Label-free proteomics identifies Calreticulin and GRP75/Mortalin as peripherally accessible protein biomarkers for spinal muscular atrophy

BACKGROUND: Spinal muscular atrophy (SMA) is a neuromuscular disease resulting from mutations in the survival motor neuron 1 (SMN1) gene. Recent breakthroughs in preclinical research have highlighted several potential novel therapies for SMA, increasing the need for robust and sensitive clinical trial platforms for evaluating their effectiveness in human patient cohorts. Given that most clinical trials for SMA are likely to involve young children, there is a need for validated molecular biomarkers to assist with monitoring disease progression and establishing the effectiveness of therapies being tested. Proteomics technologies have recently been highlighted as a potentially powerful tool for such biomarker discovery. METHODS: We utilized label-free proteomics to identify individual proteins in pathologically-affected skeletal muscle from SMA mice that report directly on disease status. Quantitative fluorescent western blotting was then used to assess whether protein biomarkers were robustly changed in muscle, skin and blood from another mouse model of SMA, as well as in a small cohort of human SMA patient muscle biopsies. RESULTS: By comparing the protein composition of skeletal muscle in SMA mice at a pre-symptomatic time-point with the muscle proteome at a late-symptomatic time-point we identified increased expression of both Calreticulin and GRP75/Mortalin as robust indicators of disease progression in SMA mice. We report that these protein biomarkers were consistently modified in different mouse models of SMA, as well as across multiple skeletal muscles, and were also measurable in skin biopsies. Furthermore, Calreticulin and GRP75/Mortalin were measurable in muscle biopsy samples from human SMA patients. CONCLUSIONS: We conclude that label-free proteomics technology provides a powerful platform for biomarker identification in SMA, revealing Calreticulin and GRP75/Mortalin as peripherally accessible protein biomarkers capable of reporting on disease progression in samples of muscle and skin

Provenance-Centered Dataset of Drug-Drug Interactions

Over the years several studies have demonstrated the ability to identify potential drug-drug interactions via data mining from the literature (MEDLINE), electronic health records, public databases (Drugbank), etc. While each one of these approaches is properly statistically validated, they do not take into consideration the overlap between them as one of their decision making variables. In this paper we present LInked Drug-Drug Interactions (LIDDI), a public nanopublication-based RDF dataset with trusty URIs that encompasses some of the most cited prediction methods and sources to provide researchers a resource for leveraging the work of others into their prediction methods. As one of the main issues to overcome the usage of external resources is their mappings between drug names and identifiers used, we also provide the set of mappings we curated to be able to compare the multiple sources we aggregate in our dataset.Comment: In Proceedings of the 14th International Semantic Web Conference (ISWC) 201

Star formation in disk galaxies driven by primordial H_2

We show that gaseous \HI disks of primordial composition irradiated by an external radiation field can develop a multiphase medium with temperatures between 10^2 and 10^4 K due to the formation of molecular hydrogen. For a given \HI column density there is a critical value of the radiation field below which only the cold \HI phase can exist. Due to a time decreasing quasar background, the gas starts cooling slowly after recombination until the lowest stable temperature in the warm phase is reached at a critical redshift $z=z_{cr}$. Below this redshift the formation of molecular hydrogen promotes a rapid transition towards the cold \HI phase. We find that disks of protogalaxies with 10^{20}\simlt N_{HI}\simlt 10^{21} cm^{-2} are gravitationally stable at $T\sim 10^4$ K and can start their star formation history only at z \simlt z_{cr}\sim 2, after the gas in the central portion of the disk has cooled to temperatures T\simlt 300 K. Such a delayed starbust phase in galaxies of low gas surface density and low dynamical mass can disrupt the disks and cause them to fade away. These objects could contribute significantly to the faint blue galaxy population.Comment: 16 pages (LaTeX), 2 Figures to be published in Astrophysical Journal Letter

Spectral Density of Sparse Sample Covariance Matrices

Applying the replica method of statistical mechanics, we evaluate the eigenvalue density of the large random matrix (sample covariance matrix) of the form $J = A^{\rm T} A$, where $A$ is an $M \times N$ real sparse random matrix. The difference from a dense random matrix is the most significant in the tail region of the spectrum. We compare the results of several approximation schemes, focusing on the behavior in the tail region.Comment: 22 pages, 4 figures, minor corrections mad

KPZ equation in one dimension and line ensembles

For suitably discretized versions of the Kardar-Parisi-Zhang equation in one space dimension exact scaling functions are available, amongst them the stationary two-point function. We explain one central piece from the technology through which such results are obtained, namely the method of line ensembles with purely entropic repulsion.Comment: Proceedings STATPHYS22, Bangalore, 200

Local and global modes of drug action in biochemical networks

It becomes increasingly accepted that a shift is needed from the traditional target-based approach of drug development to an integrated perspective of drug action in biochemical systems. We here present an integrative analysis of the interactions between drugs and metabolism based on the concept of drug scope. The drug scope represents the set of metabolic compounds and reactions that are potentially affected by a drug. We constructed and analyzed the scopes of all US approved drugs having metabolic targets. Our analysis shows that the distribution of drug scopes is highly uneven, and that drugs can be classified into several categories based on their scopes. Some of them have small scopes corresponding to localized action, while others have large scopes corresponding to potential large-scale systemic action. These groups are well conserved throughout different topologies of the underlying metabolic network. They can furthermore be associated to specific drug therapeutic properties

Atomic and Molecular Opacities for Brown Dwarf and Giant Planet Atmospheres

We present a comprehensive description of the theory and practice of opacity calculations from the infrared to the ultraviolet needed to generate models of the atmospheres of brown dwarfs and extrasolar giant planets. Methods for using existing line lists and spectroscopic databases in disparate formats are presented and plots of the resulting absorptive opacities versus wavelength for the most important molecules and atoms at representative temperature/pressure points are provided. Electronic, ro-vibrational, bound-free, bound-bound, free-free, and collision-induced transitions and monochromatic opacities are derived, discussed, and analyzed. The species addressed include the alkali metals, iron, heavy metal oxides, metal hydrides, $H_2$, $H_2O$, $CH_4$, $CO$, $NH_3$, $H_2S$, $PH_3$, and representative grains. [Abridged]Comment: 28 pages of text, plus 22 figures, accepted to the Astrophysical Journal Supplement Series, replaced with more compact emulateapj versio

Induction of Cell Stress in Neurons from Transgenic Mice Expressing Yellow Fluorescent Protein: Implications for Neurodegeneration Research

Peer reviewedPublisher PD

Spectra of Empirical Auto-Covariance Matrices

We compute spectra of sample auto-covariance matrices of second order stationary stochastic processes. We look at a limit in which both the matrix dimension $N$ and the sample size $M$ used to define empirical averages diverge, with their ratio $\alpha=N/M$ kept fixed. We find a remarkable scaling relation which expresses the spectral density $\rho(\lambda)$ of sample auto-covariance matrices for processes with dynamical correlations as a continuous superposition of appropriately rescaled copies of the spectral density $\rho^{(0)}_\alpha(\lambda)$ for a sequence of uncorrelated random variables. The rescaling factors are given by the Fourier transform $\hat C(q)$ of the auto-covariance function of the stochastic process. We also obtain a closed-form approximation for the scaling function $\rho^{(0)}_\alpha(\lambda)$. This depends on the shape parameter $\alpha$, but is otherwise universal: it is independent of the details of the underlying random variables, provided only they have finite variance. Our results are corroborated by numerical simulations using auto-regressive processes.Comment: 4 pages, 2 figure

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

BACKGROUND PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. METHODS Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. RESULTS In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. CONCLUSIONS The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer