Activation of the TRPV4 Ion Channel Is Enhanced by Phosphorylation

The TRPV4 (transient receptor potential vanilloid 4) ion channel, a member of the vanilloid subfamily of the transient receptor potential channels, is activated by membrane stretch, by non-noxious warm temperatures, and by a range of chemical activators. In the present study we examined the role of phosphorylation in modulating the activation of TRPV4. We expressed TRPV4 in HEK293 cells and activated the channel by cell swelling in a hypotonic solution. TRPV4 channel activation and serine phosphorylation were enhanced by exposure to the protein kinase C (PKC) activator phorbol 12-myristate 13-acetate or by application of bradykinin, which activates PKC via a G-protein-coupled mechanism. The enhancement was inhibited by the PKC inhibitors staurosporine, bisindolylmaleimide I, and rottlerin or by mutation of the serine/threonine residues Ser¹⁶², Thr¹⁷⁵, and Ser¹⁸⁹. The adenylate cyclase activator forskolin also enhanced activation of TRPV4, and the enhancement was antagonized by the selective cyclic AMP-dependent protein kinase (PKA) inhibitor H89 or by mutation of serine residue Ser⁸²⁴. Sensitization of TRPV4 by both PKC and PKA depended on the scaffolding protein AKAP79, because channel activation and phosphorylation were enhanced by co-transfection of AKAP79 and were antagonized by removal of AKAP79 using small interfering RNA. We conclude that the serine/threonine kinases PKC and PKA enhance activation of the TRPV4 ion channel by phosphorylation at specific sites and that phosphorylation depends on assembly of PKC and PKA by AKAP79 into a signaling complex with TRPV4.TRPV4 was cloned from kidney, hypothalamus, and auditory epithelium and was given a number of names: OTRPC4 (Osm-9-like TRP channel 4) (1), VR-OAC (2), TRP12 (3), and VRL-2 (vanilloid receptor-like channel 2) (4). The gene for human TRPV4 is located on chromosome 12q23-q24.1 and has 15 exons, which code for a full-length protein with 871 amino acids. TRPV4 is a member of the transient receptor potential vanilloid subfamily of TRP² channels, and like other members of this subfamily, it is a polymodal receptor activated by a wide variety of stimuli. TRPV4 is strongly expressed in kidney and is activated by hypotonicity, which has led to the suggestion that TRPV4 is an osmosensor important in regulating body fluid levels (2, 5–9). However, TRPV4 is also activated by innocuous heat with a threshold of >27 °C (6, 10, 11), by the phorbol ester 4α-phorbol 12,13-didecanoate (12, 13), by low pH (14), by endocannabinoids and arachidonic acid metabolites (15, 16), by the active compound, bisandrographolide A, of Andrographis paniculata, a Chinese herbal plant (17), and by nitric oxide (18). TRPV4 is expressed in a broad range of tissues, including lung, spleen, kidney, testis, fat, brain, cochlea, skin, smooth muscle, liver, and vascular endothelium (1–3); in the lamina terminalis of the mouse brain; in neurons of the arched vascular organ of the lamina terminalis; and in the median preoptic area, the optic chiasm, neurons of the subfornical organ, the ventral hippocampal commissure, anterior hypothalamic structures, and ependymal cells of the choroid plexus in the lateral ventricles, and dorsal root ganglia neurons (1–3). The broad spectrum of activators and the wide distribution of TRPV4 suggest that the functions of TRPV4 extend beyond osmosensation.TRPV4 has been proposed to play a role in the mechanical hyperalgesia that is generated by the concerted action of inflammatory mediators present in inflamed tissues (19). After tissue injury, inflammatory mediators such as bradykinin, prostaglandin E₂, 5-hydroxytryptamine, and histamine directly sensitize primary afferent neurons, resulting in hyperalgesia (reviewed in Ref. 20). Important intracellular signaling molecules contributing to inflammatory hyperalgesia include protein kinase C (PKC) (21, 22) and cyclic AMP-dependent protein kinase (PKA) (23). For example, the activation of the G_q-coupled B₁ and B₂ receptors by bradykinin leads to the release of a range of potential intracellular messengers, with a substantial body of evidence favoring the idea that the temperature threshold of TRPV1 is lowered by PKCϵ-mediated phosphorylation (21, 22, 24, 25). PKA, like PKC, is a critical intracellular signaling molecule mediating inflammatory hyperalgesia (26). In sensory neurons prostaglandin E₂ activates both the EP₁ receptor, which is G_q-coupled and therefore activates PKC, and the EP₄ receptor, which is G_s-coupled and therefore activates PKA. Cyclic AMP analogues, the adenylate cyclase activator forskolin (FSK) or phosphodiesterase inhibitors enhance the mechanical and thermal hyperalgesic effects of prostaglandin E₂ (27–29). Thus PKC and PKA have vital roles to play in the process of inflammatory hyperalgesia.The speed and specificity of the action of kinases is in many cases enhanced by binding to scaffolding proteins, which preassemble the kinases into signaling complexes with their target substrates. The AKAP (a kinase-anchoring protein) family of scaffolding proteins was originally named for their ability to target PKA to appropriate substrates but are now known to assemble a wide range of kinases and phosphatases into signaling complexes with appropriate targets (30). A number of ion channels are subject to modulation by AKAPs, including glutamate receptors, calcium channels, and the M-type potassium channels (31–34). The heat-activated ion channel TRPV1, a member of the same subfamily as TRPV4, has recently been shown to be assembled into a signaling complex with PKA, PKC, and PP2B by AKAP79, and the sensitization of TRPV1 by PKC and PKA is critically reliant on binding to AKAP79 (35). The present study shows that PKC and PKA activation can sensitize TRPV4 to mechanical stimuli, identifies the relevant phosphorylation sites, and shows that the scaffolding protein AKAP79 plays a critical role in sensitization of TRPV4.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]     

Question Processing and Clustering in INDOC: A Biomedical Question Answering System

The exponential growth in the volume of publications in the biomedical domain has made it impossible for an individual to keep pace with the advances. Even though evidence-based medicine has gained wide acceptance, the physicians are unable to access the relevant information in the required time, leaving most of the questions unanswered. This accentuates the need for fast and accurate biomedical question answering systems. In this paper we introduce INDOC—a biomedical question answering system based on novel ideas of indexing and extracting the answer to the questions posed. INDOC displays the results in clusters to help the user arrive the most relevant set of documents quickly. Evaluation was done against the standard OHSUMED test collection. Our system achieves high accuracy and minimizes user effort.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]