Proteomic Analysis of Alterations Induced by Perinatal Hypoxic–Ischemic Brain Injury

Perinatal hypoxic–ischemic brain injury is an important cause of neurological deficits still causing mortality and morbidity in the early period of life. As efficient clinical or pharmaceutical strategies to prevent or reduce the outcome of perinatal hypoxic–ischemic brain damage are limited, the development of new therapies is of utmost importance. To evolve innovative therapeutic concepts, elucidation of the mechanisms contributing to the neurological impairments upon hypoxic–ischemic brain injury is necessary. Therefore, we aimed for the identification of proteins that are affected by hypoxic–ischemic brain injury in neonatal rats. To assess changes in protein expression two days after induction of brain damage, a 2D-DIGE based proteome analysis was performed. Among the proteins altered after hypoxic–ischemic brain injury, Calcineurin A, Coronin-1A, as well as GFAP were identified, showing higher expression in lesioned hemispheres. Validation of the changes in Calcineurin A expression by Western Blot analysis demonstrated several truncated forms of this protein generated by limited proteolysis after hypoxia–ischemia. Further analysis revealed activation of calpain, which is involved in the limited proteolysis of Calcineurin. Active forms of Calcineurin are associated with the dephosphorylation of Darpp-32, an effect that was also demonstrated in lesioned hemispheres after perinatal brain injury

Diagnostic Value of the Impairment of Olfaction in Parkinson's Disease

<div><p>Background</p><p>Olfactory impairment is increasingly recognized as an early symptom in the development of Parkinson's disease. Testing olfactory function is a non-invasive method but can be time-consuming which restricts its application in clinical settings and epidemiological studies. Here, we investigate odor identification as a supportive diagnostic tool for Parkinson's disease and estimate the performance of odor subsets to allow a more rapid testing of olfactory impairment.</p><p>Methodology/Principal Findings</p><p>Odor identification was assessed with 16 Sniffin' sticks in 148 Parkinson patients and 148 healthy controls. Risks of olfactory impairment were estimated with proportional odds models. Random forests were applied to classify Parkinson and non-Parkinson patients. Parkinson patients were rarely normosmic (identification of more than 12 odors; 16.8%) and identified on average seven odors whereas the reference group identified 12 odors and showed a higher prevalence of normosmy (31.1%). Parkinson patients with rigidity dominance had a twofold greater prevalence of olfactory impairment. Disease severity was associated with impairment of odor identification (per score point of the Hoehn and Yahr rating OR 1.87, 95% CI 1.26–2.77). Age-related impairment of olfaction showed a steeper gradient in Parkinson patients. <i>Coffee</i>, <i>peppermint</i>, and <i>anise</i> showed the largest difference in odor identification between Parkinson patients and controls. Random forests estimated a misclassification rate of 22.4% when comparing Parkinson patients with healthy controls using all 16 odors. A similar rate (23.8%) was observed when only the three aforementioned odors were applied.</p><p>Conclusions/Significance</p><p>Our findings indicate that testing odor identification can be a supportive diagnostic tool for Parkinson's disease. The application of only three odors performed well in discriminating Parkinson patients from controls, which can facilitate a wider application of this method as a point-of-care test.</p></div

Cyber-physical system design with sensor networking technologies

This book describes how wireless sensor networking technologies can help in establishing and maintaining seamless communications between the physical and cyber systems to enable efficient, secure, reliable acquisition, management, and routing of data

Overlapping coalition formation games in wireless communication networks

This brief introduces overlapping coalition formation games (OCF games), a novel mathematical framework from cooperative game theory that can be used to model, design and analyze cooperative scenarios in future wireless communication networks. The concepts of OCF games are explained, and several algorithmic aspects are studied. In addition, several major application scenarios are discussed. These applications are drawn from a variety of fields that include radio resource allocation in dense wireless networks, cooperative spectrum sensing for cognitive radio networks, and resource management for crowd sourcing. For each application, the use of OCF games is discussed in detail in order to show how this framework can be used to solve relevant wireless networking problems. Overlapping Coalition Formation Games in Wireless Communication Networks provides researchers, students and practitioners with a concise overview of existing works in this emerging area, exploring the relevant fundamental theories, key techniques, and significant applications.

A: Schematic overview of the DOHH activity assay by two steps of size exclusion chromatography.

<p>B: Silver staining of a protein gel after size exclusion chromatography of DOHH activity assays with Amicon-Ultra 100 and Amicon-Ultra 30 columns. Modified EIF-5A protein was recovered in the eluates after size exclusion chromatography with Amicon Ultra 30 columns. Lane 1) Standard protein marker from Roth (Karlsruhe, Germany); Lane 2) Recovered, modified EIF-5A protein after the DOHH activity assay with the inhibitor zileuton (100 nmol); Lane 3) Flowthrough of Amicon Ultra 30 column after the DOHH activity assay with the inhibitor zileuton (100 nmol); Lane 4) Recovered, modified EIF-5A protein of a non-inhibitor treated DOHH activity assay; Lane 5) Flowthrough of a non-inhibitor treated DOHH activity assay; Identification of (C) hypusine and (D) deoxyhypusine by GC/MS analysis after DOHH activity assays obtained from a peptide hydrolysate of modified EIF-5A protein after derivatization with methyl chloroformate. Hypusine was identified by the prominent molecular fragments 88 m/z, 101 m/z and 157 m/z in the ion mass spectrum. Deoxyhypusine was identified by the prominent molecular fragments 87 m/z, 129 m/z and 143 m/z (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058318#pone-0058318-g005" target="_blank">Fig.5D</a>). Break points of the molecule are indicated by vertical lines while horizontal lines indicate the fragment and its corresponding mass, e.g. the deoxyhypusine fragment 87 m/z represents a [NH(C₄H₈)NH]⁺ fragment and the 74 m/z fragment the terminal [NHC(OO)CH₃] group.</p

Determination of DOHH activity from <i>P. vivax</i> and human.

<p>Column A) Complete DOOH activity assay with <i>Plasmodium</i> DOHH; B) Control Assay: DOHH protein from <i>P. vivax</i> was substituted by water; C) Inhibitor assay: DOHH from <i>P. vivax</i> was inhibited with 100 nmol zileuton; Column D) Complete DOOH activity assay with human DOHH; Column E) Control Assay: DOHH protein from human was substituted by water; F) Inhibitor assay: DOHH from human was inhibited with 100 nmol zileuton. Each experiment was performed in three replicates the same day.</p

A: Transcriptional profile of <i>P. vivax dohh</i> throughout the 48 h intraerythrocytic life cycle.

<p>Cellular RNA was isolated from a clinical isolate obtained from a patient infected with <i>P. vivax</i> at different developmental stages of the infection during the intraerythrocytic life cycle. Expression values were determined for all given developmental stages (x-axis) in comparison to an average value obtained from a housekeeping gene (y-axis). B: Purification of <i>P. vivax</i> DOHH by Nickel-chelate-affinity chromatography. Lane 1) Standard protein marker from Roth (Karlsruhe, Germany); Lane 2) Bacterial lysate; Lane 3) Crude extract; Lane 4) Wash fraction 1; Lane 5) Wash fraction 2; Lane 6) Eluate fraction 1; Lane 7) Eluate fraction 2. The SDS-PAGE gel was stained by Coumassie Blue.</p

Primary and corresponding secondary antibodies used for immunohistochemistry.

<p>Primary and corresponding secondary antibodies used for immunohistochemistry.</p

Assay of DOHH for phycocyanin α-84 lyase and phycoerythrocyanin α-84 lyase activities.

<p>Absorption spectra of acceptor proteins, CpcA and PecA, after treatment with PCB in the presence (–) or absence (–) of DOHH, and purification by Ni²⁺ affinity chromatography. (A) Assay for the attachment of PCB to CpcA in the additional presence of the lyase, CpcE. (B) Assay for the attachment of PCB to CpcA in the additional presence of the lyase, CpcF; (C) Assay for the attachment of PCB to PecA in the additional presence of the lyase, PecE. (D) Assay for PCB isomerizing to PVB and attachment to PecA in the additional presence of the lyase, PecF. All reactions were carried out in <i>E. coli</i> (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0058318#s2" target="_blank">Materials and methods</a> for details).</p

Detection of Patient Subgroups with Differential Expression in Omics Data: A Comprehensive Comparison of Univariate Measures

<div><p>Detection of yet unknown subgroups showing differential gene or protein expression is a frequent goal in the analysis of modern molecular data. Applications range from cancer biology over developmental biology to toxicology. Often a control and an experimental group are compared, and subgroups can be characterized by differential expression for only a subgroup-specific set of genes or proteins. Finding such genes and corresponding patient subgroups can help in understanding pathological pathways, diagnosis and defining drug targets. The size of the subgroup and the type of differential expression determine the optimal strategy for subgroup identification. To date, commonly used software packages hardly provide statistical tests and methods for the detection of such subgroups. Different univariate methods for subgroup detection are characterized and compared, both on simulated and on real data. We present an advanced design for simulation studies: Data is simulated under different distributional assumptions for the expression of the subgroup, and performance results are compared against theoretical upper bounds. For each distribution, different degrees of deviation from the majority of observations are considered for the subgroup. We evaluate classical approaches as well as various new suggestions in the context of omics data, including <i>outlier sum</i>, <i>PADGE</i>, and <i>kurtosis</i>. We also propose the new <i>FisherSum</i> score. ROC curve analysis and AUC values are used to quantify the ability of the methods to distinguish between genes or proteins with and without certain subgroup patterns. In general, FisherSum for small subgroups and -test for large subgroups achieve best results. We apply each method to a case-control study on Parkinson's disease and underline the biological benefit of the new method.</p></div