In Vivo Ligands of MDA5 and RIG-I in Measles Virus-Infected Cells

RIG-I-like receptors (RLRs: RIG-I, MDA5 and LGP2) play a major role in the innate immune response against viral infections and detect patterns on viral RNA molecules that are typically absent from host RNA. Upon RNA binding, RLRs trigger a complex downstream signaling cascade resulting in the expression of type I interferons and proinflammatory cytokines. In the past decade extensive efforts were made to elucidate the nature of putative RLR ligands. In vitro and transfection studies identified 5'-triphosphate containing blunt-ended double-strand RNAs as potent RIG-I inducers and these findings were confirmed by next-generation sequencing of RIG-I associated RNAs from virus-infected cells. The nature of RNA ligands of MDA5 is less clear. Several studies suggest that double-stranded RNAs are the preferred agonists for the protein. However, the exact nature of physiological MDA5 ligands from virus-infected cells needs to be elucidated. In this work, we combine a crosslinking technique with next-generation sequencing in order to shed light on MDA5-associated RNAs from human cells infected with measles virus. Our findings suggest that RIG-I and MDA5 associate with AU-rich RNA species originating from the mRNA of the measles virus L gene. Corresponding sequences are poorer activators of ATP-hydrolysis by MDA5 in vitro, suggesting that they result in more stable MDA5 filaments. These data provide a possible model of how AU-rich sequences could activate type I interferon signaling

Sunfall: a collaborative visual analytics system for astrophysics

Computational and experimental sciences produce and collect ever-larger and complex datasets, often in large-scale, multi-institution projects. The inability to gain insight into complex scientific phenomena using current software tools is a bottleneck facing virtually all endeavors of science. In this paper, we introduce Sunfall, a collaborative visual analytics system developed for the Nearby Supernova Factory, an international astrophysics experiment and the largest data volume supernova search currently in operation. Sunfall utilizes novel interactive visualization and analysis techniques to facilitate deeper scientific insight into complex, noisy, high-dimensional, high-volume, time-critical data. The system combines novel image processing algorithms, statistical analysis, and machine learning with highly interactive visual interfaces to enable collaborative, user-driven scientific exploration of supernova image and spectral data. Sunfall is currently in operation at the Nearby Supernova Factory; it is the first visual analytics system in production use at a major astrophysics project

Seeking supernovae in the clouds: a performance study,”

ABSTRACT Today, our picture of the Universe radically differs from that of just over a decade ago. We now know that the Universe is not only expanding as Hubble discovered in 1929, but that the rate of expansion is accelerating, propelled by mysterious new physics dubbed "Dark Energy." This revolutionary discovery was made by comparing the brightness of nearby Type Ia supernovae (which exploded in the past billion years) to that of much more distant ones (from up to seven billion years ago). The reliability of this comparison hinges upon a very detailed understanding of the physics of the nearby events. As part of its effort to further this understanding, the Nearby Supernova Factory (SNfactory) relies upon a complex pipeline of serial processes that execute various image processing algorithms in parallel on ~10TBs of data. This pipeline has traditionally been run on a local cluster. Cloud computing offers many features that make it an attractive alternative. The ability to completely control the software environment in a Cloud is appealing when dealing with a community developed science pipeline with many unique library and platform requirements. In this context we study the feasibility of porting the SNfactory pipeline to the Amazon Web Services environment. Specifically we: describe the tool set we developed to manage a virtual cluster on Amazon EC2, explore the various design options available for application data placement, and offer detailed performance results and lessons learned from each of the above design options

Causal Modeling Using Network Ensemble Simulations of Genetic and Gene Expression Data Predicts Genes Involved in Rheumatoid Arthritis

Tumor necrosis factor α (TNF-α) is a key regulator of inflammation and rheumatoid arthritis (RA). TNF-α blocker therapies can be very effective for a substantial number of patients, but fail to work in one third of patients who show no or minimal response. It is therefore necessary to discover new molecular intervention points involved in TNF-α blocker treatment of rheumatoid arthritis patients. We describe a data analysis strategy for predicting gene expression measures that are critical for rheumatoid arthritis using a combination of comprehensive genotyping, whole blood gene expression profiles and the component clinical measures of the arthritis Disease Activity Score 28 (DAS28) score. Two separate network ensembles, each comprised of 1024 networks, were built from molecular measures from subjects before and 14 weeks after treatment with TNF-α blocker. The network ensemble built from pre-treated data captures TNF-α dependent mechanistic information, while the ensemble built from data collected under TNF-α blocker treatment captures TNF-α independent mechanisms. In silico simulations of targeted, personalized perturbations of gene expression measures from both network ensembles identify transcripts in three broad categories. Firstly, 22 transcripts are identified to have new roles in modulating the DAS28 score; secondly, there are 6 transcripts that could be alternative targets to TNF-α blocker therapies, including CD86 - a component of the signaling axis targeted by Abatacept (CTLA4-Ig), and finally, 59 transcripts that are predicted to modulate the count of tender or swollen joints but not sufficiently enough to have a significant impact on DAS28

Alteration of the Cortical Actin Cytoskeleton Deregulates Ca2+ Signaling, Monospermic Fertilization, and Sperm Entry

Background: When preparing for fertilization, oocytes undergo meiotic maturation during which structural changes occur in the endoplasmic reticulum (ER) that lead to a more efficient calcium response. During meiotic maturation and subsequent fertilization, the actin cytoskeleton also undergoes dramatic restructuring. We have recently observed that rearrangements of the actin cytoskeleton induced by actin-depolymerizing agents, or by actin-binding proteins, strongly modulate intracellular calcium (Ca 2+) signals during the maturation process. However, the significance of the dynamic changes in F-actin within the fertilized egg has been largely unclear. Methodology/Principal Findings: We have measured changes in intracellular Ca 2+ signals and F-actin structures during fertilization. We also report the unexpected observation that the conventional antagonist of the InsP3 receptor, heparin, hyperpolymerizes the cortical actin cytoskeleton in postmeiotic eggs. Using heparin and other pharmacological agents that either hypo- or hyperpolymerize the cortical actin, we demonstrate that nearly all aspects of the fertilization process are profoundly affected by the dynamic restructuring of the egg cortical actin cytoskeleton. Conclusions/Significance: Our findings identify important roles for subplasmalemmal actin fibers in the process of spermegg interaction and in the subsequent events related to fertilization: the generation of Ca 2+ signals, sperm penetration

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease.

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Forward Walking Gfmc Calculation Of The Staggered Order In The Heisenberg Antiferromagnet

A disadvantage of the Green's Function Monte Carlo method is that the limiting distribution of configurations is not equal to the square of the exact groundstate wavefunction but rather the exact wavefunction multiplied by a known 'guiding' or 'trial' wavefunction. Although this feature poses no problem in computing the groundstate energy eigenvalue, it makes the computation of other observables difficult. To compute these quantities one typically extrapolates assuming the difference between the true groundstate and the guiding wavefunction is small. The method of 'forward walking' avoids the requirement of knowing exceptionally good guiding wavefunctions and yields, in principle, exact results for groundstate expectation values. One measures the observable at a certain time and then continues the random walk forward in time until the difference between the guiding function and exact wavefunction has been driven to zero. Only the measurements for configurations that survive this proces..

"Forward-walking" Quantum Monte Carlo Calculation of the Long-range Order in the Heisenberg Antiferromagnet

this paper I discuss a new method to compute the long-range order parameter, (the staggered magnetization

Finite-size study of the Ground-state Energy, Susceptibility, and Spin-wave Velocity for the Heisenberg Antiferromagnet

this paper is to report reliable Green's function Monte Carlo (GFMC) calculations of