Computational translation of genomic responses from experimental model systems to humans

The high failure rate of therapeutics showing promise in mouse models to translate to patients is a pressing challenge in biomedical science. Though retrospective studies have examined the fidelity of mouse models to their respective human conditions, approaches for prospective translation of insights from mouse models to patients remain relatively unexplored. Here, we develop a semi-supervised learning approach for inference of disease-associated human differentially expressed genes and pathways from mouse model experiments. We examined 36 transcriptomic case studies where comparable phenotypes were available for mouse and human inflammatory diseases and assessed multiple computational approaches for inferring human biology from mouse datasets. We found that semi-supervised training of a neural network identified significantly more true human biological associations than interpreting mouse experiments directly. Evaluating the experimental design of mouse experiments where our model was most successful revealed principles of experimental design that may improve translational performance. Our study shows that when prospectively evaluating biological associations in mouse studies, semi-supervised learning approaches, combining mouse and human data for biological inference, provide the most accurate assessment of human in vivo disease processes. Finally, we proffer a delineation of four categories of model system-to-human "Translation Problems" defined by the resolution and coverage of the datasets available for molecular insight translation and suggest that the task of translating insights from model systems to human disease contexts may be better accomplished by a combination of translation-minded experimental design and computational approaches.Boehringer Ingelheim PharmaceuticalsInstitute for Collaborative Biotechnologies (Grant W911NF-09-0001

A Dynamical Systems Model of Progesterone Receptor Interactions with Inflammation in Human Parturition

Background: Progesterone promotes uterine relaxation and is essential for the maintenance of pregnancy. Withdrawal of progesterone activity and increased inflammation within the uterine tissues are key triggers for parturition. Progesterone actions in myometrial cells are mediated by two progesterone receptor (PR) isoforms, PR-A and PR-B, that function as ligand-activated transcription factors. PR-B mediates relaxatory actions of progesterone, in part, by decreasing myometrial cell responsiveness to pro-inflammatory stimuli. These same pro-inflammatory stimuli promote the expression of PR-A which inhibits the anti-inflammatory activity of PR-B. Competitive interaction between the progesterone receptors then augments myometrial responsiveness to pro-inflammatory stimuli. The interaction between PR-B transcriptional activity and inflammation in the pregnancy myometrium is examined using a dynamical systems model in which quiescence and labor are represented as phase-space equilibrium points. Our model shows that PR-B transcriptional activity and the inflammatory load determine the stability of the quiescent and laboring phenotypes. The model is tested using published transcriptome datasets describing the mRNA abundances in the myometrium before and after the onset of labor at term. Surrogate transcripts were selected to reflect PR-B transcriptional activity and inflammation status. Results: The model coupling PR-B activity and inflammation predicts contractile status (i.e., laboring or quiescent) with high precision and recall and outperforms uncoupled single and two-gene classifiers. Linear stability analysis shows that phase space bifurcations exist in our model that may reflect the phenotypic states of the pregnancy uterus. The model describes a possible tipping point for the transition of the quiescent to the contractile laboring phenotype. Conclusions: Our model describes the functional interaction between the PR-A:PR-B hypothesis and tissue level inflammation in the pregnancy uterus and is a first step in more sophisticated dynamical systems modeling of human partition. The model explains observed biochemical dynamics and as such will be useful for the development of a range of systems-based models using emerging data to predict preterm birth and identify strategies for its prevention

An Assessment of Technology Learning Styles, Skills, and Perceptions Among Teachers of Grades Pre-Kindergarten Through Four.

This study investigated whether a relationship exists between learning style and the self-reported technology-related needs, beliefs, stages of adoption, software expertise, and technology competencies of teachers in a large suburban school district. The Gregorc Style Delineator was used to identify dominant learning style, and the Snapshot Survey was used to measure technology-related needs, beliefs, stages of adoption, and software expertise. Technology competencies were measured using the Technology in Education Competency Survey. Data collected from 499 participants was included in data analysis. The study was conducted at each of the 12 elementary schools of a large suburban district in the Dallas-Fort Worth Metroplex. The findings suggest that there is a significant relationship between learning style and the technology-related needs, stages of adoption, software expertise, and competencies of teachers. The relationship between learning style and technology-related needs was significant at the p < .01 level. The relationships between learning style and technology-related stages of adoption, software expertise, and technology competencies were significant at the p < .05 level. Members of the abstract sequential [AS] learning style group reported having significantly fewer needs and significantly higher stages of adoption, software expertise, and competency than members of one or more of the other learning style groups. More research is recommended to determine whether these findings could be utilized to improve teacher staff development in the area of technology. Possible applications may include mentoring programs and the customization of training models to more closely match learning style profiles

Method Development Involving Modeling Bacterial Metabolite Regulation of Vaginal Epithelial Cell Signaling in Bacterial Vaginosis

BACKGROUND Bacterial vaginosis, which is the imbalance of normal vaginal microbiota, contributes to preterm delivery, vaginitis, and decreased drug efficacy. Despite metronidazole efficacy in reducing BV contributing organisms, BV continues to recur in 50% of patients. Previous studies showing imidazole propionate’s role in the pathogenesis of type II diabetes suggest that similar metabolite-regulated pathways in vaginal microbiomes may be the key in pathogenesis of uterine diseases such as BV. Thus, the purpose of this study was to observe the relationship between vaginal metabolites, host or microbiome-derived, and transcriptomic responses in vaginal epithelial tissues stratified by vaginal microbiome composition (“microbiome group”). The hypothesis was that differences in vaginal microbiome composition result in differential regulation of metabolite-host pathway functional relationships. METHODS Transcript levels and metabolite concentrations precollected from 23 East African women were processed and analyzed via R. Transcriptomic data were converted into KEGG pathway enrichment scores via ssGSEA2.0, a package within R. Enrichment scores were correlated (Spearman) with metabolite levels by microbiome group and lactobacillus dominant phenotypes, and relationships were visualized via Heatmap3 and Cytoscape. RESULTS The results showed varying strengths in correlation among metabolites and KEGG pathway enrichment scores after filtering for strong correlations (R > |0.5|) and significance (p< 0.05). Nonlactobacillus dominant microbiomes showed fewer strongly associated metabolite-KEGG pathway relationships compared to the lactobacillus dominant microbiome group, specifically the imidazole-related networks. CONCLUSIONS In this study, variations in significant correlations among metabolites and KEGG pathways suggests that microbiome diversity may contribute to how metabolites regulate host pathways in vaginal epithelial cells. The reduced pathway interactions observed in imidazole compounds suggests that dysregulation may contribute to recurrence of bacterial vaginosis. This method of modelling could be used to characterize the regulation of critical pathways associated with the pathogenesis of bacterial vaginosis

Activation of CTP : Phosphocholine cytidylyltransferase in rat lung by fatty acids

CTP : phosphocholine cytidylyltransferase activity exists in both the microsome and cytosol fractions of adult lung, 36 and 59%, respectively. Although these enzyme activities are stimulated in vitro by added lipid activators (i.e. phosphatidylglycerol), there are significant levels of activity in the absence of added lipid. We have removed endogenous lipid material from microsome and cytosol preparations of rat lung by rapid extraction with isopropyl ether. The extraction procedure did not cause any loss of cytidylyltransferase activity in the cytosol. After the extraction the enzyme was almost completely dependent upon added lipid activator. Isopropyl ether extraction of microsome preparations produced a loss of 40% of the cytidylyltransferase activity, when measured in the presence of added phosphatidylglycerol. Lipid material extracted into isopropyl ether restored the cytidylyltransferase activity in cytosol. The predominant species of enzyme activator in the isopropyl ether extracts was fatty acid. A variety of naturally occurring unsaturated fatty acids stimulated the cytidylyltransferase to the same extent as phosphatidylglycerol. Saturated fatty acids were inactive.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/24302/1/0000568.pd

Separating Instability from Aggregation Propensity in γS-Crystallin Variants

AbstractMolecular dynamics (MD) simulations, circular dichroism (CD), and dynamic light scattering (DLS) measurements were used to investigate the aggregation propensity of the eye-lens protein γS-crystallin. The wild-type protein was investigated along with the cataract-related G18V variant and the symmetry-related G106V variant. The MD simulations suggest that local sequence differences result in dramatic differences in dynamics and hydration between these two apparently similar point mutations. This finding is supported by the experimental measurements, which show that although both variants appear to be mostly folded at room temperature, both display increased aggregation propensity. Although the disease-related G18V variant is not the most strongly destabilized, it aggregates more readily than either the wild-type or the G106V variant. These results indicate that γS-crystallin provides an excellent model system for investigating the role of dynamics and hydration in aggregation by locally unfolded proteins

Predictive Supersymmetry from Criticality

Motivated by the absence of any direct signal of new physics so far, we present a simple supersymmetric model in which the up-type Higgs mass-squared parameter crosses zero at a scale close to the weak scale. Such a theory may be motivated either by the conventional naturalness picture or by the landscape picture with certain assumptions on prior probability distributions of parameters. The model arises from a simple higher dimensional setup in which the gauge and Higgs fields propagate in the bulk while the matter fields are on a brane. The soft supersymmetry breaking parameters receive contributions from both moduli and anomaly mediations, and their weak scale values can be analytically solved for in terms of a single overall mass scale M. The expected size for M depends on whether one adopts the naturalness or landscape pictures, allowing for the possibility of distinguishing between these two cases. We also present possible variations of the model, and discuss more general implications of the landscape picture in this context.Comment: 18 pages, 4 figures, reference adde

Safety Evaluation of EXPAREL (DepoFoam Bupivacaine) Administered by Repeated Subcutaneous Injection in Rabbits and Dogs: Species Comparison

EXPAREL (bupivacaine extended-release liposome injection), DepoFoam bupivacaine, is in development for prolonged postsurgical analgesia. Repeat-dose toxicity studies were conducted in rabbits and dogs to compare the potential local and systemic toxicities of EXPAREL and bupivacaine HCl (Bsol), and the reversibility of any effects. Dogs tolerated much larger doses than rabbits. EXPAREL-related minimal-to-moderate granulomatous inflammation was noted at the injection sites. In recovery animals, the granulomatous inflammation was observed less frequently and was characterized by an increased number of multinucleated giant cells. These effects were considered a normal response to liposomes and nonadverse. Rabbits are more sensitive than dogs. In rabbits, convulsions were noted with EXPAREL and more frequently with Bsol; a NOAEL was not identified. In dogs, EXPAREL was well tolerated (NOAEL > 30 mg/kg/dose). The cumulative exposure of EXPAREL in these studies is well in excess of the proposed maximum single-dose exposure that is intended in humans

The scaling of vertical temperature gradient spectra

Tests of a formula derived for the cutoff wave number of vertical temperature gradient spectra, using data taken in the upper layers of the North Pacific, show encouraging results. To derive this formula, the cutoff wave number is assumed to be the Batchelor wave number, with kinetic energy dissipation calculated by combining a form used in the atmosphere for calculating the vertical eddy diffusivity in terms of the dissipation with the Osborn-Cox formula for calculating eddy diffusivity from the variance of the temperature gradient spectrum. Kinetic energy dissipation in the water column can be determined in this way; a vertical profile of dissipation shows values of the order of 10^(-3) cm² s^(-3) at the base of a stormtossed mixing layer. In the thermocline below, dissipation occurs in patches