UPLC-ESI-TOFMS-based metabolomics and gene expression dynamics inspector self-organizing metabolomic maps as tools for understanding the cellular response to ionizing radiation

Global transcriptomic and proteomic profiling platforms have yielded important insights into the complex response to ionizing radiation (IR). Nonetheless, little is known about the ways in which small cellular metabolite concentrations change in response to IR. Here, a metabolomics approach using ultraperformance liquid chromatography coupled with electrospray time-of-flight mass spectrometry was used to profile, over time, the hydrophilic metabolome of TK6 cells exposed to IR doses ranging from 0.5 to 8.0 Gy. Multivariate data analysis of the positive ions revealed dose- and time-dependent clustering of the irradiated cells and identified certain constituents of the water-soluble metabolome as being significantly depleted as early as 1 h after IR. Tandem mass spectrometry was used to confirm metabolite identity. Many of the depleted metabolites are associated with oxidative stress and DNA repair pathways. Included are reduced glutathione, adenosine monophosphate, nicotinamide adenine dinucleotide, and spermine. Similar measurements were performed with a transformed fibroblast cell line, BJ, and it was found that a subset of the identified TK6 metabolites were effective in IR dose discrimination. The GEDI (Gene Expression Dynamics Inspector) algorithm, which is based on self-organizing maps, was used to visualize dynamic global changes in the TK6 metabolome that resulted from IR. It revealed dose-dependent clustering of ions sharing the same trends in concentration change across radiation doses. "Radiation metabolomics," the application of metabolomic analysis to the field of radiobiology, promises to increase our understanding of cellular responses to stressors such as radiation.

Radiation metabolomics. 1. Identification of minimally invasive urine biomarkers for gamma-radiation exposure in mice

Gamma-radiation exposure has both short- and long-term adverse health effects. The threat of modern terrorism places human populations at risk for radiological exposures, yet current medical countermeasures to radiation exposure are limited. Here we describe metabolomics for gamma-radiation biodosimetry in a mouse model. Mice were gamma-irradiated at doses of 0, 3 and 8 Gy (2.57 Gy/min), and urine samples collected over the first 24 h after exposure were analyzed by ultra-performance liquid chromatography-time-of-flight mass spectrometry (UPLC-TOFMS). Multivariate data were analyzed by orthogonal partial least squares (OPLS). Both 3- and 8-Gy exposures yielded distinct urine metabolomic phenotypes. The top 22 ions for 3 and 8 Gy were analyzed further, including tandem mass spectrometric comparison with authentic standards, revealing that N-hexanoylglycine and beta-thymidine are urinary biomarkers of exposure to 3 and 8 Gy, 3-hydroxy-2-methylbenzoic acid 3-O-sulfate is elevated in urine of mice exposed to 3 but not 8 Gy, and taurine is elevated after 8 but not 3 Gy. Gene Expression Dynamics Inspector (GEDI) self-organizing maps showed clear dose-response relationships for subsets of the urine metabolome. This approach is useful for identifying mice exposed to gamma radiation and for developing metabolomic strategies for noninvasive radiation biodosimetry in humans.

Structure-activity relationships from in vitro efficacies of the thiazolide series against the intracellular apicomplexan protozoan Neospora caninum

Nitazoxanide (2-acetolyloxy-N-(5-nitro 2-thiazolyl) benzamide; NTZ) represents the parent compound of a novel class of broad-spectrum anti-parasitic compounds named thiazolides. NTZ is active against a wide variety of intestinal and tissue-dwelling helminths, protozoa, enteric bacteria and a number of viruses infecting animals and humans. While potent, this poses a problem in practice, since this obvious non-selectivity can lead to undesired side effects in both humans and animals. In this study, we used real time PCR to determine the in vitro activities of 29 different thiazolides (NTZ-derivatives), which carry distinct modifications on both the thiazole- and the benzene moieties, against the tachyzoite stage of the intracellular protozoan Neospora caninum. The goal was to identify a highly active compound lacking the undesirable nitro group, which would have a more specific applicability, such as in food animals. By applying self-organizing molecular field analysis (SOMFA), these data were used to develop a predictive model for future drug design. SOMFA performs self-alignment of the molecules, and takes into account the steric and electrostatic properties, in order to determine 3D-quantitative structure activity relationship models. The best model was obtained by overlay of the thiazole moieties. Plotting of predicted versus experimentally determined activity produced an r2 value of 0.8052 and cross-validation using the "leave one out" methodology resulted in a q2 value of 0.7987. A master grid map showed that large steric groups at the R2 position, the nitrogen of the amide bond and position Y could greatly reduce activity, and the presence of large steric groups placed at positions X, R4 and surrounding the oxygen atom of the amide bond, may increase the activity of thiazolides against Neospora caninum tachyzoites. The model obtained here will be an important predictive tool for future development of this important class of drugs.

Gene expression in cortex and hippocampus during acute pneumococcal meningitis

BACKGROUND: Pneumococcal meningitis is associated with high mortality (approximately 30%) and morbidity. Up to 50% of survivors are affected by neurological sequelae due to a wide spectrum of brain injury mainly affecting the cortex and hippocampus. Despite this significant disease burden, the genetic program that regulates the host response leading to brain damage as a consequence of bacterial meningitis is largely unknown.We used an infant rat model of pneumococcal meningitis to assess gene expression profiles in cortex and hippocampus at 22 and 44 hours after infection and in controls at 22 h after mock-infection with saline. To analyze the biological significance of the data generated by Affymetrix DNA microarrays, a bioinformatics pipeline was used combining (i) a literature-profiling algorithm to cluster genes based on the vocabulary of abstracts indexed in MEDLINE (NCBI) and (ii) the self-organizing map (SOM), a clustering technique based on covariance in gene expression kinetics. RESULTS: Among 598 genes differentially regulated (change factor > or = 1.5; p < or = 0.05), 77% were automatically assigned to one of 11 functional groups with 94% accuracy. SOM disclosed six patterns of expression kinetics. Genes associated with growth control/neuroplasticity, signal transduction, cell death/survival, cytoskeleton, and immunity were generally upregulated. In contrast, genes related to neurotransmission and lipid metabolism were transiently downregulated on the whole. The majority of the genes associated with ionic homeostasis, neurotransmission, signal transduction and lipid metabolism were differentially regulated specifically in the hippocampus. Of the cell death/survival genes found to be continuously upregulated only in hippocampus, the majority are pro-apoptotic, while those continuously upregulated only in cortex are anti-apoptotic. CONCLUSION: Temporal and spatial analysis of gene expression in experimental pneumococcal meningitis identified potential targets for therapy.

Towards an Emergent Semantic Web

In his in uential article about the evolution of the Web, Berners-Lee [1] envisions a Semantic Web in which humans and computers alike are capable of understanding and processing information. This vision is yet to materialize. The main obstacle for the Semantic Web vision is that in today's Web meaning is rooted most often not in formal semantics, but in natural language and, in the sense of semiology, emerges not before interpretation and processing. Yet, an automated form of interpretation and processing can be tackled by precisiating raw natural language. To do that, Web agents extract fuzzy grassroots ontologies through induction from existing Web content. Inductive fuzzy grassroots ontologies thus constitute organically evolved knowledge bases that resemble automated gradual thesauri, which allow precisiating natural language [2]. The Web agents' underlying dynamic, self-organizing, and best-effort induction, enable a sub-syntactical bottom up learning of semiotic associations. Thus, knowledge is induced from the users' natural use of language in mutual Web interactions, and stored in a gradual, thesauri-like lexical-world knowledge database as a top-level ontology, eventually allowing a form of computing with words [3]. Since when computing with words the objects of computation are words, phrases and propositions drawn from natural languages, it proves to be a practical notion to yield emergent semantics for the Semantic Web. In the end, an improved understanding by computers on the one hand should upgrade human- computer interaction on the Web, and, on the other hand allow an initial version of human- intelligence amplification through the Web.

Software Development in the Cloud: Exploring the Affordances of Platform-as-a-Service

Software development teams increasingly adopt platform-as-a-service (PaaS), i.e., cloud services that make software development infrastructure available over the internet. Yet, empirical evidence of whether and how software development work changes with the use of PaaS is difficult to find. We performed a grounded-theory study to explore the affordances of PaaS for software development teams. We find that PaaS enables software development teams to enforce uniformity, to exploit knowledge embedded in technology, to enhance agility, and to enrich jobs. These affordances do not arise in a vacuum. Their emergence is closely interwoven with changes in methodologies, roles, and norms that give rise to self-organizing, loosely coupled teams. Our study provides rich descriptions of PaaS-based software development and an emerging theory of affordances of PaaS for software development teams.

Characterization of Enhancing MS Lesions by Dynamic Texture Parameter Analysis of Dynamic Susceptibility Perfusion Imaging

Purpose. The purpose of this study was to investigate statistical differences with MR perfusion imaging features that reflect the dynamics of Gadolinium-uptake in MS lesions using dynamic texture parameter analysis (DTPA). Methods. We investigated 51 MS lesions (25 enhancing, 26 nonenhancing lesions) of 12 patients. Enhancing lesions () were prestratified into enhancing lesions with increased permeability (EL+; ) and enhancing lesions with subtle permeability (EL−; ). Histogram-based feature maps were computed from the raw DSC-image time series and the corresponding texture parameters were analyzed during the inflow, outflow, and reperfusion time intervals. Results. Significant differences () were found between EL+ and EL− and between EL+ and nonenhancing inactive lesions (NEL). Main effects between EL+ versus EL− and EL+ versus NEL were observed during reperfusion (mainly in mean and standard deviation (SD): EL+ versus EL− and EL+ versus NEL), while EL− and NEL differed only in their SD during outflow. Conclusion. DTPA allows grading enhancing MS lesions according to their perfusion characteristics. Texture parameters of EL− were similar to NEL, while EL+ differed significantly from EL− and NEL. Dynamic texture analysis may thus be further investigated as noninvasive endogenous marker of lesion formation and restoration.

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Towards Self-Learning Radio-Based Localization Systems

Location-awareness indoors will be an inseparable feature of mobile services/applications in future wireless networks. Its current ubiquitous availability is still obstructed by technological challenges and privacy issues. We propose an innovative approach towards the concept of indoor positioning with main goal to develop a system that is self-learning and able to adapt to various radio propagation environments. The approach combines estimation of propagation conditions, subsequent appropriate channel modelling and optimisation feedback to the used positioning algorithm. Main advantages of the proposal are decreased system set-up effort, automatic re-calibration and increased precision.

Structural analysis of B-Box 2 from MuRF1: identification of a novel self-association pattern in a RING-like fold

The B-box motif is the defining feature of the TRIM family of proteins, characterized by a RING finger-B-box-coiled coil tripartite fold. We have elucidated the crystal structure of B-box 2 (B2) from MuRF1, a TRIM protein that supports a wide variety of protein interactions in the sarcomere and regulates the trophic state of striated muscle tissue. MuRF1 B2 coordinates two zinc ions through a cross-brace alpha/beta-topology typical of members of the RING finger superfamily. However, it self-associates into dimers with high affinity. The dimerization pattern is mediated by the helical component of this fold and is unique among RING-like folds. This B2 reveals a long shallow groove that encircles the C-terminal metal binding site ZnII and appears as the defining protein-protein interaction feature of this domain. A cluster of conserved hydrophobic residues in this groove and, in particular, a highly conserved aromatic residue (Y133 in MuRF1 B2) is likely to be central to this role. We expect these findings to aid the future exploration of the cellular function and therapeutic potential of MuRF1.

Agency and ownership are independent components of 'sensing the self' in the auditory-verbal domain

'Sensing the self' relies on the ability to distinguish self-generated from external stimuli. It requires functioning mechanisms to establish feelings of agency and ownership. Agency is defined causally, where the subjects action is followed by an effect. Ownership is defined by the features of the effect, independent from the action. In our study, we manipulated these qualities separately. 13 right-handed healthy individuals performed the experiment while 76-channel EEG was recorded. Stimuli consisted of visually presented words, read aloud by the subject. The experiment consisted of six conditions: (a) subjects saw a word, read it aloud, heard it in their own voice; (b) like a, but the word was heard in an unfamiliar voice; (c) subject heard a word in his/her own voice without speaking; (d) like c, but the word was heard in an unfamiliar voice; (e) like a, but subjects heard the word with a delay; (f) subjects read without hearing. ERPs and difference maps were computed for all conditions. Effects were analysed topographically. The N100 (86-172 ms) displayed significant main effects of agency and ownership. The topographies of the two effects shared little common variance, suggesting independent effects. Later effects (174-400 ms) of agency and ownership were topographically similar, suggesting common mechanisms. Replicating earlier studies, significant N100 suppression was observed, with a topography resembling the agency effect. 'Sensing the self' appears to recruit from at least two very distinct processes: an agency assessment that represents causality and an ownership assessment that compares stimulus features with memory content.