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Posts Tagged ‘multivariate classification’


Genetic Analysis of Atrial Fibrillation

Author and Curator: Larry H Bernstein, MD, FCAP  

and 

Curator: Aviva-Lev Ari, PhD, RN

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:

Oxidized Calcium Calmodulin Kinase and Atrial Fibrillation
Author: Larry H. Bernstein, MD, FCAP and Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/10/26/oxidized-calcium-calmodulin-kinase-and-atrial-fibrillation/

Jmjd3 and Cardiovascular Differentiation of Embryonic Stem Cells

Author: Larry H. Bernstein, MD, FCAP and Curator: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2013/10/26/jmjd3-and-cardiovascular-differentiation-of-embryonic-stem-cells/

Contributions to cardiomyocyte interactions and signaling
Author and Curator: Larry H Bernstein, MD, FCAP  and Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/10/21/contributions-to-cardiomyocyte-interactions-and-signaling/

Cardiac Contractility & Myocardium Performance: Therapeutic Implications for Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses
Editor: Justin Pearlman, MD, PhD, FACC, Author and Curator: Larry H Bernstein, MD, FCAP, and Article Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/

Part I. Identification of Biomarkers that are Related to the Actin Cytoskeleton
Curator and Writer: Larry H Bernstein, MD, FCAP
https://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
https://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/

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
https://pharmaceuticalintelligence.com/2013/09/08/the-centrality-of-ca2-signaling-and-cytoskeleton-involving-calmodulin-kinases-and-ryanodine-receptors-in-cardiac-failure-arterial-smooth-muscle-post-ischemic-arrhythmia-similarities-and-differen/

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
Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/01/calcium-molecule-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/

Part VII: Cardiac Contractility & Myocardium Performance: Ventricular Arrhythmias and Non-ischemic Heart Failure – Therapeutic Implications for Cardiomyocyte Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses
Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/

Part VIII: Disruption of Calcium Homeostasis: Cardiomyocytes and Vascular Smooth Muscle Cells: The Cardiac and Cardiovascular Calcium Signaling Mechanism
Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/09/12/disruption-of-calcium-homeostasis-cardiomyocytes-and-vascular-smooth-muscle-cells-the-cardiac-and-cardiovascular-calcium-signaling-mechanism/

Part IX: Calcium-Channel Blockers, Calcium Release-related Contractile Dysfunction (Ryanopathy) and Calcium as Neurotransmitter Sensor
Justin Pearlman, MD, PhD, FACC, Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/09/16/calcium-channel-blocker-calcium-as-neurotransmitter-sensor-and-calcium-release-related-contractile-dysfunction-ryanopathy/

Part X: Synaptotagmin functions as a Calcium Sensor: How Calcium Ions Regulate the fusion of vesicles with cell membranes during Neurotransmission
Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/09/10/synaptotagmin-functions-as-a-calcium-sensor-how-calcium-ions-regulate-the-fusion-of-vesicles-with-cell-membranes-during-neurotransmission/

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.

Volume Three: Etiologies of Cardiovascular Diseases – Epigenetics, Genetics & Genomics

Curators: Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/biomed-e-books/series-a-e-books-on-cardiovascular-diseases/volume-three-etiologies-of-cardiovascular-diseases-epigenetics-genetics-genomics/

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,

  1. prognostication, and
  2. treatment of both index cases and relatives, and
  3. 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.

SOURCE:   Circulation: Cardiovascular Genetics.2011; 4: 701-709.  http://dx.doi.org/10.1161/CIRCGENETICS.110.959379

3.2.2: Myocardial Damage

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

MF Corsten, R Dennert, S Jochems, T Kuznetsova, Y Devaux, et al.
SOURCE: Circulation: Cardiovascular Genetics. 2010; 3: 499-506.  http://dx.doi.org/10.1161/CIRCGENETICS.110.957415

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/6/4/362   The 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.)

Key Words: arrhythmias, cardiac • atrial fibrillation • bioinformatics • gene coexpression • gene regulatory networks • genetics • microarrays

Introduction

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

Characteristics

MV Group (n=64)

CAD Group (n=57)

LAF Group (n=35)

P Value*

Age, median y (1st–3rd quartiles)

60 (51.75–67.25)

64 (58.00–70.00)

56 (45.50–60.50)

2.0×10−4

Sex, female (%) 19 (29.7) 6 (10.5)

7 (20.0)

0.033

BMI, median (1st–3rd quartiles)

25.97 (24.27–28.66)

29.01 (27.06–32.11)

29.71 (26.72–35.10)

2.7×10−6

Current smoker (%) 29 (45.3) 35 (61.4)

12 (21.1)

0.032

Hypertension (%) 21 (32.8) 39 (68.4)

16 (45.7)

4.4×10−4

AF severity (%)
No AF 7 (10.9) 7 (12.3)

0 (0.0)

0.033

Paroxysmal 19 (29.7) 10 (17.5)

7 (20.0)

Persistent 30 (46.9) 26 (45.6)

15 (42.9)

Permanent 8 (12.5) 14 (24.6)

13 (37.1)

Atrial rhythm at surgery (%)
No AF history in sinus rhythm 7 (10.9) 7 (12.3)

0 (0)

0.065

AF history in sinus rhythm 28 (43.8) 16 (28.1)

11 (31.4)

AF History in AF rhythm 29 (45.3) 34 (59.6)

24 (68.6)

Gene Coexpression Network Construction and Module Identificationsee document at  http://circgenetics.ahajournals.org/content/6/4/362

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).

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 mod-ules between mitral valve (MV) and coronary artery disease

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).

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.

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.

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).

see document at  http://circgenetics.ahajournals.org/content/6/4/362

Hub Gene Analysis of Salmon and Tan Modules

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

Figure 6. Cytoscape visualization of genes in the salmon module.

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.32,33 RCAN1 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

  1. actin cytoskeleton,
  2. transcription machinery, and
  3. 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

1. European Heart Rhythm Association, European Association for Cardio-Thoracic Surgery, Camm AJ, Kirchhof P, Lip GY, Schotten U, et al. Guidelines for the management of atrial fibrillation: the task force for the management of atrial fibrillation of the European Society of Cardiology (ESC). Eur Heart J. 2010;31:2369–2429.

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.

10. Clauss S, Kääb S. Is Pitx2 growing up? Circ Cardiovasc Genet. 2011;4:105–107.

11. Kirchhof P, Kahr PC, Kaese S, Piccini I, Vokshi I, Scheld HH, et al. PITX2c is expressed in the adult left atrium, and reducing Pitx2c expres­sion promotes atrial fibrillation inducibility and complex changes in gene expression. Circ Cardiovasc Genet. 2011;4:123–133.

12. Ellinor PT, Lunetta KL, Albert CM, Glazer NL, Ritchie MD, Smith AV, et al. Meta-analysis identifies six new susceptibility loci for atrial fibrillation. Nat Genet. 2012;44:670–675.

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.

Continues to 45.  see

http://circgenetics.ahajournals.org/content/6/4/362

CLINICAL PERSPECTIVE

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.

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typical changes in CK-MB and cardiac troponin ...

typical changes in CK-MB and cardiac troponin in Acute Myocardial Infarction (Photo credit: Wikipedia)

Reporter and curator:

Larry H Bernstein, MD, FCAP

This posting is a followup on two previous posts covering the design and handling of HIT to improve healthcare outcomes as well as lower costs from better workflow and diagnostics, which is self-correcting over time.

The first example is a non technology method designed by Lee Goldman (Goldman Algorithm) that was later implemented at Cook County Hospital in Chicago with great success.     It has been known that there is over triage of patients to intensive care beds, adding to the costs of medical care.  If the differentiation between acute myocardial infarction and other causes of chest pain could be made more accurate, the quantity of scare resources used on unnecessary admissions could be reduced.  The Goldman algorithm was introduced in 1982 during a training phase at Yale-New Haven Hospital based on 482 patients, and later validated at the BWH (in Boston) on 468 patients.They demonstrated improvement in sensitivity as well as specificity (67% to 77%), and positive predictive value (34% to 42%).  They modified the computer derived algorithm in 1988 to achieve better results in triage of patients to the ICU of patients with chest pain based on a study group of 1379 patients.  The process was tested prospectively on 4770 patients at two university and 4 community hospitals.  The specificity improved by 74% vs 71% in recognizing absence of AMI by the algorithm vs physician judgement. The sensitivity was not different for admission (88%).  Decisions based solely on the protocol would have decreased admissions of patients without AMI by 11.5% without adverse effects.  The study was repeated by Qamar et al. with equal success.

Pain in acute myocardial infarction (front)

Pain in acute myocardial infarction (front) (Photo credit: Wikipedia)

An ECG showing pardee waves indicating acute m...

An ECG showing pardee waves indicating acute myocardial infarction in the inferior leads II, III and aVF with reciprocal changes in the anterolateral leads. (Photo credit: Wikipedia)

Acute myocardial infarction with coagulative n...

Acute myocardial infarction with coagulative necrosis (4) (Photo credit: Wikipedia)

Goldman L, Cook EF, Brand DA, Lee TH, Rouan GW, Weisberg MC, et al. A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med. 1988;318:797-803.

A Qamar, C McPherson, J Babb, L Bernstein, M Werdmann, D Yasick, S Zarich. The Goldman algorithm revisited: prospective evaluation of a computer-derived algorithm versus unaided physician judgment in suspected acute myocardial infarction. Am Heart J 1999; 138(4 Pt 1):705-709. ICID: 825629

The usual accepted method for determining the decision value of a predictive variable is the Receiver Operator Characteristic Curve, which requires a mapping of each value of the variable against the percent with disease on the Y-axis.   This requires a review of every case entered into the study.  The ROC curve is done to validate a study to classify data on leukemia markers for research purposes as shown by Jay Magidson in his demonstation of  Correlated Component Regression (2012)(Statistical Innovations, Inc.)  The test for the contribution of each predictor is measured by Akaike Information Criteria and Bayes Information Criteria, which have proved to be critically essential tests over the last 20 years.

I go back 20 years and revisit the application of these principles in clinical diagnostics, but the ROC was introduced to medicine in radiology earlier.   A full rendering of this matter can be found in the following:
R A Rudolph, L H Bernstein, J Babb. Information induction for predicting acute myocardial infarction.Clin Chem 1988; 34(10):2031-2038. ICID: 825568.

Rypka EW. Methods to evaluate and develop the decision process in the selection of tests. Clin Lab Med 1992; 12:355

Rypka EW. Syndromic Classification: A process for amplifying information using S-Clustering. Nutrition 1996;12(11/12):827-9.

Christianson R. Foundations of inductive reasoning. 1964.  Entropy Publications. Lincoln, MA.

Inability to classify information is a major problem in deriving and validating hypotheses from PRIMARY data sets necessary to establish a measure of outcome effectiveness.  When using quantitative data, decision limits have to be determined that best distinguish the populations investigated.   We are concerned with accurate assignment into uniquely verifiable groups by information in test relationships.  Uncertainty in assigning to a supervisory classification can only be relieved by providing suffiuciuent data.

A method for examining the endogenous information in the data is used to determine decision points.  The reference or null set is defined as a class having no information.  When information is present in the data, the entropy (uncertainty in the data set) is reduced by the amount of information provided.  This is measureable and may be referred to as the Kullback-Liebler distance, which was extended by Akaike to include statistical theory.   An approach is devised using EW Rypka’s S-Clustering has been created to find optimal decision values that separate the groups being classified.  Further, it is possible to obtain PRIMARY data on-line and continually creating primary classifications (learning matrices).  From the primary classifications test-minimized sets of features are determined with optimal useful and sufficient information for accurately distinguishing elements (patients).  Primary classifications can be continually created from PRIMARY data.  More recent and complex work in classifying hematology data using a 30,000 patient data set and 16 variables to identify the anemias, moderate SIRS, sepsis, lymphocytic and platelet disorders has been  published and recently presented.  Another classification for malnutrition and stress hypermetabolism is now validated and in press in the journal Nutrition (2012), Elsevier.
G David, LH Bernstein, RR Coifman. Generating Evidence Based Interpretation of Hematology Screens via Anomaly Characterization. Open Clinical Chemistry Journal 2011; 4 (1):10-16. 1874-2416/11 2011 Bentham Open.  ICID: 939928

G David; LH Bernstein; RR Coifman. The Automated Malnutrition Assessment. Accepted 29 April 2012.
http://www.nutritionjrnl.com. Nutrition (2012), doi:10.1016/j.nut.2012.04.017.

Keywords: Network Algorithm; unsupervised classification; malnutrition screening; protein energy malnutrition (PEM); malnutrition risk; characteristic metric; characteristic profile; data characterization; non-linear differential diagnosis

Summary: We propose an automated nutritional assessment (ANA) algorithm that provides a method for malnutrition risk prediction with high accuracy and reliability. The problem of rapidly identifying risk and severity of malnutrition is crucial for minimizing medical and surgical complications. We characterized for each patient a unique profile and mapped similar patients into a classification. We also found that the laboratory parameters were sufficient for the automated risk prediction.
We here propose a simple, workable algorithm that provides assistance for interpreting any set of data from the screen of a blood analysis with high accuracy, reliability, and inter-operability with an electronic medical record. This has been made possible at least recently as a result of advances in mathematics, low computational costs, and rapid transmission of the necessary data for computation.  In this example, acute myocardial infarction (AMI) is classified using isoenzyme CKMB activity, total LD, and isoenzyme LD-1, and repeated studies have shown the high power of laboratory features for diagnosis of AMI, especially with NSTEMI.  A later study includes the scale values for chest pain and for ECG changes to create the model.

LH Bernstein, A Qamar, C McPherson, S Zarich.  Evaluating a new graphical ordinal logit method (GOLDminer) in the diagnosis of myocardial infarction utilizing clinical features and laboratory data. Yale J Biol Med 1999; 72(4):259-268. ICID: 825617

The quantitative measure of information, Shannon entropy treats data as a message transmission.  We are interested in classifying data with near errorless discrimination.  The method assigns upper limits of normal to tests computed from Rudolph’s maximum entropy definitions of group-based normal reference.  Using the Bernoulli trial to determine maximum entropy reference, we determine from the entropy in the data a probability of a positive result that is the same for each test and conditionally independent of other results by setting the binary decision level for each test.  The entropy of the discrete distribution is calculated from the probabilities of the distribution. The probability distribution of the binary patterns is not flat and the entropy decreases when there is information in the data.  The decrease in entropy is the Kullback-Liebler distance.

The basic principle of separatory clustering is extracting features from endogenous data that amplify or maximize structural information into disjointed or separable classes.  This differs from other methods because it finds in a database a theoretic – or more – number of variables with required VARIETY that map closest to an ideal, theoretic, or structural information standard. Scaling allows using variables with different numbers of message choices (number bases) in the same matrix, binary, ternary, etc (representing yes-no; small-modest, large, largest).   The ideal number of class is defined by x^n.   In viewing a variable value we think of it as low, normal, high, high high, etc.  A system works with related parts in harmony.  This frame of reference improves the applicability of S-clustering.  By definition, a unit of information is log.r r = 1.

The method of creating a syndromic classification to control variety in the system also performs a semantic function by attributing a term to a Port Royal Class.  If any of the attributes are removed, the meaning of the class is made meaningless.  Any significant overlap between the groups would be improved by adding requisite variety.  S-clustering is an objective and most desirable way to find the shortest route to diagnosis, and is an objective way of determining practice parameters.

Multiple Test Binary Decision Patterns where CK-MB = 18 u/l, LD-1 = 36 u/l, %LD1 = 32 u/l.

No.               Pattern       Freq                   P1                       Self information                Weighted information

0                    000             26                   0.1831                    2.4493                                     0.4485
1                    001                3                    0.0211                   5.5648                                     0.1176
2                    010               4                    0.0282                   5.1497                                     0.1451
3                    011                2                    0.0282                   6.1497                                     0.0866
4                    100               6                    0.0423                   4.5648                                     0.1929
6                    110                8                    0.0563                  4.1497                                     0.2338
7                    111               93                   0.6549                  0.6106                                     0.3999

Entropy: sum of weighted information (average)           1.6243 bits

The effective information values are the least-error points. Non AMI patients exhibit patterns 0, 1, 2, 3, and 4: AMI patients are 6 and 7.  There is 1 fp 4, and 1 fn 6.  The error rate is 1.4%.

Summary:

A major problem in using quantitative data is lack of a justifiable definition of reference (normal).  Our information model consists of a population group, a set of attributes derived from observations, and basic definitions using Shannon’s information measure entropy. In this model, the population set and its values for its variables are considered to be the only information available.  The finding of a flat distribution with the Bernoulli test defines the reference population that has no information.  The complementary syndromic group, treated in the same way, produces a distribution that is not flat and has a less than maximum information uncertainty.

The vector of probabilities – (1/2), (1/2), …(1/2), can be related to the path calculated from the Rypka-Fletcher equation, which

Ct = 1 – 2^-k/1 -2^-n

determines the theoretical maximum comprehension from the test of n attributes.  We constructed a ROC curve from theoriginal IRIS  data of R Fisher from four measurements of leaf and petal with a result obtained using information-based induction principles to determine discriminant points without the classification that had to be used for the discriminant analysis.   The principle of maximum entropy, as formu;ated by Jaynes and Tribus proposes that for problems of statistical inference – which as defined, are problems of induction – the probabilities should be assigned so that the entropy function is maximized.  Good proposed that maximum entropy be used to define the null hypothesis and Rudolph proposed that medical reference be defined as at maximum entropy.

Rudolph RA. A general purpose information processing automation: generating Port Royal Classes with probabilistic information. Intl Proc Soc Gen systems Res 1985;2:624-30.

Jaynes ET. Information theory and statistical mechanics. Phys Rev 1956;106:620-30.

Tribus M. Where do we stand after 30 years of maximum entropy? In: Levine RD, Tribus M, eds. The maximum entropy formalism. Cambridge, Ma: MIT Press, 1978.

Good IJ. Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. Ann Math Stat 1963;34:911-34.

The most important reason for using as many tests as is practicable is derived from the prominent role of redundancy in transmitting information (Noisy Channel Theorem).  The proof of this theorem does not tell how to accomplish nearly errorless discrimination, but redundancy is essential.

In conclusion, we have been using the effective information (derived from Kullback-Liebler distance) provided by more than one test to determine normal reference and locate decision values.  Syndromes and patterns that are extracted are empirically verifiable.

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