Concept: Complex analysis
Src homology 2 (SH2) domains play a critical role in cellular signal transduction. They bind to peptides containing phosphotyrosine (pY) with various specificities that depend on the flanking amino-acid residues. The SH2 domain of growth-factor receptor-bound protein 2 (Grb2) specifically recognizes pY-X-N-X, whereas the SH2 domains in phosphatidylinositol 3-kinase (PI3K) recognize pY-X-X-M. Binding of the pY site in CD28 (pY-M-N-M) by PI3K and Grb2 through their SH2 domains is a key step that triggers the CD28 signal transduction for T cell activation and differentiation. In this study, we determined the crystal structure of the Grb2 SH2 domain in complex with a pY-containing peptide derived from CD28 at 1.35 Å resolution. The peptide was found to adopt a twisted U-type conformation, similar to, but distinct from type-I β-turn. In all previously reported crystal structures, the peptide bound to the Grb2 SH2 domains adopts a type-I β-turn conformation, except those with a proline residue at the pY+3 position. Molecular modeling also suggests that the same peptide bound to PI3K might adopt a very different conformation.
Most imaging studies in the biological sciences rely on analyses that are relatively simple. However, manual repetition of analysis tasks across multiple regions in many images can complicate even the simplest analysis, making record keeping difficult, increasing the potential for error, and limiting reproducibility. While fully automated solutions are necessary for very large data sets, they are sometimes impractical for the small- and medium-sized data sets common in biology. Here we present the Slide Set plugin for ImageJ, which provides a framework for reproducible image analysis and batch processing. Slide Set organizes data into tables, associating image files with regions of interest and other relevant information. Analysis commands are automatically repeated over each image in the data set, and multiple commands can be chained together for more complex analysis tasks. All analysis parameters are saved, ensuring transparency and reproducibility. Slide Set includes a variety of built-in analysis commands and can be easily extended to automate other ImageJ plugins, reducing the manual repetition of image analysis without the set-up effort or programming expertise required for a fully automated solution.
Immunomodulatory drugs bind to cereblon (CRBN) to confer differentiated substrate specificity on the CRL4(CRBN) E3 ubiquitin ligase. Here we report the identification of a new cereblon modulator, CC-885, with potent anti-tumour activity. The anti-tumour activity of CC-885 is mediated through the cereblon-dependent ubiquitination and degradation of the translation termination factor GSPT1. Patient-derived acute myeloid leukaemia tumour cells exhibit high sensitivity to CC-885, indicating the clinical potential of this mechanism. Crystallographic studies of the CRBN-DDB1-CC-885-GSPT1 complex reveal that GSPT1 binds to cereblon through a surface turn containing a glycine residue at a key position, interacting with both CC-885 and a ‘hotspot’ on the cereblon surface. Although GSPT1 possesses no obvious structural, sequence or functional homology to previously known cereblon substrates, mutational analysis and modelling indicate that the cereblon substrate Ikaros uses a similar structural feature to bind cereblon, suggesting a common motif for substrate recruitment. These findings define a structural degron underlying cereblon ‘neosubstrate’ selectivity, and identify an anti-tumour target rendered druggable by cereblon modulation.
Chronic pain is a complex disabling experience that negatively affects the cognitive, affective and physical functions as well as behavior. Although the interaction between chronic pain and physical functioning is a well-accepted paradigm in clinical research, the understanding of how pain affects individuals' daily life behavior remains a challenging task. Here we develop a methodological framework allowing to objectively document disruptive pain related interferences on real-life physical activity. The results reveal that meaningful information is contained in the temporal dynamics of activity patterns and an analytical model based on the theory of bivariate point processes can be used to describe physical activity behavior. The model parameters capture the dynamic interdependence between periods and events and determine a ‘signature’ of activity pattern. The study is likely to contribute to the clinical understanding of complex pain/disease-related behaviors and establish a unified mathematical framework to quantify the complex dynamics of various human activities.
BACKGROUND: Communication is often impaired in cerebral palsy (CP). Tools are needed to describe this complex function, in order to provide effective support. AIM: To study communication ability and the relationship between the Communication Function Classification System (CFCS) and CP subtype, gross motor function, manual ability, cognitive function and neuroimaging findings in the CP register of western Sweden. METHODS: Sixty-eight children (29 girls), 14 with unilateral spastic CP, 35 with bilateral spastic CP and 19 with dyskinetic CP, participated. The CFCS, Gross Motor Function Classification System (GMFCS) and Manual Ability Classification System (MACS) levels, cognitive impairment and neuroimaging findings were recorded. RESULTS: Half the children used speech, 32% used communication boards/books and 16% relied on body movements, eye gaze and sounds. Twenty-eight per cent were at the most functional CFCS level I, 13% at level II, 21% at level III, 10% at level IV and 28% at level V. CFCS levels I-II were found in 71% of children with unilateral spastic CP, 46% in bilateral spastic CP and 11% in dyskinetic CP (p = 0.03). CFCS correlated with the GMFCS, MACS and cognitive function (p < 0.01). Periventricular lesions were associated with speech and more functional CFCS levels, while cortical/subcortical and basal ganglia lesions were associated with the absence of speech and less functional CFCS levels (p < 0.01). CONCLUSION: Communication function profiles in CP can be derived from the CFCS, which correlates to gross and fine motor and cognitive function. Good communication ability is associated with lesions acquired early, rather than late, in the third trimester.
Bone nonunion in the pediatric population usually occurs in the context of highly unfavorable biological conditions. Recently, the vascularized fibular periosteal flap has been reported as a very effective procedure for treating this condition. Even though a vascularized tibial periosteal graft (VTPG) was described long ago and has been successfully employed in one adult case, there has been no other report published on the use of this technique. We report on the use of VTPG, pedicled in the anterior tibial vessels, for the treatment of two complex pediatric bone nonunion case: a recalcitrant supracondylar femoral pseudarthrosis secondary to an infection in an 11-year-old girl, and a tibial nonunion secondary to a failed bone defect reconstruction in a 12-year-old girl. Rapid healing was obtained in both cases. In the light of the data presented, we consider VTPG as a valuable surgical option for the treatment of complex bone nonunions in children. © 2014 Wiley Periodicals, Inc. Microsurgery, 2014.
Continued advancements in sequencing technologies have fueled the development of new sequencing applications and promise to flood current databases with raw data. A number of factors prevent the seamless and easy use of these data, including the breadth of project goals, the wide array of tools that individually perform fractions of any given analysis, the large number of associated software/hardware dependencies, and the detailed expertise required to perform these analyses. To address these issues, we have developed an intuitive web-based environment with a wide assortment of integrated and cutting-edge bioinformatics tools in pre-configured workflows. These workflows, coupled with the ease of use of the environment, provide even novice next-generation sequencing users with the ability to perform many complex analyses with only a few mouse clicks and, within the context of the same environment, to visualize and further interrogate their results. This bioinformatics platform is an initial attempt at Empowering the Development of Genomics Expertise (EDGE) in a wide range of applications for microbial research.
- Proceedings of the National Academy of Sciences of the United States of America
- Published almost 6 years ago
We investigate the impact of contact structure clustering on the dynamics of multiple diseases interacting through coinfection of a single individual, two problems typically studied independently. We highlight how clustering, which is well known to hinder propagation of diseases, can actually speed up epidemic propagation in the context of synergistic coinfections if the strength of the coupling matches that of the clustering. We also show that such dynamics lead to a first-order transition in endemic states, where small changes in transmissibility of the diseases can lead to explosive outbreaks and regions where these explosive outbreaks can only happen on clustered networks. We develop a mean-field model of coinfection of two diseases following susceptible-infectious-susceptible dynamics, which is allowed to interact on a general class of modular networks. We also introduce a criterion based on tertiary infections that yields precise analytical estimates of when clustering will lead to faster propagation than nonclustered networks. Our results carry importance for epidemiology, mathematical modeling, and the propagation of interacting phenomena in general. We make a call for more detailed epidemiological data of interacting coinfections.
Collisions at high-energy particle colliders are a traditionally fruitful source of exotic particle discoveries. Finding these rare particles requires solving difficult signal-versus-background classification problems, hence machine-learning approaches are often used. Standard approaches have relied on ‘shallow’ machine-learning models that have a limited capacity to learn complex nonlinear functions of the inputs, and rely on a painstaking search through manually constructed nonlinear features. Progress on this problem has slowed, as a variety of techniques have shown equivalent performance. Recent advances in the field of deep learning make it possible to learn more complex functions and better discriminate between signal and background classes. Here, using benchmark data sets, we show that deep-learning methods need no manually constructed inputs and yet improve the classification metric by as much as 8% over the best current approaches. This demonstrates that deep-learning approaches can improve the power of collider searches for exotic particles.
The analysis of contacts is a powerful tool to understand biomolecular function in a series of contexts, from the investigation of dynamical behavior at equilibrium to the study of nonequilibrium dynamics in which the system moves between multiple states. We thus propose a tool called CONtact ANalysis (CONAN) that, from molecular dynamics (MD) trajectories, analyzes interresidue contacts, creates videos of time-resolved contact maps, and performs correlation, principal component, and cluster analysis, revealing how specific contacts relate to functionally relevant states sampled by MD. We present how CONAN can identify features describing the dynamics of ubiquitin both at equilibrium and during mechanical unfolding. Additionally, we show the analysis of MD trajectories of an α-synuclein mutant peptide that undergoes an α-β conformational transition that can be easily monitored using CONAN, which identifies the multiple states that the peptide explores along its conformational dynamics. The high versatility and ease of use of the software make CONAN a tool that can significantly facilitate the understanding of the complex dynamical behavior of proteins or other biomolecules. CONAN and its documentation are freely available for download on GitHub.