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Gap Junctions

Most cited protocols related to «Gap Junctions»

Efficient Phylogenomic Data Manipulation

File manipulation and conversion is a tedious and error-prone, but often required, component of phylogenetic analysis, made more burdensome by the volume of data available in current phylogenomics studies. phyx supports the popular formats for sequence alignments (fasta, fastq, phylip and Nexus) and trees (newick and Nexus), and provides lightweight, high-throughput utilities to convert data among formats without the user needing to provide the format of the original data as phyx will attempt to auto-detect the original format. Alignments can be further manipulated by removing individual taxa, resampling (bootstrap or jackknifing), sequence recoding, translation to protein, reverse complementation, filtering by quality scores or the amount of missing data, and concatenation across mixed alignment formats.
Processing large data matrices is only one step required for phylogenomic analyses. In order to perform downstream analyses (e.g. orthology detection (Yang and Smith, 2014 (link)), mapping gene trees to species tree (Smith et al., 2015 (link)), or gene tree/species tree reconciliation (Mirarab et al., 2014 (link))) it is now also essential to be able to manipulate individual gene trees constructed from these data. phyx enables fast, efficient manipulations such as pruning individual taxa, extracting subclades and rerooting/unrooting trees. Finally, Bayesian MCMC analyses involving phylogenies have become common in the biological sciences, and often involve large log files generated from replicated analyses. phyx enables both the concatenation and resampling (burnin and/or thinning) of MCMC tree or parameter logs for downstream summary.

Brown J.W., Walker J.F, & Smith S.A. (2017). Phyx: phylogenetic tools for unix. Bioinformatics, 33(12), 1886-1888.

Publication 2017

Gap Junctions Genes Protein Biosynthesis Sequence Alignment Strains Trees

Foot Strike Patterns in Healthy Runners

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Publication 2011

Females Foot Gap Junctions Kinetics Males Reading Frames

Drosophila Genome Nexus Alignment Pipeline

A complete set of files needed to implement this alignment pipeline is provided at https://github.com/justin-lack/Drosophila-Genome-Nexus.git, along with a step-by-step list of commands, which is also provided here as Supporting Information, File S1. From the DGN web site (http://johnpool.net/genomes.html), three types of alignment files are provided. Sequence text files provided the reference-numbered consensus sequences described above. Only these files are subject to heterozygosity filtering in the downloaded state. Scripts and instructions are provided to enable the filtering of identity-by-descent and admixture in African populations, as indicated above. Sequence text files are the recommended starting point for most users performing SNP-oriented analyses. We also provide two forms of variant call files (VCFs). Indel VCFs summarize the short insertions and deletions called for this genome relative to the reference sequence. These files are provided from both rounds of mapping (with positions in the round 2 file altered to match reference numbering). Many users may find the indel VCFs to be a useful complement to the sequence text files, which contain no explicit information about indels. We also provide site-by-site, substitution-oriented VCFs. These files are intended for advanced users. They have not been filtered around indels, for heterozygous regions, or for any other reason, instead representing a distilled raw output of the alignment pipeline. Since each genome was aligned to a distinct modified reference sequence, individual genome VCFs may differ in the “reference” column; these files are not intended for merging via common software. To reduce file size, the downloaded SNP VCFs do not contain the typical “ID,” “FORMAT,” and “FILTER” columns. A script to restore conventional VCF format is available on the DGN and GitHub web sites referenced above.

Lack J.B., Cardeno C.M., Crepeau M.W., Taylor W., Corbett-Detig R.B., Stevens K.A., Langley C.H, & Pool J.E. (2015). The Drosophila Genome Nexus: A Population Genomic Resource of 623 Drosophila melanogaster Genomes, Including 197 from a Single Ancestral Range Population. Genetics, 199(4), 1229-1241.

Publication 2015

Consensus Sequence Drosophila Gap Junctions Gene Deletion Genome Heterozygote INDEL Mutation Insertion Mutation Negroid Races Population Group

BPNet: Sequence-to-Profile Convolutional Neural Network

BPNet is a sequence-to-profile convolutional neural network that uses one-hot-encoded DNA sequence (A=[1,0,0,0], C=[0,1,0,0], G=[0,0,1,0], T=[0,0,0,1]) with adjustable length as input to predict base-resolution read count profiles as output. For flexibility, the architecture of BPNet can be compartmentalized into the body and multiple task-specific output heads. The body of BPNet consists of a sequence of convolutional layers with residual skip connections and ReLU activations^{57 (link)}. The first convolutional layer uses 64 filters of width 25 bp, followed by 9 dilated convolutional layers (64 filters of width 3 in each layer) where the dilation rate (number of skipped positions in the convolutional filter) doubles at every layer. This results in a receptive field of +/−1034 bp for any position in the genome. The output of the final convolutional layer within the BPNet body (also referred to as the bottleneck activation map) serves as input for two output heads per TF: i) a deconvolutional layer (filter width=25, typical ChIP-nexus footprint width) predicting the strand-specific probabilities of observing a particular read at a particular position in the input sequence (shape or profile prediction) and ii) a global average pooling layer followed by the fully connected layer predicting the total number of read counts aligned to the input sequence for each strand (total read count prediction). The training occurs for all TF ChIP-nexus experiments together in a multi-task fashion. BPNet architecture (without bias correction) implementation in Keras 2.2.4 is provided in Supplementary Methods.

Avsec Ž., Weilert M., Shrikumar A., Krueger S., Alexandari A., Dalal K., Fropf R., McAnany C., Gagneur J., Kundaje A, & Zeitlinger J. (2021). Base-resolution models of transcription factor binding reveal soft motif syntax. Nature genetics, 53(3), 354-366.

Publication 2021

DNA Chips DNA Sequence Gap Junctions Genome Head Human Body Pathological Dilatation

Neuron Reconstruction and Synapse Identification in Drosophila Brain

Neuron reconstructions are based on manual skeleton tracing of neuronal arbors and annotation of synapses from image stacks in CATMAID (http://www.catmaid.org) as described in Schneider-Mizell et al. (2016) (link). All neurons included in analyses are reconstructed by at least two team members, an initial tracer and a subsequent proofreader who corroborates the tracer’s work. In the event that any tracer or proofreader encounters ambiguous features (neural processes or synapses that are not identifiable with high confidence), they consult other tracers and proofreaders to determine the validity of said features, climbing the experience ladder up to expert tracers as needed. If any feature remains ambiguous after scrutiny by an expert tracer, then said feature is not included in the neural reconstruction and/or flagged to be excluded from analyses. During the proofreading phase, the proofreader and tracer iteratively consult each other until each neuron is deemed complete per the specific tracing protocol to which it belongs. An assignment of completion does not necessarily entail that an entire neuron’s processes and synapses have been reconstructed (see Tracing to classification and Tracing to completion). We traced 114 PNs, the APL, two MB-C1s, MB-CP1, and two MB-CP2 neurons to classification (120 neurons in total). We also traced the calyx sub-arbors of the 15 KCs to completion, and their remaining sub-arbors to morphological, but not synaptic, completion. The total cable length of the neurons above is 206.6 mm.
The criteria to identify a chemical synapse include at least three of the four following features, with the first as an absolute requirement: 1) an active zone with vesicles; 2) presynaptic specializations such as a ribbon or T-bar with or without a platform; 3) synaptic clefts; 4) postsynaptic membrane specializations such as postsynaptic densities (PSDs). In flies, PSDs are variable, clearer at postsynaptic sites of KCs in a microglomerulus but often subtle, unclear, or absent in other atypical synaptic contacts (Prokop and Meinertzhagen, 2006 (link)). In the absence of clear PSDs, we marked all cells with membranes that have unobstructed access to a clearly visible synaptic cleft as postsynaptic. We did not attempt to identify electrical synapses (gap junctions), since they are unlikely to be resolved at the resolution of this dataset.
Very rarely, aberrant neurons were found in the dataset. For example, two PNs with a piece of fused cell membrane were discovered in our tracing. It is unknown what factors might cause this, but cell membrane pathologies resultant from EM fixation protocols have been observed (Kopek et al., 2017 (link)). Overall, however, the ultrastructural quality of the whole brain was excellent.

Zheng Z., Lauritzen J.S., Perlman E., Robinson C.G., Nichols M., Milkie D., Torrens O., Price J., Fisher C.B., Sharifi N., Calle-Schuler S.A., Kmecova L., Ali I.J., Karsh B., Trautman E.T., Bogovic J.A., Hanslovsky P., Jefferis G.S., Kazhdan M., Khairy K., Saalfeld S., Fetter R.D, & Bock D.D. (2018). A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster. Cell, 174(3), 730-743.e22.

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Publication 2018

Brain Diptera Electrical Synapses Gap Junctions Kidney Calices Nervousness Neurites Neurons Plasma Membrane Post-Synaptic Density Reconstructive Surgical Procedures Skeleton Synapses Tissue, Membrane

Most recents protocols related to «Gap Junctions»

Bivariate Probit Model for Mobile Money Adoption and Informal Business Performance

In this subsection, we present the model estimated to assess the effect of mobile money adoption on the performance of Zambian informal businesses. Usage of mobile money is the main indicator of firms that adopted mobile money for any transaction. A firm use mobile money if it employs mobile money for some transactions. The dataset provides information on the purpose of mobile money use as to pay suppliers, to save money, to pay various current bills or to receive payments from their customers. The correlation between mobile money use and firm profit does not imply causality, but it can be explained by the effect of other factors that affect both variables simultaneously (endogeneity problem). To overcome this problem, it is advisable to consider a bivariate model with instrumental variables composed of two equations, one explaining mobile money adoption and the other explaining the firm’s profit.
The dependent variable (informal business performance) is binary and takes a value of 1 if the business made a profit in the month prior to the survey and 0 otherwise. As there is an endogeneity problem in the nexus between informal business performance and mobile money adoption, we need to first identify determinants of mobile money adoption. For modelling the joint determination of factors driving mobile money use by Zambian informal businesses and the effect of mobile money use on their performance, we use an instrumental variable probit model. This model provides a specification for analysing a case in which a probit model contains an endogenous binary variable in one of the equations (Greene, 2012 ). We suspected mobile money would be endogenous with profit made by those firms, so the bivariate probit model will help detect and correct the likelihood of this endogeneity.
Let MB be the dichotomous variable indicating the fact that the firm has adopted mobile money.

\{\begin{matrix} M B = 1 i f {MB}^{*} > 0 \\ M B = 0 o t h e r w i s e \end{matrix})

MB^∗ is a latent variable with

{MB}^{*} = α_{1} + X_{1} β_{1} + ε_{1}

Let PF be the dichotomous variable indicating the firm’s profit defined by

\{\begin{matrix} P F = 1 i f {PF}^{*} > 0 \\ P F = 0 o t h e r w i s e \end{matrix})

PF^∗ is a latent variable given by

{PF}^{*} = α_{2} + X_{2} β_{2} + δ M B + ε_{2}

The model can then be written

\{\begin{matrix} {MB}^{*} = α_{1} + X_{1} β_{1} + ε_{1} \\ {PF}^{*} = α_{2} + X_{2} β_{2} + δ M B + ε_{2} \end{matrix})

X₁ and X₂ are the independent variables, and the standard error terms (ε₁ and ε₂) follow a bivariate normal distribution of mean 0 with a variance–covariance matrix written as follows:

(\binom{ε_{1}}{ε_{2}}) \to N (0, \sum), avec \sum = (\begin{matrix} 1 & ρ_{12} \\ ρ_{12} & 1 \end{matrix})

For the model to be identified, at least one of the variables in X₁, called the instrument, must be excluded from X₂. The choice of instruments requires that they be correlated with mobile money adoption and not correlated with firm performance. The instrument chosen in this study is the average of firms that have adopted mobile money in the locality where the firm is located. We assume that the probability of a firm adopting mobile money increases with the average number of firms in its neighbourhood that have adopted this technology. Indeed, “peer effects” among entrepreneurs should exist and could contribute to the spread of mobile money. However, this variable should not have a direct effect on firm productivity.

, & Hassan R. (2023). Does Mobile Money Adoption Increase Informal Business Performance in Zambia?. Journal of the Knowledge Economy.

Publication 2023

Gap Junctions Joints

Phylogenetic Analysis of Mycobacterium Tuberculosis

After consensus genomes were combined, we used snp-sites v2.5.1 to extract the variant positions, and then generated a neighbour-joining tree of all 6037 samples with IQ-TREE v2.1.4-beta [34 (link)]. The tb-profiler results were combined with the neighbour-joining tree and the L4 genomes identified. A maximum-likelihood phylogenetic tree of the L4 genomes was then derived using IQ-TREE with built-in model selection, and the inclusion of the number of invariant sites, as identified using snp-sites. TreeBreaker v1.1 [35 (link)] was used to identify internal nodes of the tree where there was a change in the distribution of phenotypes of interest at the tips that descended from that internal node. The TreeBreaker command line used was ‘treeBreaker -x 5000000 -y 5000000 -z 10 000 input.tree phenotype.txt output_prefix’. The phenotype of interest was the geographical location. To enable easy interpretation, separate TreeBreaker runs were carried out for Vietnam, Indonesia, China and Thailand, and all the preceding countries combined into a single category (i.e. a single ‘phenotype’ of belonging to either Vietnam, Indonesia, China or Thailand). TreeBreaker outputs a text file that, on the last line of the file, has a newick format phylogenetic tree with the results annotated onto the internal nodes. This newick tree was extracted from the text file and saved as a tree file. It was then converted to a nexus format tree using FigTree (ensuring to include annotations) for reading into dendropy v4.5.2 [36 (link)]. The nexus format tree was then parsed using a script (https://gist.github.com/flashton2003/50d645a60219c0e381874a1dd4355646) to produce sub-trees and summary information for nodes above the 0.5 posterior probability threshold. Example input and output files for TreeBreaker analysis can be accessed from https://doi.org/10.6084/m9.figshare.21378312. As TreeBreaker produces results annotated onto the nodes of the input phylogenetic tree, and we used the same input tree for all analyses, we could combine the results from these different runs based on the identifiers of the internal nodes. As we were using TreeBreaker as a screening tool, to identify nodes for further analysis using SIMMAP, we filtered for nodes with a posterior probability threshold of 0.5 and at least five descendent leaves. All SIMMAP analysis [37 (link)] was carried out using the make.simmap function from PhyTools [38 (link)] in the R statistical language [39 ] using RStudio [40 ]. The fit of each model type (all rates different, symmetrical and equal rates) was assessed using the fitMk function, and the model with the best fit was used for the SIMMAP analysis. We ran 1000 simulations within SIMMAP. Nodes that were identified as being associated with changes by TreeBreaker were targeted for investigation in the output of SIMMAP. Trees (newick format) were visualized with iTOL [41 (link)], and graphs drawn with ggplot2 [42 ]. The files for replicating the iTOL trees can be downloaded from https://doi.org/10.6084/m9.figshare.21378330.v1. Phylotemporal analysis was carried out using TreeTime v0.9.0 [43 (link)] with a substitution rate and standard deviation of 0.000000061643 and 0.0000000385, respectively. These values were obtained from the estimates of the ‘BEAST constant population size, uniform prior on clock rate’ analysis of Menardo et al. [44 (link)]. The command line used was ‘treetime –clock-rate 0.000000061643 –tree input.tree –dates input_dates.csv –outdir my_analysis –sequence-length 4411532 –confidence –clock-std-dev 0.0000000385’. Input data for TreeTime analysis can be found at https://doi.org/10.6084/m9.figshare.21401307.v1.

Ashton P.M., Cha J., Anscombe C., Thuong N.T., Thwaites G.E, & Walker T.M. (2023). Distribution and origins of Mycobacterium tuberculosis L4 in Southeast Asia. Microbial Genomics, 9(2), mgen000955.

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Publication 2023

Gap Junctions Gastrointestinal Stromal Tumors Genome Phenotype Trees

Phylogenetic Analysis of Conserved Domains

Both the nucleotide and amino acid sequences were used to phylogeny analysis. All sequence files were first loaded into MEGA-X software (Molecular Evolutionary Genetics Analysis). MEGA-X Version 10.1.8 was used to align each set of sequences. MUSCLE alignment was performed on each sequence set with a -400.00 gap open penalty (default), 100 max iterations, and cluster method–UPGMA (default). Then a phylogeny was constructed in MEGA-X for each set using the maximum likelihood statistical method with 5000 number of bootstrap replications, all other options were set to their default settings. Afterwards, a “.nexus” file was exported out of MEGA-X with the MUSCLE alignment for each sequence set and loaded into MrBayes version 3.2.7a-win64. mcmcp ngen = 1,000,000 setting was used to set number of generations to a million. Lset nst = 6 setting was used to set the likelihood model parameters to allow all rates to be different, subject to the constraint of time-reversibility. All other settings were kept default. Different phylogenetic trees were constructed with either MEGA or BEAST (Bayesian Evolutionary Analysis Sampling Trees) platforms, which showed similar species positions. Because the BEAST method requires for time calibration priors, only MEGA trees are shown in “S1 File”.
Consensus sequences of the conserved domain were generated using EMBOSS CONS (www.ebi.ac.uk/Tools/msa/emboss_cons/). Pictograms of the consensus sequences were produced by using Weblogo (weblogo.Berkeley.edu).

Zhang X.H., Swait D., Jin X.L., Vichyavichien P., Nifakos N., Kaplan N., Raymond L, & Harlin J.M. (2023). Evolutionary analysis of KED-rich proteins in plants. PLOS ONE, 18(3), e0279772.

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Publication 2023

Amino Acid Sequence Biological Evolution Consensus Sequence DNA Replication Evolution, Molecular Gap Junctions MEGA 8 Muscle Tissue Nucleotides Sequence Alignment Trees

Kinematic and Kinetic Analysis of Cross-Country Skiing

The marker labeling was performed by using NEXUS 2.8.1 software. The raw 3D trajectories of all reflective markers were low-pass filtered (fourth-order, zero-lag, Butterworth filter) with a cut-off frequency of 11.3 Hz (37 (link)). Force data were low-pass filtered (eighth-order, zero-lag, Butterworth filter) with a cutoff frequency of 15 Hz (38 (link)). Filtering and parameter calculation were performed in MATLAB R2018a (MathWorks, Natick, United States). 10 cycles from each DP technique trail and 5 cycles from each G3 technique trail were analyzed in this current study. For DP technique trails, one cycle was defined as the period between two consecutive right pole plant. For G3 technique, one cycle was defined as the time between consecutive same side ski force minima after ski plant and contained the kicking, overlapping, pure gliding action of both left and right ski and two double poling action from both poles (Figure 2A).

Zhao S., Lindinger S., Ohtonen O, & Linnamo V. (2023). Contribution and effectiveness of ski and pole forces in selected roller skiing techniques on treadmill at moderate inclines. Frontiers in Sports and Active Living, 5, 948919.

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Publication 2023

Gap Junctions Plants TNFSF10 protein, human

Pharmacological modulation of retinal activity

We blocked gap junctions via application of the gap junction blocker MFA (50 µM, Sigma-Aldrich). We blocked the nicotinic acetylcholine signaling pathway via application of the broad nicotinic receptor antagonists hexamethonium (Hex, 100 µM, Sigma-Aldrich) and epibatidine (EPB, 10 nM, Sigma-Aldrich) as well as the specific antagonist dihydro-ß-erythroidine hydrobromide (DHßE, 8 µM, Sigma-Aldrich).
The following procedure was used for all pharmacology experiments: We recorded baseline activity in ACSF for 8 min before pharmacological agents were applied to the perfusion system. We then waited 15–30 min for the agents to take effect before acquiring another 8 min recording session.
To attempt to assay off-target effects of MFA, we used whole-cell voltage-clamp recordings to compare voltage-gated ion channels on RGCs in E16–18 retina but found inconsistent results (Figure 2—figure supplement 1). We associate this high variance with a rapid changing complement of ion channels during development and the quick washout of these conductances during whole-cell recordings.

Voufo C., Chen A.Q., Smith B.E., Yan R., Feller M.B, & Tiriac A. (2023). Circuit mechanisms underlying embryonic retinal waves. eLife, 12, e81983.

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Publication 2023

Biological Assay Dietary Supplements epibatidine Gap Junctions Hexamethonium Nicotinic Antagonists Perfusion Retina Signal Transduction Pathways

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What are the main functions of gap junctions?

Gap junctions are crucial for coordinating the activities of cells in tissues and organs. They play important roles in various physiological processes such as cell signaling, metabolic cooperatoin, and electrical coupling. These specialized intercellular connections allow the direct exchange of ions, small molecules, and electrical signals between adjacent cells, enabling efficient coordination and communication between cells.

What types or variations of gap junctions exist?

Gap junctions can be formed by different types of connexin proteins, which assemble into hexameric structures called connexons. The specific connexin composition of a gap junction determines its functional properties, such as the size and type of molecules that can pass through it. For example, connexin 43 (Cx43) is a common connexin found in many tissues, while connexin 26 (Cx26) is more prevalent in the inner ear and skin.

How can gap junctions be studied and optimized?

Studying gap junctions can be challenging due to the complexity of their structure and function. Researchers can utilize various techniques, such as immunohistochemistry, electrophysiology, and dye transfer assays, to investigate the localization, permeability, and regulation of gap junctions. PubCompare.ai's AI-driven platform can assist researchers by helping them efficiently screen the literature, identify the most effective protocols, and compare the performance of different methods related to gap junctions. This can enhance the reproducibility and accuracy of gap junctions studies.

What are some specific applications of gap junction research?

Gap junctions play critical roles in a wide range of physiological processes and disease states. They are important for coordinating cardiac and neural activities, regulating tissue homeostasis, and facilitating wound healing. Gap junction dysfunction has been implicated in various pathological conditions, such as cardiac arrhythmias, neurodegenerative diseases, and cancer. Understanding the regulation and function of gap junctions can lead to the development of novel therapeutic strategies targeting these specialized intercellular connections.

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PubCompare.ai's AI-driven platfrom can be invaluable for researchers studying gap junctions. The tool allows you to efficiently screen the literature, including protocols from published papers, pre-prints, and patents, to identify the most effective methods for your specific research goals. The platfrom's AI-driven analysis can highlight key differences in protocol effectiveness, enabling you to choose the best option for reproducibility and accuracy. By leveraging PubCompare.ai, you can save time, enhance the quality of your gap junctions research, and improve the overall outcomes of your studies.

More about "Gap Junctions"

Gap junctions are specialized intercellular connections that allow the direct exchange of ions, small molecules, and electrical signals between adjacent cells.
They are crucial for coordinating the activities of cells in tissues and organs, and play important roles in various physiological processes such as cell signaling, metabolic cooperation, and electrical coupling.
Gap junctions are composed of specialized proteins called connexins, which form channels that connect the cytoplasm of neighboring cells.
These channels allow the direct passage of ions, small molecules, and even electrical signals, enabling efficient communication and integration between cells.
This communication is essential for the proper function of tissues and organs, as it allows for the synchronization of cellular activities, the sharing of resources and information, and the coordinated response to external stimuli.
Gap junctions are found in a wide range of cell types, including epithelial cells, muscle cells, and neurons.
They are particularly important in the cardiovascular system, where they facilitate the rapid transmission of electrical signals between cardiomyocytes, enabling the coordinated contraction of the heart.
In the nervous system, gap junctions play a crucial role in the synchronization of neural activity, which is essential for various cognitive and sensory functions.
Researchers studying gap junctions may utilize various tools and technologies, such as the Nexus Expression 3 software, Mastercycler nexus, and Mastercycler® nexus X2, to analyze gene expression patterns and measure the expression levels of connexin proteins.
Additionally, techniques like MATLAB and S-4800 scanning electron microscopy can be employed to visualize and study the structure and dynamics of gap junctions.
The Mastercycler Nexus Gradient is a versatile thermal cycler that can be used in gap junction research, enabling precise temperature control and optimization of PCR-based experiments.
Carbenoxolone, a pharmacological agent, has been used to study the functional role of gap junctions by selectively inhibiting their activity.
Nexus Copy Number software can be utilized to analyze the copy number variations of connexin genes, which may be associated with genetic disorders or diseases related to gap junction dysfunction.
The Nicolet Nexus 470 and Nicolet Nexus 670 are high-performance Fourier-transform infrared (FTIR) spectrometers that can be employed to study the molecular composition and structure of gap junctions.
By leveraging these tools and technologies, researchers can optimize their research protocols, enhance the reproducibility and accuracy of their gap junction studies, and gain deeper insights into the fundamental mechanisms underlying the function and regulation of these crucial intercellular structures.