzed. Bioinformatics provides a platform to assist biologists in developing minimally invasive biomarkers to detect cancer, and in designing effective personalized therapies to treat cancer patients. Still, the early diagnosis, prognosis, and treatment of cancer are an open challenge for the research community. MicroRNAs (miRNAs) are small non-coding RNAs that serve to regulate gene expression. The discovery of deregulated miRNAs in cancer cells and tissues has led many to investigate the use of miRNAs as potential biomarkers for early detection, and as a therapeutic agent to treat cancer. Here we describe advancements in computational approaches to predict miRNAs and their targets, and discuss the role of bioinformatics in studying miRNAs in the context of human cancer. Published by Elsevier B.V.

Characterizing metabolic pathway diversification in the context of perturbation size.

PubMed

Yang, Laurence; Srinivasan, Shyamsundhar; Mahadevan, Radhakrishnan; Cluett, William R

2015-03-01

Cell metabolism is an important platform for sustainable biofuel, chemical and pharmaceutical production but its complexity presents a major challenge for scientists and engineers. Although in silico strains have been designed in the past with predicted performances near the theoretical maximum, real-world performance is often sub-optimal. Here, we simulate how strain performance is impacted when subjected to many randomly varying perturbations, including discrepancies between gene expression and in vivo flux, osmotic stress, and substrate uptake perturbations due to concentration gradients in bioreactors. This computational study asks whether robust performance can be achieved by adopting robustness-enhancing mechanisms from naturally evolved organisms-in particular, redundancy. Our study shows that redundancy, typically perceived as a ubiquitous robustness-enhancing strategy in nature, can either improve or undermine robustness depending on the magnitude of the perturbations. We also show that the optimal number of redundant pathways used can be predicted for a given perturbation size. Copyright © 2015. Published by Elsevier Inc.

Intersection of toxicogenomics and high throughput screening in the Tox21 program: an NIEHS perspective.

PubMed

Merrick, B Alex; Paules, Richard S; Tice, Raymond R

Humans are exposed to thousands of chemicals with inadequate toxicological data. Advances in computational toxicology, robotic high throughput screening (HTS), and genome-wide expression have been integrated into the Tox21 program to better predict the toxicological effects of chemicals. Tox21 is a collaboration among US government agencies initiated in 2008 that aims to shift chemical hazard assessment from traditional animal toxicology to target-specific, mechanism-based, biological observations using in vitro assays and lower organism models. HTS uses biocomputational methods for probing thousands of chemicals in in vitro assays for gene-pathway response patterns predictive of adverse human health outcomes. In 1999, NIEHS began exploring the application of toxicogenomics to toxicology and recent advances in NextGen sequencing should greatly enhance the biological content obtained from HTS platforms. We foresee an intersection of new technologies in toxicogenomics and HTS as an innovative development in Tox21. Tox21 goals, priorities, progress, and challenges will be reviewed.

Does climate undermine subjective well-being? A 58-nation study.

PubMed

Fischer, Ronald; Van de Vliert, Evert

2011-08-01

The authors test predictions from climato-economic theories of culture that climate and wealth interact in their influence on psychological processes. Demanding climates (defined as colder than temperate and hotter than temperate climates) create potential threats for humans. If these demands can be met by available economic resources, individuals experience challenging opportunities for self-expression and personal growth and consequently will report lowest levels of ill-being. If threatening climatic demands cannot be met by resources, resulting levels of reported ill-being will be highest. These predictions are confirmed in nation-level means of health complaints, burnout, anxiety, and depression across 58 societies. Climate, wealth, and their interaction together account for 35% of the variation in overall subjective ill-being, even when controlling for known predictors of subjective well-being. Further investigations of the process suggest that cultural individualism does not mediate these effects, but subjective well-being may function as a mediator of the impact of ecological variables on ill-being.

Correlates of androgens in wild male Barbary macaques: Testing the challenge hypothesis.

PubMed

Rincon, Alan V; Maréchal, Laëtitia; Semple, Stuart; Majolo, Bonaventura; MacLarnon, Ann

2017-10-01

Investigating causes and consequences of variation in hormonal expression is a key focus in behavioral ecology. Many studies have explored patterns of secretion of the androgen testosterone in male vertebrates, using the challenge hypothesis (Wingfield, Hegner, Dufty, & Ball, 1990; The American Naturalist, 136(6), 829-846) as a theoretical framework. Rather than the classic association of testosterone with male sexual behavior, this hypothesis predicts that high levels of testosterone are associated with male-male reproductive competition but also inhibit paternal care. The hypothesis was originally developed for birds, and subsequently tested in other vertebrate taxa, including primates. Such studies have explored the link between testosterone and reproductive aggression as well as other measures of mating competition, or between testosterone and aspects of male behavior related to the presence of infants. Very few studies have simultaneously investigated the links between testosterone and male aggression, other aspects of mating competition and infant-related behavior. We tested predictions derived from the challenge hypothesis in wild male Barbary macaques (Macaca sylvanus), a species with marked breeding seasonality and high levels of male-infant affiliation, providing a powerful test of this theoretical framework. Over 11 months, 251 hr of behavioral observations and 296 fecal samples were collected from seven adult males in the Middle Atlas Mountains, Morocco. Fecal androgen levels rose before the onset of the mating season, during a period of rank instability, and were positively related to group mating activity across the mating season. Androgen levels were unrelated to rates of male-male aggression in any period, but higher ranked males had higher levels in both the mating season and in the period of rank instability. Lower androgen levels were associated with increased rates of male-infant grooming during the mating and unstable periods. Our results generally support the challenge hypothesis and highlight the importance of considering individual species' behavioral ecology when testing this framework. © 2017 Wiley Periodicals, Inc.

Center of Excellence for Individuation of Therapy for Breast Cancer

DTIC Science & Technology

2012-03-01

Sledge, B. Leyland-Jones (2011) Gene copy number and expression of TYMP and TYMS are predictive of outcome in breast cancer patients treated with... Gene copy number and expression of TYMP and TYMS are predictive of outcome in breast cancer patients treated with capecitabine. R. Audet, C...determine if a specific gene expression signature could be used as predictive marker for treatment outcome . Results summary for Cohort A: doxorubicin

Glycogen synthase kinase-3 (GSK3) regulates TNF production and haemocyte phagocytosis in the immune response of Chinese mitten crab Eriocheir sinensis.

PubMed

Li, Xiaowei; Jia, Zhihao; Wang, Weilin; Wang, Lingling; Liu, Zhaoqun; Yang, Bin; Jia, Yunke; Song, Xiaorui; Yi, Qilin; Qiu, Limei; Song, Linsheng

2017-08-01

Glycogen synthase kinase-3 (GSK3) is a serine/threonine protein kinase firstly identified as a regulator of glycogen synthesis. Recently, it has been proved to be a key regulator of the immune reaction. In the present study, a GSK3 homolog gene (designated as EsGSK3) was cloned from Chinese mitten crab, Eriocheir sinensis. The open reading frame (ORF) was 1824 bp, which encoded a predicted polypeptide of 607 amino acids. There was a conserved Serine/Threonine Kinase domain and a DNA binding domain found in EsGSK3. Phylogenetic analysis showed that EsGSK3 was firstly clustered with GSK3-β from oriental river prawn Macrobrachium nipponense in the invertebrate branch, while GSK3s from vertebrates formed the other distinct branch. EsGSK3 mRNA transcripts could be detected in all tested tissues of the crab including haepatopancreas, eyestalk, muscle, gonad, haemocytes and haematopoietic tissue with the highest expression level in haepatopancreas. And EsGSK3 protein was mostly detected in the cytoplasm of haemocyte by immunofluorescence analysis. The expression levels of EsGSK3 mRNA increased significantly at 6 h after Aeromonas hydrophila challenge (p < 0.05) in comparison with control group, and then gradually decreased to the initial level at 48 h (p > 0.05). The mRNA expression of lipopolysaccharide-induced tumor necrosis factor (TNF)-α factor (EsLITAF) was also induced by A. hydrophila challenge. However, the mRNA expression of EsLITAF and TNF-α production was significantly suppressed after EsGSK3 was blocked in vivo with specific inhibitor lithium, while the phagocytosis of crab haemocytes was significantly promoted. These results collectively demonstrated that EsGSK3 could regulate the innate immune responses of E. sinensis by promoting TNF-α production and inhibiting haemocyte phagocytosis. Copyright © 2017 Elsevier Ltd. All rights reserved.

Emotional facial expressions differentially influence predictions and performance for face recognition.

PubMed

Nomi, Jason S; Rhodes, Matthew G; Cleary, Anne M

2013-01-01

This study examined how participants' predictions of future memory performance are influenced by emotional facial expressions. Participants made judgements of learning (JOLs) predicting the likelihood that they would correctly identify a face displaying a happy, angry, or neutral emotional expression in a future two-alternative forced-choice recognition test of identity (i.e., recognition that a person's face was seen before). JOLs were higher for studied faces with happy and angry emotional expressions than for neutral faces. However, neutral test faces with studied neutral expressions had significantly higher identity recognition rates than neutral test faces studied with happy or angry expressions. Thus, these data are the first to demonstrate that people believe happy and angry emotional expressions will lead to better identity recognition in the future relative to neutral expressions. This occurred despite the fact that neutral expressions elicited better identity recognition than happy and angry expressions. These findings contribute to the growing literature examining the interaction of cognition and emotion.

The SloR Metalloregulator is Involved in the Streptococcus mutans Oxidative Stress Response

PubMed Central

Crepps, Sarah C.; Fields, Emily E.; Galan, Diego; Corbett, John P.; Von Hasseln, Elizabeth R.; Spatafora, Grace A.

2015-01-01

SUMMARY A 25kDa SloR metalloregulatory protein in Streptococcus mutans modulates the expression of multiple genes, including the sloABC operon that encodes essential Mn2+ transport and genes that promote cariogenesis. In this study, we report on SloC- and SloR-deficient strains of S. mutans (GMS284 and GMS584, respectively) that demonstrate compromised survivorship compared to their UA159 wildtype progenitor and their complemented strains (GMS285 and GMS585, respectively), when challenged with streptonigrin and/or in growth competition experiments. The results of streptonigrin assays revealed significantly larger zones of inhibition for GMS584 than for either UA159 or GMS585, indicating weakened S. mutans survivorship in the absence of SloR. Competition assays revealed a compromised ability for GMS284 and GMS584 to survive peroxide challenge compared with their SloC- and SloR-proficient counterparts. These findings are consistent with a role for SloC and SloR in S. mutans aerotolerance. We also predicted differential expression of oxidative stress tolerance genes in GMS584 versus UA159 and GMS585 when grown aerobically. The results of qRT-PCR experiments revealed S. mutans sod, tpx, and sloC expression that was up-regulated in GMS584 compared to UA159 and GMS585, indicating that the impact of oxidative stress on S. mutans is more severe in the absence of SloR than in its presence. The results of electrophoretic mobility shift assays indicate that SloR does not bind to the sod or tpx promoter regions directly, implicating intermediaries that may arbitrate the SloR response to oxidative stress. PMID:26577188

The SloR metalloregulator is involved in the Streptococcus mutans oxidative stress response.

PubMed

Crepps, S C; Fields, E E; Galan, D; Corbett, J P; Von Hasseln, E R; Spatafora, G A

2016-12-01

SloR, a 25-kDa metalloregulatory protein in Streptococcus mutans modulates the expression of multiple genes, including the sloABC operon that encodes essential Mn 2+ transport and genes that promote cariogenesis. In this study, we report on SloC- and SloR-deficient strains of S. mutans (GMS284 and GMS584, respectively) that demonstrate compromised survivorship compared with their UA159 wild-type progenitor and their complemented strains (GMS285 and GMS585, respectively), when challenged with streptonigrin and/or in growth competition experiments. The results of streptonigrin assays revealed significantly larger zones of inhibition for GMS584 than for either UA159 or GMS585, indicating weakened S. mutans survivorship in the absence of SloR. Competition assays revealed a compromised ability for GMS284 and GMS584 to survive peroxide challenge compared with their SloC- and SloR-proficient counterparts. These findings are consistent with a role for SloC and SloR in S. mutans aerotolerance. We also predicted differential expression of oxidative stress tolerance genes in GMS584 versus UA159 and GMS585 when grown aerobically. The results of quantitative RT-PCR experiments revealed S. mutans sod, tpx, and sloC expression that was upregulated in GMS584 compared with UA159 and GMS585, indicating that the impact of oxidative stress on S. mutans is more severe in the absence of SloR than in its presence. The results of electrophoretic mobility shift assays indicate that SloR does not bind to the sod or tpx promoter regions directly, implicating intermediaries that may arbitrate the SloR response to oxidative stress. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Identifying microbially mediated transformations of DOC across season and tide from simultaneous changes in whole community gene expression and in mass spectra generated by Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS)

NASA Astrophysics Data System (ADS)

Ballantyne, F.; Medeiros, P. M.; Moran, M. A.; Song, C.; Whitman, W. B.; Washington, B.; Yu, M.; Lee, J.

2017-12-01

Despite the advent of methods enabling high resolution characterization of metabolic activity and of organic matter, linking microbial metabolism to organic matter transformations remains a challenge. By sequencing metatranscriptomes and using Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FTICR-MS) to characterize organic matter (OM) at the beginning and at the end of incubations of estuarine water across tide and season, we sought to link observed a changes in OM composition to microbial metabolism. We used linear models and K means clustering to identify clusters of genes that responded coherently across season, which accounted for most of the variability in gene expression, over tidal regime, which explained the majority of the remaining variation, and over time during the 24 hour incubations. We used an approach from the field of signal processing, that to our knowledge has not been used to analyze FTICR-MS data, to identify formulae of compounds that changed in concentration during the incubations. This approach, based on the discrete wavelet transform (DWT), allowed us to overcome some of the challenges associated with analyzing FTICR-MS data: variable ionization of organic compounds, signal suppression by high concentration compounds, and uncertainty about how to normalize changes across spectra. We were able to link clusters of metabolic and transporter genes to changes in OM composition, and uniquely identify genes based on their cross correlation with changes in FTICR mass spectra. Our approach for analyzing FTICR- MS data enables more robust inference about OM transformations, and linking high resolution changes in gene expression and in OM data during incubations represents an important step toward formulating models of microbial metabolism relevant for predicting biogeochemically relevant C fluxes.

Concholepas concholepas Ferritin H-like subunit (CcFer): Molecular characterization and single nucleotide polymorphism associated to innate immune response.

PubMed

Chávez-Mardones, Jacqueline; Valenzuela-Muñoz, Valentina; Núñez-Acuña, Gustavo; Maldonado-Aguayo, Waleska; Gallardo-Escárate, Cristian

2013-09-01

Ferritin has been identified as the principal protein of iron storage and iron detoxification, playing a pivotal role for the cellular homeostasis in living organisms. However, recent studies in marine invertebrates have suggested its association with innate immune system. In the present study, one Ferritin subunit was identified from the gastropod Concholepas concholepas (CcFer), which was fully characterized by Rapid Amplification of cDNA Ends technique. Simultaneously, a challenge test was performed to evaluate the immune response against Vibrio anguillarum. The full length of cDNA Ccfer was 1030 bp, containing 513 bp of open reading frame that encodes to 170 amino acid peptide, which was similar to the Ferritin H subunit described in vertebrates. Untranslated Regions (UTRs) were identified with a 5'UTR of 244 bp that contains iron responsive element (IRE), and a 3'UTR of 273 bp. The predicted molecular mass of deduced amino acid of CcFer was 19.66 kDa and isoelectric point of 4.92. Gene transcription analysis revealed that CcFer increases against infections with V. anguillarum, showing a peak expression at 6 h post-infection. Moreover, a single nucleotide polymorphism was detected at -64 downstream 5'UTR sequence (SNP-64). Quantitative real time analysis showed that homozygous mutant allele (TT) was significantly associated with higher expression levels of the challenged group compared to wild (CC) and heterozygous (CT) variants. Our findings suggest that CcFer is associated to innate immune response in C. concholepas and that the presence of SNPs may involve differential transcriptional expression of CcFer. Copyright © 2013 Elsevier Ltd. All rights reserved.

Adipose Gene Expression Prior to Weight Loss Can Differentiate and Weakly Predict Dietary Responders

PubMed Central

Mutch, David M.; Temanni, M. Ramzi; Henegar, Corneliu; Combes, Florence; Pelloux, Véronique; Holst, Claus; Sørensen, Thorkild I. A.; Astrup, Arne; Martinez, J. Alfredo; Saris, Wim H. M.; Viguerie, Nathalie; Langin, Dominique; Zucker, Jean-Daniel; Clément, Karine

2007-01-01

Background The ability to identify obese individuals who will successfully lose weight in response to dietary intervention will revolutionize disease management. Therefore, we asked whether it is possible to identify subjects who will lose weight during dietary intervention using only a single gene expression snapshot. Methodology/Principal Findings The present study involved 54 female subjects from the Nutrient-Gene Interactions in Human Obesity-Implications for Dietary Guidelines (NUGENOB) trial to determine whether subcutaneous adipose tissue gene expression could be used to predict weight loss prior to the 10-week consumption of a low-fat hypocaloric diet. Using several statistical tests revealed that the gene expression profiles of responders (8–12 kgs weight loss) could always be differentiated from non-responders (<4 kgs weight loss). We also assessed whether this differentiation was sufficient for prediction. Using a bottom-up (i.e. black-box) approach, standard class prediction algorithms were able to predict dietary responders with up to 61.1%±8.1% accuracy. Using a top-down approach (i.e. using differentially expressed genes to build a classifier) improved prediction accuracy to 80.9%±2.2%. Conclusion Adipose gene expression profiling prior to the consumption of a low-fat diet is able to differentiate responders from non-responders as well as serve as a weak predictor of subjects destined to lose weight. While the degree of prediction accuracy currently achieved with a gene expression snapshot is perhaps insufficient for clinical use, this work reveals that the comprehensive molecular signature of adipose tissue paves the way for the future of personalized nutrition. PMID:18094752

Facial expressions of emotion and the course of conjugal bereavement.

PubMed

Bonanno, G A; Keltner, D

1997-02-01

The common assumption that emotional expression mediates the course of bereavement is tested. Competing hypotheses about the direction of mediation were formulated from the grief work and social-functional accounts of emotional expression. Facial expressions of emotion in conjugally bereaved adults were coded at 6 months post-loss as they described their relationship with the deceased; grief and perceived health were measured at 6, 14, and 25 months. Facial expressions of negative emotion, in particular anger, predicted increased grief at 14 months and poorer perceived health through 25 months. Facial expressions of positive emotion predicted decreased grief through 25 months and a positive but nonsignificant relation to perceived health. Predictive relations between negative and positive emotional expression persisted when initial levels of self-reported emotion, grief, and health were statistically controlled, demonstrating the mediating role of facial expressions of emotion in adjustment to conjugal loss. Theoretical and clinical implications are discussed.

Targeted therapies in cancer - challenges and chances offered by newly developed techniques for protein analysis in clinical tissues

PubMed Central

Malinowsky, K; Wolff, C; Gündisch, S; Berg, D; Becker, KF

2011-01-01

In recent years, new anticancer therapies have accompanied the classical approaches of surgery and radio- and chemotherapy. These new forms of treatment aim to inhibit specific molecular targets namely altered or deregulated proteins, which offer the possibility of individualized therapies. The specificity and efficiency of these new approaches, however, bring about a number of challenges. First of all, it is essential to specifically identify and quantify protein targets in tumor tissues for the reasonable use of such targeted therapies. Additionally, it has become even more obvious in recent years that the presence of a target protein is not always sufficient to predict the outcome of targeted therapies. The deregulation of downstream signaling molecules might also play an important role in the success of such therapeutic approaches. For these reasons, the analysis of tumor-specific protein expression profiles prior to therapy has been suggested as the most effective way to predict possible therapeutic results. To further elucidate signaling networks underlying cancer development and to identify new targets, it is necessary to implement tools that allow the rapid, precise, inexpensive and simultaneous analysis of many network components while requiring only a small amount of clinical material. Reverse phase protein microarray (RPPA) is a promising technology that meets these requirements while enabling the quantitative measurement of proteins. Together with recently developed protocols for the extraction of proteins from formalin-fixed, paraffin-embedded (FFPE) tissues, RPPA may provide the means to quantify therapeutic targets and diagnostic markers in the near future and reliably screen for new protein targets. With the possibility to quantitatively analyze DNA, RNA and protein from a single FFPE tissue sample, the methods are available for integrated patient profiling at all levels of gene expression, thus allowing optimal patient stratification for individualized therapies. PMID:21197262

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. PMID:24667482

Prediction of miRNA-mRNA associations in Alzheimer's disease mice using network topology.

PubMed

Noh, Haneul; Park, Charny; Park, Soojun; Lee, Young Seek; Cho, Soo Young; Seo, Hyemyung

2014-08-03

Little is known about the relationship between miRNA and mRNA expression in Alzheimer's disease (AD) at early- or late-symptomatic stages. Sequence-based target prediction algorithms and anti-correlation profiles have been applied to predict miRNA targets using omics data, but this approach often leads to false positive predictions. Here, we applied the joint profiling analysis of mRNA and miRNA expression levels to Tg6799 AD model mice at 4 and 8 months of age using a network topology-based method. We constructed gene regulatory networks and used the PageRank algorithm to predict significant interactions between miRNA and mRNA. In total, 8 cluster modules were predicted by the transcriptome data for co-expression networks of AD pathology. In total, 54 miRNAs were identified as being differentially expressed in AD. Among these, 50 significant miRNA-mRNA interactions were predicted by integrating sequence target prediction, expression analysis, and the PageRank algorithm. We identified a set of miRNA-mRNA interactions that were changed in the hippocampus of Tg6799 AD model mice. We determined the expression levels of several candidate genes and miRNA. For functional validation in primary cultured neurons from Tg6799 mice (MT) and littermate (LM) controls, the overexpression of ARRDC3 enhanced PPP1R3C expression. ARRDC3 overexpression showed the tendency to decrease the expression of miR139-5p and miR3470a in both LM and MT primary cells. Pathological environment created by Aβ treatment increased the gene expression of PPP1R3C and Sfpq but did not significantly alter the expression of miR139-5p or miR3470a. Aβ treatment increased the promoter activity of ARRDC3 gene in LM primary cells but not in MT primary cells. Our results demonstrate AD-specific changes in the miRNA regulatory system as well as the relationship between the expression levels of miRNAs and their targets in the hippocampus of Tg6799 mice. These data help further our understanding of the function and mechanism of various miRNAs and their target genes in the molecular pathology of AD.

c-Met Expression Is a Marker of Poor Prognosis in Patients With Locally Advanced Head and Neck Squamous Cell Carcinoma Treated With Chemoradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Baschnagel, Andrew M.; Williams, Lindsay; Hanna, Alaa

2014-03-01

Purpose: To examine the prognostic significance of c-Met expression in relation to p16 and epidermal growth factor receptor (EGFR) in patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with definitive concurrent chemoradiation. Methods and Materials: Archival tissue from 107 HNSCC patients treated with chemoradiation was retrieved, and a tissue microarray was assembled. Immunohistochemical staining of c-Met, p16, and EGFR was performed. c-Met expression was correlated with p16, EGFR, clinical characteristics, and clinical endpoints including locoregional control (LRC), distant metastasis (DM), disease-free survival (DFS), and overall survival (OS). Results: Fifty-one percent of patients were positive for p16,more » and 53% were positive for EGFR. Both p16-negative (P≤.001) and EGFR-positive (P=.019) status predicted for worse DFS. Ninety-three percent of patients stained positive for c-Met. Patients were divided into low (0, 1, or 2+ intensity) or high (3+ intensity) c-Met expression. On univariate analysis, high c-Met expression predicted for worse LRC (hazard ratio [HR] 2.27; 95% CI, 1.08-4.77; P=.031), DM (HR 4.41; 95% CI, 1.56-12.45; P=.005), DFS (HR 3.00; 95% CI, 1.68-5.38; P<.001), and OS (HR 4.35; 95% CI, 2.13-8.88; P<.001). On multivariate analysis, after adjustment for site, T stage, smoking history, and EGFR status, only high c-Met expression (P=.011) and negative p16 status (P=.003) predicted for worse DFS. High c-Met expression was predictive of worse DFS in both EGFR-positive (P=.032) and -negative (P=.008) patients. In the p16-negative patients, those with high c-Met expression had worse DFS (P=.036) than did those with low c-Met expression. c-Met expression was not associated with any outcome in the p16-positive patients. Conclusions: c-Met is expressed in the majority of locally advanced HNSCC cases, and high c-Met expression predicts for worse clinical outcomes. High c-Met expression predicted for worse DFS in p16-negative patients but not in p16-positive patients. c-Met predicted for worse outcome regardless of EGFR status.« less

Ionizing Radiation Affects Gene Expression in Mouse Skin and Bone

NASA Technical Reports Server (NTRS)

Terada, Masahiro; Tahimic, Candice; Sowa, Marianne B.; Schreurs, Ann-Sofie; Shirazi-Fard, Yasaman; Alwood, Joshua; Globus, Ruth K.

2017-01-01

Future long-duration space exploration beyond low earth orbit will increase human exposure to space radiation and microgravity conditions as well as associated risks to skeletal health. In animal studies, radiation exposure (greater than 1 Gy) is associated with pathological changes in bone structure, enhanced bone resorption, reduced bone formation and decreased bone mineral density, which can lead to skeletal fragility. Definitive measurements and detection of bone loss typically require large and specialized equipment which can make their application to long duration space missions logistically challenging. Towards the goal of developing non-invasive and less complicated monitoring methods to predict astronauts' health during spaceflight, we examined whether radiation induced gene expression changes in skin may be predictive of the responses of skeletal tissue to radiation exposure. We examined oxidative stress and growth arrest pathways in mouse skin and long bones by measuring gene expression levels via quantitative polymerase chain reaction (qPCR) after exposure to total body irradiation (IR). To investigate the effects of irradiation on gene expression, we used skin and femora (cortical shaft) from the following treatment groups: control (normally loaded, sham-irradiated), and IR (0.5 Gy 56Fe 600 MeV/n and 0.5 Gy 1H 150 MeV/n), euthanized at one and 11 days post-irradiation (IR). To determine the extent of bone loss, tibiae were harvested and cancellous microarchitecture in the proximal tibia quantified ex vivo using microcomputed tomography (microCT). Statistical analysis was performed using Student's t-test. At one day post-IR, expression of FGF18 in skin was significantly greater (3.8X) than sham-irradiated controls, but did not differ at 11 days post IR. Expression levels of other genes associated with antioxidant response (Nfe2l2, FoxO3 and Sod1) and the cell cycle (Trp53, Cdkn1a, Gadd45g) did not significantly differ between the control and IR groups at either time point. Radiation exposure resulted in a 27.0% increase in FGF18-positive hair follicles at one day post-IR and returned to basal levels at 11 days post-IR. A similar trend was observed from FGF18 gene expression analysis of skin. In bone (femora), there was an increase in the expression of the pro-osteoclastogenic cytokine, MCP-1, one day after IR compared to non-irradiated controls. FGF18 expression in skin and MCP- 1 expression in bone were found to be positively correlated (P less than 0.002, r=0.8779). Further, microcomputed tomography analysis of tibia from these animals showed reduced cancellous bone volume (-9.9%) at 11 days post- IR. These results suggest that measurements of early radiation induced changes in FGF18 gene expression in skin may have value for predicting subsequent loss of cancellous bone mass. Further research may lead to the development of a relatively simple diagnostic tool for bone loss, with the advantage that hair follicles and skin are relatively easy to acquire from human subjects.

Supplemental dietary L-arginine attenuates intestinal mucosal disruption during a coccidial vaccine challenge in broiler chickens.

PubMed

Tan, Jianzhuang; Applegate, Todd J; Liu, Shasha; Guo, Yuming; Eicher, Susan D

2014-10-14

The present study investigated the effects of dietary arginine (Arg) supplementation on intestinal structure and functionality in broiler chickens subjected to coccidial challenge. The present study was a randomised complete block design employing a 3 × 2 factorial arrangement (n 8) with three dietary concentrations of Arg (11·1, 13·3 and 20·2 g/kg) with or without coccidial vaccine challenge (unchallenged and coccidial challenge). On day 14, birds were orally administered with coccidial vaccine or saline. On day 21, birds were killed to obtain jejunal tissue and mucosal samples for histological, gene expression and mucosal immunity measurements. Within 7 d of the challenge, there was a decrease in body-weight gain and feed intake, and an increase in the feed:gain ratio (P< 0·05). Jejunal inflammation was evidenced by villus damage, crypt dilation and goblet cell depletion. Coccidial challenge increased mucosal secretory IgA concentration and inflammatory gene (iNOS, IL-1β, IL-8 and MyD88) mRNA expression levels (P< 0·05), as well as reduced jejunal Mucin-2, IgA and IL-1RI mRNA expression levels (P< 0·05). Increasing Arg concentration (1) increased jejunal villus height (P< 0·05) and linearly increased jejunal crypt depth (P< 0·05); (2) quadratically increased mucosal maltase activity (P< 0·05) and linearly decreased mucosal secretory IgG concentration (P< 0·05) within the coccidiosis-challenged groups; and (3) linearly decreased jejunal Toll-like receptor 4 (TLR4) mRNA expression level (P< 0·05) within the coccidiosis-challenged groups. The mRNA expression of mechanistic target of rapamycin (mTOR) complex 1 pathway genes (mTOR and RPS6KB1) and the anti-apoptosis gene Bcl-2 quadratically responded to increasing dietary Arg supplementation (P< 0·05). These results indicate that dietary Arg supplementation attenuates intestinal mucosal disruption in coccidiosis-challenged chickens probably through suppressing TLR4 and activating mTOR complex 1 pathways.

Empathy, Challenge, and Psychophysiological Activation in Therapist–Client Interaction

PubMed Central

Voutilainen, Liisa; Henttonen, Pentti; Kahri, Mikko; Ravaja, Niklas; Sams, Mikko; Peräkylä, Anssi

2018-01-01

Two central dimensions in psychotherapeutic work are a therapist’s empathy with clients and challenging their judgments. We investigated how they influence psychophysiological responses in the participants. Data were from psychodynamic therapy sessions, 24 sessions from 5 dyads, from which 694 therapist’s interventions were coded. Heart rate and electrodermal activity (EDA) of the participants were used to index emotional arousal. Facial muscle activity (electromyography) was used to index positive and negative emotional facial expressions. Electrophysiological data were analyzed in two time frames: (a) during the therapists’ interventions and (b) across the whole psychotherapy session. Both empathy and challenge had an effect on psychophysiological responses in the participants. Therapists’ empathy decreased clients’ and increased their own EDA across the session. Therapists’ challenge increased their own EDA in response to the interventions, but not across the sessions. Clients, on the other hand, did not respond to challenges during interventions, but challenges tended to increase EDA across a session. Furthermore, there was an interaction effect between empathy and challenge. Heart rate decreased and positive facial expressions increased in sessions where empathy and challenge were coupled, i.e., the amount of both empathy and challenge was either high or low. This suggests that these two variables work together. The results highlight the therapeutic functions and interrelation of empathy and challenge, and in line with the dyadic system theory by Beebe and Lachmann (2002), the systemic linkage between interactional expression and individual regulation of emotion. PMID:29695992

Anger Expression and Persistence in Young Children

ERIC Educational Resources Information Center

He, Jie; Xu, Qinmei; Degnan, Kathryn Amey

2012-01-01

This study investigated anger expression during toy removal (TR) in 92 young Chinese children, two to five years of age, and its relations to their persistence in responding to obstacles during two challenging tasks with highly desirable goals [TR and locked box (LB)] and one challenging task with a less desirable goal [impossible perfect circles…

Cohort-specific imputation of gene expression improves prediction of warfarin dose for African Americans.

PubMed

Gottlieb, Assaf; Daneshjou, Roxana; DeGorter, Marianne; Bourgeois, Stephane; Svensson, Peter J; Wadelius, Mia; Deloukas, Panos; Montgomery, Stephen B; Altman, Russ B

2017-11-24

Genome-wide association studies are useful for discovering genotype-phenotype associations but are limited because they require large cohorts to identify a signal, which can be population-specific. Mapping genetic variation to genes improves power and allows the effects of both protein-coding variation as well as variation in expression to be combined into "gene level" effects. Previous work has shown that warfarin dose can be predicted using information from genetic variation that affects protein-coding regions. Here, we introduce a method that improves dose prediction by integrating tissue-specific gene expression. In particular, we use drug pathways and expression quantitative trait loci knowledge to impute gene expression-on the assumption that differential expression of key pathway genes may impact dose requirement. We focus on 116 genes from the pharmacokinetic and pharmacodynamic pathways of warfarin within training and validation sets comprising both European and African-descent individuals. We build gene-tissue signatures associated with warfarin dose in a cohort-specific manner and identify a signature of 11 gene-tissue pairs that significantly augments the International Warfarin Pharmacogenetics Consortium dosage-prediction algorithm in both populations. Our results demonstrate that imputed expression can improve dose prediction and bridge population-specific compositions. MATLAB code is available at https://github.com/assafgo/warfarin-cohort.

Optimizing heat shock protein expression induced by prostate cancer laser therapy through predictive computational models

NASA Astrophysics Data System (ADS)

Rylander, Marissa N.; Feng, Yusheng; Zhang, Yongjie; Bass, Jon; Stafford, Roger J.; Hazle, John D.; Diller, Kenneth R.

2006-07-01

Thermal therapy efficacy can be diminished due to heat shock protein (HSP) induction in regions of a tumor where temperatures are insufficient to coagulate proteins. HSP expression enhances tumor cell viability and imparts resistance to chemotherapy and radiation treatments, which are generally employed in conjunction with hyperthermia. Therefore, an understanding of the thermally induced HSP expression within the targeted tumor must be incorporated into the treatment plan to optimize the thermal dose delivery and permit prediction of the overall tissue response. A treatment planning computational model capable of predicting the temperature, HSP27 and HSP70 expression, and damage fraction distributions associated with laser heating in healthy prostate tissue and tumors is presented. Measured thermally induced HSP27 and HSP70 expression kinetics and injury data for normal and cancerous prostate cells and prostate tumors are employed to create the first HSP expression predictive model and formulate an Arrhenius damage model. The correlation coefficients between measured and model predicted temperature, HSP27, and HSP70 were 0.98, 0.99, and 0.99, respectively, confirming the accuracy of the model. Utilization of the treatment planning model in the design of prostate cancer thermal therapies can enable optimization of the treatment outcome by controlling HSP expression and injury.

Global Transcriptional Start Site Mapping Using Differential RNA Sequencing Reveals Novel Antisense RNAs in Escherichia coli

PubMed Central

Thomason, Maureen K.; Bischler, Thorsten; Eisenbart, Sara K.; Förstner, Konrad U.; Zhang, Aixia; Herbig, Alexander; Nieselt, Kay

2014-01-01

While the model organism Escherichia coli has been the subject of intense study for decades, the full complement of its RNAs is only now being examined. Here we describe a survey of the E. coli transcriptome carried out using a differential RNA sequencing (dRNA-seq) approach, which can distinguish between primary and processed transcripts, and an automated prediction algorithm for transcriptional start sites (TSS). With the criterion of expression under at least one of three growth conditions examined, we predicted 14,868 TSS candidates, including 5,574 internal to annotated genes (iTSS) and 5,495 TSS corresponding to potential antisense RNAs (asRNAs). We examined expression of 14 candidate asRNAs by Northern analysis using RNA from wild-type E. coli and from strains defective for RNases III and E, two RNases reported to be involved in asRNA processing. Interestingly, nine asRNAs detected as distinct bands by Northern analysis were differentially affected by the rnc and rne mutations. We also compared our asRNA candidates with previously published asRNA annotations from RNA-seq data and discuss the challenges associated with these cross-comparisons. Our global transcriptional start site map represents a valuable resource for identification of transcription start sites, promoters, and novel transcripts in E. coli and is easily accessible, together with the cDNA coverage plots, in an online genome browser. PMID:25266388

Lessons learned in induced fit docking and metadynamics in the Drug Design Data Resource Grand Challenge 2

NASA Astrophysics Data System (ADS)

Baumgartner, Matthew P.; Evans, David A.

2018-01-01

Two of the major ongoing challenges in computational drug discovery are predicting the binding pose and affinity of a compound to a protein. The Drug Design Data Resource Grand Challenge 2 was developed to address these problems and to drive development of new methods. The challenge provided the 2D structures of compounds for which the organizers help blinded data in the form of 35 X-ray crystal structures and 102 binding affinity measurements and challenged participants to predict the binding pose and affinity of the compounds. We tested a number of pose prediction methods as part of the challenge; we found that docking methods that incorporate protein flexibility (Induced Fit Docking) outperformed methods that treated the protein as rigid. We also found that using binding pose metadynamics, a molecular dynamics based method, to score docked poses provided the best predictions of our methods with an average RMSD of 2.01 Å. We tested both structure-based (e.g. docking) and ligand-based methods (e.g. QSAR) in the affinity prediction portion of the competition. We found that our structure-based methods based on docking with Smina (Spearman ρ = 0.614), performed slightly better than our ligand-based methods (ρ = 0.543), and had equivalent performance with the other top methods in the competition. Despite the overall good performance of our methods in comparison to other participants in the challenge, there exists significant room for improvement especially in cases such as these where protein flexibility plays such a large role.

D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions

NASA Astrophysics Data System (ADS)

Gathiaka, Symon; Liu, Shuai; Chiu, Michael; Yang, Huanwang; Stuckey, Jeanne A.; Kang, You Na; Delproposto, Jim; Kubish, Ginger; Dunbar, James B.; Carlson, Heather A.; Burley, Stephen K.; Walters, W. Patrick; Amaro, Rommie E.; Feher, Victoria A.; Gilson, Michael K.

2016-09-01

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (1) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (2) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor.

D3R Grand Challenge 2015: Evaluation of Protein-Ligand Pose and Affinity Predictions

PubMed Central

Gathiaka, Symon; Liu, Shuai; Chiu, Michael; Yang, Huanwang; Stuckey, Jeanne A; Kang, You Na; Delproposto, Jim; Kubish, Ginger; Dunbar, James B.; Carlson, Heather A.; Burley, Stephen K.; Walters, W. Patrick; Amaro, Rommie E.; Feher, Victoria A.; Gilson, Michael K.

2017-01-01

The Drug Design Data Resource (D3R) ran Grand Challenge 2015 between September 2015 and February 2016. Two targets served as the framework to test community docking and scoring methods: (i) HSP90, donated by AbbVie and the Community Structure Activity Resource (CSAR), and (ii) MAP4K4, donated by Genentech. The challenges for both target datasets were conducted in two stages, with the first stage testing pose predictions and the capacity to rank compounds by affinity with minimal structural data; and the second stage testing methods for ranking compounds with knowledge of at least a subset of the ligand-protein poses. An additional sub-challenge provided small groups of chemically similar HSP90 compounds amenable to alchemical calculations of relative binding free energy. Unlike previous blinded Challenges, we did not provide cognate receptors or receptors prepared with hydrogens and likewise did not require a specified crystal structure to be used for pose or affinity prediction in Stage 1. Given the freedom to select from over 200 crystal structures of HSP90 in the PDB, participants employed workflows that tested not only core docking and scoring technologies, but also methods for addressing water-mediated ligand-protein interactions, binding pocket flexibility, and the optimal selection of protein structures for use in docking calculations. Nearly 40 participating groups submitted over 350 prediction sets for Grand Challenge 2015. This overview describes the datasets and the organization of the challenge components, summarizes the results across all submitted predictions, and considers broad conclusions that may be drawn from this collaborative community endeavor. PMID:27696240

Exploring the Potential of Predictive Analytics and Big Data in Emergency Care.

PubMed

Janke, Alexander T; Overbeek, Daniel L; Kocher, Keith E; Levy, Phillip D

2016-02-01

Clinical research often focuses on resource-intensive causal inference, whereas the potential of predictive analytics with constantly increasing big data sources remains largely unexplored. Basic prediction, divorced from causal inference, is much easier with big data. Emergency care may benefit from this simpler application of big data. Historically, predictive analytics have played an important role in emergency care as simple heuristics for risk stratification. These tools generally follow a standard approach: parsimonious criteria, easy computability, and independent validation with distinct populations. Simplicity in a prediction tool is valuable, but technological advances make it no longer a necessity. Emergency care could benefit from clinical predictions built using data science tools with abundant potential input variables available in electronic medical records. Patients' risks could be stratified more precisely with large pools of data and lower resource requirements for comparing each clinical encounter to those that came before it, benefiting clinical decisionmaking and health systems operations. The largest value of predictive analytics comes early in the clinical encounter, in which diagnostic and prognostic uncertainty are high and resource-committing decisions need to be made. We propose an agenda for widening the application of predictive analytics in emergency care. Throughout, we express cautious optimism because there are myriad challenges related to database infrastructure, practitioner uptake, and patient acceptance. The quality of routinely compiled clinical data will remain an important limitation. Complementing big data sources with prospective data may be necessary if predictive analytics are to achieve their full potential to improve care quality in the emergency department. Copyright © 2015 American College of Emergency Physicians. Published by Elsevier Inc. All rights reserved.

Adult Judgments and Fine-Grained Analysis of Infant Facial Expressions: Testing the Validity of A Priori Coding Formulas.

ERIC Educational Resources Information Center

Oster, Harriet; And Others

1992-01-01

Compared subjects' judgments about emotions expressed by the faces of infants pictured in slides to predictions made by the Max system of measuring emotional expression. Judgments did not coincide with Max predictions for fear, anger, sadness, and disgust. Results indicated that expressions of negative affect by infants are not fully…

Identification of driving network of cellular differentiation from single sample time course gene expression data

NASA Astrophysics Data System (ADS)

Chen, Ye; Wolanyk, Nathaniel; Ilker, Tunc; Gao, Shouguo; Wang, Xujing

Methods developed based on bifurcation theory have demonstrated their potential in driving network identification for complex human diseases, including the work by Chen, et al. Recently bifurcation theory has been successfully applied to model cellular differentiation. However, there one often faces a technical challenge in driving network prediction: time course cellular differentiation study often only contains one sample at each time point, while driving network prediction typically require multiple samples at each time point to infer the variation and interaction structures of candidate genes for the driving network. In this study, we investigate several methods to identify both the critical time point and the driving network through examination of how each time point affects the autocorrelation and phase locking. We apply these methods to a high-throughput sequencing (RNA-Seq) dataset of 42 subsets of thymocytes and mature peripheral T cells at multiple time points during their differentiation (GSE48138 from GEO). We compare the predicted driving genes with known transcription regulators of cellular differentiation. We will discuss the advantages and limitations of our proposed methods, as well as potential further improvements of our methods.

Prediction of EST functional relationships via literature mining with user-specified parameters.

PubMed

Wang, Hei-Chia; Huang, Tian-Hsiang

2009-04-01

The massive amount of expressed sequence tags (ESTs) gathered over recent years has triggered great interest in efficient applications for genomic research. In particular, EST functional relationships can be used to determine a possible gene network for biological processes of interest. In recent years, many researchers have tried to determine EST functional relationships by analyzing the biological literature. However, it has been challenging to find efficient prediction methods. Moreover, an annotated EST is usually associated with many functions, so successful methods must be able to distinguish between relevant and irrelevant functions based on user specifications. This paper proposes a method to discover functional relationships between ESTs of interest by analyzing literature from the Medical Literature Analysis and Retrieval System Online, with user-specified parameters for selecting keywords. This method performs better than the multiple kernel documents method in setting up a specific threshold for gathering materials. The method is also able to uncover known functional relationships, as shown by a comparison with the Kyoto Encyclopedia of Genes and Genomes database. The reliable EST relationships predicted by the proposed method can help to construct gene networks for specific biological functions of interest.

Longitudinal associations between emotion regulation and depression in preadolescent girls: moderation by the caregiving environment.

PubMed

Feng, Xin; Keenan, Kate; Hipwell, Alison E; Henneberger, Angela K; Rischall, Michal S; Butch, Jen; Coyne, Claire; Boeldt, Debbie; Hinze, Amanda K; Babinski, Dara E

2009-05-01

Identifying childhood precursors for depression has been challenging and yet important for understanding the rapid increase in the rate of depression among adolescent girls. This study examined the prospective relations of preadolescent girls' emotion regulation and parenting style with depressive symptoms. Participants were 225 children and their biological mothers recruited from a larger longitudinal community study. Girls' observed positive and negative emotion during a conflict resolution task with mothers, their ability to regulate sadness and anger, and their perception of parental acceptance and psychological control were assessed at age 9. Depressive symptoms were assessed by self-report at ages 9 and 10. The results indicated interactions between child emotion characteristics and parenting in predicting later depression. Specifically, low levels of positive emotion expression predicted higher levels of depressive symptoms in the context of moderate to high parental psychological control. Low levels of sadness regulation were predictive of high levels of depressive symptoms in the context of low to moderate parental acceptance. Findings from this study support the hypothesis that the prospective association between vulnerabilities in emotion regulation and depression are moderated by the caregiving environment. Copyright 2009 APA, all rights reserved

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree.

PubMed

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen-host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules.

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree

PubMed Central

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen–host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules. PMID:26649272

Induction of indolamine 2,3-dioxygenase and kynurenine 3-monooxygenase in rat brain following a systemic inflammatory challenge: a role for IFN-gamma?

PubMed

Connor, Thomas J; Starr, Neasa; O'Sullivan, Joan B; Harkin, Andrew

2008-08-15

Inflammation-mediated dysregulation of the kynurenine pathway has been implicated as a contributor to a number of major brain disorders. Consequently, we examined the impact of a systemic inflammatory challenge on kynurenine pathway enzyme expression in rat brain. Indoleamine 2,3-dioxygenase (IDO) expression was induced in cortex and hippocampus following systemic lipopolysaccharide (LPS) administration. Whilst IDO expression was paralleled by increased circulating interferon (IFN)-gamma concentrations, IFN-gamma expression in the brain was only modestly altered following LPS administration. In contrast, induction of IDO was associated with increased central tumour necrosis factor (TNF)-alpha and interleukin (IL)-6 expression. Similarly, in cultured glial cells LPS-induced IDO expression was accompanied by increased TNF-alpha and IL-6 expression, whereas IFN-gamma was not detectable. These findings indicate that IFN-gamma is not required for LPS-induced IDO expression in brain. A robust increase in kynurenine-3-monooxygenase (KMO) expression was observed in rat brain 24h post LPS, without any change in kynurenine aminotransferase II (KAT II) expression. In addition, we report that constitutive expression of KAT II is approximately 8-fold higher than KMO in cortex and 20-fold higher in hippocampus. Similarly, in glial cells constitutive expression of KAT II was approximately 16-fold higher than KMO, and expression of KMO but not KAT II was induced by LPS. These data are the first to demonstrate that a systemic inflammatory challenge stimulates KMO expression in brain; a situation that is likely to favour kynurenine metabolism in a neurotoxic direction. However, our observation that expression of KAT II is much higher than KMO in rat brain is likely to counteract potential neurotoxicity that could arise from KMO induction following an acute inflammation.

Relationships of personality factors to perceived stress, depression, and oral lichen planus severity.

PubMed

Mohamadi Hasel, Kurosh; Besharat, Mohamad Ali; Abdolhoseini, Amir; Alaei Nasab, Somaye; Niknam, Seyran

2013-06-01

  The objective of this study is to examine relationships of hardiness and big five personality factors to depression, perceived stress, and oral lichen planus (OLP) severity. Sixty Iranian patients with oral lichen planus completed measures of perceived stress, hardiness, big five, and depression. Linear regressions revealed that control and challenge significantly predicted least perceived stress. On the contrary, big five factor of neuroticism predicted more perceived stress. Furthermore, control, commitment, and extraversion negatively predicted depression levels, but neuroticism positively predicted depression levels. Additionally, more levels of the challenge factor predicted fewer OLP scores while more levels of perceived stress predicted more OLP scores. The components of control challenge and neuroticism factors had a significant role in predicting perceived stress. On the other hand, the components of control and commitment and extraversion factors had a prominent role in predicting depression in patients with OLP, so personality constructs may have an effective role in triggering experience of stress, depression, and OLP itself. Additionally, interventions that enhance individual protective factors may be beneficial in reducing stress and depression in some severe diseases.

Predictable transcriptome evolution in the convergent and complex bioluminescent organs of squid

PubMed Central

Pankey, M. Sabrina; Minin, Vladimir N.; Imholte, Greg C.; Suchard, Marc A.; Oakley, Todd H.

2014-01-01

Despite contingency in life’s history, the similarity of evolutionarily convergent traits may represent predictable solutions to common conditions. However, the extent to which overall gene expression levels (transcriptomes) underlying convergent traits are themselves convergent remains largely unexplored. Here, we show strong statistical support for convergent evolutionary origins and massively parallel evolution of the entire transcriptomes in symbiotic bioluminescent organs (bacterial photophores) from two divergent squid species. The gene expression similarities are so strong that regression models of one species’ photophore can predict organ identity of a distantly related photophore from gene expression levels alone. Our results point to widespread parallel changes in gene expression evolution associated with convergent origins of complex organs. Therefore, predictable solutions may drive not only the evolution of novel, complex organs but also the evolution of overall gene expression levels that underlie them. PMID:25336755

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge.

PubMed

Biggerstaff, Matthew; Alper, David; Dredze, Mark; Fox, Spencer; Fung, Isaac Chun-Hai; Hickmann, Kyle S; Lewis, Bryan; Rosenfeld, Roni; Shaman, Jeffrey; Tsou, Ming-Hsiang; Velardi, Paola; Vespignani, Alessandro; Finelli, Lyn

2016-07-22

Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts.

Expression of NK Cell Surface Receptors in Breast Cancer Tissue as Predictors of Resistance to Antineoplastic Treatment

PubMed Central

Mariel, Garcia-Chagollan; Edith, Carranza-Torres Irma; Pilar, Carranza-Rosales; Elena, Guzmán-Delgado Nancy; Humberto, Ramírez-Montoya; Guadalupe, Martínez-Silva María; Ignacio, Mariscal-Ramirez; Alfredo, Barrón-Gallardo Carlos; Laura, Pereira-Suárez Ana; Adriana, Aguilar-Lemarroy; Felipe, Jave-Suárez Luis

2018-01-01

Background: Currently, one of the most used strategies for the treatment of newly diagnosed patients with breast cancer is neoadjuvant chemotherapy based on the application of taxanes and anthracyclines. However, despite the high number of patients who develop a complete pathological clinical response, resistance and relapse following this therapy continue to be a clinical challenge. As a component of the innate immune system, the cytotoxic function of Natural Killer (NK) cells plays an important role in the elimination of tumor cells. However, the role of NK cells in resistance to systemic therapy in breast cancer remains unclear. The present project aims to evaluate the gene expression profile of human NK cells in breast cancer tissue resistant to treatment with taxanes–anthracyclines. Methods: Biopsies from tumor tissues were obtained from patients with breast cancer without prior treatment. Histopathological analysis and ex vivo exposure to antineoplastic chemotherapeutics were carried out. Alamar blue and lactate dehydrogenase release assays were performed for quantitative analysis of tumor viability. Gene expression profiles from tumor tissues without prior exposure to therapeutic drugs were analyzed by gene expression microarrays and verified by polymerase chain reaction. Results: A significant decrease in gene expression of cell-surface receptors related to NK cells was observed in tumor samples resistant to antineoplastic treatment compared with those that were sensitive to treatment. Conclusion: A decrease in NK cell infiltration into tumor tissue might be a predictive marker for failure of chemotherapeutic treatment in breast cancer. PMID:29558872

Expression of NK Cell Surface Receptors in Breast Cancer Tissue as Predictors of Resistance to Antineoplastic Treatment.

PubMed

Mariel, Garcia-Chagollan; Edith, Carranza-Torres Irma; Pilar, Carranza-Rosales; Elena, Guzmán-Delgado Nancy; Humberto, Ramírez-Montoya; Guadalupe, Martínez-Silva María; Ignacio, Mariscal-Ramirez; Alfredo, Barrón-Gallardo Carlos; Laura, Pereira-Suárez Ana; Adriana, Aguilar-Lemarroy; Felipe, Jave-Suárez Luis

2018-01-01

Currently, one of the most used strategies for the treatment of newly diagnosed patients with breast cancer is neoadjuvant chemotherapy based on the application of taxanes and anthracyclines. However, despite the high number of patients who develop a complete pathological clinical response, resistance and relapse following this therapy continue to be a clinical challenge. As a component of the innate immune system, the cytotoxic function of Natural Killer (NK) cells plays an important role in the elimination of tumor cells. However, the role of NK cells in resistance to systemic therapy in breast cancer remains unclear. The present project aims to evaluate the gene expression profile of human NK cells in breast cancer tissue resistant to treatment with taxanes-anthracyclines. Biopsies from tumor tissues were obtained from patients with breast cancer without prior treatment. Histopathological analysis and ex vivo exposure to antineoplastic chemotherapeutics were carried out. Alamar blue and lactate dehydrogenase release assays were performed for quantitative analysis of tumor viability. Gene expression profiles from tumor tissues without prior exposure to therapeutic drugs were analyzed by gene expression microarrays and verified by polymerase chain reaction. A significant decrease in gene expression of cell-surface receptors related to NK cells was observed in tumor samples resistant to antineoplastic treatment compared with those that were sensitive to treatment. A decrease in NK cell infiltration into tumor tissue might be a predictive marker for failure of chemotherapeutic treatment in breast cancer.

Chronic methamphetamine administration causes differential regulation of transcription factors in the rat midbrain.

PubMed

Krasnova, Irina N; Ladenheim, Bruce; Hodges, Amber B; Volkow, Nora D; Cadet, Jean Lud

2011-04-25

Methamphetamine (METH) is an addictive and neurotoxic psychostimulant widely abused in the USA and throughout the world. When administered in large doses, METH can cause depletion of striatal dopamine terminals, with preservation of midbrain dopaminergic neurons. Because alterations in the expression of transcription factors that regulate the development of dopaminergic neurons might be involved in protecting these neurons after toxic insults, we tested the possibility that their expression might be affected by toxic doses of METH in the adult brain. Male Sprague-Dawley rats pretreated with saline or increasing doses of METH were challenged with toxic doses of the drug and euthanized two weeks later. Animals that received toxic METH challenges showed decreases in dopamine levels and reductions in tyrosine hydroxylase protein concentration in the striatum. METH pretreatment protected against loss of striatal dopamine and tyrosine hydroxylase. In contrast, METH challenges caused decreases in dopamine transporters in both saline- and METH-pretreated animals. Interestingly, METH challenges elicited increases in dopamine transporter mRNA levels in the midbrain in the presence but not in the absence of METH pretreatment. Moreover, toxic METH doses caused decreases in the expression of the dopamine developmental factors, Shh, Lmx1b, and Nurr1, but not in the levels of Otx2 and Pitx3, in saline-pretreated rats. METH pretreatment followed by METH challenges also decreased Nurr1 but increased Otx2 and Pitx3 expression in the midbrain. These findings suggest that, in adult animals, toxic doses of METH can differentially influence the expression of transcription factors involved in the developmental regulation of dopamine neurons. The combined increases in Otx2 and Pitx3 expression after METH preconditioning might represent, in part, some of the mechanisms that served to protect against METH-induced striatal dopamine depletion observed after METH preconditioning.

Expressive writing in people with traumatic brain injury and learning disability.

PubMed

Wheeler, Lisa; Nickerson, Sherry; Long, Kayla; Silver, Rebecca

2014-01-01

There is a dearth of systematic studies of expressive writing disorder (EWD) in persons with Traumatic Brain Injury (TBI). It is unclear if TBI survivors' written expression differs significantly from that experienced by persons with learning disabilities. It is also unclear which cognitive or neuropsychological variables predict problems with expressive writing (EW) or the EWD. This study investigated the EW skill, and the EWD in adults with mild traumatic brain injuries (TBI) relative to those with learning disabilities (LD). It also determined which of several cognitive variables predicted EW and EWD. Principle Component Analysis (PCA) of writing samples from 28 LD participants and 28 TBI survivors revealed four components of expressive writing skills: Reading Ease, Sentence Fluency, Grammar and Spelling, and Paragraph Fluency. There were no significant differences between the LD and TBI groups on any of the expressive writing components. Several neuropsychological variables predicted skills of written expression. The best predictors included measures of spatial perception, verbal IQ, working memory, and visual memory. TBI survivors and persons with LD do not differ markedly in terms of expressive writing skill. Measures of spatial perception, visual memory, verbal intelligence, and working memory predict writing skill in both groups. Several therapeutic interventions are suggested that are specifically designed to improve deficits in expressive writing skills in individuals with TBI and LD.

The Interrelationship between Promoter Strength, Gene Expression, and Growth Rate

PubMed Central

Klesmith, Justin R.; Detwiler, Emily E.; Tomek, Kyle J.; Whitehead, Timothy A.

2014-01-01

In exponentially growing bacteria, expression of heterologous protein impedes cellular growth rates. Quantitative understanding of the relationship between expression and growth rate will advance our ability to forward engineer bacteria, important for metabolic engineering and synthetic biology applications. Recently, a work described a scaling model based on optimal allocation of ribosomes for protein translation. This model quantitatively predicts a linear relationship between microbial growth rate and heterologous protein expression with no free parameters. With the aim of validating this model, we have rigorously quantified the fitness cost of gene expression by using a library of synthetic constitutive promoters to drive expression of two separate proteins (eGFP and amiE) in E. coli in different strains and growth media. In all cases, we demonstrate that the fitness cost is consistent with the previous findings. We expand upon the previous theory by introducing a simple promoter activity model to quantitatively predict how basal promoter strength relates to growth rate and protein expression. We then estimate the amount of protein expression needed to support high flux through a heterologous metabolic pathway and predict the sizable fitness cost associated with enzyme production. This work has broad implications across applied biological sciences because it allows for prediction of the interplay between promoter strength, protein expression, and the resulting cost to microbial growth rates. PMID:25286161

Skin prick testing and peanut-specific IgE can predict peanut challenge outcomes in preschoolchildren with peanut sensitization.

PubMed

Johannsen, H; Nolan, R; Pascoe, E M; Cuthbert, P; Noble, V; Corderoy, T; Franzmann, A; Loh, R; Prescott, S L

2011-07-01

The rise in peanut allergy is a source of considerable burden in the community. A growing number of preschoolchildren have been identified as peanut sensitized in the course of investigation of other allergic conditions. Although many have never knowingly ingested peanuts and their clinical reactivity is not known, it has been common practice to place these children on avoidance diets for many years. To determine the utility of skin prick tests (SPT) and fluorescent-enzyme immunoassays (FEIA) for identifying either peanut allergy or tolerance in preschoolchildren with peanut sensitization. Forty-nine preschoolchildren (<5 years of age) with peanut sensitization (SPT ≥ 2 mm or peanut-specific IgE ≥ 0.35 kU/L) but unknown clinical reactivity had graded open peanut challenges reaching a total of 11 g. A positive challenge was defined as an objective IgE-mediated reaction during challenge or the 2-h observation. Forty-nine percent (24/49) of children had positive challenges. An SPT of >7 mm on the day of challenge predicted a positive challenge with a sensitivity of 83% and a negative predictive value (NPV) of 84%. An FEIA of >2.0 kU/L showed a sensitivity of 79% and an NPV of 80%. Predicting challenge outcome from a combination of SPT and FEIA (SPT >7 and/or FEIA >2 is positive) increased sensitivity to 96% and NPV to 95%. At least half of preschoolchildren with peanut sensitization and no antecedent history of peanut ingestion can tolerate peanuts. A SPT<7 mm and FEIA<2 kU/L identify children most likely to tolerate peanut, with only a 5% likelihood of failing an oral challenge. This study assists clinicians considering challenges in very young peanut-sensitized children. © 2011 Blackwell Publishing Ltd.

Non-coding-regulatory regions of human brain genes delineated by bacterial artificial chromosome knock-in mice.

PubMed

Schmouth, Jean-François; Castellarin, Mauro; Laprise, Stéphanie; Banks, Kathleen G; Bonaguro, Russell J; McInerny, Simone C; Borretta, Lisa; Amirabbasi, Mahsa; Korecki, Andrea J; Portales-Casamar, Elodie; Wilson, Gary; Dreolini, Lisa; Jones, Steven J M; Wasserman, Wyeth W; Goldowitz, Daniel; Holt, Robert A; Simpson, Elizabeth M

2013-10-14

The next big challenge in human genetics is understanding the 98% of the genome that comprises non-coding DNA. Hidden in this DNA are sequences critical for gene regulation, and new experimental strategies are needed to understand the functional role of gene-regulation sequences in health and disease. In this study, we build upon our HuGX ('high-throughput human genes on the X chromosome') strategy to expand our understanding of human gene regulation in vivo. In all, ten human genes known to express in therapeutically important brain regions were chosen for study. For eight of these genes, human bacterial artificial chromosome clones were identified, retrofitted with a reporter, knocked single-copy into the Hprt locus in mouse embryonic stem cells, and mouse strains derived. Five of these human genes expressed in mouse, and all expressed in the adult brain region for which they were chosen. This defined the boundaries of the genomic DNA sufficient for brain expression, and refined our knowledge regarding the complexity of gene regulation. We also characterized for the first time the expression of human MAOA and NR2F2, two genes for which the mouse homologs have been extensively studied in the central nervous system (CNS), and AMOTL1 and NOV, for which roles in CNS have been unclear. We have demonstrated the use of the HuGX strategy to functionally delineate non-coding-regulatory regions of therapeutically important human brain genes. Our results also show that a careful investigation, using publicly available resources and bioinformatics, can lead to accurate predictions of gene expression.

STAT4 Deficiency Fails To Induce Lung Th2 or Th17 Immunity following Primary or Secondary Respiratory Syncytial Virus (RSV) Challenge but Enhances the Lung RSV-Specific CD8+ T Cell Immune Response to Secondary Challenge

PubMed Central

Dulek, Daniel E.; Newcomb, Dawn C.; Toki, Shinji; Goliniewska, Kasia; Cephus, Jacqueline; Reiss, Sara; Bates, John T.; Crowe, James E.; Boyd, Kelli L.; Moore, Martin L.; Zhou, Weisong

2014-01-01

ABSTRACT Immune-mediated lung injury is a hallmark of lower respiratory tract illness caused by respiratory syncytial virus (RSV). STAT4 plays a critical role in CD4+ Th1 lineage differentiation and gamma interferon (IFN-γ) protein expression by CD4+ T cells. As CD4+ Th1 differentiation is associated with negative regulation of CD4+ Th2 and Th17 differentiation, we hypothesized that RSV infection of STAT4−/− mice would result in enhanced lung Th2 and Th17 inflammation and impaired lung Th1 inflammation compared to wild-type (WT) mice. We performed primary and secondary RSV challenges in WT and STAT4−/− mice and used STAT1−/− mice as a positive control for the development of RSV-specific lung Th2 and Th17 inflammation during primary challenge. Primary RSV challenge of STAT4−/− mice resulted in decreased T-bet and IFN-γ expression levels in CD4+ T cells compared to those of WT mice. Lung Th2 and Th17 inflammation did not develop in primary RSV-challenged STAT4−/− mice. Decreased IFN-γ expression by NK cells, CD4+ T cells, and CD8+ T cells was associated with attenuated weight loss and enhanced viral clearance with primary challenge in STAT4−/− mice compared to WT mice. Following secondary challenge, WT and STAT4−/− mice also did not develop lung Th2 or Th17 inflammation. In contrast to primary challenge, secondary RSV challenge of STAT4−/− mice resulted in enhanced weight loss, an increased lung IFN-γ expression level, and an increased lung RSV-specific CD8+ T cell response compared to those of WT mice. These data demonstrate that STAT4 regulates the RSV-specific CD8+ T cell response to secondary infection but does not independently regulate lung Th2 or Th17 immune responses to RSV challenge. IMPORTANCE STAT4 is a protein critical for both innate and adaptive immune responses to viral infection. Our results show that STAT4 regulates the immune response to primary and secondary challenge with RSV but does not restrain RSV-induced lung Th2 or Th17 immune responses. These findings suggest that STAT4 expression may influence lung immunity and severity of illness following primary and secondary RSV infections. PMID:24920804

[Protective immune response of guinea pigs against challenge with foot and mouth disease virus by immunization with foliar extracts from transgenic tomato plants expressing the FMDV structural protein VP1].

PubMed

Pan, Li; Zhang, Yong-Guang; Wang, Yong-Lu; Wang, Bao-Qin; Xie, Qing-Ge

2006-10-01

The plant constitutive expression vector pBin438/VP1 for VP1 gene of foot-and-mouth disease virus strain O/ China/99 was constructed. Mediated with Agrobacterium tumefaciens GV3101 harboring pBin438/VP1, VP1 gene was transferred into cotyledons of tomato. After selected by Kanamysin, sixty resistant lines were obtained. The integration and transcription of the VP1 gene in transformed plants was detected by PCR and RT-PCR. After being detected by sandwich-ELISA assays, about 40% transformed plants confirmed to express the recombinant protein. The leave extracts of two positive lines were respectively emulsified in Freund's adjuvant and guinea pigs were intramuscular inoculation at days 0, 15 and 30d. According to the sera antibody levels and the protection of the vaccinated guinea pigs against challenge with 100ID50 FMDV, probed into the immunogenicity of the target protein expressed in transgenic plants. Experimental results showed that the plant expression vector was successfully constructed. PCR and RT-PCR analyses confirmed VP1 gene was transformed into tomato plants and got expression at the transcription levels. The expressed VP1 protein of FMDV, which was identified by ELISA and Western blot, can be specifically recognized by polyclonal antibodies against FMDV. Indirect-ELISA antibody titers reached 1:64 twenty-one days after the third inoculation. In the challenge test, the protection against FMDV challenge in two groups was 80% and 40% respectively. The immunization test in guinea pigs indicated that the expression product of transgenic tomato plants had immunogenicity and could effectively induce the specific antibodies against FMDV.

Do proposed facial expressions of contempt, shame, embarrassment, and compassion communicate the predicted emotion?

PubMed

Widen, Sherri C; Christy, Anita M; Hewett, Kristen; Russell, James A

2011-08-01

Shame, embarrassment, compassion, and contempt have been considered candidates for the status of basic emotions on the grounds that each has a recognisable facial expression. In two studies (N=88, N=60) on recognition of these four facial expressions, observers showed moderate agreement on the predicted emotion when assessed with forced choice (58%; 42%), but low agreement when assessed with free labelling (18%; 16%). Thus, even though some observers endorsed the predicted emotion when it was presented in a list, over 80% spontaneously interpreted these faces in a way other than the predicted emotion.

Enhancing the fathead minnow fish embryo toxicity test: Optimizing embryo production and assessing the utility of additional test endpoints.

PubMed

Roush, Kyle S; Krzykwa, Julie C; Malmquist, Jacob A; Stephens, Dane A; Sellin Jeffries, Marlo K

2018-05-30

The fathead minnow fish embryo toxicity (FET) test has been identified as a potential alternative to toxicity test methods that utilize older fish. However, several challenges have been identified with the fathead minnow FET test, including: 1) difficulties in obtaining appropriately-staged embryos for FET test initiation, 2) a paucity of data comparing fathead minnow FET test performance to the fathead minnow larval growth and survival (LGS) test and 3) a lack of sublethal endpoints that could be used to estimate chronic toxicity and/or predict adverse effects. These challenges were addressed through three study objectives. The first objective was to optimize embryo production by assessing the effect of breeding group composition (number of males and females) on egg production. Results showed that groups containing one male and four females produced the largest clutches, enhancing the likelihood of procuring sufficient numbers of embryos for FET test initiation. The second study objective was to compare the performance of the FET test to that of the fathead minnow LGS test using three reference toxicants. The FET and LGS tests were similar in their ability to predict the acute toxicity of sodium chloride and ethanol, but the FET test was found to be more sensitive than the LGS test for sodium dodecyl sulfate. The last objective of the study was to evaluate the utility and practicality of several sublethal metrics (i.e., growth, developmental abnormalities and growth- and stress-related gene expression) as FET test endpoints. Developmental abnormalities, including pericardial edema and hatch success, were found to offer the most promise as additional FET test endpoints, given their responsiveness, potential for predicting adverse effects, ease of assessment and low cost of measurement. Copyright © 2018 Elsevier Inc. All rights reserved.

Vaccination with Recombinant Cryptococcus Proteins in Glucan Particles Protects Mice against Cryptococcosis in a Manner Dependent upon Mouse Strain and Cryptococcal Species

PubMed Central

Lee, Chrono K.; Huang, Haibin; Hester, Maureen M.; Liu, Jianhua; Luckie, Bridget A.; Torres Santana, Melanie A.; Mirza, Zeynep; Khoshkenar, Payam; Abraham, Ambily; Shen, Zu T.; Lodge, Jennifer K.; Akalin, Ali; Homan, Jane; Ostroff, Gary R.

2017-01-01

ABSTRACT Development of a vaccine to protect against cryptococcosis is a priority given the enormous global burden of disease in at-risk individuals. Using glucan particles (GPs) as a delivery system, we previously demonstrated that mice vaccinated with crude Cryptococcus-derived alkaline extracts were protected against lethal challenge with Cryptococcus neoformans and Cryptococcus gattii. The goal of the present study was to identify protective protein antigens that could be used in a subunit vaccine. Using biased and unbiased approaches, six candidate antigens (Cda1, Cda2, Cda3, Fpd1, MP88, and Sod1) were selected, recombinantly expressed in Escherichia coli, purified, and loaded into GPs. Three mouse strains (C57BL/6, BALB/c, and DR4) were then vaccinated with the antigen-laden GPs, following which they received a pulmonary challenge with virulent C. neoformans and C. gattii strains. Four candidate vaccines (GP-Cda1, GP-Cda2, GP-Cda3, and GP-Sod1) afforded a significant survival advantage in at least one mouse model; some vaccine combinations provided added protection over that seen with either antigen alone. Vaccine-mediated protection against C. neoformans did not necessarily predict protection against C. gattii. Vaccinated mice developed pulmonary inflammatory responses that effectively contained the infection; many surviving mice developed sterilizing immunity. Predicted T helper cell epitopes differed between mouse strains and in the degree to which they matched epitopes predicted in humans. Thus, we have discovered cryptococcal proteins that make promising candidate vaccine antigens. Protection varied depending on the mouse strain and cryptococcal species, suggesting that a successful human subunit vaccine will need to contain multiple antigens, including ones that are species specific. PMID:29184017

Molecular Structure-Based Large-Scale Prediction of Chemical-Induced Gene Expression Changes.

PubMed

Liu, Ruifeng; AbdulHameed, Mohamed Diwan M; Wallqvist, Anders

2017-09-25

The quantitative structure-activity relationship (QSAR) approach has been used to model a wide range of chemical-induced biological responses. However, it had not been utilized to model chemical-induced genomewide gene expression changes until very recently, owing to the complexity of training and evaluating a very large number of models. To address this issue, we examined the performance of a variable nearest neighbor (v-NN) method that uses information on near neighbors conforming to the principle that similar structures have similar activities. Using a data set of gene expression signatures of 13 150 compounds derived from cell-based measurements in the NIH Library of Integrated Network-based Cellular Signatures program, we were able to make predictions for 62% of the compounds in a 10-fold cross validation test, with a correlation coefficient of 0.61 between the predicted and experimentally derived signatures-a reproducibility rivaling that of high-throughput gene expression measurements. To evaluate the utility of the predicted gene expression signatures, we compared the predicted and experimentally derived signatures in their ability to identify drugs known to cause specific liver, kidney, and heart injuries. Overall, the predicted and experimentally derived signatures had similar receiver operating characteristics, whose areas under the curve ranged from 0.71 to 0.77 and 0.70 to 0.73, respectively, across the three organ injury models. However, detailed analyses of enrichment curves indicate that signatures predicted from multiple near neighbors outperformed those derived from experiments, suggesting that averaging information from near neighbors may help improve the signal from gene expression measurements. Our results demonstrate that the v-NN method can serve as a practical approach for modeling large-scale, genomewide, chemical-induced, gene expression changes.

Differential expression of heat shock transcription factors and heat shock proteins after acute and chronic heat stress in laying chickens (Gallus gallus).

PubMed

Xie, Jingjing; Tang, Li; Lu, Lin; Zhang, Liyang; Xi, Lin; Liu, Hsiao-Ching; Odle, Jack; Luo, Xugang

2014-01-01

Heat stress due to high environmental temperature negatively influences animal performances. To better understand the biological impact of heat stress, laying broiler breeder chickens were subjected either to acute (step-wisely increasing temperature from 21 to 35°C within 24 hours) or chronic (32°C for 8 weeks) high temperature exposure. High temperature challenges significantly elevated body temperature of experimental birds (P<0.05). However, oxidation status of lipid and protein and expression of heat shock transcription factors (HSFs) and heat shock proteins (HSPs) 70 and 90 were differently affected by acute and chronic treatment. Tissue-specific responses to thermal challenge were also found among heart, liver and muscle. In the heart, acute heat challenge affected lipid oxidation (P = 0.05) and gene expression of all 4 HSF gene expression was upregulated (P<0.05). During chronic heat treatment, the HSP 70 mRNA level was increased (P<0.05) and HSP 90 mRNA (P<0.05) was decreased. In the liver, oxidation of protein was alleviated during acute heat challenge (P<0.05), however, gene expression HSF2, 3 and 4 and HSP 70 were highly induced (P<0.05). HSP90 expression was increased by chronic thermal treatment (P<0.05). In the muscle, both types of heat stress increased protein oxidation, but HSFs and HSPs gene expression remained unaltered. Only tendencies to increase were observed in HSP 70 (P = 0.052) and 90 (P = 0.054) gene expression after acute heat stress. The differential expressions of HSF and HSP genes in different tissues of laying broiler breeder chickens suggested that anti-heat stress mechanisms might be provoked more profoundly in the heart, by which the muscle was least protected during heat stress. In addition to HSP, HSFs gene expression could be used as a marker during acute heat stress.

Insect parents improve the anti-parasitic and anti-bacterial defence of their offspring by priming the expression of immune-relevant genes.

PubMed

Trauer-Kizilelma, Ute; Hilker, Monika

2015-09-01

Insect parents that experienced an immune challenge are known to prepare (prime) the immune activity of their offspring for improved defence. This phenomenon has intensively been studied by analysing especially immunity-related proteins. However, it is unknown how transgenerational immune priming affects transcript levels of immune-relevant genes of the offspring upon an actual threat. Here, we investigated how an immune challenge of Manduca sexta parents affects the expression of immune-related genes in their eggs that are attacked by parasitoids. Furthermore, we addressed the question whether the transgenerational immune priming of expression of genes in the eggs is still traceable in adult offspring. Our study revealed that a parental immune challenge did not affect the expression of immune-related genes in unparasitised eggs. However, immune-related genes in parasitised eggs of immune-challenged parents were upregulated to a higher level than those in parasitised eggs of unchallenged parents. Hence, this transgenerational immune priming of the eggs was detected only "on demand", i.e. upon parasitoid attack. The priming effects were also traceable in adult female progeny of immune-challenged parents which showed higher transcript levels of several immune-related genes in their ovaries than non-primed progeny. Some of the primed genes showed enhanced expression even when the progeny was left unchallenged, whereas other genes were upregulated to a greater extent in primed female progeny than non-primed ones only when the progeny itself was immune-challenged. Thus, the detection of transgenerational immune priming strongly depends on the analysed genes and the presence or absence of an actual threat for the offspring. We suggest that M. sexta eggs laid by immune-challenged parents "afford" to upregulate the transcription of immunity-related genes only upon attack, because they have the chance to be endowed by parentally directly transferred protective proteins. Copyright © 2015 Elsevier Ltd. All rights reserved.

Free energy of formation of a crystal nucleus in incongruent solidification: Implication for modeling the crystallization of aqueous nitric acid droplets in polar stratospheric clouds

NASA Astrophysics Data System (ADS)

Djikaev, Yuri S.; Ruckenstein, Eli

2017-04-01

Using the formalism of classical thermodynamics in the framework of the classical nucleation theory, we derive an expression for the reversible work W* of formation of a binary crystal nucleus in a liquid binary solution of non-stoichiometric composition (incongruent crystallization). Applied to the crystallization of aqueous nitric acid droplets, the new expression more adequately takes account of the effects of nitric acid vapor compared to the conventional expression of MacKenzie, Kulmala, Laaksonen, and Vesala (MKLV) [J. Geophys. Res.: Atmos. 102, 19729 (1997)]. The predictions of both MKLV and modified expressions for the average liquid-solid interfacial tension σls of nitric acid dihydrate (NAD) crystals are compared by using existing experimental data on the incongruent crystallization of aqueous nitric acid droplets of composition relevant to polar stratospheric clouds (PSCs). The predictions for σls based on the MKLV expression are higher by about 5% compared to predictions based on our modified expression. This results in similar differences between the predictions of both expressions for the solid-vapor interfacial tension σsv of NAD crystal nuclei. The latter can be obtained by using the method based on the analysis of experimental data on crystal nucleation rates in aqueous nitric acid droplets; it exploits the dominance of the surface-stimulated mode of crystal nucleation in small droplets and its negligibility in large ones. Applying that method to existing experimental data, our expression for the free energy of formation provides an estimate for σsv of NAD in the range ≈92 dyn/cm to ≈100 dyn/cm, while the MKLV expression predicts it in the range ≈95 dyn/cm to ≈105 dyn/cm. The predictions of both expressions for W* become identical for the case of congruent crystallization; this was also demonstrated by applying our method for determining σsv to the nucleation of nitric acid trihydrate crystals in PSC droplets of stoichiometric composition.

Validity of Measured Interest for Decided and Undecided Students.

ERIC Educational Resources Information Center

Bartling, Herbert C.; Hood, Albert B.

The usefulness of vocational interest measures has been questioned by those who have studied the predictive validity of expressed choice. The predictive validities of measured interest for decided and undecided students, expressed choice and measured interest, and expressed choice and measured interest when they are congruent and incongruent were…

infections bronchitis virus S2 of 4/91 expressed from recombinant virus does not protect against ark-type challenge

USDA-ARS?s Scientific Manuscript database

We previously demonstrated that chickens primed with a recombinant Newcastle disease virus LaSota (rLS) expressing the S2 gene of infectious bronchitis virus (IBV) and boosted with an attenuated IBV Massachusetts (Mass)-type vaccine were protected against IBV Arkansas (Ark)-type virulent challenge. ...

A cis-regulatory logic simulator.

PubMed

Zeigler, Robert D; Gertz, Jason; Cohen, Barak A

2007-07-27

A major goal of computational studies of gene regulation is to accurately predict the expression of genes based on the cis-regulatory content of their promoters. The development of computational methods to decode the interactions among cis-regulatory elements has been slow, in part, because it is difficult to know, without extensive experimental validation, whether a particular method identifies the correct cis-regulatory interactions that underlie a given set of expression data. There is an urgent need for test expression data in which the interactions among cis-regulatory sites that produce the data are known. The ability to rapidly generate such data sets would facilitate the development and comparison of computational methods that predict gene expression patterns from promoter sequence. We developed a gene expression simulator which generates expression data using user-defined interactions between cis-regulatory sites. The simulator can incorporate additive, cooperative, competitive, and synergistic interactions between regulatory elements. Constraints on the spacing, distance, and orientation of regulatory elements and their interactions may also be defined and Gaussian noise can be added to the expression values. The simulator allows for a data transformation that simulates the sigmoid shape of expression levels from real promoters. We found good agreement between sets of simulated promoters and predicted regulatory modules from real expression data. We present several data sets that may be useful for testing new methodologies for predicting gene expression from promoter sequence. We developed a flexible gene expression simulator that rapidly generates large numbers of simulated promoters and their corresponding transcriptional output based on specified interactions between cis-regulatory sites. When appropriate rule sets are used, the data generated by our simulator faithfully reproduces experimentally derived data sets. We anticipate that using simulated gene expression data sets will facilitate the direct comparison of computational strategies to predict gene expression from promoter sequence. The source code is available online and as additional material. The test sets are available as additional material.

HSP27 and 70 expression in thymic epithelial tumors and benign thymic alterations: diagnostic, prognostic and physiologic implications.

PubMed

Janik, S; Schiefer, A I; Bekos, C; Hacker, P; Haider, T; Moser, J; Klepetko, W; Müllauer, L; Ankersmit, H J; Moser, B

2016-04-21

Thymic Epithelial Tumors (TETs), the most common tumors in the anterior mediastinum in adults, show a unique association with autoimmune Myasthenia Gravis (MG) and represent a multidisciplinary diagnostic and therapeutic challenge. Neither risk factors nor established biomarkers for TETs exist. Predictive and diagnostic markers are urgently needed. Heat shock proteins (HSPs) are upregulated in several malignancies promoting tumor cell survival and metastases. We performed immunohistochemical staining of HSP27 and 70 in patients with TETs (n = 101) and patients with benign thymic alterations (n = 24). Further, serum HSP27 and 70 concentrations were determined in patients with TETs (n = 46), patients with benign thymic alterations (n = 33) and volunteers (n = 49) by using ELISA. HSPs were differentially expressed in histologic types and pathological tumor stages of TETs. Weak HSP tumor expression correlated with worse freedom from recurrence. Serum HSP concentrations were elevated in TETs and MG, correlated with clinical tumor stage and histologic subtype and decreased significantly after complete tumor resection. To conclude, we found HSP expression in the vast majority of TETs, in physiologic thymus and staining intensities in patients with TETs have been associated with prognosis. However, although interesting and promising the role of HSPs in TETs as diagnostic and prognostic or even therapeutic markers need to be further evaluated.

HSP27 and 70 expression in thymic epithelial tumors and benign thymic alterations: diagnostic, prognostic and physiologic implications

PubMed Central

Janik, S.; Schiefer, A. I.; Bekos, C.; Hacker, P.; Haider, T.; Moser, J.; Klepetko, W.; Müllauer, L.; Ankersmit, H. J.; Moser, B.

2016-01-01

Thymic Epithelial Tumors (TETs), the most common tumors in the anterior mediastinum in adults, show a unique association with autoimmune Myasthenia Gravis (MG) and represent a multidisciplinary diagnostic and therapeutic challenge. Neither risk factors nor established biomarkers for TETs exist. Predictive and diagnostic markers are urgently needed. Heat shock proteins (HSPs) are upregulated in several malignancies promoting tumor cell survival and metastases. We performed immunohistochemical staining of HSP27 and 70 in patients with TETs (n = 101) and patients with benign thymic alterations (n = 24). Further, serum HSP27 and 70 concentrations were determined in patients with TETs (n = 46), patients with benign thymic alterations (n = 33) and volunteers (n = 49) by using ELISA. HSPs were differentially expressed in histologic types and pathological tumor stages of TETs. Weak HSP tumor expression correlated with worse freedom from recurrence. Serum HSP concentrations were elevated in TETs and MG, correlated with clinical tumor stage and histologic subtype and decreased significantly after complete tumor resection. To conclude, we found HSP expression in the vast majority of TETs, in physiologic thymus and staining intensities in patients with TETs have been associated with prognosis. However, although interesting and promising the role of HSPs in TETs as diagnostic and prognostic or even therapeutic markers need to be further evaluated. PMID:27097982

Association of suboptimal health status with psychosocial stress, plasma cortisol and mRNA expression of glucocorticoid receptor α/β in lymphocyte.

PubMed

Yan, Yu-Xiang; Dong, Jing; Liu, You-Qin; Zhang, Jie; Song, Man-Shu; He, Yan; Wang, Wei

2015-01-01

Suboptimal health status (SHS) has become a new public health challenge in China. This study investigated whether high SHS is associated with psychosocial stress, changes in cortisol level and/or glucocorticoid receptor (GR) isoform expression. Three-hundred eighty-six workers employed in three companies in Beijing were recruited. The SHS score was derived from data collection in the SHS questionnaire (SHSQ-25). The short standard version of the Copenhagen Psychosocial Questionnaire (COPSOQ) was used to assess job-related psychosocial stress. The mean value of the five scales of COPSOQ and distribution of plasma cortisol and mRNA expression of GRα/GRβ between the high level of SHS group and the low level of SHS group were compared using a general linear model procedure. Multiple linear regression analysis was used to analyze the effect of psychosocial stress on SHS. We identified three factors that were predictive of SHS, including "demands at work", "interpersonal relations and leadership" and "insecurity at work". Significantly higher levels of plasma cortisol and GRβ/GRα mRNA ratio were observed among the high SHS group. High level of SHS is associated with decreased mRNA expression of GRα. This study confirmed the association between chronic psychosocial stress and SHS, indicating that improving the psychosocial work environment may reduce SHS and then prevent chronic diseases effectively.

Statistical approaches to maximize recombinant protein expression in Escherichia coli: a general review.

PubMed

Papaneophytou, Christos P; Kontopidis, George

2014-02-01

The supply of many valuable proteins that have potential clinical or industrial use is often limited by their low natural availability. With the modern advances in genomics, proteomics and bioinformatics, the number of proteins being produced using recombinant techniques is exponentially increasing and seems to guarantee an unlimited supply of recombinant proteins. The demand of recombinant proteins has increased as more applications in several fields become a commercial reality. Escherichia coli (E. coli) is the most widely used expression system for the production of recombinant proteins for structural and functional studies. However, producing soluble proteins in E. coli is still a major bottleneck for structural biology projects. One of the most challenging steps in any structural biology project is predicting which protein or protein fragment will express solubly and purify for crystallographic studies. The production of soluble and active proteins is influenced by several factors including expression host, fusion tag, induction temperature and time. Statistical designed experiments are gaining success in the production of recombinant protein because they provide information on variable interactions that escape the "one-factor-at-a-time" method. Here, we review the most important factors affecting the production of recombinant proteins in a soluble form. Moreover, we provide information about how the statistical design experiments can increase protein yield and purity as well as find conditions for crystal growth. Copyright © 2013 Elsevier Inc. All rights reserved.

Biosynthesis of the acetyl‐CoA carboxylase‐inhibiting antibiotic, andrimid in Serratia is regulated by Hfq and the LysR‐type transcriptional regulator, AdmX

PubMed Central

Nogellova, Veronika; Morel, Bertrand; Krell, Tino

2016-01-01

Summary Infections due to multidrug‐resistant bacteria represent a major global health challenge. To combat this problem, new antibiotics are urgently needed and some plant‐associated bacteria are a promising source. The rhizobacterium Serratia plymuthica A153 produces several bioactive secondary metabolites, including the anti‐oomycete and antifungal haterumalide, oocydin A and the broad spectrum polyamine antibiotic, zeamine. In this study, we show that A153 produces a second broad spectrum antibiotic, andrimid. Using genome sequencing, comparative genomics and mutagenesis, we defined new genes involved in andrimid (adm) biosynthesis. Both the expression of the adm gene cluster and regulation of andrimid synthesis were investigated. The biosynthetic cluster is operonic and its expression is modulated by various environmental cues, including temperature and carbon source. Analysis of the genome context of the adm operon revealed a gene encoding a predicted LysR‐type regulator, AdmX, apparently unique to Serratia strains. Mutagenesis and gene expression assays demonstrated that AdmX is a transcriptional activator of the adm gene cluster. At the post‐transcriptional level, the expression of the adm cluster is positively regulated by the RNA chaperone, Hfq, in an RpoS‐independent manner. Our results highlight the complexity of andrimid biosynthesis – an antibiotic with potential clinical and agricultural utility. PMID:26914969

Ensemble superparameterization versus stochastic parameterization: A comparison of model uncertainty representation in tropical weather prediction

NASA Astrophysics Data System (ADS)

Subramanian, Aneesh C.; Palmer, Tim N.

2017-06-01

Stochastic schemes to represent model uncertainty in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble prediction system has helped improve its probabilistic forecast skill over the past decade by both improving its reliability and reducing the ensemble mean error. The largest uncertainties in the model arise from the model physics parameterizations. In the tropics, the parameterization of moist convection presents a major challenge for the accurate prediction of weather and climate. Superparameterization is a promising alternative strategy for including the effects of moist convection through explicit turbulent fluxes calculated from a cloud-resolving model (CRM) embedded within a global climate model (GCM). In this paper, we compare the impact of initial random perturbations in embedded CRMs, within the ECMWF ensemble prediction system, with stochastically perturbed physical tendency (SPPT) scheme as a way to represent model uncertainty in medium-range tropical weather forecasts. We especially focus on forecasts of tropical convection and dynamics during MJO events in October-November 2011. These are well-studied events for MJO dynamics as they were also heavily observed during the DYNAMO field campaign. We show that a multiscale ensemble modeling approach helps improve forecasts of certain aspects of tropical convection during the MJO events, while it also tends to deteriorate certain large-scale dynamic fields with respect to stochastically perturbed physical tendencies approach that is used operationally at ECMWF.