The legend of plantar neuropraxia in long-distance athletes

Background. Legend has it that endurance athletes who develop plantar foot pain during long-distance running frequently experience an eventual relief of pain due to a transient neuropraxia brought on by continued activity.Objective. To evaluate the nature of this legend, we assessed long-distance runners for the presence of sensory deficits before and after completion of an ultramarathon, expecting to find an induced neuropraxia  and abnormal sensory results.Methods. Twenty-five adult participants of an ultramarathon were evaluated prior to their 50/100 km run and again upon completion of the race. Neurosensory testing was performed using a 10 g monofilament at 4 locations on each foot and a 128 Hz tuning fork at one location on each foot. The same techniques were used prior to, and at conclusion of the race.Results. We detected no neuropraxia or sensory deficits in any participant, despite reports by the same subjects that they had experienced the phenomenon during the race. While runners commonly report losing sensation in their feet during long runs, we were unable to demonstrate any sensory deficit with simple field-based testing.Conclusion. We believe that there is room for additional research to be performed using more sensitive means of neurosensory evaluation

Robust structure-based resonance assignment for functional protein studies by NMR

High-throughput functional protein NMR studies, like protein interactions or dynamics, require an automated approach for the assignment of the protein backbone. With the availability of a growing number of protein 3D structures, a new class of automated approaches, called structure-based assignment, has been developed quite recently. Structure-based approaches use primarily NMR input data that are not based on J-coupling and for which connections between residues are not limited by through bonds magnetization transfer efficiency. We present here a robust structure-based assignment approach using mainly HN–HN NOEs networks, as well as 1H–15N residual dipolar couplings and chemical shifts. The NOEnet complete search algorithm is robust against assignment errors, even for sparse input data. Instead of a unique and partly erroneous assignment solution, an optimal assignment ensemble with an accuracy equal or near to 100% is given by NOEnet. We show that even low precision assignment ensembles give enough information for functional studies, like modeling of protein-complexes. Finally, the combination of NOEnet with a low number of ambiguous J-coupling sequential connectivities yields a high precision assignment ensemble. NOEnet will be available under: http://www.icsn.cnrs-gif.fr/download/nmr

Neisseria gonorrhoeae Infection Induces Altered Amphiregulin Processing and Release

Adhesion of the human pathogen Neisseria gonorrhoeae has established effects on the host cell and evokes a variety of cellular events including growth factor activation. In the present study we report that infection with N. gonorrhoeae causes altered amphiregulin processing and release in human epithelial cells. Amphiregulin is a well-studied growth factor with functions in various cell processes and is upregulated in different forms cancer and proliferative diseases. The protein is prototypically cleaved on the cell surface in response to external stimuli. We demonstrate that upon infection, a massive upregulation of amphiregulin mRNA is seen. The protein changes its subcellular distribution and is also alternatively cleaved at the plasma membrane, which results in augmented release of an infection-specific 36 kDa amphiregulin product from the surface of human cervical epithelial cells. Further, using antibodies directed against different domains of the protein we could determine the impact of infection on pro-peptide processing. In summary, we present data showing that the infection of N. gonorrhoeae causes an alternative amphiregulin processing, subcellular distribution and release in human epithelial cervical cells that likely contribute to the predisposition cellular abnormalities and anti-apoptotic features of N. gonorrhoeae infections

A Real-Time PCR Antibiogram for Drug-Resistant Sepsis

Current molecular diagnostic techniques for susceptibility testing of septicemia rely on genotyping for the presence of known resistance cassettes. This technique is intrinsically vulnerable due to the inability to detect newly emergent resistance genes. Traditional phenotypic susceptibility testing has always been a superior method to assay for resistance; however, relying on the multi-day growth period to determine which antimicrobial to administer jeopardizes patient survival. These factors have resulted in the widespread and deleterious use of broad-spectrum antimicrobials. The real-time PCR antibiogram, described herein, combines universal phenotypic susceptibility testing with the rapid diagnostic capabilities of PCR. We have developed a procedure that determines susceptibility by monitoring pathogenic load with the highly conserved 16S rRNA gene in blood samples exposed to different antimicrobial drugs. The optimized protocol removes heme and human background DNA from blood, which allows standard real-time PCR detection systems to be employed with high sensitivity (<100 CFU/mL). Three strains of E. coli, two of which were antimicrobial resistant, were spiked into whole blood and exposed to three different antibiotics. After real-time PCR-based determination of pathogenic load, a ΔCt<3.0 between untreated and treated samples was found to indicate antimicrobial resistance (P<0.01). Minimum inhibitory concentration was determined for susceptible bacteria and pan-bacterial detection was demonstrated with 3 Gram-negative and 2 Gram-positive bacteria. Species identification was performed via analysis of the hypervariable amplicons. In summary, we have developed a universal diagnostic phenotyping technique that assays for the susceptibility of drug-resistant septicemia with the speed of PCR. The real-time PCR antibiogram achieves detection, susceptibility testing, minimum inhibitory concentration determination, and identification in less than 24 hours

Neisseria meningitidis Differentially Controls Host Cell Motility through PilC1 and PilC2 Components of Type IV Pili

Neisseria meningitidis is a strictly human pathogen that has two facets since asymptomatic carriage can unpredictably turn into fulminant forms of infection. Meningococcal pathogenesis relies on the ability of the bacteria to break host epithelial or endothelial cellular barriers. Highly restrictive, yet poorly understood, mechanisms allow meningococcal adhesion to cells of only human origin. Adhesion of encapsulated and virulent meningococci to human cells relies on the expression of bacterial type four pili (T4P) that trigger intense host cell signalling. Among the components of the meningococcal T4P, the concomitantly expressed PilC1 and PilC2 proteins regulate pili exposure at the bacterial surface, and until now, PilC1 was believed to be specifically responsible for T4P-mediated meningococcal adhesion to human cells. Contrary to previous reports, we show that, like PilC1, the meningococcal PilC2 component is capable of mediating adhesion to human ME180 epithelial cells, with cortical plaque formation and F-actin condensation. However, PilC1 and PilC2 promote different effects on infected cells. Cellular tracking analysis revealed that PilC1-expressing meningococci caused a severe reduction in the motility of infected cells, which was not the case when cells were infected with PilC2-expressing strains. The amount of both total and phosphorylated forms of EGFR was dramatically reduced in cells upon PilC1-mediated infection. In contrast, PilC2-mediated infection did not notably affect the EGFR pathway, and these specificities were shared among unrelated meningococcal strains. These results suggest that meningococci have evolved a highly discriminative tool for differential adhesion in specific microenvironments where different cell types are present. Moreover, the fine-tuning of cellular control through the combined action of two concomitantly expressed, but distinctly regulated, T4P-associated variants of the same molecule (i.e. PilC1 and PilC2) brings a new model to light for the analysis of the interplay between pathogenic bacteria and human host cells

Differential Modulation of TNF-α–Induced Apoptosis by Neisseria meningitidis

Infections by Neisseria meningitidis show duality between frequent asymptomatic carriage and occasional life-threatening disease. Bacterial and host factors involved in this balance are not fully understood. Cytopathic effects and cell damage may prelude to pathogenesis of isolates belonging to hyper-invasive lineages. We aimed to analyze cell–bacteria interactions using both pathogenic and carriage meningococcal isolates. Several pathogenic isolates of the ST-11 clonal complex and carriage isolates were used to infect human epithelial cells. Cytopathic effect was determined and apoptosis was scored using several methods (FITC-Annexin V staining followed by FACS analysis, caspase assays and DNA fragmentation). Only pathogenic isolates were able to induce apoptosis in human epithelial cells, mainly by lipooligosaccharide (endotoxin). Bioactive TNF-α is only detected when cells were infected by pathogenic isolates. At the opposite, carriage isolates seem to provoke shedding of the TNF-α receptor I (TNF-RI) from the surface that protect cells from apoptosis by chelating TNF-α. Ability to induce apoptosis and inflammation may represent major traits in the pathogenesis of N. meningitidis. However, our data strongly suggest that carriage isolates of meningococci reduce inflammatory response and apoptosis induction, resulting in the protection of their ecological niche at the human nasopharynx

Translating Clinical Findings into Knowledge in Drug Safety Evaluation - Drug Induced Liver Injury Prediction System (DILIps)

Drug-induced liver injury (DILI) is a significant concern in drug development due to the poor concordance between preclinical and clinical findings of liver toxicity. We hypothesized that the DILI types (hepatotoxic side effects) seen in the clinic can be translated into the development of predictive in silico models for use in the drug discovery phase. We identified 13 hepatotoxic side effects with high accuracy for classifying marketed drugs for their DILI potential. We then developed in silico predictive models for each of these 13 side effects, which were further combined to construct a DILI prediction system (DILIps). The DILIps yielded 60–70% prediction accuracy for three independent validation sets. To enhance the confidence for identification of drugs that cause severe DILI in humans, the “Rule of Three” was developed in DILIps by using a consensus strategy based on 13 models. This gave high positive predictive value (91%) when applied to an external dataset containing 206 drugs from three independent literature datasets. Using the DILIps, we screened all the drugs in DrugBank and investigated their DILI potential in terms of protein targets and therapeutic categories through network modeling. We demonstrated that two therapeutic categories, anti-infectives for systemic use and musculoskeletal system drugs, were enriched for DILI, which is consistent with current knowledge. We also identified protein targets and pathways that are related to drugs that cause DILI by using pathway analysis and co-occurrence text mining. While marketed drugs were the focus of this study, the DILIps has a potential as an evaluation tool to screen and prioritize new drug candidates or chemicals, such as environmental chemicals, to avoid those that might cause liver toxicity. We expect that the methodology can be also applied to other drug safety endpoints, such as renal or cardiovascular toxicity

A Measurement of Rb using a Double Tagging Method

The fraction of Z to bbbar events in hadronic Z decays has been measured by the OPAL experiment using the data collected at LEP between 1992 and 1995. The Z to bbbar decays were tagged using displaced secondary vertices, and high momentum electrons and muons. Systematic uncertainties were reduced by measuring the b-tagging efficiency using a double tagging technique. Efficiency correlations between opposite hemispheres of an event are small, and are well understood through comparisons between real and simulated data samples. A value of Rb = 0.2178 +- 0.0011 +- 0.0013 was obtained, where the first error is statistical and the second systematic. The uncertainty on Rc, the fraction of Z to ccbar events in hadronic Z decays, is not included in the errors. The dependence on Rc is Delta(Rb)/Rb = -0.056*Delta(Rc)/Rc where Delta(Rc) is the deviation of Rc from the value 0.172 predicted by the Standard Model. The result for Rb agrees with the value of 0.2155 +- 0.0003 predicted by the Standard Model.Comment: 42 pages, LaTeX, 14 eps figures included, submitted to European Physical Journal

A novel application of motion analysis for detecting stress responses in embryos at different stages of development.

Motion analysis is one of the tools available to biologists to extract biologically relevant information from image datasets and has been applied to a diverse range of organisms. The application of motion analysis during early development presents a challenge, as embryos often exhibit complex, subtle and diverse movement patterns. A method of motion analysis able to holistically quantify complex embryonic movements could be a powerful tool for fields such as toxicology and developmental biology to investigate whole organism stress responses. Here we assessed whether motion analysis could be used to distinguish the effects of stressors on three early developmental stages of each of three species: (i) the zebrafish Danio rerio (stages 19 h, 21.5 h and 33 h exposed to 1.5% ethanol and a salinity of 5); (ii) the African clawed toad Xenopus laevis (stages 24, 32 and 34 exposed to a salinity of 20); and iii) the pond snail Radix balthica (stages E3, E4, E6, E9 and E11 exposed to salinities of 5, 10 and 15). Image sequences were analysed using Sparse Optic Flow and the resultant frame-to-frame motion parameters were analysed using Discrete Fourier Transform to quantify the distribution of energy at different frequencies. This spectral frequency dataset was then used to construct a Bray-Curtis similarity matrix and differences in movement patterns between embryos in this matrix were tested for using ANOSIM