Under Ice Operations

The oceans of the world, and the myriad of mysteries they contain, have long been described as Inner Space

Synaptic nanomodules underlie the organization and plasticity of spine synapses.

Experience results in long-lasting changes in dendritic spine size, yet how the molecular architecture of the synapse responds to plasticity remains poorly understood. Here a combined approach of multicolor stimulated emission depletion microscopy (STED) and confocal imaging in rat and mouse demonstrates that structural plasticity is linked to the addition of unitary synaptic nanomodules to spines. Spine synapses in vivo and in vitro contain discrete and aligned subdiffraction modules of pre- and postsynaptic proteins whose number scales linearly with spine size. Live-cell time-lapse super-resolution imaging reveals that NMDA receptor-dependent increases in spine size are accompanied both by enhanced mobility of pre- and postsynaptic modules that remain aligned with each other and by a coordinated increase in the number of nanomodules. These findings suggest a simplified model for experience-dependent structural plasticity relying on an unexpectedly modular nanomolecular architecture of synaptic proteins

Ephrin-B3 controls excitatory synapse density through cell-cell competition for EphBs

Cortical networks are characterized by sparse connectivity, with synapses found at only a subset of axo-dendritic contacts. Yet within these networks, neurons can exhibit high connection probabilities, suggesting that cell-intrinsic factors, not proximity, determine connectivity. Here, we identify ephrin-B3 (eB3) as a factor that determines synapse density by mediating a cell-cell competition that requires ephrin-B-EphB signaling. In a microisland culture system designed to isolate cell-cell competition, we find that eB3 determines winning and losing neurons in a contest for synapses. In a Mosaic Analysis with Double Markers (MADM) genetic mouse model system in vivo the relative levels of eB3 control spine density in layer 5 and 6 neurons. MADM cortical neurons in vitro reveal that eB3 controls synapse density independently of action potential-driven activity. Our findings illustrate a new class of competitive mechanism mediated by trans-synaptic organizing proteins which control the number of synapses neurons receive relative to neighboring neurons

Cockroach allergy and asthma in a 30-year-old man.

A growing body of evidence has implicated allergens derived from cockroaches as an important environmental factor that may aggravate asthma in sensitized persons. We present the case of a 30-year-old man with asthma and a cockroach allergy. Allergy skin testing confirmed hypersensitivity to cockroach extract, and a home visit revealed visual evidence of infestation and the presence of Bla g 1 German cockroach allergen in vacuumed dust. As is typical of patients with a cockroach allergy and asthma, multiple factors in addition to cockroach allergen appeared to aggravate the patient's asthma. A multimodality therapeutic regimen, which included medications as well as cleaning of the home, integrated pest management, and professional application of chemical controls, resulted in substantial clinical improvement. The pathophysiology, epidemiology, and clinical features of cockroach-allergic asthma are reviewed, and an approach to diagnosis and management is suggested

Association of selenium, tocopherols, carotenoids, retinol, and 15-isoprostane F(2t) in serum or urine with prostate cancer risk: the multiethnic cohort.

We examine the association of antioxidants and 15-isoprostane F(2t) with risk of prostate cancer.We conducted a nested case-control study of serum antioxidant biomarkers (selenium, tocopherols, carotenoids, and retinol) and a urinary oxidation biomarker (15-isoprostane F(2t)) with risk of prostate cancer within the Multiethnic Cohort. Demographic, dietary, and other exposure information was collected by self-administered questionnaire in 1993-1996. We compared prediagnostic biomarker levels from 467 prostate cancer cases and 936 cancer free controls that were matched on several variables. Multivariate conditional logistic regression models were used to compute adjusted odds ratios (ORs) and 95% confidence intervals (CIs).We observed that there was no overall association of serum concentrations of antioxidants and urinary concentrations of 15-isoprostane F(2t) with risk of prostate cancer or risk of advanced prostate cancer. However, we did observe an inverse association for serum selenium only among African-American men (p trend = 0.02); men in the third tertile of selenium concentrations had a 41% lower risk (95% CI: 0.38-0.93) of prostate cancer when compared to men in the first tertile.Overall, our study found no association of serum antioxidants or 15-isoprostane F(2t) with the risk of prostate cancer. The observed inverse association of selenium with prostate cancer in African-Americans needs to be validated in other studies

Identifying cooperative transcriptional regulations using protein–protein interactions

Cooperative transcriptional activations among multiple transcription factors (TFs) are important to understand the mechanisms of complex transcriptional regulations in eukaryotes. Previous studies have attempted to find cooperative TFs based on gene expression data with gene expression profiles as a measure of similarity of gene regulations. In this paper, we use protein–protein interaction data to infer synergistic binding of cooperative TFs. Our fundamental idea is based on the assumption that genes contributing to a similar biological process are regulated under the same control mechanism. First, the protein–protein interaction networks are used to calculate the similarity of biological processes among genes. Second, we integrate this similarity and the chromatin immuno-precipitation data to identify cooperative TFs. Our computational experiments in yeast show that predictions made by our method have successfully identified eight pairs of cooperative TFs that have literature evidences but could not be identified by the previous method. Further, 12 new possible pairs have been inferred and we have examined the biological relevances for them. However, since a typical problem using protein–protein interaction data is that many false-positive data are contained, we propose a method combining various biological data to increase the prediction accuracy

Analysis of case-control association studies with known risk variants

Motivation: The question of how to best use information from known associated variants when conducting disease association studies has yet to be answered. Some studies compute a marginal P-value for each Several Nucleotide Polymorphisms independently, ignoring previously discovered variants. Other studies include known variants as covariates in logistic regression, but a weakness of this standard conditioning strategy is that it does not account for disease prevalence and non-random ascertainment, which can induce a correlation structure between candidate variants and known associated variants even if the variants lie on different chromosomes. Here, we propose a new conditioning approach, which is based in part on the classical technique of liability threshold modeling. Roughly, this method estimates model parameters for each known variant while accounting for the published disease prevalence from the epidemiological literature. Results: We show via simulation and application to empirical datasets that our approach outperforms both the no conditioning strategy and the standard conditioning strategy, with a properly controlled false-positive rate. Furthermore, in multiple data sets involving diseases of low prevalence, standard conditioning produces a severe drop in test statistics whereas our approach generally performs as well or better than no conditioning. Our approach may substantially improve disease gene discovery for diseases with many known risk variants. Availability: LTSOFT software is available online http://www.hsph.harvard.edu/faculty/alkes-price/software/ Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin