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Archive for the ‘Signaling & Cell Circuits’ Category

Summary of Transcription, Translation ond Transcription Factors

Author and Curator:  Larry H. Bernstein, MD, FCAP  

Article ID #158: Summary of Transcription, Translation and Transcription Factors. Published on 11/5/2014

WordCloud Image Produced by Adam Tubman

 

Proteins are integral to the composition of the cytoskeleton, and also to the extracellular matrix.  Many proteins are actually enzymes, carrying out the transformation of some substrate, a derivative of the food we ingest.  They have a catalytic site, and they function with a cofactor – either a multivalent metal or a nucleotide. Proteins also are critically involved in the regulation of cell metabolism, and they are involved in translation of the DNA code, as they make up transcription factors (TFs). There are 20 essential amino acids that go into protein synthesis that are derived from animal or plant protein.   Protein synthesis is carried out by the transport of mRNA out of the nucleus to the ribosome, where tRNA is paired with a matching amino acid, and the primary sequence of a protein is constructed as a linear string of amino acids.

This is illustrated in the following three pictures:

protein synthesis

protein synthesis

mcell-transcription-translation

mcell-transcription-translation

transcription_translation

transcription_translation

Proteins synthesized at distal locations frequently contain intrinsically disordered segments. These regions are generally rich in assembly-promoting modules and are often regulated by post-translational modifications. Such proteins are tightly regulated but display distinct temporal dynamics upon stimulation with growth factors. Thus, proteins synthesized on-site may rapidly alter proteome composition and act as dynamically regulated scaffolds to promote the formation of reversible cellular assemblies.
RJ Weatheritt, et al. Nature Structural & Molecular Biology 24 Aug, 2014; 21: 833–839 http://dx.do.orgi:/10.1038/nsmb.2876

An overview of the potential advantages conferred by distal-site protein synthesis

An overview of the potential advantages conferred by distal-site protein synthesis

Turquoise and red filled circle represents off-target and correct interaction partners, respectively. Wavy lines represent a disordered region within a distal site synthesis protein. Grey and red line in graphs represents profiles of t…  http://www.nature.com/nsmb/journal/v21/n9/carousel/nsmb.2876-F5.jpg

In the the transcription process an RNA sequence is read.  This is essential for protein synthesis through the ordering of the amino acids in the primary structure. However, there are microRNAs and noncoding RNAs, and there are transcription factors.  The transcription factors bind to chromatin, and the RNAs also have some role in regulating the transcription process. (see picture above)

Transcription factors (TFs) interact dynamically in vivo with chromatin binding sites. Four different techniques are currently used to measure their kinetics in live cells,

  1. fluorescence recovery after photobleaching (FRAP),
  2. fluorescence correlation spectroscopy (FCS),
  3. single molecule tracking (SMT) and
  4. competition ChIP (CC).

A comparison of data from each of these techniques raises an important question:

  • do measured transcription kinetics reflect biologically functional interactions at specific sites (i.e. working TFs) or
  • do they reflect non-specific interactions (i.e. playing TFs)?

There are five key unresolved biological questions related to

  • the functionality of transient and prolonged binding events at both
  • specific promoter response elements as well as non-specific sites.

In support of functionality,

  • there are data suggesting that TF residence times are tightly regulated, and
  • that this regulation modulates transcriptional output at single genes.

In addition to this site-specific regulatory role, TF residence times

  • also determine the fraction of promoter targets occupied within a cell
  • thereby impacting the functional status of cellular gene networks.
  • TF residence times, then, are key parameters that could influence transcription in multiple ways.

Quantifying transcription factor kinetics: At work or at play? Mueller F., et al.  http://dx.doi.org:/10.3109/10409238.2013.833891

Dr. Virginie Mattot works in the team “Angiogenesis, endothelium activation and Cancer” directed by Dr. Fabrice Soncin at the Institut de Biologie de Lille in France where she studies the roles played by microRNAs in endothelial cells during physiological and pathological processes such as angiogenesis or endothelium activation. She has been using Target Site Blockers to investigate the role of microRNAs on putative targets.

A few years ago, the team identified

  • an endothelial cell-specific gene which
  • harbors a microRNA in its intronic sequence.

They have since been working on understanding the functions of

  • both this new gene and its intronic microRNA in endothelial cells.

While they were searching for the functions of the intronic microRNA,

  • theye identified an unknown gene as a putative target.

The aim of my project was to investigate if this unknown gene was actually a genuine target and

  • if regulation of this gene by the microRNA was involved in endothelial cell function.

They had already shown the endothelial cell phenotype is associated with the inhibition of the intronic microRNA.
They then used miRCURY LNA™ Target Site Blockers to demonstrate

  • the expression of this unknown gene is actually controlled by this microRNA.
  • the microRNA regulates specific endothelial cell properties through regulation of this unknown gene.

MicroRNA function in endothelial cells – Solving the mystery of an unknown target gene using Target Site Blockers to investigate the role of microRNAs on putative targets

We first verified that this TSB was functional by analyzing

  • the expression of the miRNA target against which the TSB was directed
  • we then showed the TSB induced similar phenotypes as those when we inhibited the microRNA in the same cells.

Target Site Blockers were shown to be efficient tools to demonstrate the specific involvement of

  • putative microRNA targets
  • in the function played by this microRNA.

Some genes are known to have several different alternatively spliced protein variants, but the Scripps Research Institute’s Paul Schimmel and his colleagues have uncovered almost 250 protein splice variants of an essential, evolutionarily conserved family of human genes. The results were published July 17 in Science.

Focusing on the 20-gene family of aminoacyl tRNA synthetases (AARSs),

  • the team captured AARS transcripts from human tissues—some fetal, some adult—and showed that
  • many of these messenger RNAs (mRNAs) were translated into proteins.

Previous studies have identified several splice variants of these enzymes that have novel functions, but uncovering so many more variants was unexpected, Schimmel said. Most of these new protein products

  • lack the catalytic domain but retain other AARS non-catalytic functional domains.

This study fundamentally effects how we view protein-synthesis, according to  Michael Ibba (who was not involved in the work), The Scientist reported. “The unexpected and potentially vast expanded functional networks that emerge from this study have the potential to influence virtually any aspect of cell growth.”

The team—comprehensively captured and sequenced the AARS mRNAs from six human tissue types using high-throughput deep sequencing. They next showed that a proportion of these transcripts, including those missing the catalytic domain, indeed resulted in stable protein products:

  • 48 of these splice variants associated with polysomes.

In vitro translation assays and the expression of more than 100 of these variants in cells confirmed that

  • many of these variants could be made into stable protein products.

The AARS enzymes—of which there’s one for each of the 20 amino acids—bring together an amino acid with its appropriate transfer RNA (tRNA) molecule. This reaction allows a ribosome to add the amino acid to a growing peptide chain during protein translation. AARS enzymes can be found in all living organisms and are thought to be among the first proteins to have originated on Earth.

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly

  • play an important role in determining gene expression outputs, yet
  • the regulatory logic underlying functional transcription factor binding is poorly understood.

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and

  • it is generally accepted that much of the binding does not strongly influence gene expression.

DA Cusanovich et al. PLoS Genet 2014;10(3):e1004226.  http://dx.doi.org:/10.1371/journal.pgen.1004226

We knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line

  • to evaluate the context of functional TF binding.

We then identified genes whose expression was affected by the knockdowns

  • by intersecting the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq)
  • within 10 kb of the transcription start sites of expressed genes.

This combination of data allowed us to infer functional TF binding.
Only a small subset of genes bound by a factor were

  • differentially expressed following the knockdown of that factor,
  • suggesting that most interactions between TF and chromatin
  • do not result in measurable changes in gene expression levels
  • of putative target genes.

We found that functional TF binding is enriched

  • in regulatory elements that harbor a large number of TF binding sites,
  • at sites with predicted higher binding affinity, and
  • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

We aim to be able to predict the expression pattern of a gene based on its regulatory
sequence alone.

Combining a TF knockdown approach with TF binding data can help us to

  • distinguish functional binding from non-functional binding

This approach has previously been applied to the study of human TFs, although for the most part studies have only focused on

  • the regulatory relationship of a single factor with its downstream targets.

The FANTOM consortium knocked down 52 different transcription factors in

  • the THP-1 cell line, an acute monocytic leukemia-derived cell line, and
  • used a subset of these to validate certain regulatory predictions based on binding motif enrichments.

We and others previously studied the regulatory architecture of gene expression in

  • the model system of HapMap lymphoblastoid cell lines (LCLs) using both
  • binding map strategies and QTL mapping strategies.

We now sought to use knockdown experiments targeting transcription factors in a HapMap LCL

  • to refine our understanding of the gene regulatory circuitry of the human genome.

Therefore, We integrated the results of the knockdown experiments with previous data on TF binding to

  • better characterize the regulatory targets of 59 different factors and
  • to learn when a disruption in transcription factor binding
  • is most likely to be associated with variation in the expression level of a nearby gene.

Gene expression levels following the knockdown were compared to

  • expression data collected from six samples that were transfected with negative control siRNA.

Depending on the factor targeted, the knockdowns resulted in

  • between 39 and 3,892 differentially expressed genes at an FDR of 5%
    (Figure 1B; see Table S3 for a summary of the results).

The knockdown efficiency for the 59 factors ranged

  • from 50% to 90% (based on qPCR; Table S1).

The qPCR measurements of the knockdown level were significantly

  • correlated with estimates of the TF expression levels
  • based on the microarray data (P =0.001; Figure 1C).

 

Did the factors tended to have a consistent effect (either up- or down-regulation)

  • on the expression levels of genes they purportedly regulated?

All factors we tested are associated with both up- and down-regulation of downstream targets (Figure 6).

While there is compelling evidence for our inferences, the current chromatin functional annotations

  • do not fully explain the regulatory effects of the knockdown experiments.

For example, the enrichments for binding in ‘‘strong enhancer’’ regions of the genome range from 7.2% to 50.1% (median = 19.2%),

  • much beyond what is expected by chance alone, but far from accounting for all functional binding.

A slight majority of downstream target genes were expressed at higher levels

  • following the knockdown for 15 of the 29 factors for which we had binding information (Figure 6B).

The factor that is associated with the largest fraction (68.8%) of up-regulated target genes following the knockdown is EZH2,

  • the enzymatic component of the Polycomb group complex.

On the other end of the spectrum was JUND, a member of the AP-1 complex, for which

  • 66.7% of differentially expressed targets were down-regulated following the knockdown.

Our results, combined with the previous work from our group and others make for a complicated view

  • of the role of transcription factors in gene regulation as
  • it seems difficult to reconcile the inference from previous work that
  • many transcription factors should primarily act as activators with the results presented here.

One somewhat complicated hypothesis, which nevertheless can resolve the apparent discrepancy, is that

  • the ‘‘repressive’’ effects we observe for known activators may be
  • at sites in which the activator is acting as a weak enhancer of transcription and
  • that reducing the cellular concentration of the factor
  • releases the regulatory region to binding by an alternative, stronger activator.

Integrative study of Arabidopsis thaliana metabolomic and transcriptomic data
with the interactiveMarVis-Graph software

M Landesfeind, A Kaever, K Feussner, C Thurow, C Gatz, I Feussner and P Meinicke
PeerJ 2:e239;   http://dx.doi.org /10.7717/peerj.239

High-throughput technologies notoriously generate large datasets often including data from different omics platforms. Each dataset contains data for several thousand experimental markers, e.g., mass-to-charge ratios in mass spectrometry or spots in DNA microarray analysis. An experimental marker is associated with an intensity profile which may include several measurements according to different experimental conditions (Dettmer, Aronov & Hammock, 2007).

The combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics.We present here theMarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and

  • sub-networks, according to connected high-scoring reactions, are identified.

Finally, MarVis-Graph scores the detected sub-networks,

  • evaluates them by means of a random permutation test and
  • presents them as a ranked list.

Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results.

The key advantage ofMarVis-Graph is the analysis of reactions detached from their pathways so that

  • it is possible to identify new pathways or
  • to connect known pathways by previously unrelated reactions.

TheMarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.

Significant differences or clusters may be explained by associated annotations, e.g., in terms of metabolic pathways or biological functions. During recent years, numerous specialized tools have been developed to aid biological researchers in automating all these steps (e.g., Medina et al., 2010; Kaever et al., 2009; Waegele et al., 2012). Comprehensive studies can be performed by combining technologies from different omics fields. The combination of transcriptomic and proteomic data sets revealed a strong
correlation between both kinds of data (Nie et al., 2007) and supported the detection of complex interactions, e.g., in RNA silencing (Haq et al., 2010). Moreover, correlations
were detected between RNA expression levels and metabolite abundances (Gibon et al., 2006). Therefore, tools that integrate, analyze and visualize experimental markers from different platforms are needed. To cope with the complexity of genome-wide studies, pathway models are utilized extensively as a simple abstraction of the underlying complex mechanisms. Set Enrichment Analysis (Subramanian et al., 2005) and Over-Representation Analysis (Huang, Sherman & Lempicki, 2009) have become state-of-the-art tools for analyzing large-scale datasets: both methods evaluate predefined sets of entities, e.g., the accumulation of differentially expressed genes in a pathway.

While manually curated pathways are convenient and easy to interpret, experimental studies have shown that all metabolic and signaling pathways are heavily interconnected (Kunkel & Brooks, 2002; Laule et al., 2003). Data from biomolecular databases support these studies: the metabolic network of Arabidopsis thaliana in the KEGG database (Kanehisa et al., 2012; Kanehisa & Goto, 2000) contains 1606 reactions from which 1464 are connected in a single sub-network (>91%), i.e., they
share a metabolite as product or substrate. In the AraCyc 10.0 database (Mueller, Zhang & Rhee, 2003; Rhee et al., 2006), more than 89% of the reactions are counted in a single sub-network. In both databases, most other reactions are completely disconnected. Additionally, Set Enrichment Analyses can not identify links between the predefined sets easily. This becomes even more important when analyzing smaller pathways as provided by the MetaCyc (Caspi et al., 2008; Caspi et al., 2012) database. Moreover, methods that utilize pathways as predefined sets ignore reactions and related biomolecular entities (e.g., metabolites, genes) which are not associated with a single pathway. For example, this affects 4000 reactions in MetaCyc and 2500 in KEGG, respectively (Altman et al., 2013). Therefore, it is desirable to develop additional methods

  • that do not require predefined sets but may detect enriched sub-networks in the full metabolic network.

While several tools support the statistical analysis of experimental markers from one or more omics technologies and then utilize variants of Set Enrichment Analysis (Xia et al., 2012; Chen et al., 2013; Howe et al., 2011),

  • no tool is able to explicitly search for connected reactions that include
  • most of the metabolites, genes, and enyzmes with experimental evidence.

However, the automatic identification of sub-networks has been proven useful in other contexts, e.g., in the analysis of protein–protein-interaction networks (Alcaraz et al., 2012; Baumbach et al., 2012; Maeyer et al., 2013).

MarVis-Graph imports experimental markers from different high-throughput experiments and

  • analyses them in the context of reaction-chains in full metabolic networks.

Then, MarVis-Graph scores the reactions in the metabolic network

  • according to the number of associated experimental markers and
  • identifies sub-networks consisting of subsequent, high-scoring reactions.

The resulting sub-networks are

  • ranked according to a scoring method and visualized interactively.

Hereby, sub-networks consisting of reactions from different pathways may be identified to be important

  • whereas the single pathways may not be found to be significantly enriched.

MarVis-Graph may also connect reactions without an assigned pathway

  • to reactions within a particular pathway.

TheMarVis-Graph tool was applied in a case-study investigating the wound response in Arabidopsis thaliana to analyze combined metabolomic and transcriptomic high-throughput data.

Figure 1 Schema of the metabolic network representation in MarVis-Graph. Metabolite markers are shown in gray, metabolites in red, reactions in blue, enzymes in green, genes in yellow, transcript markers in pink, and pathways in turquoise color. The edges are shown in black with labels that comply with the biological meaning. The orange arrows depict the flow of score for the initial scoring (described in section “Initial Scoring”). (not shown)

In MarVis-Graph, metabolite markers obtained from mass-spectrometry experiments additionally contain the experimental mass. The experimental mass has to be
calculated based on the mass-to-charge ratio (m/z-value) and specific isotope- or adduct-corrections (Draper et al., 2009) by means of specialized tools, e.g.,MarVis-Filter
(Kaever et al., 2012).

For each transcript marker the corresponding annotation has to be given. In DNA microarray experiments, each spot (transcript marker) is specific for a gene and can
therefore be used for annotation. For other technologies an annotation has to be provided by external tools.

In MarVis-Graph, each reaction is scored initially based on the associated experimental data (see “Initial scoring”). This initial scoring is refined (see “Refining the scoring”) and afterwards reactions with a score below a user-defined threshold are removed. The network is

  • decomposed into subsequent high-scoring reactions that constitute the sub-networks.

The weight of each experimental marker (see “Experimental markers”) is equally distributed over all metabolites and genes associated with the metabolite marker or
transcript marker, respectively. For all vertices, this is repeated as illustrated in Fig. 1 until the weights are accumulated by the reactions.

The initial reaction scores are used as input scoring for the random walk algorithm. The algorithm is performed as described by Glaab et al. (2012) with a user-defined
restart-probability r (default value 0.8). After convergence of the algorithm, reactions with a score lower than the user-defined threshold t (default value t = 1−r) are removed from the reaction network. During the removal process,

  • the network is decomposed into pairwise disconnected sub-networks containing only high-scoring reactions.

In the following, a resulting sub-network is denoted by a prime: G′ = (V′,L′) with V′ = M′ ∪C′ ∪R′ ∪E′ ∪G′ ∪T′ ∪P′.

The scores of the identified sub-networks can be assessed using a random permutation test, evaluating the marker annotations under the null hypothesis of being connected
randomly. Here, the assignments

  • from metabolite markers to metabolites and from transcript markers to genes are randomized.

For each association between a metabolite marker and a metabolite,

  • this connection is replaced by a connection between a randomly chosen metabolite marker and a randomly chosen metabolite.

The random metabolite marker is chosen from the pool of formerly connected metabolite markers. Each connected transcript marker

  • is associated with a randomly chosen gene.

Choosing from the list of already connected experimental markers ensures that

  • the sum of weights from the original and the permuted network are equal.

This method differs from the commonly utilized XSwap permutation (Hanhij¨arvi, Garriga & Puolam¨aki, 2009) that is based on swapping endpoints of two random edges. The main difference of our permutation method is that it results in a network with different topological structure, i.e., different degree of the metabolite and gene nodes.

Finally, the sub-networks are detected and scored with the same parameters applied for the original network. Based on the scores of the networks identified in the random
permutations, the family-wise-error-rate (FWER) and false-discovery-rate (FDR) are calculated for each originally identified sub-network.

MarVis-Graph was applied in a case study investigating the A. thaliana wound response. Data from a metabolite fingerprinting (Meinicke et al., 2008) and a DNA microarray
experiment (Yan et al., 2007) were imported into a metabolic network specific for A. thaliana created from the AraCyc 10.0 database (Lamesch et al., 2011). The metabolome
and transcriptome have been measured before wounding as control and at specific time points after wounding in wild-type and in the allene oxide synthase (AOS) knock-out
mutant dde-2-2 (Park et al., 2002) of A. thaliana Columbia (see Table 1). The AOS mutant was chosen, because AOS catalyzes the first specific step in the biosynthesis of the hormone jasmonic acid, which is the key regulator in wound response of plants (Wasternack & Hause, 2013).

Both datasets have been preprocessed with theMarVis-Filter tool (Kaever et al., 2012) utilizing the Kruskal–Wallis p-value calculation on the intensity profiles. Based on the ranking of ascending p-values,

  • the first 25% of the metabolite markers and 10% of the transcript markers have been selected for further investigation (Data S2).

The filtered metabolite and transcript markers were imported into the metabolic network. For metabolite markers, metabolites were associated

  • if the metabolite marker’s detected mass differs from the metabolites monoisotopic mass by a maximum of 0.005u.

Transcript markers were linked to the genes whose ID equaled the ID given in the CATMA database (Sclep et al., 2007) for that transcript marker.

Table 2 Vertices in the A. thaliana specific metabolic network after import of experimental markers. Number of objects in the metabolic network
in absolute counts and relative abundances. For experimental markers, the with annotation column gives the number of metabolite markers and
transcript markers that were annotated with a metabolite or gene, respectively. The direct evidence column contains the number of metabolites
and genes, that are associated with a metabolite marker or transcript marker. For enzymes, this is the number of enzymes encoded by a gene with
direct evidence. The number of vertices with an association to a reaction is given in the with reaction column. In the last column, this is given for
associations to metabolic pathways. (not shown)

MarVis-Graph detected a total of 133 sub-networks. The sub-networks were ranked according to size Ss, diameter Sd, and sum-of-weights Ssow
scores (Table S4). Interestingly, the different rankings show a high correlation with all pairwise correlations higher than 0.75 (Pearson correlation
coefficient) and 0.6 (Spearman rank correlation).

Allene-oxide cyclase sub-network
In all rankings, the sub-network allene-oxide cyclase (named after the reaction with the highest score in this sub-network) appeared as top candidate.

This sub-network is constituted of reactions from different pathways related to fatty acids. Figure 2 shows a visualization of the sub-network.
Jasmonic acid biosynthesis. The main part of the sub-network is formed by reactions from the “jasmonic acid biosynthesis” (PlantMetabolic Network, 2013)
resulting in jasmonic acid (jasmonate). The presence of this pathway is very well established because of its central role in mediating the plants wound response
(Reymond & Farmer, 1998; Creelman, Tierney & Mullet, 1992). Additionally, metabolites and transcripts from this pathway were expected to show prominent
expression profiles because AOS, a key enzyme in this pathway, is knocked-out in themutant plant. Jasmonic acid derivatives and hormones.

Jasmonic acid derivatives and hormones. Jasmonate is a precursor for a broad variety of plant hormones (Wasternack & Hause, 2013), e.g., the derivative (-)-
jasmonic acid methyl ester (also Methyl Jasmonic Acid; MeJA) is a volatile, airborne signal mediating wound response between plants (Farmer&Ryan, 1990).
Reactions from the jasmonoyl-amino acid conjugates biosynthesis I (PMN, 2013a) pathway connect jasmonate to different amino acids, including L-valine,
L-leucine, and L-isoleucine. Via these amino acids, this sub-network is connected to the indole-3-acetylamino acid biosynthesis (PMN, 2013b) (IAA biosynthesis).
Again, this pathway produces a well known plant hormone: Auxine (Woodward & Bartel, 2005). Even though, jasmonate and auxin are both plant hormones, their
connection in this subnetwork is of minor relevance because amino acid conjugates are often utilized as active or storage forms of signaling molecules.While
jasmonoyl-amino acid conjugates represent the active signaling form of jasmonates, IAA amino acid conjugates are the storage form of this hormone (Staswick et al.,
2005).

polyhydroxy fatty acids synthesis

polyhydroxy fatty acids synthesis

 

Figure 2 Schema of the allene-oxide cyclase sub-network. Metabolites are shown in red, reactions in blue, and enzymes in green color. Metabolites and reactions without direct experimental evidence are marked by a dashed outline and a brighter color while enzymes without experimental evidence are hidden. The metabolic pathways described in section “Resulting sub-networks” are highlighted with different colors. The orange and green parts indicate the reaction chains required to build jasmonate and its amino acid conjugates. The coloring of pathways was done manually after export from MarVis-Graph.

The ω-3-fatty acid desaturase should catalyze a reaction from linoleate to α-linolenate. Metabolite markers that match the mass of crepenynic acid do also match α-linolenate
because both molecules have the same sum-formula and monoisotopic mass. As mentioned above, MarVis-Graph compiled the metabolic network for this study
from the AraCyc database version 10.0. On June 4th, a curator changed the database to remove theΔ12-fatty acid dehydrogenase prior to the release of AraCyc version 11.0.

The presented new software tool MarVis-Graph supports the investigation and visualization of omics data from different fields of study. The introduced algorithm for
identification of sub-networks is able to identify reaction-chains across different pathways and includes reactions that are not associated with a single pathway. The application of MarVis-Graph in the case study on A. thaliana wound response resulted in a convenient graphical representation of high-throughput data which allows the analysis of the complex dynamics in a metabolic network.

 

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Transcription Modulation

Author and Curator: Larry H. Bernstein, MD, FCAP 

 

This portion of the transcription series deals with transcription factors and the effects of their binding on metabolism. This also has implications for pharmaceutical target identification.

The Functional Consequences of Variation in Transcription Factor Binding
DA. Cusanovich, B Pavlovic, JK. Pritchard*, Y Gilad*
1 Department of Human Genetics, 2 Howard Hughes Medical Institute, University of Chicago, Chicago, IL 3 Departments of Genetics and Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA.
PLoS Genet 2014;10(3):e1004226.  http://dx.doi.org:/10.1371/journal.pgen.1004226

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly

  • play an important role in determining gene expression outputs, yet
  • the regulatory logic underlying functional transcription factor binding is poorly understood.

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and

  • it is generally accepted that much of the binding does not strongly influence gene expression.

To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then

  • examined which genes were impacted by the reduced transcription factor level.

Our results implicate some attributes

  • that might influence what binding is functional, but they also suggest that
  • a simple model of functional vs. non-functional binding may not suffice.

Many studies have focused on characterizing the genomic locations of TF binding, but

  • it is unclear whether TF binding at any specific locus has
  • functional consequences with respect to gene expression output.

We knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line

  • to evaluate the context of functional TF binding.

We then identified genes whose expression was affected by the knockdowns

  • by intersecting the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq)
  • within 10 kb of the transcription start sites of expressed genes.

This combination of data allowed us to infer functional TF binding.
Only a small subset of genes bound by a factor were

  • differentially expressed following the knockdown of that factor,
  • suggesting that most interactions between TF and chromatin
  • do not result in measurable changes in gene expression levels
  • of putative target genes.

We found that functional TF binding is enriched

  • in regulatory elements that harbor a large number of TF binding sites,
  • at sites with predicted higher binding affinity, and
  • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

We aim to be able to predict the expression pattern of a gene based on its regulatory
sequence alone. However, the regulatory code of the human genome is much more complicated than

  • the triplet code of protein coding sequences, and is highly context-specific,
  • depending on cell-type and other factors.

Moreover, regulatory regions are not necessarily organized into

  • discrete, easily identifiable regions of the genome and
  • may exert their influence on genes over large genomic distances

Genomic studies addressing questions of the regulatory logic of the human genome have largely taken one of two approaches.

  1. collecting transcription factor binding maps using techniques such as ChIPseq
    and DNase-seq
  2. mapping various quantitative trait loci (QTL), such as gene expression levels
    (eQTLs) [7], DNA methylation (meQTLs) [8] and chromatin accessibility (dsQTLs)

Cumulatively, binding map studies and QTL map studies have

  • led to many insights into the principles and mechanisms of gene regulation.

However, there are questions that neither mapping approach on its own is well equipped to address. One outstanding issue is

  • the fraction of factor binding in the genome that is ‘‘functional’’,
    which we define here to mean that
  • disturbing the protein-DNA interaction leads to a measurable
  • downstream effect on gene regulation.

Transcription factor knockdown could be used to address this problem, whereby

  • the RNA interference pathway is employed to greatly reduce
  • the expression level of a specific target gene by using small interfering RNAs (siRNAs).

The response to the knockdown can then be measured by collecting RNA after the knockdown and

  • measuring global changes in gene expression patterns
  • after specifically attenuating the expression level of a given factor.

Combining a TF knockdown approach with TF binding data can help us to

  • distinguish functional binding from non-functional binding

This approach has previously been applied to the study of human TFs, although for the most part studies have only focused on

  • the regulatory relationship of a single factor with its downstream targets.

The FANTOM consortium knocked down 52 different transcription factors in

  • the THP-1 cell line, an acute monocytic leukemia-derived cell line, and
  • used a subset of these to validate certain regulatory predictions based on binding motif enrichments.

We and others previously studied the regulatory architecture of gene expression in

  • the model system of HapMap lymphoblastoid cell lines (LCLs) using both
  • binding map strategies and QTL mapping strategies.

We now sought to use knockdown experiments targeting transcription factors in a HapMap LCL

  • to refine our understanding of the gene regulatory circuitry of the human genome.

Therefore, We integrated the results of the knockdown experiments with previous data on TF binding to

  • better characterize the regulatory targets of 59 different factors and
  • to learn when a disruption in transcription factor binding
  • is most likely to be associated with variation in the expression level of a nearby gene.

Gene expression levels following the knockdown were compared to

  • expression data collected from six samples that were transfected with negative control siRNA.

The expression data from all samples were normalized together using

  • quantile  normalization followed by batch correction using the RUV-2 method.

We then performed several quality control analyses to confirm

  1. that the quality of the data was high,
  2. that there were no outlier samples, and
  3. that the normalization methods reduced the influence of confounders

In order to identify genes that were expressed at a significantly different level

  • in the knockdown samples compared to the negative controls,
  • we used likelihood-ratio tests within the framework of a fixed effect linear model.

Following normalization and quality control of the arrays,

  • we identified genes that were differentially expressed between
  • the three knockdown replicates of each factor and the six controls.

Depending on the factor targeted, the knockdowns resulted in

  • between 39 and 3,892 differentially expressed genes at an FDR of 5%
    (Figure 1B; see Table S3 for a summary of the results).

The knockdown efficiency for the 59 factors ranged

  • from 50% to 90% (based on qPCR; Table S1).

The qPCR measurements of the knockdown level were significantly

  • correlated with estimates of the TF expression levels
  • based on the microarray data (P =0.001; Figure 1C).

Reassuringly, we did not observe a significant correlation between

  • the knockdown efficiency of a given factor and
  • the number of genes classified as differentially expressed foci.

Because we knocked down 59 different factors in this experiment

  • we were able to assess general patterns associated with the perturbation of transcription factors
  • beyond merely the number of affected target genes.

Globally, despite the range in the number of genes we identified as

  • differentially expressed in each knockdown,
  • the effect sizes of the differences in expression were relatively modest and
  • consistent in magnitude across all knockdowns.

The median effect size following the knockdown experiment for genes classified as

  • differentially expressed at an FDR of 5% in any knockdown was
  • a 9.2% difference in expression level between the controls and the knockdown (Figure 2),
  • while the median effect size for any individual knockdown experiment ranged between 8.1% and 11.0%.
    (this was true whether we estimated the knockdown effect based on qPCR (P = 0.10; Figure 1D) or microarray (P = 0.99; not shown) data.

Nor did we observe a correlation between

  • variance in qPCR-estimated knockdown efficiency (between replicates) and
  • the number of genes differentially expressed (P = 0.94; Figure 1E).

We noticed that the large variation in the number of differentially expressed genes

  • extended even to knockdowns of factors from the same gene family.

Figure 1. Differential expression analysis.
(a) Examples of differential expression analysis results for the genes HCST and IRF4. The top two panels are ‘MA plots’ of the mean Log2(expression level) between the knockdown arrays and the controls for each gene (x-axis) to the Log2(Fold-Change) between the knockdowns and controls (y-axis). Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The gene targeted by the siRNA is highlighted in red. The bottom two panels are ‘volcano plots’ of the Log2(Fold-Change) between the knockdowns and controls (x-axis) to the P-value for differential expression (y-axis). The dashed line marks the 5% FDR threshold. Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The red dot marks the gene targeted by the siRNA.
(b) Barplot of number of differentially expressed genes in each knockdown experiment.
(c) Comparison of the knockdown level measured by qPCR (RNA sample collected 48 hours posttransfection) and the knockdown level measured by microarray.
(d) Comparison of the level of knockdown of the transcription factor at 48 hrs (evaluated by qPCR; x-axis) and the number of genes differentially expressed in the knockdown experiment (y-axis).
(e) Comparison of the variance in knockdown efficiency between replicates for each transcription factor (evaluated by qPCR; x-axis) and the number of differentially expressed genes in the knockdown experiment (y-axis).

Differential expression analysis

Differential expression analysis

http://dx.doi.org:/10.1371/journal.pgen.1004226.g001

Figure 2. Effect sizes for differentially expressed genes.
Boxplots of absolute Log2(fold-change) between knockdown arrays and control arrays for all genes identified as differentially expressed in each experiment. Outliers are not plotted. The gray bar indicates the interquartile range across all genes differentially expressed in all knockdowns. Boxplots are ordered by the number of genes differentially expressed in each experiment. Outliers were not plotted.

Effect sizes for differentially expressed genes

Effect sizes for differentially expressed genes

http://dx.doi.org:/10.1371/journal.pgen.1004226.g002

Knocking down SREBF2 (1,286 genes differentially expressed), a key regulator of cholesterol homeostasis,

  • results in changes in the expression of genes that are
  • significantly enriched for cholesterol and sterol biosynthesis annotations.

While not all factors exhibited striking enrichments for relevant functional categories and pathways,

  • the overall picture is that perturbations of many of the factors
  • primarily affected pathways consistent with their known biology.

In order to assess functional TF binding, we next incorporated

  • binding maps together with the knockdown expression data.

We combined binding data based on DNase-seq footprints in 70 HapMap LCLs, reported by Degner et al. (Table S5)

  • and from ChIP-seq experiments in LCL GM12878, published by ENCODE.

We were thus able to obtain genome wide binding maps for a total of 131 factors that were either

  • directly targeted by an siRNA in our experiment (29 factors) or were
  • differentially expressed in one of the knockdown experiments.

We classified a gene as a bound target of a particular factor when

  • binding of that factor was inferred within 10kb of the transcription start site (TSS) of the target gene.

Using this approach, we found that the 131 TFs were bound

  • in proximity to a median of 1,922 genes per factor (range 11 to 7,053 target genes).

We considered binding of a factor to be functional if the target gene

  • was differentially expressed after perturbing the expression level the bound transcription factor.

We then asked about the concordance between

  • the transcription factor binding data and the knockdown expression data.
  •  the extent to which differences in gene expression levels following the knockdowns
  • might be predicted by binding of the transcription factors
  • within the putative regulatory regions of the responsive genes. and also
  • what proportion of putative target (bound) genes of a given TF were
  • differentially expressed following the knockdown of the factor.

Focusing only on the binding sites classified using the DNase-seq data
(which were assigned to a specific instance of the binding motif, unlike the ChIP data),

  • we examined sequence features that might distinguish functional binding.

In particular, whether binding at conserved sites was more likely to be functional  and

  • whether binding sites that better matched the known PWM for the factor were more likely to be functional.

We did not observe a significant shift in the conservation of functional binding sites (Wilcoxon rank sum P = 0.34),

  • but we did observe that binding around differentially expressed genes occurred at sites
  • that were significantly better matches to the canonical binding motif.

Figure 3. Intersecting binding data and expression data for each knockdown.
(a) Example Venn diagrams showing the overlap of binding and differential expression for the knockdowns of HCST and IRF4 (the same genes as in Figure 1).
(b) Boxplot summarizing the distribution of the fraction of all expressed genes that are bound by the targeted gene or downstream factors.
(c) Boxplot summarizing the distribution of the fraction of bound genes that are classified as differentially expressed, using an FDR of either 5% or 20%.

Intersecting binding data and expression data for each knockdown

Intersecting binding data and expression data for each knockdown

http://dx.doi.org:/10.1371/journal.pgen.1004226.g003

Considering bound targets determined from either the ChIP-seq or DNase-seq data, we observed that

  • differentially expressed genes were associated with both
  • a higher number of binding events for the relevant factors within 10 kb of the TSS (P,10216; Figure 4A)
  • as well as with a larger number of different binding factors
    (considering the siRNA-targeted factor and any TFs that were DE in the knockdown; P,10216; Figure 4B).

Figure 4. Degree of binding correlated with function. Boxplots comparing
(a) the number of sites bound, and
(b) the number of differentially expressed transcription factors binding events near functionally or non-functionally bound genes. We considered binding for siRNA-targeted factor and any factor differentially expressed in the knockdown.
(c) Focusing only on genes differentially expressed in common between each pairwise set of knockdowns we tested for enrichments of functional binding (y-axis). Pairwise comparisons between knockdown experiments were binned by the fraction of differentially expressed transcription factors in common between the two experiments. For these boxplots, outliers were not plotted.

Degree of binding correlated with function

Degree of binding correlated with function

http://dx.doi.org:/10.1371/journal.pgen.1004226.g004

We examined the distribution of binding about the TSS. Most factor binding was concentrated

  • near the TSS whether or not the genes were classified as differentially expressed (Figure 5A).
  • the distance from the TSS to the binding sites was significantly longer for differentially expressed genes (P,10216; Fig. 5B).

Figure 5. Distribution of functional binding about the TSS.
(a) A density plot of the distribution of bound sites within 10 kb of the TSS for both functional and non-functional genes. Inset is a zoom-in of the region +/21 kb from the TSS (b) Boxplots comparing the distances from the TSS to the binding sites for functionally bound genes and non-functionally bound genes. For the boxplots, 0.001 was added before log10 transforming the distances and outliers were not plotted.

Distribution of functional binding about the TSS

Distribution of functional binding about the TSS

http://dx.doi.doi:/10.1371/journal.pgen.1004226.g005

We investigated the distribution of factor binding across various chromatin states, as defined by Ernst et al. This dataset lists

  • regions of the genome that have been assigned to different activity states
  • based on ChIP-seq data for various histone modifications and CTCF binding.

For each knockdown, we separated binding events

  • by the genomic state in which they occurred and then
  • tested whether binding in that state was enriched around differentially expressed genes.

After correcting for multiple testing of genes that were differentially expressed.

  • 19 knockdowns showed significant enrichment for binding in ‘‘strong enhancers’’
  • four knockdowns had significant enrichments for ‘‘weak enhancers’’,
  • eight knockdowns showed significant depletion of binding in ‘‘active promoters’’ ,
  • six knockdowns had significant depletions for ‘‘transcription elongation’’,

Did the factors tended to have a consistent effect (either up- or down-regulation)

  • on the expression levels of genes they purportedly regulated?

All factors we tested are associated with both up- and down-regulation of downstream targets (Figure 6).

A slight majority of downstream target genes were expressed at higher levels

  • following the knockdown for 15 of the 29 factors for which we had binding information (Figure 6B).

The factor that is associated with the largest fraction (68.8%) of up-regulated target genes following the knockdown is EZH2,

  • the enzymatic component of the Polycomb group complex.

On the other end of the spectrum was JUND, a member of the AP-1 complex, for which

  • 66.7% of differentially expressed targets were down-regulated following the knockdown.

Figure 6. Magnitude and direction of differential expression after knockdown.
(a) Density plot of all Log2(fold-changes) between the knockdown arrays and controls for genes that are differentially expressed at 5% FDR in one of the knockdown experiments as well as bound by the targeted transcription factor.
(b) Plot of the fraction of differentially expressed putative direct targets that were up-regulated in each of the knockdown experiments.

Magnitude and direction of differential expression after knockdown

Magnitude and direction of differential expression after knockdown

http://dx.doi.org:/10.1371/journal.pgen.1004226.g006

We found no correlation between the number of paralogs and the fraction of bound targets that were differentially expressed. We also did not observe a significant correlation when we considered whether

  • the percent identity of the closest paralog might be predicative of
  • the fraction of bound genes that were differentially expressed following the knockdown (Figure S8).

While there is compelling evidence for our inferences, the current chromatin functional annotations

  • do not fully explain the regulatory effects of the knockdown experiments.

For example, the enrichments for binding in ‘‘strong enhancer’’ regions of the genome range from 7.2% to 50.1% (median = 19.2%),

  • much beyond what is expected by chance alone, but far from accounting for all functional binding.

In addition to considering

  • the distinguishing characteristics of functional binding, we also examined
  • the direction of effect that perturbing a transcription factor had on the expression level of its direct targets.

We specifically addressed whether

  • knocking down a particular factor tended to drive expression of its putatively direct (namely, bound) targets up or down,
  • which can be used to infer that the factor represses or activates the target, respectively.

Transcription factors have traditionally been thought of primarily as activators, and previous work from our group is consistent with that notion. Surprisingly, the most straightforward inference from the present study is that

  • many of the factors function as repressors at least as often as they function as activators.
  1. EZH2 had a negative regulatory relationship with the largest fraction of direct targets (68.8%),
    consistent with – the known role of EZH2 as the active member of the Polycomb group complex PC2
  2. while JUND seemed to have a positive regulatory relationship with the largest fraction of direct targets (66.7%),
    and with – the biochemical characterization of the AP-1 complex (of which JUND is a component) as a transactivator.

More generally, however, our results, combined with the previous work from our group and others make for a complicated view

  • of the role of transcription factors in gene regulation as
  • it seems difficult to reconcile the inference from previous work that
  • many transcription factors should primarily act as activators with the results presented here.

One somewhat complicated hypothesis, which nevertheless can resolve the apparent discrepancy, is that

  • the ‘‘repressive’’ effects we observe for known activators may be
  • at sites in which the activator is acting as a weak enhancer of transcription and
  • that reducing the cellular concentration of the factor
  • releases the regulatory region to binding by an alternative, stronger activator.

To more explicitly address the effect that our proximity-based definition of target genes might have on our analyses, we reanalyzed

  • the overlap between factor binding and differential expression following the knockdowns
  • using an independent, empirically determined set of target genes.

Thurman et al. used correlations in DNase hypersensitivity between

  • intergenic hypersensitive sites and promoter hypersensitive sites across diverse tissues
  • to assign intergenic regulatory regions to specific genes,
  • independently of proximity to a particular promoter.

We performed this alternative analysis in which we

  • assigned binding events to genes based on the classification of Thurman et al.

We then considered the overlap between binding and differential expression in this new data set. The results were largely

  • consistent with our proximity-based observations.

A median of 9.5% of genes that were bound by a factor were

  • also differentially expressed following the knockdown of that factor
    (compared to 11.1% when the assignment of binding sites to genes is based on proximity).

From the opposite perspective, a median of 28.0% of differentially expressed genes were bound by that factor
(compared to 32.3% for the proximity based definition). The results of this analysis are summarized in Table S7.

Our results should not be considered a comprehensive census of regulatory events in the human genome. Instead, we adopted a gene-centric approach,

  • focusing only on binding events near the genes for which we could measure expression
  • to learn some of the principles of functional transcription factor binding.

In light of our observations a reassessment of our estimates of binding may be warranted. In particular, because functional binding is skewed away from promoters (our system is apparently not well-suited to observe functional promoter binding, perhaps because of protection by large protein complexes),

  • a more conservative estimate of the fraction of binding that is indeed functional would not consider data within the promoter.

Importantly, excluding the putative promoter region from our analysis (i.e. only considering a window .1 kb from the TSS and ,10 kb from the TSS)

  • does not change our conclusions.

Considering this smaller window,

  • a median of 67.0% of expressed genes are still classified as bound by
  1. either the knocked down transcription factor or
  2. a downstream factors that is differentially expressed in each experiment,

yet a median of only 8.1% of the bound genes are

  • also differentially expressed after the knockdowns.

Much of what distinguishes functional binding (as we define it) has yet to be explained. We are unable to explain much of the differential expression observed in our experiments by the presence of least one relevant binding event. This may not be altogether surprising, as

  • we are only considering binding in a limited window around the transcription start site.

To address these issues, more factors should be perturbed to further evaluate the robustness of our results and to add insight. Together, such studies will help us develop a more sophisticated understanding of functional transcription factor binding in particular, the gene regulatory logic more generally.

Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

E Shmelkov, Z Tang, I Aifantis, A Statnikov*
Biology Direct 2011; 6(15).  http://www.biology-direct.com/content/6/1/15

Recently the biological pathways have become a common and probably the most popular form of representing biochemical information for hypothesis generation and validation. These maps store wide knowledge of complex molecular interactions and regulations occurring in the living organism in a simple and obvious way, often using intuitive graphical notation. Two major types of biological pathways could be distinguished.

  1. Metabolic pathways incorporate complex networks of protein-based interactions and modifications, while
  2. signal transduction and transcriptional regulatory pathways are usually considered to provide information on mechanisms of transcription

While there are a lot of data collected on human metabolic processes,

  • the content of signal transduction and transcriptional regulatory pathways varies greatly in quality and completeness.

An indicative comparison of MYC transcriptional targets reported in ten different pathway databases reveals that these databases differ greatly from each other (Figure 1). Given that MYC is involved

  • in the transcriptional regulation of approximately 15% of all genes,

one cannot argue that the majority of pathway databases that contain

  • less than thirty putative transcriptional targets of MYC are even close to complete.

More importantly, to date there have been no prior genome-wide evaluation studies (that are based on genome-wide binding and gene expression assays) assessing pathway databases

Background: While pathway databases are becoming increasingly important in most types of biological and translational research, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors:

  1. MYC,
  2. NOTCH1,
  3. BCL6,
  4. TP53,
  5. AR,
  6. STAT1,
  7. RELA.

The employed benchmarking methodology first involves integrating

  • genome-wide binding with functional gene expression data
  • to derive direct targets of transcription factors.

Then the lists of experimentally obtained direct targets

  • are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.

Results: The results of this study show that for the majority of pathway databases,

  • the overlap between experimentally obtained target genes and
  • targets reported in transcriptional regulatory pathway databases is
  • surprisingly small and often is not statistically significant.

The only exception is MetaCore pathway database which

  • yields statistically significant intersection with experimental results in 84% cases.

The lists of experimentally derived direct targets obtained in this study can be used

  • to reveal new biological insight in transcriptional regulation,  and we
  • suggest novel putative therapeutic targets in cancer.

Conclusions: Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by

  • solid scientific evidence and rigorous empirical evaluation.

In the current study we perform

(1) an evaluation of ten commonly used pathway databases,

  • assessing the transcriptional regulatory pathways, considered in the current study as
  • the interactions of the type ‘transcription factor-transcriptional targets’.

This involves integration of human genome wide functional microarray or RNA-seq gene expression data with

  • protein-DNA binding data from ChIP-chip, ChIP-seq, or ChIP-PET platforms
  • to find direct transcriptional targets of the seven well known transcription factors:
  • MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA.

The choice of transcription factors is based on their important role in oncogenesis and availability of binding and expression data in the public domain.

(2) the lists of experimentally derived direct targets are used to assess the quality and completeness of 84 transcriptional regulatory pathways from four publicly available (BioCarta, KEGG, WikiPathways and Cell Signaling Technology) and six commercial (MetaCore, Ingenuity Pathway Analysis, BKL TRANSPATH, BKL TRANSFAC, Pathway Studio and GeneSpring Pathways) pathway databases.

(3) We measure the overlap between pathways and experimentally obtained target genes and assess statistical significance of this overlap, and we demonstrate that experimentally derived lists of direct transcriptional targets

  • can be used to reveal new biological insight on transcriptional regulation.

We show this by analyzing common direct transcriptional targets of

  • MYC, NOTCH1 and RELA
  • that act in interconnected molecular pathways.

Detection of such genes is important as it could reveal novel targets of cancer therapy.

Figure 1 Number of genes in common between MYC transcriptional targets derived from ten different pathway databases. Cells are colored according to their values from white (low values) to red (high values). (not shown)

statistical methodology for comparison

statistical methodology for comparison

Figure 2 Illustration of statistical methodology for comparison between a gold-standard and a pathway database

Since we are seeking to compare gene sets from different studies/databases, it is essential to transform genes to standard identifiers. That is why we transformed all
gene sets to the HUGO Gene Nomenclature Committee approved gene symbols and names. In order to assess statistical significance of the overlap between the resulting gene sets, we used the hypergeometric test at 5% a-level with false discovery rate correction for multiple comparisons by the method of Benjamini and Yekutieli. The alternative hypothesis of this test is that two sets of genes (set A from pathway
database and set B from experiments) have greater number of genes in common than two randomly selected gene sets with the same number of genes as in sets A and B. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 700 in the experimentally derived list (gold-standard), and their intersection is 16 genes (Figure 2a). If we select on random from a total of
20,000 genes two sets with 300 and 700 genes each, their overlap would be greater or equal to 16 genes in 6.34% times. Thus, this overlap will not be statistically significant at 5% a-level (p = 0.0634). On the other hand, consider that for the pathway database #2, there are 30 direct targets of that transcription factor, and their intersection with the 700-gene gold-standard is only 6 genes. Even though the size of this intersection is rather small, it is unlikely to randomly select 30 genes (out of 20,000) with an overlap greater or equal to 6 genes with a 700-gene gold-standard (p = 0.0005, see Figure 2a). This overlap is statistically significant at 5% a-level.

We also calculate an enrichment fold change ratio (EFC) for every intersection between a gold-standard and a pathway database. For a given pair of a gold-standard and a pathway database, EFC is equal to the observed number of genes in their intersection, divided by the expected size of intersection under the null hypothesis (plus machine epsilon, to avoid division by zero). Notice however that larger values of EFC may correspond to databases that are highly incomplete and contain only a few relations. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 50 in the experimentally derived list (gold-standard), and their intersection is 30 genes (Figure 2b). If we select on random from a total of 20,000 genes two sets with 300 and 50 genes each, their expected overlap under the null hypothesis will be equal to 0.75. Thus, the EFC ratio will be equal to 40 (= 30/0.75). On the other hand, consider that for the pathway database #2, there are 2 direct
targets of that transcription factor, and their intersection with the 50-gene gold-standard is only 1 gene. Even though the expected overlap under the null hypothesis will be equal to 0.005 and EFC equal to 200 (5 times bigger than for the database #1), the size of this intersection with the gold-standard is 30 times less than for database #1 (Figure 2b).

Figure 3 Comparison between different pathway databases and experimentally derived gold-standards for all considered transcription factors. Value in a given cell is a number of overlapping genes between a gold-standard and a pathway-derived gene set. Cells
are colored according to their values from white (low values) to red (high values). Underlined values in red represent statistically significant intersections. (not shown)

Figure 4 Summary of the pathway databases assessment. Green cells represent statistically significant intersections between experimentally derived gold-standards and transcriptional regulatory pathways. White cells denote results that are not statistically significant. Numbers are the enrichment fold change ratios (EFC) calculated for each intersection. (not shown)

At the core of this study was creation of gold-standards of transcriptional regulation in humans that can be compared with target genes reported in transcriptional regulatory pathways. We focused on seven well known transcription factors and obtained gold-standards

  • by integrating genome-wide transcription factor-DNA binding data (from ChIP-chip, ChIP-seq, or ChIP-PET platforms)
  • with functional gene expression microarray and RNA-seq data.

The latter data allows to survey changes in the transcriptomes on a genome-wide scale

  • after the inhibition or over-expression of the transcription factor in question.

However, change in the expression of a particular gene could be caused either by the direct effect of the removal or introduction of a given transcription factor, as well as by an indirect effect, through the change in expression level of some other gene(s). It is essential

  • to integrate data from these two sources to
  • obtain an accurate list of gene targets that are directly regulated by a transcription factor.

It is worth noting that tested pathway databases typically do not give distinction between cell-lines, experimental conditions, and other details relevant to experimental systems in which data were obtained. These databases in a sense propose a ‘universal’ list of transcriptional targets. However, it is known that

  • transcriptional regulation in a cell is dynamic and works differently for different systems and stimuli.

This accentuates the major limitation of pathway databases and emphasizes

  • importance of deriving a specific list of transcriptional targets for the current experimental system.

In this study we followed the latter approach by developing gold-standards for specific cell characterized biological systems and experimental conditions.

The approach used here  for building gold-standards of direct mechanistic knowledge has several limitations. (see article).  Nevertheless, our results suggest that multiple transcription factors can co-operate and control both physiological differentiation and malignant transformation, as demonstrated utilizing combinatorial gene-profiling for

  • NOTCH1, MYC and RELA targets.

These studies might lead us to multi-pathway gene expression “signatures”

  • essential for the prediction of genes that could be targeted in cancer treatments.

In agreement with this hypothesis, several of the genes identified in our analysis have been suggested to be putative therapeutic targets in leukemia, with either preclinical or clinical trials underway (CDK4, CDK6, GSK3b, MYC, LCK, NFkB2, BCL2L1, NOTCH1).

Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus

I Izeddin†, V Récamier†‡, L Bosanac, II Cissé, L Boudarene, et al.
1Functional Imaging of Transcription, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Inserm, and CNRS UMR; 2Laboratoire Kastler Brossel, CNRS UMR, Departement de Physique et Institut de Biologie
de l’Ecole Normale Supérieure (IBENS), Paris, Fr; 3Transcription Imaging Consortium, Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, US; + more.
Biophysics and structural biology | Cell biology eLife 2014;3:e02230. http://dx.doi.org:/10.7554/eLife.02230

Transcription factors are

  • proteins that control the expression of genes in the nucleus, and
  • they do this by binding to other proteins or DNA.

First, however, these regulatory proteins need to overcome the challenge of

  • finding their targets in the nucleus, which is crowded with other proteins and DNA.

Much research to date has focused on measuring how fast proteins can diffuse and spread out throughout the nucleus. However these measurements only make sense if these proteins have access to the same space within the nucleus.

Now, Izeddin, Récamier et al. have developed a new technique to track

  • single protein molecules in the nucleus of mammalian cells.

A transcription factor called c-Myc and another protein called P-TEFb

  • were tracked and while they diffused at similar rates,
  • they ‘explored’ the space inside the nucleus in very different ways.

Izeddin, Récamier et al. found that c-Myc explores the nucleus in a so-called ‘non-compact’ manner: this means that it

  • can move almost everywhere inside the nucleus, and has an equal chance
  • of reaching any target regardless of its position in this space.

P-TEFb, on the other hand, searches

  • the nucleus in a ‘compact’ way.

This means that it is constrained to follow a specific path

  • through the nucleus and is therefore guided to its potential targets.

Izeddin, Récamier et al. explain that

  • the different ‘search strategies’ used by these two proteins
  • influence how long it takes them to find their targets and
  • how far they can travel in a given time.

These findings, together with information about

  • where and when different proteins interact in the nucleus,

will be essential to understand how the organization of the genome within the nucleus

  • can control the expression of genes.

The next challenge will now be to

  • uncover what determines a
  • protein’s search strategy in the nucleus, as well as
  • the potential ways that this strategy might be regulated.

Mueller et al., 2010; Normanno et al., 2012). These transient interactions are essential to ensure a fine regulation of binding site occupancy—by competition or by altering the TF concentration—but must also be persistent enough to enable the assembly of multicomponent complexes (Dundr, 2002; Darzacq and Singer, 2008; Gorski et al., 2008; Cisse et al., 2013).
In parallel to the experimental evidence of the fast diffusive motion of nuclear factors, our understanding of the intranuclear space has evolved from a homogeneous environment to an organelle where spatial arrangement among genes and regulatory sequences play an important role in transcriptional control (Heard and Bickmore, 2007). The nucleus of eukaryotes displays a hierarchy of organized structures (Gibcus and Dekker, 2013) and is often referred to as a
crowded environment.
How crowding influences transport properties of macromolecules and organelles in the cell is a fundamental question in quantitative molecular biology. While a restriction of the available space for diffusion can slow down transport processes, it can also channel molecules towards their targets increasing their chance to meet interacting partners. A widespread observation in quantitative cell biology is that the diffusion of molecules is anomalous, often attributed to crowding in the nucleoplasm, cytoplasm, or in the membranes of the cell (Höfling and Franosch, 2013). An open debate remains on how to determine whether diffusion is anomalous or normal (Malchus and Weiss, 2009; Saxton, 2012), and the mechanisms behind anomalous diffusion (Saxton, 2007). The answer to these questions bears important consequences for the understanding of the biochemical reactions of the cell.
The problem of diffusing molecules in non-homogenous media has been investigated in different fields. Following the seminal work of de Gennes (1982a), (1982b) in polymer physics, the study of diffusivity of particles and their reactivity has been generalized to random or disordered media (Kopelman, 1986; Lindenberg et al., 1991). These works have set a framework to interpret the mobility of macromolecular complexes in the cell, and recently in terms of kinetics of biochemical reactions (Condamin et al., 2007). Experimental evidence has also been found, showing the influence
of the glass-like properties of the bacterial cytoplasm in the molecular dynamics of intracellular processes (Parry et al., 2014). These studies demonstrate that the geometry of the medium in which diffusion takes place has important repercussions for the search kinetics of molecules. The notion of compact and non-compact exploration was introduced by de Gennes (1982a) in the context of dense polymers and describes two fundamental types of diffusive behavior. While a non-compact explorer leaves a significant number of available sites unvisited, a compact explorer performs a redundant
exploration of the space. In chemistry, the influence of compactness is well established to describe dimensional effects on reaction rates (Kopelman, 1986).
In this study, we aim to elucidate the existence of different types of mobility of TFs in the eukaryotic nucleus, as well as the principles governing nuclear exploration of factors relevant to transcriptional control. To this end, we used single-molecule (SM) imaging to address the relationship between the nuclear geometry and the search dynamics of two nuclear factors having distinct functional roles: the proto-oncogene c-Myc and the positive transcription elongation factor (P-TEFb). c-Myc is a basic helix-loop-helix DNA-binding transcription factor that binds to E-Boxes; 18,000 E-boxes are found in the genome, and c-Myc affects the transcription of numerous genes (Gallant and Steiger, 2009).
Recently, c-Myc has been demonstrated to be a general transcriptional activator upregulating transcription of nearly all genes (Lin et al., 2012; Nie et al., 2012). P-TEFb is an essential actor in the transcription regulation driven by RNA Polymerase II. P-TEFb is a cyclin-dependent kinase, comprising a CDK9 and a Cyclin T subunit. It phosphorylates the elongation control factors SPT5 and NELF to allow productive elongation of class II gene transcription (Wada et al., 1998). The carboxy-terminal domain (CTD) of the catalytic subunit RPB1 of polymerase II is also a major target of P-TEFb (Zhou et al., 2012). c-Myc and P-TEFb are therefore two good examples of transcriptional regulators binding to numerous sites in the nucleus; the latter binds to the transcription machinery itself and the former directly to DNA.

Single particle tracking (SPT) constitutes a powerful method to probe the mobility of molecules in living cells (Lord et al., 2010). In the nucleus, SPT has been first employed to investigate the dynamics of mRNAs (Fusco et al., 2003; Shav-Tal et al., 2004) or for rheological measurements of the nucleoplasm using inert probes (Bancaud et al., 2009). Recently, the tracking of single nuclear factors has been facilitated by the advent of efficient in situ tagging methods such as Halo
tags (Mazza et al., 2012). An alternative approach takes advantage of photoconvertible tags (Lippincott-Schwartz and Patterson, 2009) and photoactivated localization microscopy (PALM) (Betzig et al., 2006; Hess et al., 2006). Single particle tracking PALM (sptPALM) was first used to achieve high-density diffusion maps of membrane proteins (Manley et al., 2008). However, spt-PALM experiments have typically been limited to proteins with slow mobility (Manley et al., 2008) or those that undergo restricted motions (Frost et al., 2010; English et al., 2011).

Recently, by inclusion of light-sheet illumination, it has been used to determine the binding characteristics of TFs to DNA (Gebhardt et al., 2013). In this study, we developed a new sptPALM procedure adapted for the recording of individual proteins rapidly diffusing in the nucleus of mammalian cells. We used the photoconvertible fluorophore Dendra2 (Gurskaya et al., 2006) and took advantage of tilted illumination (Tokunaga et al., 2008). A careful control of the photoconversion rate minimized the background signal due to out-of-focus activated molecules, and we could thus follow the motion of individual proteins freely diffusing within the nuclear volume. With this sptPALM technique, we recorded large data sets (on the order of 104 single translocations in a single imaging session), which were essential for a proper statistical analysis of the search dynamics.
We applied our technique to several nuclear proteins and found that diffusing factors do not sense a unique nucleoplasmic architecture: c-Myc and P-TEFb adopt different nuclear space-exploration strategies, which drastically change the way they reach their specific targets. The differences observed between the two factors were not due to their diffusive kinetic parameters but to the geometry of their exploration path. c-Myc and our control protein, ‘free’ Dendra2, showed free diffusion in a three-dimensional nuclear space. In contrast, P-TEFb explored the nuclear volume by sampling a space of reduced dimensionality, displaying characteristics of exploration constrained in fractal structures.
The role of the space-sampling mode in the search strategy has long been discussed from a theoretical point of view (de Gennes, 1982a; Kopelman, 1986; Lindenberg et al., 1991). Our experimental results support the notion that it could indeed be a key parameter for diffusion-limited chemical reactions in the closed environment of the nucleus (Bénichou et al., 2010). We discuss the implications of our observations in terms of gene expression control, and its relation to the spatial organization of genes within the nucleus.

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Summary of Signaling and Signaling Pathways

Summary of Signaling and Signaling Pathways

Author and Curator: Larry H Bernstein, MD, FCAP

In the imtroduction to this series of discussions I pointed out JEDS Rosalino’s observation about the construction of a complex molecule of acetyl coenzyme A, and the amount of genetic coding that had to go into it.  Furthermore, he observes –  Millions of years later, or as soon as, the information of interaction leading to activity and regulation could be found in RNA, proteins like reverse transcriptase move this information to a more stable form (DNA). In this way it is easier to understand the use of CoA to make two carbon molecules more reactive.

acetylCoA

acetylCoA

In the tutorial that follows we find support for the view that mechanisms and examples from the current literature, which give insight into the developments in cell metabolism, are achieving a separation from inconsistent views introduced by the classical model of molecular biology and genomics, toward a more functional cellular dynamics that is not dependent on the classic view.  The classical view fits a rigid framework that is to genomics and metabolomics as Mendelian genetics if to multidimentional, multifactorial genetics.  The inherent difficulty lies in two places:

  1. Interactions between differently weighted determinants
  2. A large part of the genome is concerned with regulatory function, not expression of the code

The goal of the tutorial was to achieve an understanding of how cell signaling occurs in a cell.  Completion of the tutorial would provide

  1. a basic understanding signal transduction and
  2. the role of phosphorylation in signal transduction.
Regulation of the integrity of endothelial cell–cell contacts by phosphorylation of VE-cadherin

Regulation of the integrity of endothelial cell–cell contacts by phosphorylation of VE-cadherin

In addition – detailed knowledge of –

  1. the role of Tyrosine kinases and
  2. G protein-coupled receptors in cell signaling.
serine

serine

threonine

threonine

protein kinase

protein kinase

We are constantly receiving and interpreting signals from our environment, which can come

  • in the form of light, heat, odors, touch or sound.

The cells of our bodies are also

  • constantly receiving signals from other cells.

These signals are important to

  • keep cells alive and functioning as well as
  • to stimulate important events such as
  • cell division and differentiation.

Signals are most often chemicals that can be found

  • in the extracellular fluid around cells.

These chemicals can come

  • from distant locations in the body (endocrine signaling by hormones), from
  • nearby cells (paracrine signaling) or can even
  • be secreted by the same cell (autocrine signaling).

Notch-mediated juxtacrine signal between adjacent cells. 220px-Notchccr

Signaling molecules may trigger any number of cellular responses, including

  • changing the metabolism of the cell receiving the signal or
  • result in a change in gene expression (transcription) within the nucleus of the cell or both.
controlling the output of ribosomes.

controlling the output of ribosomes.

To which I would now add..

  • result in either an inhibitory or a stimulatory effect

The three stages of cell signaling are:

Cell signaling can be divided into 3 stages:

Reception: A cell detects a signaling molecule from the outside of the cell.

Transduction: When the signaling molecule binds the receptor it changes the receptor protein in some way. This change initiates the process of transduction. Signal transduction is usually a pathway of several steps. Each relay molecule in the signal transduction pathway changes the next molecule in the pathway.

Response: Finally, the signal triggers a specific cellular response.

signal transduction

signal transduction

http://www.hartnell.edu/tutorials/biology/images/signaltransduction_simple.jpg

The initiation is depicted as follows:

Signal Transduction – ligand binds to surface receptor

Membrane receptors function by binding the signal molecule (ligand) and causing the production of a second signal (also known as a second messenger) that then causes a cellular response. These types of receptors transmit information from the extracellular environment to the inside of the cell.

  • by changing shape or
  • by joining with another protein
  • once a specific ligand binds to it.

Examples of membrane receptors include

  • G Protein-Coupled Receptors and
Understanding these receptors and identifying their ligands and the resulting signal transduction pathways represent a major conceptual advance.

Understanding these receptors and identifying their ligands and the resulting signal transduction pathways represent a major conceptual advance.

  • Receptor Tyrosine Kinases.
intracellular signaling

intracellular signaling

http://www.hartnell.edu/tutorials/biology/images/membrane_receptor_tk.jpg

Intracellular receptors are found inside the cell, either in the cytopolasm or in the nucleus of the target cell (the cell receiving the signal).

Note that though change in gene expression is stated, the change in gene expression does not here imply a change in the genetic information – such as – mutation.  That does not have to be the case in the normal homeostatic case.

This point is the differentiating case between what JEDS Roselino has referred as

  1. a fast, adaptive reaction, that is the feature of protein molecules, and distinguishes this interaction from
  2. a one-to-one transcription of the genetic code.

The rate of transcription can be controlled, or it can be blocked.  This is in large part in response to the metabolites in the immediate interstitium.

This might only be

  • a change in the rate of a transcription or a suppression of expression through RNA.
  • Or through a conformational change in an enzyme
 Swinging domains in HECT E3 enzymes

Swinging domains in HECT E3 enzymes

Since signaling systems need to be

  • responsive to small concentrations of chemical signals and act quickly,
  • cells often use a multi-step pathway that transmits the signal quickly,
  • while amplifying the signal to numerous molecules at each step.

Signal transduction pathways are shown (simplified):

Signal Transduction

Signal Transduction

Signal transduction occurs when an

  1. extracellular signaling molecule activates a specific receptor located on the cell surface or inside the cell.
  2. In turn, this receptor triggers a biochemical chain of events inside the cell, creating a response.
  3. Depending on the cell, the response alters the cell’s metabolism, shape, gene expression, or ability to divide.
  4. The signal can be amplified at any step. Thus, one signaling molecule can cause many responses.

In 1970, Martin Rodbell examined the effects of glucagon on a rat’s liver cell membrane receptor. He noted that guanosine triphosphate disassociated glucagon from this receptor and stimulated the G-protein, which strongly influenced the cell’s metabolism. Thus, he deduced that the G-protein is a transducer that accepts glucagon molecules and affects the cell. For this, he shared the 1994 Nobel Prize in Physiology or Medicine with Alfred G. Gilman.

Guanosine monophosphate structure

Guanosine monophosphate structure

In 2007, a total of 48,377 scientific papers—including 11,211 e-review papers—were published on the subject. The term first appeared in a paper’s title in 1979. Widespread use of the term has been traced to a 1980 review article by Rodbell: Research papers focusing on signal transduction first appeared in large numbers in the late 1980s and early 1990s.

Signal transduction involves the binding of extracellular signaling molecules and ligands to cell-surface receptors that trigger events inside the cell. The combination of messenger with receptor causes a change in the conformation of the receptor, known as receptor activation.

This activation is always the initial step (the cause) leading to the cell’s ultimate responses (effect) to the messenger. Despite the myriad of these ultimate responses, they are all directly due to changes in particular cell proteins. Intracellular signaling cascades can be started through cell-substratum interactions; examples are the integrin that binds ligands in the extracellular matrix and steroids.

Integrin

Integrin

Most steroid hormones have receptors within the cytoplasm and act by stimulating the binding of their receptors to the promoter region of steroid-responsive genes.

steroid hormone receptor

steroid hormone receptor

Various environmental stimuli exist that initiate signal transmission processes in multicellular organisms; examples include photons hitting cells in the retina of the eye, and odorants binding to odorant receptors in the nasal epithelium. Certain microbial molecules, such as viral nucleotides and protein antigens, can elicit an immune system response against invading pathogens mediated by signal transduction processes. This may occur independent of signal transduction stimulation by other molecules, as is the case for the toll-like receptor. It may occur with help from stimulatory molecules located at the cell surface of other cells, as with T-cell receptor signaling. Receptors can be roughly divided into two major classes: intracellular receptors and extracellular receptors.

Signal transduction cascades amplify the signal output

Signal transduction cascades amplify the signal output

Signal transduction cascades amplify the signal output

G protein-coupled receptors (GPCRs) are a family of integral transmembrane proteins that possess seven transmembrane domains and are linked to a heterotrimeric G protein. Many receptors are in this family, including adrenergic receptors and chemokine receptors.

Arrestin binding to active GPCR kinase (GRK)-phosphorylated GPCRs blocks G protein coupling

signal transduction pathways

signal transduction pathways

Arrestin binding to active GPCR kinase (GRK)-phosphorylated GPCRs blocks G protein coupling

Arrestin binding to active GPCR kinase (GRK)-phosphorylated GPCRs blocks G protein coupling

Signal transduction by a GPCR begins with an inactive G protein coupled to the receptor; it exists as a heterotrimer consisting of Gα, Gβ, and Gγ. Once the GPCR recognizes a ligand, the conformation of the receptor changes to activate the G protein, causing Gα to bind a molecule of GTP and dissociate from the other two G-protein subunits.

The dissociation exposes sites on the subunits that can interact with other molecules. The activated G protein subunits detach from the receptor and initiate signaling from many downstream effector proteins such as phospholipases and ion channels, the latter permitting the release of second messenger molecules.

Receptor tyrosine kinases (RTKs) are transmembrane proteins with an intracellular kinase domain and an extracellular domain that binds ligands; examples include growth factor receptors such as the insulin receptor.

 insulin receptor and and insulin receptor signaling pathway (IRS)

insulin receptor and and insulin receptor signaling pathway (IRS)

To perform signal transduction, RTKs need to form dimers in the plasma membrane; the dimer is stabilized by ligands binding to the receptor.

RTKs

RTKs

The interaction between the cytoplasmic domains stimulates the autophosphorylation of tyrosines within the domains of the RTKs, causing conformational changes.

Allosteric_Regulation.svg

Subsequent to this, the receptors’ kinase domains are activated, initiating phosphorylation signaling cascades of downstream cytoplasmic molecules that facilitate various cellular processes such as cell differentiation and metabolism.

Signal-Transduction-Pathway

Signal-Transduction-Pathway

As is the case with GPCRs, proteins that bind GTP play a major role in signal transduction from the activated RTK into the cell. In this case, the G proteins are

  • members of the Ras, Rho, and Raf families, referred to collectively as small G proteins.

They act as molecular switches usually

  • tethered to membranes by isoprenyl groups linked to their carboxyl ends.

Upon activation, they assign proteins to specific membrane subdomains where they participate in signaling. Activated RTKs in turn activate

  • small G proteins that activate guanine nucleotide exchange factors such as SOS1.

Once activated, these exchange factors can activate more small G proteins, thus

  • amplifying the receptor’s initial signal.

The mutation of certain RTK genes, as with that of GPCRs, can result in the expression of receptors that exist in a constitutively activate state; such mutated genes may act as oncogenes.

Integrin

 

Integrin

Integrin

Integrin-mediated signal transduction

An overview of integrin-mediated signal transduction, adapted from Hehlgens et al. (2007).

Integrins are produced by a wide variety of cells; they play a role in

  • cell attachment to other cells and the extracellular matrix and
  • in the transduction of signals from extracellular matrix components such as fibronectin and collagen.

Ligand binding to the extracellular domain of integrins

  • changes the protein’s conformation,
  • clustering it at the cell membrane to
  • initiate signal transduction.

Integrins lack kinase activity; hence, integrin-mediated signal transduction is achieved through a variety of intracellular protein kinases and adaptor molecules, the main coordinator being integrin-linked kinase.

As shown in the picture, cooperative integrin-RTK signaling determines the

  1. timing of cellular survival,
  2. apoptosis,
  3. proliferation, and
  4. differentiation.
integrin-mediated signal transduction

integrin-mediated signal transduction

Integrin signaling

Integrin signaling

ion channel

A ligand-gated ion channel, upon binding with a ligand, changes conformation

  • to open a channel in the cell membrane
  • through which ions relaying signals can pass.

An example of this mechanism is found in the receiving cell of a neural synapse. The influx of ions that occurs in response to the opening of these channels

  1. induces action potentials, such as those that travel along nerves,
  2. by depolarizing the membrane of post-synaptic cells,
  3. resulting in the opening of voltage-gated ion channels.
RyR and Ca+ release from SR

RyR and Ca+ release from SR

An example of an ion allowed into the cell during a ligand-gated ion channel opening is Ca2+;

  • it acts as a second messenger
  • initiating signal transduction cascades and
  • altering the physiology of the responding cell.

This results in amplification of the synapse response between synaptic cells

  • by remodelling the dendritic spines involved in the synapse.

In eukaryotic cells, most intracellular proteins activated by a ligand/receptor interaction possess an enzymatic activity; examples include tyrosine kinase and phosphatases. Some of them create second messengers such as cyclic AMP and IP3,

cAMP

cAMP

Inositol_1,4,5-trisphosphate.svg

Inositol_1,4,5-trisphosphate.svg

  • the latter controlling the release of intracellular calcium stores into the cytoplasm.

Many adaptor proteins and enzymes activated as part of signal transduction possess specialized protein domains that bind to specific secondary messenger molecules. For example,

  • calcium ions bind to the EF hand domains of calmodulin,
  • allowing it to bind and activate calmodulin-dependent kinase.
calcium movement and RyR2 receptor

calcium movement and RyR2 receptor

PIP3 and other phosphoinositides do the same thing to the Pleckstrin homology domains of proteins such as the kinase protein AKT.

Signals can be generated within organelles, such as chloroplasts and mitochondria, modulating the nuclear
gene expression in a process called retrograde signaling.

Recently, integrative genomics approaches, in which correlation analysis has been applied on transcript and metabolite profiling data of Arabidopsis thaliana, revealed the identification of metabolites which are putatively acting as mediators of nuclear gene expression.

http://fpls.com/unraveling_retrograde_signaling_pathways:_finding_candidate_signaling_molecules_via_metabolomics_and_systems_biology_driven_approaches

Related articles

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  3. Activation of the Jasmonic Acid Plant Defence Pathway Alters the Composition of Rhizosphere

Nutrients 2014, 6, 3245-3258; http://dx.doi.org:/10.3390/nu6083245

Omega-3 (ω-3) fatty acids are one of the two main families of long chain polyunsaturated fatty acids (PUFA). The main omega-3 fatty acids in the mammalian body are

  • α-linolenic acid (ALA), docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA).

Central nervous tissues of vertebrates are characterized by a high concentration of omega-3 fatty acids. Moreover, in the human brain,

  • DHA is considered as the main structural omega-3 fatty acid, which comprises about 40% of the PUFAs in total.

DHA deficiency may be the cause of many disorders such as depression, inability to concentrate, excessive mood swings, anxiety, cardiovascular disease, type 2 diabetes, dry skin and so on.

On the other hand,

  • zinc is the most abundant trace metal in the human brain.

There are many scientific studies linking zinc, especially

  • excess amounts of free zinc, to cellular death.

Neurodegenerative diseases, such as Alzheimer’s disease, are characterized by altered zinc metabolism. Both animal model studies and human cell culture studies have shown a possible link between

  • omega-3 fatty acids, zinc transporter levels and
  • free zinc availability at cellular levels.

Many other studies have also suggested a possible

  • omega-3 and zinc effect on neurodegeneration and cellular death.

Therefore, in this review, we will examine

  • the effect of omega-3 fatty acids on zinc transporters and
  • the importance of free zinc for human neuronal cells.

Moreover, we will evaluate the collective understanding of

  • mechanism(s) for the interaction of these elements in neuronal research and their
  • significance for the diagnosis and treatment of neurodegeneration.

Epidemiological studies have linked high intake of fish and shellfish as part of the daily diet to

  • reduction of the incidence and/or severity of Alzheimer’s disease (AD) and senile mental decline in

Omega-3 fatty acids are one of the two main families of a broader group of fatty acids referred to as polyunsaturated fatty acids (PUFAs). The other main family of PUFAs encompasses the omega-6 fatty acids. In general, PUFAs are essential in many biochemical events, especially in early post-natal development processes such as

  • cellular differentiation,
  • photoreceptor membrane biogenesis and
  • active synaptogenesis.

Despite the significance of these

two families, mammals cannot synthesize PUFA de novo, so they must be ingested from dietary sources. Though belonging to the same family, both

  • omega-3 and omega-6 fatty acids are metabolically and functionally distinct and have
  • opposing physiological effects. In the human body,
  • high concentrations of omega-6 fatty acids are known to increase the formation of prostaglandins and
  • thereby increase inflammatory processes [10].

the reverse process can be seen with increased omega-3 fatty acids in the body.

Many other factors, such as

  1. thromboxane A2 (TXA2),
  2. leukotriene
  3. B4 (LTB4),
  4. IL-1,
  5. IL-6,
  6. tumor necrosis factor (TNF) and
  7. C-reactive protein,

which are implicated in various health conditions, have been shown to be increased with high omega-6 fatty acids but decreased with omega-3 fatty acids in the human body.

Dietary fatty acids have been identified as protective factors in coronary heart disease, and PUFA levels are known to play a critical role in

  • immune responses,
  • gene expression and
  • intercellular communications.

omega-3 fatty acids are known to be vital in

  • the prevention of fatal ventricular arrhythmias, and
  • are also known to reduce thrombus formation propensity by decreasing platelet aggregation, blood viscosity and fibrinogen levels

.Since omega-3 fatty acids are prevalent in the nervous system, it seems logical that a deficiency may result in neuronal problems, and this is indeed what has been identified and reported.

The main

In another study conducted with individuals of 65 years of age or older (n = 6158), it was found that

  • only high fish consumption, but
  • not dietary omega-3 acid intake,
  • had a protective effect on cognitive decline

In 2005, based on a meta-analysis of the available epidemiology and preclinical studies, clinical trials were conducted to assess the effects of omega-3 fatty acids on cognitive protection. Four of the trials completed have shown

a protective effect of omega-3 fatty acids only among those with mild cognitive impairment conditions.

A  trial of subjects with mild memory complaints demonstrated

  • an improvement with 900 mg of DHA.

We review key findings on

  • the effect of the omega-3 fatty acid DHA on zinc transporters and the
  • importance of free zinc to human neuronal cells.

DHA is the most abundant fatty acid in neural membranes, imparting appropriate

  • fluidity and other properties,

and is thus considered as the most important fatty acid in neuronal studies. DHA is well conserved throughout the mammalian species despite their dietary differences. It is mainly concentrated

  • in membrane phospholipids at synapses and
  • in retinal photoreceptors and
  • also in the testis and sperm.

In adult rats’ brain, DHA comprises approximately

  • 17% of the total fatty acid weight, and
  • in the retina it is as high as 33%.

DHA is believed to have played a major role in the evolution of the modern human –

  • in particular the well-developed brain.

Premature babies fed on DHA-rich formula show improvements in vocabulary and motor performance.

Analysis of human cadaver brains have shown that

  • people with AD have less DHA in their frontal lobe
  • and hippocampus compared with unaffected individuals

Furthermore, studies in mice have increased support for the

  • protective role of omega-3 fatty acids.

Mice administrated with a dietary intake of DHA showed

  • an increase in DHA levels in the hippocampus.

Errors in memory were decreased in these mice and they demonstrated

  • reduced peroxide and free radical levels,
  • suggesting a role in antioxidant defense.

Another study conducted with a Tg2576 mouse model of AD demonstrated that dietary

  • DHA supplementation had a protective effect against reduction in
  • drebrin (actin associated protein), elevated oxidation, and to some extent, apoptosis via
  • decreased caspase activity.

 

Zinc

Zinc is a trace element, which is indispensable for life, and it is the second most abundant trace element in the body. It is known to be related to

  • growth,
  • development,
  • differentiation,
  • immune response,
  • receptor activity,
  • DNA synthesis,
  • gene expression,
  • neuro-transmission,
  • enzymatic catalysis,
  • hormonal storage and release,
  • tissue repair,
  • memory,
  • the visual process

and many other cellular functions. Moreover, the indispensability of zinc to the body can be discussed in many other aspects,  as

  • a component of over 300 different enzymes
  • an integral component of a metallothioneins
  • a gene regulatory protein.

Approximately 3% of all proteins contain

  • zinc binding motifs .

The broad biological functionality of zinc is thought to be due to its stable chemical and physical properties. Zinc is considered to have three different functions in enzymes;

  1. catalytic,
  2. coactive and

Indeed, it is the only metal found in all six different subclasses

of enzymes. The essential nature of zinc to the human body can be clearly displayed by studying the wide range of pathological effects of zinc deficiency. Anorexia, embryonic and post-natal growth retardation, alopecia, skin lesions, difficulties in wound healing, increased hemorrhage tendency and severe reproductive abnormalities, emotional instability, irritability and depression are just some of the detrimental effects of zinc deficiency.

Proper development and function of the central nervous system (CNS) is highly dependent on zinc levels. In the mammalian organs, zinc is mainly concentrated in the brain at around 150 μm. However, free zinc in the mammalian brain is calculated to be around 10 to 20 nm and the rest exists in either protein-, enzyme- or nucleotide bound form. The brain and zinc relationship is thought to be mediated

  • through glutamate receptors, and
  • it inhibits excitatory and inhibitory receptors.

Vesicular localization of zinc in pre-synaptic terminals is a characteristic feature of brain-localized zinc, and

  • its release is dependent on neural activity.

Retardation of the growth and development of CNS tissues have been linked to low zinc levels. Peripheral neuropathy, spina bifida, hydrocephalus, anencephalus, epilepsy and Pick’s disease have been linked to zinc deficiency. However, the body cannot tolerate excessive amounts of zinc.

The relationship between zinc and neurodegeneration, specifically AD, has been interpreted in several ways. One study has proposed that β-amyloid has a greater propensity to

  • form insoluble amyloid in the presence of
  • high physiological levels of zinc.

Insoluble amyloid is thought to

  • aggregate to form plaques,

which is a main pathological feature of AD. Further studies have shown that

  • chelation of zinc ions can deform and disaggregate plaques.

In AD, the most prominent injuries are found in

  • hippocampal pyramidal neurons, acetylcholine-containing neurons in the basal forebrain, and in
  • somatostatin-containing neurons in the forebrain.

All of these neurons are known to favor

  • rapid and direct entry of zinc in high concentration
  • leaving neurons frequently exposed to high dosages of zinc.

This is thought to promote neuronal cell damage through oxidative stress and mitochondrial dysfunction. Excessive levels of zinc are also capable of

  • inhibiting Ca2+ and Na+ voltage gated channels
  • and up-regulating the cellular levels of reactive oxygen species (ROS).

High levels of zinc are found in Alzheimer’s brains indicating a possible zinc related neurodegeneration. A study conducted with mouse neuronal cells has shown that even a 24-h exposure to high levels of zinc (40 μm) is sufficient to degenerate cells.

If the human diet is deficient in zinc, the body

  • efficiently conserves zinc at the tissue level by compensating other cellular mechanisms

to delay the dietary deficiency effects of zinc. These include reduction of cellular growth rate and zinc excretion levels, and

  • redistribution of available zinc to more zinc dependent cells or organs.

A novel method of measuring metallothionein (MT) levels was introduced as a biomarker for the

  • assessment of the zinc status of individuals and populations.

In humans, erythrocyte metallothionein (E-MT) levels may be considered as an indicator of zinc depletion and repletion, as E-MT levels are sensitive to dietary zinc intake. It should be noted here that MT plays an important role in zinc homeostasis by acting

  • as a target for zinc ion binding and thus
  • assisting in the trafficking of zinc ions through the cell,
  • which may be similar to that of zinc transporters

Zinc Transporters

Deficient or excess amounts of zinc in the body can be catastrophic to the integrity of cellular biochemical and biological systems. The gastrointestinal system controls the absorption, excretion and the distribution of zinc, although the hydrophilic and high-charge molecular characteristics of zinc are not favorable for passive diffusion across the cell membranes. Zinc movement is known to occur

  • via intermembrane proteins and zinc transporter (ZnT) proteins

These transporters are mainly categorized under two metal transporter families; Zip (ZRT, IRT like proteins) and CDF/ZnT (Cation Diffusion Facilitator), also known as SLC (Solute Linked Carrier) gene families: Zip (SLC-39) and ZnT (SLC-30). More than 20 zinc transporters have been identified and characterized over the last two decades (14 Zips and 8 ZnTs).

Members of the SLC39 family have been identified as the putative facilitators of zinc influx into the cytosol, either from the extracellular environment or from intracellular compartments (Figure 1).

The identification of this transporter family was a result of gene sequencing of known Zip1 protein transporters in plants, yeast and human cells. In contrast to the SLC39 family, the SLC30 family facilitates the opposite process, namely zinc efflux from the cytosol to the extracellular environment or into luminal compartments such as secretory granules, endosomes and synaptic vesicles; thus decreasing intracellular zinc availability (Figure 1). ZnT3 is the most important in the brain where

  • it is responsible for the transport of zinc into the synaptic vesicles of
  • glutamatergic neurons in the hippocampus and neocortex,

Figure 1: Subcellular localization and direction of transport of the zinc transporter families, ZnT and ZIP. Arrows show the direction of zinc mobilization for the ZnT (green) and ZIP (red) proteins. A net gain in cytosolic zinc is achieved by the transportation of zinc from the extracellular region and organelles such as the endoplasmic reticulum (ER) and Golgi apparatus by the ZIP transporters. Cytosolic zinc is mobilized into early secretory compartments such as the ER and Golgi apparatus by the ZnT transporters. Figures were produced using Servier Medical Art, http://www.servier.com/.   http://www.hindawi.com/journals/jnme/2012/173712.fig.001.jpg

Figure 2: Early zinc signaling (EZS) and late zinc signaling (LZS). EZS involves transcription-independent mechanisms where an extracellular stimulus directly induces an increase in zinc levels within several minutes by releasing zinc from intracellular stores (e.g., endoplasmic reticulum). LSZ is induced several hours after an external stimulus and is dependent on transcriptional changes in zinc transporter expression. Components of this figure were produced using Servier Medical Art, http://www.servier.com/ and adapted from Fukada et al. [30].

omega-3 fatty acids in the mammalian body are

  1. α-linolenic acid (ALA),
  2. docosahexenoic acid (DHA) and
  3. eicosapentaenoic acid (EPA).

In general, seafood is rich in omega-3 fatty acids, more specifically DHA and EPA (Table 1). Thus far, there are nine separate epidemiological studies that suggest a possible link between

  • increased fish consumption and reduced risk of AD
  • and eight out of ten studies have reported a link between higher blood omega-3 levels

DHA and Zinc Homeostasis

Many studies have identified possible associations between DHA levels, zinc homeostasis, neuroprotection and neurodegeneration. Dietary DHA deficiency resulted in

  • increased zinc levels in the hippocampus and
  • elevated expression of the putative zinc transporter, ZnT3, in the rat brain.

Altered zinc metabolism in neuronal cells has been linked to neurodegenerative conditions such as AD. A study conducted with transgenic mice has shown a significant link between ZnT3 transporter levels and cerebral amyloid plaque pathology. When the ZnT3 transporter was silenced in transgenic mice expressing cerebral amyloid plaque pathology,

  • a significant reduction in plaque load
  • and the presence of insoluble amyloid were observed.

In addition to the decrease in plaque load, ZnT3 silenced mice also exhibited a significant

  • reduction in free zinc availability in the hippocampus
  • and cerebral cortex.

Collectively, the findings from this study are very interesting and indicate a clear connection between

  • zinc availability and amyloid plaque formation,

thus indicating a possible link to AD.

DHA supplementation has also been reported to limit the following:

  1. amyloid presence,
  2. synaptic marker loss,
  3. hyper-phosphorylation of Tau,
  4. oxidative damage and
  5. cognitive deficits in transgenic mouse model of AD.

In addition, studies by Stoltenberg, Flinn and colleagues report on the modulation of zinc and the effect in transgenic mouse models of AD. Given that all of these are classic pathological features of AD, and considering the limiting nature of DHA in these processes, it can be argued that DHA is a key candidate in preventing or even curing this debilitating disease.

In order to better understand the possible links and pathways of zinc and DHA with neurodegeneration, we designed a study that incorporates all three of these aspects, to study their effects at the cellular level. In this study, we were able to demonstrate a possible link between omega-3 fatty acid (DHA) concentration, zinc availability and zinc transporter expression levels in cultured human neuronal cells.

When treated with DHA over 48 h, ZnT3 levels were markedly reduced in the human neuroblastoma M17 cell line. Moreover, in the same study, we were able to propose a possible

  • neuroprotective mechanism of DHA,

which we believe is exerted through

  • a reduction in cellular zinc levels (through altering zinc transporter expression levels)
  • that in turn inhibits apoptosis.

DHA supplemented M17 cells also showed a marked depletion of zinc uptake (up to 30%), and

  • free zinc levels in the cytosol were significantly low compared to the control

This reduction in free zinc availability was specific to DHA; cells treated with EPA had no significant change in free zinc levels (unpublished data). Moreover, DHA-repleted cells had

  • low levels of active caspase-3 and
  • high Bcl-2 levels compared to the control treatment.

These findings are consistent with previous published data and further strengthen the possible

  • correlation between zinc, DHA and neurodegeneration.

On the other hand, recent studies using ZnT3 knockout (ZnT3KO) mice have shown the importance of

  • ZnT3 in memory and AD pathology.

For example, Sindreu and colleagues have used ZnT3KO mice to establish the important role of

  • ZnT3 in zinc homeostasis that modulates presynaptic MAPK signaling
  • required for hippocampus-dependent memory

Results from these studies indicate a possible zinc-transporter-expression-level-dependent mechanism for DHA neuroprotection.

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Complex Models of Signaling: Therapeutic Implications

Complex Models of Signaling: Therapeutic Implications

Curator: Larry H. Bernstein, MD, FCAP

Updated 6/24/2019

Fishy Business: Effect of Omega-3 Fatty Acids on Zinc Transporters and Free Zinc Availability in Human Neuronal Cells

Damitha De Mel and Cenk Suphioglu *

NeuroAllergy Research Laboratory (NARL), School of Life and Environmental Sciences, Faculty of Science, Engineering and Built Environment, Waurn Ponds, Victoria, Australia.

Nutrients 2014, 6, 3245-3258; http://dx.doi.org:/10.3390/nu6083245

Omega-3 (ω-3) fatty acids are one of the two main families of long chain polyunsaturated fatty acids (PUFA). The main omega-3 fatty acids in the mammalian body are

  • α-linolenic acid (ALA), docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA).

Central nervous tissues of vertebrates are characterized by a high concentration of omega-3 fatty acids. Moreover, in the human brain,

  • DHA is considered as the main structural omega-3 fatty acid, which comprises about 40% of the PUFAs in total.

DHA deficiency may be the cause of many disorders such as depression, inability to concentrate, excessive mood swings, anxiety, cardiovascular disease, type 2 diabetes, dry skin and so on.

On the other hand,

  • zinc is the most abundant trace metal in the human brain.

There are many scientific studies linking zinc, especially

  • excess amounts of free zinc, to cellular death.

Neurodegenerative diseases, such as Alzheimer’s disease, are characterized by altered zinc metabolism. Both animal model studies and human cell culture studies have shown a possible link between

  • omega-3 fatty acids, zinc transporter levels and
  • free zinc availability at cellular levels.

Many other studies have also suggested a possible

  • omega-3 and zinc effect on neurodegeneration and cellular death.

Therefore, in this review, we will examine

  • the effect of omega-3 fatty acids on zinc transporters and
  • the importance of free zinc for human neuronal cells.

Moreover, we will evaluate the collective understanding of

  • mechanism(s) for the interaction of these elements in neuronal research and their
  • significance for the diagnosis and treatment of neurodegeneration.

Epidemiological studies have linked high intake of fish and shellfish as part of the daily diet to

  • reduction of the incidence and/or severity of Alzheimer’s disease (AD) and senile mental decline in

Omega-3 fatty acids are one of the two main families of a broader group of fatty acids referred to as polyunsaturated fatty acids (PUFAs). The other main family of PUFAs encompasses the omega-6 fatty acids. In general, PUFAs are essential in many biochemical events, especially in early post-natal development processes such as

  • cellular differentiation,
  • photoreceptor membrane biogenesis and
  • active synaptogenesis.

Despite the significance of these

two families, mammals cannot synthesize PUFA de novo, so they must be ingested from dietary sources. Though belonging to the same family, both

  • omega-3 and omega-6 fatty acids are metabolically and functionally distinct and have
  • opposing physiological effects. In the human body,
  • high concentrations of omega-6 fatty acids are known to increase the formation of prostaglandins and
  • thereby increase inflammatory processes [10].

the reverse process can be seen with increased omega-3 fatty acids in the body.

Many other factors, such as

  1. thromboxane A2 (TXA2),
  2. leukotriene
  3. B4 (LTB4),
  4. IL-1,
  5. IL-6,
  6. tumor necrosis factor (TNF) and
  7. C-reactive protein,

which are implicated in various health conditions, have been shown to be increased with high omega-6 fatty acids but decreased with omega-3 fatty acids in the human body.

Dietary fatty acids have been identified as protective factors in coronary heart disease, and PUFA levels are known to play a critical role in

  • immune responses,
  • gene expression and
  • intercellular communications.

omega-3 fatty acids are known to be vital in

  • the prevention of fatal ventricular arrhythmias, and
  • are also known to reduce thrombus formation propensity by decreasing platelet aggregation, blood viscosity and fibrinogen levels

.Since omega-3 fatty acids are prevalent in the nervous system, it seems logical that a deficiency may result in neuronal problems, and this is indeed what has been identified and reported.

The main omega-3 fatty acids in the mammalian body are

  1. α-linolenic acid (ALA),
  2. docosahexenoic acid (DHA) and
  3. eicosapentaenoic acid (EPA).

In general, seafood is rich in omega-3 fatty acids, more specifically DHA and EPA (Table 1). Thus far, there are nine separate epidemiological studies that suggest a possible link between

  • increased fish consumption and reduced risk of AD
  • and eight out of ten studies have reported a link between higher blood omega-3 levels

Table 1. Total percentage of omega-3 fatty acids in common foods and supplements.

Food/Supplement EPA DHA ALA Total %
Fish
SalmonSardine

Anchovy

Halibut

Herring

Mackerel

Tuna

Fresh Bluefin

XX

X

X

X

X

X

X

XX

X

X

X

X

X

X

>50%>50%

>50%

>50%

>50%

>50%

>50%

>50%

Oils/Supplements
Fish oil capsulesCod liver oils

Salmon oil

Sardine oil

XX

X

X

XX

X

X

>50%>50%

>50%

>50%

Black currant oilCanola oil Mustard seed oils

Soybean oil

Walnut oil

Wheat germ oil

XX

X

X

X

X

10%–50%10%–50%

10%–50%

10%–50%

10%–50%

10%–50%

Seeds and other foods
Flaxseeds/LinseedsSpinach

Wheat germ Human milk

Peanut butter

Soybeans

Olive oil

Walnuts

XX

X

X

X

X

X

X

>50%>50%

10%–50%

10%–50%

<10%

<10%

<10%

<10%

 

Table adopted from Maclean C.H. et al. [18].

In another study conducted with individuals of 65 years of age or older (n = 6158), it was found that

  • only high fish consumption, but
  • not dietary omega-3 acid intake,
  • had a protective effect on cognitive decline

In 2005, based on a meta-analysis of the available epidemiology and preclinical studies, clinical trials were conducted to assess the effects of omega-3 fatty acids on cognitive protection. Four of the trials completed have shown

a protective effect of omega-3 fatty acids only among those with mild cognitive impairment conditions.

A  trial of subjects with mild memory complaints demonstrated

  • an improvement with 900 mg of DHA.

We review key findings on

  • the effect of the omega-3 fatty acid DHA on zinc transporters and the
  • importance of free zinc to human neuronal cells.

DHA is the most abundant fatty acid in neural membranes, imparting appropriate

  • fluidity and other properties,

and is thus considered as the most important fatty acid in neuronal studies. DHA is well conserved throughout the mammalian species despite their dietary differences. It is mainly concentrated

  • in membrane phospholipids at synapses and
  • in retinal photoreceptors and
  • also in the testis and sperm.

In adult rats’ brain, DHA comprises approximately

  • 17% of the total fatty acid weight, and
  • in the retina it is as high as 33%.

DHA is believed to have played a major role in the evolution of the modern human –

  • in particular the well-developed brain.

Premature babies fed on DHA-rich formula show improvements in vocabulary and motor performance.

Analysis of human cadaver brains have shown that

  • people with AD have less DHA in their frontal lobe
  • and hippocampus compared with unaffected individuals

Furthermore, studies in mice have increased support for the

  • protective role of omega-3 fatty acids.

Mice administrated with a dietary intake of DHA showed

  • an increase in DHA levels in the hippocampus.

Errors in memory were decreased in these mice and they demonstrated

  • reduced peroxide and free radical levels,
  • suggesting a role in antioxidant defense.

Another study conducted with a Tg2576 mouse model of AD demonstrated that dietary

  • DHA supplementation had a protective effect against reduction in
  • drebrin (actin associated protein), elevated oxidation, and to some extent, apoptosis via
  • decreased caspase activity.

 

Zinc

Zinc is a trace element, which is indispensable for life, and it is the second most abundant trace element in the body. It is known to be related to

  • growth,
  • development,
  • differentiation,
  • immune response,
  • receptor activity,
  • DNA synthesis,
  • gene expression,
  • neuro-transmission,
  • enzymatic catalysis,
  • hormonal storage and release,
  • tissue repair,
  • memory,
  • the visual process

and many other cellular functions. Moreover, the indispensability of zinc to the body can be discussed in many other aspects,  as

  • a component of over 300 different enzymes
  • an integral component of a metallothioneins
  • a gene regulatory protein.

Approximately 3% of all proteins contain

  • zinc binding motifs .

The broad biological functionality of zinc is thought to be due to its stable chemical and physical properties. Zinc is considered to have three different functions in enzymes;

  1. catalytic,
  2. coactive and

Indeed, it is the only metal found in all six different subclasses

of enzymes. The essential nature of zinc to the human body can be clearly displayed by studying the wide range of pathological effects of zinc deficiency. Anorexia, embryonic and post-natal growth retardation, alopecia, skin lesions, difficulties in wound healing, increased hemorrhage tendency and severe reproductive abnormalities, emotional instability, irritability and depression are just some of the detrimental effects of zinc deficiency.

Proper development and function of the central nervous system (CNS) is highly dependent on zinc levels. In the mammalian organs, zinc is mainly concentrated in the brain at around 150 μm. However, free zinc in the mammalian brain is calculated to be around 10 to 20 nm and the rest exists in either protein-, enzyme- or nucleotide bound form. The brain and zinc relationship is thought to be mediated

  • through glutamate receptors, and
  • it inhibits excitatory and inhibitory receptors.

Vesicular localization of zinc in pre-synaptic terminals is a characteristic feature of brain-localized zinc, and

  • its release is dependent on neural activity.

Retardation of the growth and development of CNS tissues have been linked to low zinc levels. Peripheral neuropathy, spina bifida, hydrocephalus, anencephalus, epilepsy and Pick’s disease have been linked to zinc deficiency. However, the body cannot tolerate excessive amounts of zinc.

The relationship between zinc and neurodegeneration, specifically AD, has been interpreted in several ways. One study has proposed that β-amyloid has a greater propensity to

  • form insoluble amyloid in the presence of
  • high physiological levels of zinc.

Insoluble amyloid is thought to

  • aggregate to form plaques,

which is a main pathological feature of AD. Further studies have shown that

  • chelation of zinc ions can deform and disaggregate plaques.

In AD, the most prominent injuries are found in

  • hippocampal pyramidal neurons, acetylcholine-containing neurons in the basal forebrain, and in
  • somatostatin-containing neurons in the forebrain.

All of these neurons are known to favor

  • rapid and direct entry of zinc in high concentration
  • leaving neurons frequently exposed to high dosages of zinc.

This is thought to promote neuronal cell damage through oxidative stress and mitochondrial dysfunction. Excessive levels of zinc are also capable of

  • inhibiting Ca2+ and Na+ voltage gated channels
  • and up-regulating the cellular levels of reactive oxygen species (ROS).

High levels of zinc are found in Alzheimer’s brains indicating a possible zinc related neurodegeneration. A study conducted with mouse neuronal cells has shown that even a 24-h exposure to high levels of zinc (40 μm) is sufficient to degenerate cells.

If the human diet is deficient in zinc, the body

  • efficiently conserves zinc at the tissue level by compensating other cellular mechanisms

to delay the dietary deficiency effects of zinc. These include reduction of cellular growth rate and zinc excretion levels, and

  • redistribution of available zinc to more zinc dependent cells or organs.

A novel method of measuring metallothionein (MT) levels was introduced as a biomarker for the

  • assessment of the zinc status of individuals and populations.

In humans, erythrocyte metallothionein (E-MT) levels may be considered as an indicator of zinc depletion and repletion, as E-MT levels are sensitive to dietary zinc intake. It should be noted here that MT plays an important role in zinc homeostasis by acting

  • as a target for zinc ion binding and thus
  • assisting in the trafficking of zinc ions through the cell,
  • which may be similar to that of zinc transporters

Zinc Transporters

Deficient or excess amounts of zinc in the body can be catastrophic to the integrity of cellular biochemical and biological systems. The gastrointestinal system controls the absorption, excretion and the distribution of zinc, although the hydrophilic and high-charge molecular characteristics of zinc are not favorable for passive diffusion across the cell membranes. Zinc movement is known to occur

  • via intermembrane proteins and zinc transporter (ZnT) proteins

These transporters are mainly categorized under two metal transporter families; Zip (ZRT, IRT like proteins) and CDF/ZnT (Cation Diffusion Facilitator), also known as SLC (Solute Linked Carrier) gene families: Zip (SLC-39) and ZnT (SLC-30). More than 20 zinc transporters have been identified and characterized over the last two decades (14 Zips and 8 ZnTs).

Members of the SLC39 family have been identified as the putative facilitators of zinc influx into the cytosol, either from the extracellular environment or from intracellular compartments (Figure 1).

The identification of this transporter family was a result of gene sequencing of known Zip1 protein transporters in plants, yeast and human cells. In contrast to the SLC39 family, the SLC30 family facilitates the opposite process, namely zinc efflux from the cytosol to the extracellular environment or into luminal compartments such as secretory granules, endosomes and synaptic vesicles; thus decreasing intracellular zinc availability (Figure 1). ZnT3 is the most important in the brain where

  • it is responsible for the transport of zinc into the synaptic vesicles of
  • glutamatergic neurons in the hippocampus and neocortex,

 

Figure 1. Putative cellular localization of some of the different human zinc transporters (i.e., Zip1- Zip4 and ZnT1- ZnT7). Arrows indicate the direction of zinc passage by the appropriate putative zinc transporters in a generalized human cell. Although there are fourteen Zips and eight ZnTs known so far, only the main zinc transporters are illustrated in this figure for clarity and brevity.

Figure 1: Subcellular localization and direction of transport of the zinc transporter families, ZnT and ZIP. Arrows show the direction of zinc mobilization for the ZnT (green) and ZIP (red) proteins. A net gain in cytosolic zinc is achieved by the transportation of zinc from the extracellular region and organelles such as the endoplasmic reticulum (ER) and Golgi apparatus by the ZIP transporters. Cytosolic zinc is mobilized into early secretory compartments such as the ER and Golgi apparatus by the ZnT transporters. Figures were produced using Servier Medical Art, http://www.servier.com/.   http://www.hindawi.com/journals/jnme/2012/173712.fig.001.jpg

zinc transporters

zinc transporters

 

 

Early zinc signaling (EZS) and late zinc signaling (LZS)

Early zinc signaling (EZS) and late zinc signaling (LZS)

http://www.hindawi.com/journals/jnme/2012/floats/173712/thumbnails/173712.fig.002_th.jpg

 

Figure 2: Early zinc signaling (EZS) and late zinc signaling (LZS). EZS involves transcription-independent mechanisms where an extracellular stimulus directly induces an increase in zinc levels within several minutes by releasing zinc from intracellular stores (e.g., endoplasmic reticulum). LSZ is induced several hours after an external stimulus and is dependent on transcriptional changes in zinc transporter expression. Components of this figure were produced using Servier Medical Art, http://www.servier.com/ and adapted from Fukada et al. [30].

 

DHA and Zinc Homeostasis

Many studies have identified possible associations between DHA levels, zinc homeostasis, neuroprotection and neurodegeneration. Dietary DHA deficiency resulted in

  • increased zinc levels in the hippocampus and
  • elevated expression of the putative zinc transporter, ZnT3, in the rat brain.

Altered zinc metabolism in neuronal cells has been linked to neurodegenerative conditions such as AD. A study conducted with transgenic mice has shown a significant link between ZnT3 transporter levels and cerebral amyloid plaque pathology. When the ZnT3 transporter was silenced in transgenic mice expressing cerebral amyloid plaque pathology,

  • a significant reduction in plaque load
  • and the presence of insoluble amyloid were observed.

In addition to the decrease in plaque load, ZnT3 silenced mice also exhibited a significant

  • reduction in free zinc availability in the hippocampus
  • and cerebral cortex.

Collectively, the findings from this study are very interesting and indicate a clear connection between

  • zinc availability and amyloid plaque formation,

thus indicating a possible link to AD.

DHA supplementation has also been reported to limit the following:

  1. amyloid presence,
  2. synaptic marker loss,
  3. hyper-phosphorylation of Tau,
  4. oxidative damage and
  5. cognitive deficits in transgenic mouse model of AD.

In addition, studies by Stoltenberg, Flinn and colleagues report on the modulation of zinc and the effect in transgenic mouse models of AD. Given that all of these are classic pathological features of AD, and considering the limiting nature of DHA in these processes, it can be argued that DHA is a key candidate in preventing or even curing this debilitating disease.

In order to better understand the possible links and pathways of zinc and DHA with neurodegeneration, we designed a study that incorporates all three of these aspects, to study their effects at the cellular level. In this study, we were able to demonstrate a possible link between omega-3 fatty acid (DHA) concentration, zinc availability and zinc transporter expression levels in cultured human neuronal cells.

When treated with DHA over 48 h, ZnT3 levels were markedly reduced in the human neuroblastoma M17 cell line. Moreover, in the same study, we were able to propose a possible

  • neuroprotective mechanism of DHA,

which we believe is exerted through

  • a reduction in cellular zinc levels (through altering zinc transporter expression levels)
  • that in turn inhibits apoptosis.

DHA supplemented M17 cells also showed a marked depletion of zinc uptake (up to 30%), and

  • free zinc levels in the cytosol were significantly low compared to the control

This reduction in free zinc availability was specific to DHA; cells treated with EPA had no significant change in free zinc levels (unpublished data). Moreover, DHA-repleted cells had

  • low levels of active caspase-3 and
  • high Bcl-2 levels compared to the control treatment.

These findings are consistent with previous published data and further strengthen the possible

  • correlation between zinc, DHA and neurodegeneration.

On the other hand, recent studies using ZnT3 knockout (ZnT3KO) mice have shown the importance of

  • ZnT3 in memory and AD pathology.

For example, Sindreu and colleagues have used ZnT3KO mice to establish the important role of

  • ZnT3 in zinc homeostasis that modulates presynaptic MAPK signaling
  • required for hippocampus-dependent memory

Results from these studies indicate a possible zinc-transporter-expression-level-dependent mechanism for DHA neuroprotection.

Collectively from these studies, the following possible mechanism can be proposed (Figure 2).

possible benefits of DHA in neuroprotection through reduction of ZnT3 transporter

possible benefits of DHA in neuroprotection through reduction of ZnT3 transporter

 

Figure 2. Proposed neuroprotection mechanism of docosahexaenoic acid (DHA) in reference to synaptic zinc. Schematic diagram showing possible benefits of DHA in neuroprotection through reduction of ZnT3 transporter expression levels in human neuronal cells, which results in a reduction of zinc flux and thus lowering zinc concentrations in neuronal synaptic vesicles, and therefore contributing to a lower incidence of neurodegenerative diseases (ND), such as Alzheimer’s disease (AD).

More recent data from our research group have also shown a link between the expression levels of histone H3 and H4 proteins in human neuronal cells in relation to DHA and zinc. Following DHA treatment, both H3 and H4 levels were up-regulated. In contrast, zinc treatment resulted in a down-regulation of histone levels. Both zinc and DHA have shown opposing effects on histone post-translational modifications, indicating a possible distinctive epigenetic pattern. Upon treatment with zinc, M17 cells displayed an increase in histone deacetylase (HDACs) and a reduction in histone acetylation. Conversely, with DHA treatment, HDAC levels were significantly reduced and the acetylation of histones was up-regulated. These findings also support a possible interaction between DHA and zinc availability.

Conclusions

It is possible to safely claim that there is more than one potential pathway by which DHA and zinc interact at a cellular level, at least in cultured human neuronal cells. Significance and importance of both DHA and zinc in neuronal survival is attested by the presence of these multiple mechanisms.
Most of these reported studies were conducted using human neuroblastoma cells, or similar cell types, due to the lack of live mature human neuronal cells. Thus, the results may differ from results achieved under actual human physiological conditions due to the structural and functional differences between these cells and mature human neurons. Therefore, an alternative approach that can mimic the human neuronal cells more effectively would be advantageous.

Sphingosine-1-phosphate signaling as a therapeutic target          

E Giannoudaki, DJ Swan, JA Kirby, S Ali

Applied Immunobiology and Transplantation Research Group, Institute of Cellular Medicine, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK

Cell Health and Cytoskeleton 2012; 4: 63–72

S1P is a 379Da member of the lysophospholipid family. It is the direct metabolite of sphingosine through the action of two sphingosine kinases, SphK1 and SphK2. The main metabolic pathway starts with the hydrolysis of sphingomyelin, a membrane sphingolipid, into ceramide by the enzyme sphingomyelinase and the subsequent production of sphingosine by ceramidase (Figure 1). Ceramide can also be produced de novo in the endoplasmic reticulum (ER) from serine and palmitoyl coenzyme A through multiple intermediates. S1P production is regulated by various S1P-specific and general lipid phosphatases, as well as S1P lyase, which irreversibly degrades S1P into phosphoethanolamine and hexadecanal. The balance between intracellular S1P and its metabolite ceramide can determine cellular fate. Ceramide promotes apoptosis, while S1P suppresses cell death and promotes cell survival. This creates an S1P ceramide “rheostat” inside the cells. S1P lyase expression in tissue is higher than it is in erythrocytes and platelets, the main “suppliers” of S1P in blood. This causes a tissue–blood gradient of S1P, which is important in many S1P-mediated responses, like the lymphocyte egress from lymphoid organs.

S1P signaling overview

S1P is produced inside cells; however, it can also be found extracellularly, in a variety of different tissues. It is abundant in the blood, at concentrations of 0.4–1.5 μM, where it is mainly secreted by erythrocytes and platelets. Blood S1P can be found separately, but mainly it exists in complexes with high-density lipoprotein (HDL) (∼60%).  Many of the cardioprotective effects of HDL are hypothesized to involve S1P. Before 1996, S1P was thought to act mainly intracellularly as a second messenger. However, the identification of several GPCRs that bind S1P led to the initiation of many studies on

  • extracellular S1P signaling through those receptors.

There are five receptors that have been identified currently. These can be coupled with different G-proteins. Assuming that each receptor coupling with a G protein has a slightly different function, one can recognize the complexity of S1P receptor signaling.

S1P as a second messenger

S1P is involved in many cellular processes through its GPCR signaling; studies demonstrate that S1P also acts at an intracellular level. Intracellular S1P plays a role in maintaining the balance of cell survival signal toward apoptotic signals, creating a

  • cell “rheostat” between S1P and its precursor ceramide.

Important evidence that S1P can act intracellularly as a second messenger came from yeast (Saccharomyces cerevisiae) and plant (Arabidopsis thaliana) cells. Yeast cells do not express any S1P receptors, although they can be affected by S1P during heat-shock responses. Similarly, Arabidopsis has only one GPCR-like protein, termed “GCR1,” which does not bind S1P, although S1P regulates stomata closure during drought.

Sphingosine-1-phosphate

Sphingosine-1-phosphate

In mammals, the sphingosine kinases have been found to localize in different cell compartments, being responsible for the accumulation of S1P in those compartments to give intracellular signals. In mitochondria, for instance,

  • S1P was recently found to interact with prohibitin 2,

a conserved protein that maintains mitochondria assembly and function. According to the same study,

SphK2 is the major producer of S1P in mitochondria and the knockout of its gene can cause

  • disruption of mitochondrial respiration and cytochrome c oxidase function.

SphK2 is also present in the nucleus of many cells and has been implicated to cause cell cycle arrest, and it causes S1P accumulation in the nucleus. It seems that nuclear S1P is affiliated with the histone deacetylases HDAC1 and HDAC2,

  • inhibiting their activity, thus having an indirect effect in epigenetic regulation of gene expression.

In the ER, SphK2 has been identified to translocate during stress, and promote apoptosis. It seems that S1P has specific targets in the ER that cause apoptosis, probably through calcium mobilization signals.

Sphingosine 1-phosphate (S1P) is a small bioactive lipid molecule that is involved in several processes both intracellularly and extracellularly. It acts intracellularly

  • to promote the survival and growth of the cell,

through its interaction with molecules in different compartments of the cell.

It can also exist at high concentrations extracellularly, in the blood plasma and lymph. This causes an S1P gradient important for cell migration. S1P signals through five G protein-coupled receptors, S1PR1–S1PR5, whose expression varies in different types of cells and tissue. S1P signaling can be involved in physiological and pathophysiological conditions of the cardiovascular, nervous, and immune systems and diseases such as ischemia/reperfusion injury, autoimmunity, and cancer. In this review, we discuss how it can be used to discover novel therapeutic targets.

The involvement of S1P signaling in disease

In a mouse model of myocardial ischemia-reperfusion injury (IRI), S1P and its carrier, HDL, can help protect myocardial tissue and decrease the infarct size. It seems they reduce cardiomyocyte apoptosis and neutrophil recruitment to the ischemic tissue and may decrease leukocyte adhesion to the endothelium. This effect appears to be S1PR3 mediated, since in S1PR3 knockout mice it is alleviated.

Ischemia activates SphK1, which is then translocated to the plasma membrane. This leads to an increase of intracellular S1P, helping to promote cardiomyocyte survival against apoptosis, induced by ceramide. SphK1 knockout mice cannot be preconditioned against IRI, whereas SphK1 gene induction in the heart protects it from IRI. Interestingly, a recent study shows SphK2 may also play a role, since its knockout reduces the cardioprotective effects of preconditioning. Further, administration of S1P or sphingosine during reperfusion results in better recovery and attenuation of damage to cardiomyocytes. As with preconditioning, SphK1 deficiency also affects post-conditioning of mouse hearts after ischemia reperfusion (IR).

S1P does not only protect the heart from IRI. During intestinal IR, multiple organs can be damaged, including the lungs. S1P treatment of mice during intestinal IR seems to have a protective effect on lung injury, probably due to suppression of iNOS-induced nitric oxide generation. In renal IRI, SphK1 seems to be important, since its deficiency increased the damage in kidney tissue, whereas the lentiviral overexpression of the SphK1 gene protected from injury. Another study suggests that, after IRI, apoptotic renal cells release S1P, which recruits macrophages through S1PR3 activation and might contribute to kidney regeneration and restoration of renal epithelium. However, SphK2 is negatively implicated in hepatic IRI, its inhibition helping protect hepatocytes and restoring mitochondrial function.

Further studies are implicating S1P signaling or sphingosine kinases in several kinds of cancer as well as autoimmune diseases.

Figure 2 FTY720-P causes retention of T cells in the lymph nodes.

Notes: C57BL/6 mice were injected with BALB/c splenocytes in the footpad to create an allogenic response then treated with FTY720-P or vehicle every day on days 2 to 5. On day 6, the popliteal lymph nodes were removed. Popliteal node-derived cells were mixed with BALB/c splenocytes in interferon gamma (IFN-γ) cultured enzyme-linked immunosorbent spot reactions. Bars represent the mean number of IFN-γ spot-forming cells per 1000 popliteal node-derived cells, from six mice treated with vehicle and seven with FTY720-P. **P , 0.01.  (not shown)

Fingolimod (INN, trade name Gilenya, Novartis) is an immunomodulating drug, approved for treating multiple sclerosis. It has reduced the rate of relapses in relapsing-remitting multiple sclerosis by over half. Fingolimod is a sphingosine-1-phosphate receptor modulator, which sequesters lymphocytes in lymph nodes, preventing them from contributing to an autoimmune reaction.

Fingolimod3Dan

Fingolimod3Dan

 

http://upload.wikimedia.org/wikipedia/commons/thumb/4/48/Fingolimod3Dan.gif/200px-Fingolimod3Dan.gif

The S1P antagonist FTY720 has been approved by the US Food and Drug Administration to be used as a drug against multiple sclerosis (MS). FTY720 is in fact a prodrug, since it is phosphorylated in vivo by SphK2 into FTY720-P, an S1P structural analog, which can activate S1PR1, 3, 4, and 5. FTY720-P binding to S1PR1 causes internalization of the receptor, as does S1P – but instead of recycling it back to the cell surface, it promotes its ubiquitination and degradation at the proteasome. This has a direct effect on lymphocyte trafficking through the lymph nodes, since it relies on S1PR1 signaling and S1P gradient (Figure 2). In MS, it stops migrating lymphocytes into the brain, but it may also have direct effects on the CNS through neuroprotection. FTY720 can pass the blood–brain barrier and it could be phosphorylated by local sphingosine kinases to act through S1PR1 and S1PR3 receptors that are mainly expressed in the CNS. In MS lesions, astrocytes upregulate those two receptors and it has been shown that FTY720-P treatment in vitro inhibits astrocyte production of inflammatory cytokines. A recent study confirms the importance of S1PR3 signaling on activated astrocytes, as well as SphK1, that are upregulated and promote the secretion of the potentially neuroprotective cytokine CXCL-1.

There are several studies implicating the intracellular S1P ceramide rheostat to cancer cell survival or apoptosis and resistance to chemotherapy or irradiation in vitro. Studies with SphK1 inhibition in pancreatic, prostate cancers, and leukemia, show increased ceramide/S1P ratio and induction of apoptosis. However, S1P receptor signaling plays conflicting roles in cancer cell migration and metastasis.

Modulation of S1P signaling: therapeutic potential

S1P signaling can be involved in many pathophysiological conditions. This means that we could look for therapeutic targets in all the molecules taking part in S1P signaling and production, most importantly the S1P receptors and the sphingosine kinases. S1P agonists and antagonists could also be used to modulate S1P signaling during pathological conditions.

S1P can have direct effects on the cardiovascular system. During IRI, intracellular S1P can protect the cardiomyocytes and promote their survival. Pre- or post-conditioning of the heart with S1P could be used as a treatment, but upregulation of sphingosine kinases could also increase intracellular S1P bioavailability. S1P could also have effects on endothelial cells and neutrophil trafficking. Vascular endothelial cells mainly express S1PR1 and S1PR3; only a few types express S1PR2. S1PR1 and S1PR3 activation on these cells has been shown to enhance their chemotactic migration, probably through direct phosphorylation of S1PR1 by Akt, in a phosphatidylinositol 3-kinase and Rac1-dependent signaling pathway. Moreover, it stimulates endothelial cell proliferation through an ERK pathway. S1PR2 activation, however, inhibits endothelial cell migration, morphogenesis, and angiogenesis, most likely through Rho-dependent inhibition of Rac signaling pathway, as Inoki et al showed in mouse cells with the use of S1PR1 and S1PR3 specific antagonists.

Regarding permeability of the vascular endothelium and endothelial barrier integrity, S1P receptors can have different effects. S1PR1 activation enhances endothelial barrier integrity by stimulation of cellular adhesion and upregulation of adhesion molecules. However, S1PR2 and S1PR3 have been shown to have barrier-disrupting effects in vitro, and vascular permeability increasing effects in vivo. All the effects S1P can have on vascular endothelium and smooth muscle cells suggest that activation of S1PR2, not S1PR1 and S1PR3, signaling, perhaps with the use of S1PR2 specific agonists, could be used therapeutically to inhibit angiogenesis and disrupt vasculature, suppressing tumor growth and progression.

An important aspect of S1P signaling that is being already therapeutically targeted, but could be further investigated, is immune cell trafficking. Attempts have already been made to regulate lymphocyte cell migration with the use of the drug FTY720, whose phosphorylated form can inhibit the cells S1PR1-dependent egress from the lymph nodes, causing lymphopenia. FTY720 is used as an immunosuppressant for MS but is also being investigated for other autoimmune conditions and for transplantation. Unfortunately, Phase II and III clinical trials for the prevention of kidney graft rejection have not shown an advantage over standard therapies. Moreover, FTY720 can have some adverse cardiac effects, such as bradycardia. However, there are other S1PR1 antagonists that could be considered instead, including KRP-203, AUY954, and SEW2871. KRP-203 in particular has been shown to prolong rat skin and heart allograft survival and attenuate chronic rejection without causing bradycardia, especially when combined with other immunomodulators.

There are studies that argue S1P pretreatment has a negative effect on neutrophil chemotaxis toward the chemokine CXCL-8 (interleukin-8) or the potent chemoattractant formyl-methionyl-leucyl-phenylalanine. S1P pretreatment might also inhibit trans-endothelial migration of neutrophils, without affecting their adhesion to the endothelium. S1P effects on neutrophil migration toward CXCL-8 might be the result of S1PRs cross-linking with the CXCL-8 receptors in neutrophils, CXCR-1 and CXCR-2. Indeed, there is evidence suggesting S1PR4 and S1PR3 form heterodimers with CXCR-1 in neutrophils. Another indication that S1P plays a role in neutrophil trafficking is a recent paper on S1P lyase deficiency, a deficiency that impairs neutrophil migration from blood to tissue in knockout mice.

S1P lyase and S1PRs in neutrophils may be new therapeutic targets against IRI and inflammatory conditions in general. Consistent with these results, another study has shown that inhibition of S1P lyase can have a protective effect on the heart after IRI and this effect is alleviated when pretreated with an S1PR1 and S1PR3 antagonist. Inhibition was achieved with a US Food and Drug Administration-approved food additive, 2-acetyl-4-tetrahydroxybutylimidazole, providing a possible new drug perspective. Another S1P lyase inhibitor, LX2931, a synthetic analog of 2-acetyl-4-tetrahydroxybutylimidazole, has been shown to cause peripheral lymphopenia when administered in mice, providing a potential treatment for autoimmune diseases and prevention of graft rejection in transplantation. This molecule is currently under Phase II clinical trials in rheumatoid arthritis patients.

S1P signaling research has the potential to discover novel therapeutic targets. S1P signaling is involved in many physiological and pathological processes. However, the complexity of S1P signaling makes it necessary to consider every possible pathway, either through its GPCRs, or intracellularly, with S1P as a second messenger. Where the activation of one S1P receptor may lead to the desired outcome, the simultaneous activation of another S1P receptor may lead to the opposite outcome. Thus, if we are to target a specific signaling pathway, we might need specific agonists for S1P receptors to activate one S1P receptor pathway, while, at the same time, we might need to inhibit another through S1P receptor antagonists.

Evidence of sphingolipid signaling in cancer

Biologically active lipids are important cellular signaling molecules and play a role in cell communication and cancer cell proliferation, and cancer stem cell biology.  A recent study in ovarian cancer cell lines shows that exogenous sphingosine 1 phosphate (SIP1) or overexpression of the sphingosine kinase (SPHK1) increases ovarian cancer cell proliferation, invasion and contributes to cancer stem cell like phenotype.  The diabetes drug metformin was shown to be an inhibitor of SPHK1 and reduce ovarian cancer tumor growth.

 2019 Apr;17(4):870-881. doi: 10.1158/1541-7786.MCR-18-0409. Epub 2019 Jan 17.

SPHK1 Is a Novel Target of Metformin in Ovarian Cancer.

Abstract

The role of phospholipid signaling in ovarian cancer is poorly understood. Sphingosine-1-phosphate (S1P) is a bioactive metabolite of sphingosine that has been associated with tumor progression through enhanced cell proliferation and motility. Similarly, sphingosine kinases (SPHK), which catalyze the formation of S1P and thus regulate the sphingolipid rheostat, have been reported to promote tumor growth in a variety of cancers. The findings reported here show that exogenous S1P or overexpression of SPHK1 increased proliferation, migration, invasion, and stem-like phenotypes in ovarian cancer cell lines. Likewise, overexpression of SPHK1 markedly enhanced tumor growth in a xenograft model of ovarian cancer, which was associated with elevation of key markers of proliferation and stemness. The diabetes drug, metformin, has been shown to have anticancer effects. Here, we found that ovarian cancer patients taking metformin had significantly reduced serum S1P levels, a finding that was recapitulated when ovarian cancer cells were treated with metformin and analyzed by lipidomics. These findings suggested that in cancer the sphingolipid rheostat may be a novel metabolic target of metformin. In support of this, metformin blocked hypoxia-induced SPHK1, which was associated with inhibited nuclear translocation and transcriptional activity of hypoxia-inducible factors (HIF1α and HIF2α). Further, ovarian cancer cells with high SPHK1 were found to be highly sensitive to the cytotoxic effects of metformin, whereas ovarian cancer cells with low SPHK1 were resistant. Together, the findings reported here show that hypoxia-induced SPHK1 expression and downstream S1P signaling promote ovarian cancer progression and that tumors with high expression of SPHK1 or S1P levels might have increased sensitivity to the cytotoxic effects of metformin. IMPLICATIONS: Metformin targets sphingolipid metabolism through inhibiting SPHK1, thereby impeding ovarian cancer cell migration, proliferation, and self-renewal.

Nrf2:INrf2(Keap1) Signaling in Oxidative Stress

James W. Kaspar, Suresh K. Niture, and Anil K. Jaiswal*

Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD

Free Radic Biol Med. 2009 Nov 1; 47(9): 1304–1309. http://dx.doi.org:/10.1016/j.freeradbiomed.2009.07.035

Nrf2:INrf2(Keap1) are cellular sensors of chemical and radiation induced oxidative and electrophilic stress. Nrf2 is a nuclear transcription factor that

  • controls the expression and coordinated induction of a battery of defensive genes encoding detoxifying enzymes and antioxidant proteins.

This is a mechanism of critical importance for cellular protection and cell survival. Nrf2 is retained in the cytoplasm by an inhibitor INrf2. INrf2 functions as an adapter for

  • Cul3/Rbx1 mediated degradation of Nrf2.
  • In response to oxidative/electrophilic stress,
  • Nrf2 is switched on and then off by distinct

early and delayed mechanisms.

Oxidative/electrophilic modification of INrf2cysteine151 and/or PKC phosphorylation of Nrf2serine40 results in the escape or release of Nrf2 from INrf2. Nrf2 is stabilized and translocates to the nucleus, forms heterodimers with unknown proteins, and binds antioxidant response element (ARE) that leads to coordinated activation of gene expression. It takes less than fifteen minutes from the time of exposure

  • to switch on nuclear import of Nrf2.

This is followed by activation of a delayed mechanism that controls

  • switching off of Nrf2 activation of gene expression.

GSK3β phosphorylates Fyn at unknown threonine residue(s) leading to

  • nuclear localization of Fyn.

Fyn phosphorylates Nrf2tyrosine568 resulting in

  • nuclear export of Nrf2,
  • binding with INrf2 and
  • degradation of Nrf2.

The switching on and off of Nrf2 protects cells against free radical damage, prevents apoptosis and promotes cell survival.

NPRA-mediated suppression of AngII-induced ROS production contributes to the antiproliferative effects of B-type natriuretic peptide in VSMC

Pan Gao, De-Hui Qian, Wei Li,  Lan Huang
Mol Cell Biochem (2009) 324:165–172

http://dx.doi.org/10.1007/s11010-008-9995-y

Excessive proliferation of vascular smooth cells (VSMCs) plays a critical role in the pathogenesis of diverse vascular disorders, and inhibition of VSMCs proliferation has been proved to be beneficial to these diseases.

In this study, we investigated the antiproliferative effect of

  • B-type natriuretic peptide (BNP), a natriuretic peptide with potent antioxidant capacity,

on rat aortic VSMCs, and the possible mechanisms involved. The results indicate that

  • BNP potently inhibited Angiotensin II (AngII)-induced VSMCs proliferation,

as evaluated by [3H]-thymidine incorporation assay. Consistently, BNP significantly decreased

  • AngII-induced intracellular reactive oxygen species (ROS)
  • and NAD(P)H oxidase activity.

8-Br-cGMP, a cGMP analog,

  • mimicked these effects.

To confirm its mechanism, siRNA of natriuretic peptide receptor-A(NRPA) strategy technology was used

  • to block cGMP production in VSMCs, and
  • siNPRA attenuated the inhibitory effects of BNP in VSMCs.

Taken together, these results indicate that

  • BNP was capable of inhibiting VSMCs proliferation by
  • NPRA/cGMP pathway,

which might be associated with

  • the suppression of ROS production.

These results might be related, at least partly, to the anti-oxidant property of BNP.

Cellular prion protein is required for neuritogenesis: fine-tuning of multiple signaling pathways involved in focal adhesions and actin cytoskeleton dynamics

A Alleaume-Butaux, C Dakowski, M Pietri, S Mouillet-Richard, Jean-Marie Launay, O Kellermann, B Schneider

1INSERM, UMR-S 747, 2Paris Descartes University, Sorbonne Paris, 3Public Hospital of Paris, Department of Biochemistry, Paris, France; 4Pharma Research Department, Hoffmann La Roche Ltd, Basel, Switzerland

Cell Health and Cytoskeleton 2013; 5: 1–12

Neuritogenesis is a complex morphological phenomena accompanying neuronal differentiation. Neuritogenesis relies on the initial breakage of the rather spherical symmetry of neuroblasts and the formation of buds emerging from the postmitotic neuronal soma. Buds then evolve into neurites, which later convert into an axon or dendrites. At the distal tip of neurites, the growth cone integrates extracellular signals and guides the neurite to its target. The acquisition of neuronal polarity depends on deep modifications of the neuroblast cytoskeleton characterized by the remodeling and activation of focal adhesions (FAs) and localized destabilization of the actin network in the neuronal sphere.Actin instability in unpolarized neurons allows neurite sprouting, ie, the protrusion of microtubules, and subsequent neurite outgrowth. Once the neurite is formed, actin microfilaments recover their stability and exert a sheathed action on neurites, a dynamic process necessary for the maintenance and integrity of neurites.

A combination of extrinsic and intrinsic cues pilots the architectural and functional changes in FAs and the actin network along neuritogenesis. This process includes neurotrophic factors (nerve growth factor, brain derived neurotrophic factor, neurotrophin, ciliary neurotrophic factor, glial derived neurotrophic factor) and their receptors, protein components of the extracellular matrix (ECM) (laminin, vitronectin, fibronectin), plasma membrane integrins and neural cell adhesion molecules (NCAM), and intracellular molecular protagonists such as small G proteins (RhoA, Rac, Cdc42) and their downstream targets.

Neuritogenesis is a dynamic phenomenon associated with neuronal differentiation that allows a rather spherical neuronal stem cell to develop dendrites and axon, a prerequisite for the integration and transmission of signals. The acquisition of neuronal polarity occurs in three steps:

(1) neurite sprouting, which consists of the formation of buds emerging from the postmitotic neuronal soma;

(2) neurite outgrowth, which represents the conversion of buds into neurites, their elongation and evolution into axon or dendrites; and

(3) the stability and plasticity of neuronal polarity.

In neuronal stem cells, remodeling and activation of focal adhesions (FAs) associated with deep modifications of the actin cytoskeleton is a prerequisite for neurite sprouting and subsequent neurite outgrowth. A multiple set of growth factors and interactors located in the extracellular matrix and the plasma membrane orchestrate neuritogenesis

  • by acting on intracellular signaling effectors,
  • notably small G proteins such as RhoA, Rac, and Cdc42,
  • which are involved in actin turnover and the dynamics of FAs.

The cellular prion protein (PrPC), a glycosylphosphatidylinositol

  • (GPI)-anchored membrane protein

mainly known for its role in a group of fatal

  • neurodegenerative diseases,

has emerged as a central player in neuritogenesis.

Here, we review the contribution of PrPC to neuronal polarization and detail the current knowledge on the

  • signaling pathways fine-tuned by PrPC
  • to promote neurite sprouting, outgrowth, and maintenance.

We emphasize that PrPC-dependent neurite sprouting is a process in which PrPC

  • governs the dynamics of FAs and the actin cytoskeleton
  • via β1 integrin signaling.

The presence of PrPC is necessary to render neuronal stem cells

  • competent to respond to neuronal inducers and
  • to develop neurites.

In differentiating neurons, PrPC exerts

  • a facilitator role towards neurite elongation.

This function relies on the interaction of PrPC with a set of diverse partners such as

  1. elements of the extracellular matrix,
  2. plasma membrane receptors,
  3. adhesion molecules, and
  4. soluble factors that control actin cytoskeleton turnover through Rho-GTPase signaling.

Once neurons have reached their terminal stage of differentiation and acquired their polarized morphology, PrPC also

  • takes part in the maintenance of neurites.

By acting on tissue nonspecific alkaline phosphatase, or

  • matrix metalloproteinase type 9,

PrPC stabilizes interactions between

  • neurites and the extracellular matrix.

Keywords: prion, neuronal differentiation

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Integrins, Cadherins, Signaling and the Cytoskeleton

Curator: Larry H. Bernstein, MD, FCAP 

 

We have reviewed the cytoskeleton, cytoskeleton pores and ionic translocation under lipids. We shall now look at this again, with specific attention to proteins, transporters and signaling.

Integrins and extracellular matrix in mechanotransduction

Lindsay Ramage
Queen’s Medical Research Institute, University of Edinburgh,

Edinburgh, UK
Cell Health and Cytoskeleton 2012; 4: 1–9

https://s3.amazonaws.com/academia.edu.documents/37116869/CHC-21829-integrins-and-extracellular-matrix-in-mechanotransduction_122311.pdf?response-content-disposition=inline%3B%20filename%3DCell_Health_and_Cytoskeleton_Integrins_a.pdf&X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWOWYYGZ2Y53UL3A%2F20191231%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20191231T021009Z&X-Amz-Expires=3600&X-Amz-SignedHeaders=host&X-Amz-Signature=b376084e0e1c31c399ee7fe96eb81b1b65d3346d647192e9ebeff96f577e117d

Integrins are a family of cell surface receptors which

  • mediate cell–matrix and cell–cell adhesions.

Among other functions they provide an important

  • mechanical link between the cells external and intracellular environments while
  • the adhesions that they form also have critical roles in cellular signal-transduction.

Cell–matrix contacts occur at zones in the cell surface where

  • adhesion receptors cluster and when activated
  • the receptors bind to ligands in the extracellular matrix.

The extracellular matrix surrounds the cells of tissues and forms the

  • structural support of tissue which is particularly important in connective tissues.

Cells attach to the extracellular matrix through

  • specific cell-surface receptors and molecules
  • including integrins and transmembrane proteoglycans.

Integrins work alongside other proteins such as

  • cadherins,
  • immunoglobulin superfamily
  • cell adhesion molecules,
  • selectins, and
  • syndecans

to mediate

  • cell–cell and
  • cell–matrix interactions and communication.

Activation of adhesion receptors triggers the formation of matrix contacts in which

  • bound matrix components,
  • adhesion receptors,
  • and associated intracellular cytoskeletal and signaling molecules

form large functional, localized multiprotein complexes.

Cell–matrix contacts are important in a variety of different cell and

tissue properties including

  1. embryonic development,
  2. inflammatory responses,
  3. wound healing,
  4. and adult tissue homeostasis.

This review summarizes the roles and functions of integrins and extracellular matrix proteins in mechanotransduction.

Integrins are a family of αβ heterodimeric receptors which act as

  • cell adhesion molecules
  • connecting the ECM to the actin cytoskeleton.

The actin cytoskeleton is involved in the regulation of

  1. cell motility,
  2. cell polarity,
  3. cell growth, and
  4. cell survival.

The integrin family consists of around 25 members which are composed of differing

  • combinations of α and β subunits.

The combination of αβ subunits determines

  • binding specificity and
  • signaling properties.

In mammals around 19 α and eight β subunits have been characterized.

Both α and β integrin subunits contain two separate tails, which

  • penetrate the plasma membrane and possess small cytoplasmic domains which facilitate
  • the signaling functions of the receptor.

There is some evidence that the β subunit is the principal

site for

  • binding of cytoskeletal and signaling molecules,

whereas the α subunit has a regulatory role. The integrin

tails

  • link the ECM to the actin cytoskeleton within the cell and with cytoplasmic proteins,

such as talin, tensin, and filamin. The extracellular domains of integrin receptors bind the ECM ligands.

The ECM is a complex mixture of matrix molecules, including -glycoproteins, collagens, laminins, glycosaminoglycans, proteoglycans,
and nonmatrix proteins, – including growth factors.
These can be categorized as insoluble molecules within the ECM, soluble molecules, and/or matrix-associated biochemicals, such as systemic hormones or growth factors and cytokines that act locally.

The integrin receptor formed from the binding of α and β subunits is shaped like a globular head supported by two rod-like legs (Figure 1). Most of the contact between the two subunits occurs in the head region, with the intracellular tails of the subunits forming the legs of the receptor.6 Integrin recognition of ligands is not constitutive but is regulated by alteration of integrin affinity for ligand binding. For integrin binding to ligands to occur the integrin must be primed and activated, both of which involve conformational changes to the receptor.

The integrins are composed of well-defined domains used for protein–protein interactions. The α-I domains of α integrin subunits comprise the ligand binding sites. X-ray crystallography has identified an α-I domain within the β subunit and a β propeller domain within the α subunit which complex to form the ligand-binding head of the integrin.

The use of activating and conformation-specific antibodies also suggests that the β chain is extended in the active integrin. It has since been identified that the hybrid domain in the β chain is critical for integrin activation, and a swing-out movement of this leg activates integrins.

http://www.ks.uiuc.edu/Publications/Stories/tcbg_ytt/pdfs/dbp6.pdf

DBP6: Integrin

Integrin

Integrin

Integrin.large

Integrin.large

Linking integrin conformation to function

Figure  Integrin binding to extracellular matrix (ECM). Conformational changes to integrin structure and clustering of subunits which allow enhanced function of the receptor.

integrin coupled to F-actin via linker

integrin coupled to F-actin via linker

http://dx.dio.org:/integrin-coupled-to-f-actin-via-linker-nrm3896-f4.jpg

Integrin extracellular binding activity is regulated from inside the cell and binding to the ECM induces signals that are transmitted into the cell.15 This bidirectional signaling requires

  • dynamic,
  • spatially, and
  • temporally regulated formation and
  • disassembly of multiprotein complexes that
    form around the short cytoplasmic tails of integrins.

Ligand binding to integrin family members leads to clustering of integrin molecules in the plasma membrane and recruitment of actin filaments and intracellular signaling molecules to the cytoplasmic domain of the integrins. This forms focal adhesion complexes which are able to maintain

  • not only adhesion to the ECM
  • but are involved in complex signaling pathways

which include establishing

  1. cell polarity,
  2. directed cell migration, and
  3. maintaining cell growth and survival.

Initial activation through integrin adhesion to matrix recruits up to around 50 diverse signaling molecules

  • to assemble the focal adhesion complex
  • which is capable of responding to environmental stimuli efficiently.

Mapping of the integrin

  • adhesome binding and signaling interactions

identified a network of 156 components linked together which can be modified by 690 interactions.

The binding of the adaptor protein talin to the β subunit cytoplasmic tail is known to have a key role in integrin activation. This is thought to occur through the disruption of

  • inhibitory interactions between α and β subunit cytoplasmic tails.

Talin also binds

  • to actin and to cytoskeletal and signaling proteins.

This allows talin to directly link activated integrins

to signaling events and the cytoskeleton.

 

Genetic programming occurs with the binding of integrins to the ECM

Signal transduction pathway activation arising from integrin-

ECM binding results in changes in gene expression of cells

and leads to alterations in cell and tissue function. Various

different effects can arise depending on the

  1. cell type,
  2. matrix composition, and
  3. integrins activated.

One way in which integrin expression is important in genetic programming is in the fate and differentiation of stem cells.
Osteoblast differentiation occurs through ECM interactions

with specific integrins

  • to initiate intracellular signaling pathways leading to osteoblast-specific gene expression
  • disruption of interactions between integrins and collagen;
  • fibronectin blocks osteoblast differentiation and

Disruption of α2 integrin prevents osteoblast differentiation, and activation of the transcription factor

  • osteoblast-specific factor 2/core-binding factor α1.

It was found that the ECM-integrin interaction induces osteoblast-specific factor 2/core-binding factor α1 to

  • increase its activity as a transcriptional enhancer
  • rather than increasing protein levels.

It was also found that modification of α2 integrin alters

  • induction of the osteocalcin promoter;
  • inhibition of α2 prevents activation of the osteocalcin promoter,
  • overexpression enhanced osteocalcin promoter activity.

It has been suggested that integrin-type I collagen interaction is necessary for the phosphorylation and activation of osteoblast-specific transcription factors present in committed osteoprogenitor cells.

A variety of growth factors and cytokines have been shown to be important in the regulation of integrin expression and function in chondrocytes. Mechanotransduction in chondrocytes occurs through several different receptors and ion channels including integrins. During osteoarthritis the expression of integrins by chondrocytes is altered, resulting in different cellular transduction pathways which contribute to tissue pathology.

In normal adult cartilage, chondrocytes express α1β1, α10β1 (collagen receptors), α5β1, and αvβ5 (fibronectin) receptors. During mechanical loading/stimulation of chondrocytes there is an influx of ions across the cell membrane resulting from activation of mechanosensitive ion channels which can be inhibited by subunit-specific anti-integrin blocking antibodies or RGD peptides. Using these strategies it was identified that α5β1 integrin is a major mechanoreceptor in articular chondrocyte responses to mechanical loading/stimulation.

Osteoarthritic chondrocytes show a depolarization response to 0.33 Hz stimulation in contrast to the hyperpolarization response of normal chondrocytes. The mechanotransduction pathway in chondrocytes derived from normal and osteoarthritic cartilage both involve recognition of the mechanical stimulus by integrin receptors resulting in the activation of integrin signaling pathways leading to the generation of a cytokine loop. Normal and osteoarthritic chondrocytes show differences at multiple stages of the mechanotransduction cascade (Figure 3). Early events are similar; α5β1 integrin and stretch activated ion channels are activated and result in rapid tyrosine phosphorylation events. The actin cytoskeleton is required for the integrin-dependent Mechanotransduction leading to changes in membrane potential in normal but not osteoarthritic chondrocytes.

Cell–matrix interactions are essential for maintaining the integrity of tissues. An intact matrix is essential for cell survival and proliferation and to allow efficient mechanotransduction and tissue homeostasis. Cell–matrix interactions have been extensively studied in many tissues and this knowledge is being used to develop strategies to treat pathology. This is particularly important in tissues subject to abnormal mechanical loading, such as musculoskeletal tissues. Integrin-ECM interactions are being used to enhance tissue repair mechanisms in these tissues through differentiation of progenitor cells for in vitro and in vivo use. Knowledge of how signaling cascades are differentially regulated in response to physiological and pathological external stimuli (including ECM availability and mechanical loading/stimulation) will enable future strategies to be developed to prevent and treat the progression of pathology associated with integrin-ECM interactions.

Cellular adaptation to mechanical stress: role of integrins, Rho, cytoskeletal tension and mechanosensitive ion channels

  1. Matthews, DR. Overby, R Mannix and DE. Ingber
    1Vascular Biology Program, Departments of Pathology and Surgery, Children’s Hospital, and 2Department of Pediatrics, Massachusetts General Hospital, Harvard Medical School, Boston, MA J Cell Sci 2006; 119: 508-518. http://dx.doi.org:/10.1242/jcs.02760

To understand how cells sense and adapt to mechanical stress, we applied tensional forces to magnetic microbeads bound to cell-surface integrin receptors and measured changes in bead isplacement with sub-micrometer resolution using optical microscopy. Cells exhibited four types of mechanical responses: (1) an immediate viscoelastic response;

(2) early adaptive behavior characterized by pulse-to-pulse attenuation in response to oscillatory forces;

(3) later adaptive cell stiffening with sustained (>15 second) static stresses; and

(4) a large-scale repositioning response with prolonged (>1 minute) stress.

Importantly, these adaptation responses differed biochemically. The immediate and early responses were affected by

  • chemically dissipating cytoskeletal prestress (isometric tension), whereas
  • the later adaptive response was not.

The repositioning response was prevented by

  • inhibiting tension through interference with Rho signaling,

similar to the case of the immediate and early responses, but it was also prevented by

  • blocking mechanosensitive ion channels or
  • by inhibiting Src tyrosine kinases.

All adaptive responses were suppressed by cooling cells to 4°C to slow biochemical remodeling. Thus, cells use multiple mechanisms to sense and respond to static and dynamic changes in the level of mechanical stress applied to integrins.

Microtubule-Stimulated ADP Release, ATP Binding, and Force Generation In Transport Kinesins

J Atherton, I Farabella, I-Mei Yu, SS Rosenfeld, A Houdusse, M Topf, CA Moores

1Institute of Structural and Molecular Biology, Department of Biological Sciences, Birkbeck College, University of London, London, United Kingdom; 2Structural Motility, Institut Curie, Centre National de la Recherche Scientifique, Paris, France; 3Department of Cancer Biology, Lerner Research Institute, Cleveland Clinic, Cleveland, United States
eLife 2014;3:e03680. http://dx.doi.org:/10.7554/eLife.03680

Kinesins are a large family of microtubule (MT)-based motors that play important roles in many cellular activities including

  • mitosis,
  • motility, and
  • intracellular transport

Their involvement in a range of pathological processes also highlights their significance as therapeutic targets and the importance of understanding the molecular basis of their function They are defined by their motor domains that contain both the microtubule (MT) and ATP binding sites. Three ATP binding motifs—the P-loop, switch I, switch II–are highly conserved among kinesins, myosin motors, and small GTPases. They share a conserved mode of MT binding such that MT binding, ATP binding, and hydrolysis are functionally coupled for efficient MT-based work.

The interior of a cell is a hive of activity, filled with proteins and other items moving from one location to another. A network of filaments called microtubules forms tracks along which so-called motor proteins carry these items. Kinesins are one group of motor proteins, and a typical kinesin protein has one end (called the ‘motor domain’) that can attach itself to the microtubules.

The other end links to the cargo being carried, and a ‘neck’ connects the two. When two of these proteins work together, flexible regions of the neck allow the two motor domains to move past one another, which enable the kinesin to essentially walk along a microtubule in a stepwise manner.

Atherton et al. use a technique called cryo-electron microscopy to study—in more detail than previously seen—the structure of the motor domains of two types of kinesin called kinesin-1 and kinesin-3. Images were taken at different stages of the cycle used by the motor domains to extract the energy from ATP molecules. Although the two kinesins have been thought to move along the microtubule tracks in different ways, Atherton et al. find that the core mechanism used by their motor domains is the same.

When a motor domain binds to the microtubule, its shape changes, first stimulating release of the breakdown products of ATP from the previous cycle. This release makes room for a new ATP molecule to bind. The structural changes caused by ATP binding are relatively small but produce larger changes in the flexible neck region that enable individual motor domains within a kinesin pair to co-ordinate their movement and move in a consistent direction. This mechanism involves tight coupling between track binding and fuel usage and makes kinesins highly efficient motors.

A number of kinesins drive long distance transport of cellular cargo with dimerisation allowing them to take multiple 8 nm ATP-driven steps toward MT plus ends. Their processivity depends on communication between the two motor domains, which is achieved via the neck linker that connects each motor domain to the dimer-forming coiled-coil

Kinesins are a superfamily of microtubule-based

  • ATP-powered motors, important for multiple, essential cellular functions.

How microtubule binding stimulates their ATPase and controls force generation is not understood. To address this fundamental question, we visualized microtubule-bound kinesin-1 and kinesin-3 motor domains at multiple steps in their ATPase cycles—including their nucleotide-free states—at ∼7 Å resolution using cryo-electron microscopy.

All our reconstructions have, as their asymmetric unit, a triangle-shaped motor domain bound to an αβ-tubulin dimer within the MT lattice (Figure 1). The structural comparisons below are made with respect to the MT surface, which, at the resolution of our structures (∼7 Å, Table 1), is the same (CCC > 0.98 for all). As is well established across the superfamily, the major and largely invariant point of contact between kinesin motor domains and the MT is helix-α4, which lies at the tubulin intradimer interface (Figure 1C, Kikkawa et al., 2001).

However, multiple conformational changes are seen throughout the rest of each domain in response to bound nucleotide (Figure 1D). Below, we describe the conformational changes in functionally important regions of each motor domain starting with the nucleotide-binding site, from which all other conformational changes emanate.

The nucleotide-binding site (Figure 2) has three major elements: (1) the P-loop (brown) is visible in all our reconstructions;

(2) loop9 (yellow, contains switch I) undergoes major conformational changes through the ATPase cycle; and

(3) loop11 (red, contains switch II) that connects strand-β7 to helix-α4,

the conformation and flexibility of which is determined by MT binding and motor nucleotide state.

Movement and extension of helix-α6 controls neck linker docking

the N-terminus of helix-α6 is closely associated with elements of the nucleotide binding site suggesting that its conformation alters in response to different nucleotide states. In addition, because the orientation of helix-α6 with respect to helix-α4 controls neck linker docking and because helix-α4 is held against the MT during the ATPase cycle,

  • conformational changes in helix-α6 control movement of the neck linker.

Mechanical amplification and force generation involves conformational changes across the motor domain

A key conformational change in the motor domain following Mg-ATP binding is peeling of the central β-sheet from the C-terminus of helix-α4 increasing their separation (Figure 3—figure supplement 2); this is required to accommodate rotation of helix-α6 and consequent neck linker docking (Figure 3B–E).

Peeling of the central β-sheet has previously been proposed to arise from tilting of the entire motor domain relative to static MT contacts, pivoting around helix-α4 (the so-called ‘seesaw’ model; Sindelar, 2011). Specifically, this model predicts that the major difference in the motor before and after Mg-ATP binding would be the orientation of the motor domain with respect to helix-α4.

Kinesin mechanochemistry and the extent of mechanistic conservation within the motor superfamily are open questions, critical to explain how MT binding, and ATP binding and hydrolysis drive motor activity. Our structural characterisation of two transport motors now allows us to propose a model that describes the roles of mechanochemical elements that together drive conserved MT-based motor function.

Model of conserved MT-bound kinesin mechanochemistry. Loop11/N-terminus of helix-α4 is flexible in ADP-bound kinesin in solution, the neck linker is also flexible while loop9 chelates ADP. MT binding is sensed by loop11/helix-α4 N-terminus, biasing them towards more ordered conformations.

We propose that this favours crosstalk between loop11 and loop9, stimulating ADP release. In the NN conformation, both loop11 and loop9 are well ordered and primed to favour ATP binding, while helix-α6—which is required for mechanical amplification–is closely associated with the MT on the other side of the motor domain. ATP binding draws loop11 and loop9 closer together; causing

(1) tilting of most of the motor domain not contacting the MT towards the nucleotide-binding site,

(2) rotation, translation, and extension of helix-α6 which we propose contributes to force generation, and

(3) allows neck linker docking and biases movement of the 2nd head towards the MT plus end.

In both motors, microtubule binding promotes

  • ordered conformations of conserved loops that
  • stimulate ADP release,
  • enhance microtubule affinity and
  • prime the catalytic site for ATP binding.

ATP binding causes only small shifts of these nucleotide-coordinating loops but induces

  • large conformational changes elsewhere that
  • allow force generation and
  • neck linker docking towards the microtubule plus end.

Family-specific differences across the kinesin–microtubule interface account for the

  • distinctive properties of each motor.

Our data thus provide evidence for a

conserved ATP-driven

  • mechanism for kinesins and
  • reveal the critical mechanistic contribution of the microtubule interface.

Phosphorylation at endothelial cell–cell junctions: Implications for VE-cadherin function

I Timmerman, PL Hordijk, JD van Buul

Cell Health and Cytoskeleton 2010; 2: 23–31
Endothelial cell–cell junctions are strictly regulated in order to

  • control the barrier function of endothelium.

Vascular endothelial (VE)-cadherin is one of the proteins that is crucial in this process. It has been reported that

  • phosphorylation events control the function of VE-cadherin.

This review summarizes the role of VE-cadherin phosphorylation in the regulation of endothelial cell–cell junctions and highlights how this affects vascular permeability and leukocyte extravasation.

The vascular endothelium is the inner lining of blood vessels and

  • forms a physical barrier between the vessel lumen and surrounding tissue;
  • controlling the extravasation of fluids,
  • plasma proteins and leukocytes.

Changes in the permeability of the endothelium are tightly regulated. Under basal physiological conditions, there is a continuous transfer of substances across the capillary beds. In addition the endothelium can mediate inducible,

  • transient hyperpermeability
  • in response to stimulation with inflammatory mediators,
  • which takes place primarily in postcapillary venules.

However, when severe, inflammation may result in dysfunction of the endothelial barrier in various parts of the vascular tree, including large veins, arterioles and capillaries. Dysregulated permeability is observed in various pathological conditions, such as tumor-induced angiogenesis, cerebrovascular accident and atherosclerosis.

Two fundamentally different pathways regulate endothelial permeability,

  • the transcellular and paracellular pathways.

Solutes and cells can pass through the body of endothelial cells via the transcellular pathway, which includes

  • vesicular transport systems, fenestrae, and biochemical transporters.

The paracellular route is controlled by

  • the coordinated opening and closing of endothelial junctions and
  • thereby regulates traffic across the intercellular spaces between endothelial cells.

Endothelial cells are connected by

  • tight, gap and
  • adherens junctions,

of which the latter, and particularly the adherens junction component,

  • vascular endothelial (VE)-cadherin,
  • are of central importance for the initiation and stabilization of cell–cell contacts.

Although multiple adhesion molecules are localized at endothelial junctions, blocking the adhesive function of VE-cadherin using antibodies is sufficient to disrupt endothelial junctions and to increase endothelial monolayer permeability both in vitro and in vivo. Like other cadherins, VE-cadherin mediates adhesion via homophilic, calcium-dependent interactions.

This cell–cell adhesion

  • is strengthened by binding of cytoplasmic proteins, the catenins,
  • to the C-terminus of VE-cadherin.

VE-cadherin can directly bind β-catenin and plakoglobin, which

  • both associate with the actin binding protein α-catenin.

Initially, α-catenin was thought to directly anchor cadherins to the actin cytoskeleton, but recently it became clear that

  • α-catenin cannot bind to both β-catenin and actin simultaneously.

Data using purified proteins show that

  • monomeric α-catenin binds strongly to cadherin-bound β-catenin;
  • in contrast to the dimer which has a higher affinity for actin filaments,
  • indicating that α-catenin might function as a molecular switch regulating cadherin-mediated cell–cell adhesion and actin assembly.

Thus, interactions between the cadherin complex and the actin cytoskeleton are more complex than previously thought. Recently, Takeichi and colleagues reported that

  • the actin binding protein EPLIN (epithelial protein lost in neoplasm)
  • can associate with α-catenin and thereby
  • link the E-cadherin–catenin complex to the actin cytoskeleton.

Although this study was performed in epithelial cells,

  • an EPLIN-like molecule might serve as
  • a bridge between the cadherin–catenin complex and
  • the actin cytoskeleton in endothelial cells.

Next to β-catenin and plakoglobin, p120-catenin also binds directly to the intracellular tail of VE-cadherin.

Numerous lines of evidence indicate that

  • p120-catenin promotes VE-cadherin surface expression and stability at the plasma membrane.

Different models are proposed that describe how p120-catenin regulates cadherin membrane dynamics, including the hypothesis

  • that p120-catenin functions as a ‘cap’ that prevents the interaction of VE-cadherin
  • with the endocytic membrane trafficking machinery.

In addition, p120-catenin might regulate VE-cadherin internalization through interactions with small GTPases. Cytoplasmic p120-catenin, which is not bound to VE-cadherin, has been shown to

  • decrease RhoA activity,
  • elevate active Rac1 and Cdc42, and thereby is thought
  • to regulate actin cytoskeleton organization and membrane trafficking.

The intact cadherin-catenin complex is required for proper functioning of the adherens junction. Mutant forms of VE-cadherin which

  • lack either the β-catenin, plakoglobin or p120 binding regions reduce the strength of cell–cell adhesion.

Moreover, our own results showed that

  • interfering with the interaction between α-catenin and β-catenin,
  • using a cell-permeable peptide which encodes the binding site in α-catenin for β-catenin,
  • resulted in an increased permeability of the endothelial monolayer.

Several mechanisms may be involved in the regulation of the organization and function of the cadherin–catenin complex, including endocytosis of the complex, VE-cadherin cleavage and actin cytoskeleton reorganization. The remainder of this review primarily focuses on the

  • role of tyrosine phosphorylation in the control of VE-cadherin-mediated cell–cell adhesion.

Regulation of the adhesive function of VE-cadherin by tyrosine phosphorylation

It is a widely accepted concept that tyrosine phosphorylation of components of the VE–cadherin-catenin complex

  • Correlates with the weakening of cell–cell adhesion.

One of the first reports that supported this idea showed that the level of phosphorylation of VE-cadherin was

  • high in loosely confluent endothelial cells, but
  • low in tightly confluent monolayers,

when intercellular junctions are stabilized.

In addition, several conditions that induce tyrosine phosphorylation

of adherens junction components, like

  • v-Src transformation
  • and inhibition of phosphatase activity by pervanadate,

have been shown to shift cell–cell adhesion from a strong to a weak state. More physiologically relevant;

permeability-increasing agents such as

  • histamine,
  • tumor necrosis factor-α (TNF-α),
  • thrombin,
  • platelet-activating factor (PAF) and
  • vascular endothelial growth factor (VEGF)

increase tyrosine phosphorylation of various components of the cadherin–catenin complex.

A general idea has emerged that

  • tyrosine phosphorylation of the VE-cadherin complex
  • leads to the uncoupling of VE-cadherin from the actin cytoskeleton
  • through dissociation of catenins from the cadherin.

However, tyrosine phosphorylation of VE-cadherin is required for efficient transmigration of leukocytes.

This suggests that VE-cadherin-mediated cell–cell contacts

  1. are not just pushed open by the migrating leukocytes, but play
  2. a more active role in the transmigration process.

A schematic overview of leukocyte adhesion-induced signals leading to VE-cadherin phosphorylation

Regulation of the integrity of endothelial cell–cell contacts by phosphorylation of VE-cadherin

Regulation of the integrity of endothelial cell–cell contacts by phosphorylation of VE-cadherin

Regulation of the integrity of endothelial cell–cell contacts by phosphorylation of VE-cadherin.

Notes: A) Permeability-inducing agents such as thrombin, histamine and VEGF, induce tyrosine phosphorylation (pY) of VE-cadherin and the associated catenins. Although the specific consequences of catenin tyrosine phosphorylation in endothelial cells are still unknown, VE-cadherin tyrosine phosphorylation results in opening of the cell–cell junctions (indicated by arrows) and enhanced vascular permeability. How tyrosine phosphorylation affects VE-cadherin adhesiveness is not yet well understood; disrupted binding of catenins, which link the cadherin to the actin cytoskeleton, may be involved. VEGF induces phosphorylation of VE-cadherin at specific residues, Y658 and Y731, which have been reported to regulate p120-catenin and β-catenin binding, respectively. Moreover, VEGF stimulation results in serine phosphorylation (pSer) of VE-cadherin, specifically at residue S665, which leads to its endocytosis. B) Adhesion of leukocytes to endothelial cells via ICAM-1 increases endothelial permeability by inducing phosphorylation of VE-cadherin on tyrosine residues. Essential mediators, such as the kinases Pyk2 and Src, and signaling routes involving reactive oxygen species (ROS) and Rho, have been shown to act downstream of ICAM-1. Different tyrosine residues within the cytoplasmic domain of VE-cadherin are involved in the extravasation of neutrophils and lymphocytes, including Y658 and Y731. (β: β-catenin, α: α-catenin, γ: γ-catenin/plakoglobin).

N-glycosylation status of E-cadherin controls cytoskeletal dynamics through the organization of distinct β-catenin- and γ-catenin-containing AJs

BT Jamal, MN Nita-Lazar, Z Gao, B Amin, J Walker, MA Kukuruzinska
Cell Health and Cytoskeleton 2009; 1: 67–80

N-glycosylation of E-cadherin has been shown to inhibit cell–cell adhesion. Specifically, our recent studies have provided evidence that the reduction of E-cadherin N-glycosylation promoted the recruitment of stabilizing components, vinculin and serine/ threonine protein phosphatase 2A (PP2A), to adherens junctions (AJs) and enhanced the association of AJs with the actin cytoskeleton. Here, we examined the details of how

  • N-glycosylation of E-cadherin affected the molecular organization of AJs and their cytoskeletal interactions.

Using the hypoglycosylated E-cadherin variant, V13, we show that

  • V13/β-catenin complexes preferentially interacted with PP2A and with the microtubule motor protein dynein.

This correlated with dephosphorylation of the microtubule-associated protein tau, suggesting that

  • increased association of PP2A with V13-containing AJs promoted their tethering to microtubules.

On the other hand, V13/γ-catenin complexes associated more with vinculin, suggesting that they

  • mediated the interaction of AJs with the actin cytoskeleton.
  • N-glycosylation driven changes in the molecular organization of AJs were physiologically significant because transfection of V13 into A253 cancer cells, lacking both mature AJs and tight junctions (TJs), promoted the formation of stable AJs and enhanced the function of TJs to a greater extent than wild-type E-cadherin.

These studies provide the first mechanistic insights into how N-glycosylation of E-cadherin drives changes in AJ composition through

  • the assembly of distinct β-catenin- and γ-catenin-containing scaffolds that impact the interaction with different cytoskeletal components.

Cytoskeletal Basis of Ion Channel Function in Cardiac Muscle

Matteo Vatta, and Georgine Faulkner,

1 Departments of Pediatrics (Cardiology), Baylor College of Medicine, Houston, TX 2 Department of Reproductive and Developmental Sciences, University of Trieste, Trieste, Italy
3 Muscular Molecular Biology Unit, International Centre for Genetic Engineering and Biotechnology, Padriciano, Trieste, Italy

Future Cardiol. 2006 July 1; 2(4): 467–476. http://dx.doi.org:/10.2217/14796678.2.4.467

The heart is a force-generating organ that responds to

  • self-generated electrical stimuli from specialized cardiomyocytes.

This function is modulated

  • by sympathetic and parasympathetic activity.

In order to contract and accommodate the repetitive morphological changes induced by the cardiac cycle, cardiomyocytes

  • depend on their highly evolved and specialized cytoskeletal apparatus.

Defects in components of the cytoskeleton, in the long term,

  • affect the ability of the cell to compensate at both functional and structural levels.

In addition to the structural remodeling,

  • the myocardium becomes increasingly susceptible to altered electrical activity leading to arrhythmogenesis.

The development of arrhythmias secondary to structural remodeling defects has been noted, although the detailed molecular mechanisms are still elusive. Here I will review

  • the current knowledge of the molecular and functional relationships between the cytoskeleton and ion channels

and, I will discuss the future impact of new data on molecular cardiology research and clinical practice.

Myocardial dysfunction in the end-stage failing heart is very often associated with increasing

  • susceptibility to ventricular tachycardia (VT) and ventricular fibrillation (VF),

both of which are common causes of sudden cardiac death (SCD).

Among the various forms of HF,

myocardial remodeling due to ischemic cardiomyopathy (ICM) or dilated cardiomyopathy (DCM)

  • is characterized by alterations in baseline ECG,

which includes the

  • prolongation of the QT interval,
  • as well as QT dispersion,
  • ST-segment elevation, and
  • T-wave abnormalities,

especially during exercise. In particular, subjects with

severe left ventricular chamber dilation such as in DCM can have left bundle branch block (LBBB), while right bundle branch block (RBBB) is more characteristic of right ventricular failure.  LBBB and RBBB have both been repeatedly associated with AV block in heart failure.

The impact of volume overload on structural and electro-cardiographic alterations has been noted in cardiomyopathy patients treated with left ventricular assist device (LVAD) therapy, which puts the heart at mechanical rest. In LVAD-treated subjects,

  • QRS- and both QT- and QTc duration decreased,
  • suggesting that QRS- and QT-duration are significantly influenced by mechanical load and
  • that the shortening of the action potential duration contributes to the improved contractile performance after LVAD support.

Despite the increasing use of LVAD supporting either continuous or pulsatile blood flow in patients with severe HF, the benefit of this treatment in dealing with the risk of arrhythmias is still controversial.

Large epidemiological studies, such as the REMATCH study, demonstrated that the

  • employment of LVAD significantly improved survival rate and the quality of life, in comparison to optimal medical management.

An early postoperative period study after cardiac unloading therapy in 17 HF patients showed that in the first two weeks after LVAD implantation,

  • HF was associated with a relatively high incidence of ventricular arrhythmias associated with QTc interval prolongation.

In addition, a recent retrospective study of 100 adult patients with advanced HF, treated with an axial-flow HeartMate LVAD suggested that

  • the rate of new-onset monomorphic ventricular tachycardia (MVT) was increased in LVAD treated patients compared to patients given only medical treatment,

while no effect was observed on the development of polymorphic ventricular tachycardia (PVT)/ventricular fibrillation (VF).

The sarcomere

The myocardium is exposed to severe and continuous biomechanical stress during each contraction-relaxation cycle. When fiber tension remains uncompensated or simply unbalanced,

  • it may represent a trigger for arrhythmogenesis caused by cytoskeletal stretching,
  • which ultimately leads to altered ion channel localization, and subsequent action potential and conduction alterations.

Cytoskeletal proteins not only provide the backbone of the cellular structure, but they also

  • maintain the shape and flexibility of the different sub-cellular compartments, including the
  1. plasma membrane,
  2. the double lipid layer, which defines the boundaries of the cell and where
  • ion channels are mainly localized.

The interaction between the sarcomere, which is the basic for the passive force during diastole and for the restoring force during systole. Titin connects

  • the Z-line to the M-line of the sarcomeric structure
    (Figure 1).

In addition to the strategic

  • localization and mechanical spring function,
  • titin is a length-dependent sensor during
  • stretch and promotes actin-myosin interaction

Titin is stabilized by the cross-linking protein

  • telethonin (T-Cap), which localizes at the Z-line and is also part of titin sensor machinery (Figure 1).

The complex protein interactions in the sarcomere entwine telethonin to other

  • Z-line components through the family of the telethonin-binding proteins of the Z-disc, FATZ, also known as calsarcin and myozenin.

FATZ binds to

  1. calcineurin,
  2. γ-filamin as well as the
  3. spectrin-like repeats (R3–R4) of α-actinin-2,

the major component of the Z-line and a pivotal

  • F-actin cross-linker (Figure 1).contractile unit of striated muscles, and
  • the sarcolemma,

the plasma membrane surrendering the muscle fibers in skeletal muscle and the muscle cell of the cardiomyocyte,

  • determines the mechanical plasticity of the cell, enabling it to complete and re-initiate each contraction-relaxation cycle.

At the level of the sarcomere,

  • actin (thin) and myosin (thick) filaments generate the contractile force,

while other components such as titin, the largest protein known to date, are responsible for

  • the passive force during diastole and for the restoring force during systole, and (titin).
  • the Z-line to the M-line of the sarcomeric structure
    (Figure 1).

In addition to the strategic

  • localization and mechanical spring function,
  • it acts as a length-dependent sensor during stretch and
  • promotes actin-myosin interaction.

Stabilized by the cross-linking protein telethonin (T-Cap),

  • titin localizes at the Z-line and is
  • part of titin sensor machinery

Another cross-linker of α-actinin-2 in the complex Z-line scaffold is

  • the Z-band alternatively spliced PDZ motif protein (ZASP),
  • which has an important role in maintaining Z-disc stability

in skeletal and cardiac muscle (Figure 1).

ZASP contains a PDZ motif at its N-terminus,

  • which interacts with C-terminus of α-actinin-2,
  • and a conserved sequence called the ZASP like motif (ZM)
  • found in the alternatively spliced exons 4 and 6.

It has also been reported

  • to bind to the FATZ (calsarcin) family of Z-disc proteins (Figure 1).

The complex protein interactions in the sarcomere entwine telethonin to other Z-line components through the family of the telethonin-binding proteins of the

  1. Z-disc,
  2. FATZ, also known as calsarcin and
  3. myozenin

FATZ binds to calcineurin,

  1. γ-filamin as well as the
  2. spectrin-like repeats (R3–R4) of α-actinin-2, the major component of the Z-line and a pivotal F-actin cross-linker (Figure 1).
sarcomere structure

sarcomere structure

Figure 1. Sarcomere structure

The diagram illustrates the sarcomeric structure. The Z-line determines the boundaries of the contractile unit, while Titin connects the Z-line to the M-line and acts as a functional spring during contraction/relaxation cycles.

Sarcomeric Proteins and Ion Channels

In addition to systolic dysfunction characteristic of dilated cardiomyopathy (DCM) and diastolic dysfunction featuring hypertrophic cardiomyopathy (HCM), the clinical phenotype of patients with severe cardiomyopathy is very often associated with a high incidence of cardiac arrhythmias. Therefore, besides fiber stretch associated with mechanical and hemodynamic impairment, cytoskeletal alterations due to primary genetic defects or indirectly to alterations in response to cellular injury can potentially

  1. affect ion channel anchoring, and trafficking, as well as
  2. functional regulation by second messenger pathways,
  3. causing an imbalance in cardiac ionic homeostasis that will trigger arrhythmogenesis.

Intense investigation of

  • the sarcomeric actin network,
  • the Z-line structure, and
  • chaperone molecules docking in the plasma membrane,

has shed new light on the molecular basis of

  • cytoskeletal interactions in regulating ion channels.

In 1991, Cantiello et al., demonstrated that

  • although the epithelial sodium channel and F-actin are in close proximity,
  • they do not co-localize.

Actin disruption using cytochalasin D, an agent that interferes with actin polymerization, increased Na+ channel activity in 90% of excised patches tested within 2 min, which indicated that

  • the integrity of the filamentous actin (F-actin) network was essential
  • for the maintenance of normal Na+ channel function.

Later, the group of Dr. Jonathan Makielski demonstrated that

  • actin disruption induced a dramatic reduction in Na+ peak current and
  • slowed current decay without affecting steady-state voltage-dependent availability or recovery from inactivation.

These data were the first to support a role for the cytoskeleton in cardiac arrhythmias.

F-actin is intertwined in a multi-protein complex that includes

  • the composite Z-line structure.

Further, there is a direct binding between

  • the major protein of the Z-line, α-actinin-2 and
  • the voltage-gated K+ channel 1.5 (Kv1.5), (Figure 2).

The latter is expressed in human cardiomyocytes and localizes to

  • the intercalated disk of the cardiomyocyte
  • in association with connexin and N-cadherin.

Maruoka et al. treated HEK293 cells stably expressing Kv1.5 with cytochalasin D, which led to

  • a massive increase in ionic and gating IK+ currents.

This was prevented by pre-incubation with phalloidin, an F-actin stabilizing agent. In addition, the Z-line protein telethonin binds to the cytoplasmic domain of minK, the beta subunit of the potassium channel KCNQ1 (Figure 2).

Molecular interactions between the cytoskeleton and ion channels

Molecular interactions between the cytoskeleton and ion channels

Figure 2. Molecular interactions between the cytoskeleton and ion channels

The figure illustrates the interactions between the ion channels on the sarcolemma, and the sarcomere in cardiac myocytes. Note that the Z-line is connected to the cardiac T-tubules. The diagram illustrates the complex protein-protein interactions that occur between structural components of the cytoskeleton and ion channels. The cytoskeleton is involved in regulating the metabolism of ion channels, modifying their expression, localization, and electrical properties. The cardiac sodium channel Nav1.5 associates with the DGC, while potassium channels such as Kv1.5, associate with the Z-line.

Ion Channel Subunits and Trafficking

Correct localization is essential for ion channel function and this is dependent upon the ability of auxiliary proteins to

  • shuttle ion channels from the cytoplasm to their final destination such as
  • the plasma membrane or other sub-cellular compartments.

In this regard, Kvβ-subunits are

  • cytoplasmic components known to assemble with the α-subunits of voltage-dependent K+ (Kv) channels
  • at their N-terminus to form stable Kvα/β hetero-oligomeric channels.

When Kvβ is co-expressed with Kv1.4 or Kv1.5, it enhances Kv1.x channel trafficking to the cell membrane without changing the overall protein channel content. The regulatory Kvβ subunits, which are also expressed in cardiomyocytes, directly decrease K+ current by

  • accelerating Kv1.x channel inactivation.

Therefore, altered expression or mutations in Kvβ subunits could cause abnormal ion channel transport to the cell surface, thereby increasing the risk of cardiac arrhythmias.

Ion Channel Protein Motifs and Trafficking

Cell membrane trafficking in the Kv1.x family may occur in a Kvβ subunit-independent manner through specific motifs in their C-terminus. Mutagenesis of the final asparagine (N) in the Kv1.2 motif restores the leucine (L) of the Kv1.4 motif

  • re-establishing high expression levels at the plasma membrane in a Kvβ-independent manner

Cytoskeletal Proteins and Ion Channel Trafficking

Until recently, primary arrhythmias such as LQTS have been almost exclusively regarded as ion channelopathies. Other mutations have been identified with regard to channelopathies. However, the conviction that primary mutations in ion channels were solely responsible for

  • the electrical defects associated with arrhythmias

has been shaken by the identification of mutations in the

  • ANK2 gene encoding the cytoskeletal protein ankyrin-B

that is associated with LQTS in animal models and humans.

Ankyrin-B acts as a chaperone protein, which shuttles the cardiac sodium channel from the cytoplasm to the membrane. Immunohistochemical analysis has localized ankyrin-B to the Zlines/T-tubules on the plasma membrane in the myocardium. Mutations in ankyrin-B associated with LQTS

  • alter sodium channel trafficking due to loss of ankyrin-B localization at the Z-line/transverse (T)-tubules.

Reduced levels of ankyrin-B at cardiac Z-lines/T-tubules were associated with the deficiency of ankyrin-B-associated proteins such as Na/K-ATPase, Na/Ca exchanger (NCX) and inositol-1, 4, 5-trisphosphate receptors (InsP3R).

Dystrophin component of the Dystrophin Glycoprotien Complex (DGC)

Synchronized contraction is essential for cardiomyocytes, which are connected to each other via the extracellular matrix (ECM) through the DGC. The N-terminus domain of dystrophin

  • binds F-actin, and connects it to the sarcomere, while
  • the cysteine-rich (CR) C-terminus domain ensures its connection to the sarcolemma (Figure 2).

The central portion of dystrophin, the rod domain, is composed of

  • rigid spectrin-like repeats and four hinge portions (H1–H4) that determine the flexibility of the protein.

Dystrophin possesses another F-actin binding domain in the Rod domain region, between the basic repeats 11- 17 (DysN-R17).

Dystrophin, originally identified as the gene responsible for Duchenne and Becker muscular dystrophies (DMD/BMD), and later for the X-linked form of dilated cardiomyopathy (XLCM), exerts a major function in physical force transmission in striated muscle. In addition to its structural significance, dystrophin and other DGC proteins such as syntrophins are required for the

  • correct localization,
  • clustering and
  • regulation of ion channel function.

Syntrophins have been implicated in ion channel regulation.  Syntrophins contain two pleckstrin homology (PH) domains, a PDZ domain, and a syntrophin-unique (SU) C-terminal region. The interaction between syntrophins and dystrophin occurs at the PH domain distal to the syntrophin N-terminus and through the highly conserved SU domain. Conversely, the PH domain proximal to the N-terminal portion of the protein and the PDZ domain interact with other membrane components such as

  1. phosphatidyl inositol-4, 5-bisphosphate,
  2. neuronal NOS (nNOS),
  3. aquaporin-4,
  4. stress-activated protein kinase-3, and
  5. 5,

thereby linking all these molecules to the dystrophin complex (Figure 2).

Among the five known isoforms of syntrophin, the 59 KDa α1-syntrophin isoform is the most highly represented in human heart, whereas in skeletal muscle it is only present on the

  • sarcolemma of fast type II fibers.

In addition, the skeletal muscle γ2-syntrophin was found at high levels only at the

  • postsynaptic membrane of the neuromuscular junctions.

In addition to syntrophin, other scaffolding proteins such as caveolin-3 (CAV3), which is present in the caveolae, flask-shaped plasma membrane microdomains, are involved

  • in signal transduction and vesicle trafficking in myocytes,
  • modulating cardiac remodeling during heart failure.

CAV3 and α1-syntrophin, localizes at the T-tubule and are part of the DGC. In addition, α1-syntrophin binds Nav1.5, while

  • caveolin-3 binds the Na+/Ca2+ exchanger, Nav1.5 and the L-type Ca2+ channel as well as nNOS and the DGC (Figure 2).

Although ankyrin-B is the only protein found mutated in patients with primary arrhythmias, other proteins such as caveolin-3 and the syntrophins if mutated may alter ion channel function.

Conclusions

It is important to be aware of the enormous variety of clinical presentations that derive from distinct variants in the same pool of genetic factors. Knowledge of these variants could facilitate tailoring the therapy of choice for each patient. In particular, the recent findings of structural and functional links between

  • the cytoskeleton and ion channels

could expand the therapeutic interventions in

  • arrhythmia management in structurally abnormal myocardium, where aberrant binding
  • between cytoskeletal proteins can directly or indirectly alter ion channel function.

Executive Summary

Arrhythmogenesis and myocardial structure

  • Rhythm alterations can develop as a secondary consequence of myocardial structural abnormalities or as a result of a primary defect in the cardiac electric machinery.
  • Until recently, no molecular mechanism has been able to fully explain the occurrence of arrhythmogenesis in heart failure, however genetic defects that are found almost exclusively in ion channel genes account for the majority of primary arrhythmias such as long QT syndromes and Brugada syndrome. The contractile apparatus is linked to ion channels
  • The sarcomere, which represents the contractile unit of the myocardium not only generates the mechanical force necessary to exert the pump function, but also provides localization and anchorage to ion channels.
  • Alpha-actinin-2, and telethonin, two members of the Z-line scaffolding protein complex in the striated muscle associate with the potassium voltage-gated channel alpha subunit Kv1.5 and the beta subunit KCNE1 respectively.
  • Mutations in KCNE1 have previously been associated with the development of arrhythmias in LQTS subjects.
  • Mutations in both alpha-actinin-2, and telethonin were identified in individuals with cardiomyopathy. The primary defect is structural leading to ventricular dysfunction, but the secondary consequence is arrhythmia.

Ion channel trafficking and sub-cellular compartments

  • Ion channel trafficking from the endoplasmic reticulum (ER) to the Golgi complex is an important check-point for regulating the functional channel molecules on the plasma membrane. Several molecules acting as chaperones bind to and shuttle the channel proteins to their final localization on the cell surface
  • Ion channel subunits such as Kvβ enhance Kv1.x ion channel presentation on the sarcolemma. The α subunits of the Kv1.x potassium channels can be shuttled in a Kvβ-independent manner through specific sequence motif at Kv1.x protein level.
  • In addition, cytoskeletal proteins such as ankyrin-G bind Nav1.5 and are involved in the sodium channel trafficking. Another member of the ankyrin family, ankyrin-B was found mutated in patients with LQTS but the pathological mechanism of ankyrin-B mutations is still obscure, although the sodium current intensity is dramatically reduced.

The sarcolemma and ion channels

  • The sarcolemma contains a wide range of ion channels, which are responsible for the electrical propagating force in the myocardium.
  • The DGC is a protein complex, which forms a scaffold for cytoskeletal components and ion channels.
  • Dystrophin is the major component of the DGC and mutations in dystrophin and DGC cause muscular dystrophies and X-linked cardiomyopathies (XLCM) in humans. Cardiomyopathies are associated with arrhythmias
  • Caveolin-3 and syntrophins associate with Nav1.5, and are part of the DGC. Syntrophins can directly modulate Nav1.5 channel function.

Conclusions

  • The role of the cytoskeleton in ion channel function has been hypothesized in the past, but only recently the mechanism underlying the development of arrhythmias in structurally impaired myocardium has become clearer.
  • The recently acknowledged role of the cytoskeleton in ion channel function suggests that genes encoding cytoskeletal proteins should be regarded as potential candidates for variants involved in the susceptibility to arrhythmias, as well as the primary target of genetic mutations in patients with arrhythmogenic syndromes such as LQTS and Brugada syndrome.
  • Studies of genotype-phenotype correlation and and patient risk stratification for mutations in cytoskeletal proteins will help to tailor the therapy and management of patients with arrhythmias.

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Introduction to Signaling

Curator: Larry H. Bernstein, MD, FCAP

 

We have laid down a basic structure and foundation for the remaining presentations.  It was essential to begin with the genome, which changed the course of teaching of biology and medicine in the 20th century, and introduced a central dogma of translation by transcription.  Nevertheless, there were significant inconsistencies and unanswered questions entering the twenty first century, accompanied by vast improvements in technical advances to clarify these issues. We have covered carbohydrate, protein, and lipid metabolism, which function in concert with the development of cellular structure, organ system development, and physiology.  To be sure, the progress in the study of the microscopic and particulate can’t be divorced from the observation of the whole.  We were left in the not so distant past with the impression of the Sufi story of the elephant and the three blind men, who one at a time held the tail, the trunk, and the ear, each proclaiming that it was the elephant.

I introduce here a story from the Brazilian biochemist, Jose

Eduardo des Salles Rosalino, on a formativr experience he had with the Nobelist, Luis Leloir.

Just at the beginning, when phosphorylation of proteins is presented, I assume you must mention that some proteins are activated by phosphorylation. This is fundamental in order to present self –organization reflex upon fast regulatory mechanisms. Even from an historical point of view. The first observation arrived from a sample due to be studied on the following day of glycogen synthetase. It was unintended left overnight out of the refrigerator. The result was it has changed from active form of the previous day to a non-active form. The story could have being finished here, if the researcher did not decide to spent this day increasing substrate levels (it could be a simple case of denaturation of proteins that changes its conformation despite the same order of amino acids). He kept on trying and found restoration of maximal activity. This assay was repeated with glycogen phosphorylase and the result was the opposite – it increases its activity. This led to the discovery

  • of cAMP activated protein kinase and
  • the assembly of a very complex system in the glycogen granule
  • that is not a simple carbohydrate polymer.

Instead, it has several proteins assembled and

  • preserves the capacity to receive from a single event (rise in cAMP)
  • two opposing signals with maximal efficiency,
  • stops glycogen synthesis,
  • as long as levels of glucose 6 phosphate are low
  • and increases glycogen phosphorylation as long as AMP levels are high).

I did everything I was able to do by the end of 1970 in order to repeat the assays with PK I, PKII and PKIII of M. Rouxii and using the Sutherland route to cAMP failed in this case. I then asked Leloir to suggest to my chief (SP) the idea of AA, AB, BB subunits as was observed in lactic dehydrogenase (tetramer) indicating this as his idea. The reason was my “chief”(SP) more than once, had said to me: “Leave these great ideas for the Houssay, Leloir etc…We must do our career with small things.” However, as she also had a faulty ability for recollection she also used to arrive some time later, with the very same idea but in that case, as her idea.
Leloir, said to me: I will not offer your interpretation to her as mine. I think it is not phosphorylation, however I think it is glycosylation that explains the changes in the isoenzymes with the same molecular weight preserved. This dialogue explains why during the reading and discussing “What is life” with him he asked me if as a biochemist in exile, talking to another biochemist, I expressed myself fully. I had considered that Schrödinger would not have confronted Darlington & Haldane because he was in U.K. in exile. This might explain why Leloir could have answered a bad telephone call from P. Boyer, Editor of The Enzymes, in a way that suggested that the pattern could be of covalent changes over a protein. Our FEBS and Eur J. Biochemistry papers on pyruvate kinase of M. Rouxii is wrongly quoted in this way on his review about pyruvate kinase of that year (1971).

 

Another aspect I think you must call attention to the following. Show in detail with different colors what carbons belongs to CoA, a huge molecule in comparison with the single two carbons of acetate that will produce the enormous jump in energy yield

  • in comparison with anaerobic glycolysis.

The idea is

  • how much must have been spent in DNA sequences to build that molecule in order to use only two atoms of carbon.

Very limited aspects of biology could be explained in this way. In case we follow an alternative way of thinking, it becomes clearer that proteins were made more stable by interaction with other molecules (great and small). Afterwards, it’s rather easy to understand how the stability of protein-RNA complexes where transmitted to RNA (vibrational +solvational reactivity stability pair of conformational energy).

Millions of years later, or as soon as, the information of interaction leading to activity and regulation could be found in RNA, proteins like reverse transcriptase move this information to a more stable form (DNA). In this way it is easier to understand the use of CoA to make two carbon molecules more reactive.

The discussions that follow are concerned with protein interactions and signaling.

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Summary of Lipid Metabolism

 

Author: Larry H. Bernstein, MD, FCAP

 

Lipid Classification System

The LIPID MAPS Lipid Classification System is comprised of eight lipid categories, each with its own sublassification hierarchy.

http://www.lipidmaps.org/resources/tutorials/lipid_cns.html

Each LMSD record contains an image of the

  • molecular structure,
  • common and systematic names,
  • links to external databases,
  • Wikipedia pages (where available),
  • other annotations and links to structure viewing tools.

All lipids in the LIPID MAPS Structure Database (LMSD) have been classified using this system and have been assigned LIPID MAPS ID’s (LM_ID) which reflects their position in the classification hierarchy.

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. As of May 3, 2013, LMSD contains over 37,500 unique lipid structures, making it the largest public lipid-only database in the world. Structures of lipids in the database come from several sources:

  • LIPID MAPS Consortium’s core laboratories and partners;
  • lipids identified by LIPID MAPS experiments;
  • biologically relevant lipids manually curated from LIPID BANK, LIPIDAT, Lipid Library, Cyberlipids, ChEBI and other public sources;
  • novel lipids submitted to peer-reviewed journals;
  • computationally generated structures for appropriate classes.

All the lipid structures in LMSD adhere to the structure drawing rules proposed by the LIPID MAPS consortium. A number of structure viewing options are offered: gif image (default), Chemdraw (requires Chemdraw ActiveX/Plugin), MarvinView (Java applet) and JMol (Java applet).

(as of 10/8/14)

Number of lipids per category

Fatty acyls          5869

Glycerolipids       7541

Glycerophospholipids       8002

Sphingolipids      4338

Sterol lipids         2715

Prenol lipids        1259

Sacccharolipids  1293

Polyketides         6742

TOTAL  37,759 structures

References

Sud M, Fahy E, Cotter D, Brown A, Dennis EA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, Subramaniam S. LMSD: LIPID MAPS structure database Nucleic Acids Research 35: p. D527-32. PMID:17098933 [doi:10.1093/nar/gkl838] PMID: 17098933

Fahy E, Sud M, Cotter D & Subramaniam S. LIPID MAPS online tools for lipid research Nucleic Acids Research (2007) 35: p. W606-12.PMID:17584797 [doi:10.1093/nar/gkm324] PMID: 17584797 

The Recognition of Essential Fatty Acids

Dietary fat has long been recognized as an important source of energy for mammals, but in the late 1920s, researchers demonstrated the dietary requirement for particular fatty acids, which came to be called essential fatty acids. It was not until the advent of intravenous feeding, however, that the importance of essential fatty acids was widely accepted: Clinical signs of essential fatty acid deficiency are generally observed only in patients on total parenteral nutrition who received mixtures devoid of essential fatty acids or in those with malabsorption syndromes.

These signs include dermatitis and changes in visual and neural function. Over the past 40 years, an increasing number of physiological functions, such as immunomodulation, have been attributed to the essential fatty acids and their metabolites, and this area of research remains quite active.1, 2

Fatty Acid Nomenclature

The fat found in foods consists largely of a heterogeneous mixture of triacylglycerols (triglycerides)–glycerol molecules that are each combined with three fatty acids. The fatty acids can be divided into two categories, based on chemical properties: saturated fatty acids, which are usually solid at room temperature, and unsaturated fatty acids, which are liquid at room temperature. The term “saturation” refers to a chemical structure in which each carbon atom in the fatty acyl chain is bound to (saturated with) four other atoms, these carbons are linked by single bonds, and no other atoms or molecules can attach; unsaturated fatty acids contain at least one pair of carbon atoms linked by a double bond, which allows the attachment of additional atoms to those carbons (resulting in saturation). Despite their differences in structure, all fats contain approximately the same amount of energy (37 kilojoules/gram, or 9 kilocalories/gram).

The class of unsaturated fatty acids can be further divided into monounsaturated and polyunsaturated fatty acids. Monounsaturated fatty acids (the primary constituents of olive and canola oils) contain only one double bond. Polyunsaturated fatty acids (PUFAs) (the primary constituents of corn, sunflower, flax seed and many other vegetable oils) contain more than one double bond. Fatty acids are often referred to using the number of carbon atoms in the acyl chain, followed by a colon, followed by the number of double bonds in the chain (e.g., 18:1 refers to the 18-carbon monounsaturated fatty acid, oleic acid; 18:3 refers to any 18-carbon PUFA with three double bonds).

PUFAs are further categorized on the basis of the location of their double bonds. An omega or n notation indicates the number of carbon atoms from the methyl end of the acyl chain to the first double bond. Thus, for example, in the omega-3 (n-3) family of PUFAs, the first double bond is 3 carbons from the methyl end of the molecule.  Finally, PUFAs can be categorized according to their chain length. The 18-carbon n-3 and n-6 short-chain PUFAs are precursors to the longer 20- and 22-carbon PUFAs, called long-chain PUFAs (LCPUFAs).

Fatty Acid Metabolism

Mammalian cells can introduce double bonds into all positions on the fatty acid chain except the n-3 and n-6 position. Thus, the short-chain alpha- linolenic acid (ALA, chemical abbreviation: 18:3n-3) and linoleic acid (LA, chemical abbreviation: 18:2n-6) are essential fatty acids.

No other fatty acids found in food are considered ‘essential’ for humans, because they can all be synthesized from the short chain fatty acids.

Following ingestion, ALA and LA can be converted in the liver to the long chain, more unsaturated n-3 and n-6 LCPUFAs by a complex set of synthetic pathways that share several enzymes (Figure 1). LC PUFAs retain the original sites of desaturation (including n-3 or n-6). The omega-6 fatty acid LA is converted to gamma-linolenic acid (GLA, 18:3n-6), an omega- 6 fatty acid that is a positional isomer of ALA. GLA, in turn, can be converted to the longerchain omega-6 fatty acid, arachidonic acid (AA, 20:4n-6). AA is the precursor for certain classes of an important family of hormone- like substances called the eicosanoids (see below).

The omega-3 fatty acid ALA (18:3n-3) can be converted to the long-chain omega-3 fatty acid, eicosapentaenoic acid (EPA; 20:5n-3). EPA can be elongated to docosapentaenoic acid (DPA 22:5n-3), which is further desaturated to docosahexaenoic acid (DHA; 22:6n-3). EPA and DHA are also precursors of several classes of eicosanoids and are known to play several other critical roles, some of which are discussed further below.

The conversion from parent fatty acids into the LC PUFAs – EPA, DHA, and AA – appears to occur slowly in humans. In addition, the regulation of conversion is not well understood, although it is known that ALA and LA compete for entry into the metabolic pathways.

Physiological Functions of EPA and AA

As stated earlier, fatty acids play a variety of physiological roles. The specific biological functions of a fatty acid are determined by the number and position of double bonds and the length of the acyl chain.

Both EPA (20:5n-3) and AA (20:4n-6) are precursors for the formation of a family of hormone- like agents called eicosanoids. Eicosanoids are rudimentary hormones or regulating – molecules that appear to occur in most forms of life. However, unlike endocrine hormones, which travel in the blood stream to exert their effects at distant sites, the eicosanoids are autocrine or paracrine factors, which exert their effects locally – in the cells that synthesize them or adjacent cells. Processes affected include the movement of calcium and other substances into and out of cells, relaxation and contraction of muscles, inhibition and promotion of clotting, regulation of secretions including digestive juices and hormones, and control of fertility, cell division, and growth.3

The eicosanoid family includes subgroups of substances known as prostaglandins, leukotrienes, and thromboxanes, among others. As shown in Figure 1.1, the long-chain omega-6 fatty acid, AA (20:4n-6), is the precursor of a group of eicosanoids that include series-2 prostaglandins and series-4 leukotrienes. The omega-3 fatty acid, EPA (20:5n-3), is the precursor to a group of eicosanoids that includes series-3 prostaglandins and series-5 leukotrienes. The AA-derived series-2 prostaglandins and series-4 leukotrienes are often synthesized in response to some emergency such as injury or stress, whereas the EPA-derived series-3 prostaglandins and series-5 leukotrienes appear to modulate the effects of the series-2 prostaglandins and series-4 leukotrienes (usually on the same target cells). More specifically, the series-3 prostaglandins are formed at a slower rate and work to attenuate the effects of excessive levels of series-2 prostaglandins. Thus, adequate production of the series-3 prostaglandins seems to protect against heart attack and stroke as well as certain inflammatory diseases like arthritis, lupus, and asthma.3.

EPA (22:6 n-3) also affects lipoprotein metabolism and decreases the production of substances – including cytokines, interleukin 1ß (IL-1ß), and tumor necrosis factor a (TNF-a) – that have pro-inflammatory effects (such as stimulation of collagenase synthesis and the expression of adhesion molecules necessary for leukocyte extravasation [movement from the circulatory system into tissues]).2 The mechanism responsible for the suppression of cytokine production by omega-3 LC PUFAs remains unknown, although suppression of omega-6-derived eicosanoid production by omega-3 fatty acids may be involved, because the omega-3 and omega-6 fatty acids compete for a common enzyme in the eicosanoid synthetic pathway, delta-6 desaturase.

DPA (22:5n-3) (the elongation product of EPA) and its metabolite DHA (22:6n-3) are frequently referred to as very long chain n-3 fatty acids (VLCFA). Along with AA, DHA is the major PUFA found in the brain and is thought to be important for brain development and function. Recent research has focused on this role and the effect of supplementing infant formula with DHA (since DHA is naturally present in breast milk but not in formula).

Overview of Lipid Catabolism:

http://www.elmhurst.edu/~chm/vchembook/622overview.html

The major aspects of lipid metabolism are involved with

  • Fatty Acid Oxidation to produce energy or
  • the synthesis of lipids which is called Lipogenesis.

The metabolism of lipids and carbohydrates are related by the conversion of lipids from carbohydrates. This can be seen in the diagram. Notice the link through actyl-CoA, the seminal discovery of Fritz Lipmann. The metabolism of both is upset by diabetes mellitus, which results in the release of ketones (2/3 betahydroxybutyric acid) into the circulation.

 

fatty acid metabolism

fatty acid metabolism

 

http://www.elmhurst.edu/~chm/vchembook/images/590metabolism.gif

The first step in lipid metabolism is the hydrolysis of the lipid in the cytoplasm to produce glycerol and fatty acids.

Since glycerol is a three carbon alcohol, it is metabolized quite readily into an intermediate in glycolysis, dihydroxyacetone phosphate. The last reaction is readily reversible if glycerol is needed for the synthesis of a lipid.

The hydroxyacetone, obtained from glycerol is metabolized into one of two possible compounds. Dihydroxyacetone may be converted into pyruvic acid, a 3-C intermediate at the last step of glycolysis to make energy.

In addition, the dihydroxyacetone may also be used in gluconeogenesis (usually dependent on conversion of gluconeogenic amino acids) to make glucose-6-phosphate for glucose to the blood or glycogen depending upon what is required at that time.

Fatty acids are oxidized to acetyl CoA in the mitochondria using the fatty acid spiral. The acetyl CoA is then ultimately converted into ATP, CO2, and H2O using the citric acid cycle and the electron transport chain.

There are two major types of fatty acids – ω-3 and ω-6.  There are also saturated and unsaturated with respect to the existence of double bonds, and monounsaturated and polyunsatured.  Polyunsaturated fatty acids (PUFAs) are important in long term health, and it will be seen that high cardiovascular risk is most associated with a low ratio of ω-3/ω-6, the denominator being from animal fat. Ω-3 fatty acids are readily available from fish, seaweed, and flax seed. More can be said of this later.

Fatty acids are synthesized from carbohydrates and occasionally from proteins. Actually, the carbohydrates and proteins have first been catabolized into acetyl CoA. Depending upon the energy requirements, the acetyl CoA enters the citric acid cycle or is used to synthesize fatty acids in a process known as LIPOGENESIS.

The relationships between lipid and carbohydrate metabolism are
summarized in Figure 2.

fatty acid spiral

 

Energy Production Fatty Acid Oxidation:

Visible” ATP:

In the fatty acid spiral, there is only one reaction which directly uses ATP and that is in the initiating step. So this is a loss of ATP and must be subtracted later.

A large amount of energy is released and restored as ATP during the oxidation of fatty acids. The ATP is formed from both the fatty acid spiral and the citric acid cycle. 

Connections to Electron Transport and ATP:

One turn of the fatty acid spiral produces ATP from the interaction of the coenzymes FAD (step 1) and NAD+ (step 3) with the electron transport chain. Total ATP per turn of the fatty acid spiral is:

Electron Transport Diagram – (e.t.c.)

Step 1 – FAD into e.t.c. = 2 ATP
Step 3 – NAD+ into e.t.c. = 3 ATP
Total ATP per turn of spiral = 5 ATP

In order to calculate total ATP from the fatty acid spiral, you must calculate the number of turns that the spiral makes. Remember that the number of turns is found by subtracting one from the number of acetyl CoA produced. See the graphic on the left bottom.

Example with Palmitic Acid = 16 carbons = 8 acetyl groups

Number of turns of fatty acid spiral = 8-1 = 7 turns

ATP from fatty acid spiral = 7 turns and 5 per turn = 35 ATP.

This would be a good time to remember that single ATP that was needed to get the fatty acid spiral started. Therefore subtract it now.

NET ATP from Fatty Acid Spiral = 35 – 1 = 34 ATP

SREBPs: activators of the complete program of cholesterol and fatty acid synthesis in the liver

Jay D. Horton1,2, Joseph L. Goldstein1 and Michael S. Brown1

1Department of Molecular Genetics, and
2Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA

J Clin Invest. 2002;109(9):1125–1131.
http://dx.doi.org:/10.1172/JCI15593
Lipid homeostasis in vertebrate cells is regulated by a family of membrane-bound transcription factors designated sterol regulatory element–binding proteins (SREBPs). SREBPs directly activate the expression of more than 30 genes dedicated to the synthesis and uptake of cholesterol, fatty acids, triglycerides, and phospholipids, as well as the NADPH cofactor required to synthesize these molecules (14). In the liver, three SREBPs regulate the production of lipids for export into the plasma as lipoproteins and into the bile as micelles. The complex, interdigitated roles of these three SREBPs have been dissected through the study of ten different lines of gene-manipulated mice. These studies form the subject of this review.

SREBPs: activation through proteolytic processing

SREBPs belong to the basic helix-loop-helix–leucine zipper (bHLH-Zip) family of transcription factors, but they differ from other bHLH-Zip proteins in that they are synthesized as inactive precursors bound to the endoplasmic reticulum (ER) (1, 5). Each SREBP precursor of about 1150 amino acids is organized into three domains: (a) an NH2-terminal domain of about 480 amino acids that contains the bHLH-Zip region for binding DNA; (b) two hydrophobic transmembrane–spanning segments interrupted by a short loop of about 30 amino acids that projects into the lumen of the ER; and (c) a COOH-terminal domain of about 590 amino acids that performs the essential regulatory function described below.

In order to reach the nucleus and act as a transcription factor, the NH2-terminal domain of each SREBP must be released from the membrane proteolytically (Figure1). Three proteins required for SREBP processing have been delineated in cultured cells, using the tools of somatic cell genetics (see ref. 5for review). One is an escort protein designated SREBP cleavage–activating protein (SCAP). The other two are proteases, designated Site-1 protease (S1P) and Site-2 protease (S2P). Newly synthesized SREBP is inserted into the membranes of the ER, where its COOH-terminal regulatory domain binds to the COOH-terminal domain of SCAP (Figure 1).

Figure 1

Model for the sterol-mediated proteolytic release of SREBPs from membranes JCI0215593.f1

Model for the sterol-mediated proteolytic release of SREBPs from membranes. SCAP is a sensor of sterols and an escort of SREBPs. When cells are depleted of sterols, SCAP transports SREBPs from the ER to the Golgi apparatus, where two proteases, Site-1 protease (S1P) and Site-2 protease (S2P), act sequentially to release the NH2-terminal bHLH-Zip domain from the membrane. The bHLH-Zip domain enters the nucleus and binds to a sterol response element (SRE) in the enhancer/promoter region of target genes, activating their transcription.

SCAP is both an escort for SREBPs and a sensor of sterols. When cells become depleted in cholesterol, SCAP escorts the SREBP from the ER to the Golgi apparatus, where the two proteases reside. In the Golgi apparatus, S1P, a membrane-bound serine protease, cleaves the SREBP in the luminal loop between its two membrane-spanning segments, dividing the SREBP molecule in half. (Fig 1)  The NH2-terminal bHLH-Zip domain is then released from the membrane via a second cleavage mediated by S2P, a membrane-bound zinc metalloproteinase. The NH2-terminal domain, designated nuclear SREBP (nSREBP), translocates to the nucleus, where it activates transcription by binding to nonpalindromic sterol response elements (SREs) in the promoter/enhancer regions of multiple target genes.

SREBPs: two genes, three proteins

The mammalian genome encodes three SREBP isoforms, designated SREBP-1a, SREBP-1c, and SREBP-2.

SREBP-1a is a potent activator of all SREBP-responsive genes, including those that mediate the synthesis of cholesterol, fatty acids, and triglycerides. High-level transcriptional activation is dependent on exon 1a, which encodes a longer acidic transactivation segment than does the first exon of SREBP-1c. The roles of SREBP-1c and SREBP-2 are more restricted than that of SREBP-1a. SREBP-1c preferentially enhances transcription of genes required for fatty acid synthesis but not cholesterol synthesis.

SREBP-1c and SREBP-2 activate three genes required to generate NADPH, which is consumed at multiple stages in these lipid biosynthetic pathways (8) (Figure 2).

major metabolic intermediates in the pathways for synthesis of cholesterol, fatty acids, and triglycerides

major metabolic intermediates in the pathways for synthesis of cholesterol, fatty acids, and triglycerides

Steroids

A major class of lipids, steroids, have a ring structure of three cyclohexanes and one
cyclopentane in a fused ring system as shown below. There are a variety of functional
groups that may be attached. The main feature, as in all lipids, is the large number of
carbon-hydrogens which make steroids non-polar.

Steroids include such well known compounds as cholesterol, sex hormones, birth
control pills, cortisone, and anabolic steroids.

 

sex hormones

sex hormones

cortisone

cortisone

Adrenocorticoid Hormones

The adrenocorticoid hormones are products of the adrenal glands.

The most important mineralcorticoid is aldosterone, which regulates the
reabsorption of sodium and chloride ions in the kidney tubules and increases
the loss of potassium ions.Aldosterone is secreted when blood sodium ion
levels are too low to cause the kidney to retain sodium ions. If sodium
levels are elevated, aldosterone is not secreted, so that some sodium
will be lost in the urine. Aldosterone also controls swelling in the tissues.

Cortisol, the most important glucocortinoid, has the function of increasing
glucose and glycogen concentrations in the body. These reactions are
completed in the liver by taking fatty acids from lipid storage cells and
amino acids from body proteins to make glucose and glycogen.

In addition, cortisol is elevated in the circulation with cytokine mediated
(IL1, IL1, TNFα) inflammatory reaction, called the systemic inflammatory
response syndrome. Its ketone derivative, cortisone, has the ability
to relieve inflammatory effects. Cortisone or similar synthetic derivatives
such as prednisolone are used to treat inflammatory diseases, rheumatoid
arthritis, and bronchial asthma. There are many side effects with the use
of cortisone drugs, such as bone resorption, so there use must be
monitored carefully.

Hormone Receptors

Steroid hormone receptors are found on the plasma membrane, in the cytosol and also in the nucleus of target cells. They are generally intracellular receptors (typically cytoplasmic) and initiate signal transduction for steroid hormones which lead to changes in gene expression over a time period of hours to days. The best studied steroid hormone receptors are members of the nuclear receptor subfamily 3 (NR3) that include receptors for estrogen (group NR3A)[1] and 3-ketosteroids (group NR3C).[2] In addition to nuclear receptors, several G protein-coupled receptors and ion channels act as cell surface receptors for certain steroid hormones.

 

Steroid Hormone Receptors and their Response Elements

Steroid hormone receptors are proteins that have a binding site for a particular steroid molecule. Their response elements are DNA sequences that are bound by the complex of the steroid bound to its Steroid receptor.

The response element is part of the promoter of a gene. Binding by the receptor activates or represses, as the case may be, the gene controlled by that promoter.

It is through this mechanism that steroid hormones turn genes on (or off).

steroid hormone receptor

steroid hormone receptor

http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/S/Sigler.jpg

The glucocorticoid receptor, like all steroid hormone receptors, is a zinc-finger transcription factor; the zinc atoms are the four yellow spheres. Each is attached to four cysteines.

For a steroid hormone to regulate (turn on or off) gene transcription, its receptor must:

  1. bind to the hormone (cortisol in the case of the glucocorticoid receptor)
  2. bind to a second copy of itself to form a homodimer
  3. be in the nucleus, moving from the cytosol if necessary
  4. bind to its response element
  5. bind to other protein cofactors

Each of these functions depend upon a particular region of the protein (e.g., the zinc fingers for binding DNA).

Each of these functions depend upon a particular region of the protein (e.g., the zinc fingers for binding DNA). Mutations in any one region may upset the function of that region without necessarily interfering with other functions of the receptor.

Positive and Negative Response Elements

Some of the hundreds of glucocorticoid response elements in the human genome activate gene transcription when bound by the hormone/receptor complex. Others inhibit gene transcription when bound by the hormone/receptor complex.

Example: When the stress hormone cortisol — bound to its receptor — enters the nucleus of a liver cell, the complex binds to the positive response elements of the many genes needed for gluconeogenesis — the conversion of protein and fat into glucose resulting in a rise in the level of blood sugar.

the negative response element of the insulin receptor gene thus diminishing the ability of the cells to remove glucose from the blood. (This hyperglycemic effect is enhanced by the binding of the cortisol/receptor complex to a negative response element in the beta cells of the pancreas thus reducing the production of insulin.)

Note that every type of cell in the body contains the same response elements in its genome. What determines if a given cell responds to the arrival of a hormone depends on the presence of the hormone’s receptor in the cell.

The Nuclear Receptor Superfamily

Retinoids

Retinoids

http://users.rcn.com/jkimball.ma.ultranet/BiologyPages/R/Retinoids.png

 The zinc-finger proteins that serve as receptors for glucocorticoids and progesterone are members of a large family of similar proteins that serve as receptors for a variety of small, hydrophobic molecules. These include:

  1. other steroid hormones like
  2. the mineralocorticoid aldosterone
  3. estrogens
  4. the thyroid hormone, T3
  5. calcitriol, the active form of vitamin D
  6. retinoids: vitamin A (retinol) and its relatives
    1. retinal
    2. retinoic acid (tretinoin — also available as the drug Retin-A®); and its isomer
  7. isotretinoin (sold as Accutane® for the treatment of acne).
  8. bile acids
  9. fatty acids.

These bind members of the superfamily called peroxisome-proliferator-activated receptors (PPARs). They got their name from their initial discovery as the receptors for

  • drugs that increase the number and size of peroxisomes in cells.

In every case, the receptors consist of at least

  • three functional modules or domains.

From N-terminal to C-terminal, these are:

  1. a domain needed
  2. the zinc-finger domain needed for DNA binding (to the response element)
  3. the domain responsible for binding the particular hormone as well as the second unit of the dimer.
  4. for the receptor to activate the promoters of the genes being controlled

Schematic diagram of type II zinc finger proteins characteristic of the DNA-binding domain structure of members of the steroid hormone receptor superfamily. Zinc fingers are common features of many transcription factors, allowing proteins to bind to DNA. Each circle represents one amino acid. The CI zinc finger interacts specifically with five base pairs of DNA and determines the DNA sequence recognized by the particular steroid receptor. The three shaded amino acids indicated by the arrows in the knuckle of the CI zinc finger are in the “P box” that allows HRE sequence discrimination between the GR and ERα. The vertically striped aa within the knuckle of the CII zinc finger constitutes the “D box” that is important for dimerization and contacts with the DNA phosphate backbone. Adapted from Tsai M-J, O’Malley BW. Molecular mechanisms of action of steroid/thyroid receptor superfamily members. Annu Rev Biochem 1994;63:451-483; Gronemeyer H. Transcription activation by estrogen and progesterone receptors. Annu Rev Genet 1991;25:89-123.

type II zinc finger proteins

type II zinc finger proteins

Cytoskeleton and Cell Membrane Physiology

http://pharmaceuticalinnovation.com/10/28/2014/larryhbern/Cytoskeleton_
and_Cell_Membrane_Physiology

Definition and Function

The cytoskeleton is a series of intercellular proteins that help a cell with

  1. shape,
  2. support, and
  3. movement.

Cytoskeleton has three main structural components:

  1. microfilaments,
  2. intermediate filaments, and
  3. movement

The cytoskeleton mediates movement by

  • helping the cell move in its environment and
  • mediating the movement of the cell’s components.

Thereby it provides an important structural framework for the cell –

  • the framework for the movement of organelles, contiguous with the cell membrane, around the cytoplasm. By the activity of
  • the network of protein microfilaments, intermediate filaments, and microtubules.

The structural framework supports cell function as follows:

Cell shape. For cells without cell walls, the cytoskeleton determines the shape of the cell. This is one of the functions of the intermediate filaments.

Cell movement. The dynamic collection of microfilaments and microtubles can be continually in the process of assembly and disassembly, resulting in forces that move the cell. There can also be sliding motions of these structures. Audesirk and Audesirk give examples of white blood cells “crawling” and the migration and shape changes of cells during the development of multicellular organisms.

Organelle movement. Microtubules and microfilaments can help move organelles from place to place in the cell. In endocytosis a vesicle formed engulfs a particle abutting the cell. Microfilaments then attach to the vesicle and pull it into the cell. Much of the complex synthesis and distribution function of the endoplasmic reticulum and the Golgi complex makes use of transport vescicles,  associated with the cytoskeleton.

Cell division. During cell division, microtubules accomplish the movement of the chromosones to the daughter nucleus. Also, a ring of microfilaments helps divide two developing cells by constricting the central region between the cells (fission).

References:
Hickman, et al. Ch 4 Hickman, Cleveland P., Roberts, Larry S., and Larson, Allan, Integrated Principles of Zoology, 9th. Ed., Wm C. Brown, 1995.
Audesirk & Audesirk Ch 6 Audesirk, Teresa and Audesirk, Gerald, Biology, Life on Earth, 5th Ed., Prentice-Hall, 1999.
http://hyperphysics.phy-astr.gsu.edu/hbase/biology/bioref.html#c1
http://hyperphysics.phy-astr.gsu.edu/hbase/biology/cytoskel.html

 

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Cytoskeleton and Cell Membrane Physiology

 

Curator: Larry H Bernstein, MD, FCAP

 

cell-membrane

cell-membrane

early evolution of lipid membranes and the three domains of life

early evolution of lipid membranes and the three domains of life

Definition and Function

The cytoskeleton is a series of intercellular proteins that help a cell with

  1. shape,
  2. support, and
  3. movement.

Cytoskeleton has three main structural components:

  1. microfilaments,
  2. intermediate filaments, and
  3. movement

The cytoskeleton mediates movement by

  • helping the cell move in its environment and
  • mediating the movement of the cell’s components.

Thereby it provides an important structural framework for the cell –

  • the framework for the movement of organelles, contiguous with the cell membrane, around the cytoplasm. By the activity of
  • the network of protein microfilaments, intermediate filaments, and microtubules.

The structural framework supports cell function as follows:

Cell shape. For cells without cell walls, the cytoskeleton determines the shape of the cell. This is one of the functions of the intermediate filaments.

Cell movement. The dynamic collection of microfilaments and microtubles can be continually in the process of assembly and disassembly, resulting in forces that move the cell. There can also be sliding motions of these structures. Audesirk and Audesirk give examples of white blood cells “crawling” and the migration and shape changes of cells during the development of multicellular organisms.

Organelle movement. Microtubules and microfilaments can help move organelles from place to place in the cell. In endocytosis a vesicle formed engulfs a particle abutting the cell. Microfilaments then attach to the vesicle and pull it into the cell. Much of the complex synthesis and distribution function of the endoplasmic reticulum and the Golgi complex makes use of transport vescicles,  associated with the cytoskeleton.

Cell division. During cell division, microtubules accomplish the movement of the chromosones to the daughter nucleus. Also, a ring of microfilaments helps divide two developing cells by constricting the central region between the cells (fission).

References:
Hickman, et al. Ch 4 Hickman, Cleveland P., Roberts, Larry S., and Larson, Allan, Integrated Principles of Zoology, 9th. Ed., Wm C. Brown, 1995.
Audesirk & Audesirk Ch 6 Audesirk, Teresa and Audesirk, Gerald, Biology, Life on Earth, 5th Ed., Prentice-Hall, 1999.
http://hyperphysics.phy-astr.gsu.edu/hbase/biology/bioref.html#c1
http://hyperphysics.phy-astr.gsu.edu/hbase/biology/cytoskel.html

Intermediate filaments are 8-12 nanometers in diameter and are twisted together in a cord shape. They are composed of keratin and keratin-like proteins.  These filaments are tough and resist tension.

Microtubules are composed of alpha and beta tubulin that form long, hollow cylinders.  These are fairly strong proteins and are the largest component of cytoskeleton at 25 nanometers. Tubular monomers can be lengthened or shortened from the positive end.

Microtubules have three different functions.

They make up the cell’s

  1. centriole
  2. the flagella and cilia of a cell, and
  3. they serve as “tracks” for transport vesicles to move along.

http://biology.kenyon.edu/HHMI/Biol113/cytoskeleton.htm

Key Points 

Microtubules

  1. help the cell resist compression,
  2. provide a track along which vesicles can move throughout the cell, and
  3. are the components of cilia and flagella.

Cilia and flagella are hair-like structures that

  1. assist with locomotion in some cells, as well as
  2. line various structures to trap particles.

The structures of cilia and flagella are a “9+2 array,” meaning that

  • a ring of nine microtubules is surrounded by two more microtubules.

Microtubules attach to replicated chromosomes

  • during cell division and
  • pull them apart to opposite ends of the pole,
  • allowing the cell to divide with a complete set of chromosomes in each daughter cell.

Microtubules are the largest element of the cytoskeleton.

The walls of the microtubule are made of

  • polymerized dimers of α-tubulin and β-tubulin, two globular proteins.

https://figures.boundless.com/18608/full/figure-04-05-04ab.jpe

With a diameter of about 25 nm, microtubules are the widest components of the cytoskeleton.

https://figures.boundless.com/18608/full/figure-04-05-04ab.jpe

They help the cell

  • resist compression,
  • provide a track along which vesicles move through the cell, and
  • pull replicated chromosomes to opposite ends of a dividing cell.

Like microfilaments, microtubules can dissolve and reform quickly.

Microtubules are also the structural elements of flagella, cilia, and centrioles (the latter are the two perpendicular bodies of the centrosome). In animal cells, the centrosome is the microtubule-organizing center. In eukaryotic cells, flagella and cilia are quite different structurally from their counterparts in prokaryotes.

Intermediate Filaments

Intermediate filaments (IFs) are cytoskeletal components found in animal cells. They are composed of a family of related proteins sharing common structural and sequence features.

epithelial cells

epithelial cells

https://figures.boundless.com/22035/full/epithelial-cells.jpe

flagella and cilia share a common structural arrangement of microtubules called a “9 + 2 array.” This is an appropriate name because a single flagellum or cilium is made of a ring of nine microtubule doublets surrounding a single microtubule doublet in the center.

9+2 array

9+2 array

https://figures.boundless.com/18609/full/figure-04-05-05.jpe

https://www.boundless.com/physiology/textbooks/boundless-anatomy-and-physiology-textbook/cellular-structure-and-function-3/the-cytoskeleton-46/the-composition-and-function-of-the-cytoskeleton-348-11460/

http://jcs.biologists.org/content/115/22/4215/F4.large.jpg

The `Spectraplakins’: cytoskeletal giants with characteristics of both spectrin and plakin families

Katja Röper, Stephen L. Gregory and Nicholas H. Brown
J Cell Sci Nov 15, 2002; 115: 4215-4225
http://dx.doi.org:/10.1242/​jcs.00157

cytoskel

cytoskel

http://plantphys.info/plant_physiology/images/cytoskelfcns.gif

cytoskeleton

cytoskeleton

http://img.sparknotes.com/figures/D/d479f5da672c08a54f986ae699069d7a/cytoskeleton.gif

The sequential endosymbiotic origins of eukaryotes: Compared to bacteria and archaea, the typical eukaryotic cell is much more structurally complex.

While the prokaryotes have a rigid cell wall, the ancestral eukaryote appears to have been wall-less (the walls of plant cells appear to represent a adaptation, and are not homologous to prokaryotic cell walls).

In addition to a nucleus (wherein the cell’s DNA is located, and which we will return to in the next section), there are cytoskeletal structures, including distinctive flagella (quite different from those found in prokaryotes), an active (motile) plasma membrane, capable of engulfing other cells, and multiple internal membrane systems. (A more complete description of cell structure is beyond this version of Biofundamentals).

In aerobic bacteria and cyanobacteria, the electron transport chains associated with ATP synthesis (through either photosynthesis or aerobic respiration) located within the plasma membrane (and in the case of cyanobacteria, internal membrane systems as well).

The same processes (aerobic respiration and photosynthesis) occur within eukaryotic cells. Animals have aerobic respiration, while plants have both).

However, these processes do not occur on the plasma membrane, but rather within distinct cytoplasmic organelles: mitochondria for aerobic respiration and chloroplasts for photosynthesis. All eukaryotic cells have mitochondria, plants (which are eukaryotic) have both mitochondria and chloroplasts.

An intriguing evolutionary question was, are these processes related, that is, are the processes of aerobic respiration and photosynthesis found in eukaryotes homologous to the processes found in bacteria and cyanobacteria, or did they originate independently.

The path to understanding that homologous nature of these processes began with studies of cell structure.

http://virtuallaboratory.colorado.edu/Biofundamentals/lectureNotes-Revision/Topic2I_Symbiosis.htm

spectrin protein superfamily.large

spectrin protein superfamily.large

http://mmbr.asm.org/content/70/3/605/F4.large.jpg

The role of secreted factors and extracellular matrix

The role of secreted factors and extracellular matrix

Focal Adhesions: Transmembrane Junctions Between the Extracellular Matrix and the Cytoskeleton

K Burridge, K Fath, T Kelly, G Nuckolls, and C Turner
Ann Rev Cell Biol Nov 1988; 4: 487-525

http://dx.doi.org:/10.1146/annurev.cb.04.110188.002415

the extracellular matrix (ECM) is a collection of extracellular molecules secreted by cells that

  • provides structural and biochemical support to the surrounding cells.[1]

Because multicellularity evolved independently in different multicellular lineages, the composition of ECM varies between multicellular structures; however,

  • cell adhesion,
  • cell-to-cell communication and
  • differentiation

are common functions of the ECM.[2]

The animal extracellular matrix includes

  • the interstitial matrix and
  • the basement membrane.[3]

Interstitial matrix is present between various animal cells (i.e., in the intercellular spaces).

Gels of polysaccharides and fibrous proteins

  • fill the interstitial space and act as
  • a compression buffer against the stress placed on the ECM.[4]

Basement membranes are sheet-like depositions of ECM on which various epithelial cells rest.

The Extracellular Matrix (ECM)
http://userpage.chemie.fu-berlin.de/biochemie/aghaucke/lehre/cytoskelet-ECM.pdf

Mechanical support to tissues

http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/14707425/U4CP5-1_FibronectinIntegri_ksm.jpg

http://www.nature.com/scitable/content/integrin-connects-the-extracellular-matrix-with-the-14707425

Organization of cells into tissues

  1. Activation of signaling pathways (cell growth, proliferation; development); examples:
  2. TGF-β, integrins
  3. specialized roles (tendon, bone; cartilage; cell movement during development; basal lamina in epithelia)

Components

  1. proteoglycans
  2. collagen fibers (mechanical strength)
  3. multiadhesive matrix proteins (linking cell surface receptors to the (ECM)

Integrin connects the extracellular matrix with the actin cytoskeleton inside the cell

Fibronectin Integrin

Fibronectin Integrin

http://www.nature.com/scitable/content/ne0000/ne0000/ne0000/ne0000/14707425/U4CP5-1_FibronectinIntegri_ksm.jpg

http://www.nature.com/scitable/content/integrin-connects-the-extracellular-matrix-with-the-14707425

Continuous membrane-cytoskeleton adhesion requires continuous accommodation to lipid and cytoskeleton dynamics.

Sheetz MP, Sable JE, Döbereiner HG.
Annu Rev Biophys Struct Biomol. 2006;35:417-34.

The plasma membrane of most animal cells conforms to the cytoskeleton and only occasionally separates to form blebs. Previous studies indicated that

  • many weak interactions between cytoskeleton and the lipid bilayer
  • kept the surfaces together to counteract the normal outward pressure of cytoplasm.

Either the loss of adhesion strength or the formation of gaps in the cytoskeleton enables the pressure to form blebs. Membrane-associated cytoskeleton proteins, such as spectrin and filamin, can

  • control the movement and aggregation of membrane proteins and lipids,
    e.g., phosphoinositol phospholipids (PIPs), as well as blebbing.

At the same time, lipids (particularly PIPs) and membrane proteins affect

  • cytoskeleton and signaling dynamics.

We consider here the roles of the major phosphatidylinositol-4,5-diphosphate (PIP2) binding protein, MARCKS, and PIP2 levels in controlling cytoskeleton dynamics. Further understanding of dynamics will provide important clues about how membrane-cytoskeleton adhesion rapidly adjusts to cytoskeleton and membrane dynamics. http://www.ncbi.nlm.nih.gov/pubmed/16689643

Interaction of membrane/lipid rafts with the cytoskeleton: impact on signaling and function: membrane/lipid rafts, mediators of cytoskeletal arrangement and cell signaling.

Head BP, Patel HH, Insel PA   Epub 2013 Jul 27.
Biochim Biophys Acta. 2014 Feb;1838(2):532-45.
http://dx.doi.org:/10.1016/j.bbamem.2013.07.018

The plasma membrane in eukaryotic cells contains microdomains that are

  • enriched in certain glycosphingolipids, gangliosides, and sterols (such as cholesterol) to form membrane/lipid rafts (MLR).

These regions exist as caveolae, morphologically observable flask-like invaginations, or as a less easily detectable planar form. MLR are scaffolds for many molecular entities, including

  • signaling receptors and ion channels that
  • communicate extracellular stimuli to the intracellular milieu.

Much evidence indicates that this organization and/or the clustering of MLR into more active signaling platforms

  • depends upon interactions with and dynamic rearrangement of the cytoskeleton.

Several cytoskeletal components and binding partners, as well as enzymes that regulate the cytoskeleton, localize to MLR and help

  • regulate lateral diffusion of membrane proteins and lipids in response to extracellular events
    (e.g., receptor activation, shear stress, electrical conductance, and nutrient demand).

MLR regulate

  • cellular polarity,
  • adherence to the extracellular matrix,
  • signaling events (including ones that affect growth and migration), and
  • are sites of cellular entry of certain pathogens, toxins and nanoparticles.

The dynamic interaction between MLR and the underlying cytoskeleton thus regulates many facets of the function of eukaryotic cells and their adaptation to changing environments. Here, we review general features of MLR and caveolae and their role in several aspects of cellular function, including

  • polarity of endothelial and epithelial cells,
  • cell migration,
  • mechanotransduction,
  • lymphocyte activation,
  • neuronal growth and signaling, and
  • a variety of disease settings.

This article is part of a Special Issue entitled: Reciprocal influences between cell cytoskeleton and membrane channels, receptors and transporters. Guest Editor: Jean Claude Hervé.

Cell control by membrane–cytoskeleton adhesion

Michael P. Sheetz
Nature Reviews Molecular Cell Biology 2, 392-396 (May 2001) | http://dx.doi.doi:/10.1038/35073095

The rates of mechanochemical processes, such as endocytosis, membrane extension and membrane resealing after cell wounding, are known to be controlled biochemically, through interaction with regulatory proteins. Here, I propose that these rates are also controlled physically, through an apparently continuous adhesion between plasma membrane lipids and cytoskeletal proteins.

Lipid Rafts, Signalling and the Cytoskeleton
http://www.bms.ed.ac.uk/research/others/smaciver/Cyto-Topics/lipid_rafts_and_the_cytoskeleton.htm

Lipid rafts are specialised membrane domains enriched in certain lipids cholesterol and proteins. The existence of lipid rafts was first hypothesised in 1988 (Simons & van Meer, 1988; Simon & Ikonen, 1997), but what we know as “caveolae” were first observed  much earlier (Palade, 1953; Yamada, 1955).  Caveolae are flask shaped invaginations on the cell surface that are a type of membrane raft, these were named “caveolae intracellulare” (Yamada, 1955).  After a long argument (Jacobson & Dietrich, 1999), most now consider that these rafts actually exist, however, there is some confusion surrounding the classification of these rafts. It presently seems that there could be three types; caveolae, glycosphingolipid enriched membranes (GEM), and polyphospho inositol rich rafts. It may also be that there are inside rafts (PIP2 rich and caveolae) and outside rafts (GEM).

The fatty-acid chains of lipids within the rafts tend to be extended and so more tightly packed, creating domains with higher order. It is therefore thought that  rafts exist in a separate ordered phase that floats in a sea of poorly ordered lipids.  Glycosphingolipids, and other lipids with long, straight acyl chains are preferentially incorporated into the rafts.

Caveolae are similar in composition to GEMs that lack caveolae and in fact cells that lack caveolin-1 do not have morphologically identifiable caveolae but instead have extra GEM.  These cells can then be transfected with caveolin-1 cDNA and the caveolae then appear.  This suggests that GEM are merely caveolae without caveolin-1.  Caveolin-1 is a 21kDa integral membrane protein that binds cholesterol (Maruta et al, 1995). In cells lacking caveolin-1, caveolin-2 is synthesised but remains in the Golgi.  Caveolin 1 and 2 colocalise when expressed in the same cells and they may form hetero-dimers (Scherer et al, 1997). Caveolin-3 is expressed in muscle where it forms muscle-type caveolae.  Caveolin-3 is involved in certain types of muscular dystrophy (Galbiati et al, ). A slightly confusing finding is that caveolae are the reported site of integrin signalling ().  It is difficult to imagine integrins being available in the depths of membrane invaginations for binding extra-cellular ligands.

The function of rafts

Many functions have been attributed to rafts, from cholesterol transport, endocytosis and signal transduction.  The later is almost certainly the case. It has been suggested that the primary function of caveolae was in constitutive endocytic trafficking but recent data show that this is not the case, instead caveolae are very stable regions of membranes that are not involved in  endocytosis (Thompsen et al, 2002).

lipid raft

lipid raft

Rafts and the Cytoskeleton

Many actin binding proteins are known to bind to polyphosphoinositides and to be regulated by them (see PI and ABPs), by a series of protein domains such as PH, PX and ENTH (see Domains).  It is consequently scarcely surprising that some ABPs are suggested to link the actin cytoskeleton and PIP2-enriched rafts. One of these is gelsolin, a Ca2+, pH and polyphosphoinositide regulated actin capping and severing protein (see Gelsolin Family), that partitions into rafts isolated biochemically from brain (Fanatsu et al, 2000).

GEMs too are suggested to link to the actin cytoskeleton through ABPs particularly ERM proteins through EBP50, a protein that binds members of the ERM proteins through the ERM C-terminus (Brdickova et al, 2001).

References:

Brdickova, N., Brdicka, T., Andrea, L., Spicka, J., Angelisova, P., Milgram, S. L. & Horejsi, V. (2001) Interaction between two adaptor proteins, PAG and EBP50: a possible link between membrane rafts and actin cytoskeleton.  FEBS letters. 507, 133-136.

Cary, L. A. & Cooper, J. A. (2000) Molecular switches in lipid rafts.  Nature. 404, 945-947.

Czarny, M., Fiucci, G., Lavie, Y., Banno, Y., Nozawa, Y. & Liscovitch, M. (2000) Phospholipase D2: functional interaction with caveolin in low-density membrane microdomains.,  FEBS letters.

Foger, N., Funatsu, N., Kumanogoh, H., Sokawa, Y. & Maekawa, S. (2000) Identification of gelsolin as an actin regulatory component in a Triton insoluble low density fraction (raft) of newborn bovine brain.  Neuroscience Research. 36, 311-317.

Galbiati, F., Engelman, J. A., Volonte, D., Zhang, X. L., Minetti, C., Li, M., Hou jr, H., Kneitz, B., Edelman, W. & Lisanti, M. P. (2001) Caveolin-3 null mice show a loss of caveolae, changes in the microdomain distribution of the dystrophin-glycoprotein complex, and T-tubule abnormalities.  J. Biol.Chem. 276, 21425-21433.

…  (more)

centralpore-small  Gating and Ion Conductivity

centralpore-small Gating and Ion Conductivity

Interaction of epithelial ion channels with the actin-based cytoskeleton.

Mazzochi C, Benos DJ, Smith PR.
Am J Physiol Renal Physiol. 2006 Dec;291(6):F1113-22. Epub 2006 Aug 22

The interaction of ion channels with the actin-based cytoskeleton in epithelial cells

  • not only maintains the polarized expression of ion channels within specific membrane domains,
  • it also functions in the intracellular trafficking and regulation of channel activity.

Initial evidence supporting an interaction between

  • epithelial ion channels and the
  • actin-based cytoskeleton

came from patch-clamp studies of the effects of cytochalasins on channel activity. Cytochalasins were shown to

  • either activate or inactivate epithelial ion channels.

An interaction between

  • the actin-based cytoskeleton and epithelial ion channels

was further supported by the fact that the addition of monomeric or filamentous actin to excised patches had an effect on channel activity comparable to that of cytochalasins. Through the recent application of molecular and proteomic approaches, we now know that

  • the interactions between epithelial ion channels and actin can either be direct or indirect,
  • the latter being mediated through scaffolding or actin-binding proteins
  • that serve as links between the channels and the actin-based cytoskeleton.

This review discusses recent advances in our understanding of the interactions between epithelial ion channels and the actin-based cytoskeleton, and the roles these interactions play in regulating the cell surface expression, activity, and intracellular trafficking of epithelial ion channels.

epithelial ion channels

epithelial ion channels

Actin cytoskeleton regulates ion channel activity in retinal neurons.

Maguire G, Connaughton V, Prat AG, Jackson GR Jr, Cantiello HF.
Neuroreport. 1998 Mar 9;9(4):665-70

The actin cytoskeleton is an important contributor to the integrity of cellular shape and responses in neurons. However, the molecular mechanisms associated with functional interactions between the actin cytoskeleton and neuronal ion channels are largely unknown. Whole-cell and single channel recording techniques were thus applied to identified retinal bipolar neurons of the tiger salamander (Ambystoma tigrinum) to assess the role of acute changes in actin-based cytoskeleton dynamics in the regulation of voltage-gated ion channels. Disruption of endogenous actin filaments after brief treatment (20-30 min) with cytochalasin D (CD) activated voltage-gated K+ currents in bipolar cells, which were largely prevented by intracellular perfusion with the actin filament-stabilizer agent, phalloidin. Either CD treatment under cell-attached conditions or direct addition of actin to excised, inside-out patches of bipolar cells activated and/or increased single K+ channels. Thus, acute changes in actin-based cytoskeleton dynamics regulate voltage-gated ion channel activity in bipolar cells.

Cytoskeletal Basis of Ion Channel Function in Cardiac Muscle

Matteo Vatta, Ph.D1,2 and Georgine Faulkner, Ph.D3

The publisher’s final edited version of this article is available at Future Cardiol

The heart is a force-generating organ that responds to self-generated electrical stimuli from specialized cardiomyocytes. This function is modulated by sympathetic and parasympathetic activity.

In order to contract and accommodate the repetitive morphological changes induced by the cardiac cycle,

  • cardiomyocytes depend on their highly evolved and specialized cytoskeletal apparatus.

Defects in components of the cytoskeleton, in the long term, affect

  • the ability of the cell to compensate at
  • both functional and structural levels.

In addition to the structural remodeling, the myocardium becomes

  • increasingly susceptible to altered electrical activity leading to arrhythmogenesis.

The development of arrhythmias secondary to structural remodeling defects has been noted, although the detailed molecular mechanisms are still elusive. Here I will review the current knowledge of the molecular and functional relationships between the cytoskeleton and ion channels and, I will discuss the future impact of new data on molecular cardiology research and clinical practice. 

Stretch-activated ion channel

Stretch-activated or stretch-gated ion channels are

  • ion channels which open their pores in response to
  • mechanical deformation of a neuron’s plasma membrane.

[Also see mechanosensitive ion channels and mechanosensitive channels, with which they may be synonymous]. Opening of the ion channels

  • depolarizes the afferent neuron producing an action potential with sufficient depolarization.[1]

Channels open in response to two different mechanisms: the prokaryotic model and the mammalian hair cell model.[2][3] Stretch-activated ion channels have been shown to detect vibration, pressure, stretch, touch, sounds, tastes, smell, heat, volume, and vision.[4][5][6] Stretch-activated ion channels have been categorized into

three distinct “superfamilies”:

  1. the ENaC/DEG family,
  2. the TRP family, and
  3. the K1 selective family.

These channels are involved with bodily functions such as blood pressure regulation.[7] They are shown to be associated with many cardiovascular diseases.[3] Stretch-activated channels were first observed in chick skeletal muscles by Falguni Guharay and Frederick Sachs in 1983 and the results were published in 1984.[8] Since then stretch-activated channels have been found in cells from bacteria to humans as well as plants.

Mechanosensitivity of cell membranes. Ion channels, lipid matrix and cytoskeleton.

Petrov AG, Usherwood PN.
Eur Biophys J. 1994;23(1):1-19

Physical and biophysical mechanisms of mechano-sensitivity of cell membranes are reviewed. The possible roles of

  • the lipid matrix and of
  • the cytoskeleton in membrane mechanoreception

are discussed. Techniques for generation of static strains and dynamic curvatures of membrane patches are considered. A unified model for

  • stress-activated and stress-inactivated ion channels

under static strains is described. A review of work on

  • stress-sensitive pores in lipid-peptide model membranes

is presented. The possible role of flexoelectricity in mechano-electric transduction, e.g. in auditory receptors is discussed. Studies of

  • flexoelectricity in model lipid membranes, lipid-peptide membranes and natural membranes containing ion channels

are reviewed. Finally, possible applications in molecular electronics of mechanosensors employing some of the recognized principles of mechano-electric transduction in natural membranes are discussed.Marhaba, R. & Zoller, M. (2001) Involvement of CD44 in cytoskeleton rearrangement and raft reorganization in T cells.  J.Cell Sci. 114, 1169-1178.

FIGURE 2 | The transient pore model.

peroxisomal matrix protein

peroxisomal matrix protein

FROM THE FOLLOWING ARTICLE:
Peroxisomal matrix protein import: the transient pore model

Ralf Erdmann & Wolfgang Schliebs
Nature Reviews Molecular Cell Biology 6, 738-742 (September 2005)
http://dx.doi.org:/10.1038/nrm1710

Peroxisomal matrix protein import: the transient pore model
The transient pore model

The peroxisomal import receptor peroxin-5 (Pex5) recognizes peroxisomal targeting signal-1 (PTS1)-containing cargo proteins in the cytosol. It then moves to the peroxisome where it inserts into the peroxisomal membrane to become an integral part of the protein-import apparatus. Pex14 and/or Pex13, which are associated with Pex17, are proposed to be involved in tethering the receptor to the membrane and in the assembly, stabilization and rearrangement of the translocon. Cargo release into the peroxisomal matrix is thought to be initiated by intraperoxisomal factors — for example, the competitive binding of the intraperoxisomal Pex8, which also has a PTS1. The disassembly and recycling of Pex5 is triggered by a cascade of protein–protein interactions at the peroxisomal membrane that results in the Pex1-, Pex6-driven, ATP-dependent dislocation of Pex5 from the peroxisomal membrane to the cytosol. Pex1 and Pex6 are AAA+ (ATPases associated with a variety of cellular activities) peroxins that are associated with the peroxisome membrane through Pex15 in yeast or its orthologue PEX26 in mammals. Pex4, which is membrane-anchored through Pex22, is a member of the E2 family of ubiquitin-conjugating enzymes, and Pex2, Pex10 and Pex12 contain the RING-finger motif that is a characteristic element of E3 ubiquitin ligases. Mono- or di-ubiquitylation are reversible steps that seem to be required for the efficient recycling of import receptors, whereas polyubiquitylation might signal the proteasome-dependent degradation of receptors when the physiological dislocation of receptors is blocked. Ub, ubiquitin.

Nature Reviews Molecular Cell Biology 6, 738-742 (September 2005) |
http://dx.doi.org:/10.1038/nrm1710

FROM THE FOLLOWING ARTICLE:

peroxisomal protein pore model

peroxisomal protein pore model


Peroxisomal matrix protein import: the transient pore model

Ralf Erdmann & Wolfgang Schliebs
Nature Reviews Molecular Cell Biology 6, 738-742 (September 2005)
http://dx.doi.org:/10.1038/nrm1710

Peroxisomal matrix protein import: the transient pore model

Peroxin-13 (Pex13), Pex14 and Pex17 are constituents of the docking complex for cycling peroxisomal import receptors. Another protein assembly in the peroxisomal membrane comprises the RING-finger-motif-containing peroxins Pex2, Pex10 and Pex12. This motif is a characteristic element of E3 ubiquitin ligases, and this subcomplex is linked to the docking complex by Pex8, which is peripherally attached to the lumenal side of the peroxisomal membrane. Pex4 is a member of the E2 family of ubiquitin-conjugating enzymes and is anchored to the peroxisomal membrane through the cytosolic domain of Pex22. Pex1 and Pex6 are interacting AAA+ proteins (ATPases associated with a variety of cellular activities), which are attached to the membrane through binding to Pex15 in yeast or to its mammalian counterpart PEX26.

Peroxisomal matrix protein import: the transient pore model

Ralf Erdmann & Wolfgang Schliebs

Peroxisomes import folded, even oligomeric, proteins, which distinguishes the peroxisomal translocation machinery from the well-characterized translocons of other organelles. How proteins are transported across the peroxisomal membrane is unclear. Here, we propose a mechanistic model that conceptually divides the import process into three consecutive steps: the formation of a

  • translocation pore by the import receptor,
  • the ubiquitylation of the import receptors, and
  • pore disassembly/receptor recycling.

Phytosphingosine

Masoud Naderi Maralani

Identification of the phytosphingosine metabolic pathway leading to odd-numbered fatty acids

The long-chain base ​phytosphingosine is a component of sphingolipids and exists in yeast, plants and some mammalian tissues. ​Phytosphingosine is unique in that it possesses an additional hydroxyl group compared with other long-chain bases. However, its metabolism is unknown. Here we show that ​phytosphingosine is metabolized to odd-numbered fatty acids and is incorporated into glycerophospholipids both in yeast and mammalian cells. Disruption of the yeast gene encoding long-chain base 1-phosphate lyase, which catalyzes the committed step in the metabolism of ​phytosphingosine to glycerophospholipids, causes an ~40% reduction in the level of phosphatidylcholines that contain a C15 fatty acid. We also find that ​2-hydroxypalmitic acid is an intermediate of the phytosphingosine metabolic pathway. Furthermore, we show that the yeast ​MPO1 gene, whose product belongs to a large, conserved protein family of unknown function, is involved in ​phytosphingosine metabolism. Our findings provide insights into fatty acid diversity and identify a pathway by which hydroxyl group-containing lipids are metabolized.  nature.com nature.com

About GPCRs

G-protein-coupled receptors (GPCRs) are a class of membrane proteins that allow the transmission of a wide variety of signals over the cell membrane, between different cells and over long distances inside the body. The molecular mechanisms of action of GPCRs were worked in great detail by Brian Kobilka and Robert Lefkowitz for which they were jointly awarded the Nobel Prize in Chemistry for 2012. Read More

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Lipid Classification System

Curator: Larry H. Bernstein, MD, FCAP

Lipid Classification, Nomenclature and Structure Drawing

 

The LIPID MAPS consortium has developed a comprehensive classification, nomenclature, and chemical representation system for lipids, the details of which are described in the May 2009 issue of the Journal of Lipid Research:

Fahy E, Subramaniam S, Murphy R, Nishijima M, Raetz C, Shimizu T, Spener F, van Meer G, Wakelam M and Dennis E.A.,Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. (2009) 50: S9-S14.PubMed ID:19098281.

Fahy E, Subramaniam S, Brown H, Glass C, Merrill JA, Murphy R, Raetz C, Russell D, Seyama Y, Shaw W, Shimizu T, Spener F, van Meer G, Vannieuwenhze M, White S, Witztum J and Dennis E.A.,A comprehensive classification system for lipids. J. Lipid Res. (2005) 46: 839-861.PubMed ID:15722563.

http://www.lipidmaps.org/resources/tutorials/lipid_cns.html

Lipid Classification System

The LIPID MAPS Lipid Classification System is comprised of eight lipid categories, each with its own sublassification hierarchy.

All lipids in the LIPID MAPS Structure Database (LMSD) have been classified using this system and have been assigned LIPID MAPS ID’s (LM_ID) which reflects their position in the classification hierarchy.

LMSD can be searched by lipid class, common name, systematic name or synonym, mass, InChIKey or LIPID MAPS ID with the “Quick Search” tool on the home page, or alternatively, by

LIPID MAPS ID, systematic or common name, mass, formula, category, main class, subclass data, or structure or sub-structure with one of the search interfaces in the LMSD database section.

Each LMSD record contains an image of the

  • molecular structure,
  • common and systematic names,
  • links to external databases,
  • Wikipedia pages (where available),
  • other annotations and links to structure viewing tools.

In addition to LMSD search interfaces, you can drill down through the classification hierarchy below to the LMSD record for an individual lipid.

 

Lipid Classes
Fatty Acyls [FA] Fatty Acids and Conjugates [FA01]Octadecanoids [FA02]Eicosanoids [FA03]

Docosanoids [FA04]

Fatty alcohols [FA05]

Fatty aldehydes [FA06]

Fatty esters [FA07]

Fatty amides [FA08]

Fatty nitriles [FA09]

Fatty ethers [FA10]

Hydrocarbons [FA11]

Oxygenated hydrocarbons [FA12]

Fatty acyl glycosides [FA13]

Other Fatty Acyls [FA00]

Glycerophospholipids [GP] Glycerophosphocholines [GP01]Glycerophosphoethanolamines [GP02]Glycerophosphoserines [GP03]

Glycerophosphoglycerols [GP04]

Glycerophosphoglycerophosphates [GP05]

Glycerophosphoinositols [GP06]

Glycerophosphoinositol monophosphates [GP07]

Glycerophosphoinositol bisphosphates [GP08]

Glycerophosphoinositol trisphosphates [GP09]

Glycerophosphates [GP10]

Glyceropyrophosphates [GP11]

Glycerophosphoglycerophosphoglycerols [GP12]

CDP-Glycerols [GP13]

Glycosylglycerophospholipids [GP14]

Glycerophosphoinositolglycans [GP15]

Glycerophosphonocholines [GP16]

Glycerophosphonoethanolamines [GP17]

Di-glycerol tetraether phospholipids (caldarchaeols) [GP18]

Glycerol-nonitol tetraether phospholipids [GP19]

Oxidized glycerophospholipids [GP20]

Other Glycerophospholipids [GP00]

Glycerolipids [GL] Monoradylglycerols [GL01]Diradylglycerols [GL02]Triradylglycerols [GL03]

Glycosylmonoradylglycerols [GL04]

Glycosyldiradylglycerols [GL05]

Other Glycerolipids [GL00]

Sphingolipids [SP] Sphingoidbases [SP01]Ceramides [SP02]Phosphosphingolipids [SP03]

Phosphonosphingolipids [SP04]

Neutral glycosphingolipids [SP05]

Acidic glycosphingolipids [SP06]

Basic glycosphingolipids [SP07]

Amphoteric glycosphingolipids [SP08]

Arsenosphingolipids [SP09]

Other Sphingolipids [SP00]

Sterol Lipids [ST] Sterols [ST01]Steroids [ST02]Secosteroids [ST03]

Bile acids and derivatives [ST04]

Steroid conjugates [ST05]

Other Sterol lipids [ST00]

Prenol Lipids [PR] Isoprenoids [PR01]Quinones andhydroquinones [PR02]Polyprenols [PR03]

Hopanoids [PR04]

Other Prenol lipids [PR00]

Saccharolipids [SL] Acylaminosugars [SL01]Acylaminosugarglycans [SL02]Acyltrehaloses [SL03]

Acyltrehalose glycans [SL04]

Other acyl sugars [SL05]

Other Saccharolipids [SL00]

Polyketides [PK] Linearpolyketides [PK01]Halogenatedacetogenins [PK02]Annonaceae acetogenins [PK03]

Macrolides and lactone polyketides [PK04]

Ansamycins and related polyketides [PK05]

Polyenes [PK06]

Linear tetracyclines [PK07]

Angucyclines [PK08]

Polyether polyketides [PK09]

Aflatoxins and related substances [PK10]

Cytochalasins [PK11]

Flavonoids [PK12]

Aromatic polyketides [PK13]

Non-ribosomal peptide/polyketide hybrids [PK14]

Other Polyketides [PK00]

 

 

LIPID MAPS Structure Database (LMSD)

 

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. As of May 3, 2013, LMSD contains over 37,500 unique lipid structures, making it the largest public lipid-only database in the world. Structures of lipids in the database come from several sources:

  • LIPID MAPS Consortium’s core laboratories and partners;
  • lipids identified by LIPID MAPS experiments;
  • biologically relevant lipids manually curated from LIPID BANK, LIPIDAT, Lipid Library, Cyberlipids, ChEBI and other public sources;
  • novel lipids submitted to peer-reviewed journals;
  • computationally generated structures for appropriate classes.

All the lipid structures in LMSD adhere to the structure drawing rules proposed by the LIPID MAPS consortium. A number of structure viewing options are offered: gif image (default), Chemdraw (requires Chemdraw ActiveX/Plugin), MarvinView (Java applet) and JMol (Java applet).

All lipids in the LMSD have been classified using the LIPID MAPS Lipid Classification System. Each lipid structure has been assigned a LIPID MAPS ID (LM_ID) which reflects its position in the classification hierarchy. In addition to a classification-based retrieval of lipids, users can search LMSD using either text-based or structure-based search options.

 

The text-based search implementation supports data retrieval by any combination of these data fields: LIPID MAPS ID, systematic or common name, mass, formula, category, main class, and subclass data fields. The structure-based search, in conjunction with optional data fields, provides the capability to perform a substructure search or exact match for the structure drawn by the user. Search results, in addition to structure and annotations, also include relevant links to external databases.

Statistics

(as of 10/8/14)

Number of lipids per category

Fatty acyls          5869

Glycerolipids       7541

Glycerophospholipids       8002

Sphingolipids      4338

Sterol lipids         2715

Prenol lipids        1259

Sacccharolipids  1293

Polyketides         6742

TOTAL  37,759 structures

References

Sud M, Fahy E, Cotter D, Brown A, Dennis EA, Glass CK, Merrill AH Jr, Murphy RC, Raetz CR, Russell DW, Subramaniam S. LMSD: LIPID MAPS structure database Nucleic Acids Research 35: p. D527-32. PMID:17098933 [http://dx.doi.org:/10.1093/nar/gkl838]     PMID: 17098933

Fahy E, Sud M, Cotter D & Subramaniam S. LIPID MAPS online tools for lipid research Nucleic Acids Research (2007) 35: p. W606-12.PMID:17584797 [http://dx.doi.org:/10.1093/nar/gkm324] PMID: 17584797

 

Proteome Database (LMPD)

– over 2,400 lipid-associated proteins from human and mouse

Pathways

– manually curated lipid metabolism and signaling pathways

MS analysis tools

– tools for searching various lipid classes by precursor or product ion

Structure Drawing Tools

– draw and save lipid structures using online menus

 

References

Time-varying causal inference from phosphoproteomic measurements in macrophage cells.

IEEE Trans Biomed Circuits Syst. 2014 Feb;8(1):74-86.
http://dx.doi.org:/10.1109/TBCAS.2013.2880235.

 

 

research highlights icon Modeling of eicosanoid fluxes reveals functional coupling between cyclooxygenases and terminal synthases.

Biophys J. 2014 Feb 18;106(4):966-75.
http://dx.doi.org:/10.1016/j.bpj.2014.01.015.

 

Lipid Classification

Starting from a lipid category, the user can navigate through the hierarchy by clicking on the “[+]” icon next to a main class name.

This will expand that item to reveal its sub classes.

Clicking on hyperlinks to the right of main classes, sub classes or level 4 classes will display a tabular listing of all lipids corresponding to that particular subset in the LMSD database.

Finally, clicking on the LM_ID hyperlink displays the LMSD record for an individual lipid, which contains

  • an image of the molecular structure,
  • common and systematic names,
  • links to external databases,
  • Wikipedia pages (where available),
  • other annotations and links to structure viewing tools.

LIPID MAPS classification hierarchy

Category (Example: Prenol lipids [LMPR])

Main class (Example: Isoprenoids [LMPR01])

Sub class (where applicable) (Example: C15 Isoprenoids (sesquiterpenes) [LMPR0103])

Level 4 class (where applicable) (Example: Bisabolane sesquiterpenoids [LMPR010306])

Pathways

We have carefully constructed these lipid pathways based on LIPID MAPS experimental data and data from the literature. LIPID MAPS experimental data obtained from our lipid time course experiments and microarray experiments on macrophagese were mapped to corresponding lipids and genes, respectively.

Pathway maps created using VANTED

VANTED is a tool for the visualization and analysis of networks with related experimental data. For more information on VANTED, please refer to: Björn H. Junker, Christian Klukas and Falk Schreiber (2006): VANTED: A system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics, 7:109 (http://www.biomedcentral.com/1471-2105/7/109)

References

Fahy E, Subramaniam S, Murphy R, Nishijima M, Raetz C, Shimizu T, Spener F, van Meer G, Wakelam M and Dennis E.A.,Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. (2009) 50: S9-S14.PubMed ID:19098281.

Fahy E, Subramaniam S, Brown H, Glass C, Merrill JA, Murphy R, Raetz C, Russell D, Seyama Y, Shaw W, Shimizu T, Spener F, van Meer G, Vannieuwenhze M, White S, Witztum J and Dennis E.A.,A comprehensive classification system for lipids. J. Lipid Res. (2005) 46: 839-861.PubMed ID:15722563.

Introduction to lipids

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Introduction to Metabolomics

Introduction to Metabolomics

Author: Larry H. Bernstein, MD, FCAP

 

This is the first volume of the Series D: e-Books on BioMedicine – Metabolomics, Immunology, Infectious Diseases.  It is written for comprehension at the third year medical student level, or as a reference for licensing board exams, but it is also written for the education of a first time bachalaureate degree reader in the biological sciences.  Hopefully, it can be read with great interest by the undergraduate student who is undecided in the choice of a career.

In the Preface, I failed to disclose that the term Metabolomics applies to plants, animals, bacteria, and both prokaryotes and eukaryotes.  The metabolome for each organism is unique, but from an evolutionary perspective has metabolic pathways in common, and expressed in concert with the environment that these living creatures exist. The metabolome of each has adaptive accommodation with suppression and activation of pathways that are functional and necessary in balance, for its existence.  Was it William Faulkner who said in his Nobel Prize acceptance that mankind shall not merely exist, but survive? That seems to be the overlying theme for all of life. If life cannot persist, a surviving “remnant” might continue. The history of life may well be etched into the genetic code, some of which is not expressed.

This work is apportioned into chapters in a sequence that is first directed at the major sources for the energy and the structure of life, in the carbohydrates, lipids, and fats, which are sourced from both plants and animals, and depending on their balance, results in an equilibrium, and a disequilibrium we refer to as disease.  There is also a need to consider the nonorganic essentials which are derived from the soil, from water, and from the energy of the sun and the air we breathe, or in the case of water-bound metabolomes, dissolved gases.

In addition to the basic essential nutrients and their metabolic utilization, they are under cellular metabolic regulation that is tied to signaling pathways.  In addition, the genetic expression of the organism is under regulatory control by the interaction of RNAs that interact with the chromatin genetic framework, with exosomes, and with protein modulators.This is referred to as epigenetics, but there are also drivers of metabolism that are shaped by the interactions between enzymes and substartes, and are related to the tertiary structure of a protein.  The framework for diseases in a separate chapter.  Pharmaceutical interventions that are designed to modulate specific metabolic targets are addressed as the pathways are unfolded. Neutraceuticals and plant based nutrition are covered in Chapter 8.

Chapter 1: Metabolic Pathways

Chapter 2. Lipid Metabolism

Chapter 3. Cell Signaling

Chapter 4. Protein Synthesis and Degradation

Chapter 5: Sub-cellular Structure

Chapter 6: Proteomics

Chapter 7: Metabolomics

Chapter 8. Impairments in Pathological States: Endocrine Disorders; Stress Hypermetabolism and Cancer

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