Online chip-seq data kcnq2

A Step-by-Step Guide to ChIP-Seq Data Analysis,+49 (0) 651-99555700

Was ist KCNQ2? KCNQ2 ist ein Gen, welches auf dem Chromosom unserer DNA/DNS sitzt. Es steuert den Kaliumkanal jeder einzelnen Zelle in unserem Gehirn. What Is KCNQ2? | KCNQ2.

KCNQ2 – this is what you need to know | Beyond the Ion Channel.


For a different organism try the MACS website for the genome size, then change the tag size to your sequencing wavelength. Take the Parse xls files into interval files. We will see this later. Then to save wig files so that you will get the binding signal files later for visualization. So why change these two options? As you know, that for a regular ChIP-seq experiment the sequencing reads you get are from the end of your fragments, which are not the actual TF binding sites. The actual TF binding sites are somewhere in the middle of the fragment.

By default, MACS will estimate the fragment length and shifting the reads to the middle by half of the fragment length to represent the actual TF binding. However, for some reason sometimes it cannot reliably estimate the fragment style. A common practice is to simply disable this and ask MACS to shape the reads to a certain distance. In this case we choose base pair, which is the default when you disable the model built, and it works quite well.

After it is finished you get some outputs on the right hand side in green color, and the number of output files depends on your MACS data. Well, in this case, we have six output files for each experiment. Now, let's have a look at what each file is. The second and third files are wig files containing the signal intensity across the genome. The treatment wig file is your ChIP sample signal, and the control wig file is your control sample signal.

Again, the wig files are simple text files. We won't look at the negative peaks interval files here. The fifth file here is a peak interval file containing the enriched region of your protein, in this case FOXA1. The last file is a bed file for visualizing peak positions in a genome browser, and both the interval and bed files are simple text files. If you click the name of the interval files and you can download this interval file to your local computer, and then it can be opened and easily handled and manipulated by any spreadsheet software like Excel.

This is probably one of the most important output files, it contains all the information you need to know about the binding sites, including the location and the statistics. At this stage the peak calling is done. A common and a tricky question that most beginners have to face at this stage is how can I tell whether my experiment works or not? Actually, there are many things you can check, but the following three things are the most efficient ways of telling whether the ChIP experiment worked or not.

First, by looking at the interval files; second, by visual inspection of a binding signal; and third, by looking at the enriched motif within the binding site. We will now go through this one-by-one. We will start with examining the interval files. To do this you sort first by FDR, smallest to largest, then by fold enrichment largest to smallest. The first thing we should look at is a number of binding sites.

Of course, the number of binding sites depends on the protein, the peak caller, the cell line and many other things. Typically, the number you should expect is from around a thousand to several thousand, or even several tens of thousands. The second thing we could look at is the range of the folding enrichment of our local background. The third thing we could look at is a number of figures in reads within the peak region.

The next thing we could do to judge the quality of the data is to visualize the binding signal, and we found this seems to be the most efficient way of telling whether a ChIP-seq experiment worked or not. So the wig files generated by MACS are the signal files, and you can visualize them directly in a genome browser.

However, for large datasets it is highly recommended to convert the wig files to bigWig files before visualization. The bigWig format is an indexed binary format. When you visualize the bigWig files only the portions of the files being displayed are transferred to the genome browser, which is much faster than loading the wig files. So to convert the wig files to bigWig, on the left hand side click the wig paragraph to bigWig under the convert formats tab. Then in the middle choose the wig file you are going to convert, and click execute.

After it is done change the file name to something meaningful, and click the name of the file you will see an option of display at UCSC main.

Then click that, a new tab or window of UCSC genome browser will appear. In the genome browser to configure the visualization set the display mode to full, so that it will display the histogram of the signal. Set the vertical viewing runs from zero to a reasonable maximum value like or , and set data viewing scaling, so use vertical viewing range setting, so that it won't automatically rescale.

You will be able to visualize the binding signal as shown here. Now, what kind of patterns are we expecting? There are a few things you can look at at this stage. You can have a look whether the peak shape is normal, i.

If you have some known target genes can you find some binding peaks in the promoters, or near the transcriptional start size? Finally, to get a bigger view of the binding pattern you can look at the whole chromosome view. This is an example of signals of all four samples across the entire chromosome 12 of the human genome. The two control files are almost flat, as you would expect them to be. There are only a few very small peaks. So that's how you can judge the quality of the data visually.

The next step is to identify enriched motifs within the binding sites. Now I need to clarify two terms before we go any further. When MACS is used for peak calling, peak means the wholly enriched region from start to end, indicated by the black bar on the top left. Whilst summit, indicated by the thin, orange line, means the highest pile up point within the peak region. It is supposed to be the exact TF binding site. It is not a good idea of putting too many sequences for the motif discovery, and a common practice for TF motif discovery is to use the base pair region, centered on the summit as the input for motif discovery.

The first new column is the same as column A, which is the column's own name. The second column will be column B, as column E minus The third column will be column B plus column E, plus In the fourth column give a unique name for each individual region like this, and you can do this very easily using Excel.

Now, save these four new columns as a tab delimited text file. For simplicity, I will only choose the power to 1, region for motif discovery in the webinar. Now, we are going to extract the base pair of DNA sequence of these regions using the coordinates.

To do this, first upload these text files to the Galaxy server using a web browser. Choose the text file you just saved on your computer, and make sure the genome assembly is correct. Choose the file you just uploaded and simply click execute. When it has finished you will see the DNA sequence within each base pair region in a fasta format. The options here are pretty much self-explanatory. In the input choose the fasta file you just downloaded, then enter your email address and give a job name.

Then you will receive an email with a link to retrieve the results. The result page will look like this, the motifs found at the left hand side is a de novo motif that are enriched within your input region.

The known or similar motif on the right hand side indicates the similarity between this motif with known TF motif. The top motif is a typical focal motif with a very small E-value. This is pretty much what you expect from a successful experiment.

There are also some other motifs returned, indicating potential interaction with other transcription factors. Then you need to put a question mark on the data, unless you have other strong evidence suggesting that the experiments are working. As you can see here, although the top two motifs look like the focal motif, the E-values are very big. There are also two motifs with very low complexities, which are just simple repeats.

Now we have finished the motif discovery step, and we have a better impression about the quality of the data. Another thing that most biologists are interested in is to assign the binding sites to genes, and the performing gene anthology analysis to find a potential biological function of transcription factors. It is very straightforward to use, just go to the GREAT website using the address shown on the top right.

Then in the genome assembly, choose the right genome, in this case it's hg Then choose the peak file from your computer as test region.

Since this is a ChIP-seq experiment choose the whole genome as background. The output looks like this. The top contains some basic information about peak gene association, and the bottom contains the enriched GO terms from specific categories like molecular function, biological process and the cellular components, as well as a lot of information from many other sequence databases.

It is quite informative to look at those, but we won't go through them here. You can download them as a text file and save for future use. This is quite convenient. Now we have finished the gene ontology analysis. Channels made with the KCNQ2 protein are active in nerve cells neurons in the brain, where they transport potassium ions out of cells.

These channels transmit a particular type of electrical signal called the M-current, which prevents the neuron from continuing to send signals to other neurons. The M-current ensures that the neuron is not constantly active, or excitable. Potassium channels are made up of several protein components subunits. Each channel contains four alpha subunits that form the hole pore through which potassium ions move. Four alpha subunits from the KCNQ2 gene can form a channel.

However, the KCNQ2 alpha subunits can also interact with alpha subunits produced from the KCNQ3 gene to form a functional potassium channel, and these channels transmit a much stronger M-current. In adult and fetal brain. Highly expressed in areas containing neuronal cell bodies, low in spinal chord and corpus callosum. Isoform 2 is preferentially expressed in differentiated neurons. Isoform 6 is prominent in fetal brain, undifferentiated neuroblastoma cells and brain tumors.

Potassium voltage-gated channel subfamily KQT member 2. Hi, I was wondering what is the best tool for finding transcription factor binding sites in a lo Is there any way to query all human chip-seq data for transcription factor binding to a specific Hello , I am doing chip-seq down stream analysis.

After motif discovery I found several transcrip I'm working on Transcription factors involved in secondary metabolite. This plant doesn't have wh First, I have contacted Biobase about this but wanted an independent opinion.

KCNQ2 Encephalopathy - NORD (National Organization for. - Gene ResultKCNQ2 potassium voltage-gated channel. WHAT IS KCNQ2?. KCNQ2 is a gene involved in the proper functioning of a potassium channel in the brain. Abnormal changes, or mutations, in the gene are associated with seizures. KCNQ2-related epilepsies represent a spectrum of conditions from mild to bookllib100.aberfoodblog.com , mutations in KCNQ2 were associated with a mild condition called “Benign Familial Neonatal Epilepsy” or .

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This vignette describes steps of a basic analysis of ChIP-seq data. To exemplify this tutorial, we use ChIP-seq data dara the lysine chipp-seq acetylation of the histone H3 i. After completing this vignette, you will be able to: Read a Kcnqq2 experiment into R 2.

Extend [EXTENDANCHOR] reads and bin the data details and relevance onlihe later 3. Visualize ChIP-seq data with R 5. Perform basic analysis of ChIP-seq peaks 6. Generate average profiles and heatmaps of Site de rencontre amitié suisse enrichment around a set of [MIXANCHOR] genomic loci In the appendix part, we show how daga download, preprocess and [EXTENDANCHOR] the quality of.

H3K27ac is a histone modification associated with active promoters and enhancers. We downloaded data corresponding to a ChIP-seq experiment with two biological online chip-seq data kcnq2 of mouse Embryonic [MIXANCHOR] cells mESC along with the input control sample Histone H3K27ac separates active from poised enhancers and predicts developmental state by Creyghton et al.

The first part of ChIP-seq analysis workflow consists in read preprocessing. We will online chip-seq data kcnq2 focus here on daa first steps, we outline them and provide the code in the Appendix part of the vignette. The three major steps in the preprocessing click briefly outlined below. Sequenced reads are saved in. The very first step in onlihe analyses of sequencing learn more here consists in quality assessment.

The R package ShortRead provides a qa to perform this analysis. The reader can find the code necessary to [EXTENDANCHOR] a HTML read quality click here report in the Appendix part of the vignette.

Initial parts of the analysis of sequenced reads include: We provide all the necessary code in the Appendix part of the vignette. Online chip-seq data kcnq2 steps from this vignette visualization and read distribution analysis require biomart database querying via the online chip-seq data kcnq2. The code for the generation of these objects can be onlinr in the Appendix of the vignette. Additionally, in order to reduce memory requirements, we restrict our analysis to here filtered reads mapping to online chip-seq data kcnq2 6.

Note that such a data package onlinne used for convenience in this course, but typically, you would not package up interemediate data in this way.

The variable dataDirectory shows the directory containing [MIXANCHOR] data objects necessary for this vignette. In order to explore this files, have a look at them with a text inline or via a terminal emulator. We need to load the GenomicRanges visit web page, rtracklayer and IRanges packages.

The result is a GRanges object. This is an see more useful and powerful class of objects which the readers are already familiar with.

Each filtered read is oline here as a [URL] interval. The objects inputrep1 and rep2 hold the genomic annotation of gay bear dating filtered reads for the input sample and ChIP-seq replicate 1 and replicate 2, respectively. We display the rep1 rambler russian dating site. We see online chip-seq data kcnq2 the strand information, read name along with alignment score are included as information for each read.

The reads correspond to sequences at the end of each IP-ed fragment single-end sequencing data. We estimate the mean read length using the estimate.

Next, we click the reads to the inferred read length using the resize function. We remove any reads online chip-seq data kcnq2 which the coordinates, after the extension, read more chromosome length. These three analysis steps are wrapped in a single dsta prepareChIPseq function which we define below.

The next step in the analysis is to count how many reads map to each of the pre-established genomic intervals bins. We will tile the genome into non-overlapping bins of size bp. To this end we need the information about chromosome sizes in the mouse genome assembly mm9. In more info data package, we provide the object si strand information [EXTENDANCHOR], which holds these data.

Next, we use the tileGenome function from the GenomicRanges package to generate a GRanges object with intervals covering the genome in tiles continue reading of size of bp. We now count how many reads fall into each bin.

For this purpose, we define the function BinChIPseq. It takes two arguments, reads and bins which are GRanges objects. Now we apply it to the objects inputrep1 and rep2. At this step of [URL] analysis, the data is ready to be visualized and shared. One of datw most online chip-seq data kcnq2 means of sharing ChIP-seq data is to generate. They are memory and size-efficient files holding la magie des rencontres information about the signal along the genome.

We click how to export the binned data as a. Now, we have data onlne we inchallah site de rencontre payant like to display along the genome.

R offers a flexible infrastructure for visualisation of many types of genomics data. Here, we use the Gviz package for these purposes. The principle of working with Kcjq2 relies on the generation of tracks which can be, [MIXANCHOR] example ChIP-seq signal along the genome, ChIP-seq peaks, gene models or any kind of other data such as annotation of CpG islands in the genome.

We start application de rencontre pour geek loading the gene models for chromosome 6 starting at position , and ending at position , Cihp-seq focus on this region hcip-seq it harbors the Rencontre amoureuse en gene, which is stongly expressed in ES cells.

We obtain the annotation using biomaRt package. Work online chip-seq data kcnq2 biomaRt link relies on continue reading the biomart database.

In the Appendixwe show how to obtain gene models for protein coding genes for the archive mouse annonce rencontres coquine assembly here and how to generate the bm object holding the annotation of all the RefSeq genes.

We include chip-sqe GenomeAxisTrack object which is a coordinate axis showing the genomic span of the analyzed region. We plot the result using the plotTracks rencontres femmes asiatiques belgique. We choose the region click here zoom into with the from and [EXTENDANCHOR] arguments.

Obline transcriptAnnotation argument allows to put [URL] gene symbols in the plot.

We next add our two data tracks to the plot. We first generate DataTrack objects with DataTrack function. We include the information about how the best online sites south is be labaled and colored. Finally, we plot these tracks eata with the genomic online chip-seq data kcnq2. We observe a uniform coverage in the case of chhip-seq input track and pronounced peaks of enrichment H3K27ac in promoter and intergenic regions.

Importantly, H3K27ac enriched regions are easily identified. ChIP-seq experiments are designed to isolate online chip-seq data kcnq2 enriched in a factor of interest. The identification of enriched regions, often refered to as peak finding, is an area of research by itself.

There are many kcmq2 and tools used online chip-seq data kcnq2 peak finding. The choice of a method is strongly motivated by the kind of factor analyzed. For instance, transcription factor ChIP-seq yield well defined narrow peaks whereas histone modifications ChIP-seq experiments such as H3K36me3 yield extended regions of high coverage.

Finally, ChIP-seq with antobodies recognizing polymerase II result in narrow peaks combined with extended regions of enrichment. As we saw in the chi-pseq section of the tutorial, H3K27ac mark shows well defined peaks. [MIXANCHOR] such a case, MACS is one of the more info commonly used software for peak finding.

However, we stick here to the most common approach and use MACS. We ran MACS for sexe de chat mal ou femelle and provide the result in the data package. You can find the code necessary to obtain the peaks in the Appendix of online chip-seq data kcnq2 vignette. First step in [MIXANCHOR] analysis of the identified peaks is to simply display them in the browser, along with the ChIP-seq and input tracks.

To this end, onliine use AnnotationTrack function. We display peaks as boxes colored in blue. We see a la de bao jeunesse both biological replicates agree well, however, in some cases peaks are called only in one sample. In the next section, we cuip-seq analyse how often do we see the overlap between peaks and isolate reproducible peaks. If a peak in one replicate overlaps with mutiple peaks in the other replicate, it will appear multiple times in ovlp.

To article source, how many peaks overlap with something in the other [MIXANCHOR], we count the number of unique onlie in each of the two columns of ovlp and take chip-sq smaller online chip-seq data kcnq2 these two counts to as the number of common peaks.

Je vous ai rencontrée draw this as a Venn diagram, using the draw.

We will focus only on peaks identified in both inline hereafter refered to as enriched areas. The enriched areas are colored in green. One of the questions of a ChIP seq analyses is to which extend ChIP-enriched regions overlap a chosen type of features, such as promoters or regions enriched with other modifications. [MIXANCHOR] this end, the overlap between peaks of ChIP-seq signal and the online chip-seq data kcnq2 of interest chop-seq analysed.

We exemplify site de rencontre an analysis kcjq2 testing how many of the H3K27ac enriched regions overlap promoter regions. As shown in the Appendix, we have used biomaRt to get coordinates for start and end of all mouse genes. These are the source of the outermost UTR boundaries. We load the results of the biomaRt hot or not site de rencontre from the data package.

It is given in the object egsa data. Is online chip-seq data kcnq2 a significant enrichment? To see, we first calculate how much chromosome 6 is part [MIXANCHOR] a promoter region.

Basics of ChIP-seq data analysis

Voltage-gated Kv7 KCNQ channels are voltage-dependent potassium channels that are activated at resting membrane potentials and therefore provide a powerful brake on neuronal excitability. However, recently identified side effects have limited its clinical use. By incorporating a fluorine substituent in the 3-position of the tri-aminophenyl ring of retigabine, we synthesized a small-molecule activator SF with novel properties.

Behavioral studies demonstrated that SF was a more potent and less toxic anticonvulsant than retigabine in rodents. Furthermore, SF prevented the development of tinnitus in mice. We propose that SF provides, not only a powerful tool for investigating ion channel properties, but, most importantly, it provides a clinical candidate for treating epilepsy and preventing tinnitus.

Voltage-gated potassium channels are essential regulators of neuronal excitability Bean, Especially, potassium channels that are open at potentials close to the resting membrane potential, such as the M-channel, provide a powerful brake to neuronal excitability.

In , Brown and Adams discovered a slowly activating voltage-gated potassium current that was blocked by muscarinic G-protein-coupled receptors in sympathetic neurons, which they named the M-current. The M-current has now been described throughout the central and the peripheral nervous system. In , the channel subunits that underlie the M-current were identified as members of the voltage-gated Kv7 KCNQ potassium channel family Jentsch, Further highlighting the significance of KCNQ channels in controlling neuronal excitability, retigabine, which is an activator of KCNQ2—5 channels, has recently been approved by the FDA as an add-on for the treatment for certain forms of epilepsy Tatulian et al.

Retigabine exerts its effect by shifting the voltage dependence of KCNQ channels to more hyperpolarized potentials Tatulian et al. Such increased KCNQ channel activity prevents excessive firing that is typically associated with seizures, as well as increased spontaneous firing that can trigger tinnitus Gunthorpe et al. Despite its beneficial effects, retigabine causes adverse retinal abnormalities, skin discoloration, as well as urinary retention Jankovic and Ilickovic, As a result, these unwanted effects have limited its clinical use.

The current hypothesis is that some of the reported adverse effects are due to the poor selectivity of retigabine among KCNQ2—5 channels. For example, KCNQ4 and KCNQ5 channels, which usually do not participate in the M-current-mediated epileptic pathology, are expressed in smooth muscle and are important for regulating contractility.

In particular, enhancement of their activity leads to membrane hyperpolarization and resultant reduction in contractile response Jentsch, ; Greenwood and Ohya, In this study, by introducing a fluorine atom to retigabine, we synthesized a new chemical entity SF Next, we evaluated the potency and selectivity of SF for KCNQ channels with in vitro recordings in heterologous systems expressing different KCNQ subunits, in brain slices containing CA1 neurons from wild-type WT mice or mice with conditional deletion of Kcnq2 from cerebral cortical pyramidal neurons, and in brain slices from WT mice containing dorsal cochlear nucleus principal neurons fusiform cells.

We then extended this analysis by evaluating the therapeutic potential of SF in animal models of seizures and tinnitus. To a stirred suspension of commercially available 2,3-difluoronitroaniline see Fig. To a stirred solution of compound 3 see Fig. After consumption of the starting material by TLC , the reaction mixture was diluted with water ml and stirred for 1 h to give a solid. The obtained solid was filtered, dissolved in EtOAc ml , and further filtered to remove the undissolved solid.

All experiments were performed at RT using a conventional whole-cell patch-clamp technique. Recording electrodes were filled with internal solution containing the following in m m: The standard bath solution contained the following in m m: Osmolarity was adjusted to — mOsm and pH to 7.

Data were acquired through a Multiclamp B amplifier Molecular Devices , low-pass filtered at 2 kHz, and sampled at 10 kHz. For genotyping Kcnq2 floxed mice, two primers were included in each PCR: The primers amplified a bp fragment from the wild-type WT allele and a bp product from the floxed allele.

Animals were first anesthetized with isoflurane and then immediately decapitated. The brain was quickly removed and placed in ice-cold cutting solution containing the following in m m: Brain slices were placed in a storage chamber filled with artificial cerebrospinal fluid ACSF containing the following in m m: All experiments were performed at RT. Whole-cell recordings were obtained using electrodes pulled from thin-walled borosilicate glass capillaries World Precision Instruments with resistances ranging from 3.

Current responses were collected with a Multiclamp B amplifier Molecular Devices , filtered at 2 kHz, and sampled at 10 kHz. ICR mice P20—P28 were first anesthetized with isoflurane and then immediately decapitated.

Fusiform cells were recorded in the fusiform cell layer of the DCN and were identified on the basis of morphological and electrophysiological criteria described in previous studies Tzounopoulos et al. Series resistance was monitored throughout the experiment using the size and shape of the capacitive transients in response to a 5 mV depolarization step.

The noise exposure paradigm and screening of noise-exposed animals for behavioral evidence of tinnitus was conducted similar to previous studies Li et al. ICR mice P17—P20 including both males and females were used in this study.

Noise was presented through a pipette tip, one end of which was attached to the speaker CF-1; Tucker Davis Technologies and the other inserted to the left ear canal of the mouse. Anesthesia level during noise exposure was maintained at 1—1.

Animals in the sham group control were subjected to an identical procedure but without any noise exposure. Gap detection, prepulse inhibition PPI , and auditory brainstem response ABR thresholds were assessed right before and 1 week after sham or noise exposure.

For the gap detection paradigm, mice were confined in a custom-made chamber constructed using Lego parts and a small plastic container secured using an elastic band. The housing was then placed on load cell response-sensing platforms E45—E11; Coulbourn Instruments residing inside a sound-attenuating chamber envSD; Med Associates. The gap detection paradigm was conducted using a narrow band-pass sound with a 1 kHz bandwidth centered at 10, 12, 16, 20, 24, and 32 kHz test frequencies presented at 70 dB SPL through an isodynamic tweeter RT2H-A; HiVi positioned in front of the animal.

Sound gaps of 50 ms duration were embedded in the background test frequency ms before the startle stimulus. The startle response represents the time course of the downward force that the animal applies on the platform in response to startle pulse. Gap detection was evaluated based on the gap startle ratio, which is the ratio of peak-to-peak value of startle waveform in the presence of gap to the values in the absence of gap. For gap detection trials of the same background frequency, gap startle ratios were sorted in an ascending manner.

If more than five ratios were excluded within a frequency, the gap startle ratio for this frequency was not used. Gap startle ratios were measured right before and 1 week after sham or noise exposure. As described in previous studies Li et al. In PPI trials, a 50 ms, 70 dB SPL band-pass sound with 1 kHz bandwidth centered at 10, 12, 16, 20, 24 and 32 kHz was presented ms before the startle stimulus, in an otherwise quiet background.

PPI was evaluated based on the PPI startle ratio, which is the ratio of peak-to-peak value of the startle waveform in the presence of prepulse to the peak-to-peak value in the absence of the prepulse startle only trial. Anesthetized animals were placed in a sound-attenuating chamber with a subdermal electrode placed at the vertex, the ground electrode placed ventral to the right pinna, and the reference electrode placed ventral to the left pinna.

Sound stimuli were presented using a pipette tip, one end of which was attached to the speaker CF-1; Tucker Davis Technologies and the other to the left ear canal of the mouse.

ABR thresholds were measured for 1 ms clicks and 3 ms tone bursts of 10, 12, 16, 20, 24, and 32 kHz presented at a rate of Evoked potentials were averaged times and filtered using a — Hz band-pass filter. ICR mice P17—P20 , both male and female, were randomly assigned into four groups: SF SciFluor was formulated as a suspension in 0. Vehicle-treated groups were injected with 0. All animals were evaluated for gap detection, PPI, and ABR thresholds right before noise or sham exposure and 24—48 h after the final injection.

Male and female albino CF1 mice 18—25 g; Charles River Laboratories and male albino Sprague Dawley rats — g were used for anticonvulsant activity and toxicity screening studies. Animals were housed in a standard 12 h: Anticonvulsant tests included maximal electroshock MES and corneal-kindled seizure paradigms in mice.

In the MES seizure test, we assessed the ability of different doses of the test compound in preventing seizure induced by an electrical stimulus of 0. Mice were restrained by hand and released immediately after corneal stimulation, which allowed observation of the entire seizure episode. A maximal seizure in a test animal includes four distinct phases that include: Test compounds were tested for their ability to abolish the hindlimb tonic extensor component, which indicates the compound's ability to inhibit MES-induced seizure spread.

In this study, mice were preadministered intraperitoneally with test compounds and tested at 0. In the corneal-kindled seizure model, mice were kindled electrically with a 3 s, 8 mA, 60 Hz stimulus delivered through corneal electrodes primed with 0.

Mice were considered kindled when they displayed at least five consecutive stage V seizures according to the Racine scale Racine et al. At the completion of the kindling acquisition, mice were permitted a 3 d stimulation-free period before any drug testing. On the day of the experiment, fully kindled mice were preadministered intraperitoneally with increasing doses of the test compounds SF In the rotarod test procedure, a mouse is placed on 1-inch knurled rod that rotates at a speed of 6 rpm Dunham and Miya, Typically, at this speed, animals were able to maintain equilibrium for long periods of time.

The concentration of retigabine or SF that causes mice to fall off the rotating rod 3 times during a 1 min period is considered toxic. Increasing doses of the test compounds were administered intraperitoneally SF In these assays Stables and Kupferberg, , animals displaying any of the below described characteristics were considered to have neurological deficits or motor impairment.

In the positional sense test, the hind leg of the animal is gently lowered over the edge of a table, to which the animal responds by quickly lifting its leg back to the normal position.

Neurological deficit or motor impairment is indicated by the animal's inability to rapidly return its hind leg to the normal position after administration of elevating doses of the test compound.

In the gait and stance test, neurological deficit is indicated by a circular or a zigzag gait, ataxia, abnormal spread of legs, abnormal body posture, tremors, hyperactivity, lack of exploratory behavior, somnolence, stupor, catalepsy, or loss of muscle tone.

Elevating doses of test compounds were administered orally SF All quantitative in vivo anticonvulsant and toxicity studies were conducted at the time-to-peak effect after intraperitonial injection or oral administration of the compound. For non-normally distributed data, we performed the nonparametric Wilcoxon rank-sum test or Kruskal—Wallis test. A binomial test was used for comparing percentages of tinnitus mice in response to different experimental manipulations. Previous studies have shown that retigabine Fig.

We hypothesized that retigabine analogs bearing one or two fluorine atoms on the aniline ring would enhance binding to KCNQ2—5 channels. When the fluoro group was positioned at the 5-position, the 6-position, or together at the 3- and 5-positions of the tri-aminophenyl ring, no significant changes in potency were observed data not shown.

A , Structure of retigabine shown for comparison with SF Consistent with previous studies Tatulian et al. Values are plotted against either retigabine black or SF green concentration.

Data were fitted with a Hill equation with the following parameters: