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Myocardial Damage in Cardiovascular Disease: Circulating MicroRNA-208b and MicroRNA-499

Posted in Biological Networks, Gene Regulation and Evolution, Cardiomyopathy, Cardiovascular Research, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, Myocardial metabolism, Myocardial ischemia, myocardial perfusion, Myocardial adenine nucleotide metabolism, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, tagged , , , , on December 12, 2013| Leave a Comment »

Myocardial Damage in Cardiovascular Disease: Circulating MicroRNA-208b and MicroRNA-499

Reporter: Aviva Lev-Ari, PhD, RN

Circulating MicroRNA-208b and MicroRNA-499 Reflect Myocardial Damage in Cardiovascular Disease

Maarten F. Corsten, MD, Robert Dennert, MD, Sylvia Jochems, BSc, Tatiana Kuznetsova, MD, PhD, Yvan Devaux, PhD, Leon Hofstra, MD, PhD, Daniel R. Wagner, MD, PhD, Jan A. Staessen, MD, PhD, Stephane Heymans, MD, PhD and Blanche Schroen, PhD

Author Affiliations

From the Center for Heart Failure Research (M.F.C., R.D., S.J., S.H., B.S.), Cardiovascular Research Institute, Maastricht, The Netherlands; the Division of Hypertension and Cardiovascular Rehabilitation (T.K., J.A.S.), Department of Cardiovascular Diseases, University of Leuven, Leuven, Belgium and Department of Epidemiology, Maastricht University Medical Center, Maastricht, The Netherlands; Centre de Recherche Public–Santé, Luxembourg (Y.D., D.R.W.), Luxembourg; Maastricht University Medical Center (L.H.), Maastricht, The Netherlands; and Centre Hospitalier Luxembourg (D.R.W.), Luxembourg.

Correspondence to Blanche Schroen, PhD, Center for Heart Failure Research, Cardiovascular Research Institute Maastricht, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands. E-mail b.schroen@cardio.unimaas.nl

Drs Heymans and Schroen contributed equally to this work.

Abstract

Background— Small RNA molecules, called microRNAs, freely circulate in human plasma and correlate with varying pathologies. In this study, we explored their diagnostic potential in a selection of prevalent cardiovascular disorders.

Methods and Results— MicroRNAs were isolated from plasmas from well-characterized patients with varying degrees of cardiac damage:

(1) acute myocardial infarction,

(2) viral myocarditis,

(3) diastolic dysfunction, and

(4) acute heart failure.

Plasma levels of selected microRNAs, including heart-associated (miR-1, -133a, -208b, and -499), fibrosis-associated (miR-21 and miR-29b), and leukocyte-associated (miR-146, -155, and -223) candidates, were subsequently assessed using real-time polymerase chain reaction. Strikingly, in plasma from acute myocardial infarction patients, cardiac myocyte–associated miR-208b and -499 were highly elevated, 1600-fold (P<0.005) and 100-fold (P<0.0005), respectively, as compared with control subjects. Receiver operating characteristic curve analysis revealed an area under the curve of 0.94 (P<10−10) for miR-208b and 0.92 (P<10−9) for miR-499. Both microRNAs correlated with plasma troponin T, indicating release of microRNAs from injured cardiomyocytes. In viral myocarditis, we observed a milder but significant elevation of these microRNAs, 30-fold and 6-fold, respectively. Plasma levels of leukocyte-expressed microRNAs were not significantly increased in acute myocardial infarction or viral myocarditis patients, despite elevated white blood cell counts. In patients with acute heart failure, only miR-499 was significantly elevated (2-fold), whereas no significant changes in microRNAs studied could be observed in diastolic dysfunction. Remarkably, plasma microRNA levels were not affected by a wide range of clinical confounders, including age, sex, body mass index, kidney function, systolic blood pressure, and white blood cell count.

Conclusions— Cardiac damage initiates the detectable release of cardiomyocyte-specific microRNAs-208b and -499 into the circulation.

SOURCE:

Circulation: Cardiovascular Genetics. 2010; 3: 499-506

Published online before print October 4, 2010,

doi: 10.1161/ CIRCGENETICS.110.957415

 

 

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MicroRNA in Serum as Biomarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis, diastolic dysfunction, and acute heart failure

Posted in Biological Networks, Gene Regulation and Evolution, Cardiomyopathy, Cardiovascular Research, Cell Biology, Signaling & Cell Circuits, Chemical Genetics, Computational Biology/Systems and Bioinformatics, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Myocardial metabolism, Myocardial ischemia, myocardial perfusion, Myocardial adenine nucleotide metabolism, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Population Health Management, Genetics & Pharmaceutical, Translational Science, tagged , , , on December 12, 2013| Leave a Comment »

MicroRNA in Serum as Biomarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis,  diastolic dysfunction, and acute heart failure

Reporter: Aviva Lev-Ari, PhD, RN

Increased MicroRNA-1 and MicroRNA-133a Levels in Serum of Patients With Cardiovascular Disease Indicate Myocardial Damage

Yasuhide Kuwabara, MD, Koh Ono, MD, PhD, Takahiro Horie, MD, PhD, Hitoo Nishi, MD, PhD, Kazuya Nagao, MD, PhD, Minako Kinoshita, MD, PhD, Shin Watanabe, MD, PhD, Osamu Baba, MD, Yoji Kojima, MD, PhD, Satoshi Shizuta, MD, Masao Imai, MD,Toshihiro Tamura, MD, Toru Kita, MD, PhD and Takeshi Kimura, MD, PhD

Author Affiliations

From the Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan (Y. Kuwabara, K.O., T.H., H.N., K.N., M.K., S.W., O.B., Y. Kojima, S.S., M.I., T.T., T. Kimura); and Kobe City Medical Center General Hospital, Kobe, Japan (T. Kita).

Correspondence to Koh Ono, MD, PhD, Department of Cardiovascular Medicine, Graduate School of Medicine, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto, Japan 606-8507. E-mail kohono@kuhp.kyoto-u.ac.jp

Abstract

Background—Recently, elevation of circulating muscle-specific microRNA (miRNA) levels has been reported in patients with acute myocardial infarction. However, it is still unclear from which part of the myocardium or under what conditions miRNAs are released into circulating blood. The purpose of this study was to identify the source of elevated levels of circulating miRNAs and their function in cardiovascular diseases.

Conclusions—These results suggest that elevated levels of circulating miRNA-133a in patients with cardiovascular diseases originate mainly from the injured myocardium. Circulating miR-133a can be used as a marker for cardiomyocyte death, and it may have functions in cardiovascular diseases.

SOURCE:

Circulation: Cardiovascular Genetics. 2011; 4: 446-454

Published online before print June 2, 2011,

doi: 10.1161/ CIRCGENETICS.110.958975

 

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Implications of Inheritance for Clinical Management: Common Cardiovascular Disorders When There Is a Family History

Posted in Frontiers in Cardiology and Cardiovascular Disorders, Genomic Testing: Methodology for Diagnosis, Personalized and Precision Medicine & Genomic Research, tagged , , , , , on December 12, 2013| Leave a Comment »

Implications of Inheritance for Clinical Management: Common Cardiovascular Disorders When There Is a Family History

Reporter: Aviva Lev- Ari, PhD, RN

 

A Clinical Approach to Common Cardiovascular Disorders When There Is a Family History:  The Implications of Inheritance for Clinical Management

Srijita Sen-Chowdhry, MBBS, MD, FESC, Daniel Jacoby, MD and William J. McKenna, MD, DSc, FESC

Author Affiliations

From the Institute of Cardiovascular Science, University College London, London, United Kingdom (S.S-C., W.J.M.); Department of Epidemiology, Imperial College, London, London, United Kingdom (S.S-C.); Division of Cardiology, Yale School of Medicine, New Haven, CT (D.J., W.J.M.).

Correspondence to Professor William J. McKenna, MD, DSc, FESC, Institute of Cardiovascular Science, University College London, The Heart Hospital, 16-18 Westmoreland Street, London, E-mail william.mckenna@uclh.nhs.uk

Introduction

Since the advent of genotyping, recognition of heritable disease has been perceived as an opportunity for genetic diagnosis or new gene identification studies to advance understanding of pathogenesis. Until recently, however, clinical application of DNA-based testing was confined largely to Mendelian disorders. Even within this remit, predictive testing of relatives is cost-effective only in diseases in which the majority of families harbor mutations in known causal genes, such as adult polycystic kidney disease and hypertrophic cardiomyopathy, but not dilated cardiomyopathy. Confirmatory genetic testing of index cases with borderline clinical features may be economic in the still smaller subset of diseases with limited locus heterogeneity, such as Marfan syndrome. Furthermore, Mendelian diseases account for ≈5% of total disease burden.1 Genome-wide association studies have made headway in elucidating the genetic contribution to the more common, complex diseases, and high throughput techniques promise to facilitate integration of genetic analysis into clinical practice. Nevertheless, many genes remain to be identified and implementation of genomic profiling as a population screening tool would not be cost-effective at present. The implications of heredity, however, extend beyond serving as a platform for genetic analysis, influencing diagnosis, prognostication, and treatment of both index cases and relatives, and enabling rational targeting of genotyping resources. This review covers acquisition of a family history, evaluation of heritability and inheritance patterns, and the impact of inheritance on subsequent components of the clinical pathway.

SOURCE:

Circulation: Cardiovascular Genetics.2012; 5: 467-476

doi: 10.1161/ CIRCGENETICS.110.959361

 

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Ischemic Stable CAD (FFR): In >5000 Patients – Medical Therapy and PCI no difference in End Point: Meta-Analysis of Contemporary Randomized Clinical Trials

Posted in Atherogenic Processes & Pathology, Cardiac and Cardiovascular Surgical Procedures, Cardiovascular Research, Frontiers in Cardiology and Cardiovascular Disorders, Medical Devices R&D and Inventions, Myocardial metabolism, Myocardial ischemia, myocardial perfusion, Myocardial adenine nucleotide metabolism, Origins of Cardiovascular Disease, PCI, Pharmacotherapy of Cardiovascular Disease, Stents & Tools, tagged , , , , , on December 3, 2013| Leave a Comment »

Ischemic Stable CAD: Medical Therapy and PCI no difference in End Point: Meta-Analysis of Contemporary Randomized Clinical Trials

Reporter: Aviva Lev-Ari, PhD, RN

 

SOURCE

Stergiopoulos K, Boden WE, Hartigan P, et al. Percutaneous coronary intervention outcomes in patients with stable obstructive coronary artery disease and myocardial ischemia: A collaborative meta-analysis of contemporary randomized clinical trials. JAMA Intern Med 2013; DOI:10.1001/jamainternmed.2013.12855. Available at:http://www.jamainternalmedicine.com.

 

PCI No Benefit Over Medical Therapy in Ischemic Stable CAD

December 02, 2013

NEW YORK, NY — A new analysis is calling into question the de facto rationale for many of the revascularization procedures taking place today, at least in patients with stable coronary artery disease[1]. In a meta-analysis of more than 5000 patients, PCI was no better than medical therapy in patients with documented ischemia by stress testing or fractional flow reserve (FFR).

“Cardiology has a long history of finding a marker of a bad outcome and treating that marker of that bad outcome as if it were the cause of the bad outcome,” senior author on the study, Dr David Brown (State University of New York [SUNY]–Stony Brook School of Medicine), told heartwire . In the case of proceeding to PCI on the basis of documented ischemia, that stems from evidence that patients with ischemia have a worse prognosis than patients who don’t.”It has gotten to the point that a positive stress test [is the gateway] to doing an intervention, even if the ischemia is not in the same ischemic territory as the vessel being treated,” he said. “The medical/industrial complex in cardiology is now focused on finding and treating ischemia, and I think that’s not justified, and these data suggest that that’s not justified.”

Brown and colleagues, with first author Dr Kathleen Stergiopoulus (SUNY–Stony Brook School of Medicine), reviewed the literature for randomized clinical trials of PCI and medical therapy for stable CAD conducted over the past 40 years, ultimately including five trials of 5286 patients. These were a small German trial published in 2004, plus MASS II , COURAGE , BARI 2D , and FAME 2 . In all, 4064 patients had myocardial ischemia documented by exercise, nuclear or echo stress imaging, or FFR.

Over a median follow-up of five years, mortality, nonfatal MI, unplanned revascularization, and angina were no different between patients treated medically vs those treated with PCI.

Odds Ratio, PCI vs Medical Therapy

Outcome Odds ratio 95% CI
Death 0.90 0.71–1.16
Nonfatal MI 1.24 0.99–1.56
Unplanned revascularization 0.64 0.35–1.17
Angina 0.91 0.57–1.44

“These findings are unique in that this is the first meta-analysis to our knowledge limited to patients with documented, objective findings of myocardial ischemia, almost all of whom underwent treatment with intracoronary stents and disease-modifying secondary-prevention therapy,” Stergiopoulus et al write.

The findings, they continue, “strongly suggest that the relationship between ischemia and mortality is not altered or ameliorated by catheter-based revascularization of obstructive, flow-limiting coronary stenosis.”

To heartwire , Brown pointed out that their analysis could not separate out patients who had small amounts of ischemia from those with larger ischemic territories. “Maybe that’s where the differentiating factor will be,” he acknowledged, adding that the 8000-patient ISCHEMIA trial, still ongoing, will hopefully yield some insights.

Current practice, however, is to check for ischemia and to proceed with catheterization and, usually, revascularization when ischemia is confirmed by stress testing or during FFR. “But if that doesn’t improve outcomes, why are we doing it?” Brown asked. “We think that needs to be rethought.”

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Commenting on the study for heartwire , Dr Peter Berger(Geisinger Health System, Danville, PA) pointed out: “There is no question that PCI is more effective than medical therapy for relief of symptoms: the more severe the angina and the more active the patient, the greater the superiority of PCI.” And, as Berger noted, most of the studies included in this analysis documented ischemia but did not report on the frequency or severity of angina at baseline.

That said, “Patients with minimal angina—and certainly those with silent ischemia but no angina—are unlikely to have a significantly greater reduction of symptoms with PCI, and PCI is rarely beneficial in such patients.”

Moreover, Berger continued, it has been clearly established that PCI does not reduce the risk of death or MI in most such patients.

“I very much agree with the authors, however, that just because more severe ischemia has been shown to be associated with a worse long-term prognosis, reducing the ischemic burden ought not be assumed to reduce the likelihood of death or MI. In most such patients, it does not.”

Stergiopoulos and Brown had no disclosures. Disclosures for the coauthors are listed in the paper.

SOURCE 

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Cardiology, Genomics and Individualized Heart Care: Framingham Heart Study (65 y-o study) & Jackson Heart Study (15 y-o study)

Posted in Atherogenic Processes & Pathology, Biological Networks, Gene Regulation and Evolution, Biomarkers & Medical Diagnostics, Cardiovascular Pharmacogenomics, Cardiovascular Research, Cerebrovascular and Neurodegenerative Diseases, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Coagulation Therapy and Internal Bleeding, Computational Biology/Systems and Bioinformatics, Diabetes Mellitus, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Drug Delivery Platform Technology, Electrophysiology, FDA Regulatory Affairs, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, HTN, HTN in Youth, Interviews with Scientific Leaders, Medical and Population Genetics, Molecular Genetics & Pharmaceutical, Origins of Cardiovascular Disease, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmaceutical R&D Investment, Pharmacogenomics, Pharmacotherapy of Cardiovascular Disease, Population Health Management, Genetics & Pharmaceutical, Proteomics, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, Translational Research, tagged , , , , , on December 1, 2013| Leave a Comment »

Cardiology, Genomics and Individualized Heart Care

Curator: Aviva Lev-Ari, PhD, RN

Article ID #90: Cardiology, Genomics and Individualized Heart Care: Framingham Heart Study (65 y-o study) & Jackson Heart Study (15 y-o study). Published on 12/1/2014

WordCloud Image Produced by Adam Tubman

 

The topic of Cardiology, Genomics and Individualized Heart Care is been developed in the following forthcoming e-Book on a related subject matter:

Curators: Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

This e-Book has the following Parts:

PART 1
Genomics and Medicine

Introduction to Volume Three
1.1: Genomics and Medicine: The Physician’s View
1.2: Ribozymes and RNA Machines – Work of Jennifer A. Doudn
1.3: Genomics and Medicine: The Geneticist’s View
1.4: Genomics in Medicine – Establishing a Patient-Centric View of Genomic Data

PART 2
Epigenetics- Modifiable Factors Causing Cardiovascular Diseases

2.1 Diseases Etiology

2.1.1 Environmental Contributors Implicated as Causing Cardiovascular Diseases
2.1.2 Diet: Solids and Fluid Intake
2.1.3 Physical Activity and Prevention of Cardiovascular Diseases
2.1.4 Psychological Stress and Mental Health: Risk for Cardiovascular Diseases
2.1.5 Correlation between Cancer and Cardiovascular Diseases
2.1.6 Medical Etiologies for Cardiovascular Diseases: Evidence-based Medicine – Leading DIAGNOSES of Cardiovascular Diseases, Risk Biomarkers and Therapies
2.1.7 Signaling Pathways
2.1.8 Proteomics and Metabolomics

2.2 Assessing Cardiovascular Disease with Biomarkers

2.2.1 Issues in Genomics of Cardiovascular Diseases
2.2.2 Endothelium, Angiogenesis, and Disordered Coagulation
2.2.3 Hypertension BioMarkers
2.2.4 Inflammatory, Atherosclerotic and Heart Failure Markers
2.2.5 Myocardial Markers

2.3  Therapeutic Implications: Focus on Ca(2+) signaling, platelets, endothelium

2.3.1 The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors

2.3.2 Platelets in Translational Research ­ 2

2.3.3 The Final Considerations of the Role of Platelets and Platelet Endothelial Reactions in Atherosclerosis

2.3.4 Nitric Oxide Synthase Inhibitors (NOS-I)

2.3.5 Resistance to Receptor of Tyrosine Kinase

2.3.6 Oxidized Calcium Calmodulin Kinase and Atrial Fibrillation

2.3.7 Advanced Topics in Sepsis and the Cardiovascular System at its End Stage

2.4 Comorbidity of Diabetes and Aging

PART 3
Determinants of Cardiovascular Diseases
Genetics, Heredity and Genomics Discoveries

Introduction
3.1 Why cancer cells contain abnormal numbers of chromosomes (Aneuploidy)
3.2 Functional Characterization of Cardiovascular Genomics: Disease Case Studies @ 2013 ASHG
3.3 Leading DIAGNOSES of Cardiovascular Diseases covered in Circulation: Cardiovascular Genetics, 3/2010 – 3/2013
3.4  Commentary on Biomarkers for Genetics and Genomics of Cardiovascular Disease

PART 4
Individualized Medicine Guided by Genetics and Genomics Discoveries

4.1 Preventive Medicine: Cardiovascular Diseases
4.2 Gene-Therapy for Cardiovascular Diseases
4.3 Congenital Heart Disease/Defects
4.4 Pharmacogenomics for Cardiovascular Diseases

SOURCE

http://pharmaceuticalintelligence.com/biomed-e-books/series-a-e-books-on-cardiovascular-diseases/volume-three-etiologies-of-cardiovascular-diseases-epigenetics-genetics-genomics/

The Next Frontier in Heart Care

Research Aims to Personalize Treatment With Genetics

Nov. 25, 2013 7:18 p.m. ET

VIEW VIDEO

http://online.wsj.com/news/articles/SB10001424052702304281004579220373600912930#!

Two influential heart studies are joining forces to bring the power of genetics and other 21st century tools to battle against heart disease and stroke. Ron Winslow and study co-director Dr. Vasan Ramachandran explain. Photo: Shubhangi Ganeshrao Kene/Corbis.

Scientists from two landmark heart-disease studies are joining forces to wield the power of genetics in battling the leading cause of death in the U.S.

Cardiologists have struggled in recent years to score major advances against heart disease and stroke. Although death rates have been dropping steadily since the 1960s, progress combating the twin diseases has plateaued by other measures.

Genetics has had a profound impact on cancer treatment in recent years. Now, heart-disease specialists hope genetics will reveal fresh insight into the interaction between a

  • person’s biology,
  • living habits and
  • medications

that can better predict who is at risk of a heart attack or stroke.

“There’s a promise of new treatments with this research,” said Daniel Jones, chancellor of the University of Mississippi and former principal investigator of the 15-year-old Jackson Heart Study, a co-collaborator in the new genetics initiative.

Scienc e Source /Photo Researchers Inc. (hearts); below, l-r: Boston University; Robert Jordan/Univ. of Miss.; Jay Ferchaud/Univ. of Miss Medical Center

Prevention efforts also could improve with the help of genetics research, Dr. Jones said. For example, an estimated 75 million Americans currently have high blood pressure, or hypertension, but only about half of those are able to control it with medication. It can take months of trial-and-error for a doctor to get the right dose or combination of pills for a patient. Researchers hope genetic and other information might enable doctors to identify subgroups of hypertension that respond to specific treatments and target patients with an appropriate therapy.

Also collaborating on the genetics project is the 65-year-old Framingham Heart Study. Its breakthrough findings decades ago linked heart disease to such factors as smoking, high blood pressure and high cholesterol. Framingham findings have been a foundation of cardiovascular disease prevention policy for a half-century.

More than 15,000 people have participated in the Framingham study. The Jackson study, with more than 5,000 participants, was launched in 1998 to better understand risk factors in African-Americans, who were underrepresented in Framingham and who bear a higher burden of cardiovascular disease than the rest of the population. Both studies are funded by the National Heart, Lung, and Blood Institute, part of the National Institutes of Health.

Exactly how the collaboration, announced last week, will proceed hasn’t been determined. One promising area is the “biobank,” the collection of more than one million blood and other biological samples gathered during biennial checkups of Framingham study participants going back more than a half century.

The samples are stored in freezers in an underground earthquake-proof facility in Massachusetts, said Vasan Ramachandran, a Boston University scientist who takes over at the beginning of next year as principal investigator of the Framingham Heart Study. Another 40,000 samples from the Jackson study are kept in freezers in Vermont. By subjecting samples to DNA sequencing and other tests, researchers say they may be able to identify variations linked to progression of cardiovascular disease—or protection from it.

Each study is likely to enroll new participants as part of the collaboration to allow tracking of risk factors and diet and exercise habits, for instance, in real time instead of only during infrequent checkups.

Heart disease is linked to about 800,000 deaths a year in the U.S. In 2010, some 200,000 of those deaths could have been avoided, including more than 112,300 deaths among people younger than 65, according to a recent analysis by the Centers for Disease Control and Prevention. But those avoidable deaths reflected a 3.8% per year decline in mortality rates during the previous 10 years.

Now, widespread prevalence of obesity and diabetes threatens to undermine such gains. And a large gap remains between how white patients and minorities—especially African-Americans—benefit from effective strategies.

There have been few new transformative cardiovascular treatments since the mid-1980s to early 1990s, when a stream of large-scale trials of new agents ranging from clot-busters to treat heart attacks to the mega class of statins electrified the cardiology field with evidence of significant improvements in survival from the disease. One reason: Some of those remedies have proven tough to beat with new treatments.

What’s more, use of the current menu of medicines for reducing heart risk remains an imprecise art. Besides

  • blood pressure drugs,
  • cholesterol-lowering statins

also are widely prescribed. Drug-trial statistics show that to prevent a single first heart attack in otherwise healthy patients can require prescribing a statin to scores of patients, but no one knows for sure who actually benefits and who doesn’t.

“It would be great if we could make some more paradigm-shifting discoveries,” said Michael Lauer, director of cardiovascular sciences at the NHLBI, which is a part of the National Institutes of Health.

Finding new treatments isn’t the only aim of the new project. “You could use existing therapies smarter,” said Joseph Loscalzo, chairman of medicine at Brigham and Women’s Hospital in Boston.

The American Heart Association launched the initiative and has committed $30 million to it over the next five years. The AHA sees the project as critical to its goal to achieve a 20% improvement in cardiovascular health in the U.S. while also reducing deaths from heart disease and stroke by 20% for the decade ending in 2020, said Nancy Brown, the nonprofit organization’s chief executive.

The Jackson study has already identified characteristics of cardiovascular risk among African-American patients “that may have promise for new insights” in a collaborative effort, said Adolfo Correa, professor of medicine and pediatrics at University of Mississippi Medical Center and interim director of the Jackson study.

For instance, there is a higher prevalence of obesity among Jackson participants than seen in the Framingham cohorts. Obesity is associated with high blood pressure, diabetes and cardiovascular risk. Diabetes is also more prevalent among blacks than whites.

But African-Americans of normal weight appear to have higher rates of hypertension and diabetes than whites of normal weight. “The question is, should [measures] for defining diabetes be different or the same for the [different] populations and are they associated with the same risk of cardiovascular disease?” said Dr. Correa. The collaboration, he said, may provide better comparisons.

Researchers, who plan to use tools other than genetics, think more might be learned about blood pressure and heart and stroke risk by monitoring patients in real time using mobile devices rather than taking readings only in periodic office visits. For example, high blood pressure during sleep or spikes during exercise could indicate risks that don’t show up in a routine measurement in the doctors’ office.

A big challenge is making sense of the huge amounts of data involved in sequencing DNA and linking it to

  • medical records,
  • diet and
  • exercise habits and other variables that influence risk.

“The analytical methods for sorting out these complex relationships are still in evolution,” said Dr. Loscalzo, of Brigham and Women’s Hospital. “The cost of sequencing is getting cheaper and cheaper. The hard part is analyzing the data.”

Write to Ron Winslow at ron.winslow@wsj.com

SOURCE

http://online.wsj.com/news/articles/SB10001424052702304281004579220373600912930#!

The e-Reader is advised to to review tightly related articles in

http://pharmaceuticalintelligence.com/biomed-e-books/series-a-e-books-on-cardiovascular-diseases/volume-three-etiologies-of-cardiovascular-diseases-epigenetics-genetics-genomics/

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Stem Cell Therapy for Coronary Artery Disease (CAD)

Posted in Cardiovascular Research, Frontiers in Cardiology and Cardiovascular Disorders, Population Health Management, Genetics & Pharmaceutical, Pre-Clinical Animal Model Development, Regulated Clinical Trials: Design, Methods, Components and IRB related issues, Stem Cells for Regenerative Medicine, Technology Transfer: Biotech and Pharmaceutical, tagged , , , , , , , , , , , , , , , , on November 2, 2013| Leave a Comment »

Stem Cell Therapy for Coronary Artery Disease (CAD)

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

and

Curator: Aviva Lev-Ari, PhD, RN

 

There is great interest and future promise for stem cell therapy in ischemic heart disease.  This is another report for the active work in cardiology with stem cell therapy by MA Gaballa and associates at University of Arizona.

Stem Cell Therapy for Coronary Heart Disease

Julia N. E. Sunkomat and Mohamed A. Gaballa

The University ofArizona Sarver Heart Center, Section of Cardiology, Tucson, Ar
Cardiovascular Drug Reviews 2003: 21(4): 327–342

Keywords: Angiogenesis — Cardiac therapy — Coronary heart disease — Heart failure — Myoblasts — Myocardial ischemia — Myocardial regenera­tion — Stem cells

ABSTRACT

Coronary artery disease (CAD) remains the leading cause of death in the Western world. The high impact of its main sequelae, acute myocardial infarction and congestive heart failure (CHF), on the quality of life of patients and the cost of health care drives the search for new therapies. The recent finding that

stem cells contribute to neovascularization and possibly improve cardiac function after myocardial infarction makes stem cell therapy the most highly active research area in cardiology. Although the concept of stem cell therapy may revolutionize heart failure treatment, several obstacles need to be ad­dressed. To name a few:

  1.  Which patient population should be considered for stem cell therapy?
  2.  What type of stem cell should be used?
  3.  What is the best route for cell de­livery?
  4.  What is the optimum number of cells that should be used to achieve functional effects?
  5.  Is stem cell therapy safer and more effective than conventional therapies?

The published studies vary significantly in design, making it difficult to draw conclusions on the efficacy of this treatment. For example, different models of

  1. ischemia,
  2. species of donors and recipients,
  3. techniques of cell delivery,
  4. cell types,
  5. cell numbers and
  6. timing of the experiments

have been used. However, these studies highlight the landmark concept that stem cell therapy may play a major role in treating cardiovascular diseases in the near future. It should be noted that stem cell therapy is not limited to the treatment of ischemic cardiac disease.

  • Non-ischemic cardiomyopathy,
  • peripheral vascular disease, and
  • aging may be treated by stem cells.

Stem cells could be used as vehicle for gene therapy and eliminate the use of viral vectors. Finally, stem cell therapy may be combined with phar­macological, surgical, and interventional therapy to improve outcome. Here we attempt a systematic overview of the science of stem cells and their effects when transplanted into ischemic myocardium.

INTRODUCTION

Background

Congestive heart failure (CHF) is the leading discharge diagnosis in patients over the age of 65 with estimates of $24 billion spent on health care in the US (1,11). The number one cause of CHF is coronary artery diseases (CAD). Coronary care units, reperfusion therapy (lytic and percutaneous coronary intervention) and medical therapy with anti-pla­telet agents, statins, ACE-inhibitors and â-adrenoceptor antagonists all significantly reduce morbidity and mortality of CAD and CHF (9), but it is very difficult to regenerate new viable myocardium and new blood vessels.

Identification of circulating endothelial progenitor cells in peripheral blood that incor­porated into foci of neovascularization in hindlimb ischemia (4) and the successful engraftment of embryonic stem cells into myocardium of adult dystrophic mice (31) intro­duced a new therapeutic strategy to the field of cardiovascular diseases: tissue regeneration. This approach is supported by the discovery of primitive cells of extracardiac origin in cardiac tissues after sex-mismatched transplants suggesting that an endogenous repair mechanism may exist in the heart (35,45,54). The number of recruited cells varied significantly from 0 (19) to 18% (54), but the natural course of ischemic cardiomyopathy implies that cell recruitment for tissue repair in most cases is insufficient to prevent heart failure. Therefore, investigational efforts are geared towards

  • augmenting the number of multipotent stem cells and endothelial and myocardial progenitor cells at the site of ischemia to induce clinically significant angiogenesis and potentially myogenesis.

Stem and Progenitor Cells

Stem cells are defined by their ability to give rise to identical stem cells and progenitor cells that continue to differentiate into a specific tissue cell phenotype (23,33). The po­tential of mammalian stem cells varies with stage of development and age (Table 1).

In mammals, the fertilized oocyte and blastomere cells of embryos of the two to eight cell stage can generate a complete organism when implanted into the uterus; they are called totipotent stem cells. After the blastocyst stage, embryonic stem cells retain the ability to differentiate into all cell types, but

  • cannot generate a complete organism and thus are denoted pluripotent stem cells.

Other examples of pluripotent stem cells are embryo­nic germ cells that are derived from the gonadal ridge of aborted embryos and embryonic carcinoma cells that are found in gonadal tumors (teratocarcinomas) (23,33). Both these cell types can also differentiate into cells of all three germ layers, but are not as well inves­tigated as embryonic stem cells.

It is well established that embryonic stem cells can differentiate into cardiomyocytes (7,10,13,14,31,37,76), endothelial cells (55), and smooth muscle cells (5,22,78) in vitro, but it is unclear whether

  • pure populations of embryonic stem cell-derived cardiomyocytes can integrate and function appropriately in the heart after transplantation.
  • one study reported arrhythmogenic potential of embryonic stem cell-derived cardiomyocytes in vitro (80).

Adult somatic stem cells are cells that have already committed to one of the three germ layers: endoderm, ectoderm, or mesoderm (76). While embryonic stem cells are defined by their origin (the inner cell mass of the blastocyst), the origin of adult stem cells in mature tissues is still unknown. The primary role of adult stem cells in a living organism is thought to be maintaining and repairing the tissue in which they reside. They are the source of more identical stem cells and cells with a progressively more distinct phenotype of specialized tissue cells (progenitor and precursor cells) (Fig. 1). Until recently adult stem cells were thought to be lineage-specific, meaning that they can only differentiate into the cell-type of their original tissue. This concept has now been challenged with the discovery of multipotent stem and progenitor cells (26, 50, 51).

The presence of multipotent stem and progenitor cells in adult mammals has vast im­plications on the availability of stem cells to research and clinical medicine. Recent publi­cations, however, have questioned whether the adaptation of a phenotype in those dogma-challenging studies is really a result of trans-differentiation or rather a result of cell and nuclear fusion (60,68,75,79). Spontaneous fusion between mammalian cells was first re­ported in 1961 (8), but how frequently fusion occurs and whether it occurs in vivo is not clear.

The bone marrow is a known source of stem cells. Hematopoietic stem cells are fre­quently used in the field of hematology. Surface receptors are used to differentiate hematopoietic stem and progenitor cells from mature cells. For example, virtually all

  • hematopoietic stem and progenitor cells express the CD34+ glycoprotein antigen on their cell membrane (73),

though a small proportion of primitive cells have been shown to be CD34 negative (58).

The function of the CD34+ receptor is not yet fully understood. It has been suggested that it may act as a regulator of hematopoietic cell adhesion in the bone marrow microenvironment. It also appears to be involved in the maintenance of the hematopoietic stem/progenitor cell phenotype and function (16,21). The frequency of immature CD34+ cells in peripheral circulation diminishes with age.

  • It is the highest (up to 11%) in utero (69) and decreases to 1% of nucleated cells in term cord blood (63).
  • This equals the per­centage of CD34+ cells in adult bone marrow.
  • The number of circulating stem cells in adult peripheral blood is even lower at 0.1% of nucleated cells.

Since hematopoietic stem cells have been identified as endothelial progenitor cells (29,30,32) their low density in adult bone marrow and blood could explain the inadequacy of endogenous recruitment of cells to injured organs such as an ischemic heart. The bone marrow is also home to another stem cell population the so-called mesenchymal stem cells. These may constitute a subset of the bone marrow stromal cells (2,43). Bone marrow stromal cells are a mixed cell popu­lation that generates

  1. bone,
  2. cartilage,
  3. fat,
  4. connective tissue, and
  5. reticular network that sup­ports cell formation (23).

Mesenchymal stem cells have been described as multipotent (51,52) and as a source of myocardial progenitor cells (41,59). They are, however, much less defined than the hematopoietic stem cells and a characteristic antigen constellation has not yet been identified (44).

Another example of an adult tissue containing stem cells is the skeletal muscle. The cells responsible for renewal and growth of the skeletal muscle are called satellite cells or myoblasts and are located between the sarcolemma and the basal lamina of the muscle fiber (5). Since skeletal muscle and cardiac muscle share similar characteristics such as they both are striated muscle cells, satellite cells are considered good candidates for the repair of damaged myocardium and have been extensively studied (20,25,38–40,48,56, 64–67). Myoblasts are particularly attractive, because they can be autotransplanted, so that issues of donor availability, ethics, tumorigenesis and immunological compatibility can be avoided. They also have been shown to have a high growth potential in vitro and a strong resistance to ischemia in vivo (20). On the down side

  • they may have more arrhythmogenic potential when transplanted into myocardium than bone marrow or peripheral blood de­rived stem cells and progenitor cells (40).

Isolation of Cells Prior to Transplantation

Hematopoietic stem and progenitor cells are commonly identified by the expression of a profile of surface receptors (cell antigens). For example, human hematopoietic stem cells are defined as CD34+/CD59+/Thy-1+/CD38low/–/c-kit–/low/lin–, while mouse hema-topoietic stem cells are defined as CD34low/–/Sca-1+/Thy-1+/low/CD38+/c-kit+/lin– (23). Additional cell surface receptors have been identified as markers for subgroups of hema-topoietic stem cells with the ability to differentiate into non-hematopoetic tissues, such as endothelial cells (57,78). These can be specifically targeted by isolation methods that use the receptors for cell selection (positive selection with antibody coated magnetic beads or fluorescence-activated cell sorting, FACS). Other stem cell populations are identified by their behavior in cell culture (mesenchymal stem cells) or dye exclusion (SP cells). Finally, embryonic stem cells are isolated from the inner cell mass of the blastocyst and skeletal myoblasts are mechanically and enzymatically dissociated from an easily acces­sible skeletal muscle and expanded in cell culture.

FIG. 1. Maturation process of adult stem cells: with acquisition of a certain phenotype the cell gradually loses its self-renewal capability.  (unable to transfer)

METHODICAL APPROACHES 

j.1527-3466.2003.tb00125.x fig stem cell

FIG. 2. Intramyocardial injection:

the cells are injected directly into the myocardium through the epicardium. Usually a thoracotomy or sternotomy is required. Transendocardial injection: access can be gained from the ar­terial vasculature. Cells are injected through the endocardium into the myocardium, ideally after identifying the ischemic myocardium by perfusion studies and/or electromechanical mapping. Intracoronary injection: the coronary artery is accessed from the arterial vasculature. Stem cells are injected into the lumen of the coronary artery. Proximal washout is prevented by inflation of a balloon. Cells are then distributed through the capillary system. They eventually cross the endothelium and migrate towards ischemic areas.

The intracoronary delivery of stem cells (Fig. 2) and distribution through the coronary system has also been explored (6,62,74). This approach was pioneered by Robinson et al. (56), who demonstrated successful engraftment within the coronary distribution after intracoronary delivery of genetically labeled skeletal myoblasts. The risk of intracoronary injection is comparable to that of a coronary angiogram and percutaneous transluminal coronary angioplasty (PTCA) (62), which are safe and clinically well established.

RESULTS IN ANIMAL STUDIES AND HUMAN TRIALS

Dif­ferentiation into cardiomyocytes was observed after transplantation of embryonic stem cells, mesenchymal stem cells, lin–/c-kit+ and SP cells. The induction of angiogenesis was observed after transplantation of embryonic stem cells, mesenchymal stem cells, bone marrow-derived mononuclear cells, circulating endothelial progenitor cells, SP cells and lin–/c-kit+ cells.

The use of embryonic stem cells in ischemia was examined in two studies (42,43). These studies demonstrated that mice embryonic stem cells transplanted into rat myo­cardium exhibited cardiomyocyte phenotype at 6 weeks after transplantation. In addition, generation of myocardium and angiogenesis were observed at 32 weeks after allogenic transplantation in rats. In these two studies no arrhythmias or cardiac tumors were reported.

Several studies have shown retardation of LV remodeling and improvement of cardiac function after administration of bone marrow-derived mononuclear cells. For example, decreases in infarct size, and increase in ejection fraction (EF), and left ventricular (LV) time rate change of pressure (dP/dtmax) were observed after direct injection of bone marrow-derived mononuclear cells 60 min after ischemia in swine (28). In humans, intra-coronary delivery and transendocardial injection of mononuclear cells leads to a decrease in LV dimensions and improvement of cardiac function and perfusion (49,62). A decrease in end systolic volume (ESV) and an increase in EF as well as regional wall motion were observed following intracoronary administration of CD34+/CD45+ human circulating en­dothelial cells (6). Injection of circulating human CD34+/CD117+ cells into infarcted rat myocardium induced neoangiogenesis and improved cardiac function (32). This study suggests that the improvement in LV remodeling after infarction appears to be in part me­diated by a decrease in apoptosis within the noninfarcted myocardium. Two other studies reported increased fractional shortening, improved regional wall motion and decreased left ventricular dimensions after transplantation of human CD34+ cells (29,30). Improved global left ventricular function and infarct perfusion was demonstrated after intramyo-cardial injection of autologous endothelial progenitor cells in humans (61).

DISCUSSION AND OUTLOOK

The idea of replacing damaged myocardium by healthy cardiac tissue is exciting and has received much attention in the medical field and the media. Therefore, it is important for the scientist to know what is established and what is based on premature conclusions. Currently, there are data from animal studies and human trials (Table 2). However, some of these data are not very concrete. For example,

  • many animal studies do not report the level of achieved neoangiogenesis and/or regeneration of myocardium.
  • In studies where the numbers of neovessels and new cardiomyocytes are specified, these numbers are often very low.

While these experiments confirm the concept that bone marrow and peripheral blood-derived stem and progenitor cells can differentiate into cardiomyocytes and endo­thelial cells when transplanted into ischemic myocardium, they also raise the question how effective this treatment is.

The results of the clinical trials that have been conducted are encouraging, but they need to be interpreted with caution. The common endpoints of these studies include left ventricular dimensions, perfusion, wall motion and hemodynamic function. While all studies report improvement after mononuclear cell, myoblast or endothelial progenitor cell transplantation, it is difficult to separate the effects of stem cell transplantation from the effects of the state-of-the art medical care that the patients typically received.

CONCLUSION

While the majority of studies demonstrate neoangiogenesis and some studies also show regeneration of myocardium after stem/progenitor cell transplantation, it remains unclear whether the currently achieved level of tissue regeneration is sufficient to affect clinical outcome. Long-term follow-up of patients that received stem/progenitor cells in clinical trials will provide important information on the potential risks of neoplasm and arrhythmias and, therefore, safety of this treatment. Ultimately, postmortem histological confirmation of scar tissue repair by transplanted cells and randomized placebo control trials with long-term follow-up are required to prove efficacy of this treatment.

REFERENCES (10)

1. American Heart Association Disease and Stroke Statistics-2003 Update, Dallas TX, American Heart Associ­ation; 2002 http://http://www.americanheart.org/downloadable/heart/10461207852142003HDSStatsBook.pdf

2. Arai A, Sheikh F, Agyeman K, et al. Lack of benefit from cytokine mobilized stem cell therapy for acute myocardial infarction in nonhuman primates. J Am Coll Cardiol 2003;41(Suppl 6A):371.

3. Asahara T, Masuda H, Takahashi T, et al. Bone marrow origin of endothelial progenitor cells responsible for postnatal vasculogenesis in physiological and pathological neovascularization. Circ Res 1999;85:221–228.

4. Asahara T, Murohara T, Sullivan A, et al. Isolation of putative progenitor endothelial cells for angiogenesis. Science 1997;275:964–967.

5. Asakura A, Seale P, Girgis-Gabardo A, Rudnicki M. Myogenic specification of side population cells in skeletal muscle. J Cell Biol 2002;159(1):123–134.

6. Assmus B, Schaechinger V, Teupe C, et al. Transplantation of progenitor cells and regeneration en­hancement in acute myocardial infarction (TOPCARE-AMI). Circulation 2002;106:r53–r61.

7. Bader A, Al-Dubai H, Weitzer G. Leukemia inhibitory factor modulates cardiogenesis in embryoid bodies in opposite fashions. Circ Res 2000;86(7):787–794.

8. Barski G, Sorieul S, Cornefert F. “Hybrid” type cells in combined cultures of two different mammalian cell strains. J Natl Cancer Inst 1961;26:1269–1291.

9. Boersma E, Mercado N, Poldermans D, Gardien M, Vos J, Simoons M. Acute myocardial infarction. Lancet 2003;361:847–58.

  1. 10.          Boheler K, Czyz J, Tweedie D, Yang H, Anisimov S, Wobus A. Differentiation of pluripotent embryonic stem cells into cardiomyocytes. Circ Res 2002;91:189–201.

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Sensors and Signaling in Oxidative Stress

Posted in CANCER BIOLOGY & Innovations in Cancer Therapy, Cancer Prevention: Research & Programs, Cardiovascular Research, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Chemical Genetics, Cytoskeleton, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Frontiers in Cardiology and Cardiovascular Disorders, Genome Biology, Genomic Testing: Methodology for Diagnosis, Metabolomics, Molecular Genetics & Pharmaceutical, Myocardial metabolism, Myocardial ischemia, myocardial perfusion, Myocardial adenine nucleotide metabolism, Pharmacogenomics, Population Health Management, Genetics & Pharmaceutical, tagged , , , , , , , , , , , , , , , , , , , on November 1, 2013| Leave a Comment »

Sensors and Signaling in Oxidative Stress

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

Article XI Sensors and Signaling in Oxidative Stress

Image created by Adina Hazan 06/30/2021

This is article ELEVEN in the following series on Calcium Role in Cardiovascular Diseases

Part I: Identification of Biomarkers that are Related to the Actin Cytoskeleton
Larry H Bernstein, MD, FCAP
http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-
that-are-related-to-the-actin-cytoskeleton/

Part II: Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility
Larry H. Bernstein, MD, FCAP, Stephen Williams, PhD and Aviva Lev-Ari, PhD, RN
http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-
skeleton-and-lipid-structures-in-signaling-and-cell-motility/

Part III: Renal Distal Tubular Ca2+ Exchange Mechanism in Health and Disease
Larry H. Bernstein, MD, FCAP, Stephen J. Williams, PhD
and Aviva Lev-Ari, PhD, RN
http://pharmaceuticalintelligence.com/2013/09/02/renal-distal-tubular-ca2-
exchange-mechanism-in-health-and-disease/

Part IV: The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and
Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia,
Similarities and Differences, and Pharmaceutical Targets
Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN
http://pharmaceuticalintelligence.com/2013/09/08/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 V: Ca2+-Stimulated Exocytosis:  The Role of Calmodulin and Protein Kinase C in Ca2+ Regulation of Hormone and Neurotransmitter

Larry H Bernstein, MD, FCAP
and
Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/12/23/calmodulin-and-protein-kinase-c-drive-the-ca2-regulation-of-hormone-and-neurotransmitter-release-that-triggers-ca2-stimulated-exocytosis/

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
http://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
http://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
http://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
http://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
http://pharmaceuticalintelligence.com/2013/09/10/synaptotagmin-functions-as-a-calcium-sensor-how-calcium-ions-
regulate-the-fusion-of-vesicles-with-cell-membranes-during-neurotransmission/

Part XI: Sensors and Signaling in Oxidative Stress
Larry H. Bernstein, MD, FCAP
http://pharmaceuticalintelligence.com/2013/11/01/sensors-and-signaling-in-oxidative-stress/

Part XII: Atherosclerosis Independence: Genetic Polymorphisms of Ion Channels Role in the Pathogenesis of Coronary Microvascular Dysfunction and Myocardial Ischemia (Coronary Artery Disease (CAD))

Larry H Bernstein, MD, FCAP and Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/12/21/genetic-polymorphisms-of-ion-channels-have-a-role-in-the-pathogenesis-of-coronary-microvascular-dysfunction-and-ischemic-heart-disease/

This important article on oxidative stress was published in Free Radical Biol. and Med.

Nrf2:INrf2(Keap1) Signaling in Oxidative Stress

James W. Kaspar, Suresh K. Niture, and Anil K. Jaiswal*
Department of Pharmacology, University of Maryland School of Medicine, Baltimore, MD

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

Nrf2:INrf2(Keap1) are cellular sensors of chemical and radiation induced oxidative and electrophilic stress. Nrf2 is a nuclear transcription factor that controls the expression and coordinated induction of a battery of defensive genes encoding detoxifying enzymes and antioxidant proteins. This is a mechanism of critical importance for cellular protection and cell survival. Nrf2 is retained in the cytoplasm by an inhibitor INrf2. INrf2 functions as an adapter for Cul3/Rbx1 mediated degradation of Nrf2. In response to oxidative/electrophilic stress, Nrf2 is switched on and then off by distinct early and delayed mechanisms. Oxidative/electrophilic modification of INrf2cysteine151 and/or PKC phosphorylation of Nrf2serine40 results in

  • the escape or release of Nrf2 from INrf2.

Nrf2 is stabilized and

  • translocates to the nucleus,
  • forms heterodimers with unknown proteins, and
  • binds antioxidant response element (ARE) that
  • leads to coordinated activation of gene expression.
  • It takes less than fifteen minutes from the time of exposure to switch on nuclear import of Nrf2. This is followed by activation of a delayed mechanism that controls switching off of Nrf2 activation of gene expression. GSK3β phosphorylates Fyn at unknown threonine residue(s) leading to nuclear localization of Fyn. Fyn phosphorylates Nrf2tyrosine568
  • resulting in nuclear export of Nrf2, binding with INrf2 and
  • degradation of Nrf2.

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

Introduction

Oxidative stress is induced by a vast range of factors including xenobiotics, drugs, heavy metals and ionizing radiation. Oxidative stress leads to the generation of Reactive Oxygen Species (ROS) and electrophiles. ROS and electrophiles generated can have a profound impact on survival, growth development and evolution of all living organisms [1,2] ROS include

  • both free radicals, such as the superoxide anion and the hydroxyl radical, and
  • oxidants such as hydrogen peroxide [3].

ROS and electrophiles can cause diseases such as cancer, cardiovascular complications, acute and chronic inflammation, and neurodegenerative diseases [1]. Therefore, it is obvious that

  • cells must constantly labor to control levels of ROS, preventing them from accumulation.

Much of what we know about the mechanisms of protection against oxidative stress has come from the study of prokaryotic cells [4,5]. Prokaryotic cells utilize transcription factors OxyR and SoxRS to sense the redox state of the cell, and

  • during oxidative stress these factors induce the expression of nearly eighty defensive genes [5].

Eukaryotic cells have similar mechanisms to protect against oxidative stress [Fig. 1; ref. 3,6–9]. Initial effect of oxidative/electrophilic stress leads to activation of a battery of defensive gene expression that leads to detoxification of chemicals and ROS and prevention of free radical generation and cell survival [Fig. 1].

Fig 1. Chemical and radiation exposure and coordinated induction of defensive genes.

Fig. 1. Chemical and radiation exposure and coordinated induction of defensive genes.

Of these genes, some are enzymes such as NAD(P)H:quinine oxidoreductase 1 (NQO1), NRH:quinone oxidoreductase 2 (NQO2), glutathione S-transferase Ya subunit (GST Ya Subunit), heme oxygenase 1 (HO-1), and γ-glutamylcysteine synthetase (γ-GCS), also known as glutamate cysteine ligase (GCL). Other genes have end products that regulate a wide variety of cellular activities including

  • signal transduction,
  • proliferation, and
  • immunologic defense reactions.

There is a wide variety of factors associated with the cellular response to oxidative stress. For example,

  • NF-E2 related factor 2 (Nrf2),
  • heat shock response activator protein 1, and
  • NF-kappaB promote cell survival,

whereas activation of c-jun, N-terminal kinases (JNK), p38 kinase and TP53 may lead to cell cycle arrest and apoptosis [10]. The Nrf2 pathway is regarded as the most important in the cell to protect against oxidative stress. [3,6–9]. It is noteworthy that accumulation of ROS and/or electrophiles leads to oxidative/electrophile stress,

  • membrane damage,
  • DNA adducts formation and
  • mutagenicity [Fig. 1].

These changes lead to degeneration of tissues and premature aging, apoptotic cell death, cellular transformation and cancer.

Antioxidant Response Element and Nrf2

Promoter analysis identified a cis-acting enhancer sequence designated as the antioxidant response element (ARE) that

  • controls the basal and inducible expression of antioxidant genes in response to xenobiotics, antioxidants, heavy metals and UV light [11].

The ARE sequence is responsive to a broad range of structurally diverse chemicals apart from β-nafthoflavone and phenolic antioxidants [12]. Mutational analysis revealed GTGACA***GC to be the core sequence of the ARE [11,13–14]. This core sequence is present in all Nrf2 downstream genes that respond to antioxidants and xenobiotics [3,6–9]. Nrf2 binds to the ARE and regulates ARE-mediated antioxidant enzyme genes expression and induction in response to a variety of stimuli including antioxidants, xenobiotics, metals, and UV irradiation [6,15–21].

Nrf2 is ubiquitously expressed in a wide range of tissue and cell types [22–24] and belongs to a subset of basic leucine zipper genes (bZIP) sharing a conserved structural domain designated as a cap’n’collar domain which is highly conserved in Drosphila transcription factor CNC (Fig. 2; ref. 25].

Fig. 2. Schematic Presentation of Various Domains of Nrf (Nrf1, Nrf2, Nrf3) and INrf2

Fig. 2. Schematic Presentation of Various Domains of Nrf (Nrf1, Nrf2, Nrf3) and INrf2

Nrf, NF-E2 Related Factor; INrf2, Inhibitor of Nrf2; NTR, N-Terminal Region; BTB, Broad complex, Tramtrack, Bric-a-brac; IVR, Intervening/linker Region; DGR, Kelch domain/ diglycine repeats; CTR, C-Terminal Region.

The basic region, just upstream of the leucine zipper region,

  • is responsible for DNA binding [3] and
  • the acidic region is required for transcriptional activation.

ARE-mediated transcriptional activation requires heterodimerization of Nrf2 with other bZIP proteins including Jun (c-Jun, Jun-D, and Jun-B) and small Maf (MafG, MafK, MafF) proteins [18– 20,26–27].

Initial evidence demonstrating the role of Nrf2 in antioxidant-induction of detoxifying enzymes came from studies on

  • the role of Nrf2 in ARE-mediated regulation of NQO1 gene expression [17].

Nrf2 was subsequently shown to be involved in

  • the transcriptional activation of other ARE-responsive genes such as
    • GST Ya, γ-GCS, HO-1, antioxidants, proteasomes, and drug transporters [3,6–9,28–33].

Overexpression of Nrf2 cDNA was shown to upregulate the expression and induction of the NQO1 gene in response to antioxidants and xenobiotics [17]. In addition, Nrf2-null mice exhibited a marked

  • decrease in the expression and induction of NQO1,
  • indicating that Nrf2 plays an essential role in the in vivo regulation of NQO1 in response to oxidative stress [26].

The importance of this transcription factor in upregulating ARE-mediated gene expression has been demonstrated by several in vivo and in vitro studies [reviewed in ref. 3]. The results indicate that Nrf2 is an important activator of phase II antioxidant genes [3,8].

Negative Regulation of Nrf2 mediated by INrf2

A cytosolic inhibitor (INrf2), also known as Keap1 (Kelch-like ECH-associating protein 1), of Nrf2 was identified and reported [Fig. 2; ref. 34–35]. INrf2, existing as a dimer [36], retains Nrf2 in the cytoplasm. Analysis of the INrf2 amino acid sequence and domain structure-function analyses have revealed that

  • INrf2 has a BTB (broad complex, tramtrack, bric-a-brac)/ POZ (poxvirus, zinc finger) domain and
  • a Kelch domain [34–35] also known as the DGR domain (Double glycine repeat) [37].

Keap1 has three additional domains/regions:

  1. the N-terminal region (NTR),
  2. the invervening region (IVR), and
  3. the C-terminal region (CTR) [8].

The BTB/POZ domain has been shown to be

  • a protein-protein interaction domain.

In the Drosophila Kelch protein, and in IPP,

  • the Kelch domain binds to actin [38–39]
  • allowing the scaffolding of INrf2 to the actin cytoskeleton
    • which plays an important role in Nrf2 retention in the cytosol [40].

The main function of INrf2 is to serve as

  • an adapter for the Cullin3/Ring Box 1 (Cul3/Rbx1) E3 ubiquitin ligase complex [41–43].

Cul3 serves as a scaffold protein that forms the E3 ligase complex with Rbx1 and recruits a cognate E2 enzyme [8].

INrf2

  1. via its N-terminal BTB/POZ domain binds to Cul3 [44] and
  2. via its C-terminal Kelch domain binds to the substrate Nrf2
  • leading to the ubiquitination and degradation of Nrf2 through the 26S proteasome [45–49].

Under normal cellular conditions, the cytosolic INrf2/Cul3-Rbx1 complex is constantly degrading Nrf2. When a cell is exposed to oxidative stress Nrf2 dissociates from the INrf2 complex, stabilizes and translocates into the nucleus leading to activation of ARE-mediated gene expression [3,6–9]. An alternative theory is that Nrf2 in response to oxidative stress escapes INrf2 degradation, stabilizes and translocates in the nucleus [49–50]. We suggested the theory of escape of Nrf2 from INrf2 [49] and similar suggestion was also made in another report [50]. However, the follow up studies in our laboratory could not support the escape theory. Escape theory is a possibility but has to be proven by experiments before it can be adapted. Therefore, we will use the release of Nrf2 from INrf2 in the rest of this review.

Numerous reports have suggested that

  • any mechanism that modifies INrf2 and/or Nrf2 disrupting the Nrf2:INrf2 interaction will result in the upregulation of ARE-mediated gene expression.

A model Nrf2:INrf2 signaling from antioxidant and xenobiotic to activation of ARE-mediated defensive gene expression is shown in Fig. 3.

Fig. 3. Nrf2 signaling in ARE-mediated coordinated activation of defensive genes

Fig. 3. Nrf2 signaling in ARE-mediated coordinated activation of defensive genes

Since the metabolism of antioxidants and xenobiotics results in the generation of ROS and electrophiles [51], it is thought that these molecules might act as second messengers, activating ARE-mediated gene expression. Several protein kinases including PKC, ERK, MAPK, p38, and PERK [49,52– 56] are known to modify Nrf2 and activate its release from INrf2. Among these mechanisms,

  1. oxidative/electrophilic stress mediated phosphorylation of Nrf2 at serine40 by PKC is necessary for Nrf2 release from INrf2, but
  2. is not required for Nrf2 accumulation in the nucleus [49,52–53].

In addition to post-translational modification in Nrf2, several crucial residues in INrf2 have also been proposed to be important for activation of Nrf2. Studies based on

  • the electrophile mediated modification,
  • location and
  • mutational analyses revealed
    • that three cysteine residues, Cys151, Cys273 and Cys288 are crucial for INrf2 activity [50].

INrf2 itself undergoes ubiquitination by the Cul3 complex, via a proteasomal independent pathway,

  • which was markedly increased in response to phase II inducers such as antioxidants [57].

It has been suggested that normally INrf2 targets Nrf2 for ubiquitin mediated degradation but

  • electrophiles may trigger a switch of Cul3 dependent ubiquitination from Nrf2 to INrf2 resulting in ARE gene induction.

The redox modulation of cysteines in INrf2

  • might be a mechanism redundant to the phosphorylation of Nrf2 by PKC, or that
  • the two mechanisms work in concert.

In addition to cysteine151 modification,

  • phosphorylation of Nrf2 has also been shown to play a role in INrf2 retention and release of Nrf2.

Serine104 of INrf2 is required for dimerization of INrf2, and

  • mutations of serine104 led to the disruption of the INrf2 dimer leading to the release of Nrf2 [36].

Recently, Eggler at al. demonstrated that modifying specific cysteines of the electrophile-sensing human INrf2 protein is insufficient to disrupt binding to the Nrf2 domain Neh2 (58). Upon introduction of electrophiles, modification of INrf2C151 leads to a change in the conformation of the BTB domain by means of perturbing the homodimerization site, disrupting Neh2 ubiquitination, and causing ubiquitination of INrf2. Modification of INrf2 cysteines by electrophiles does not lead to disruption of the INrf2–Nrf2 complex. Rather, the switch of ubiquitination from Nrf2 to INrf2 leads to Nrf2 nuclear accumulation.

More recently, our laboratory demonstrated that phosphorylation and de-phosphorylation of tyrosine141 in INrf2 regulates its stability and degradation, respectively [59]. The de-phosphorylation of tyrosine141 caused destabilization and degradation of INrf2 leading to the release of Nrf2. Furthermore, we showed that prothymosin-α mediates nuclear import of the INrf2/Cul3-Rbx1 complex [60]. The INrf2/Cul3-Rbx1 complex inside the nucleus exchanges prothymosin-α with Nrf2 resulting in degradation of Nrf2. These results led to the conclusion that prothymosin-α mediated nuclear import of INrf2/Cul3-Rbx1 complex leads to ubiquitination and degradation of nuclear Nrf2 presumably to regulate nuclear level of Nrf2 and rapidly switch off the activation of Nrf2 downstream gene expression. An auto-regulatory loop also exists within the Nrf2 pathway [61]. An ARE was identified in the INrf2 promoter that facilitates Nrf2 binding causing induction of the INrf2 gene. Nrf2 regulates INrf2 by controlling its transcription, and INrf2 controls Nrf2 by serving as an adaptor for degradation.

Other Regulatory Mediators of Nrf2

Bach1 (BTB and CNC homology 1, basic leucine zipper transcription factor 1) is a transcription repressor [62] that is ubiquitously expressed in tissues [63–64] and distantly related to Nrf2 [8]. In the absence of cellular stress, Bach1 heterodimers with small Maf proteins [65] that bind to the (ARE) [66] repressing gene expression. In the presence of oxidative stress, Bach1 releases from the ARE and is replaced by Nrf2. Bach1 competes with Nrf2 for binding to the ARE leading to suppression of Nrf2 downstream genes [66].

Nuclear import of Nrf2, from time of exposure to stabilization, takes roughly two hours [67]. This is followed by activation of a delayed mechanism involving Glycogen synthase kinase 3 beta (GSK3f3) that controls switching off of Nrf2 activation of gene expression (Fig. 3). GSK3f3 is a multifunctional serine/threonine kinase, which plays a major role in various signaling pathways [68]. GSK3f3 phosphorylates Fyn, a tyrosine kinase, at unknown threonine residue(s) leading to nuclear localization of Fyn [69]. Fyn phosphorylates Nrf2 tyrosine 568 resulting in nuclear export of Nrf2, binding with INrf2 and degradation of Nrf2 [70].

The negative regulation of Nrf2 by Bach1 and GSK3f3/Fyn are important in repressing Nrf2 downstream genes that were induced in response to oxidative/electrophilic stress. The tight control of Nrf2 is vital for the cells against free radical damage, prevention of apoptosis and cell survival [3,6–9,70].

Nrf2 in Cytoprotection, Cancer and Drug Resistance

Nrf2 is a major protective mechanism against xenobiotics capable of damaging DNA and initiating carcinogenesis [71]. Inducers of Nrf2 function as blocking agents that prevents carcinogens from reaching target sites, inhibits parent molecules undergoing metabolic activation, or subsequently preventing carcinogenic species from interacting with crucial cellular macromolecules, such as DNA, RNA, and proteins [72]. A plausible mechanism by which blocking agents impart their chemopreventive activity is the induction of detoxification and antioxidant enzymes [73]. Oltipraz, 3H-1,2,-dithiole-3-thione (D3T), Sulforaphane, and Curcumin can be considered potential chemopreventive agents because

  • these compounds have all been shown to induce Nrf2 [74–81].

Studies have shown a role of Nrf2 in protection against cadmium and manganese toxicity [82]. Nrf2 also plays an important role in reduction of methyl mercury toxicity [83]. Methylmercury activates Nrf2 and the activation of Nrf2 is essential for reduction of methylmercury by facilitating its excretion into extracellular space. In vitro and in vivo studies have shown a role of Nrf2 in neuroprotection and protection against Parkinson’s disease [84– 86]. Disruption of Nrf2 impairs the resolution of hyperoxia-induced acute lung injury and inflammation in mice [87]. Nrf2-knockout mice were more prone to

  • tumor growth when exposed to carcinogens such as benzo[a]pyrene, diesel exhaust, and N-nitrosobutyl (4-hydroxybutyl) amine [88–90].

INrf2/Nrf2 signaling is also shown to regulate oxidative stress tolerance and lifespan in Drosophila [91].

A role of Nrf2 in drug resistance is suggested based on its property to induce detoxifying and antioxidant enzymes (92–97). The loss of INrf2 (Keap1) function is shown to

  • lead to nuclear accumulation of Nrf2, activation of metabolizing enzymes and drug resistance (95).

Studies have reported mutations resulting in dysfunctional INrf2 in lung, breast and bladder cancers (96–100). A recent study reported that somatic mutations also occur in the coding region of Nrf2, especially in cancer patients with a history of smoking or suffering from squamous cell carcinoma (101). These mutations abrogate its interaction with INrf2 and nuclear accumulation of Nrf2. This gives advantage to

  • cancer cell survival and
  • undue protection from anti-cancer treatments.

However, the understanding of the mechanism of Nrf2 induced drug resistance remains in its infancy. In addition, the studies on Nrf2 regulated downstream pathways that contribute to drug resistance remain limited.

Future Perspectives

Nrf2 creates a new paradigm in cytoprotection, cancer prevention and drug resistance. Considerable progress has been made to better understand all mechanisms involved within the intracellular pathways regulating Nrf2 and its downstream genes. Preliminary studies demonstrate that

  • deactivation of Nrf2 is as important as activation of Nrf2.

Further studies are needed to better understand the negative regulation of Nrf2. Also better understanding of the negative regulation of Nrf2 could help design a new class of effective chemopreventive compounds not only targeting Nrf2 activation, but also

  • targeting the negative regulators of Nrf2.

Abbreviations: 

Nrf2    NF-E2 related factor 2;  INrf2   Inhibitor of Nrf2 also known as Keap1;   ROS    Reactive oxygen species.

References (1-15 of 101)

1. Breimer LH. Molecular Mechanisms of oxygen radical carcinogenesis and mutagenesis: the role of DNA base damage. Mol Carcinog 1990;3:188–197. [PubMed: 2206282]

2. Meneghini R. Iron homeostasis, oxidative stress, and DNA damage. Free Radic Biol Med 1997;23:783– 792. [PubMed: 9296456]

3. Jaiswal AK. Nrf2 signaling in coordinated activation of antioxidant gene expression. Free Radic Biol Med 2004;36:1199–1207. [PubMed: 15110384]

4. Bauer CE, Elsen S, Bird TH. Mechanisms for redox control of gene expression. Annu Rev Microbiol 1999;53:495–523. [PubMed: 10547699]

5. Zheng M, Storz G. Redox sensing by prokaryotic transcription factors. Biochem Pharm 2000;59:1–6. [PubMed: 10605928]

6. Dhakshinamoorthy S, Long DJ II, Jaiswal AK. Antioxidant regulation of genes encoding enzymes that detoxify xenobiotics and carcinogens. Current Topics in Cellular Regulation 2000;36:201–206. [PubMed: 10842753]

7. Zhang DD. Mechanistic studies of the Nrf2-Keap1 signaling pathway. Drug Metab Rev 2006;38:769– 789. [PubMed: 17145701]

8. Kobayashi M, Yamamoto M. Nrf2-Keap1 regulation of cellular defense mechanisms against electrophiles and reactive oxygen species. Adv Enzyme Regul 2006;46:113–140. [PubMed: 16887173]

9. Copple IM, Goldring CE, Kitteringham NR, Park BK. The Nrf2-Keap1 defense pathway: role in protection against drug-induced toxicity. Toxicology 2008;246:24–33. [PubMed: 18083283]

10. Halliwell, B.; Gutteridge, JMC. Free radicals in biology and medicine. Vol. 4. Oxford University Press; 2007.

  • 11. Rushmore TH, Morton MR, Pickett CB. The antioxidant responsive element. Actiavtion by oxidative stress and identification of the DNA consensus sequence required for functional activity. J Biol Chem 1991;266:11632–11639. [PubMed: 1646813]
  • 12. Rushmore TH, King RG, Paulson KE, Pickett CB. Regulation of glutathione S-transferase Ya subunit gene expression: identification of a unique xenobiotics-responsive element controlling inducible expression by planar aromatic compounds. Proc Natl Acad Sci USA 1990;87:3826–3830. [PubMed: 2160079]
  • 13. Xie T, Belinsky M, Xu Y, Jaiswal AK. ARE and TRE-mediated regulation of gene expression: response to xenobiotics and antioxidants. J Biol Chem 1995;270:6894–6900. [PubMed: 7896838]
  • 14.

Rushmore TH, Pickett CB. Glutathione S-transferases, structures, regulation, and therapeutic implications. J Biol Chem 1993;268:11475–11478. [PubMed: 8505281]

15. Jaiswal AK. Regulation of genes encoding NAD(P)H:quinone oxidoreductases. Free Radic Biol Med 2000;29:254–252. [PubMed: 11035254]

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Heroes in Medical Research: Dr. Carmine Paul Bianchi Pharmacologist, Leader, and Mentor

Posted in Cardiovascular Pharmacogenomics, Cardiovascular Research, Cell Biology, Signaling & Cell Circuits, Chemical Biology and its relations to Metabolic Disease, Health Economics and Outcomes Research, Human Sensation and Cellular Transduction: Physiology and Therapeutics, Interviews with Scientific Leaders, Ionic Transporters Na+, K, Scientist: Career considerations, tagged , , , , , , , , , , on October 29, 2013| Leave a Comment »

Heroes in Medical Research: Dr. Carmine Paul Bianchi Pharmacologist, Leader, and Mentor

Writer/Curator: Stephen J. Williams, Ph.D.

Article ID #83: Heroes in Medical Research: Dr. Carmine Paul Bianchi Pharmacologist, Leader, and Mentor. Published on 10/29/2013

WordCloud Image Produced by Adam Tubman

Past articles in this Heroes in Medical Research series had focused on those seemingly small discoveries, sometimes gained serendipitously and through careful observation and experimentation, which led to some of our most important breakthroughs of our time.  I have tried to make the posts more about the people and less about the discoveries

However, though seminal discoveries are so important to the future of science (and should be celebrated), equally if not MORE IMPORTANT is the MENTORING of future scientists and the PROMOTION of fields of study.  One person who exemplified these values was Dr. Carmine Paul Bianchi, who had recently just passed away this August, and will be sorely missed in the field of pharmacology and toxicology.

For those who were not familiar with Dr. Bianchi I have curated some pertinent information about his work as a scientist, professor and Chairman in pharmacology, and leader and spokesperson for the field of pharmacology.  He was one of the founders of the Mid-Atlantic Pharmacology Society and was an advocate and influential in the careers of many pharmacologists and toxicologists.

Comments from fellow colleagues are very welcome (in comment section at end of post)

The following is separated in 3 sections:

  • An obituary from the Philadelphia Inquirer
  •  A section of the history of the Pharmacology Department at Thomas Jefferson University where Dr. Bianchi was Chairman
  • A few important textbooks and scientific articles he had authored

 

Carmine Paul Bianchi, 86, pharmacology professor

Paul Bianchi

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Carmine Paul Bianchi

By Bonnie L. Cook, Inquirer Staff Writer

Posted: August 20, 2013

Carmine Paul Bianchi, 86, of Boothwyn, a professor of pharmacology in Philadelphia for many years, died Tuesday, Aug. 13, of a digestive ailment at Taylor Hospice House in Ridley Park.

Born in Newark, N.J., and raised in Maplewood, Dr. Bianchi served as an Army surgical technician in Tilton General Hospital at Fort Dix from 1945 to 1947.

He earned a bachelor’s degree in chemistry from Columbia University in 1950, a master’s in physiology and biochemistry from Rutgers University in 1953, and a doctorate in physiology and physical chemistry in 1956 from Rutgers.

In the 1950s, he did research at Rutgers and was a public health fellow and visiting scientist at the National Institutes of Health in Maryland.

From 1961 to 1976, he held a number of jobs in the department of pharmacology in the University of Pennsylvania School of Medicine. That culminated in his being named professor of pharmacology.

Dr. Bianchi left in 1976 for Jefferson Medical College of Thomas Jefferson University, where he became pharmacology professor and chairman of the pharmacology department from 1976 to 1987. In 1987, he stepped down from the chairmanship but remained professor of pharmacology. He retired in 1997 as professor emeritus.

Dr. Bianchi was a member of many professional groups, including the New York Academy of Sciences and the American Association for the Advancement of Science.

He was a leader and author in pharmacology, helping edit an industry journal and making himself available for consultation to medical examiners and experts in toxicology.

He wrote or contributed to three books and 200 scientific papers and lectured widely. He enjoyed mentoring medical and graduate students.

His family called Dr. Bianchi “a true renaissance man” who was as comfortable discussing English, history, and politics as he was the sciences.

 

 

 

The following was taken from a history of  Department of Pharmacology  at Thomas Jefferson University  and can be viewed at: http://jdc.jefferson.edu/cgi/viewcontent.cgi?article=1008&context=wagner2

 

 

Carmine Paul Bianchi, Ph.D;

Third Chairman (1976-1986)

The new Chairman of the Department, effective

July 1, 1976, was Carmine Paul Bianchi, Ph.D.

(Figure 8-3) from the University of Pennsylvania

School of Medicine, where he had been Professor

of Pharmacology since [969 and a member of the

faculty of that Department since 1961.

Dr. Bianchi was born on April 9, [927, in

Newark, New Jersey. After receiving his diploma

at Columbia High School in 1945, he spent two

years in the Army Medical Corps as Technical Sgt.

Fourth Grade. He then attended Columbia

University, where he majored in chemistry and

obtained the B.A. degree in 1950. Like Dr.

Gruber, the first Chairman of the Pharmacology

Department at Jefferson, Bianchi earned his Ph.D.

in physiology. He pursued his graduate studies at

Rutgers University, supplementing his physiology

major with a biochemistry minor for the M.S.

degree in [953 and with a physical chemistry minor

for the Ph.D. degree in 1956. Dr. Bianchi then

spent several years at the National Institutes of

Health-two years as a Public Health Fellow and

one as a Visiting Scientist. Following that he was

Assistant Member of the Institute for Muscle

Disease in New York for one year. In 1961 Dr.

Bianchi became classified professionally as a

pharmacologist by becoming an Associate in the

Department of Pharmacology at the University of

Pennsylvania School of Medicine. There he

advanced to Professorship in 1969 and remained

until he came to Jefferson. The evolution of Dr.

Bianchi’s career from physiology to pharmacology

was the logical result of his investigations of the

effect of various drugs on the metabolism and

distribution of some of the important elements of

the body, notably calcium. His major field of

interest became classified and remained in

electrolyte pharmacology.

Throughout his career Dr. Bianchi has been

very active in the affairs of outside professional

organizations. He is a member of the American

Society for Pharmacology and Experimental

Therapeutics, the American Physiological Society,

the American Chemical Society, and the

International Society of Toxicology, to name

only a few. He served as President of both the

Philadelphia Physiological Society and the John

Morgan Society in the same year (1973-1974), and

of the Philadelphia Chapter of the Society for

Neuroscience (1979-1980). He gave much time

and valuable services as Field Editor for the

Journal of Pharmacology and Experimental

Therapeutics ([970-1979) and as a member of the

Pharmacology Section of the National Board of

Medical Examiners (1981-1985).

After Dr. Bianchi became Chairman no

immediate changes in the general structure and

activities of the Department took place. He

enlarged the Department and filled vacancies

occasioned by the retirement of some faculty

members. The didactic schedules and subject

matter offered to the medical and graduate

students underwent only minor annual changes.

Research activities were augmented by the

addition of Dr. Bianchi’s specialty in electrolyte

pharmacology and the appointments of new staff

members for investigations in that and related

flelds. Through the following decade there was a

marked change in the faculty structure of the

Department. The [975 Jefferson catalogue, for

example, listed 15 faculty appointments in

Pharmacology, of which eight were on a primary

full-time basis with offices and laboratories in the

Department. In 1985 there were 36 faculty

appointments of which eight were on a primary

full-time basis. The large increase in the total

number of faculty resulted from adjunct

appointments from outside organizations and from

secondary appointments of faculty members of the

Clinical Departments at Jefferson. This expansion

reflected a broadening of interests and interactions

on both the scientific and clinical fronts in clinical

pharmacology and clinical toxicology.

A notable addition to the faculty of the

Department in 1978 was Dr. Hyman Menduke

as Professor of Pharmacology

(Biostatistics). After receiving his Ph.D. in

Economic Statistics at the University of

Pennsylvania, Menduke came to Jefferson in 1953

as Assistant Professor of Biostatistics with no

official Departmental affiliation until 1963, when

he was appointed Professor of Preventive

Medicine (Biostatistics). When Dr. Menduke first

came to Jefferson he gave a ten-hour course in

biostatistics to the second-year medical students in

time provided during their pharmacology course.

Through the years his offerings expanded to a

12-hour course for freshman medical students and

introductory and advanced courses for graduate

students. An early and valuable contribution was a

series of individual conferences with graduate

students on the statistical planning of their

research problems and the later analysis of their

data.

 

The interests and activities of the Department in

research in toxicology have been emphasized.

Toxicology continued as an important part of the

research program after Dr. Bianchi became

Chairman in 1976, although under his direction

the major emphasis in research became redirected

toward the general areas of cell pharmacology and

neuropharmacology.

In accord with its continuing research and

teaching activities in toxicology, the Department

starting in 1977 organized a series of annual

workshops on Industrial Toxicology sponsored by

the College of Graduate Studies. These were

four-day symposia on important toxicologic

problems in industry and the general environment,

presented by toxicologically involved Jefferson

faculty and by invited experts from other

universities, industry, and government.

In 1979 the Department was awarded a training

grant in Industrial and Environmental Toxicology

by the National Institute of Environmental Health

Sciences. The purpose of this award was to

provide postdoctoral training in toxicology for

individuals who had previously received their

Ph.D. degrees in other sciences. Ten M.S. degrees

were subsequently awarded in this program

through the years from 1981 to 1986.

On December 14, 1978, a full day’s workshop

with outside invited experts was held to discuss

the formation of a Toxicology Center and the

establishment of a Chair in Toxicology-Pathology

to broaden the base of research and training in

toxicology at Jefferson. It was envisioned that the

Center would be an administrative Division within

the Department of Pharmacology, with research

participation from other basic science departments

and the Department of Medicine. Although funds

accumulated in support of a Toxicology Center,

disagreements developed relating to the

administrative base of the Center.

 

A few articles from Dr. Bianchi showing the diversity of his research interests including calcium mobilization, neurotoxicology, and cellular metabolism and physiology.

Muscle fatigue and the role of transverse tubules.

Bianchi CP, Narayan S.

Science. 1982 Jan 15;215(4530):295-6. No abstract available.

 

Effect of adenosine on oxygen uptake and electrolyte content of frog sartorius muscle.

Prosdocimi M, Bianchi CP.

J Pharmacol Exp Ther. 1981 Jul;218(1):92-6.

 

The effect of diazepam on tension and electrolyte distribution in frog muscle.

Degroof RC, Bianchi CP, Narayan S.

Eur J Pharmacol. 1980 Aug 29;66(2-3):193-9.

 

Steady state maintenance of electrolytes in the spinal cord of the frog.

Bianchi CP, Erulkar SD.

J Neurochem. 1979 Jun;32(6):1671-7. No abstract available.

An in-vitro model of anesthetic hypertonic hyperpyrexia, halothane–caffeine-induced muscle contractures: prevention of contracture by procainamide.

Strobel GE, Bianchi CP.

Anesthesiology. 1971 Nov;35(5):465-73. No abstract available.

 

The effects of psychoactive agents on calcium uptake by preparations of rat brain mitochondria.

Tjioe S, Haugaard N, Bianchi CP.

J Neurochem. 1971 Nov;18(11):2171-8. No abstract available.

 

The effect of veratridine on sodium-sensitive radiocalcium uptake in frog sartorius muscle.

Johnson P, Bianchi CP.

Eur J Pharmacol. 1971 Sep;16(1):90-9. No abstract available.

 

The function of ATP in Ca2+ uptake by rat brain mitochondria.

Tjioe S, Bianchi CP, Haugaard N.

Biochim Biophys Acta. 1970 Sep 1;216(2):270-3. No abstract availabl

 

The effects of pH gradients on the uptake and distribution of C14-procaine and lidocaine in intact and desheathed sciatic nerve trunks.

Strobel GE, Bianchi CP.

J Pharmacol Exp Ther. 1970 Mar;172(1):18-32. No abstract available

 

 

More articles by CP Bianchi  can be found at: http://www.ncbi.nlm.nih.gov/pubmed/?term=Bianchi%20CP[auth]

The following is one of the seminal books Dr. Bianchi authored:

 

Role of Calcium Channels of the Sarcolemma and the Sarcoplasmic Reticulum in Skeletal Muscle Functions

http://link.springer.com/article/10.1007%2F978-1-4615-3362-7_17/lookinside/000.png

AND

Advances in General and Cellular Pharmacology (1976)

Toshio Narahashi; Carmine Paul Bianchi

The author of the Advances in General and Cellular Pharmacology is Toshio Narahashi; Carmine Paul Bianchi – very good writer. You can download this e-book absolutely for free. This ebook’s ISBN number is 9781461582007. if you were searching for for free download of kindle books, google books, free pdf books, pdf ebooks, e-books, pdf files or pdf ebooks just stay here for a while, download what you wanted for free and enjoy!

Advances in General and Cellular Pharmacology – Toshio Narahashi; Carmine Paul Bianchi – PDF Free Download Ebook also for Kindle

 

Other articles in this series published on this site include:

Heroes in Medical Research: Dr. Robert Ting, Ph.D. and Retrovirus in AIDS and Cancer

Heroes in Medical Research: Barnett Rosenberg and the Discovery of Cisplatin

Volume Two: Interviews with Scientific Leaders

 

 

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Genetic Analysis of Atrial Fibrillation

Posted in Calcium Signaling, Calmodulin Kinase and Contraction, Electrophysiology, Frontiers in Cardiology and Cardiovascular Disorders, tagged , , , , , , , , , , , , , , , , , , , , , , , on October 27, 2013| Leave a Comment »

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
http://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

http://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
http://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
http://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
http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/

Part II: Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility
Larry H. Bernstein, MD, FCAP, Stephen Williams, PhD and Aviva Lev-Ari, PhD, RN
http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/

Part IV: The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets
Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN
http://pharmaceuticalintelligence.com/2013/09/08/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
http://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
http://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
http://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
http://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
http://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
http://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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Heart Metabolism or Metabolic Cardiology: The Role of Ribose (D-ribose) for the Ischemic Heart – The Work of John St. Cyr, M.D., Ph.D.

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Heart Metabolism or Metabolic Cardiology: The Role of Ribose (D-ribose) for the Ischemic Heart -The Work of John St. Cyr, M.D., Ph.D.

Reporter: Aviva Lev-Ari, PhD, RN

REVIEW

An interview with John St. Cyr, M.D., Ph.D. on Ribose : A Key to Heart Health and Energy

By Richard A. Passwater, Ph.D.

 

© Whole Foods Magazine

January 2005

Ribose : A Key to Heart Health and Energy

An interview with John St. Cyr, M.D., Ph.D.

By Richard A. Passwater, Ph.D.

SOURCE

http://www.drpasswater.com/nutrition_library/John_St_Cyr.html

 

John St. Cyr, M.D., Ph.D. — PATENTS:

Issued:

Suture removal device, USP5250052

Double layer prophylactic incorporating pharmacological fluid and spiral barrier layer, USP5623945

Compositions for increasing energy in vivo, USP6159942

Method for determining viability of a myocardial segment, USP6339716

Method for raising the hypoxic threshold, USP6218366

Use of ribose to prevent cramping and soreness in muscles, USP6159943

Compositions for increasing athletic performance in mammals, USP6429198

Dual lumen adjustable length cannulae for liquid perfusion or lavage, USP6692473

Method for treating acute mountain sickness, USP6511964

Compositions for increasing energy in vivo, USP6534480

Compositions for the storage of platelets, USP6790603

Compositions for enhancing the immune response, USP6663859

Composition methods for improving cardiovascular function, USP7553817

Rejuvenation of stored blood, USP7687468

 

John St. Cyr, M.D., Ph.D. — Pending applications:

Method for improving ventilatory efficiency, SN20050277598

Storage of blood SN20070111191

Ventilatory benefits of ribose in COPD, smoking, SN

Use of ribose in recovery from anesthesia, SN20070105787

Use of ribose to alleviate rhabdomyolysis and the side effects of statin drugs, SN20060135440

Use of ribose in first response to acute myocardial infarction, SN20100055206

Compositions and methods for improving cardiovascular function, SN20100009924

Use of ribose in lessening the clinical symptoms of aberrant firing of neurons, SN20090286750

Compositions for indoor tanning, SN20090232750

Compositions for improving and repairing skin, SN20090197819

Use of ribose for recovery from anesthesia, SN20090197818

Cosmetic use of D-ribose, SN20080312169

Method for improving ventilator efficiency SN20100099630

Method and compositions for improving pulmonary hypertension, SN20080146514

Storage of blood, SN20070111191

Compositions and methods for feeding poultry, SN201100221446

Use of D-ribose for fatigued subjects, SN20100189785

Fibrin sealants and platelet concentrates applied to effect hemostasis in the interface of an implantable medical device with body tissue, SN20060190017

Compositions for reducing the deleterious effects of stress and aging, SN20120045426

 

John St. Cyr, M.D., Ph.D. — Provisional patents:

Use of ribose in pre-slaughtering of animals

Rescue therapy for acute decompensated heart failure

Combination of D-ribose plus caffeine

Role of ribose in reducing joint swelling in mammals

Role of D-ribose in cardiac remodeling

Role of D-ribose in cachexia

Use of ribose in stem cells

Use of ribose in cardioplegia

Use of ribose for doping blood for cardioplegia

Surgical adhesive for bleeding situations

Metabolic approach with EECP

Role of ribose in mitral regurgitation

Compositions for the preservation of morphology in stored blood

Methods and nutritional supplements for improving the quality of meat

 

John St. Cyr, M.D., Ph.D. — Publications 2011 to 2013

This list does not include Publication #1 to #219

220. Shecterle LM, Wagner S, St.Cyr JA.  A sugar for congestive heart failure patients.  Ther Adv Cardiovasc Dis 5(2):95-97, 2011.

221. Perkowski D, Wagner S, Schneider JR, St.Cyr JA.  A targeted metabolic protocol with D-ribose for off pump coronary artery bypass procedures: A retrospective analysis.  Ther Adv Cardiovasc Dis 5(4):185-192, 2011.

222. Foker J, Berry J, Harvey B, Befera N, Tveter K, St.Cyr J, Bianco R.  Heart failure is initiated by and progresses because of normal responses of energy metabolism to stress.  Circ Res   , 2011.

223. Rakow N, Barka N, Gerhart R, Rothstein P, Green M, Schu C, Grassl E, St.Cyr JA, Kopcak MW, Jr.  Chronic aortic root pressure-loading assessment model.  J Invest Surg 25(2):137, 2012.

224. Shecterle LM, St.Cyr JA.  Chapter 11; Myocardial Ischemia: Alterations in myocardial cellular energy and diastolic function, a potential role for D-ribose. In: Novel Strategies in Ischemia Heart Disease. Lakshmanadoss U(Ed). InTech, Croatia.  219-228, 2012.

225. Addis P, Shecterle LM, St.Cyr JA.  Cellular protection during oxidative stress: a potential role for D-ribose and antioxidants.  Journal of Dietary Supplements 9(3):178-182, 2012.

226. Holsworth R, Shecterle L, St.Cyr J, Sloop G.  Letter to the Editor.  Importance of monitoring blood viscosity during cardiopulmonary bypass.  Perfusion 28(1):91-2, 2013.

227. Seifert JG, Frost J, ST.Cyr JA.  Recovery benefits of a heat and moisture exchange mask when performing sprint exercise in cold temperature environments.  Aviation, Space and Environmental Medicine.    , 2013.

228. Seifert JG, McNair M, DeClercq P, St.Cyr JA.  A heat and moisture mask attenuates cardiovascular stress during cold air exposure.  Ther Adv Cardiovasc Dis 7(3):123-129, 2013.

229. Holsworth R, Cho Y, Weldman J, Sloop G, St.Cyr, J.  Cardiovascular benefits of phlebotomy: Relationship to changes in hemorheological variables.  Perfusion,   2013.

 

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