Association of Epileptic and Nonepileptic Seizures and Changes in Circulating Plasma Proteins Linked to Neuroinflammation.

OBJECTIVE: To develop a diagnostic test that stratifies epileptic seizures (ES) from psychogenic nonepileptic seizures (PNES) by developing a multimodal algorithm that integrates plasma concentrations of selected immune response-associated proteins and patient clinical risk factors for seizure. METHODS: Daily blood samples were collected from patients evaluated in the epilepsy monitoring unit within 24 hours after EEG confirmed ES or PNES and plasma was isolated. Levels of 51 candidate plasma proteins were quantified using an automated, multiplexed, sandwich ELISA and then integrated and analyzed using our diagnostic algorithm. RESULTS: A 51-protein multiplexed ELISA panel was used to determine the plasma concentrations of patients with ES, patients with PNES, and healthy controls. A combination of protein concentrations, tumor necrosis factor-related apoptosis-inducing ligand (TRAIL), intercellular adhesion molecule 1 (ICAM-1), monocyte chemoattractant protein-2 (MCP-2), and tumor necrosis factor-receptor 1 (TNF-R1) indicated a probability that a patient recently experienced a seizure, with TRAIL and ICAM-1 levels higher in PNES than ES and MCP-2 and TNF-R1 levels higher in ES than PNES. The diagnostic algorithm yielded an area under the receiver operating characteristic curve (AUC) of 0.94 ± 0.07, sensitivity of 82.6% (95% confidence interval [CI] 62.9-93.0), and specificity of 91.6% (95% CI 74.2-97.7). Expanding the diagnostic algorithm to include previously identified PNES risk factors enhanced diagnostic performance, with AUC of 0.97 ± 0.05, sensitivity of 91.3% (95% CI 73.2-97.6), and specificity of 95.8% (95% CI 79.8-99.3). CONCLUSIONS: These 4 plasma proteins could provide a rapid, cost-effective, and accurate blood-based diagnostic test to confirm recent ES or PNES. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that variable levels of 4 plasma proteins, when analyzed by a diagnostic algorithm, can distinguish PNES from ES with sensitivity of 82.6% and specificity of 91.6%.

Selective VIP Receptor Agonists Facilitate Immune Transformation for Dopaminergic Neuroprotection in MPTP-Intoxicated Mice.

Vasoactive intestinal peptide (VIP) mediates a broad range of biological responses by activating two related receptors, VIP receptor 1 and 2 (VIPR1 and VIPR2). Although the use of native VIP facilitates neuroprotection, clinical application of the hormone is limited due to VIP's rapid metabolism and inability to distinguish between VIPR1 and VIPR2 receptors. In addition, activation of both receptors by therapeutics may increase adverse secondary toxicities. Therefore, we developed metabolically stable and receptor-selective agonists for VIPR1 and VIPR2 to improve pharmacokinetic and pharmacodynamic therapeutic end points. Selective agonists were investigated for their abilities to protect mice against MPTP-induced neurodegeneration used to model Parkinson's disease (PD). Survival of tyrosine hydroxylase neurons in the substantia nigra was determined by stereological tests after MPTP intoxication in mice pretreated with either VIPR1 or VIPR2 agonist or after adoptive transfer of splenic cell populations from agonist-treated mice administered to MPTP-intoxicated animals. Treatment with VIPR2 agonist or splenocytes from agonist-treated mice resulted in increased neuronal sparing. Immunohistochemical tests showed that agonist-treated mice displayed reductions in microglial responses, with the most pronounced effects in VIPR2 agonist-treated, MPTP-intoxicated mice. In parallel studies, we observed reductions in proinflammatory cytokine release that included IL-17A, IL-6, and IFN-γ and increases in GM-CSF transcripts in CD4(+) T cells recovered from VIPR2 agonist-treated animals. Moreover, a phenotypic shift of effector to regulatory T cells was observed. These results support the use of VIPR2-selective agonists as neuroprotective agents for PD treatment. SIGNIFICANCE STATEMENT: Vasoactive intestinal peptide receptor 2 can elicit immune transformation in a model of Parkinson's disease (PD). Such immunomodulatory capabilities can lead to neuroprotection by attenuating microglial activation and by slowing degradation of neuronal cell bodies and termini in MPTP-intoxicated mice. The protective mechanism arises from altering a Th1/Th2 immune cytokine response into an anti-inflammatory and neuronal sparing profile. These results are directly applicable for the development of novel PD therapies.

Solution structure of the core SMN-Gemin2 complex.

In humans, assembly of spliceosomal snRNPs (small nuclear ribonucleoproteins) begins in the cytoplasm where the multi-protein SMN (survival of motor neuron) complex mediates the formation of a seven-membered ring of Sm proteins on to a conserved site of the snRNA (small nuclear RNA). The SMN complex contains the SMN protein Gemin2 and several additional Gemins that participate in snRNP biosynthesis. SMN was first identified as the product of a gene found to be deleted or mutated in patients with the neurodegenerative disease SMA (spinal muscular atrophy), the leading genetic cause of infant mortality. In the present study, we report the solution structure of Gemin2 bound to the Gemin2-binding domain of SMN determined by NMR spectroscopy. This complex reveals the structure of Gemin2, how Gemin2 binds to SMN and the roles of conserved SMN residues near the binding interface. Surprisingly, several conserved SMN residues, including the sites of two SMA patient mutations, are not required for binding to Gemin2. Instead, they form a conserved SMN/Gemin2 surface that may be functionally important for snRNP assembly. The SMN-Gemin2 structure explains how Gemin2 is stabilized by SMN and establishes a framework for structure-function studies to investigate snRNP biogenesis as well as biological processes involving Gemin2 that do not involve snRNP assembly.

Al NMR: a novel NMR data processing program optimized for sparse sampling.

Sparse sampling in biomolecular multidimensional NMR offers increased acquisition speed and resolution and, if appropriate conditions are met, an increase in sensitivity. Sparse sampling of indirectly detected time domains combined with the direct truly multidimensional Fourier transform has elicited particular attention because of the ability to generate a final spectrum amenable to traditional analysis techniques. A number of sparse sampling schemes have been described including radial sampling, random sampling, concentric sampling and variations thereof. A fundamental feature of these sampling schemes is that the resulting time domain data array is not amenable to traditional Fourier transform based processing and phasing correction techniques. In addition, radial sampling approaches offer a number of advantages and capabilities that are also not accessible using standard NMR processing techniques. These include sensitivity enhancement, sub-matrix processing and determination of minimal sets of sampling angles. Here we describe a new software package (Al NMR) that enables these capabilities in the context of a general NMR data processing environment.

Optimized linear prediction for radial sampled multidimensional NMR experiments.

Radial sampling in multidimensional NMR experiments offers greatly decreased acquisition times while also providing an avenue for increased sensitivity. Digital resolution remains a concern and depends strongly upon the extent of sampling of individual radial angles. Truncated time domain data leads to spurious peaks (artifacts) upon FT and 2D FT. Linear prediction is commonly employed to improve resolution in Cartesian sampled NMR experiments. Here, we adapt the linear prediction method to radial sampling. Significantly more accurate estimates of linear prediction coefficients are obtained by combining quadrature frequency components from the multiple angle spectra. This approach results in significant improvement in both resolution and removal of spurious peaks as compared to traditional linear prediction methods applied to radial sampled data. The 'averaging linear prediction' (ALP) method is demonstrated as a general tool for resolution improvement in multidimensional radial sampled experiments.

Optimization of NMR spectroscopy of encapsulated proteins dissolved in low viscosity fluids.

Comprehensive application of solution NMR spectroscopy to studies of macromolecules remains fundamentally limited by the molecular rotational correlation time. For proteins, molecules larger than 30 kDa require complex experimental methods, such as TROSY in conjunction with isotopic labeling schemes that are often expensive and generally reduce the potential information available. We have developed the reverse micelle encapsulation strategy as an alternative approach. Encapsulation of proteins within the protective nano-scale water pool of a reverse micelle dissolved in ultra-low viscosity nonpolar solvents overcomes the slow tumbling problem presented by large proteins. Here, we characterize the contributions from the various components of the protein-containing reverse micelle system to the rotational correlation time of the encapsulated protein. Importantly, we demonstrate that the protein encapsulated in the reverse micelle maintains a hydration shell comparable in size to that seen in bulk solution. Using moderate pressures, encapsulation in ultra-low viscosity propane or ethane can be used to magnify this advantage. We show that encapsulation in liquid ethane can be used to reduce the tumbling time of the 43 kDa maltose binding protein from ~23 to ~10 ns. These conditions enable, for example, acquisition of TOCSY-type data resolved on the adjacent amide NH for the 43 kDa encapsulated maltose binding protein dissolved in liquid ethane, which is typically impossible for proteins of such size without use of extensive deuteration or the TROSY effect.

SEnD NMR: sensitivity enhanced n-dimensional NMR.

Sparse sampling offers tremendous potential for overcoming the time limitations imposed by traditional Cartesian sampling of indirectly detected dimensions of multidimensional NMR data. However, in many instances sensitivity rather than time remains of foremost importance when collecting data on protein samples. Here we explore how to optimize the collection of radial sampled multidimensional NMR data to achieve maximal signal-to-noise. A method is presented that exploits a rigorous definition of the minimal set of radial sampling angles required to resolve all peaks of interest in combination with a fundamental statistical property of radial sampled data. The approach appears general and can achieve a substantial sensitivity advantage over Cartesian sampling for the same total data acquisition time. Termed Sensitivity Enhanced n-Dimensional or SEnD NMR, the method involves three basic steps. First, data collection is optimized using routines to determine a minimal set of radial sampling angles required to resolve frequencies in the radially sampled chemical shift evolution dimensions. Second, appropriate combinations of experimental parameters (transients and increments) are defined by simple statistical considerations in order to optimize signal-to-noise in single angle frequency domain spectra. Finally, the data is processed with a direct multidimensional Fourier transform and a statistical artifact and noise removal step is employed.

AMORE-HX: a multidimensional optimization of radial enhanced NMR-sampled hydrogen exchange.

The Cartesian sampled three-dimensional HNCO experiment is inherently limited in time resolution and sensitivity for the real time measurement of protein hydrogen exchange. This is largely overcome by use of the radial HNCO experiment that employs the use of optimized sampling angles. The significant practical limitation presented by use of three-dimensional data is the large data storage and processing requirements necessary and is largely overcome by taking advantage of the inherent capabilities of the 2D-FT to process selective frequency space without artifact or limitation. Decomposition of angle spectra into positive and negative ridge components provides increased resolution and allows statistical averaging of intensity and therefore increased precision. Strategies for averaging ridge cross sections within and between angle spectra are developed to allow further statistical approaches for increasing the precision of measured hydrogen occupancy. Intensity artifacts potentially introduced by over-pulsing are effectively eliminated by use of the BEST approach.

Optimized angle selection for radial sampled NMR experiments.

Sparse sampling offers tremendous potential for overcoming the time limitations imposed by traditional Cartesian sampling of indirectly detected dimensions of multidimensional NMR data. Unfortunately, several otherwise appealing implementations are accompanied by spectral artifacts that have the potential to contaminate the spectrum with false peak intensity. In radial sampling of linked time evolution periods, the artifacts are easily identified and removed from the spectrum if a sufficient set of radial sampling angles is employed. Robust implementation of the radial sampling approach therefore requires optimization of the set of radial sampling angles collected. Here we describe several methods for such optimization. The approaches described take advantage of various aspects of the general simultaneous multidimensional Fourier transform in the analysis of multidimensional NMR data. Radially sampled data are primarily contaminated by ridges extending from authentic peaks. Numerical methods are described that definitively identify artifactual intensity and the optimal set of sampling angles necessary to eliminate it under a variety of scenarios. The algorithms are tested with both simulated and experimentally obtained triple resonance data.

Phasing arbitrarily sampled multidimensional NMR data.

The recent re-introduction of the two-dimensional Fourier transformation (2D-FT) has allows for the transformation of arbitrarily sampled time domain signals. In this respect, radial sampling, where two incremented time dimensions (t(1) and t(2)) are sampled such that t(1)=taucosalpha and t(2)=tausinalpha, is especially appealing because of the relatively small leakage artifacts that occur upon Fourier transformation. Unfortunately radially sampled time domain data results in a fundamental artifact in the frequency domain manifested as a ridge of intensity extending through the peak positions perpendicular to +/- the radial sampling angle. Successful removal of the ridge artifacts using existing algorithms requires absorptive line shapes. Here we present two procedures for retrospective phase correction of arbitrarily sampled data.