FGF13 Is Required for Histamine-Induced Itch Sensation by Interaction with NaV1.7.

Itch can be induced by activation of small-diameter DRG neurons, which express abundant intracellular fibroblast growth factor 13 (FGF13). Although FGF13 is revealed to be essential for heat nociception, its role in mediating itch remains to be investigated. Here, we reported that loss of FGF13 in mouse DRG neurons impaired the histamine-induced scratching behavior. Calcium imaging showed that the percentage of histamine-responsive DRG neurons was largely decreased in FGF13-deficient mice; and consistently, electrophysiological recording exhibited that histamine failed to evoke action potential firing in most DRG neurons from these mice. Given that the reduced histamine-evoked neuronal response was caused by knockdown of FGF13 but not by FGF13A deficiency, FGF13B was supposed to mediate this process. Furthermore, overexpression of histamine Type 1 receptor H1R, but not H2R, H3R, nor H4R, increased the percentage of histamine-responsive DRG neurons, and the scratching behavior in FGF13-deficient mice was highly reduced by selective activation of H1R, suggesting that H1R is mainly required for FGF13-mediated neuronal response and scratching behavior induced by histamine. However, overexpression of H1R failed to rescue the histamine-evoked neuronal response in FGF13-deficient mice. Histamine enhanced the FGF13 interaction with NaV1.7. Disruption of this interaction by a membrane-permeable competitive peptide, GST-Flag-NaV1.7CT-TAT, reduced the percentage of histamine-responsive DRG neurons, and impaired the histamine-induced scratching, indicating that the FGF13/NaV1.7 interaction is a key molecular determinant in the histamine-induced itch sensation. Therefore, our study reveals a novel role of FGF13 in mediating itch sensation via the interaction of NaV1.7 in the peripheral nervous system.SIGNIFICANCE STATEMENT Scratching induced by itch brings serious tissue damage in chronic itchy diseases, and targeting itch-sensing molecules is crucial for its therapeutic intervention. Here, we reveal that FGF13 is required for the neuronal excitation and scratching behavior induced by histamine. We further provide the evidence that the histamine-evoked neuronal response is mainly mediated by histamine Type 1 receptor H1R, and is largely attenuated in FGF13-deficent mice. Importantly, we identify that histamine enhances the FGF13/NaV1.7 interaction, and disruption of this interaction reduces histamine-evoked neuronal excitation and highly impairs histamine-induced scratching behavior. Additionally, we also find that FGF13 is involved in 5-hydroxytryptamine-induced scratching behavior and hapten 1-fluoro-2,4-dinitrobenzene-induced chronic itch.

Nerve Injury-Induced Neuronal PAP-I Maintains Neuropathic Pain by Activating Spinal Microglia.

Pancreatitis-associated proteins (PAPs) display multiple functions in visceral diseases. Previous studies showed that the expression level of PAP-I was low in the DRG of naive rats but was de novo expressed after peripheral nerve injury. However, its role in neuropathic pain remains unknown. We found that PAP-I expression was continuously upregulated in the DRG neurons from rat spared nerve injury models, and transported toward the spinal dorsal horn to act as a proinflammatory factor. Intrathecal delivery of PAP-I enhanced sensory hyperalgesia, whereas PAP-I deficiency by either gene knockout or antibody application alleviated tactile allodynia at the maintenance phase after spared nerve injury. Furthermore, PAP-I functioned by activating the spinal microglia via C-C chemokine receptor Type 2 that participated in neuropathic pain. Inhibition of either microglial activation or C-C chemokine receptor Type 2 abolished the PAP-I-induced hyperalgesia. Thus, PAP-I mediates the neuron-microglial crosstalk after peripheral nerve injury and contributes to the maintenance of neuropathic pain.SIGNIFICANCE STATEMENT Neuropathic pain is maladaptive pain condition, and the maintaining mechanism is largely unclear. Here we reveal that, after peripheral nerve injury, PAP-I can be transported to the spinal dorsal horn and is crucial in the progression of neuropathic pain. Importantly, we prove that PAP-I mainly functions through activating the spinal microglia via the CCR2-p38 MAPK pathway. Furthermore, we confirm that the proinflammatory effect of PAP-I is more prominent after the establishment of neuropathic pain, thus indicating that microglia also participate in the maintenance phase of neuropathic pain.

[Clinical Value of Serum Fructosamine Measurement in IgA Type Multiple Myeloma].

OBJECTIVE: To investigate the value of serum fructosamine (FMN) measurement in clinical typing and staging of multiple myeloma(MM) as well as evaluation of curative efficacy and prognosis of IgA type MM. METHODS: The levels of FMN were detected by nitro-blue-tetrazolium colorimetric method in 62 IgA type MM patients, 65 IgG type MM patients, 24 IgM type MM patients. The difference between the serum FMN levels and different MM types and its relationship with IgA type MM, patients' stages, efficacy and as well as, the effect of FMN level on overall survival (OS) time of IgA type MM patients were analyzed. RESULTS: FMN level of IgA type patients was obviously higher than that of IgG type and IgM type patients. Patients of DS stage III had higher FMN level than that of stage II, and patients of DS stage II had higher FMN level than that of stage I, the differences were statistical significant(P<0.05). Patients of ISS stage III also had higher FMN level than that of stage II, and patients of ISS stage II also had higher FMN level than that of stage I, the differences were statistical significant(P<0.05). After treatment, FMN level of patients obtained complete remission (CR) or very good partial remission(VGPR) obviously decreased, FMN level of patients obtained partial remission(PR) decreased, FMN level of patients obtained less than PR increased. Patients with normal FMN level had longer OS time, while patients with high FMN level had obviously short OS time. CONCLUSIONS: Serum FMN is obviously elevated in the IgA type MM, there is a certain association between staging and serum FMN level, the IgA type MM patients with FMN normalization after treatment have better prognosis.

Parallel mutual information estimation for inferring gene regulatory networks on GPUs.

BACKGROUND: Mutual information is a measure of similarity between two variables. It has been widely used in various application domains including computational biology, machine learning, statistics, image processing, and financial computing. Previously used simple histogram based mutual information estimators lack the precision in quality compared to kernel based methods. The recently introduced B-spline function based mutual information estimation method is competitive to the kernel based methods in terms of quality but at a lower computational complexity. RESULTS: We present a new approach to accelerate the B-spline function based mutual information estimation algorithm with commodity graphics hardware. To derive an efficient mapping onto this type of architecture, we have used the Compute Unified Device Architecture (CUDA) programming model to design and implement a new parallel algorithm. Our implementation, called CUDA-MI, can achieve speedups of up to 82 using double precision on a single GPU compared to a multi-threaded implementation on a quad-core CPU for large microarray datasets. We have used the results obtained by CUDA-MI to infer gene regulatory networks (GRNs) from microarray data. The comparisons to existing methods including ARACNE and TINGe show that CUDA-MI produces GRNs of higher quality in less time. CONCLUSIONS: CUDA-MI is publicly available open-source software, written in CUDA and C++ programming languages. It obtains significant speedup over sequential multi-threaded implementation by fully exploiting the compute capability of commonly used CUDA-enabled low-cost GPUs.

A parallel algorithm for error correction in high-throughput short-read data on CUDA-enabled graphics hardware.

Emerging DNA sequencing technologies open up exciting new opportunities for genome sequencing by generating read data with a massive throughput. However, produced reads are significantly shorter and more error-prone compared to the traditional Sanger shotgun sequencing method. This poses challenges for de novo DNA fragment assembly algorithms in terms of both accuracy (to deal with short, error-prone reads) and scalability (to deal with very large input data sets). In this article, we present a scalable parallel algorithm for correcting sequencing errors in high-throughput short-read data so that error-free reads can be available before DNA fragment assembly, which is of high importance to many graph-based short-read assembly tools. The algorithm is based on spectral alignment and uses the Compute Unified Device Architecture (CUDA) programming model. To gain efficiency we are taking advantage of the CUDA texture memory using a space-efficient Bloom filter data structure for spectrum membership queries. We have tested the runtime and accuracy of our algorithm using real and simulated Illumina data for different read lengths, error rates, input sizes, and algorithmic parameters. Using a CUDA-enabled mass-produced GPU (available for less than US$400 at any local computer outlet), this results in speedups of 12-84 times for the parallelized error correction, and speedups of 3-63 times for both sequential preprocessing and parallelized error correction compared to the publicly available Euler-SR program. Our implementation is freely available for download from http://cuda-ec.sourceforge.net .

A gradual noisy chaotic neural network for solving the broadcast scheduling problem in packet radio networks.

In this paper, we propose a gradual noisy chaotic neural network (G-NCNN) to solve the NP-complete broadcast scheduling problem (BSP) in packet radio networks. The objective of the BSP is to design an optimal time-division multiple-access (TDMA) frame structure with minimal TDMA frame length and maximal channel utilization. A two-phase optimization is adopted to achieve the two objectives with two different energy functions, so that the G-NCNN not only finds the minimum TDMA frame length but also maximizes the total node transmissions. In the first phase, we propose a G-NCNN which combines the noisy chaotic neural network (NCNN) and the gradual expansion scheme to find a minimal TDMA frame length. In the second phase, the NCNN is used to find maximal node transmissions in the TDMA frame obtained in the first phase. The performance is evaluated through several benchmark examples and 600 randomly generated instances. The results show that the G-NCNN outperforms previous approaches, such as mean field annealing, a hybrid Hopfield network-genetic algorithm, the sequential vertex coloring algorithm, and the gradual neural network.

Broadcast scheduling in wireless multihop networks using a neural-network-based hybrid algorithm.

In wireless multihop networks, the objective of the broadcast scheduling problem is to find a conflict free transmission schedule for each node at different time slots in a fixed length time cycle, called TDMA cycle. The optimization criterion is to find an optimal TDMA schedule with minimal TDMA cycle length and maximal node transmissions. In this paper we propose a two-stage hybrid method to solve this broadcast scheduling problem in wireless multihop networks. In the first stage, we use a sequential vertex-coloring algorithm to obtain a minimal TDMA frame length. In the second stage, we apply the noisy chaotic neural network to find the maximum node transmission based on the results obtained in the previous stage. Simulation results show that this hybrid method outperforms previous approaches, such as mean field annealing, a hybrid of the Hopfield neural network and genetic algorithms, the sequential vertex coloring algorithm, and the gradual neural network.