ALBERT

Description: Motivation: Discerning genetic contributions to diseases not only enhances our understanding of disease mechanisms, but also leads to translational opportunities for drug discovery. Recent computational approaches incorporate disease phenotypic similarities to improve the prediction power of disease gene discovery. However, most current studies used only one data source of human disease phenotype. We present an innovative and generic strategy for combining multiple different data sources of human disease phenotype and predicting disease-associated genes from integrated phenotypic and genomic data. Results: To demonstrate our approach, we explored a new phenotype database from biomedical ontologies and constructed Disease Manifestation Network (DMN). We combined DMN with mimMiner, which was a widely used phenotype database in disease gene prediction studies. Our approach achieved significantly improved performance over a baseline method, which used only one phenotype data source. In the leave-one-out cross-validation and de novo gene prediction analysis, our approach achieved the area under the curves of 90.7% and 90.3%, which are significantly higher than 84.2% ( P 〈 e –4 ) and 81.3% ( P 〈 e –12 ) for the baseline approach. We further demonstrated that our predicted genes have the translational potential in drug discovery. We used Crohn’s disease as an example and ranked the candidate drugs based on the rank of drug targets. Our gene prediction approach prioritized druggable genes that are likely to be associated with Crohn’s disease pathogenesis, and our rank of candidate drugs successfully prioritized the Food and Drug Administration-approved drugs for Crohn’s disease. We also found literature evidence to support a number of drugs among the top 200 candidates. In summary, we demonstrated that a novel strategy combining unique disease phenotype data with system approaches can lead to rapid drug discovery. Availability and implementation: nlp.case.edu/public/data/DMN Contact: rxx@case.edu

Description: We present high signal-to-noise ratio, multifrequency polarization pulse profiles for 24 millisecond pulsars that are being observed as part of the Parkes Pulsar Timing Array project. The pulsars are observed in three bands, centred close to 730, 1400 and 3100 MHz, using a dual-band 10 cm/50 cm receiver and the central beam of the 20-cm multibeam receiver. Observations spanning approximately six years have been carefully calibrated and summed to produce high S/N profiles. This allows us to study the individual profile components and in particular how they evolve with frequency. We also identify previously undetected profile features. For many pulsars we show that pulsed emission extends across almost the entire pulse profile. The pulse component widths and component separations follow a complex evolution with frequency; in some cases these parameters increase and in other cases they decrease with increasing frequency. The evolution with frequency of the polarization properties of the profile is also non-trivial. We provide evidence that the pre- and post-cursors generally have higher fractional linear polarization than the main pulse. We have obtained the spectral index and rotation measure for each pulsar by fitting across all three observing bands. For the majority of pulsars, the spectra follow a single power-law and the position angles follow a 2 relation, as expected. However, clear deviations are seen for some pulsars. We also present phase-resolved measurements of the spectral index, fractional linear polarization and rotation measure. All these properties are shown to vary systematically over the pulse profile.

Description: In this paper, we study a vector disease model with a saturation incidence rate and a time delay describing a fixed time during which the infectious agents develop in the vector and it is only after that time that the infected vector can infect a susceptible human. By analysing the corresponding characteristic equations, the local stability of a disease-free equilibrium and an endemic equilibrium is discussed. The existence of Hopf bifurcations is established. By using the persistence theory for infinite-dimensional dynamic systems, it is proved that when the basic reproduction number is greater than unity, the disease is permanent. By comparison arguments, it is verified that if the basic reproduction number is less than unity, the disease-free equilibrium is globally asymptotically stable. When the basic reproduction number is greater than unity, sufficient conditions are obtained for the global asymptotic stability of the endemic equilibrium by means of an iteration scheme.

Description: In this paper, a delayed human immunodeficiency virus (HIV)-1 infection model with absorption and cytotoxic T lymphocytes (CTL) immune response is studied. By using suitable Lyapunov functionals and LaSalle's invariance principle, it is proved that if the basic reproductive ratio is less than unity, the infection-free equilibrium is globally asymptotically stable; if the immune response reproductive ratio is less than unity and the basic reproductive ratio is greater than unity, sufficient conditions are derived for the global stability of the CTL-inactivated infection equilibrium; if the immune response reproductive ratio is greater than unity, sufficient conditions are obtained for the global stability of the CTL-activated infection equilibrium. Numerical simulations are carried out to illustrate the theoretical results.

Description: Motivation: Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles. Results: Here, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well. Availability and implementation: For users’ convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at http://bioinformatics.hitsz.edu.cn/main/~binliu/remote/ Contact: bliu@insun.hit.edu.cn or bliu@gordonlifescience.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Glitch (sudden spin-up) is a common phenomenon in pulsar observations. However, the physical mechanism of glitch is still a matter of debate because it depends on the puzzle of pulsar's inner structure, i.e. the equation of state of dense matter. Some pulsars (e.g. Vela like) show large glitches (/ ~ 10 –6 ) but release negligible energy, whereas the large glitches of AXPs/SGRs (anomalous X-ray pulsars/soft gamma repeaters) are usually (but not always) accompanied with detectable energy releases manifesting as X-ray bursts or outbursts. We try to understand this aspect of glitches in a starquake model of solid quark stars. There are two kinds of glitches in this scenario: bulk-invariable (type I) and bulk-variable (type II) ones. The total stellar volume changes (and then energy releases) significantly for the latter but not for the former. Therefore, glitches accompanied with X-ray bursts (e.g. that of AXP/SGRs) could originate from type II starquakes induced probably by accretion, while the others without evident energy release (e.g. that of Vela pulsar) would be the result of type I starquakes due to, simply, a change of stellar ellipticity.

Description: Roughly 3 million years ago, an inactivating deletion occurred in CMAH , the human gene encoding CMP-Neu5Ac (cytidine-5'-monophospho-N-acetylneuraminic acid) hydroxylase (Chou HH, Takematsu H, Diaz S, Iber J, Nickerson E, Wright KL, Muchmore EA, Nelson DL, Warren ST, Varki A. 1998. A mutation in human CMP-sialic acid hydroxylase occurred after the Homo-Pan divergence. Proc Natl Acad Sci USA . 95:11751–11756). This inactivating deletion is now homozygous in all humans, causing the loss of N -glycolylneuraminic acid (Neu5Gc) biosynthesis in all human cells and tissues. The CMAH enzyme is active in other mammals, including mice, where Neu5Gc is an abundant form of sialic acid on cellular membranes, including those in cardiac and skeletal muscle. We recently demonstrated that the deletion of mouse Cmah worsened the severity of pathophysiology measures related to muscular dystrophy in mdx mice, a model for Duchenne muscular dystrophy (Chandrasekharan K, Yoon JH, Xu Y, deVries S, Camboni M, Janssen PM, Varki A, Martin PT. 2010. A human-specific deletion in mouse Cmah increases disease severity in the mdx model of Duchenne muscular dystrophy. Sci Transl Med. 2:42–54). Here, we demonstrate similar changes in cardiac and skeletal muscle pathology and physiology resulting from Cmah deletion in α-sarcoglycan-deficient ( Sgca –/– ) mice, a model for limb girdle muscular dystrophy 2D. These experiments demonstrate that loss of mouse Cmah can worsen disease severity in more than one form of muscular dystrophy and suggest that Cmah may be a general genetic modifier of muscle disease.

Description: The study of dense matter at ultrahigh density has a very long history, which is meaningful for us to understand not only cosmic events in extreme circumstances but also fundamental laws of physics. It is well known that the state of cold matter at supranuclear density depends on the non-perturbative nature of quantum chromodynamics (QCD) and is essential for modelling pulsars. A so-called H-cluster matter is proposed in this paper as the nature of dense matter in reality. In compact stars at only a few nuclear densities but low temperature, quarks could be interacting strongly with each other there. That might render quarks grouped in clusters, although the hypothetical quark clusters in cold dense matter have not been confirmed due to the lack of both theoretical and experimental evidence. Motivated by recent lattice QCD simulations of the H-dibaryons (with structure uuddss ), we therefore consider here a possible kind of quark clusters, H-clusters, that could emerge inside compact stars during their initial cooling as the dominant components inside (the degree of freedom could then be H-clusters there). Taking into account the in-medium stiffening effect, we find that at baryon densities of compact stars H-cluster matter could be more stable than nuclear matter. We also find that for the H-cluster matter with lattice structure, the equation of state could be so stiff that it would seem to be ‘superluminal’ in the most dense region. However, the real sound speed for H-cluster matter is in fact difficult to calculate, so at this stage we do not put constraints on our model from the usual requirement of causality. We study the stars composed of H-clusters, i.e. H-cluster stars, and derive the dependence of their maximum mass on the in-medium stiffening effect, showing that the maximum mass could be well above 2 M as observed and that the resultant mass–radius relation fits the measurement of the rapid burster under reasonable parameters. Besides a general understanding of different manifestations of compact stars, we expect further observational and experimental tests for the H-cluster stars in the future.

Description: Motivation: Systems approaches to studying phenotypic relationships among diseases are emerging as an active area of research for both novel disease gene discovery and drug repurposing. Currently, systematic study of disease phenotypic relationships on a phenome-wide scale is limited because large-scale machine-understandable disease–phenotype relationship knowledge bases are often unavailable. Here, we present an automatic approach to extract disease–manifestation (D-M) pairs (one specific type of disease–phenotype relationship) from the wide body of published biomedical literature. Data and Methods: Our method leverages external knowledge and limits the amount of human effort required. For the text corpus, we used 119 085 682 MEDLINE sentences (21 354 075 citations). First, we used D-M pairs from existing biomedical ontologies as prior knowledge to automatically discover D-M–specific syntactic patterns. We then extracted additional pairs from MEDLINE using the learned patterns. Finally, we analysed correlations between disease manifestations and disease-associated genes and drugs to demonstrate the potential of this newly created knowledge base in disease gene discovery and drug repurposing. Results: In total, we extracted 121 359 unique D-M pairs with a high precision of 0.924. Among the extracted pairs, 120 419 (99.2%) have not been captured in existing structured knowledge sources. We have shown that disease manifestations correlate positively with both disease-associated genes and drug treatments. Conclusions: The main contribution of our study is the creation of a large-scale and accurate D-M phenotype relationship knowledge base. This unique knowledge base, when combined with existing phenotypic, genetic and proteomic datasets, can have profound implications in our deeper understanding of disease etiology and in rapid drug repurposing. Availability: http://nlp.case.edu/public/data/DMPatternUMLS/ Contact: rxx@case.edu

Description: α-Melanocyte-stimulating hormone (α-MSH) functions as a mediator of inflammation and immunity; however, the short half-life and high dose needed limit the comprehensive clinical application of α-MSH. The aim of this study was to generate human bone marrow-derived mesenchymal stem cells (MSCs) that express and secrete high levels of bioactive α-MSH. MSCs were obtained from a normal donor and assessed for proliferation, surface markers, and adipogenic and osteogenic differentiation. A lentivirus-encoding α-MSH was constructed. MSCs were infected with this lentivirus-encoding α-MSH and assessed for stability and the expression and secretion of bioactive α-MSH. The cumulative MSC expansion rates pre- and post-lentivirus infection were not significantly different ( P 〉 0.05). The MSCs remained stable after infection with the lentivirus-encoding α-MSH. The concentration of α-MSH in the supernatants of MSCs infected with the lentivirus-encoding α-MSH was 17.55 ng/ml ( P 〈 0.001), and a melanin assay indicated that bioactive α-MSH was secreted from MSCs infected with the lentivirus-encoding α-MSH, with an optical absorbance at OD 405 of 0.886 ( P 〈 0.001). These results suggested that MSCs were promising cell carriers for the expression and secretion of high levels of bioactive α-MSH.