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Store-Operated Ca 2+ Entry (SOCE) Contributes to Normal Skeletal Muscle Contractility in young but not in aged skeletal muscle
research-article
Author(s):
Angela M Thornton
1
,
2 ,
Xiaoli Zhao
1 ,
Noah Weisleder
1 ,
Leticia S. Brotto
3 ,
Sylvain Bougoin
4 ,
Thomas M. Nosek
5 ,
Michael Reid
6 ,
Brian Hardin
6
,
+ ,
Zui Pan
1 ,
Jianjie Ma
1 ,
Jerome Parness
4 ,
Marco Brotto
3
Publication date (Electronic):
6 June 2011
Journal:
Aging (Albany NY)
Publisher:
Impact Journals LLC
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Abstract
Muscle atrophy alone is insufficient to explain the significant decline in contractile force of skeletal muscle during normal aging. One contributing factor to decreased contractile force in aging skeletal muscle could be compromised excitation-contraction (E-C) coupling, without sufficient available Ca 2+ to allow for repetitive muscle contractility, skeletal muscles naturally become weaker. Using biophysical approaches, we previously showed that store-operated Ca 2+ entry (SOCE) is compromised in aged skeletal muscle but not in young ones. While important, a missing component from previous studies is whether or not SOCE function correlates with contractile function during aging. Here we test the contribution of extracellular Ca 2+ to contractile function of skeletal muscle during aging. First, we demonstrate graded coupling between SR Ca 2+ release channel-mediated Ca 2+ release and activation of SOCE. Inhibition of SOCE produced significant reduction of contractile force in young skeletal muscle, particularly at high frequency stimulation, and such effects were completely absent in aged skeletal muscle. Our data indicate that SOCE contributes to the normal physiological contractile response of young healthy skeletal muscle and that defective extracellular Ca 2+ entry through SOCE contributes to the reduced contractile force characteristic of aged skeletal muscle.
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Most cited references38
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Transcriptional Profiling of Aging in Human Muscle Reveals a Common Aging Signature
Jacob M Zahn, Rebecca Sonu, Rebecca Sonu … (2006)
Introduction Aging is marked by the gradual decline of a multitude of physiological functions leading to an increasing probability of death. Some aging-related changes affect one's appearance, such as wrinkled skin, whereas others affect organ function, such as decreased kidney filtration rate and decreased muscular strength. At the molecular level, we are just beginning to assemble protein and gene expression changes that can be used as markers for aging. Rather than search for molecular aging markers by focusing on only one gene or pathway at a time, an attractive approach is to screen all genetic pathways in parallel for age-related changes by using full-genome oligonucleotide chips to search for gene expression changes in the elderly. A genome-wide transcriptional profile of aging may identify molecular markers of the aging process, and would provide insight into the molecular mechanisms that ultimately limit human lifespan. Molecular markers of aging must reflect physiological function rather than simple chronological age because individuals age at different rates [1]. In the mouse, changes in the levels of CD4 immunocytes and changes in the expression of cell-cycle genes such as p16INK4a are molecular markers of aging, as they predict both the remaining lifespan and the physiological age of the mouse [2–4]. In the human, gene expression profiling experiments identified 447 age-regulated genes that could predict the physiological age of the kidney [5]. Whole-genome expression profiling has also been used to identify genes that change expression with chronological age in the brain [6], skeletal muscle [7,8], and dermal fibroblasts [9], but changes in expression of these marker genes have not yet been shown to correlate with physiological aging. In this paper, we have performed a genome wide analysis of gene expression changes in the human skeletal muscle. As age increases, skeletal muscle degenerates, loses mass, loses total aerobic capacity, and becomes markedly weaker [10]. One measure of muscle physiology is the ratio of the diameters of the type I and type II muscle fibers. A decrease in the size of type II muscle fibers (fast twitch) has been found to be correlated with decline in muscle function in both human [11] and rat [12]. Type II muscle fibers are known to atrophy and become smaller with age in the human, partially accounting for decreased muscle strength and flexibility in old age. As type II muscle fibers become smaller with age, the ratio of the diameters of type II fibers to type I fibers becomes smaller. The extent to which age regulation of genetic pathways is specific to a particular tissue or common across many tissues is unknown. Age regulation of gene expression between the cortex and medulla regions of the human kidney was found to be highly correlated [5]. There was a high correlation in gene expression changes with age in different regions of the brain cortex, but no similarity was found between the cortex and the cerebellum [13]. Thus, there are similarities in patterns of age regulation between different areas of the kidney and between different areas of the brain cortex, but a common signature for aging across many diverse tissues has not been found. Another key issue is whether there are genetic pathways that are commonly age regulated in different species with vastly different lifespans, such as human, mouse, fly, and worm. Transcriptional profiles of aging have been performed on both skeletal muscle and brains in the mouse [14,15], in Drosophila melanogaster [16,17], and in Caenorhabditis elegans [18]. A comparison of the patterns of gene expression changes during aging in the fly and the worm concluded that genes encoding mitochondrial components decreased expression with age in both species [19]. In this work, we present a transcriptional expression profile of 81 human skeletal muscle samples as a function of age. The symporter activity, sialyltransferase activity, and chloride transport pathways all decrease expression with age in human muscle. The age-regulated genes were found to be markers of physiological age, not just chronological age. By comparing our results on aging in muscle to previous transcriptional profiles of aging in the kidney and the brain, we found a common signature for aging across different human tissues consisting of six genetic pathways that showed common patterns of age regulation in all three tissues. Finally, by comparing the signature for aging in humans to transcriptional profiles of aging in mice, flies, and worms, we found that expression of the electron transport chain decreases with age in humans, mice, and flies, constituting a public signature for aging across species with extremely different lifespans. Results A Global Gene Expression Profile for Aging in Human Muscle In order to study the effects of aging in human muscle, we obtained 81 samples of human skeletal muscle from individuals spanning 16 to 89 y of age (Table 1). Sixty-three samples were obtained from the abdomen, 5 were obtained from the arm, 2 were obtained from the deltoid muscle, 2 were obtained from the inner thigh, and 9 were obtained from the quadriceps (Table S1). We used Affymetrix DNA arrays to generate a transcriptional profile of aging in human muscle. We isolated total RNA from each muscle sample, and synthesized biotinylated cRNA from total RNA. We then hybridized the cRNA to Affymetrix 133 2.0 Plus oligonucleotide arrays, representing nearly the entire human genome (54,675 individual probe sets corresponding to 31,948 individual human genes). We plotted the expression of each gene as a function of age, resulting in a dataset that shows the expression of nearly every gene in the genome as a function of age in human muscle (data are publicly available on the Gene Expression Omnibus at http://www.ncbi.nlm.nih.gov/geo). Table 1 Patients Recruited by Age Group We used a multiple regression technique on each gene to determine how its expression changes with age, as had been done previously for age regulation in the kidney (Materials and Methods) [5]. We analyzed age regulation in skeletal muscle in two ways. In the first way, we found individual genes that met a stringent statistical significance threshold for correlation with age. In the second way, we found groups of genes (defined by the Gene Ontology consortium) in which there is subtle but consistent age regulation. To identify individual genes showing strong age regulation, we examined the slope with respect to age for each gene, and identified 250 genes in which the slope was significantly positive or negative (p 0.2 from a set of random genes. We performed a Monte Carlo experiment by randomly selecting sets of 250 genes from the genome, and calculating how many genes in the set had |r| > 0.2 as in (C). The procedure was repeated 1,000 times and the histogram shows the number of genes from each random selection that have |r| > 0.2. The arrow shows the number of genes exceeding this threshold (92) from the set of 250 age-regulated genes (p 0.2, and then showed that 92 genes from a set of 250 is significant (hypergeometric distribution; p 0.2). We compared the distribution of partial correlations of the 250 age-regulated genes with type II/type I ratios to the distribution of partial correlations of the rest of the genes in the genome using nonparametric methods (Figure 3C). Using a Kolmogorov-Smirnov goodness-of-fit test, we found that the distribution of the 250 age-regulated genes is wider than the total distribution in a two-sided test (p 0.2), indicating a significant overlap (p < 0.02; hypergeometric distribution) (Table S7). The 92 genes found in the analysis shown in Figure 3 and the 114 genes found in this analysis share a common set of 7 genes, indicating a statistically significant overlap (p < 1 × 10−8; hypergeometric distribution). Second, we repeated our age analysis taking into consideration the effect of type II/type I muscle fiber diameter ratio on age regulation. To do this, we used a four-term multiple regression model that includes terms for both age and type II/type I ratio: Using Equation 6, we found 543 genes that were regulated by age (p < 0.01) and 12,786 genes regulated by type II/type I ratio (p < 0.01; Table S8). There are 271 genes shared in common between these two sets of genes, which is a significantly larger number than would be expected by chance (hypergeometric p < 1 × 10−5; Table S8). We repeated this experiment using a threshold of p < 0.001 and found similar enrichment, confirming our results. This analysis shows that the set of genes that are regulated by age is enriched for those that mark the physiology of aging muscle. False discovery rate determined by permutation analysis. We used a permutation analysis to simulate the number of genes that would pass our cutoff by chance (p < 0.001). We randomized the age variables of muscle samples 1,000 times while maintaining the sex and anatomy variables with the sample. Equation 1 was used to recalculate regression coefficients and p values in every randomization. Theory predicts, and our simulation verifies, that on average about 32 genes pass our threshold (p < 0.001) by chance. This result suggests that there are about 13% false positives in our set of 250 age-regulated genes in the muscle. In 95% of the permuted datasets, 107 or fewer genes were significant at the 0.001 level. Cluster analysis of pathological and pharmaceutical factors. To examine whether pathological or pharmaceutical factors were confounding the analysis of age regulation in muscle, we performed unsupervised, average-linkage hierarchical clustering of the 81 muscle samples using the Cluster software [39]. The 81 muscle samples were clustered on the basis of the 250 genes previously determined to be age regulated in human muscle. Modified gene set enrichment analysis. GSEA [40] uses a nonparametric test to decide when the n genes in a group G have age coefficients that differ significantly from the N-n genes that are not in G. The model is that the n age coefficients in G are sampled from a distribution G, while the N-n coefficients not in G are sampled from a distribution F. We then test the null hypothesis that F = G. The Kolmogorov-Smirnov test is based on counting how many genes from G are in the top K genes of the combined list of age coefficients and comparing it to the number expected when F = G. By letting K vary from 1 to N, the test is sensitive to any alternative F ≠ G. GSEA employs a weighted Kolmogorov-Smirnov test obtained by using a weighted count of genes (with more weight on the extreme ones). In our analysis, we have replaced the weighted Kolmogorov-Smirnov test by a weighted sum, the van der Waerden normal scores test. The van der Waerden test conforms more closely to our interpretation of what it means for a group G of genes to be age related than does the weighted Kolmogorov-Smirnov test. When N is large, then any small group that contains the single most age-related gene is significantly age related by the weighted Kolmogorov-Smirnov test. Such a group displays a genuine statistical significance and comprises strong evidence that F ≠ G, but isn't necessarily biologically increasing or decreasing expression as a mechanistic unit with age. For example, a group of 30 genes with two of the most age-increasing genes and 2 of the most age-decreasing genes could be found to be both an age-increasing group and also an age-decreasing group with significance, even when the other 26 genes are not particularly age related. Here it is clear that F ≠ G, but perhaps it is simply because G has higher variance than F. To compute the van der Waerden test, we first find the rank r(j) for every gene j ∈ G. This rank is the number of the original N genes with an age coefficient smaller than that of gene j. The raw van der Waerden score is where Φ is the standard normal cumulative distribution function. When the N age coefficients are independent with a common continuous distribution F = G, then the distribution of Y is very nearly normally distributed with mean 0 and a variance V(Y) close to N-n. We replaced the GSEA enrichment score by the van der Waerden statistic, Z = Y/ , which is very nearly N(0,1) under the null hypothesis. When distribution G is shifted left or right relative to F, then the value of Z tends to increase beyond what we would expect from the N(0,1) distribution. Bootstrap test for significance of GSEA. It is better to use resampling methods instead of the N(0,1) null distribution to assess the significance of the enrichment score Z. The reason is that there are ordinarily correlations among the expression levels of the genes in G. When the expression levels of two genes in G are correlated, the age coefficients for those genes are correlated as well [41]. It then follows that their ranks are correlated, and this typically increases the variance of Y so that ultimately Z is no longer N(0,1). The value of Z can become large either because the genes are age related or because they are correlated with each other. Both may be biologically real, but the second is not an interesting finding, except possibly as confirmation that the group G is well constructed. The original GSEA [40] randomly permutes the labels of two groups being tested while keeping the gene expression data intact. This preserves correlations within the groups so that any significant findings are relative to a null simulation that includes correlations among genes. In many random permutations, one gets a histogram of enrichment scores for age that is centered around zero. If the sample value is far outside the histogram then that enrichment score is statistically significant. We adopted instead a bootstrap approach. We resampled the data and recomputed enrichment scores, obtaining a histogram roughly centered over the observed enrichment score. If the null value (zero) is far outside the resampled histogram, then the enrichment score is statistically significant. The bootstrap approach also preserves correlations among genes as well as correlations between genes and covariates. The primary motivation for bootstrapping is the presence of covariates in our problems. Consider for example data with age, sex, and expression variables. If we permute the ages with respect to the expression data and repeat the regression, we have to decide whether the sex variable should be attached to the ages or to the expressions in the random permutation. Attaching sex to the age variables will leave us with simulated data sets in which females express Y chromosome genes as much as males. Because of such artifacts, this is not a suitable null distribution. Attaching a covariate to the expression variables is also problematic. Suppose that one of the covariates is somewhat correlated with age. The effect will be to increase the variance of the originally sampled age coefficient. In permutation samples where the covariate is attached to the expression data, it is resampled independently of age. Such independence reduces the variance of the age coefficient in the permutation data. The consequence is that the permutation-based histogram of age coefficients is then too narrow and false discoveries will result. In the bootstrap approach we generated 1,000 sample datasets. In each sample dataset we mimicked the sampling process that gave rise to the data by resampling 81 subjects from the population of 81 subjects. The resampling keeps age, expression, and all covariates of any given subject together. Bootstrap sampling mimics the random process that generated the data. We remark that both bootstrap and permutation sampling of the van der Waerden scores gave rise to Z scores that were nearly normally distributed, but not necessarily N(0,1) (unpublished data). In permutation sampling, the histogram of enrichment scores tended to have means near zero, but several groups had variances larger than 1.0. In bootstrap sampling, the variances often differed from 1 and the means were usually between zero and the original enrichment score. Venn and correlation analysis of human muscle, the kidney, and the brain. The most direct way to compare aging in muscle, the kidney, and the brain is via a Venn analysis: we find which genes attain a stringent significance level for each tissue and judge whether the overlap is statistically significant according to a hypergeometric distribution. We did a pairwise comparison between each tissue to find genes that are aging-regulated in both sets. There are six aging-regulated genes in both the muscle and the kidney (p < 0.09, hypergeometric distribution), five aging-regulated genes in both muscle and the brain (p < 0.07), and 13 aging-regulated genes in common between the kidney and the brain (p < 0.29). There were no genes that were strongly age regulated in all three datasets. The Venn analysis approach is very interpretable but lacks power because it replaces actual measured correlations by a less informative notion of whether they are over a threshold. A more sensitive comparison can be based on correlating the age coefficients of genes in two tissues. We selected all genes that are age regulated in either of two tissues, plotted the age coefficient of each gene in one tissue versus that gene's coefficient in the other tissue, and computed the Pearson correlation (r) of the resulting points (Table S11). We found the strongest overlap in aging between the kidney and the brain (r = 0.219), and smaller but positive overlaps in aging between the muscle and the kidney (r = 0.103) or the muscle and the brain (r = 0.078). Because the genes are correlated we cannot use textbook formulas to judge the statistical significance of these Pearson scores. To get a p value for a Pearson correlation between kidney and muscle, we used 1,000 sets of random genes. The number of genes in each set was the same as the number we used to compute the correlation in Table S11. For each random gene group we computed the Pearson correlation between age coefficients in kidney and muscle. Of the 1,000 samples, there were six in which the random gene group gave rise to a larger Pearson correlation than the one we saw in the real data. This corresponds to a p value of 0.006 for kidney–muscle. We similarly found a p value of 0.001 for kidney–brain but only 0.058 for muscle–brain. With the possible exception of the kidney–brain pair, the age-related genes have more consistent age coefficients across tissues than randomly selected genes do. We also ran a bootstrap test of the tissue comparisons. In this test we resampled the microarray data with replacement 1,000 times. Each time we recomputed the correlations between age coefficients for genes in the kidney and muscle. In 1,000 trials we saw 39 in which the sample correlation was less than or equal to zero. After converting to a two-tailed test, this corresponds to a p value of 0.078 for kidney–muscle. To save computation, we used the same set of genes in each bootstrap sample instead of making the age-related gene set vary with the sample separately. The p value for muscle–brain was 0.07 while that for kidney–brain was 0.001. Based on these individual gene-level analyses, the age-related genes in the kidney and brain tended to be very similar. The muscle–kidney and muscle–brain comparisons were weaker. Tests for correlation of tissues within commonly age-regulated gene sets. To test for the correlation of gene ranks between tissues within those gene sets found to be commonly age regulated in the human, we used a two-tailed Spearman correlation method to first calculate a correlation coefficient for every pairwise combination of tissues (i.e., muscle–kidney, kidney–brain, muscle–brain) for that age-regulated gene set (e.g., extracellular matrix genes). In order to test for the significance of the calculated correlations, we used a permutation-based Monte Carlo method, randomizing the ranks for each gene and tissue in the gene set and recalculating Spearman correlations 1,000 times. We found that most of the correlations between tissues were not significant (Table S12). Supporting Information Figure S1 Age Distribution of Anatomical, Medical, and Pharmaceutical Factors Each row denotes a medical or pharmaceutical factor. Age of patients is shown on the x-axis. Sex, biopsy location, and 12 medical factors are shown in the legend. Only hypothyroidism shows any overt association with age. (253 KB TIF) Click here for additional data file. Figure S2 Medical and Pharmaceutical Factors do not Affect Age Regulation (A) Coronary artery disease was included as an additional term in Equation 1, and the model was recalculated for the 250 genes that significantly change expression with age. The slope of expression with age (age coefficient) from models with (y-axis) and without (x-axis) the coronary artery disease term was plotted. If coronary artery disease affected expression, we would expect a large deviation in age coefficient. No significant deviation was seen for any of the 250 age-regulated genes, indicating that coronary artery disease does not adversely affect our study of age regulation. (B–L) Similar to (A) for 11 other medical factors. (B) Coronary artery disease. (C) Colorectal cancer. (D) End-stage renal disease. (E) Hyperlipidemia. (F) Hypertension. (G) Hypothyroidism. (H) Pancreatic cancer. (I) Prostate cancer. (J) Radiotherapy. (K) Statins. (L) Villous adenoma. (319 KB TIF) Click here for additional data file. Figure S3 Cluster Analysis of Medical and Pharmaceutical Factors Samples are clustered on the basis of 250 age-regulated genes in muscle, shown by the top dendrogram. Columns are individual muscle samples, marked by age of the patient. Top seven rows correspond to the expression of the first seven age-regulated genes. The diagram shows anatomical, medical, and pharmaceutical factors for each patient. Each row corresponds to one medical or pharmaceutical factor. (1.2 MB TIF) Click here for additional data file. Table S1 Clinical Data (2.1 MB XLS) Click here for additional data file. Table S2 250 Age-Regulated Genes (p < 0.001), Arranged by Slope with Age (59 KB XLS) Click here for additional data file. Table S3 624 Gene Sets Assayed for Age Regulation (63 KB XLS) Click here for additional data file. Table S4 Contributing Genes in Gene Sets Age Regulated in Muscle (45 KB XLS) Click here for additional data file. Table S5 Type II/Type I Muscle Fiber Diameter Ratios of 32 Patients (16 KB XLS) Click here for additional data file. Table S6 92 Age-Regulated Genes that Predict Type II/Type I Ratio (27 KB XLS) Click here for additional data file. Table S7 114 Type II/Type I–Regulated Genes that Predict Age (32 KB XLS) Click here for additional data file. Table S8 Significant Overlap between Sets of Age-Regulated and Type II/Type I–Regulated Genes (14 KB XLS) Click here for additional data file. Table S9 Contributing Genes in Gene Sets Commonly Age Regulated in Muscle, the Kidney, and the Brain (101 KB XLS) Click here for additional data file. Table S10 Aging Coefficients of 11,512 Genes in Mouse Kidneys (982 KB XLS) Click here for additional data file. Table S11 Correlation of Age-Regulated Genes in Three Tissues (15 KB XLS) Click here for additional data file. Table S12 Spearman Correlations between Tissues for Age-Regulated Gene Sets (15 KB XLS) Click here for additional data file.
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STIM1 signalling controls store-operated calcium entry required for development and contractile function in skeletal muscle.
It is now well established that stromal interaction molecule 1 (STIM1) is the calcium sensor of endoplasmic reticulum stores required to activate store-operated calcium entry (SOC) channels at the surface of non-excitable cells. However, little is known about STIM1 in excitable cells, such as striated muscle, where the complement of calcium regulatory molecules is rather disparate from that of non-excitable cells. Here, we show that STIM1 is expressed in both myotubes and adult skeletal muscle. Myotubes lacking functional STIM1 fail to show SOC and fatigue rapidly. Moreover, mice lacking functional STIM1 die perinatally from a skeletal myopathy. In addition, STIM1 haploinsufficiency confers a contractile defect only under conditions where rapid refilling of stores would be needed. These findings provide insight into the role of STIM1 in skeletal muscle and suggest that STIM1 has a universal role as an ER/SR calcium sensor in both excitable and non-excitable cells.
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Calcium ions in neuronal degeneration.
Urszula Wojda, Elżbieta Salińska, Jacek Kuznicki (2008)
Neuronal Ca(2+) homeostasis and Ca(2+) signaling regulate multiple neuronal functions, including synaptic transmission, plasticity, and cell survival. Therefore disturbances in Ca(2+) homeostasis can affect the well-being of the neuron in different ways and to various degrees. Ca(2+) homeostasis undergoes subtle dysregulation in the physiological ageing. Products of energy metabolism accumulating with age together with oxidative stress gradually impair Ca(2+) homeostasis, making neurons more vulnerable to additional stress which, in turn, can lead to neuronal degeneration. Neurodegenerative diseases related to aging, such as Alzheimer's disease, Parkinson's disease, or Huntington's disease, develop slowly and are characterized by the positive feedback between Ca(2+) dyshomeostasis and the aggregation of disease-related proteins such as amyloid beta, alfa-synuclein, or huntingtin. Ca(2+) dyshomeostasis escalates with time eventually leading to neuronal loss. Ca(2+) dyshomeostasis in these chronic pathologies comprises mitochondrial and endoplasmic reticulum dysfunction, Ca(2+) buffering impairment, glutamate excitotoxicity and alterations in Ca(2+) entry routes into neurons. Similar changes have been described in a group of multifactorial diseases not related to ageing, such as epilepsy, schizophrenia, amyotrophic lateral sclerosis, or glaucoma. Dysregulation of Ca(2+) homeostasis caused by HIV infection or by sudden accidents, such as brain stroke or traumatic brain injury, leads to rapid neuronal death. The differences between the distinct types of Ca(2+) dyshomeostasis underlying neuronal degeneration in various types of pathologies are not clear. Questions that should be addressed concern the sequence of pathogenic events in an affected neuron and the pattern of progressive degeneration in the brain itself. Moreover, elucidation of the selective vulnerability of various types of neurons affected in the diseases described here will require identification of differences in the types of Ca(2+) homeostasis and signaling among these neurons. This information will be required for improved targeting of Ca(2+) homeostasis and signaling components in future therapeutic strategies, since no effective treatment is currently available to prevent neuronal degeneration in any of the pathologies described here. Copyright 2008 IUBMB
Author and article information
Journal
Journal ID (nlm-ta): Aging (Albany NY)
Journal ID (publisher-id): ImpactJ
Title:
Aging (Albany NY)
Publisher:
Impact Journals LLC
ISSN
(Electronic):
1945-4589
Publication date Collection:
June
2011
Publication date
(Electronic):
6
June
2011
Volume: 3
Issue: 6
Pages: 621-634
Affiliations
2 Rutgers University Physiology and Integrative Biology and Department of Biomedical
Engineering, Piscataway, New Jersey, 08854
3 The Muscle Biology Research Group-MUBIG, Schools of Nursing and Medicine, University
of Missouri-Kansas City, Missouri, Kansas City, Missouri, 64108
Author notes
Corresponding author:
Marco Brotto, PhD,
brottom@
123456umkc.edu
Article
DOI: 10.18632/aging.100335
PMC ID: 3164370
PubMed ID: 21666285
SO-VID: 1b9a9e9b-4abf-4a54-ac29-cc9e0265d60a
Copyright © Copyright: © 2011 Thornton et al.
License:
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
History
Date
received
: 30
May
2011
Date
accepted
: 4
June
2011
Categories
Subject:
Research Paper
ScienceOpen disciplines: Cell biology