A massive stellar bulge in a regularly rotating galaxy 1.2 billion years after the Big Bang.

Cosmological models predict that galaxies forming in the early Universe experience a chaotic phase of gas accretion and star formation, followed by gas ejection due to feedback processes. Galaxy bulges may assemble later via mergers or internal evolution. Here we present submillimeter observations (with spatial resolution of 700 parsecs) of ALESS 073.1, a starburst galaxy at redshift [Formula: see text] when the Universe was 1.2 billion years old. This galaxy's cold gas forms a regularly rotating disk with negligible noncircular motions. The galaxy rotation curve requires the presence of a central bulge in addition to a star-forming disk. We conclude that massive bulges and regularly rotating disks can form more rapidly in the early Universe than predicted by models of galaxy formation.ERC STF

Borrelia burgdorferi BBK32 Inhibits the Classical Pathway by Blocking Activation of the C1 Complement Complex

Citation: Garcia, B. L., Zhi, H., Wager, B., Hook, M., & Skare, J. T. (2016). Borrelia burgdorferi BBK32 Inhibits the Classical Pathway by Blocking Activation of the C1 Complement Complex. Plos Pathogens, 12(1), 28. doi:10.1371/journal.ppat.1005404Pathogens that traffic in blood, lymphatics, or interstitial fluids must adopt strategies to evade innate immune defenses, notably the complement system. Through recruitment of host regulators of complement to their surface, many pathogens are able to escape complement-mediated attack. The Lyme disease spirochete, Borrelia burgdorferi, produces a number of surface proteins that bind to factor H related molecules, which function as the dominant negative regulator of the alternative pathway of complement. Relatively less is known about how B. burgdorferi evades the classical pathway of complement despite the observation that some sensu lato strains are sensitive to classical pathway activation. Here we report that the borrelial lipoprotein BBK32 potently and specifically inhibits the classical pathway by binding with high affinity to the initiating C1 complex of complement. In addition, B. burgdorferi cells that produce BBK32 on their surface bind to both C1 and C1r and a serum sensitive derivative of B. burgdorferi is protected from killing via the classical pathway in a BBK32-dependent manner. Subsequent biochemical and biophysical approaches localized the anti-complement activity of BBK32 to its globular C-terminal domain. Mechanistic studies reveal that BBK32 acts by entrapping C1 in its zymogen form by binding and inhibiting the C1 subcomponent, C1r, which serves as the initiating serine protease of the classical pathway. To our knowledge this is the first report of a spirochetal protein acting as a direct inhibitor of the classical pathway and is the only example of a biomolecule capable of specifically and noncovalently inhibiting C1/C1r. By identifying a unique mode of complement evasion this study greatly enhances our understanding of how pathogens subvert and potentially manipulate host innate immune systems

Particle-Hole Symmetry Breaking in the Pseudogap State of Bi2201

In conventional superconductors, a gap exists in the energy absorption spectrum only below the transition temperature (Tc), corresponding to the energy price to pay for breaking a Cooper pair of electrons. In high-Tc cuprate superconductors above Tc, an energy gap called the pseudogap exists, and is controversially attributed either to pre-formed superconducting pairs, which would exhibit particle-hole symmetry, or to competing phases which would typically break it. Scanning tunnelling microscopy (STM) studies suggest that the pseudogap stems from lattice translational symmetry breaking and is associated with a different characteristic spectrum for adding or removing electrons (particle-hole asymmetry). However, no signature of either spatial or energy symmetry breaking of the pseudogap has previously been observed by angle-resolved photoemission spectroscopy (ARPES). Here we report ARPES data from Bi2201 which reveals both particle-hole symmetry breaking and dramatic spectral broadening indicative of spatial symmetry breaking without long range order, upon crossing through T* into the pseudogap state. This symmetry breaking is found in the dominant region of the momentum space for the pseudogap, around the so-called anti-node near the Brillouin zone boundary. Our finding supports the STM conclusion that the pseudogap state is a broken-symmetry state that is distinct from homogeneous superconductivity.Comment: Nature Physics advance online publication, 04/04/2010 (doi:10.1038/nphys1632) Author's version of the paper

Accurate Prediction of Protein Structural Class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods

From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays

Why Are Clinicians Not Embracing the Results from Pivotal Clinical Trials in Severe Sepsis? A Bayesian Analysis

BACKGROUND: Five pivotal clinical trials (Intensive Insulin Therapy; Recombinant Human Activated Protein C [rhAPC]; Low-Tidal Volume; Low-Dose Steroid; Early Goal-Directed Therapy [EGDT]) demonstrated mortality reduction in patients with severe sepsis and expert guidelines have recommended them to clinical practice. Yet, the adoption of these therapies remains low among clinicians. OBJECTIVES: We selected these five trials and asked: Question 1--What is the current probability that the new therapy is not better than the standard of care in my patient with severe sepsis? Question 2--What is the current probability of reducing the relative risk of death (RRR) of my patient with severe sepsis by meaningful clinical thresholds (RRR >15%; >20%; >25%)? METHODS: Bayesian methodologies were applied to this study. Odds ratio (OR) was considered for Question 1, and RRR was used for Question 2. We constructed prior distributions (enthusiastic; mild, moderate, and severe skeptic) based on various effective sample sizes of other relevant clinical trials (unfavorable evidence). Posterior distributions were calculated by combining the prior distributions and the data from pivotal trials (favorable evidence). MAIN FINDINGS: Answer 1--The analysis based on mild skeptic prior shows beneficial results with the Intensive Insulin, rhAPC, and Low-Tidal Volume trials, but not with the Low-Dose Steroid and EGDT trials. All trials' results become unacceptable by the analyses using moderate or severe skeptic priors. Answer 2--If we aim for a RRR>15%, the mild skeptic analysis shows that the current probability of reducing death by this clinical threshold is 88% for the Intensive Insulin, 62-65% for the Low-Tidal Volume, rhAPC, EGDT trials, and 17% for the Low-Dose Steroid trial. The moderate and severe skeptic analyses show no clinically meaningful reduction in the risk of death for all trials. If we aim for a RRR >20% or >25%, all probabilities of benefits become lower independent of the degree of skepticism. CONCLUSIONS: Our clinical threshold analysis offers a new bedside tool to be directly applied to the care of patients with severe sepsis. Our results demonstrate that the strength of evidence (statistical and clinical) is weak for all trials, particularly for the Low-Dose Steroid and EGDT trials. It is essential to replicate the results of each of these five clinical trials in confirmatory studies if we want to provide patient care based on scientifically sound evidence

A Bioinformatics Filtering Strategy for Identifying Radiation Response Biomarker Candidates

The number of biomarker candidates is often much larger than the number of clinical patient data points available, which motivates the use of a rational candidate variable filtering methodology. The goal of this paper is to apply such a bioinformatics filtering process to isolate a modest number (<10) of key interacting genes and their associated single nucleotide polymorphisms involved in radiation response, and to ultimately serve as a basis for using clinical datasets to identify new biomarkers. In step 1, we surveyed the literature on genetic and protein correlates to radiation response, in vivo or in vitro, across cellular, animal, and human studies. In step 2, we analyzed two publicly available microarray datasets and identified genes in which mRNA expression changed in response to radiation. Combining results from Step 1 and Step 2, we identified 20 genes that were common to all three sources. As a final step, a curated database of protein interactions was used to generate the most statistically reliable protein interaction network among any subset of the 20 genes resulting from Steps 1 and 2, resulting in identification of a small, tightly interacting network with 7 out of 20 input genes. We further ranked the genes in terms of likely importance, based on their location within the network using a graph-based scoring function. The resulting core interacting network provides an attractive set of genes likely to be important to radiation response