Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
Lesson 4 Cell Signaling And Motility: G Proteins, Signal Transduction: Curations and Articles of reference as supplemental information: #TUBiol3373
Curator: Stephen J. Williams, Ph.D.
Updated 7/15/2019
Below please find the link to the Powerpoint presentation for lesson #4 for #TUBiol3373. The lesson first competes the discussion on G Protein Coupled Receptors, including how cells terminate cell signals. Included are mechanisms of receptor desensitization. Please NOTE that desensitization mechanisms like B arrestin decoupling of G proteins and receptor endocytosis occur after REPEATED and HIGH exposures to agonist. Hydrolysis of GTP of the alpha subunit of G proteins, removal of agonist, and the action of phosphodiesterase on the second messenger (cAMP or cGMP) is what results in the downslope of the effect curve, the termination of the signal after agonist-receptor interaction.
Calcium Signaling, Cardiac Mitochondria and Metabolic Syndrome
Larry H Bernstein: Author
and
Reporter: Aviva Lev-Ari, PhD, RN
Mitochondria, the cardiovascular system and metabolic syndrome
Start date
April 24, 2013
End date
April 24, 2013
Venue
London, UK / Kennedy Lecture Theatre, Institute of Child Health
Location
London, UK
Topics
– Mitochondrial ROS metabolism in the heart
– Mitochondrial permeability transition pore
– Mitochondria in vascular smooth muscle
– Therapeutic targets for cardiac disease
Invited speakers
This event has now passed – please visit our Conference calendar for future Abcam events
Confirmed speakers:
Paolo Bernardi, University of Padova, Italy ‘The mitochondrial permeability transition pore: A mystery solved?’
Susan Chalmers, University of Strathclyde Glasgow ‘Mitochondria in vascular smooth muscle: from regulation of calcium signals to control of proliferation’
Andrew Hall, UCL ‘The role of sirtuin 3 in cardiac dysfunction’
Derek Hausenloy, UCL ‘Mitochondrial dynamics as a therapeutic target for cardiac disease’
Guy Rutter, Imperial College London ‘Mitochondria and insulin secretion – links to diabetes’
Michael Murphy, MRC Mitochondiral Biology Unit, Cambridge ‘Exploring mitochondrial ROS metabolism in the heart using targeted probes and bioactive molecules’
Toni Vidal Puig, Institute of Metabolic Science, University of Cambridge ‘Adipose tissue expandability, lipotoxicity and the metabolic syndrome’
Calcium signaling is instrumental for excitation-contraction coupling (ECC). The involvement of mitochondria in establishing rapid cytosolic calcium transients in this process remain debated.
Two models have emerged:
slow integration versus rapid and
ample beat-to-beat changes of
cytosolic calcium transients into the mitochondria matrix.
a brief outline of cardiac calcium signaling »
Mitochondrial Calcium transport mechanisms
Calcium influx can be mediated by:
Mitochondrial Calcium Uniporter (MCU)
Mitochondrial Ryanodine receptor type 1 (mRyR1)
Leucine-zipper-EF-hand-containing transmembrane protein 1 (LETM1)
Heroes in Medical Research: Dr. Carmine Paul Bianchi Pharmacologist, Leader, and Mentor
Writer/Curator: Stephen J. Williams, Ph.D.
Past articles in this Heroes in Medical Research series had focused on those seemingly small discoveries, sometimes gained serendipitously and through careful observation and experimentation, which led to some of our most important breakthroughs of our time. I have tried to make the posts more about the people and less about the discoveries
However, though seminal discoveries are so important to the future of science (and should be celebrated), equally if not MORE IMPORTANT is the MENTORING of future scientists and the PROMOTION of fields of study. One person who exemplified these values was Dr. Carmine Paul Bianchi, who had recently just passed away this August, and will be sorely missed in the field of pharmacology and toxicology.
For those who were not familiar with Dr. Bianchi I have curated some pertinent information about his work as a scientist, professor and Chairman in pharmacology, and leader and spokesperson for the field of pharmacology. He was one of the founders of the Mid-Atlantic Pharmacology Society and was an advocate and influential in the careers of many pharmacologists and toxicologists.
Comments from fellow colleagues are very welcome (in comment section at end of post)
Carmine Paul Bianchi, 86, of Boothwyn, a professor of pharmacology in Philadelphia for many years, died Tuesday, Aug. 13, of a digestive ailment at Taylor Hospice House in Ridley Park.
Born in Newark, N.J., and raised in Maplewood, Dr. Bianchi served as an Army surgical technician in Tilton General Hospital at Fort Dix from 1945 to 1947.
He earned a bachelor’s degree in chemistry from Columbia University in 1950, a master’s in physiology and biochemistry from Rutgers University in 1953, and a doctorate in physiology and physical chemistry in 1956 from Rutgers.
In the 1950s, he did research at Rutgers and was a public health fellow and visiting scientist at the National Institutes of Health in Maryland.
From 1961 to 1976, he held a number of jobs in the department of pharmacology in the University of Pennsylvania School of Medicine. That culminated in his being named professor of pharmacology.
Dr. Bianchi left in 1976 for Jefferson Medical College of Thomas Jefferson University, where he became pharmacology professor and chairman of the pharmacology department from 1976 to 1987. In 1987, he stepped down from the chairmanship but remained professor of pharmacology. He retired in 1997 as professor emeritus.
Dr. Bianchi was a member of many professional groups, including the New York Academy of Sciences and the American Association for the Advancement of Science.
He was a leader and author in pharmacology, helping edit an industry journal and making himself available for consultation to medical examiners and experts in toxicology.
He wrote or contributed to three books and 200 scientific papers and lectured widely. He enjoyed mentoring medical and graduate students.
His family called Dr. Bianchi “a true renaissance man” who was as comfortable discussing English, history, and politics as he was the sciences.
degree in [953 and with a physical chemistry minor
for the Ph.D. degree in 1956. Dr. Bianchi then
spent several years at the National Institutes of
Health-two years as a Public Health Fellow and
one as a Visiting Scientist. Following that he was
Assistant Member of the Institute for Muscle
Disease in New York for one year. In 1961 Dr.
Bianchi became classified professionally as a
pharmacologist by becoming an Associate in the
Department of Pharmacology at the University of
Pennsylvania School of Medicine. There he
advanced to Professorship in 1969 and remained
until he came to Jefferson. The evolution of Dr.
Bianchi’s career from physiology to pharmacology
was the logical result of his investigations of the
effect of various drugs on the metabolism and
distribution of some of the important elements of
the body, notably calcium. His major field of
interest became classified and remained in
electrolyte pharmacology.
Throughout his career Dr. Bianchi has been
very active in the affairs of outside professional
organizations. He is a member of the American
Society for Pharmacology and Experimental
Therapeutics, the American Physiological Society,
the American Chemical Society, and the
International Society of Toxicology, to name
only a few. He served as President of both the
Philadelphia Physiological Society and the John
Morgan Society in the same year (1973-1974), and
of the Philadelphia Chapter of the Society for
Neuroscience (1979-1980). He gave much time
and valuable services as Field Editor for the
Journal of Pharmacology and Experimental
Therapeutics ([970-1979) and as a member of the
Pharmacology Section of the National Board of
Medical Examiners (1981-1985).
After Dr. Bianchi became Chairman no
immediate changes in the general structure and
activities of the Department took place. He
enlarged the Department and filled vacancies
occasioned by the retirement of some faculty
members. The didactic schedules and subject
matter offered to the medical and graduate
students underwent only minor annual changes.
Research activities were augmented by the
addition of Dr. Bianchi’s specialty in electrolyte
pharmacology and the appointments of new staff
members for investigations in that and related
flelds. Through the following decade there was a
marked change in the faculty structure of the
Department. The [975 Jefferson catalogue, for
example, listed 15 faculty appointments in
Pharmacology, of which eight were on a primary
full-time basis with offices and laboratories in the
Department. In 1985 there were 36 faculty
appointments of which eight were on a primary
full-time basis. The large increase in the total
number of faculty resulted from adjunct
appointments from outside organizations and from
secondary appointments of faculty members of the
Clinical Departments at Jefferson. This expansion
reflected a broadening of interests and interactions
on both the scientific and clinical fronts in clinical
pharmacology and clinical toxicology.
A notable addition to the faculty of the
Department in 1978 was Dr. Hyman Menduke
as Professor of Pharmacology
(Biostatistics). After receiving his Ph.D. in
Economic Statistics at the University of
Pennsylvania, Menduke came to Jefferson in 1953
as Assistant Professor of Biostatistics with no
official Departmental affiliation until 1963, when
he was appointed Professor of Preventive
Medicine (Biostatistics). When Dr. Menduke first
came to Jefferson he gave a ten-hour course in
biostatistics to the second-year medical students in
time provided during their pharmacology course.
Through the years his offerings expanded to a
12-hour course for freshman medical students and
introductory and advanced courses for graduate
students. An early and valuable contribution was a
series of individual conferences with graduate
students on the statistical planning of their
research problems and the later analysis of their
data.
The interests and activities of the Department in
research in toxicology have been emphasized.
Toxicology continued as an important part of the
research program after Dr. Bianchi became
Chairman in 1976, although under his direction
the major emphasis in research became redirected
toward the general areas of cell pharmacology and
neuropharmacology.
In accord with its continuing research and
teaching activities in toxicology, the Department
starting in 1977 organized a series of annual
workshops on Industrial Toxicology sponsored by
the College of Graduate Studies. These were
four-day symposia on important toxicologic
problems in industry and the general environment,
presented by toxicologically involved Jefferson
faculty and by invited experts from other
universities, industry, and government.
In 1979 the Department was awarded a training
grant in Industrial and Environmental Toxicology
by the National Institute of Environmental Health
Sciences. The purpose of this award was to
provide postdoctoral training in toxicology for
individuals who had previously received their
Ph.D. degrees in other sciences. Ten M.S. degrees
were subsequently awarded in this program
through the years from 1981 to 1986.
On December 14, 1978, a full day’s workshop
with outside invited experts was held to discuss
the formation of a Toxicology Center and the
establishment of a Chair in Toxicology-Pathology
to broaden the base of research and training in
toxicology at Jefferson. It was envisioned that the
Center would be an administrative Division within
the Department of Pharmacology, with research
participation from other basic science departments
and the Department of Medicine. Although funds
accumulated in support of a Toxicology Center,
disagreements developed relating to the
administrative base of the Center.
A few articles from Dr. Bianchi showing the diversity of his research interests including calcium mobilization, neurotoxicology, and cellular metabolism and physiology.
The author of the Advances in General and Cellular Pharmacology is Toshio Narahashi; Carmine Paul Bianchi – very good writer. You can download this e-book absolutely for free. This ebook’s ISBN number is 9781461582007. if you were searching for for free download of kindle books, google books, free pdf books, pdf ebooks, e-books, pdf files or pdf ebooks just stay here for a while, download what you wanted for free and enjoy!
This article is a followup of the wonderful study of the effect of oxidation of a methionine residue in calcium dependent-calmodulin kinase Ox-CaMKII on stabilizing the atrial cardiomyocyte, giving protection from atrial fibrillation. It is also not so distant from the work reviewed, mostly on the ventricular myocyte and the calcium signaling by initiation of the ryanodyne receptor (RyR2) in calcium sparks and the CaMKII d isoenzyme.
We refer to the following related articles published in pharmaceutical Intelligence:
The material presented is very focused, and cannot be found elsewhere in Pharmaceutical Intelligence with respedt to genetics and heart disease. However, there are other articles that may be of interest to the reader.
PART 3. Determinants of Cardiovascular Diseases: Genetics, Heredity and Genomics Discoveries
3.2 Leading DIAGNOSES of Cardiovascular Diseases covered in Circulation: Cardiovascular Genetics, 3/2010 – 3/2013
The Diagnoses covered include the following – relevant to this discussion
MicroRNA in Serum as Bimarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis, diastolic dysfunction, and acute heart failure
Genomics of Ventricular arrhythmias, A-Fib, Right Ventricular Dysplasia, Cardiomyopathy
Heredity of Cardiovascular Disorders Inheritance
3.2.1: Heredity of Cardiovascular Disorders Inheritance
The implications of heredity extend beyond serving as a platform for genetic analysis, influencing diagnosis,
prognostication, and
treatment of both index cases and relatives, and
enabling rational targeting of genotyping resources.
This review covers acquisition of a family history, evaluation of heritability and inheritance patterns, and the impact of inheritance on subsequent components of the clinical pathway.
3.2.2.1 MicroRNA in Serum as Biomarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis, diastolic dysfunction, and acute heart failure
Increased MicroRNA-1 and MicroRNA-133a Levels in Serum of Patients With Cardiovascular Disease Indicate Myocardial Damage
Y Kuwabara, Koh Ono, T Horie, H Nishi, K Nagao, et al.
SOURCE: Circulation: Cardiovascular Genetics. 2011; 4: 446-454 http://dx.doi.org/10.1161/CIRCGENETICS.110.958975
3.2.2.2 Circulating MicroRNA-208b and MicroRNA-499 Reflect Myocardial Damage in Cardiovascular Disease
3.2.4.2 Large-Scale Candidate Gene Analysis in Whites and African Americans Identifies IL6R Polymorphism in Relation to Atrial Fibrillation
The National Heart, Lung, and Blood Institute’s Candidate Gene Association Resource (CARe) Project
RB Schnabel, KF Kerr, SA Lubitz, EL Alkylbekova, et al.
SOURCE: Circulation: Cardiovascular Genetics.2011; 4: 557-564 http://dx.doi.org/10.1161/CIRCGENETICS.110.959197
Weighted Gene Coexpression Network Analysis of Human Left Atrial Tissue Identifies Gene Modules Associated With Atrial Fibrillation
N Tan, MK Chung, JD Smith, J Hsu, D Serre, DW Newton, L Castel, E Soltesz, G Pettersson, AM Gillinov, DR Van Wagoner and J Barnard
From the Cleveland Clinic Lerner College of Medicine (N.T.), Department of Cardiovascular Medicine (M.K.C., D.W.N.), and Department of Thoracic & Cardiovascular Surgery (E.S., G.P., A.M.G.); and Department of Cellular & Molecular Medicine (J.D.S., J.H.), Genomic Medicine Institute (D.S.), Department of Molecular Cardiology (L.C.), and Department of Quantitative Health Sciences (J.B.), Cleveland Clinic Lerner Research Institute, Cleveland, OH
Circ Cardiovasc Genet. 2013;6:362-371; http://dx.doi.org/10.1161/CIRCGENETICS.113.000133 http://circgenetics.ahajournals.org/content/6/4/362The online-only Data Supplement is available at http://circgenetics.ahajournals.org/lookup/suppl/doi:10.1161/CIRCGENETICS.113.000133/-/DC1
Background—Genetic mechanisms of atrial fibrillation (AF) remain incompletely understood. Previous differential expression studies in AF were limited by small sample size and provided limited understanding of global gene networks, prompting the need for larger-scale, network-based analyses.
Methods and Results—Left atrial tissues from Cleveland Clinic patients who underwent cardiac surgery were assayed using Illumina Human HT-12 mRNA microarrays. The data set included 3 groups based on cardiovascular comorbidities: mitral valve (MV) disease without coronary artery disease (n=64), coronary artery disease without MV disease (n=57), and lone AF (n=35). Weighted gene coexpression network analysis was performed in the MV group to detect modules of correlated genes. Module preservation was assessed in the other 2 groups. Module eigengenes were regressed on AF severity or atrial rhythm at surgery. Modules whose eigengenes correlated with either AF phenotype were analyzed for gene content. A total of 14 modules were detected in the MV group; all were preserved in the other 2 groups. One module (124 genes) was associated with AF severity and atrial rhythm across all groups. Its top hub gene, RCAN1, is implicated in calcineurin-dependent signaling and cardiac hypertrophy. Another module (679 genes) was associated with atrial rhythm in the MV and coronary artery disease groups. It was enriched with cell signaling genes and contained cardiovascular developmental genes including TBX5.
Conclusions—Our network-based approach found 2 modules strongly associated with AF. Further analysis of these modules may yield insight into AF pathogenesis by providing novel targets for functional studies. (Circ Cardiovasc Genet. 2013;6:362-371.)
trial fibrillation (AF) is the most common sustained cardiac arrhythmia, with a prevalence of ≈1% to 2% in the general population.1,2 Although AF may be an isolated condition (lone AF [LAF]), it often occurs concomitantly with other cardiovascular diseases, such as coronary artery disease (CAD) and valvular heart disease.1 In addition, stroke risk is increased 5-fold among patients with AF, and ischemic strokes attributed to AF are more likely to be fatal.1 Current antiarrhythmic drug therapies are limited in terms of efficacy and safety.1,3,4 Thus, there is a need to develop better risk prediction tools as well as mechanistically targeted therapies for AF. Such developments can only come about through a clearer understanding of its pathogenesis.
Family history is an established risk factor for AF. A Danish Twin Registry study estimated AF heritability at 62%, indicating a significant genetic component.5 Substantial progress has been made to elucidate this genetic basis. For example, genome-wide association studies (GWASs) have identified several susceptibility loci and candidate genes linked with AF. Initial studies performed in European populations found 3 AF-associated genomic loci.6–9 Of these, the most significant single-nucleotide polymor-phisms (SNPs) mapped to an intergenic region of chromosome 4q25. The closest gene in this region, PITX2, is crucial in left-right asymmetrical development of the heart and thus seems promising as a major player in initiating AF.10,11 A large-scale GWAS meta-analysis discovered 6 additional susceptibility loci, implicating genes involved in cardiopulmonary development, ion transport, and cellular structural integrity.12
Differential expression studies have also provided insight into the pathogenesis of AF. A study by Barth et al13 found that about two-thirds of the genes expressed in the right atrial appendage were downregulated during permanent AF, and that many of these genes were involved in calcium-dependent signaling pathways. In addition, ventricular-predominant genes were upregulated in right atrial appendages of subjects with AF.13 Another study showed that inflammatory and transcription-related gene expression was increased in right atrial appendages of subjects with AF versus controls.14 These results highlight the adaptive responses to AF-induced stress and ischemia taking place within the atria.
Despite these advances, much remains to be discovered about the genetic mechanisms of AF. The AF-associated SNPs found thus far only explain a fraction of its heritability15; furthermore, the means by which the putative candidate genes cause AF have not been fully established.9,15,16 Additionally, previous differential expression studies in human tissue were limited to the right atrial appendage, had small sample sizes, and provided little understanding of global gene interactions.13,14 Weighted gene coexpression network analysis (WGCNA) is a technique to construct gene modules within a network based on correlations in gene expression (ie, coexpression).17,18 WGCNA has been used to study genetically complex diseases, such as metabolic syndrome,19 schizophrenia,20 and heart failure.21 Here, we obtained mRNA expression profiles from human left atrial appendage tissue and implemented WGCNA to identify gene modules associated with AF phenotypes.
Methods
Subject Recruitment
From 2001 to 2008, patients undergoing cardiac surgery at the Cleveland Clinic were prospectively screened and recruited. Informed consent for research use of discarded atrial tissues was obtained from each patient by a study coordinator during the presurgical visit. Demographic and clinical data were obtained from the Cardiovascular Surgery Information Registry and by chart review. Use of human atrial tissues was approved by the Institutional Review Board of the Cleveland Clinic.
Table S1: Clinical definitions of cardiovascular phenotype groups
Criterion Type
Mitral Valve (MV) Disease
Coronary Artery Disease (CAD)
Lone Atrial Fibrillation (LAF)
Inclusion Criteria
Surgical indication –
Surgical indication –
History of atrial fibrillation
mitral valve repair or replacement
coronary artery bypass graft
Surgical indication
– MAZE procedure
Preserved ejection fraction (≥50%)
Exclusion Criteria
Significant coronary artery disease:
Significant mitral valve disease:
Significant
coronary artery
– Significant (≥50%) stenosis
– Documented echocardiography
disease:
in at least
finding of
– Significant
one coronary artery
mitral regurgitation (≥3) or
(≥50%) stenosis in
via cardiac catheterization
mitral stenosis
at least one
– History of revascularization
– History of mitral valve
coronary artery via
(percutaneous coronary intervention or coronary artery bypass graft surgery)
repair or replacement
cardiac catheterization
– History of revascularization
(percutaneous coronary intervention or coronary artery bypass graft surgery)
Significant valvular heart disease:
-Documented echocardiography finding of valvular regurgitation (≥3) or stenosis
-History of valve repair or replacement
RNA Microarray Isolation and Profiling
Left atria appendage specimens were dissected during cardiac surgery and stored frozen at −80°C. Total RNA was extracted using the Trizol technique. RNA samples were processed by the Cleveland Clinic Genomics Core. For each sample, 250-ng RNA was reverse transcribed into cRNA and biotin-UTP labeled using the TotalPrep RNA Amplification Kit (Ambion, Austin, TX). cRNA was quantified using a Nanodrop spectrophotometer, and cRNA size distribution was assessed on a 1% agarose gel. cRNA was hybridized to Illumina Human HT-12 Expression BeadChip arrays (v.3). Arrays were scanned using a BeadArray reader.
Expression Data Preprocessing
Raw expression data were extracted using the beadarray package in R, and bead-level data were averaged after log base-2 transformation. Background correction was performed by fitting a normal-gamma deconvolution model using the NormalGamma R package.22 Quantile normalization and batch effect adjustment with the ComBat method were performed using R.23 Probes that were not detected (at a P<0.05 threshold) in all samples as well as probes with relatively lower variances (interquartile range ≤log2[1.2]) were excluded.
The WGCNA approach requires that genes be represented as singular nodes in such a network. However, a small proportion of the genes in our data have multiple probe mappings. To facilitate the representation of singular genes within the network, a probe must be selected to represent its associated gene. Hence, for genes that mapped to multiple probes, the probe with the highest mean expression level was selected for analysis (which often selects the splice isoform with the highest expression and signal-to-noise ratio), resulting in a total of 6168 genes.
Defining Training and Test Sets
Currently, no large external mRNA microarray data from human left atrial tissues are publicly available. To facilitate internal validation of results, we divided our data set into 3 groups based on cardiovascular comorbidities: mitral valve (MV) disease without CAD (MV group; n=64), CAD without MV disease (CAD group; n=57), and LAF (LAF group; n=35). LAF was defined as the presence of AF without concomitant structural heart disease, according to the guidelines set by the European Society of Cardiology.1 The MV group, which was the largest and had the most power for detecting significant modules, served as the training set for module derivation, whereas the other 2 groups were designated test sets for module reproducibility. To minimize the effect of population stratification, the data set was limited to white subjects. Differences in clinical characteristics among the groups were assessed using Kruskal–Wallis rank-sum tests for continuous variables and Pearson x2 test for categorical variables.
Weight Gene Coexpression Network Analysis
WGCNA is a systems-biology method to identify and characterize gene modules whose members share strong coexpression. We applied previously validated methodology in this analysis.17 Briefly, pair-wise gene (Pearson) correlations were calculated using the MV group data set. A weighted adjacency matrix was then constructed. I is a soft-thresholding parameter that provides emphasis on stronger correlations over weaker and less meaningful ones while preserving the continuous nature of gene–gene relationships. I=3 was selected in this analysis based on the criterion outlined by Zhang and Horvath17 (see the online-only Data Supplement).
Next, the topological overlap–based dissimilarity matrix was computed from the weighted adjacency matrix. The topological overlap, developed by Ravasz et al,24 reflects the relative interconnectedness (ie, shared neighbors) between 2 genes.17 Hence, construction of the network dendrogram based on this dissimilarity measure allows for the identification of gene modules whose members share strong intercon-nectivity patterns. The WGCNA cutreeDynamic R function was used to identify a suitable cut height for module identification via an adaptive cut height selection approach.18 Gene modules, defined as branches of the network dendrogram, were assigned colors for visualization.
Network Preservation Analysis
Module preservation between the MV and CAD groups as well as the MV and LAF groups was assessed using network preservation statistics as described in Langfelder et al.25 Module density–based statistics (to assess whether genes in each module remain highly connected in the test set) and connectivity-based statistics (to assess whether connectivity patterns between genes in the test set remain similar compared with the training set) were considered in this analysis.25 In each comparison, a Z statistic representing a weighted summary of module density and connectivity measures was computed for every module (Zsummary). The Zsummary score was used to evaluate module preservation, with values ≥8 indicating strong preservation, as proposed by Langfelder et al.25 The WGCNA R function network preservation was used to implement this analysis.25
Table S2: Network preservation analysis between the MV and CAD groups – size and Zsummary scores of gene modules detected.
Module
Module Size
ZSummary
Black
275
15.52
Blue
964
44.79
Brown
817
12.80
Cyan
119
13.42
Green
349
14.27
Green-Yellow
215
19.31
Magenta
239
15.38
Midnight-Blue
83
15.92
Pink
252
23.31
Purple
224
16.96
Red
278
17.30
Salmon
124
13.84
Tan
679
28.48
Turquoise
1512
44.03
Table S3: Network preservation analysis between the MV and LAF groups – size and Zsummary scores of gene modules detected
Module
Module Size
ZSummary
Black
275
13.14
Blue
964
39.26
Brown
817
14.98
Cyan
119
11.46
Green
349
14.91
Green-Yellow
215
20.99
Magenta
239
18.58
Midnight-Blue
83
13.87
Pink
252
19.10
Purple
224
8.80
Red
278
16.62
Salmon
124
11.57
Tan
679
28.61
Turquoise
1512
42.07
Clinical Significance of Preserved Modules
Principal component analysis of the expression data for each gene module was performed. The first principal component of each module, designated the eigengene, was identified for the 3 cardiovascular disease groups; this served as a summary expression measure that explained the largest proportion of the variance of the module.26 Multivariate linear regression was performed with the module ei-gengenes as the outcome variables and AF severity (no AF, paroxysmal AF, persistent AF, permanent AF) as the predictor of interest (adjusting for age and sex). A similar regression analysis was performed with atrial rhythm at surgery (no AF history, AF history in sinus rhythm, AF history in AF rhythm) as the predictor of interest. The false discovery rate method was used to adjust for multiple comparisons. Modules whose eigengenes associated with AF severity and atrial rhythm were identified for further analysis.
In addition, hierarchical clustering of module eigengenes and selected clinical traits (age, sex, hypertension, cholesterol, left atrial size, AF state, and atrial rhythm) was used to identify additional module–trait associations. Clusters of eigengenes/traits were detected based on a dissimilarity measure D, as given by
D=1−cor(Vi,Vj),i≠j (3)
where V=the eigengene or clinical trait.
Enrichment Analysis
Gene modules significantly associated with AF severity and atrial rhythm were submitted to Ingenuity Pathway Analysis (IPA) to determine enrichment for functional/disease categories. IPA is an application of gene set over-representation analysis; for each dis-ease/functional category annotation, a P value is calculated (using Fisher exact test) by comparing the number of genes from the module of interest that participate in the said category against the total number of participating genes in the background set.27 All 6168 genes in the current data set served as the background set for the enrichment analysis.
Hub Gene Analysis
Hub genes are defined as genes that have high intramodular connectivity17,20
Alternatively, they may also be defined as genes with high module membership21,25
Both definitions were used to identify the hub genes of modules associated with AF phenotype.
To confirm that the hub genes identified were themselves associated with AF phenotype, the expression data of the top 10 hub genes (by intramodular connectivity) were regressed on atrial rhythm (adjusting for age and sex). In addition, eigengenes of AF-associated modules were regressed on their respective (top 10) hub gene expression profiles, and the model R2 indices were computed.
Membership of AF-Associated Candidate Genes From Previous Studies
Previous GWAS studies identified multiple AF-associated SNPs.8,9,12,15,28 We selected candidate genes closest to or containing these SNPs and identified their module locations as well as their closest within-module partners (absolute Pearson correlations).
Sensitivity Analysis of Soft-Thresholding Parameter
To verify that the key results obtained from the above analysis were robust with respect to the chosen soft-thresholding parameter (I=3), we repeated the module identification process using I=5. The eigen-genes of the detected modules were computed and regressed on atrial rhythm (adjusting for age and sex). Modules significantly associated with atrial rhythm in ≥2 groups of data set were compared with the AF phenotype–associated modules from the original analysis.
Results
Subject Characteristics
Table 1 describes the clinical characteristics of the cardiac surgery patients who were recruited for the study. Subjects in the LAF group were generally younger and less likely to be a current smoker (P=2.0×10−4 and 0.032, respectively). Subjects in the MV group had lower body mass indices (P=2.7×10−6), and a larger proportion had paroxysmal AF compared with the other 2 groups (P=0.033).
Table 1. Clinical Characteristics of Study Subjects
A total of 14 modules were detected using the MV group data set (Figure 1), with module sizes ranging from 83 genes to 1512 genes; 38 genes did not share similar coexpression with the other genes in the network and were therefore not included in any of the identified modules
Figure 1. Network dendrogram (top) and colors of identified modules (bottom). The dendrogram was constructed using the topological overlap matrix as the similarity measure. Modules corresponded to branches of the dendrogram and were assigned colors for visualization.
Network Preservation Analysis Revealed Strong Preservation of All Modules Between the Training and Test Sets
All 14 modules showed strong preservation across the CAD and LAF groups in both comparisons, with Z [summary] scores of >10 in most modules (Figure 2). No major deviations in the Z [summary] score distributions for the 2 comparisons were noted, indicating that modules were preserved to a similar extent across the 2 groups
Figure 2. Preservation of modules between mitral valve (MV) and coronary artery disease (CAD) groups (left), and MV and lone atrial fibrillation (LAF) groups (right). A Zsummary statistic was computed for each module as an overall measure of its preservation relating to density and connectivity. All modules showed strong preservation in both comparisons with Zsummary scores >8 (red dotted line).
Regression Analysis of Module Eigengene Profiles Identified 2 Modules Associated With AF Severity and Atrial Rhythm
Table IV in the online-only Data Supplement summarizes the proportion of variance explained by the first 3 principal components for each module. On average, the first principal component (ie, the eigengene) explained ≈18% of the total variance of its associated module. For each group, the module eigengenes were extracted and regressed on AF severity (with age and sex as covariates). The salmon module (124 genes) eigengene was strongly associated with AF severity in the MV and CAD groups (P=1.7×10−6 and 5.2×10−4, respectively); this association was less significant in the LAF group (P=9.0×10−2). Eigengene levels increased with worsening AF severity across all 3 groups, with the greatest stepwise change taking place between the paroxysmal AF and persistent AF categories (Figure 3A). When the module eigen-genes were regressed on atrial rhythm, the salmon module eigengene showed significant association in all groups (MV: P=1.1×10−14; CAD: P=1.36×10−6; LAF: P=2.1×10−4). Eigen-gene levels were higher in the AF history in AF rhythm category (Figure 3B).
Table S4:Proportion of variance explained by the principal components for each module.
Dataset
Group
Principal
Component
Black
Blue
Brown
Cyan
Green
Green-
Yellow
Magenta
Mitral
1
20.5%
22.2%
20.1%
21.8%
21.4%
22.8%
19.6%
2
4.1%
3.6%
4.8%
5.7%
4.5%
5.9%
3.9%
3
3.4%
3.1%
3.8%
4.4%
3.9%
3.7%
3.7%
CAD
1
12.5%
18.6%
7.1%
16.8%
12.2%
20.3%
12.8%
2
6.0%
5.5%
5.0%
7.0%
5.5%
6.1%
6.4%
3
4.9%
4.1%
4.4%
6.5%
4.8%
4.4%
4.8%
LAF
1
14.0%
16.6%
11.7%
14.3%
14.7%
20.8%
20.2%
2
8.9%
8.5%
7.6%
9.3%
7.3%
11.1%
6.9%
3
6.5%
6.3%
5.5%
8.2%
6.1%
5.3%
6.2%
Dataset
Group
Principal
Component
Midnight- Blue
Pink
Purple
Red
Salmon
Tan
Turquoise
Mitral
1
28.5%
22.6%
18.7%
20.5%
22.3%
19.0%
25.8%
2
4.6%
6.0%
4.7%
4.1%
6.9%
4.0%
3.5%
3
4.2%
4.2%
4.2%
3.5%
4.0%
3.6%
3.3%
CAD
1
23.4%
17.1%
15.5%
15.0%
18.0%
14.6%
18.2%
2
7.4%
8.6%
6.0%
6.4%
7.2%
5.8%
6.6%
3
5.1%
5.4%
5.3%
5.4%
6.2%
5.1%
4.5%
LAF
1
23.5%
18.4%
12.0%
15.9%
16.9%
13.7%
16.5%
2
7.9%
8.5%
9.8%
9.4%
9.5%
9.1%
9.6%
3
6.7%
7.0%
6.6%
6.0%
6.9%
6.8%
6.3%
Figure 3. Boxplots of salmon module eigengene expression levels with respect to atrial fibrillation (AF) severity (A) and atrial rhythm (B).
A, Eigengene expression correlated positively with AF severity, with the largest stepwise increase between the paroxysmal AF and permanent AF categories. B, Eigengene expression was highest in the AF history in AF rhythm category in all 3 groups. CAD indicates coronary artery disease; LAF, lone AF; and MV, mitral valve.
The regression analysis also revealed statistically significant associations between the tan module (679 genes) eigengene and atrial rhythm in the MV and CAD groups (P=5.8×10−4 and 3.4×10−2, respectively). Eigengene levels were lower in the AF history in AF rhythm category compared with the AF history in sinus rhythm category (Figure 4); this trend was also observed in the LAF group, albeit with weaker statistical evidence (P=0.15).
Figure 4. Boxplots of tan module eigengene expression levels with respect to atrial rhythm. Eigengene expression levels were lower in the atrial fibrillation (AF) history in AF rhythm category compared with the AF history in sinus rhythm category. CAD indicates coronary artery disease; LAF, lone AF; and MV, mitral valve
Hierarchical Clustering of Eigengene Profiles With Clinical Traits
Hierarchical clustering was performed to identify relationships between gene modules and selected clinical traits. The salmon module clustered with AF severity and atrial rhythm; in addition, left atrial size was found in the same cluster, suggesting a possible relationship between salmon module gene expression and atrial remodeling (Figure 5A). Although the tan module was in a separate cluster from the salmon module, it was negatively correlated with both atrial rhythm and AF severity (Figure 5B).
Figure 5. Dendrogram (A) and correlation heatmap (B) of module eigengenes and clinical traits.
A, The salmon module eigengene but not the tan module eigengene clustered with atrial fibrillation (AF) severity, atrial rhythm, and left atrial size. B, AF severity and atrial rhythm at surgery correlated positively with the salmon module eigengene and negatively with the tan module eigengene. Arhythm indicates atrial rhythm at surgery; Chol, cholesterol; HTN, hypertension; and LASize, left atrial size.
IPA Enrichment Analysis of Salmon and Tan Modules
The salmon module was enriched in genes involved in cardiovascular function and development (smallest P=4.4×10−4) and organ morphology (smallest P=4.4×10−4). In addition, the top disease categories identified included endocrine system disorders (smallest P=4.4×10−4) and cardiovascular disease (smallest P=2.59×10−3).
The tan module was enriched in genes involved in cell-to-cell signaling and interaction (smallest P=8.9×10−4) and cell death and survival (smallest P=1.5×10−3). Enriched disease categories included cancer (smallest P=2.2×10−4) and cardiovascular disease (smallest P=4.5×10−4).
We identified hub genes in the 2 modules based on intramod-ular connectivity and module membership. For the salmon module, the gene RCAN1 exhibited the highest intramodular connectivity and module membership. The top 10 hub genes (by intramodular connectivity) were significantly associated with atrial rhythm, with false discovery rate–adjusted P values ranging from 1.5×10−5 to 4.2×10−12. These hub genes accounted for 95% of the variation in the salmon module eigengene.
In the tan module, the top hub gene was CPEB3. The top 10 hub genes (by intramodular connectivity) correlated with atrial rhythm as well, although the statistical associations in the lower-ranked hub genes were relatively weaker (false discovery rate–adjusted P values ranging from 1.1×10−1 to 3.4×10−4). These hub genes explained 94% of the total variation in the tan module eigengene.
The names and connectivity measures of the hub genes found in both modules are presented in Table 2.
Table 2. Top 10 Hub Genes in the Salmon (Left) and Tan (Right) Modules as Defined by Intramodular Connectivity and Module Membership
Salmon Module
Tan Module
Gene
IMC
Gene
MM
Gene
IMC
Gene
MM
RCAN1
8.2
RCAN1
0.81
CPEB3
43.3
CPEB3
0.85
DNAJA4
7.7
DNAJA4
0.81
CPLX3
42.4
CPLX3
0.84
PDE8B
7.7
PDE8B
0.80
NEDD4L
40.8
NEDD4L
0.83
PRKAR1A
6.9
PRKAR1A
0.77
SGSM1
40.7
SGSM1
0.82
PTPN4
6.7
PTPN4
0.75
UCKL1
39.0
UCKL1
0.81
SORBS2
6.0
FHL2
0.69
SOSTDC1
37.2
SOSTDC1
0.79
ADCY6
5.7
ADCY6
0.69
PRDX1
35.5
RCOR2
0.78
FHL2
5.7
SORBS2
0.68
RCOR2
35.4
EEF2K
0.77
BVES
5.4
DHRS9
0.67
NPPB
35.3
PRDX1
0.76
TMEM173
5.3
LAPTM4B
0.65
LRRN3
34.6
MMP11
0.76
A visualization of the salmon module is shown using the Cytoscape tool (Figure 6). A full list of the genes in the salmon and tan modules is provided in the online-only Data Supplement.
Figure 6. Cytoscape visualization of genes in the salmon module.
Nodes representing genes with high intramodu-lar connectivities, such as RCAN1 and DNAJA4, appear larger in the network. Strong connections are visualized with darker lines, whereas weak connections appear more translucent
Membership of AF-Associated Candidate Genes From Previous Studies
The tan module contained MYOZ1, which was identified as a candidate gene from the recent AF meta-analysis. PITX2 was located in the green module (n=349), and ZFHX3 was located in the turquoise module (n=1512). The locations of other candidate genes (and their closest partners) are reported in the online-only Data Supplement.
Sensitivity Analysis of Key Results
We repeated the WGCNA module identification approach using a different soft-thresholding parameter (β=5). One module (n=121) was found to be strongly associated with atrial rhythm at surgery across all 3 groups of data set, whereas another module (n=244) was associated with atrial rhythm at surgery in the MV and CAD groups. The first module overlapped significantly with the salmon module in terms of gene membership, whereas most of the second modules’ genes were contained within the tan module. The top hub genes found in the salmon and tan modules remained present and highly connected in the 2 new modules identified with the different soft-thresholding parameter.
Discussion
To our knowledge, our study is the first implementation of an unbiased, network-based analysis in a large sample of human left atrial appendage gene expression profiles. We found 2 modules associated with AF severity and atrial rhythm in 2 to 3 of our cardiovascular comorbidity groups. Functional analyses revealed significant enrichment of cardiovascular-related categories for both modules. In addition, several of the hub genes identified are implicated in cardiovascular disease and may play a role in AF initiation and progression.
In our study, WGCNA was used to construct modules based on gene coexpression, thereby reducing the net-work’s dimensionality to a smaller set of elements.17,21 Relating modulewise changes to phenotypic traits allowed statistically significant associations to be detected at a lower false discovery rate compared with traditional differential expression studies. Furthermore, shared functions and pathways among genes in the modules could be inferred via enrichment analyses.
We divided our data set into 3 groups to verify the reproducibility of the modules identified by WGCNA; 14 modules were identified in the MV group in our gene network. All were strongly preserved in the CAD and LAF groups, suggesting that gene coexpression patterns are robust and reproducible despite differences in cardiovascular comorbidities.
The use of module eigengene profiles as representative summary measures has been validated in a number of studies.20,26 Additionally, we found that the eigengenes accounted for a significant proportion (average 18%) of gene expression variability in their respective modules. Regression analysis of the module eigengenes found 2 modules associated with AF severity and atrial rhythm in ≥2 groups of data set. The association between the salmon module eigengene and AF severity was statistically weaker in the LAF group (adjusted P=9.0×10−2). This was probably because of its significantly smaller sample size compared with the MV and CAD groups. Despite this weaker association, the relationship between the salmon module eigengene and AF severity remained consistent among the 3 groups (Figure 3A). Similarly, the lack of statistical significance for the association between the tan module eigengene and atrial rhythm at surgery in the LAF group was likely driven by the smaller sample size and (by definition) lack of samples in the no AF category.
A major part of our analysis focused on the identification of module hub genes. Hubs are connected with a large number of nodes; disruption of hubs therefore leads to widespread changes within the network. This concept has powerful applications in the study of biology, genetics, and disease.29,30 Although mutations of peripheral genes can certainly lead to disease, gene network changes are more likely to be motivated by changes in hub genes, making them more biologically interesting targets for further study.17,29,31 Indeed,
the hub genes of the salmon and tan modules accounted for the vast majority of the variation in their respective module eigengenes, signaling their importance in driving gene module behavior.
The hub genes identified in the salmon and tan modules were significantly associated with AF phenotype overall. It was noted that this association was statistically weaker for the lower-ranked hub genes in the tan module. This highlights an important aspect and strength of WGCNA—to be able to capture module-wide changes with respect to disease despite potentially weaker associations among individual genes.
The implementation of WGCNA necessitated the selection of a soft-thresholding parameter 13. Unlike hard-thresholding (where gene correlations below a certain value are shrunk to zero), the soft-thresholding approach gives greater weight to stronger correlations while maintaining the continuous nature of gene–gene relationships. We selected a 13 value of 3 based on the criteria outlined by Zhang and Horvath.17 His team and other investigators have demonstrated that module identification is robust with respect to the 13 parameter.17,19–21 In our data, we were also able to reproduce the key findings reported with a different, larger 13 value, thereby verifying the stability of our results relating to 13.
The salmon module (124 genes) was associated with both AF phenotypes; furthermore, IPA analysis of its gene contents suggested enrichment in cardiovascular development as well as disease. Its eigengene increased with worsening AF severity, with the largest stepwise change occurring between the paroxysmal AF and persistent AF categories (Figure 3). Hence,
the gene expression changes within the salmon module may reflect the later stages of AF pathophysiology.
The top hub gene of the salmon module was RCAN1 (regulator of calcineurin 1). Calcineurin is a cytoplasmic Ca2+/ calmodulin-dependent protein phosphatase that stimulates cardiac hypertrophy via its interactions with NFAT and L-type Ca2+ channels.32,33RCAN1 is known to inhibit calcineurin and its associated pathways.32,34 However, some data suggest that RCAN1 may instead function as a calcineurin activator when highly expressed and consequently potentiate hypertrophic signaling.35 Thus,
perturbations in RCAN1 levels (attributable to genetic variants or mutations) may cause an aberrant switching in function, which in turn triggers atrial remodeling and arrhythmogenesis.
Other hub genes found in the salmon module are also involved in cardiovascular development and function and may be potential targets for further study.
DNAJA4 (DnaJ homolog, subfamily A, member 4) regulates the trafficking and maturation of KCNH2 potassium channels, which have a prominent role in cardiac repolarization and are implicated in the long-QT syndromes.36
FHL2 (four-and-a-half LIM domain protein 2) interacts with numerous cellular components, including
actin cytoskeleton,
transcription machinery, and
ion channels.37
FHL2 was shown to enhance the hypertrophic effects of isoproterenol, indicating that
FHL2 may modulate the effect of environmental stress on cardiomyocyte growth.38
FHL2 also interacts with several potassium channels in the heart, such as KCNQ1, KCNE1, and KCNA5.37,39
Additionally, blood vessel epicardial substance (BVES) and other members of its family were shown to be highly expressed in cardiac pacemaker cells. BVES knockout mice exhibited sinus nodal dysfunction, suggesting that BVES regulates the development of the cardiac pacemaking and conduction system40 and may therefore be involved in the early phase of AF development.
The tan module (679 genes) eigengene was negatively correlated with atrial rhythm in the MV and CAD groups (Figure 4); this may indicate a general decrease in gene expression of its members in fibrillating atrial tissue. IPA analysis revealed enrichment in genes involved in cell signaling as well as apoptosis. The top-ranked hub gene, cytoplasmic polyade-nylation element binding protein 3 (CPEB3), regulates mRNA translation and has been associated with synaptic plasticity and memory formation.41 The role of CPEB3 in the heart is currently unknown, so further exploration via animal model studies may be warranted.
Natriuretic peptide-precursor B (NPPB), another highly interconnected hub gene, produces a precursor peptide of brain natriuretic peptide, which
regulates blood pressure through natriuresis and vasodilation.42
(NPPB) gene variants have been linked with diabetes mellitus, although associations with cardiac phenotypes are less clear.42 TBX5 and GATA4, which play important roles in the embryonic heart development,43 were members of the tan module. Although not hub genes, they may also contribute toward developmental susceptibility of AF. In addition, TBX5 was previously reported to be near an SNP associated with PR interval and AF in separate large-scale GWAS studies.12,28 MYOZ1, another candidate gene identified in the recent AF GWAS meta-analysis, was found to be a member as well; it associates with proteins found in the Z-disc of skeletal and cardiac muscle and may suppress calcineurin-dependent hypertrophic signaling.12
Some, but not all, of the candidate genes found in previous GWAS studies were located in the AF-associated modules. One possible explanation for this could be the difference in sample sizes. The meta-analysis involved thousands of individuals, whereas the current study had <100 in each group of data set, which limited the power to detect significant differences between levels of AF phenotype even with the module-wise approach. Additionally, transcription factors like PITX2 are most highly expressed during the fetal phase of development. Perturbations in these genes (attributable to genetic variants or mutations) may therefore initiate the development of AF at this stage and play no significant role in adults (when we obtained their tissue samples).
Limitations in Study
We noted several limitations in this study. First, no human left atrial mRNA data set of adequate size currently exists publicly. Hence, we were unable to validate our results with an external, independent data set. However, the network preservation assessment performed within our data set showed strong preservation in all modules, indicating that our findings are robust and reproducible.
Although the module eigengenes captured a significant proportion of module variance, a large fraction of variability did remain unaccounted for, which may limit their use as representative summary measures.
We extracted RNA from human left atrial appendage tissue, which consists primarily of cardiomyocytes and fibroblasts. Atrial fibrosis is known to occur with AF-associated remodeling.44 As such, the cardiomyocyte to fibroblast ratio is likely to change with different levels of AF severity, which in turn influences the amount of RNA extracted from each cell type. Hence, true differences in gene expression (and coexpression) within cardiomyocytes may be confounded by changes in cellular composition attributable to atrial remodeling. Also, there may be significant regional heterogeneity in the left atrium with respect to structure, cellular composition, and gene expression,45 which may limit the generaliz-ability of our results to other parts of the left atrium.
All subjects in the study were whites to minimize the effects of population stratification. However, it is recognized that the genetic basis of AF may differ among ethnic groups.9 Thus, our results may not be generalizable to other ethnicities.
Finally, it is possible for genes to be involved in multiple processes and functions that require different sets of genes. However, WGCNA does not allow for overlapping modules to be formed. Thus,
this limits the method’s ability to characterize such gene interactions.
Conclusions
In summary, we constructed a weighted gene coexpression network based on RNA expression data from the largest collection of human left atrial appendage tissue specimens to date. We identified 2 gene modules significantly associated with AF severity or atrial rhythm at surgery. Hub genes within these modules may be involved in the initiation or progression of AF and may therefore be candidates for functional studies.
Refererences
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2. Lemmens R, Hermans S, Nuyens D, Thijs V. Genetics of atrial fibrillation and possible implications for ischemic stroke. Stroke Res Treat. 2011;2011:208694.
3. Wann LS, Curtis AB, January CT, Ellenbogen KA, Lowe JE, Estes NA III, et al; ACCF/AHA/HRS. 2011 ACCF/AHA/HRS focused update on the management of patients with atrial fibrillation (Updating the 2006 Guideline): a report of the American College of Cardiology Foundation/ American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2011;57:223–242.
4. Dobrev D, Carlsson L, Nattel S. Novel molecular targets for atrial fibrillation therapy. Nat Rev Drug Discov. 2012;11:275–291.
5. Christophersen IE, Ravn LS, Budtz-Joergensen E, Skytthe A, Haunsoe S, Svendsen JH, et al. Familial aggregation of atrial fibrillation: a study in Danish twins. Circ Arrhythm Electrophysiol. 2009;2:378–383.
6. Gudbjartsson DF, Arnar DO, Helgadottir A, Gretarsdottir S, Holm H, Sig-urdsson A, et al. Variants conferring risk of atrial fibrillation on chromosome 4q25. Nature. 2007;448:353–357.
7. Ellinor PT, Lunetta KL, Glazer NL, Pfeufer A, Alonso A, Chung MK, et al. Common variants in KCNN3 are associated with lone atrial fibrillation. Nat Genet. 2010;42:240–244.
8. Benjamin EJ, Rice KM, Arking DE, Pfeufer A, van Noord C, Smith AV, et al. Variants in ZFHX3 are associated with atrial fibrillation in individuals of European ancestry. Nat Genet. 2009;41:879–881.
9. Sinner MF, Ellinor PT, Meitinger T, Benjamin EJ, Kääb S. Genome-wide association studies of atrial fibrillation: past, present, and future. Cardio-vasc Res. 2011;89:701–709.
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13. Barth AS, Merk S, Arnoldi E, Zwermann L, Kloos P, Gebauer M, et al. Reprogramming of the human atrial transcriptome in permanent atrial fibrillation: expression of a ventricular-like genomic signature. Circ Res. 2005;96:1022–1029.
Atrial fibrillation is the most common sustained cardiac arrhythmias in the United States. The genetic and molecular mechanisms governing its initiation and progression are complex, and our understanding of these mechanisms remains incomplete despite recent advances via genome-wide association studies, animal model experiments, and differential expression studies. In this study, we used weighted gene coexpression network analysis to identify gene modules significantly associated with atrial fibrillation in a large sample of human left atrial appendage tissues. We further identified highly interconnected genes (ie, hub genes) within these gene modules that may be novel candidates for functional studies. The discovery of the atrial fibrillation-associated gene modules and their corresponding hub genes provide novel insight into the gene network changes that occur with atrial fibrillation, and closer study of these findings can lead to more effective targeted therapies for disease management.
Part IV: The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets
Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN
Part VI: Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD
In the first part, we discussed common MOTIFs across cell-types that are essential for cell division, embryogenesis, cancer metastasis, osteogenesis, musculoskeletal function, vascular compliance, and cardiac contractility. This second article concentrates on specific functionalities for cardiac contractility based on Ca++ signaling in excitation-contraction coupling. The modifications discussed apply specifically to cardiac muscle and not to skeletal muscle. Considering the observations described might raise additional questions specifically address to the unique requirements of smooth muscle, abundant in the GI tract and responsible for motility in organ function, and in blood vessel compliance or rigidity. Due to the distinctly different aspects of the cardiac contractility and contraction force, and the interactions with potential pharmaceutical targets, there are two separate articles on calcium signaling and cardiac arrhythmias or heart failure (Part 2 and Part 3). Part 2 focuses on the RYANODINE role in cardiac Ca(2+) signaling and its effect in heart failure. Part 3 takes up other aspects of heart failure and calcium signaling with respect to phosporylation/dephosphorylation. I add a single review and classification of genetic cardiac disorders of the same cardiac Ca(2+) signaling and the initiation and force of contraction. Keep in mind that the heart is a syncytium, and this makes a huge difference compared with skeletal muscle dynamics. In Part 1 there was some discussion of the importance of Ca2+ signaling on innate immune system, and the immunology will be further expanded in a fourth of the series.
SUMMARY:
This second article on the cardiomyocyte and the Ca(2+) cycling between the sarcomere and the cytoplasm, takes a little distance from the discussion of the ryanodine that precedes it. In this discussion we found that there is a critical phosphorylation/dephosphorylation balance that exists between Ca(+) ion displacement and it occurs at a specific amino acid residue on the CaMKIId, specific for myocardium, and there is a 4-fold increase in contraction and calcium release associated with this CAM kinase (ser 2809) dependent exchange. These events are discussed in depth, and the research holds promise for therapeutic application. We also learn that Ca(2+) ion channels are critically involved in the generation of arrhythmia as well as dilated and hypertrophic cardiomyopathy. In the case of arrhythmiagenesis, there are two possible manners by which this occurs. One trigger is Ca(2+) efflux instability. The other is based on the finding that when the cellular instability is voltage driven, the steady-state wavelength (separation of nodes in space) depends on electrotonic coupling between cells and the steepness of APD and CV restitution. The last article is an in depth review of the genetic mutations that occur in cardiac diseases. It is an attempt at classifying them into reasonable groupings. What are the therapeutic implications of this? We see that the molecular mechanism of cardiac function has been substantially elucidated, although there are contradictions in experimental findings that are unexplained. However, for the first time, it appears that personalized medicine is on a course that will improve health in the population, and the findings will allow specific targets designed for the individual with a treatable impairment in cardiac function that is identifiable early in the course of illness. This article is a continuation to the following articles on tightly related topics: Part I: Identification of Biomarkers that are Related to the Actin Cytoskeleton Larry H Bernstein, MD, FCAP http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/ Part II: Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility Larry H. Bernstein, MD, FCAP, Stephen Williams, PhD and Aviva Lev-Ari, PhD, RN http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/ Part III: Renal Distal Tubular Ca2+ Exchange Mechanism in Health and Disease Larry H. Bernstein, MD, FCAP, Stephen J. Williams, PhD and Aviva Lev-Ari, PhD, RN http://pharmaceuticalintelligence.com/2013/09/02/renal-distal-tubular-ca2-exchange-mechanism-in-health-and-disease/ Part IV: The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN http:/pharmaceuticalintelligence.com/2013.09.089/lhbern/The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets
Part V: Heart Smooth Muscle and Cardiomyocyte Cells: Excitation-Contraction Coupling & Ryanodine Receptor (RyR) type-1/type-2 in Cytoskeleton Cellular Dynamics and Ca2+ Signaling
an increase in the initial rates of Ca(2+) transport by SR vesicles
which reflects an increased turnover of elementary steps of the calcium ATPase reaction sequence.
The stimulatory effects of the protein kinases on the calcium pump may be reversed by an endogenous protein phosphatase, which
can dephosphorylate both the CAMP-dependent and the calcium calmodulin-dependent sites on phospholamban.
Thus, the calcium pump in cardiac sarcoplasmic reticulum appears to be under reversible regulation mediated by protein kinases and protein phosphatases.
Regulation of the Cardiac Ryanodine Receptor Channel by Luminal Ca2+ involves Luminal Ca2+ Sensing Sites
I Györke, S Györke. Biophysical Journal 01/1999; 75(6):2801-10. · 3.65 Impact factor http:// www.researchgate.net/publication/13459335/Regulation_of_the_cardiac_ryanodine_receptor_channel_by_luminal_Ca2_involves_luminal_Ca2_sensing_sites The mechanism of activation of the cardiac calcium release channel/ryanodine receptor (RyR) by luminal Ca(2+) was investigated in native canine cardiac RyRs incorporated into lipid bilayers in the presence of 0.01 microM to 2 mM Ca(2+) (free) and 3 mM ATP (total) on the cytosolic (cis) side and 20 microM to 20 mM Ca(2+) on the luminal (trans) side of the channel and with Cs+ as the charge carrier. Under conditions of low [trans Ca(2+)] (20 microM), increasing [cis Ca(2+)] from 0.1 to 10 microM caused a gradual increase in channel open probability (Po). Elevating [cis Ca(2+)] [cytosolic] above 100 microM resulted in a gradual decrease in Po. Elevating trans [Ca(2+)] [luminal] enhanced channel activity (EC50 approximately 2.5 mM at 1 microM cis Ca2+) primarily by increasing the frequency of channel openings. The dependency of Po on trans [Ca2+] [luminal] was similar at negative and positive holding potentials and was not influenced by high cytosolic concentrations of the fast Ca(2+) chelator, 1,2-bis(2-aminophenoxy)ethane-N,N,N, N-tetraacetic acid. Elevated luminal Ca(2+)
enhanced the sensitivity of the channel to activating cytosolic Ca(2+), and it
essentially reversed the inhibition of the channel by high cytosolic Ca(2+).
Potentiation of Po by increased luminal Ca(2+) occurred irrespective of whether the electrochemical gradient for Ca(2+) supported a cytosolic-to-luminal or a luminal-to-cytosolic flow of Ca(2+) through the channel. These results rule out the possibility that under our experimental conditions, luminal Ca(2+) acts by interacting with the cytosolic activation site of the channel and suggest that the effects of luminal Ca2+ are mediated by distinct Ca(2+)-sensitive site(s) at the luminal face of the channel or associated protein.
Protein phosphatases Decrease Sarcoplasmic Reticulum Calcium Content by Stimulating Calcium Release in Cardiac Myocytes
D Terentyev, S Viatchenko-Karpinski, I Gyorke, R Terentyeva and S Gyorke Texas Tech University Health Sciences Center, Lubbock, TX J Physiol 2003; 552(1), pp. 109–118. http://dx.doi.org/10.1113/jphysiol.2003.046367 Phosphorylation/dephosphorylation of Ca2+ transport proteins by cellular kinases and phosphatases plays an important role in regulation of cardiac excitation–contraction coupling; furthermore,
abnormal protein kinase and phosphatase activities have been implicated in heart failure.
However, the precise mechanisms of action of these enzymes on intracellular Ca2+ handling in normal and diseased hearts remains poorly understood. We have investigated
the effects of protein phosphatases PP1 and PP2A on spontaneous Ca(2+) sparks and SR Ca(2+) load in myocytes permeabilized with saponin.
Exposure of myocytes to PP1 or PP2A caused a dramatic increase in frequency of Ca(2+) sparks followed by a nearly complete disappearance of events, which were accompanied by depletion of the SR Ca(2+) stores, as determined by application of caffeine. These changes in
Ca(2+) release and
SR Ca(2+) load
could be prevented by the inhibitors of PP1 and PP2A phosphatase activities okadaic acid and calyculin A. At the single channel level, PP1 increased the open probability of RyRs incorporated into lipid bilayers. PP1-medited RyR dephosphorylation in our permeabilized myocytes preparations was confirmed biochemically by quantitative immunoblotting using a phosphospecific anti-RyR antibody. Our results suggest that
leading to depleted SR Ca(2+) stores in cardiac myocytes.
In heart muscle cells, the process of excitation–contraction (EC) coupling is mediated by
Ca(2+) influx through sarcolemmal L-type Ca(2+) channels
activating Ca(2+) release channels (ryanodine receptors, RyRs) in the sarcoplasmic reticulum (SR).
Once activated, the RyR channels allow Ca(2+) to be released from the SR into the cytosol to induce contraction. This mechanism is known as Ca(2+)-induced calcium release (CICR) (Fabiato, 1985; Bers, 2002). During relaxation, most of the Ca(2+) is resequestered into the SR by the Ca(2+)-ATPase. The amount of Ca(2+) released and the force of contraction depend on
the magnitude of the Ca(2+) trigger signal,
the functional state of the RyRs and
the amount of Ca(2+) stored in the SR.
F1.large calcium movement and RyR2 receptor calcium release calmodulin + ER Reversible phosphorylation of proteins composing the EC coupling machinery plays an important role in regulation of cardiac contractility (Bers, 2002). Thus, during stimulation of the b-adrenergic pathway, phosphorylation of several target proteins, including
the L-type Ca(2+) channels,
RyRs and
phospholamban,
by protein kinase A (PKA) leads to an overall increase in SR Ca2+ release and contractile force in heart cells (Callewaert et al. 1988, Spurgeon et al. 1990; Hussain & Orchard, 1997; Zhou et al. 1999; Song et al. 2001; Viatchenko-Karpinski & Gyorke, 2001). PKA-dependent phosphorylation of the L-type Ca(2+) channels increases the Ca2+ current (ICa), increasing both
the Ca2+ trigger for SR Ca2+ release and
the SR Ca(2+) content
(Callewaert et al. 1988; Hussain & Orchard, 1997; Del Principe et al. 2001). Phosphorylation of phospholamban (PLB) relieves the tonic inhibition dephosphorylated PLB exerts on the SR Ca(2+)-ATPase (SERCA) resulting in enhanced SR Ca(2+) accumulation and enlarged Ca(2+) release (Kranias et al. 1985; Simmermann & Jones, 1998). With regard to the RyR, despite clear demonstration of phosphorylation of the channel in biochemical studies (Takasago et al. 1989; Yoshida et al. 1992), the consequences of this reaction to channel function have not been clearly defined. RyR phosphorylation by PKA and Ca(2+)–calmodulin dependent protein kinase (CaMKII) has been reported to increase RyR activity in lipid bilayers (Hain et al. 1995; Marx et al. 2000; Uehara et al. 2002). Moreover, it has been reported that in heart failure (HF), hyperphosphorylation of RyR causes
the release of FK-506 binding protein (FKBP12.6) from the RyR,
rendering the channel excessively leaky for Ca(2+) (Marx et al. 2000).
However, other studies have reported no functional effects (Li et al. 2002) or even found phosphorylation to reduce RyR channel steady-state open probability (Valdivia et al. 1995; Lokuta et al. 1995). The action of protein kinases is opposed by dephosphorylating phosphatases. Three types of protein phosphatases (PPs), referred to as PP1, PP2A and PP2B (calcineurin), have been shown to influence cardiac performance (Neumann et al. 1993; Rusnak & Mertz, 2000). Overall, according to most studies phosphatases appear to downregulate SR Ca(2+) release and contractile performance (Neumann et al. 1993; duBell et al. 1996, 2002; Carr et al. 2002; Santana et al. 2002). Furthermore, PP1 and PP2A activities appear to be increased in heart failure (Neumann, 2002; Carr et al. 2002). However, again the precise mode of action of these enzymes on intracellular Ca(2+) handling in normal and diseased hearts remains poorly understood. In the present study, we have investigated the effects of protein phosphatases PP1 and PP2A on local Ca(2+) release events, Ca(2+) sparks, in cardiac cells. Our results show that
phosphatases activate RyR mediated SR Ca(2+) release
leading to depletion of SR Ca(2+) stores.
These results provide novel insights into the mechanisms and potential role of protein phosphorylation/dephosphorylation in regulation of Ca(2+) signaling in normal and diseased hearts.
RESULTS
Effects of PP1 and PP2A on Ca2+ sparks and SR Ca(2+) content.
[1] PP1 caused an early transient potentiation of Ca2+ spark frequency followed by a delayed inhibition of event occurrence. [2] PP1 produced similar biphasic effects on the magnitude and spatio-temporal characteristics of Ca(2+) sparks Specifically, during the potentiatory phase (1 min after addition of the enzyme), PP1 significantly increased
the amplitude,
rise-time,
duration and
width of Ca(2+) sparks;
during the inhibitory phase (5 min after addition of the enzyme),
all these parameters were significantly suppressed by PP1.
The SR Ca(2+) content decreased by 35 % or 69 % following the exposure of myocytes to either 0.5 or 2Uml_1 PP1, respectively (Fig. 1C). Qualitatively similar results were obtained with phosphatase PP2A. Similar to the effects of PP1, PP2A (5Uml_1) produced a transient increase in Ca(2+) spark frequency (~4-fold) followed by a depression of event occurrence and decreased SR Ca(2+) content (by 82 % and 65 %, respectively). Also similar to the action of PP1, PP2A increased
the amplitude and
spatio-temporal spread (i.e. rise-time, duration and width) of Ca(2+) sparks at 1 min
and suppressed the same parameters at 5 min of exposure to the enzyme (Table 1).
Together, these results suggest that phosphatases enhance spark-mediated SR Ca2+ release, leading to decreased SR Ca(2+) content.Preventive effects of calyculin A and okadaic acid Preventive effects of ryanodine
PP1-mediated RyR dephosphorylation
F3.large cardiomyocyte SR F2.large RyR and calcium coupled receptors The cardiac RyR is phosphorylated at Ser-2809 (in the rabbit sequence) by both PKA and CAMKII (Witcher et al. 1991; Marx et al. 2000). Although additional phosphorylation sites may exist on the RyR (Rodriguez et al. 2003), but Ser-2809 is believed to be the only site that is phosphorylated by PKA, and RyR hyperphosphorylation at this site has been reported in heart failure (Marx et al. 2000). To test whether indeed phosphatases dephosphorylated the RyR in our permeabilized myocyte experiments we performed quantitative immunoblotting using an antibody that specifically recognizes the phosphorylated form of the RyR at Ser-2809 (Rodriguez et al. 2003). Myocytes exhibited a significant level of phosphorylation under baseline conditions. Maximal phosphorylation was 201 % of control. When exposed to 2Uml_1 PP1, RyR phosphorylation was 58 % of the control basal condition. Exposing to a higher PP1 concentration (10Uml_1) further reduced RyR phosphorylation to 22% of control. Thus, consistent with the results of our functional measurements,
PP1 decreased RyR phosphorylation in cardiac myocytes.
Figure 1. Effects of PP1 on properties of Ca(2+) sparks and SR Ca(2+) content in rat permeabilized myocytes see . http://dx.doi.org/10.1113/jphysiol.2003.046367 A, spontaneous Ca(2+) spark images recorded under reference conditions, and 1 or 5 min after exposure of the cell to 2Uml_1 PP1. Traces below the images are Ca(2+) transients induced by application of 10 mM caffeine immediately following the acquisition of sparks before (3 min) and after (5 min) application of PP1 in the same cell. The Ca(2+) transients were elicited by a whole bath application of 10 mM caffeine. B, averaged spark frequency at early (1 min) and late (5 min) times following the addition of either 0.5 or 2Uml_1 of PP1 to the bathing solution. C, averaged SR Ca(2+) content for 0.5 or 2Uml_1 of PP1 measured before and 5 min after exposure to the enzyme. Data are presented as means ± S.E.M. of 6 experiments in different cells. Figure 2. Effects of PP2A on properties of Ca2+ sparks and SR Ca2+ content in rat permeabilized myocytes see . http://dx.doi.org/10.1113/jphysiol.2003.046367 A, spontaneous Ca(2+) spark images recorded under reference conditions, and 1 or 5 min after exposure of the cell to 5Uml_1 PP2A. Traces below the images are Ca(2+) transients induced by application of 10 mM caffeine immediately following the acquisition of sparks before (3 min) and after (5 min) application of PP2A in the same cell. B and C, averaged spark frequency (B) and SR Ca(2+) content (C) for the same conditions as in A. Data are presented as means ± S.E.M. of 6 experiments in different cells.
DISCUSSION
In the present study, we have investigated the impact of physiologically relevant exogenous protein phosphatases PP1 and PP2A on RyR-mediated SR Ca(2+) release (measured as Ca(2+) sparks) in permeabilized heart cells. Our principal finding is that
phosphatases stimulated RyR channels lead to depleted SR Ca(2+) stores.
These results have important ramifications for understanding the mechanisms and role of protein phosphorylation/dephosphorylation in
modulation of Ca(2+) handling in normal and diseased heart.
Modulation of SR Ca2+ release by protein phosphorylation/dephophorylation
Since protein dephosphorylation clearly resulted in increased functional activity of the Ca(+)release channel, our results imply that a reverse, phosphorylation reaction should reduce RyR activity. If indeed such effects take place, why do they not manifest in inhibition of Ca(+)sparks? One possibility is that enhanced Ca(+) uptake by SERCA
masks or overcomes the effects phosphorylation may have on RyRs.
In addition, the potential inhibitory influence of protein phosphorylation on RyR activity in myocytescould be countered by feedback mechanisms involving changes in luminal Ca(2+)(Trafford et al. 2002; Gyorke et al. 2002). In particular, reduced open probability of RyRs would be expected to lead to
increased Ca2+ accumulation in the SR;
and increased intra-SR [Ca(2+)], in turn would
increase activity of RyRs at their luminal Ca(2+) regulatory sites
as demonstrated for the RyR channel inhibitor tetracaine (Gyorke et al. 1997; Overend et al. 1997). Thus
potentiation of SERCA
combined with the intrinsic capacity of the release mechanism to self-regulate
could explain at least in part why PKA-mediated protein phoshorylation results in maintained potentiation of Ca(2+) sparks despite a potential initial decrease in RyR activity.
Role of altered RyR Phosphorylation in Heart Failure
Marx et al. (2000) have proposed that enhanced levels of circulating catecholamines lead to increased phosphorylation of RyR in heart failure. Based on biochemical observations as well as on studying properties of single RyRs incorporated into artificial lipid bilayers, these investigators have hypothesized that
hyperphosphorylation of RyRs contributes to pathogenesis of heart failure
by making the channel excessively leaky due to dissociation of FKBP12.6 from the channel.
We show that the mode of modulation of RyRs by phosphatases does not support this hypothesis as
dephosphorylation caused activation instead of
Interestingly, our results provide the basis for a different possibility in which
dephophosphorylation of RyR rather than its phosphorylation causes depletion of SR Ca(2+) stores by stimulating RyRs in failing hearts.
It has been reported thatPP1 and PP2 activities are increased in heart failure (Huang et al. 1999; Neumann et al. 1997; Neuman, 2002). Furthermore, overexpression of PP1 or ablation of the endogenous PP1 inhibitor, l-1, results in
depressed contractile performance and heart failure (Carr et al. 2002).
Our finding that PP1 causes depletion of SR Ca(2+) stores by activating RyRs could account for, or contribute to, these results.
References
1 DelPrincipe F, Egger M, Pignier C & Niggli E (2001). Enhanced E-C coupling efficiency after beta-stimulation of cardiac myocytes. Biophys J 80, 64a. 2 Gyorke I & Gyorke S (1998). Regulation of the cardiac ryanodine receptor channel by luminal Ca2+ involves luminal Ca2+ sensing sites. Biophys J 75, 2801–2810. 3 Gyorke S, Gyorke I, Lukyanenko V, Terentyev D, Viatchenko-Karpinski S & Wiesner TF (2002). Regulation of sarcoplasmic reticulum calcium release by luminal calcium in cardiac muscle. Front Biosci 7, d1454–d1463. 4 Gyorke I, Lukyanenko V & Gyorke S (1997). Dual effects of tetracaine on spontaneous calcium release in rat ventricular myocytes. J Physiol 500, 297–309. 5 MacDougall LK, Jones LR & Cohen P (1991). Identification of the major protein phosphatases in mammalian cardiac muscle which dephosphorylate phospholamban. Eur J Biochem 196, 725–734. 6 Marx SO, Reiken S, Hisamatsu Y, Jayaraman T, Burkhoff D, Rosemblit N & Marks AR (2000). PKA phosphorylation dissociates FKBP12.6 from the calcium release channel (ryanodine receptor): defective regulation in failing hearts. Cell 101, 365–376. 7 Rodriguez P, Bhogal MS & Colyer J (2003). Stoichiometric phosphorylation of cardiac ryanodine receptor on serine-2809 by calmodulin-dependent kinase II and protein kinase A. J Biol Chem (in press).
The δC Isoform of CaMKII Is Activated in Cardiac Hypertrophy and Induces Dilated Cardiomyopathy and Heart Failure
T Zhang, LS Maier, ND Dalton, S Miyamoto, J Ross, DM Bers, JH Brown. University of California, San Diego, La Jolla, Calif; and Loyola University, Chicago, Ill. Circ Res. 2003;92:912-919. http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5 Recent studies have demonstrated that transgenic (TG) expression of either Ca(2+)/calmodulin-dependent protein kinase IV (CaMKIV) or CaMKIIδB, both of which localize to the nucleus, induces cardiac hypertrophy. However,
CaMKIV is not present in heart, and
cardiomyocytes express not only the nuclear CaMKIIδB
but also a cytoplasmic isoform, CaMKII δC.
In the present study, we demonstrate that
expression of the δC isoform of CaMKII is selectively increased and
its phosphorylation elevated as early as 2 days and continuously for up to 7 days after pressure overload.
To determine whether enhanced activity of this cytoplasmic δC isoform of CaMKII can lead to phosphorylation of Ca(2+) regulatory proteins and induce hypertrophy, we generated TG mice that expressed the δC isoform of CaMKII. Immunocytochemical staining demonstrated that the expressed transgene is confined to the cytoplasm of cardiomyocytes isolated from these mice. These mice develop a dilated cardiomyopathy with up to a 65% decrease in fractional shortening and die prematurely. Isolated myocytes are enlarged and exhibit reduced contractility and altered Ca2(2+) handling. Phosphorylation of the ryanodine receptor (RyR) at a CaMKII site is increased even before development of heart failure, and
CaMKII is found associated with the RyR from the CaMKII TG mice.
Phosphorylation of phospholamban is increased specifically at the CaMKII but not at the PKA phosphorylation site.
These findings are the first to demonstrate that CaMKIIδC can mediate phosphorylation of Ca(2+) regulatory proteins in vivo and provide evidence for the involvement of CaMKIIδC activation in the pathogenesis of dilated cardiomyopathy and heart failure. Multifunctional Ca(2+)/calmodulin-dependent protein kinases (CaM kinases or CaMKs) are transducers of Ca2+ signals that phosphorylate a wide range of substrates and thereby affect Ca(2+)-mediated cellular responses.1 The family includes CaMKI and CaMKIV, monomeric enzymes activated by CaM kinase kinase,2,3 and CaMKII, a multimer of 6 to 12 subunits activated by autophosphorylation.1 The CaMKII subunits α, β, γ, and δ show different tissue distributions,1 with
the δ isoform predominating in the heart.4–7
Splice variants of the δ isoform, characterized by the presence of a second variable domain,4,7 include δB, which contains a nuclear localization signal (NLS), and
δC, which does not. CaMKII composed of δB subunits localizes to the nucleus, whereas CaMKIIδC localizes to the cytoplasm.4,8,9
CaMKII has been implicated in several key aspects of acute cellular Ca(2+) regulation related to cardiac excitation-contraction (E-C) coupling. CaMKII
phosphorylates sarcoplasmic reticulum (SR) proteins including the ryanodine receptors (RyR2) and
phospholamban (PLB).10–14
Phosphorylation of RyR has been suggested to alter the channel open probability,14,15 whereas phosphorylation of PLB has been suggested to regulate SR Ca(2+) uptake.14 It is also likely that CaMKII phosphorylates the L-type Ca(2+) channel complexor an associated regulatory protein and thus
mediates Ca(2+) current (ICa) facilitation.16-18 and
the development of early after-depolarizations and arrhythmias.19
Thus, CaMKII has significant effects on E-C coupling and cellular Ca(2 +) regulation. Nothing is known about the CaMKII isoforms regulating these responses. Contractile dysfunction develops with hypertrophy, characterizes heart failure, and is associated with changes in cardiomyocyte (Ca2+) homeostasis.20 CaMKII expression and activity are altered in the myocardium of rat models of hypertensive cardiac hypertrophy21,22 and heart failure,23 and
in cardiac tissue from patients with dilated cardiomyopathy.24,25
Several transgenic mouse models have confirmed a role for CaMK in the development of cardiac hypertrophy, as originally suggested by studies in isolated neonatal rat ventricular myocytes.9,26–28 Hypertrophy develops in transgenic mice that overexpress CaMKIV,27 but this isoform is not detectable in the heart,4,29 and CaMKIV knockout mice still develop hypertrophy after transverse aortic constriction (TAC).29 Transgenic mice overexpressing calmodulin developed severe cardiac hypertrophy,30 later shown to be associated with an increase in activated CaMKII31; the isoform of CaMKII involved in hypertrophy could not be determined from these studies. We recently reported that transgenic mice that overexpress CaMKIIδB, which is highly concentrated in cardiomyocyte nuclei, develop hypertrophy and dilated cardiomyopathy.32 To determine whether
in vivo expression of the cytoplasmic CaMKIIδC can phosphorylate cytoplasmic Ca(2+) regulatory proteins and
induce hypertrophy or heart failure,
we generated transgenic (TG) mice that expressed the δC isoform of CaMKII under the control of the cardiac specific α-myosin heavy chain (MHC) promoter. Our findings implicate CaMKIIδC in the pathogenesis of dilated cardiomyopathy and heart failure and suggest that
this occurs at least in part via alterations in Ca(2+) handling proteins.33
Ca(2+) and contraction RyR yuan_image3 Ca++ exchange
Results
Expression and Activation of CaMKIIδC Isoform After TAC
To determine whether CaMKII was regulated in pressure overload–induced hypertrophy, CaMKIIδ expression and phosphorylation were examined by Western blot analysis using left ventricular samples obtained at various times after TAC. A selective increase (1.6-fold) in the lower band of CaMKIIδwas observed as early as 1 day and continuously for 4 days (2.3-fold) and 7 days (2-fold) after TAC (Figure 1A). To confirm that CaMKIIδC was increased and determine whether this occurred at the transcriptional level, we performed semiquantitative RT-PCR using primers specific for the CaMKIIδC isoform. These experiments revealed that
mRNA levels for CaMKIIδC were increased 1 to 7 days after TAC (Figure 1B).
In addition to examining CaMKII expression, the activation state of CaMKII was monitored by its autophosphorylation, which confers Ca2-independent activity.
Figure 1. Expression and activation of CaMKII δC isoform after TAC.
see http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5 A, Western blot analysis of total CaMKII in left ventricular (LV) homogenates obtained at indicated times after TAC. Cardiomyocytes transfected with CaMKIIδB and δC (right) served as positive controls and molecular markers. Top band (58 kDa) represents CaMKIIδB plus δ9, and the bottom band (56 kDa) corresponds to CaMKIIδC. *P0.05 vs control. B, Semiquantitative RT-PCR using primers specific for CaMKIIδC isoform (24 cycles) and GAPDH (19 cycles) using total RNA isolated from the same LV samples. C, Western blot analysis of phospho-CaMKII in LV homogenates obtained at various times after TAC. Three bands seen for each sample represent CaMKIIγ subunit (uppermost), CaMKIIδB plus δ9 (58 kDa), and CaMKIIδC (56 kDa). Quantitation is based on the sum of all of the bands. *P0.05 vs control.
Figure 2. Expression and activation of CaMKII in CaMKIIδC transgenic mice.
see http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5 A, Transgene copy number based on Southern blots using genomic DNA isolated from mouse tails (digested with EcoRI). Probe (a 32P-labeled 1.7-kb EcoRI-SalI -MHC fragment) was hybridized to a 2.3-kb endogenous fragment (En) and a 3.9-kb transgenic fragment (TG). Transgene copy number was determined from the ratio of the 3.9-kb/2.3-kb multiplied by 2. B, Immunocytochemical staining of ventricular myocytes isolated from WT and CaMKIIδTG mice. Myocytes were cultured on laminin-coated slides overnight. Transgene was detected by indirect immunofluorescence staining using rabbit anti-HA antibody (1:100 dilution) followed by FITC-conjugated goat antirabbit IgG antibody (1:100 dilution). CaMKIIδB localization to the nucleus in CaMKIIδB TG mice (see Reference 32) is shown here for comparative purpose. C, Quantitation of the fold increase in CaMKIIδprotein expression in TGL and TGM lines. Different amounts of ventricular protein (numbers) from WT control, TG () and their littermates () were immunoblotted with an anti-CaMKIIδ antibody. Standard curve from the WT control was used to calculate fold increases in protein expression in TGL and TGM lines. D, Phosphorylated CaMKII in ventricular homogenates was measured by Western blot analysis (n5 for each group). **P0.01 vs WT.
Generation and Identification of CaMKIIδC Transgenic Mice
TG mice expressing HA-tagged rat wild-type CaMKIIδC under the control of the cardiac-specific α-MHC promoter were generated as described in Materials and Methods. By Southern blot analysis, 3 independent TG founder lines carrying 3, 5, and 15 copies of the transgene were identified. They were designated as TGL (low copy number), TGM (medium copy number), and TGH (high copy number), The founder mice from the TGH line died at 5 weeks of age with marked cardiac enlargement. The other two lines showed germline transmission of the transgene. The transgene was expressed only in the heart. Although CaMKII protein levels in TGL and TGM hearts were increased 12- and 17-fold over wild-type (WT) controls (Figure 2C), the amount of activated CaMKII was only increased 1.7- and 3-fold in TGL and TGM hearts (Figure 2D). The relatively small increase in CaMKII activity in the TG lines probably reflects the fact that the enzyme is not constitutively activated and that the availability of Ca2/CaM, necessary for activation of the overexpressed CaMKII, is limited. Importantly,
the extent of increase in active CaMKII in the TG lines was similar to that elicited by TAC.
Cardiac Overexpression of CaMKIIδC Induces Cardiac Hypertrophy and Dilated Cardiomyopathy
There was significant enlargement of hearts from CaMKIIδC TGM mice by 8 to 10 weeks [see http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5%5D (Figure 3A) and from TGL mice by 12 to 16 weeks. Histological analysis showed ventricular dilation (Figure 3B), cardiomyocyte enlargement (Figure 3C), and mild fibrosis (Figure 3D) in CaMKIIδC TG mice. Quantitative analysis of cardiomyocyte cell volume from 12-week-old TGM mice gave values of 54.7 + 0.1 pL for TGM (n = 96) versus 28.6 + 0.1 pL for WT littermates (n=94; P0.001). Ventricular dilation and cardiac dysfunction developed over time in proportion to the extent of transgene expression. Left ventricular end diastolic diameter (LVEDD) was increased by 35% to 45%, left ventricular posterior wall thickness (LVPW) decreased by 26% to 29% and fractional shortening decreased by 50% to 60% at 8 weeks for TGM and at 16 weeks for TGL. None of these parameters were significantly altered at 4 weeks in TGM or up to 11 weeks in TGL mice, indicating that heart failure had not yet developed. Contractile function was significantly decreased. Figure 6. Dilated cardiomyopathy and dysfunction in CaMKIIδC TG mice at both whole heart and single cell levels. [see Fig 6: http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5] C, Decreased contractile function in ventricular myocytes isolated from 12-week old TGM and WT controls presented as percent change of resting cell length (RCL) stimulated at 0.5 Hz. Representative trace and mean values are shown. *P0.05 vs WT. Figure 7. Phosphorylation of PLB in CaMKIIδC TG mice. [see Fig 7: http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5] Thr17 and Ser16 phosphorylated PLB was measured by Western blots using specific anti-phospho antibodies. Ventricular homogenates were from 12- to 14-week-old WT and TGM mice (A) or 4 to 5-week-old WT and TGM mice (B). Data were normalized to total PLB examined by Western blots (data not shown here). n = 6 to 8 mice per group; *P0.05 vs WT.
Cardiac Overexpression of CaMKIIδC Results in Changes in the Phosphorylation of Ca2 Handling Proteins
To assess the possible involvement of phosphorylation of Ca2cycling proteins in the phenotypic changes observed in the CaMKIIC TG mice, we first compared PLB phosphorylation state in homogenates from 12- to 14-week-old TGM and WT littermates. Western blots using antibodies specific for phosphorylated PLB showed a 2.3-fold increase in phosphorylation of Thr17 (the CaMKII site) in hearts from TGM versus WT (Figure 7A). Phosphorylation of PLB at the CaMKII site was also increased 2-fold in 4- to 5-week-old TGM mice (Figure 7B). Significantly, phosphorylation of the PKA site (Ser16) was unchanged in either the older or the younger TGM mice (Figures 7A and 7B). (see http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5) To demonstrate that the RyR2 phosphorylation changes observed in the CaMKII transgenic mice are not secondary to development of heart failure, we performed biochemical studies examining RyR2 phosphorylation in 4- to 5-week-old TGM mice. At this age, most mice showed no signs of hypertrophy or heart failure (see Figure 6B) and there was no significant increase in myocyte size (21.3 + 1.3 versus 27.7 + 4.6 pL; P0.14). Also, twitch Ca2 transient amplitude was not yet significantly depressed, and mean δ [Ca2+]i (1 Hz) was only 20% lower (192 + 36 versus 156 + 13 nmol/L; P0.47) versus 50% lower in TGM at 13 weeks.33 The in vivo phosphorylation of RyR2, determined by back phosphorylation, was significantly (2.10.3-fold; P0.05) increased in these 4- to 5-week-old TGM animals (Figure 8C), an increase equivalent to that seen in 12- to 14-week-old mice. We also performed the RyR2 back-phosphorylation assay using purified CaMKII rather than PKA. RyR2 phosphorylation at the CaMKII site was also significantly increased (2.2 + 0.3-fold; P0.05) in 4- to 5-week-old TGM mice (Figure 8C). (http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5) The association of CaMKII with the RyR2 is consistent with a physical interaction between this protein kinase and its substrate. The catalytic subunit of PKA and the phosphatases PP1 and PP2A were also present in the RyR2 immunoprecipitates, but not different in WT versus TG mouse hearts (Figure 8D). These data provide further evidence that
the increase in RyR2 phosphorylation, which precedes development of failure in the 4- to 5-week-old CaMKIIδC TG hearts, can be attributed to the increased activity of CaMKII.
Discussion
CaMKII is involved in the dynamic modulation of cellular
Ca2 regulation and has been implicated in the development of cardiac hypertrophy and heart failure.14
Published data from CaMK-expressing TG mice demonstrate that forced expression of CaMK can induce cardiac hypertrophy and lead to heart failure.27,32
However, the CaMK genes expressed in these mice are neither the endogenous isoforms of the enzyme nor the isoforms likely to regulate cytoplasmic Ca(2+) handling, because they localize to the nucleus.
the cytoplasmic cardiac isoform of CaMKII is upregulated at the expression level and is in the active state (based on autophosphorylation) after pressure overload induced by TAC.
two cytoplasmic CaMKII substrates (PLB and RyR) are phosphorylated in vivo when CaMKII is overexpressed and its activity increased to an extent seen under pathophysiological conditions.
CaMKIIδ is found to associate physically with the RyR in the heart.
heart failure can result from activation of the cytoplasmic form of CaMKII and this may be due to altered Ca(2+) handling.
Differential Regulation of CaMKIIδ Isoforms in Cardiac Hypertrophy
The isoform of CaMKII that predominates in the heart is the δ isoform.4–7 Neither the α nor the β isoforms are expressed and there is only a low level of expression of the γ isoforms.39
Both δB and δC splice variants of CaMKIIδ are present in the adult mammalian myocardium36,40 and expressed in distinct cellular compartments.4,8,9
We suggest that the CaMKIIδ isoforms are differentially regulated in pressure-overload–induced hypertrophy, because the expression of CaMKIIδC is selectively increased as early as 1 day after TAC. Studies using RT-PCR confirm that
CaMKIIδC is regulated at the transcriptional level in response to TAC. In addition,
activation of both CaMKIIδB and CaMKIIδC, as indexed by autophosphorylation, increases as early as 2 days after TAC.
Activation of CaMKIIδB by TAC is relevant to our previous work indicating its role in hypertrophy.9,32
The increased expression, as well as activation of the CaMKIIδC isoform, suggests that it could also play a critical role in both the acute and longer responses to pressure overload.
In conclusion, we demonstrate here that CaMKIIδC can phosphorylate RyR2 and PLB when expressed in vivo at levels leading to 2- to 3-fold increases in its activity. Similar increases in CaMKII activity occur with TAC or in heart failure. Data presented in this study and in the accompanying article33 suggest that altered phosphorylation of Ca(2+) cycling proteins is a major component of the observed decrease in contractile function in CaMKIIδC TG mice. The occurrence of increased CaMKII activity after TAC, and of RyR and PLB phosphorylation in the CaMKIIδC TG mice suggest that
CaMKIIδC plays an important role in the pathogenesis of dilated cardiomyopathy and heart failure.
These results have major implications for considering CaMKII and its isoforms in exploring new treatment strategies for heart failure.
Cardiac Electrophysiological Dynamics From the Cellular Level to the Organ Level
Daisuke Sato and Colleen E. Clancy Department of Pharmacology, University of California – Davis, Davis, CA. Biomedical Engineering and Computational Biology 2013:5: 69–75 http://www.la-press.com. http://dx.doi.org/10.4137/BECB.S10960 Abstract: Cardiac alternans describes contraction of the ventricles in a strong-weak-strong-weak sequence at a constant pacing frequency. Clinically, alternans manifests as alternation of the T-wave on the ECG and predisposes individuals to arrhythmia and sudden cardiac death. In this review, we focus on the fundamental dynamical mechanisms of alternans and show how alternans at the cellular level underlies alternans in the tissue and on the ECG. A clear picture of dynamical mechanisms underlying alternans is important to allow development of effective anti-arrhythmic strategies. The cardiac action potential is the single cellular level electrical signal that triggers contraction of the heart.1 Under normal conditions, the originating activation signal comes from a small bundle of tissue in the right atrium called the sinoatrial node (SAN). The action potentials generated by the SAN initiate an excitatory wave that, in healthy tissue, propagates smoothly through a well-defined path and causes excitation and contraction in the ventricles. In disease states, the normal excitation pathway is disrupted and a variety of abnormal rhythms can occur, including cardiac alternans, a well-known precursor to sudden cardiac death. Cardiac alternans was initially documented in 1872 by a German physician, Ludwig Traube.2 He observed contraction of the ventricles in a strong-weak-strong-weak sequence even though the pacing frequency was constant. Clinically, alternans manifests as alternation of the T-wave on the ECG, typically in the microvolt range. It is well established that individuals with microvolt T-wave alternans are at much higher risk for arrhythmia and sudden cardiac death. A clear picture of physiological mechanisms underlying alternans is important to allow development of effective anti-arrhythmic drugs. It is also important to understand dynamical mechanisms because while the cardiac action potential is composed of multiple currents, each of which confers specific properties, revelation of dynamical mechanisms provides a unified fundamental view of the emergent phenomena that holds independently of specific current interactions. The ventricular myocyte is an excitable cell providing the cellular level electrical activity that underlies cardiac contraction. Under resting conditions, the membrane potential is about -80 mV. When the cell is stimulated, sodium (Na) channels open and the membrane potential goes above 0 mV. Then, a few ms later, the inward current L-type calcium (Ca) current activates and maintains depolarization of the membrane potential. During this action potential plateau, several types of outward current potassium (K) channels also activate. Depending on the balance between inward and outward currents, the action potential duration (APD) is determined.The diastolic interval (DI) that follows cellular repolarization describes the duration the cell resides in the resting state until the next excitation. During the DI, channels recover with kinetics determined by intrinsic time constants. APD restitution defines the relationship between the APD and the previous DI (Fig. 1 top panel). In most cases1, the APD becomes longer as the previous DI becomes longer due to recovery of the L-type Ca channel (Fig. 1, bottom panel), and thus the APD restitution curve has a positive slope. Figure 1. (Top): APD and DI. (Bottom): The physiological mechanism of APD alternans involves recovery from inactivation of ICaL. [see http://dx.doi.org/10.4137/BECB.S10960]
Action Potential Duration Restitution
In 1968 Nolasco and Dahlen showed graphically that APD alternans occurs when the slope of the APD restitution curve exceeds unity. Why is the steepness of the slope important? As shown graphically in Figure 2, APD alternans amplitude is multiplied by the slope of the APD restitution curve in each cycle. When the slope is larger than one, then the alternans amplitude will be amplified until the average slope reaches 1 or the cell shows a 2:1 stimulus to response ratio. The one-dimensional mapping between APD and DI fails to explain quasi-periodic oscillation of the APD. Figure 2. APD restitution and dynamical mechanism of APD alternans. [see http://dx.doi.org/10.4137/BECB.S10960]
Calcium Driven Alternans
A strong-weak-strong-weak oscillation in contraction implies that the Ca transient (CaT) is alternating. Until 1999 it was assumed that if the APD is alternating then the CaT alternates because the CaT follows APD changes. However, Chudin et al showed that CaT can alternate even when APD is kept constant during pacing with a periodic AP clamp waveform.14 This implies that the intracellular Ca cycling has intrinsic nonlinear dynamics. A critical component in this process is the sarcoplasmic reticulum (SR), a subcellular organelle that stores Ca inside the cell. When Ca enters a cell through the L-type Ca channel (or reverse mode Na-Ca exchanger (NCX) ryanodine receptors open and large Ca releases occur from the SR (Ca induced Ca release). The amount of Ca release steeply depends on SR Ca load. This steep relation between Ca release and SR Ca load is the key to induce CaT alternans. A one-dimensional map between Ca release and SR calcium load can be constructed to describe the relationship21 similar to the map used in APD restitution.
Subcellular Alternans
A number of experimental and computational studies have been undertaken to identify molecular mechanisms of CaT alternans by identifying the specific components in the calcium cycling process critical to formation of CaT alternans. These components include SR Ca leak and load, Ca spark frequency and amplitude, and rate of SR refilling. For example, experiments have shown that alternation in diastolic SR Ca is not required for CaT alternans.24 In addition, stochastic openings of ryanodine receptors (RyR) lead to Ca sparks that occur randomly, not in an alternating sequence that would be expected to underlie Ca altern-ans. So, how do local random sparks and constant diastolic SR calcium load lead to global CaT alternans? Mathematical models with detailed representations of subcellular Ca cycling have been developed in order to elucidate the underlying mechanisms. Modeling studies have shown that even when SR Ca load is not changing, RyRs, which are analogous to ICaL in APD alternans, recover gradually from refractoriness. As RyR availability increases (for example during a long diastolic interval) a single Ca spark from a RyR will be larger in amplitude and recruit neighboring Ca release units to generate more sparks. The large resultant CaT causes depletion of the SR and when complete recovery of RyRs does not occur prior to the arrival of the next stimulus, the subsequent CaT will be small. This process results in an alternans of CaT amplitude from beat-to-beat.
Coupling Between the Membrane Potential and Subcellular Calcium Dynamics
Importantly, the membrane voltage and intracellular Ca cycling are coupled via Ca sensitive channels such as the L-type Ca channel and the sodium-calcium exchanger (NCX). The membrane voltage dynamics and the intracellular Ca dynamics are bi-directionally coupled. One direction is from voltage to Ca. As the DI becomes longer, the CaT usually becomes larger since the recovery time for the L-type Ca channel in increased and the SR Ca release becomes larger. The other direction is from Ca to voltage. Here we consider two major currents, NCX and ICaL. As the CaT becomes larger, forward mode NCX becomes larger and prolongs APD. On the other hand, as the CaT becomes larger, ICaL becomes smaller due to Ca-induced inactivation, and thus, larger CaT shortens the APD. Therefore, depending on which current dominates, larger CaT can prolong or shorten APD. If a larger CaT prolongs (shortens) the APD, then the coupling is positive (negative). The coupled dynamics of the membrane voltage and the intracellular Ca cycling can be categorized by the instability of membrane voltage (steep APD restitution), instability of the intracellular Ca cycling (steep relation between Ca release versus SR Ca load), and the coupling (positive or negative). If the coupling is positive, alternans is electromechanically concordant (long-short-long-short APD corresponds to large-small-large-small CaT sequence) regardless of the underlying instability mechanism. On the other hand, if the coupling is negative, alternans is electromechanically concordant in a voltage-driven regime. However, if alternans is Ca driven, alternans becomes electromechanically discordant (long-short-long-short APD corresponds to small-large-small-large CaT sequence). It is also possible to induce quasi- periodic oscillation of APD and CaT when voltage and Ca instabilities contribute equally.
Alternans in Higher Dimensions
Tissue level alternans in APD and CaT also occur and here we describe how the dynamical mechanism of alternans at the single cell level determines the phenomena in tissue. Spatially discordant alternans (SDA) where APDs in different regions of tissue alternate out-of-phase, is more arrhythmogenic since it causes large gradients of refractoriness and wave-break, which can initiate ventricular tachycardia and ventricular fibrillation. How is SDA induced? As the APD is a function of the previous DI, conduction velocity (CV) is also function of the previous DI (CV restitution) since the action potential propagation speed depends on the availability of the sodium channel. As the DI becomes shorter, sodium channels have less time to recover. Therefore, in general, as the DI becomes shorter, the CV becomes slower. When tissue is paced rapidly, action potentials propagate slowly near the stimulus, and thenac-celerate downstream as the DI becomes longer. This causes heterogeneity in APD (APD is shorter near the stimulus). During the following tissue excitation, APD becomes longer and the CV becomes faster at the pacing site then gradually APD becomes shorter and the CV becomes slower. The interaction between steep APD restitution and steep CV restitution creates SDA. This mechanism applies only when the cellular instability is voltage driven. When the cellular instability is Ca driven, the mechanism of SDA formation is different. If the voltage-Ca coupling is negative, SDA can form without steep APD and CV restitution. The mechanism can be understood as follows. First, when cells are uncoupled, alternans of APD and Ca are electromechanically discordant. If two cells are alternating in opposite phases, once these cells are coupled by voltage, due to electrotonic coupling, the membrane voltage of both cells is synchronized and thus APD becomes the same. This synchronization of APD amplifies the difference of CaT between two cells (Fig. 5 in). In other words it desynchronizes CaT. This instability mechanism is also found in subcellular SDA. In the case where the instability is Ca driven and the coupling is positive, there are several interesting distinctive phenomena that can occur. First, the profile of SDA of Ca contains a much steeper gradient at the node (point in space where no alternans occurs–cells downstream of the node are alternating out of phase with those upstream of the node) compared to the case of voltage driven SDA. Thus, the cellular mechanism of instability can be identified by evaluating the steepness of the alternans amplitude gradient in space around the node. When the cellular instability is voltage driven, the steady-state wavelength (separation of nodes in space) depends on electrotonic coupling between cells and the steepness of APD and CV restitution, regardless of the initial conditions. However, if the cellular instability is Ca driven, the location of nodes depends on the pacing history, which includes pacing cycle length and other parameters affected by pacing frequency. In this case, once the node is formed, the location of the node may be fixed, especially when Ca instability is strong. Such an explanation may apply to recent experimental results. Summary In this review, we described how the origin of alternans at the cellular level (voltage driven, Ca drive, coupling between voltage and Ca) affects the formation of spatially discordant alternans at the tissue level. Cardiac alternans is a multi-scale emergent phenomenon. Channel properties determine the instability mechanism at the cellular level. Alternans mechanisms at cellular level determine SDA patterns at the tissue level. In order to understand alternans and develop anti-arrhythmic drug and therapy, multi-scale modeling of the heart is useful, which is increasingly enabled by emerging technologies such as general-purpose computing on graphics processing units (GPGPU) and cloud computing.
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