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Cardiovascular Research 2005 67(3):438-447; doi:10.1016/j.cardiores.2005.04.021
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Copyright © 2005, European Society of Cardiology

Functional genomics of cardiac ion channel genes

Sophie Demolombe, Céline Marionneau, Sabrina Le Bouter, Flavien Charpentier and Denis Escande*

l'institut du thorax, INSERM U533, Faculté de Médecine, 1 rue Gaston Veil, 44035 Nantes cedex, France

* Corresponding author. Tel.: +33 240 41 29 49; fax: +33 240 41 29 50. Email address: denis.escande{at}nantes.inserm.fr

Received 17 January 2005; revised 13 April 2005; accepted 20 April 2005


   Abstract
Top
 Abstract
1. Introduction
 2. Pangenomic versus specialized...
 3. From microarrays to...
 4. Clinical genomics of...
 5. Future directions
 6. Limits to the...
 References
 
Ion channels are an ensemble of specialized membrane proteins that act in concert to create and modulate the electrical activity of many excitable cells, including cardiac myocytes. Following completion of the sequencing of various genomes, including that of the human, the complete repertoire of ion channel genes has been elucidated for different species. How transcripts issued from this gene collection are expressed and modulated in relation to variable physiological and pathological situations is the subject of functional or physiological genomics. Specialized microarrays (IonChips) comprising probes for the ensemble of ion channel and regulatory genes were developed as an alternative to whole-genome DNA chips. Physiological genomics of cardiac ion channel genes is a growing field that, in combination with genetics, should markedly increase our comprehension of the molecular mechanisms leading to arrhythmias.

KEYWORDS Ion channel expression; DNA microarrays; Molecular remodeling; Arrhythmias


   1. Introduction
Top
 Abstract
 1. Introduction
2. Pangenomic versus specialized...
 3. From microarrays to...
 4. Clinical genomics of...
 5. Future directions
 6. Limits to the...
 References
 
Completion of the sequencing of various genomes including human and mouse has resulted in the elucidation of the complete repertoire of ion channel genes. Cellular electrophysiologists are now informed of the ensemble of ion channel proteins, which interplay to create the electrical activity, a central function in excitable cells. They have thus reached their quest for understanding the system with a knowable core of information.

The developing field of genomics is based on our capacity to obtain large comprehensive data sets on genes (wet laboratory investigation) and to analyze computationally this information (dry laboratory investigation) searching for subtle relations between genes. Expression genomics describes the investigation of transcription in a whole-genome manner and includes the investigation of gene regulation [1]. Functional or physiological genomics aims to link transcript expression with function. Turning on or turning off a gene at a different time or in a different place is an adaptive and coordinated response of the organism to a novel physiological or pathological environment. Expression genomics has emerged from a recently invented technology, gene chip or DNA microarray, which allows the study of the expression of thousands of genes at once. At its core, the DNA microarray utilizes a unique feature of DNA known as complementary hybridization. As such, it is not different from Southern blot or Northern blot with the major exception that it allows expression profiling of the entire human genome in a single hybridization experiment.

Applying genomics to the field of ion channels, a collection of functionally connected proteins, has already provided important information on how their transcripts are regionally expressed, and on how they are regulated by drugs or diseases in the context of a well-established cellular function. Further investigations will certainly explore how this gene network is dynamically regulated in clusters. Ultimately, expression genomics of ion channels should be integrated into mathematical models of the cellular electrical activity [2]. Then, the expression profile of cardiac ion channels could be interpreted in relation to body surface ECG via mathematical models of the heart which include the spatial distribution of cardiac ion channels throughout the myocardium for each of the approximately 70 types of ion channels known to be expressed in the heart.


   2. Pangenomic versus specialized microarrays
Top
 Abstract
 1. Introduction
 2. Pangenomic versus specialized...
3. From microarrays to...
 4. Clinical genomics of...
 5. Future directions
 6. Limits to the...
 References
 
2.1. Pangenomic microarrays
A DNA chip or microarray is a solid support on which cDNA fragments of different genes (called probes) are robotically spotted (or synthesized in situ) on a grid. The principle of DNA chip experiments is based on hybridization between these probes and a complex mixture of complementary cDNA sequences (called targets; labeled with fluorescence probes usually Cy3 and Cy5) representative of the RNA transcripts isolated from a tissue sample. The hybridization signal intensity measured at each spot is related to the corresponding RNA transcript abundance in the tissue sample. A standard DNA chip experiment aims to detect whether genes are differentially or equally expressed between test and control RNA samples. Usually, the two targets (e.g. one is labeled with Cy3, the other with Cy5) are competitively cohybridized on the same chip to provide a set of relative expression ratios (Cy3/Cy5 fluorescence ratios). Various sources of experimental noise are present at the different steps of DNA chip experiments and can lead to false knowledge discovery. Technical noises arise from uncontrolled variations in the RNA quality and labeling as well as from mechanical or optical distortions of the support and the chip scanner. Biological noises may arise from many sources including contamination, storage conditions and heterogeneity of the tissue samples. Spotting of replicates for each gene within one chip and hybridization of replicate chip experiments with the same RNA extract and with different extracts of the same biological sample should be carried out. Replicate values allow statistical evaluation of the ‘true’ biological signal amidst numerous experimental noises. They also allow the identification of outlier values and the calculation of the variance of the expression value for each gene. Power analyses show that at least eight true replicate values are required to significantly detect expression variations of 20% between two distinct biological samples [3].

The human genome contains approximately 25–30,000 genes. Alternative splicing should produce at least 3-times more mRNA transcript species. However, a specialized cell, e.g. a cardiomyocyte, expresses only a portion of the whole genome. Serial analysis of gene expression (SAGE) has identified a total of 10,000 expressed genes in human cardiac cells [4]. Thus, a specialized cell is expected to express only 1/3 of the whole genome although this proportion may not be a fixed amount but inversely may vary with development. Whole-genome microarrays (e.g. Affymetrix® GeneChip U133 Plus 2.0 or Applied® Human Genome Survey Microarray) aim to produce expression data for all possibly expressed genes. This approach may be view as a guiding tool to identify novel genes that are not suspected to be altered in a given physiological or pathological process (non-targeted approach) and to guide investigators into unexpected new directions. Species limitation for whole-genome microarrays should be kept in mind. Among mammals, such tools are currently available for human and mouse and more recently for rat, dog and pig.

Whole-genome microarrays have yielded important results in the context of cardiac diseases. As an example, Yang et al. [5] have identified decreased expression of SLIM1 (encoding a protein which functions downstream of integrin activation to promote cell spreading and migration) and increased expression of gelsolin (which regulates the assembly and turnover of thin filaments) in failing human hearts. The same approach can also provide molecular fingerprints, which may be useful indicators of heart failure etiologies [6] or severity [7]. Finally, a pangenomic approach can identify confounding factors such as age and sex, which may interfere with heart failure to remodel the transcriptome [8]. Whole-genome microarrays thus provide an enormous collection of data and point to novel pathophysiological mechanisms.

In the field of expression genomics, another strategy is to focus investigations on a collection of genes involved in a well-defined cellular function: e.g. apoptosis, transcription regulation, ion channels... Pangenomic microarrays are not well suitable for this aim because of their high cost and because they usually do not contain replicates. In addition, a rate of 80% of genes on the microarray with interpretable expression data among a total of say 30,000 genes is considered as excellent in whole-genome microarrays. This is no longer acceptable when a small size collection of genes is considered. Clearly, genomics data on cardiac ion channels would be of limited interest if they were lacking information on key ion channel genes such as SCN5A (the main cardiac Na+ channel {alpha}-subunit encoding gene), CANCA1C (encoding Cav1.2, the major L-type Ca2+ channel gene in the heart), KCNQ1 (KvLQT1 or Kv7.1, a delayed rectifier K+ channel gene) and KCND3 (Kv4.3, a transient outward current K+ channel gene).

2.2. Ion channel microarrays
An alternative strategy to whole-genome microarrays was to develop specialized microarrays. These chips can be dedicated to an organ (e.g. CardioChips, MyoChips, NeuroChips...) or to a function (e.g. IonChips, ApoptosisChips...). We have developed a specialized DNA microarray comprising PCR-amplified probes for most voltage-gated Na+, Ca2+, Cl– and K+ channel {alpha}- and β-subunits, inward rectifier and 2-pore K+ channels, epithelial Na+ channel subunits and connexins [9]. Because ion channels possess strong structural similarities, we used 3' untranslated region (3'UTR) sequences specific for each channel gene as probes. Because the 3'UTR is poorly conserved among species, two different probe collections were designed for human and for mouse. Our arrays also contained an additional population of probes corresponding to 3'UTR PCR-amplified fragments encoding human and mouse proteins involved in Ca2+ homeostasis. The human and mouse genomes contain about 230–250 genes encoding ion channel {alpha}-subunits and β-subunits (receptor operated channels excluded; sources: www.celera.com, www.ncbi.nih.gov/entrez, www.informatics.jax.org). However, as stated above the heart does not express the whole collection of ion channel genes but only a subset ({approx}70 ion channel genes in the human or mouse heart; source: www.ncbi.nih.gov/entrez (Geo profiles), www.informatics.jax.org). Our human IonChips include 148 ion channel probes whereas the mouse IonChips include 147 ion channel probes each spotted in triplicate. Using conventional RT-PCR experiments and primers designed to generate our ion channel probes, we have initially checked the list of ion channel genes with significant expression in the adult mouse (56/147) and human heart (61/148; SD, unpublished results). A collection of 3000 cardiac specific probes is also added on chips in order to normalize Cy3 and Cy5 fluorescent signals.

2.3. Remodeling ion channel transcripts by thyroid hormones
In an initial study [9], we used IonChips to explore ion channel remodeling as induced by hypo- and hyperthyroidism states in the mouse. Thyroid hormones regulate gene transcription mostly through the triodothyronine (T3) form, which binds to nuclear receptors encoded by the Tr{alpha} and TRβ genes [10]. Thyroid hormone receptors are transcription factors with ligand-regulated activity. Inbred mice were treated either with 5'-propyl-2-thiouracil in low iodine food for 5 weeks or with daily injection of T3 hormone for 8 days. At the end of the treatment period, the hearts were collected and further analyzed for ion channel expression using our IonChip tool. This investigation demonstrates that not every ion channel gene family is perturbed by thyroid hormone deregulation and that inward rectifier K+ channel and Na+ and Cl– ion channel genes are not concerned by cardiac remodeling. It has been known for a long time that hypothyroidism slows cardiac repolarization and prolongs the action potential in relation to decreased expression of voltage-dependent K+ channels [11]. In hypothyroidism, we observed that the remodeling of cardiac K+ channel genes is in fact more complex and involves not only down-regulation (e.g. Kv1.5, Kv2.1 and Kv4.2) but also up-regulation (e.g. KvLQT1 and KCNE1) of K+ channel genes. Transcripts for the sarcolemmal L-type Ca2+ channel, Cav1.2, and for the Na+–Ca2+ exchanger NCX1 are markedly up-regulated. These alterations at the mRNA level are accompanied by consistent changes in the expression of corresponding proteins (as assessed with Western blots) and most importantly by consistent changes at the functional level in ion current amplitude (as assessed with patch-clamp) and in the global activity of the heart (as assessed with surface ECG). Most of the alterations produced by hyperthyroidism mirror the profile induced in hypothyroidism with the exception of a few genes (e.g. Kv4.2 and Cx43), which did not show significant changes. This genomics approach demonstrates that thyroid hormone selectively and differentially regulates gene expression of a cluster of at least 9 ion channel {alpha}- and β-subunits. The data also suggest that the impact of thyroid hormones on the integrated cardiac electrical activity results from remodeling of ion channel gene expression with a degree of complexity not previously foreseen by gene-by-gene investigations. The findings do not imply that all 9 ion channel genes possess thyroid hormone regulatory elements since part of the ion channel remodeling could indirectly result from the effects of thyroid hormones on cardiac rhythm or from their effects on intracellular Ca2+ homeostasis [12].

2.4. Pharmacogenomics of chronic amiodarone
One important motivation to develop IonChips was the new possibility it offered to investigate remodeling of ion channels as a possible pharmacological action of drugs. In this context, we thought that the drug amiodarone might be prototypic. Although amiodarone has a remarkable antiarrhythmic efficacy, the basis for its unique effectiveness during long-term intake is still poorly understood. The pharmacological profile of the drug is complex and comprises direct modulation of ion channel function in the cell membrane. The drug is also known to modify thyroid function extensively due to its iodinated nature [13]. The question arose as to whether the long-term effects of amiodarone might stem from its molecular interaction with thyroid hormone receptors or other mechanisms including modulation of gene expression in addition to its direct effect on cell-membrane channels [14]. When choosing amiodarone for this initial work, our reasoning was that if the drug were found not to affect ion channel expression in the heart, then there would be little chance that another drug does. Adult mice from the same inbred strain (C57BL/6) as used for the thyroid hormone study, were treated for 6 weeks with oral amiodarone [15]. Because mice poorly absorb amiodarone, very high daily doses were required to obtain plasma concentrations of the parent compound and of its main metabolite N-desethylamiodarone (DEA) compatible with therapeutic levels obtained in patients. As expected, oral amiodarone consistently decreased the levels of circulating T3 and increased the levels of reverse T3 in treated mice and also dose-dependently prolonged cardiac repolarization and slowed conduction. The effects of the drug on cardiac ion channel transcripts was complex with down-expression of genes encoding Na+ channels (Nav1.5, Nav1.4, Navβ1), Ca2+ channels (Cav1.2, Cavβ1, Cavβ2), Ca2+ regulatory proteins (calsequestrin type 2, calmodulin type 3, NCX1) and connexins (Cx43). Within the repertoire of K+ channel genes, amiodarone produced both up-regulation of a large number of genes (e.g. Kv1.4, Kvβ1, MirP1, MirP2, TWIK-1, TASK-2) and down-regulation of other genes (e.g. Kv1.5, Kv2.1, Kv4.2, Kir6.2, SUR2). This complex rearrangement was in nature comparable to that induced by thyroid hormone although different in the genes involved (Fig. 1). As with thyroid hormones, alterations at the mRNA level correlated with functional changes in corresponding ion currents and with the global electrical activity of the heart. Most importantly, remodeling of ion channel expression as induced by amiodarone was dose-dependent and correlated with the dose-dependent effects of the drug on the ECG. Remarkably, the effects of amiodarone on the cardiac ion channel gene collection were compatible with the pharmacological profile of the drug, which associates decreased conduction (decreased expression of Na+ channel and connexin genes) and prolonged repolarization (decreased K+ channel expression) thus excluding the possibility that remodeling was exclusively compensatory. Different non-exclusive hypotheses can explain ionic remodeling including: (i) the hypothyroidism syndrome induced by amiodarone; (ii) a direct effect of amiodarone/DEA on gene promoters [16]; (iii) an effect of amiodarone/DEA on transcription factors [17]; and (iv) primary physiological alteration induced by direct effects of the drug on membrane proteins. Our coupled studies on the effects of hypothyroidism and amiodarone on cardiac ion channel genomics show that amiodarone affects ion channel transcripts differently than hypothyroidism (Fig. 1). A common profile concerns only down-expression of voltage-gated K+ channel genes such as Kv4.2, Kv2.1 and Kv1.5. These channels however are major determinants of the ventricular repolarization kinetics in the mouse [18]. Bosch et al. [19] have previously shown at the functional level that amiodarone and hypothyroidism have different effects on cardiac ion channels in guinea-pig ventricular myocytes.


Figure 1
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  		Fig. 1 Comparative expression profiles of the effects of hypothyroidism (yellow bars) and chronic amiodarone treatment (red bars) in the mouse heart. Bar graphs are depicted in relation to the different phases of the mouse ventricular action potential. Data expressed as mean ± S.E.M. are taken from the experiments reported in Ref. [4,10]. In various instances, the error bar is too small to be seen. Stars denote significant changes versus control mice.

 
A common effect of amiodarone treatment and hypothyroidism is moderate bradycardia. In a subsequent study, we thus decided to investigate whether moderate bradycardia would per se remodel ion channel expression in the mouse ventricle. To this aim, inbred mice were treated for 3 weeks with oral ivabradine, a specific blocker of pacemaker channels, so as to obtain a 15% reduction in the heart rate. This investigation (unpublished) showed a very limited ion channel remodeling in the ventricle, which markedly differed from that of the hypothyroidism- or amiodarone situation. It is therefore unlikely that bradycardia participates to the ionic remodeling induced by hypothyroidism or chronic amiodarone treatment.


   3. From microarrays to high throughput TaqMan
Top
 Abstract
 1. Introduction
 2. Pangenomic versus specialized...
 3. From microarrays to...
4. Clinical genomics of...
 5. Future directions
 6. Limits to the...
 References
 
Technically, the DNA chip approach has numerous drawbacks and limitations. The technique requires large amounts of RNA (about 10 µg of total RNA), which prevents small size samples to be processed individually and thus requiring tissue pooling or amplification steps. The sensitivity of the method is low and the noise/ratio unfavorable for those genes with low expression levels (e.g. ion channel genes). Data generated by DNA chips have an intrinsic variability, particularly again for those genes with low expression. This requires processing of several chips per individual situation and spotting the probes at multiple places on the chip. Normalization of the Cy3–Cy5 fluorescent signal is still a source of bias even if a large collection of control genes is used. The cost of the technique is high, not only because of the equipment but also because of reagents and manpower costs. The power of the technique resides in its very large scale allowing concomitant expression measurement for several thousands of genes (whole-genome approach). When one considers a gene collection of limited size such as the ensemble of ion channel genes (< 250 genes), alternative techniques such as RT-PCR may be more appropriate. TaqMan real-time PCR requires less precious RNA samples (about 200 ng, i.e. 50-fold less than DNA chips), and shows better sensitivity and lower variability than DNA chips in detecting low-expressed genes. However, a gene-by-gene approach cannot be envisaged for collections of several hundred genes. Most recently, TaqMan assays have been made available in a pre-formulated low-throughput assay (microfluidic cards®; www.appliedbiosystems.com) well adapted for gene collections of less than 384 genes. The TaqMan assay does not provide absolute quantification. However, it has been established that for a PCR efficiency of 100%, a CT value of 30 corresponds approximately to 1000 copies of the transcript and that this relation is linear between CT values from 10 to 36 [20]. Thus, contrary to DNA chips, TaqMan assays provide important information on the relative levels of gene expression in a sample, provided that the PCR efficacy is close to 100%.

3.1. Regional distribution of ion channel gene expression
We used high throughput TaqMan assay to investigate the regional distribution of ion channel expression in the mouse heart [20]. Our microfluidic cards contain primers for 71 {alpha}- and β-ion channel subunits. It is known for long that expression of ion channel expression is heterogeneous within the heart muscle and that this heterogeneity is key to specialization of cardiac areas including the nodes. Two-way hierarchical agglomerative clustering was applied to the gene expression data of 71 genes and 24 samples [6 ventricles, 6 atria, 6 atrioventricular nodes (AVN) and 6 sinoatrial nodes (SAN)]. This computer analysis revealed a clear separation of each cardiac region (SAN, AVN, A and V) indicating that the expression profile of transcripts involved in electrical signaling characterized each individual sample. Hierarchical clustering also demonstrated sub-classification within the cardiac regions: the SAN and AVN pools were parts of the same tree branch whereas atrial and ventricular pools clustered in a distinct branch. Thus, clustering demonstrates that the ionic channel expression patterns accurately discriminate specialized regions from the mouse heart and distinguish automatic tissue from the working myocardium. The remarkable portrait of the nodal tissues results from increased expression of a large panel of ion channel genes rather than decreased expression. Among the Ca2+ channels, the L-type Cav1.3 and T-type Cav3.1 Ca2+ channels are predominantly expressed in the nodes albeit not specifically. Cav{alpha}2{delta}2 are the only transcripts, which exhibit high expression in the nodal tissues and almost no expression in working myocytes. We speculate that Cav{alpha}2{delta}2 transcripts constitute markers of the nodal function. Among the pacemaker channels, HCN1 and HCN4 largely predominate in the SAN. Among the voltage-gated sodium channel {alpha}- and β-subunits, pacemaker tissues distinguish themselves by predominant expression of Navβ1 and Navβ3. Functional expression of these subunits in vitro has shown that both Navβ1 [21] and Navβ3 [22] accelerate INa inactivation kinetics. The neuronal Kv1.1 and Kv1.6 (together with the Kvβ1 subunits) targeted nodal cells, even if their expression level remains low in comparison with other K+ channels. The remarkable portrait of the working myocardium is characterized by predominant expression of K+ channel Kv4.2 (together with KChip2) and Cx43. The expression of genes involved in calcium homeostasis did not univocally characterize tissue with intrinsic automaticity with the exception of the ryanodine receptor, RYR2, which was expressed at a lower level in the SAN than in the other cardiac regions.

3.2. Effects of the genetic background on ion channel transcripts
We thought to evaluate whether the expression profile of ion channel genes in the normal mouse heart is dependent on the genetic background. We observed consistent differences between inbred mouse strains. Hierarchical clustering as shown in Fig. 2, demonstrates that individual mice from three different strains (129/Sv, FVB and C57BL/6) cluster separately indicating strain-specificity in their ion channel expression profile. Figs. 3, 4 and 5GoGo further detail these differences. Remarkably, the FVB strain expresses less Nav1.5 transcripts but more Nav1.4, Nav1.3 and Navβ transcripts than the other strains. Similarly, FVB mice have less Cx43 transcripts in their ventricle but more Cx40 transcripts. In addition, they have less Kv1.4 but more Kv4.3. MinK (or KCNE1) is virtually absent in FVB whereas consistent levels of expression are found in 129/Sv and C57BL/6. Strain-specificity of ion channel expression may account for the previously reported differences in ECG characteristics and susceptibility to PES-induced ventricular arrhythmias [23].


Figure 2
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  		Fig. 2 Two-way hierarchical agglomerative clustering applied to 57 selected genes (vertically) and to 21 ventricular samples from 129/Sv, C57BL/6 and FVB mice. The input consisted of the ratio for each pool and gene versus an internal reference. Each gene is represented by a single row of colored boxes and each mouse by a single column. Three selected gene clusters shown on the right (A, B, C) contain genes up-regulated in 129/SV (A), C57BL/6 (B) and FVB (C). Each color patch in the resulting visual map represents gene expression level, with a continuum of expression levels from dark green (lowest) to bright red (highest). Missing values are coded as silver.

 

Figure 3
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  		Fig. 3 Expression profile of Na+, Ca2+, Cx and Cl– channel genes in mouse ventricles from three inbred strains. Bar graphs show the relative quantification versus an internal control (y-axis) of genes selected for their cardiac expression (x-axis) in 3 distinct mouse strains. Bottom panel contains low expressed genes. Data are means ± S.E.M. from 5 to 10 mice. *p<0.05, #p<0.01, §p<0.001 versus the 2 other strains. Cx indicates connexin; Cl– ch, chloride channels.

 

Figure 4
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  		Fig. 4 Expression profile of K+ channels and of hyperpolarization-activated HCN cation channels in mouse ventricles from three different inbred strains. Same symbols as in Fig. 3.

 

Figure 5
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  		Fig. 5 Expression profile of intracellular calcium regulators in mouse ventricles from three distinct inbred strains. Same symbols as in Figs. 3 and 4Go. Calm indicates calmodulin; NCX1, Na+/Ca2+ exchanger type 1; PLB, phospoholamban; RIP3, the IP3 receptor; RYR, the ryanodine receptor and SERCA the sarco(endo)plasmic reticulum Ca2+ ATPase.

 

   4. Clinical genomics of ion channel genes
Top
 Abstract
 1. Introduction
 2. Pangenomic versus specialized...
 3. From microarrays to...
 4. Clinical genomics of...
5. Future directions
 6. Limits to the...
 References
 
Clinical genomics combines genome science and human clinical research. Using real-time semi-quantitative RT-PCR, Borlak and Thum [24] explored ion channel remodeling as induced by end-stage heart failure in human. Among genes involved in phases 0 and 1 of the action potential, genes encoding for Nav1.5, Cav1.2 and Kv4.3 were found down-regulated whereas the T-type Cav3.2 was unaffected. Among transcripts involved in repolarization, Cav1.2 and Kv1.5 were downregulated whereas KvLQT1 was up-regulated and HERG was unchanged. Among transcripts controlling the resting potential, Kir2.1, Kir3.4 and Kir6.2 were down-regulated whereas Kir3.1 was unchanged. A group of patients with ischemic or dilated cardiomyopathy and receiving a left ventricular assist device (LVAD) for more than 6 months was also included in the study. These patients show lower levels of ANF in comparison with end-stage heart failure patients not equipped with LVAD. In patients with LVAD, the profile of ion channel expression differed from that of patients not receiving LVAD. LVAD patients had nearly normal Nav1.5, Cav1.2, Kv4.3 and KvLQT1 expression and less decreased Cav3.2. LVAD-assisted hearts showed consistent increase in Kv1.5, Kir3.1 and Kir6.2 expression. Genes encoding for calcium regulatory proteins were also markedly affected by LVAD in comparison with patients not receiving assistance device. This study demonstrates that unloading the failing heart has beneficial effects on ion channel expression in concordance with the effects of the procedure on cardiac electrophysiology [25].

Recently, the ionic remodeling induced by chronic atrial fibrillation (AF) was investigated in patients undergoing open-heart surgery [26]. Limitations of previous studies of ion channel expression changes in patients with AF have included the arbitrary selection of subunits for study and a lack of adequate disease-matched controls. A group of patients with chronic AF and underlying valvular heart disease was compared with a group of patients with valvular heart disease in sinus rhythm. Two-way hierarchical clustering analysis revealed a clear separation of patients with valvular heart disease in sinus rhythm from patients with valvular heart disease plus AF. Both groups of patients with valvular heart disease in sinus rhythm or in AF displayed a substantial impact on ion transport gene expression, with significant changes relative to control in 24 and 23 genes respectively. There was significant overlap between the two, with 66% of genes altered in valvular heart disease patients in sinus rhythm being similarly modified in valvular heart disease patients with AF. The specific molecular portrait of patients with AF was restricted to a cluster of 12 ion transport genes. It was concluded that the transcriptional profile of genes involved in ion transport is profoundly affected by valvular heart disease. AF is associated with a specific pattern of expression changes, but whether these are caused by AF or characterize valvular heart disease patients more likely to develop AF is at present unknown.

Clinical genomics of ion channel genes in the heart is still in its infancy as testified by the very small number of related publications. An obvious limitation to clinical genomics in cardiology originates with the difficulties and ethical issues associated with sampling biopsies from the diseased myocardium. However, simultaneous measurement of the expression of 96-192 transcripts is now possible without amplification steps in a single endocardial biopsy, using recently developed microtechniques such as Taqman microfluidic cards®.


   5. Future directions
Top
 Abstract
 1. Introduction
 2. Pangenomic versus specialized...
 3. From microarrays to...
 4. Clinical genomics of...
 5. Future directions
6. Limits to the...
 References
 
In the coming years, genomics will certainly demonstrate its power in investigating the plasticity of ion channel expression in the face of changing physiological or pathological environment. Cardiac channelopathies is one such example. This group of diseases comprises various monogenic disorders, which impact the function of ion channel in the heart muscle and include (among others) the long QT syndrome, the Brugada syndrome and the inherited Lenègre disease [27]. A simplistic view of cardiac channelopathies would be that a genetic defect selectively alters the function of a key ion channel leading to abnormal electrogenesis and arrhythmias. However, we have learned from transgenesis that this view is short because cardiac cells are able to adapt to the deficit by remodeling the expression of other ion channel and regulator genes in such a way that the deficit is partially compensated [28]. Adaptation implies the existence of a sensor (e.g. dynamic changes in intracellular calcium) to detect the anomaly and a feed-back loop (e.g. calcium-dependent transcription factors) to tentatively correct the deficit. Homeostatic regulation of ion channel expression has been the focus of an excellent review by Rosati and McKinnon [29]. According to this paradigm, the severity of the phenotype should depend not only on the nature of the mutated gene but also on the efficacy of the compensation, whether beneficial or detrimental. It is known that the penetrance of cardiac channelopathies is low even when a population of patients carrying the same gene mutation is considered [30,31]. Whether low penetrance results from inter-individual differences in the efficacy of compensatory remodeling should be the subject of future genomics research. In this context, the mouse KO model may prove to be extremely useful. We have investigated in vivo the phenotype of heterozygous mice deficient for Scn5a (encoding Nav1.5) [32]. Although from the same inbred strain (129/Sv), Scn5a +/– mice exhibit a conduction deficit very variable in severity (some mice even show conduction parameters in the normal range) much like Lenègre disease patients haplo-insufficient for SCN5A [33]. This suggests that even with a stable genetic background, the molecular program for adaptation may have a different inter-individual impact. It is easily conceivable that because of the complexity of the molecular program, which is likely to involve numerous regulatory elements acting in concert, the efficacy of the process is gradual and certainly escapes an all-or-none logic. Depending on the nature of the causative haplo-insufficient gene, molecular remodeling may restore the phenotype close to normal, leading to subtle anomalies undetectable at baseline and thereafter a low penetrance in the gene carrier population.


   6. Limits to the approach
Top
 Abstract
 1. Introduction
 2. Pangenomic versus specialized...
 3. From microarrays to...
 4. Clinical genomics of...
 5. Future directions
 6. Limits to the...
References
 
The presence of a transcript does not necessarily imply the presence of a functional protein. Regulation of ion channel function is not achieved only at the transcriptional level but is also likely to include a number of critical post-transcriptional mechanisms such as mRNA processing [34,35], translation and post-translational modifications [36], protein processing and targeting to the cell membrane [37] and ultimately regulation by phosphorylation [38] of the channel activity in the multi-protein ion channel complex. Yet, transcriptional regulation appears an efficient mechanism to control precisely and (most importantly) specifically the channel activity in the long term. In addition, no technology is currently available to explore on a large-scale the expression of a collection of membrane proteins and this is the subject of active research in the growing field of proteomics. Although we realize that the genomics approach is unlikely to reflect the whole story of molecular remodeling, it represents a powerful means to increase our understanding of the mechanisms used by the heart muscle to adapt to variable environmental constraints.

In the first paragraph of this review, it is stated that the complete repertoire of ion channel genes is now elucidated. One major limit to this statement is that ion channels are not constituted by lone proteins but rather by an ensemble of proteins that form the channel complex constituted not only of {alpha}-subunits (the channel pore) and β-subunits (the regulators) but also of additional proteins that are keys for channel targeting and regulation by second messengers. At present, the precise constitution of channel complexes is largely unknown.


   Acknowledgements
 
We thank Marja Steenman for carefully reading the manuscript. This work was supported by grants from Ouest genopole to the Nantes functional genomics platform. SD is a recipient of a tenure position from the CNRS.


   Notes
 
Time for primary review 27 days


   References
Top
 Abstract
 1. Introduction
 2. Pangenomic versus specialized...
 3. From microarrays to...
 4. Clinical genomics of...
 5. Future directions
 6. Limits to the...
 References
 

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