Funding, Deals & Partnerships: BIOLOGICS & MEDICAL DEVICES; BioMed e-Series; Medicine and Life Sciences Scientific Journal – http://PharmaceuticalIntelligence.com
The removal of thrombus under the image guidance, endovascular thrombectomy is preferred for an arterial embolism which is characteristic for an arterial blockage frequently caused by atrial fibrillation, a heart rhythm disorder. An arterial embolism causes restricted blood supply which leads to pain in the affected area. A thrombectomy can too be used to treat conditions in your organs which is usually associated with less benefit and more risk, a large retrospective study found.
Alejandro Spiotta, MD from Medical University of South Carolina in Charleston stated that functional independence rates were 45% for those treated in less than 30 minutes, 33% with procedures 30 to 60 minutes long, and 27% when procedures took more than 60 minutes. The results indicate that complications double after 50 minutes and the mortality risk is significantly for the over 60-minute group than in those treated in 30 to 60 minutes.
Earlier research has shown that when it comes to mechanical thrombectomy, procedure time has a noteworthy effect on patient outcomes. Based on these findings, it seems reasonable to conclude that at 60 minutes, one should consider the futility of continuing the procedure. However, procedures that last longer were connected with increased cost, worse outcomes, and increased incidence of complications, the investigators noted. Yet, the findings underscore the importance of timely recanalization and suggest there’s a point at which continuing to manipulate the intracranial artery may not be helpful for the patient.
Spiotta’s group evaluated 1,357 participants at seven U.S. medical centers, but only 12% out of the patients showed signs of posterior circulation stroke and 46% of cases received IV tissue-type plasminogen activator. The scientists use a prospectively-maintained database which consists of clinical and technical outcomes and baseline variables and can evaluate patients that underwent endovascular thrombectomy with direct aspiration as first pass technique or a stent retriever.
They collected their experience with the benefit of hindsight and joint it together, so there’s always a chance of case ascertain bias or other bias in the collection of the cases. One limitation is the fact that these are quality, busy centers, and the results might even worse if less experienced centers were included. It’s a little bit like getting the cream of the crop and analyzing their data. Upcoming studies should gather data on the relationship between specific thrombectomy devices and techniques and the success of recanalization procedures for patients with AIS.
3.3.8 The 3rd STATONC Annual Symposium, April 25-27, 2019, Hilton Hartford, CT, 315 Trumbull St., Hartford, CT 06103, Volume 2 (Volume Two: Latest in Genomics Methodologies for Therapeutics: Gene Editing, NGS and BioInformatics, Simulations and the Genome Ontology), Part 2: CRISPR for Gene Editing and DNA Repair
SYMPOSIUM OBJECTIVES
The three-day symposium aims to bring oncologists and statisticians together to share new research, discuss novel ideas, ask questions and provide solutions for cancer clinical trials. In the era of big data, precision medicine, and genomics and immune-based oncology, it is crucial to provide a platform for interdisciplinary dialogues among clinical and quantitative scientists. The Stat4Onc Annual Symposium serves as a venue for oncologists and statisticians to communicate their views on trial design and conduct, drug development, and translations to patient care. To be discussed includes big data and genomics for oncology clinical trials, novel dose-finding designs, drug combinations, immune oncology clinical trials, and umbrella/basket oncology trials. An important aspect of Stat4Onc is the participation of researchers across academia, industry, and regulatory agency.
Meeting Agenda will be announced coming soon. For Updated Agenda and Program Speakers please CLICK HERE
Unexpected Genetic Vulnerability to Menthol Cigarette Use
Reporter: Irina Robu, PhD
According to a study published in PLOS genetics, a group of international researchers supported by U.S. Food and Drug Administration and the National Institute of Health have found a genetic variant of MRGPRX4 gene in people of African descent that increases a smoker’s preference for cigarettes containing menthol. The FDA determined that
nearly 20 million people of African American origin in the United States smoke menthol cigarette.
Research has shown that 86 percent of African-American smokers use menthol cigarettes in comparison to the smokers of European descent which are less than 30 percent.
In this study, the researcher Andrew Griffith uncovered clues as to how menthol may reduce the irritation and harshness of smoking cigarettes. The results can help public health agencies to develop strategies to lower the rates of harmful cigarette smoking among groups particularly vulnerable.
At the same time, researchers at University of Texas Southwestern Medical Center led by Dennis Drayna, conducted a detail genetic analyses on 13000 adults using data from a multiethnic, population-based group of smokers from the Dallas Heart Study and from an African-American group of smokers from the Dallas Biobank.
The researchers report that
5 to 8 percent of the African-American study participants had the gene variant.
None of the participants of European, Asian, or Native American descent had the variant.
Recognizing the genetic variant, pointed the researchers in an unanticipated direction, leading them to offer
the first characterization of this naturally-occurring MRGPRX4 variant in humans.
The gene codes for a sensor/receptor is believed to be involved in detecting and responding to irritants from the environment in the lungs and airways.
Drayna further stated that while the gene variant can’t explain all of the increased use of menthol cigarettes by African-Americans, the results show that this variant is a theoretically vital factor that motivates the predilection for mentholcigarettes in the population.
Metastatic Gastric Cancer Treatments Indicates Mixed Results
Reporter: Irina Robu, PhD
Two novel therapies for patients with metastatic gastric cancer were evaluated by Dr. David H. Ilson of Memorial Sloan Kettering Cancer Center in New York City. In one study, trifluridine/tipiracil (FTD/TPI) was revealed to be an effective treatment for patients with metastatic gastric cancer. On the other hand, the monoclonal antibody andecaliximab plus the chemotherapy regimen mFOLFOX6 (mFOLFOX + ADX) as a first-line treatment in patients with advanced gastric or gastroesophageal junction adenocarcinoma failed to improve overall survival. He has shown the only potentially curative treatment for early stage gastric cancer is surgery, with 5-year survival rates after gastrectomy of 90% or more in Japan and Korea and 40% to 75% in non-Asian countries.
Still, the disease reappears in up to half of patients, while 40% of patients with metastatic disease have had a previous gastrectomy. The phase III TAGS study had established that FTD/TPI is safe for patients with severely pretreated metastatic gastric cancer. In this study, Ilson and his colleagues appraised the efficacy and safety of the FTD/TPI in patients with or without gastrectomy. Of the 507 patients in the study, 147 in the FTD/TPI arm had a prior gastrectomy compared to 74 in the placebo arm. The study enhances the benefit of TPI as prolonging surviving versus placebo.
According to Dr. Manish A. Shah, the mixture of mFOLFOX6 and ADX exposed encouraging anti-tumor activity in patients with gastric or gastroesophageal junction adenocarcinoma according to a prior I/IB study. He showed a phase III, randomized, double-blind, multicenter study associating the efficiency and safety of mFOLFOX with or without ADX in patients with untreated HER2-negative gastric or gastroesophageal junction adenocarcinoma.
The main endpoint was complete survival with secondary endpoints of evolution free survival, objective response rate and safety. According to Dr. Shah, thee apparent increased activity of the mixture of mFOLFOX with ADX in patients ages 65 or older needs further study.
Robert A. Harrington, MD: Hi. This is Bob Harrington from Stanford University on Medscape Cardiology and the heart.org. Thanks for joining us today. Over the last couple of months, I have interviewed a couple of physician authors who have commented on some of the issues of the time, whether that’s related to the electronic health records, as in our discussions with Bob Wachter, or the revolution that’s taking place with wearables and patient centric data, as with my conversation with Eric Topol. These shows have been well received, and those conversations were useful to the community to have a frame of reference not just in regard to the daily practice of medicine but also in terms of how the bigger issues in society around digital health, wearables, electronic records, etc., might influence the practice of medicine.
Today we’re also going to interview a physician author but with a different approach to some of the issues within medicine. He is trying to get at how physicians and other clinical providers take what we learn from the literature, from scientific studies that have been done, and use some combination of statistics coupled with the art of caring for other human beings in a way that ends up with first-rate medical care. And this author not only wondered how we do that but also how we begin to think about teaching that to our trainees, to our students, our residents, our fellows, etc. I’m really pleased to have with me today the author of the book, The Science of the Art of Medicine, which is just coming out in its second edition. The author is Dr John Brush. John is a professor of medicine at Eastern Virginia Medical School and a practicing cardiologist in Norfolk, Virginia. John and I are friends and colleagues through his service as a recent board of trustee member for the American College of Cardiology (ACC). John, thanks for joining us here today on Medscape Cardiology.
John E. Brush, MD: Thanks very much, Bob. It’s a pleasure being here, and I look forward to our conversation.
Cognitive Psychology
Dr Harrington: You heard my opening remarks reflecting on you as a physician author. What made you write this book? What were some of the issues that compelled you to put pen to paper, so to speak, and try to formulate some view of the world in medicine as you saw it?
Dr Brush: One of the origins of this book was actually my work with the ACC related to quality of care and quality and care initiatives. I was chairman of the Quality Strategic Direction Committee for 3 years and really got thinking about quality and why people make errors. What leads to good judgment? What leads to bad judgment? And that led me to stumble upon the cognitive psychology literature. When I was in college, there was no such thing as cognitive psychology; it was mostly behavioral psychology. The field of cognitive psychology has arisen in the past 30 years. Cognitive psychologists are interested in understanding how people make decisions. Doctors make decisions every single day, and yet we don’t teach anything about cognitive psychology, and we don’t learn anything about it in medical school. It seemed like it was a relevant area to think about, and so I jumped in. I taught myself cognitive psychology. I bought book after book after book and just decided I was going read about it.
What cognitive psychologists have figured out is that we’re not purely rational; we think we are, but we make mistakes. We fall into typical traps. We have biases because we use heuristics or mental short cuts to try to make rapid decisions under conditions of uncertainty. If you think about it, that’s what doctors do every single day: make rapid decisions under conditions of uncertainty. I wanted to learn more about it myself. And then the more I read about it, I jokingly said to my wife, “You know, I could write a book on this stuff.” And then I stumbled on this ibook authoring tool, and I figured out that it’s actually easy to write an ibook—easier than I thought. And so I said, “I am going to write a book about it.” I thought it was going to take me 5 years; it took me about 3 months because I was so consumed by this.
Dr Harrington: Before you dive further, let me ask you a couple of things about cognitive psychology because it is interesting that you took this observation of people making errors and then did the deeper dive, which I applaud you for. I’m thinking of some other authors in this area that some of our listeners might know about, such as Dan Ariely from Duke and the book,Predictably Irrational. Would you put that in the context of cognitive psychology?
Dr Brush: Yes, he’s a well-known cognitive psychologist. There’s also Daniel Kahneman who wrote, Thinking Fast, Thinking Slow. Daniel Kahneman is from Princeton and a very prominent writer in cognitive psychology. A guy named Gerd Gigerenzer from Germany has written books on risk and how to evaluate risk—basically how to think about probability. His books were very influential for me. There’s Herbert Simon who was a computer scientist in Pittsburgh, and he is generally thought of as the father of cognitive psychology; he was at Carnegie Mellon. Cognitive psychology is also referred to as behavioral economics, by the way, and both Herbert Simon and Daniel Kahneman have won the Nobel Prize in Economics.
Dr Harrington: That’s what I wanted you to get at for the audience—that it’s not just interesting, but it’s useful to read outside of medicine and then bring those ideas into how we think about medicine. You’ve hit upon something in that we don’t teach our students and resident how people actually make decisions, but, as you’ve described, that’s what a clinician does every day. You said that the ibook was an easy thing to do. Why did you pick a book and not write a commentary for a journal or give a lecture on the topic? Why did you go right into a book? That’s a pretty big leap.
Natural Bayesians Placing Bets
Dr Brush: The topic is too broad to cover in a single lecture or single commentary. I did write a series of blog posts that were posted on cardioexchange.org. Harlan Krumholz invited me to do that, and it was very helpful because it enabled me to collect my thoughts and to test out ways of explaining this. One of the challenges is explaining a subject matter that is outside medicine. You’re bringing a new area to people’s attention, and so you have to figure out how to go about explaining it. I’d done this series of blog posts; I did a series of lectures for our residents here in Norfolk. And I have to tell you that the first lectures I did on this utterly failed because they were dull, they were boring. I had to figure out how to make it interesting because if you discuss it in the setting of a patient and you study it on rounds, it’s absolutely fascinating. But if you talk about it in isolation, it’s like a statistics course in isolation where the subject matter is too dry. The way you make it interesting is by relating it to patients, so I started collecting examples, and there are simple examples in cardiology.
Cardiology is the ideal place to teach medical reasoning because we almost get immediate feedback. You can put your money down and make your bets as to whether somebody is having a myocardial infarction or whether somebody has heart failure or whether somebody has a pulmonary embolism. Generally by the next day you find out, and so you get this immediate feedback where you can train your intuition. We have so many numerical things from troponin levels, D-dimer, stress tests, and what have you. You can begin to develop estimates of probability so that you can start to calibrate your intuition. And there’s no better place in medicine than cardiology to that as an exercise.
Dr Harrington: One of the things I love about the book is that you spend time, particularly at the beginning of the book, going through the basics of statistics that help us in the practice of medicine. And one area you spend time talking about is Bayes’ theorem and the notion that while we use both frequentist and Bayesian statistics in medicine, in many ways clinicians are natural Bayesians, aren’t we?
Dr Brush: We are, but we don’t think hard enough about what that means. We wander into being Bayesians, and we pick that up in our training. It’s a good idea to step back, dig a little bit deeper, and ask exactly what that means. The cognitive psychologists tell us that we make mistakes by deviating from a normative mode of thinking in one of two ways. We either do things that are illogical, or we make bad probability estimates. I wanted to think about the logic of what we do as well as the probability.
I talk a little bit in the book about deductive reasoning and inductive reasoning, but really what we do is something called abductive reasoning, where we reason to the most plausible hypothesis. Most of the time, we don’t really know. If somebody comes in with heart failure, we think it may be untreated hypertension or alcoholism or whatever, but we don’t really know. We reason towards the most plausible hypothesis. When we’re using Bayesian reasoning, we’re using probability estimates, and so knowing something about probability is a good idea. We think that we know about probability because we play cards, we throw dice, we play games of chance all the time, but probability can be simple and complex and complementary and cumulative probability and conditional probability. Bayesian reasoning is all about conditional probability.
We think like Bayesians, but we never do the calculations. Instead we use a heuristic called anchoring and adjusting. Anchoring and adjusting is a natural way that you use Bayesian reasoning where you come up with an anchor, which is your pretest probability, and you adjust that anchor (that pretest probability) based on new information to give you a posttest probability. We intuitively know how to use this heuristic called anchoring and adjusting, and that’s actually what we do in practice, rather than pulling out our calculator and calculating the Bayesian probabilities.
Dr Harrington: What you’re describing in part is what makes the great clinician, and the great clinician as you described has to have a grounding in statistical methods, has to understand what probability is and how one thinks about it. At the same time, the great clinician has to have a body of experience upon which to anchor that probability; and then finally, the great clinician has to be able to be observant and trust his or her intuition. Is that a reasonable way to put it?
Teaching Good Decision-Making
Dr Brush: When you look back on your colleagues or mentors who just seemed to get it, one characteristic that describes the person who just seemed to get it is that they made good bets. They had a good sense of what’s most likely, what’s least likely. They were savvy about how they made those calculations. They had good intuition about how they made those judgments.
How do you teach that? Well, you can teach people about the processes and give them an idea of where they want to be at the end of the day. If you can break it down a little bit, it helps. You can use simple numbers. If you simplify the numbers, you can use the numbers to calibrate your intuition. There’s something in medicine that we are leaving out. We talk all the time about sensitivity and specificity, and yet when you ask physicians, “give me a quick definition of sensitivity and specificity,” they’re not able to do it. You lose track of what’s in the numerator and what’s in the denominator.
People get all balled up with those definitions that sensitivity relates to true positive and specificity to true negative. That’s a little bit more descriptive of what you’re actually talking about. But there’s something even better, and that’s likelihood ratios because a positive likelihood ratio is the probability of something divided by the probability that something is not there. It’s the evidence for something divided by the evidence against it, and so it’s a dimensionless number. You don’t even have to keep track of what’s in the numerator or the denominator because it’s a dimensionless number.
Likelihood ratios are incredibly powerful. Say you’re trying to teach a trainee how important a positive troponin is. How important is a positive nuclear stress test? How important is a D dimer? If a D dimer test is negative, that is a very important negative piece of information; but if it’s positive, it’s not quite so strong. That’s because the positive likelihood ratio for a D dimer is about 2, and the negative likelihood ratio is more like 0.1. You can use likelihood ratios to really hone your intuition, which is very useful when you’re trying to import years of wisdom to new trainees. You can’t wait 30 years for them to finally get it; you want then to get it on Day 1.
We’ve been talking about diagnosis so far, but for therapeutics you need to figure out how to calibrate your intuition on treatment decisions. The inverse of the absolute risk reduction is the number needed to treat, and the smaller the better. If you’ve got some sense of what a good number needed to treat is, then suddenly you can calibrate your intuition about what’s really important and what’s not so important. Beta blockers and aspirin and angiotensin-converting enzyme inhibitors are really important; maybe glycoprotein IIb/IIIa antagonists are less important. The number needed to treat gives you a way to gauge the importance of therapeutic interventions.
Dr Harrington: I love the direction you’re going, which is to take what we do and recognize that there’s a science behind it. For a lot of the therapeutics you just mentioned, there are clinical trials that inform us about what those numbers needed to treat are and what the numbers needed to harm are. But as you rightly said, the art of medicine is understanding who is likely to benefit from this particular therapy when you make that bet. We’re not yet in this era of precision health of being able to say, “Based on your characteristics (which may include genomic characteristics), you are more likely to benefit from this and less likely to benefit from that.” But you’re helping move us to that, and I love that you’re helping us think about a framework for teaching it. You clearly wrote this as a teacher to students, and whether that’s a teacher to a colleague like me or a teacher to residents, students, and fellows in some ways doesn’t matter. What’s been the reaction from the student community, “student” being broadly defined?
Dr Brush: I use it to teach internal medicine residents. I have a new internal medicine resident with me each month; we have 12 locally and 12 months of the year, so I have a new one each month. I have them read the book at night, and we talk about the book during the day, but we talk about the book in the context of taking care of patients. In a way, it’s an inverted classroom where they’re getting the lecture from me by reading the book (it would be odd for me to sit and lecture them on these things), but we talk about it in the setting of a real patient. We’ll go in and see a patient in the emergency department who’s short of breath, and maybe our pretest probability that its heart failure is 50/50, and so the pretest odds is 1. You can then multiply that by the likelihood ratios of paroxysmal nocturnal dyspnea and orthopnea and rales and S3 gallop and congestion or a chest x-ray. And you end up with a posttest probability that you can calculate in your head; when we do that during the day, it suddenly makes what they’ve read at night very real. I’ve gotten really good feedback on that. It’s one of those things where you hit your forehead and go, “Oh, now I get it.”
You can see the light bulb turning on, and they really appreciate it. It also helps them get a handle on uncertainty. Uncertainly scares the heck out of us when we’re in training. Some of us run away from it and create this illusion of certainty, but—let’s face it—the uncertainty and the ambiguity of medicine scares the heck out of all of us. But it really scares people during training, and you don’t know whether you don’t know it or whether it’s unknown to everybody. You don’t know the level of uncertainty of things, and by addressing that straight on, you can try to get a handle on how you can reason through that uncertainty and how you can move from a place where you don’t know to a place where you have a better idea but also understand that there are some things that you’ll never know for sure but you’ve got to get as close as you can. For the residents that I work with, that takes a little bit of pressure off and helps them. It helps them grasp what the challenge is, and the challenge is learning as much as they possibly can, getting as much experience as they can so that they can put it all into action in a way that’s systematic and organized and tends to make sense.
Dr Harrington: John, my last question for you: What have you learned from this? You entered this as a student trying to understand decision-making, and it led you to an exploration of cognitive psychology. You felt that it would help make you a better teacher, and you wrote a book. What did you learn from the whole exercise of the last few years?
Dr Brush: I’ve learned so much about cognitive psychology, about probability, about logic, and that has helped me be a better decision-maker in general. Even beyond medicine, but certainly it’s made me a better clinician and a better teacher. I’ve been able to articulate what I’m thinking in a way that makes better sense to students. What I’ve learned, though, is that we’re not teaching this, and we’re also not testing on this. It’s much easier to test on content; you can turn some medical fact into a multiple choice question a whole lot easier than you can figure out whether somebody actually gets it and is able to put the knowledge into action. We don’t want to just impart knowledge; we want to impart wisdom. We want to be able to use knowledge wisely. We’ve now got this rush of information that’s coming at as from every direction over the Internet and in so many different ways. We need to turn that information into knowledge that helps us get closer to the truth, and to do that we’ve got to learn how to use the knowledge wisely. As a community of physicians, of professionals, and also a community of academicians, we need to think about how to teach this stuff. How do we test it? How do we develop competencies in this area? For me, it’s created a whole new way of looking at medical education and really what matters and what we really ought to be concentrating on.
Dr Harrington: This has been a fascinating conversation, John, and I applaud you for taking the initiative not just to learn the background material but to write the book and share it with the rest of us. And I certainly recommend to our listener audience John Brush’s book, The Science of the Art of Medicine. You can get it through iBooks or you can get a hard copy of it. So my guest today has been Dr John Brush, a professor of medicine at Eastern Virginia Medical School and a practicing cardiologist in Norfolk, Virginia. John, thanks for joining us on Medscape Cardiology.
This article is a followup of the wonderful study of the effect of oxidation of a methionine residue in calcium dependent-calmodulin kinase Ox-CaMKII on stabilizing the atrial cardiomyocyte, giving protection from atrial fibrillation. It is also not so distant from the work reviewed, mostly on the ventricular myocyte and the calcium signaling by initiation of the ryanodyne receptor (RyR2) in calcium sparks and the CaMKII d isoenzyme.
We refer to the following related articles published in pharmaceutical Intelligence:
The material presented is very focused, and cannot be found elsewhere in Pharmaceutical Intelligence with respedt to genetics and heart disease. However, there are other articles that may be of interest to the reader.
PART 3. Determinants of Cardiovascular Diseases: Genetics, Heredity and Genomics Discoveries
3.2 Leading DIAGNOSES of Cardiovascular Diseases covered in Circulation: Cardiovascular Genetics, 3/2010 – 3/2013
The Diagnoses covered include the following – relevant to this discussion
MicroRNA in Serum as Bimarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis, diastolic dysfunction, and acute heart failure
Genomics of Ventricular arrhythmias, A-Fib, Right Ventricular Dysplasia, Cardiomyopathy
Heredity of Cardiovascular Disorders Inheritance
3.2.1: Heredity of Cardiovascular Disorders Inheritance
The implications of heredity extend beyond serving as a platform for genetic analysis, influencing diagnosis,
prognostication, and
treatment of both index cases and relatives, and
enabling rational targeting of genotyping resources.
This review covers acquisition of a family history, evaluation of heritability and inheritance patterns, and the impact of inheritance on subsequent components of the clinical pathway.
3.2.2.1 MicroRNA in Serum as Biomarker for Cardiovascular Pathologies: acute myocardial infarction, viral myocarditis, diastolic dysfunction, and acute heart failure
Increased MicroRNA-1 and MicroRNA-133a Levels in Serum of Patients With Cardiovascular Disease Indicate Myocardial Damage
Y Kuwabara, Koh Ono, T Horie, H Nishi, K Nagao, et al.
SOURCE: Circulation: Cardiovascular Genetics. 2011; 4: 446-454 http://dx.doi.org/10.1161/CIRCGENETICS.110.958975
3.2.2.2 Circulating MicroRNA-208b and MicroRNA-499 Reflect Myocardial Damage in Cardiovascular Disease
3.2.4.2 Large-Scale Candidate Gene Analysis in Whites and African Americans Identifies IL6R Polymorphism in Relation to Atrial Fibrillation
The National Heart, Lung, and Blood Institute’s Candidate Gene Association Resource (CARe) Project
RB Schnabel, KF Kerr, SA Lubitz, EL Alkylbekova, et al.
SOURCE: Circulation: Cardiovascular Genetics.2011; 4: 557-564 http://dx.doi.org/10.1161/CIRCGENETICS.110.959197
Weighted Gene Coexpression Network Analysis of Human Left Atrial Tissue Identifies Gene Modules Associated With Atrial Fibrillation
N Tan, MK Chung, JD Smith, J Hsu, D Serre, DW Newton, L Castel, E Soltesz, G Pettersson, AM Gillinov, DR Van Wagoner and J Barnard
From the Cleveland Clinic Lerner College of Medicine (N.T.), Department of Cardiovascular Medicine (M.K.C., D.W.N.), and Department of Thoracic & Cardiovascular Surgery (E.S., G.P., A.M.G.); and Department of Cellular & Molecular Medicine (J.D.S., J.H.), Genomic Medicine Institute (D.S.), Department of Molecular Cardiology (L.C.), and Department of Quantitative Health Sciences (J.B.), Cleveland Clinic Lerner Research Institute, Cleveland, OH
Circ Cardiovasc Genet. 2013;6:362-371; http://dx.doi.org/10.1161/CIRCGENETICS.113.000133 http://circgenetics.ahajournals.org/content/6/4/362The online-only Data Supplement is available at http://circgenetics.ahajournals.org/lookup/suppl/doi:10.1161/CIRCGENETICS.113.000133/-/DC1
Background—Genetic mechanisms of atrial fibrillation (AF) remain incompletely understood. Previous differential expression studies in AF were limited by small sample size and provided limited understanding of global gene networks, prompting the need for larger-scale, network-based analyses.
Methods and Results—Left atrial tissues from Cleveland Clinic patients who underwent cardiac surgery were assayed using Illumina Human HT-12 mRNA microarrays. The data set included 3 groups based on cardiovascular comorbidities: mitral valve (MV) disease without coronary artery disease (n=64), coronary artery disease without MV disease (n=57), and lone AF (n=35). Weighted gene coexpression network analysis was performed in the MV group to detect modules of correlated genes. Module preservation was assessed in the other 2 groups. Module eigengenes were regressed on AF severity or atrial rhythm at surgery. Modules whose eigengenes correlated with either AF phenotype were analyzed for gene content. A total of 14 modules were detected in the MV group; all were preserved in the other 2 groups. One module (124 genes) was associated with AF severity and atrial rhythm across all groups. Its top hub gene, RCAN1, is implicated in calcineurin-dependent signaling and cardiac hypertrophy. Another module (679 genes) was associated with atrial rhythm in the MV and coronary artery disease groups. It was enriched with cell signaling genes and contained cardiovascular developmental genes including TBX5.
Conclusions—Our network-based approach found 2 modules strongly associated with AF. Further analysis of these modules may yield insight into AF pathogenesis by providing novel targets for functional studies. (Circ Cardiovasc Genet. 2013;6:362-371.)
trial fibrillation (AF) is the most common sustained cardiac arrhythmia, with a prevalence of ≈1% to 2% in the general population.1,2 Although AF may be an isolated condition (lone AF [LAF]), it often occurs concomitantly with other cardiovascular diseases, such as coronary artery disease (CAD) and valvular heart disease.1 In addition, stroke risk is increased 5-fold among patients with AF, and ischemic strokes attributed to AF are more likely to be fatal.1 Current antiarrhythmic drug therapies are limited in terms of efficacy and safety.1,3,4 Thus, there is a need to develop better risk prediction tools as well as mechanistically targeted therapies for AF. Such developments can only come about through a clearer understanding of its pathogenesis.
Family history is an established risk factor for AF. A Danish Twin Registry study estimated AF heritability at 62%, indicating a significant genetic component.5 Substantial progress has been made to elucidate this genetic basis. For example, genome-wide association studies (GWASs) have identified several susceptibility loci and candidate genes linked with AF. Initial studies performed in European populations found 3 AF-associated genomic loci.6–9 Of these, the most significant single-nucleotide polymor-phisms (SNPs) mapped to an intergenic region of chromosome 4q25. The closest gene in this region, PITX2, is crucial in left-right asymmetrical development of the heart and thus seems promising as a major player in initiating AF.10,11 A large-scale GWAS meta-analysis discovered 6 additional susceptibility loci, implicating genes involved in cardiopulmonary development, ion transport, and cellular structural integrity.12
Differential expression studies have also provided insight into the pathogenesis of AF. A study by Barth et al13 found that about two-thirds of the genes expressed in the right atrial appendage were downregulated during permanent AF, and that many of these genes were involved in calcium-dependent signaling pathways. In addition, ventricular-predominant genes were upregulated in right atrial appendages of subjects with AF.13 Another study showed that inflammatory and transcription-related gene expression was increased in right atrial appendages of subjects with AF versus controls.14 These results highlight the adaptive responses to AF-induced stress and ischemia taking place within the atria.
Despite these advances, much remains to be discovered about the genetic mechanisms of AF. The AF-associated SNPs found thus far only explain a fraction of its heritability15; furthermore, the means by which the putative candidate genes cause AF have not been fully established.9,15,16 Additionally, previous differential expression studies in human tissue were limited to the right atrial appendage, had small sample sizes, and provided little understanding of global gene interactions.13,14 Weighted gene coexpression network analysis (WGCNA) is a technique to construct gene modules within a network based on correlations in gene expression (ie, coexpression).17,18 WGCNA has been used to study genetically complex diseases, such as metabolic syndrome,19 schizophrenia,20 and heart failure.21 Here, we obtained mRNA expression profiles from human left atrial appendage tissue and implemented WGCNA to identify gene modules associated with AF phenotypes.
Methods
Subject Recruitment
From 2001 to 2008, patients undergoing cardiac surgery at the Cleveland Clinic were prospectively screened and recruited. Informed consent for research use of discarded atrial tissues was obtained from each patient by a study coordinator during the presurgical visit. Demographic and clinical data were obtained from the Cardiovascular Surgery Information Registry and by chart review. Use of human atrial tissues was approved by the Institutional Review Board of the Cleveland Clinic.
Table S1: Clinical definitions of cardiovascular phenotype groups
Criterion Type
Mitral Valve (MV) Disease
Coronary Artery Disease (CAD)
Lone Atrial Fibrillation (LAF)
Inclusion Criteria
Surgical indication –
Surgical indication –
History of atrial fibrillation
mitral valve repair or replacement
coronary artery bypass graft
Surgical indication
– MAZE procedure
Preserved ejection fraction (≥50%)
Exclusion Criteria
Significant coronary artery disease:
Significant mitral valve disease:
Significant
coronary artery
– Significant (≥50%) stenosis
– Documented echocardiography
disease:
in at least
finding of
– Significant
one coronary artery
mitral regurgitation (≥3) or
(≥50%) stenosis in
via cardiac catheterization
mitral stenosis
at least one
– History of revascularization
– History of mitral valve
coronary artery via
(percutaneous coronary intervention or coronary artery bypass graft surgery)
repair or replacement
cardiac catheterization
– History of revascularization
(percutaneous coronary intervention or coronary artery bypass graft surgery)
Significant valvular heart disease:
-Documented echocardiography finding of valvular regurgitation (≥3) or stenosis
-History of valve repair or replacement
RNA Microarray Isolation and Profiling
Left atria appendage specimens were dissected during cardiac surgery and stored frozen at −80°C. Total RNA was extracted using the Trizol technique. RNA samples were processed by the Cleveland Clinic Genomics Core. For each sample, 250-ng RNA was reverse transcribed into cRNA and biotin-UTP labeled using the TotalPrep RNA Amplification Kit (Ambion, Austin, TX). cRNA was quantified using a Nanodrop spectrophotometer, and cRNA size distribution was assessed on a 1% agarose gel. cRNA was hybridized to Illumina Human HT-12 Expression BeadChip arrays (v.3). Arrays were scanned using a BeadArray reader.
Expression Data Preprocessing
Raw expression data were extracted using the beadarray package in R, and bead-level data were averaged after log base-2 transformation. Background correction was performed by fitting a normal-gamma deconvolution model using the NormalGamma R package.22 Quantile normalization and batch effect adjustment with the ComBat method were performed using R.23 Probes that were not detected (at a P<0.05 threshold) in all samples as well as probes with relatively lower variances (interquartile range ≤log2[1.2]) were excluded.
The WGCNA approach requires that genes be represented as singular nodes in such a network. However, a small proportion of the genes in our data have multiple probe mappings. To facilitate the representation of singular genes within the network, a probe must be selected to represent its associated gene. Hence, for genes that mapped to multiple probes, the probe with the highest mean expression level was selected for analysis (which often selects the splice isoform with the highest expression and signal-to-noise ratio), resulting in a total of 6168 genes.
Defining Training and Test Sets
Currently, no large external mRNA microarray data from human left atrial tissues are publicly available. To facilitate internal validation of results, we divided our data set into 3 groups based on cardiovascular comorbidities: mitral valve (MV) disease without CAD (MV group; n=64), CAD without MV disease (CAD group; n=57), and LAF (LAF group; n=35). LAF was defined as the presence of AF without concomitant structural heart disease, according to the guidelines set by the European Society of Cardiology.1 The MV group, which was the largest and had the most power for detecting significant modules, served as the training set for module derivation, whereas the other 2 groups were designated test sets for module reproducibility. To minimize the effect of population stratification, the data set was limited to white subjects. Differences in clinical characteristics among the groups were assessed using Kruskal–Wallis rank-sum tests for continuous variables and Pearson x2 test for categorical variables.
Weight Gene Coexpression Network Analysis
WGCNA is a systems-biology method to identify and characterize gene modules whose members share strong coexpression. We applied previously validated methodology in this analysis.17 Briefly, pair-wise gene (Pearson) correlations were calculated using the MV group data set. A weighted adjacency matrix was then constructed. I is a soft-thresholding parameter that provides emphasis on stronger correlations over weaker and less meaningful ones while preserving the continuous nature of gene–gene relationships. I=3 was selected in this analysis based on the criterion outlined by Zhang and Horvath17 (see the online-only Data Supplement).
Next, the topological overlap–based dissimilarity matrix was computed from the weighted adjacency matrix. The topological overlap, developed by Ravasz et al,24 reflects the relative interconnectedness (ie, shared neighbors) between 2 genes.17 Hence, construction of the network dendrogram based on this dissimilarity measure allows for the identification of gene modules whose members share strong intercon-nectivity patterns. The WGCNA cutreeDynamic R function was used to identify a suitable cut height for module identification via an adaptive cut height selection approach.18 Gene modules, defined as branches of the network dendrogram, were assigned colors for visualization.
Network Preservation Analysis
Module preservation between the MV and CAD groups as well as the MV and LAF groups was assessed using network preservation statistics as described in Langfelder et al.25 Module density–based statistics (to assess whether genes in each module remain highly connected in the test set) and connectivity-based statistics (to assess whether connectivity patterns between genes in the test set remain similar compared with the training set) were considered in this analysis.25 In each comparison, a Z statistic representing a weighted summary of module density and connectivity measures was computed for every module (Zsummary). The Zsummary score was used to evaluate module preservation, with values ≥8 indicating strong preservation, as proposed by Langfelder et al.25 The WGCNA R function network preservation was used to implement this analysis.25
Table S2: Network preservation analysis between the MV and CAD groups – size and Zsummary scores of gene modules detected.
Module
Module Size
ZSummary
Black
275
15.52
Blue
964
44.79
Brown
817
12.80
Cyan
119
13.42
Green
349
14.27
Green-Yellow
215
19.31
Magenta
239
15.38
Midnight-Blue
83
15.92
Pink
252
23.31
Purple
224
16.96
Red
278
17.30
Salmon
124
13.84
Tan
679
28.48
Turquoise
1512
44.03
Table S3: Network preservation analysis between the MV and LAF groups – size and Zsummary scores of gene modules detected
Module
Module Size
ZSummary
Black
275
13.14
Blue
964
39.26
Brown
817
14.98
Cyan
119
11.46
Green
349
14.91
Green-Yellow
215
20.99
Magenta
239
18.58
Midnight-Blue
83
13.87
Pink
252
19.10
Purple
224
8.80
Red
278
16.62
Salmon
124
11.57
Tan
679
28.61
Turquoise
1512
42.07
Clinical Significance of Preserved Modules
Principal component analysis of the expression data for each gene module was performed. The first principal component of each module, designated the eigengene, was identified for the 3 cardiovascular disease groups; this served as a summary expression measure that explained the largest proportion of the variance of the module.26 Multivariate linear regression was performed with the module ei-gengenes as the outcome variables and AF severity (no AF, paroxysmal AF, persistent AF, permanent AF) as the predictor of interest (adjusting for age and sex). A similar regression analysis was performed with atrial rhythm at surgery (no AF history, AF history in sinus rhythm, AF history in AF rhythm) as the predictor of interest. The false discovery rate method was used to adjust for multiple comparisons. Modules whose eigengenes associated with AF severity and atrial rhythm were identified for further analysis.
In addition, hierarchical clustering of module eigengenes and selected clinical traits (age, sex, hypertension, cholesterol, left atrial size, AF state, and atrial rhythm) was used to identify additional module–trait associations. Clusters of eigengenes/traits were detected based on a dissimilarity measure D, as given by
D=1−cor(Vi,Vj),i≠j (3)
where V=the eigengene or clinical trait.
Enrichment Analysis
Gene modules significantly associated with AF severity and atrial rhythm were submitted to Ingenuity Pathway Analysis (IPA) to determine enrichment for functional/disease categories. IPA is an application of gene set over-representation analysis; for each dis-ease/functional category annotation, a P value is calculated (using Fisher exact test) by comparing the number of genes from the module of interest that participate in the said category against the total number of participating genes in the background set.27 All 6168 genes in the current data set served as the background set for the enrichment analysis.
Hub Gene Analysis
Hub genes are defined as genes that have high intramodular connectivity17,20
Alternatively, they may also be defined as genes with high module membership21,25
Both definitions were used to identify the hub genes of modules associated with AF phenotype.
To confirm that the hub genes identified were themselves associated with AF phenotype, the expression data of the top 10 hub genes (by intramodular connectivity) were regressed on atrial rhythm (adjusting for age and sex). In addition, eigengenes of AF-associated modules were regressed on their respective (top 10) hub gene expression profiles, and the model R2 indices were computed.
Membership of AF-Associated Candidate Genes From Previous Studies
Previous GWAS studies identified multiple AF-associated SNPs.8,9,12,15,28 We selected candidate genes closest to or containing these SNPs and identified their module locations as well as their closest within-module partners (absolute Pearson correlations).
Sensitivity Analysis of Soft-Thresholding Parameter
To verify that the key results obtained from the above analysis were robust with respect to the chosen soft-thresholding parameter (I=3), we repeated the module identification process using I=5. The eigen-genes of the detected modules were computed and regressed on atrial rhythm (adjusting for age and sex). Modules significantly associated with atrial rhythm in ≥2 groups of data set were compared with the AF phenotype–associated modules from the original analysis.
Results
Subject Characteristics
Table 1 describes the clinical characteristics of the cardiac surgery patients who were recruited for the study. Subjects in the LAF group were generally younger and less likely to be a current smoker (P=2.0×10−4 and 0.032, respectively). Subjects in the MV group had lower body mass indices (P=2.7×10−6), and a larger proportion had paroxysmal AF compared with the other 2 groups (P=0.033).
Table 1. Clinical Characteristics of Study Subjects
A total of 14 modules were detected using the MV group data set (Figure 1), with module sizes ranging from 83 genes to 1512 genes; 38 genes did not share similar coexpression with the other genes in the network and were therefore not included in any of the identified modules
Figure 1. Network dendrogram (top) and colors of identified modules (bottom). The dendrogram was constructed using the topological overlap matrix as the similarity measure. Modules corresponded to branches of the dendrogram and were assigned colors for visualization.
Network Preservation Analysis Revealed Strong Preservation of All Modules Between the Training and Test Sets
All 14 modules showed strong preservation across the CAD and LAF groups in both comparisons, with Z [summary] scores of >10 in most modules (Figure 2). No major deviations in the Z [summary] score distributions for the 2 comparisons were noted, indicating that modules were preserved to a similar extent across the 2 groups
Figure 2. Preservation of modules between mitral valve (MV) and coronary artery disease (CAD) groups (left), and MV and lone atrial fibrillation (LAF) groups (right). A Zsummary statistic was computed for each module as an overall measure of its preservation relating to density and connectivity. All modules showed strong preservation in both comparisons with Zsummary scores >8 (red dotted line).
Regression Analysis of Module Eigengene Profiles Identified 2 Modules Associated With AF Severity and Atrial Rhythm
Table IV in the online-only Data Supplement summarizes the proportion of variance explained by the first 3 principal components for each module. On average, the first principal component (ie, the eigengene) explained ≈18% of the total variance of its associated module. For each group, the module eigengenes were extracted and regressed on AF severity (with age and sex as covariates). The salmon module (124 genes) eigengene was strongly associated with AF severity in the MV and CAD groups (P=1.7×10−6 and 5.2×10−4, respectively); this association was less significant in the LAF group (P=9.0×10−2). Eigengene levels increased with worsening AF severity across all 3 groups, with the greatest stepwise change taking place between the paroxysmal AF and persistent AF categories (Figure 3A). When the module eigen-genes were regressed on atrial rhythm, the salmon module eigengene showed significant association in all groups (MV: P=1.1×10−14; CAD: P=1.36×10−6; LAF: P=2.1×10−4). Eigen-gene levels were higher in the AF history in AF rhythm category (Figure 3B).
Table S4:Proportion of variance explained by the principal components for each module.
Dataset
Group
Principal
Component
Black
Blue
Brown
Cyan
Green
Green-
Yellow
Magenta
Mitral
1
20.5%
22.2%
20.1%
21.8%
21.4%
22.8%
19.6%
2
4.1%
3.6%
4.8%
5.7%
4.5%
5.9%
3.9%
3
3.4%
3.1%
3.8%
4.4%
3.9%
3.7%
3.7%
CAD
1
12.5%
18.6%
7.1%
16.8%
12.2%
20.3%
12.8%
2
6.0%
5.5%
5.0%
7.0%
5.5%
6.1%
6.4%
3
4.9%
4.1%
4.4%
6.5%
4.8%
4.4%
4.8%
LAF
1
14.0%
16.6%
11.7%
14.3%
14.7%
20.8%
20.2%
2
8.9%
8.5%
7.6%
9.3%
7.3%
11.1%
6.9%
3
6.5%
6.3%
5.5%
8.2%
6.1%
5.3%
6.2%
Dataset
Group
Principal
Component
Midnight- Blue
Pink
Purple
Red
Salmon
Tan
Turquoise
Mitral
1
28.5%
22.6%
18.7%
20.5%
22.3%
19.0%
25.8%
2
4.6%
6.0%
4.7%
4.1%
6.9%
4.0%
3.5%
3
4.2%
4.2%
4.2%
3.5%
4.0%
3.6%
3.3%
CAD
1
23.4%
17.1%
15.5%
15.0%
18.0%
14.6%
18.2%
2
7.4%
8.6%
6.0%
6.4%
7.2%
5.8%
6.6%
3
5.1%
5.4%
5.3%
5.4%
6.2%
5.1%
4.5%
LAF
1
23.5%
18.4%
12.0%
15.9%
16.9%
13.7%
16.5%
2
7.9%
8.5%
9.8%
9.4%
9.5%
9.1%
9.6%
3
6.7%
7.0%
6.6%
6.0%
6.9%
6.8%
6.3%
Figure 3. Boxplots of salmon module eigengene expression levels with respect to atrial fibrillation (AF) severity (A) and atrial rhythm (B).
A, Eigengene expression correlated positively with AF severity, with the largest stepwise increase between the paroxysmal AF and permanent AF categories. B, Eigengene expression was highest in the AF history in AF rhythm category in all 3 groups. CAD indicates coronary artery disease; LAF, lone AF; and MV, mitral valve.
The regression analysis also revealed statistically significant associations between the tan module (679 genes) eigengene and atrial rhythm in the MV and CAD groups (P=5.8×10−4 and 3.4×10−2, respectively). Eigengene levels were lower in the AF history in AF rhythm category compared with the AF history in sinus rhythm category (Figure 4); this trend was also observed in the LAF group, albeit with weaker statistical evidence (P=0.15).
Figure 4. Boxplots of tan module eigengene expression levels with respect to atrial rhythm. Eigengene expression levels were lower in the atrial fibrillation (AF) history in AF rhythm category compared with the AF history in sinus rhythm category. CAD indicates coronary artery disease; LAF, lone AF; and MV, mitral valve
Hierarchical Clustering of Eigengene Profiles With Clinical Traits
Hierarchical clustering was performed to identify relationships between gene modules and selected clinical traits. The salmon module clustered with AF severity and atrial rhythm; in addition, left atrial size was found in the same cluster, suggesting a possible relationship between salmon module gene expression and atrial remodeling (Figure 5A). Although the tan module was in a separate cluster from the salmon module, it was negatively correlated with both atrial rhythm and AF severity (Figure 5B).
Figure 5. Dendrogram (A) and correlation heatmap (B) of module eigengenes and clinical traits.
A, The salmon module eigengene but not the tan module eigengene clustered with atrial fibrillation (AF) severity, atrial rhythm, and left atrial size. B, AF severity and atrial rhythm at surgery correlated positively with the salmon module eigengene and negatively with the tan module eigengene. Arhythm indicates atrial rhythm at surgery; Chol, cholesterol; HTN, hypertension; and LASize, left atrial size.
IPA Enrichment Analysis of Salmon and Tan Modules
The salmon module was enriched in genes involved in cardiovascular function and development (smallest P=4.4×10−4) and organ morphology (smallest P=4.4×10−4). In addition, the top disease categories identified included endocrine system disorders (smallest P=4.4×10−4) and cardiovascular disease (smallest P=2.59×10−3).
The tan module was enriched in genes involved in cell-to-cell signaling and interaction (smallest P=8.9×10−4) and cell death and survival (smallest P=1.5×10−3). Enriched disease categories included cancer (smallest P=2.2×10−4) and cardiovascular disease (smallest P=4.5×10−4).
We identified hub genes in the 2 modules based on intramod-ular connectivity and module membership. For the salmon module, the gene RCAN1 exhibited the highest intramodular connectivity and module membership. The top 10 hub genes (by intramodular connectivity) were significantly associated with atrial rhythm, with false discovery rate–adjusted P values ranging from 1.5×10−5 to 4.2×10−12. These hub genes accounted for 95% of the variation in the salmon module eigengene.
In the tan module, the top hub gene was CPEB3. The top 10 hub genes (by intramodular connectivity) correlated with atrial rhythm as well, although the statistical associations in the lower-ranked hub genes were relatively weaker (false discovery rate–adjusted P values ranging from 1.1×10−1 to 3.4×10−4). These hub genes explained 94% of the total variation in the tan module eigengene.
The names and connectivity measures of the hub genes found in both modules are presented in Table 2.
Table 2. Top 10 Hub Genes in the Salmon (Left) and Tan (Right) Modules as Defined by Intramodular Connectivity and Module Membership
Salmon Module
Tan Module
Gene
IMC
Gene
MM
Gene
IMC
Gene
MM
RCAN1
8.2
RCAN1
0.81
CPEB3
43.3
CPEB3
0.85
DNAJA4
7.7
DNAJA4
0.81
CPLX3
42.4
CPLX3
0.84
PDE8B
7.7
PDE8B
0.80
NEDD4L
40.8
NEDD4L
0.83
PRKAR1A
6.9
PRKAR1A
0.77
SGSM1
40.7
SGSM1
0.82
PTPN4
6.7
PTPN4
0.75
UCKL1
39.0
UCKL1
0.81
SORBS2
6.0
FHL2
0.69
SOSTDC1
37.2
SOSTDC1
0.79
ADCY6
5.7
ADCY6
0.69
PRDX1
35.5
RCOR2
0.78
FHL2
5.7
SORBS2
0.68
RCOR2
35.4
EEF2K
0.77
BVES
5.4
DHRS9
0.67
NPPB
35.3
PRDX1
0.76
TMEM173
5.3
LAPTM4B
0.65
LRRN3
34.6
MMP11
0.76
A visualization of the salmon module is shown using the Cytoscape tool (Figure 6). A full list of the genes in the salmon and tan modules is provided in the online-only Data Supplement.
Figure 6. Cytoscape visualization of genes in the salmon module.
Nodes representing genes with high intramodu-lar connectivities, such as RCAN1 and DNAJA4, appear larger in the network. Strong connections are visualized with darker lines, whereas weak connections appear more translucent
Membership of AF-Associated Candidate Genes From Previous Studies
The tan module contained MYOZ1, which was identified as a candidate gene from the recent AF meta-analysis. PITX2 was located in the green module (n=349), and ZFHX3 was located in the turquoise module (n=1512). The locations of other candidate genes (and their closest partners) are reported in the online-only Data Supplement.
Sensitivity Analysis of Key Results
We repeated the WGCNA module identification approach using a different soft-thresholding parameter (β=5). One module (n=121) was found to be strongly associated with atrial rhythm at surgery across all 3 groups of data set, whereas another module (n=244) was associated with atrial rhythm at surgery in the MV and CAD groups. The first module overlapped significantly with the salmon module in terms of gene membership, whereas most of the second modules’ genes were contained within the tan module. The top hub genes found in the salmon and tan modules remained present and highly connected in the 2 new modules identified with the different soft-thresholding parameter.
Discussion
To our knowledge, our study is the first implementation of an unbiased, network-based analysis in a large sample of human left atrial appendage gene expression profiles. We found 2 modules associated with AF severity and atrial rhythm in 2 to 3 of our cardiovascular comorbidity groups. Functional analyses revealed significant enrichment of cardiovascular-related categories for both modules. In addition, several of the hub genes identified are implicated in cardiovascular disease and may play a role in AF initiation and progression.
In our study, WGCNA was used to construct modules based on gene coexpression, thereby reducing the net-work’s dimensionality to a smaller set of elements.17,21 Relating modulewise changes to phenotypic traits allowed statistically significant associations to be detected at a lower false discovery rate compared with traditional differential expression studies. Furthermore, shared functions and pathways among genes in the modules could be inferred via enrichment analyses.
We divided our data set into 3 groups to verify the reproducibility of the modules identified by WGCNA; 14 modules were identified in the MV group in our gene network. All were strongly preserved in the CAD and LAF groups, suggesting that gene coexpression patterns are robust and reproducible despite differences in cardiovascular comorbidities.
The use of module eigengene profiles as representative summary measures has been validated in a number of studies.20,26 Additionally, we found that the eigengenes accounted for a significant proportion (average 18%) of gene expression variability in their respective modules. Regression analysis of the module eigengenes found 2 modules associated with AF severity and atrial rhythm in ≥2 groups of data set. The association between the salmon module eigengene and AF severity was statistically weaker in the LAF group (adjusted P=9.0×10−2). This was probably because of its significantly smaller sample size compared with the MV and CAD groups. Despite this weaker association, the relationship between the salmon module eigengene and AF severity remained consistent among the 3 groups (Figure 3A). Similarly, the lack of statistical significance for the association between the tan module eigengene and atrial rhythm at surgery in the LAF group was likely driven by the smaller sample size and (by definition) lack of samples in the no AF category.
A major part of our analysis focused on the identification of module hub genes. Hubs are connected with a large number of nodes; disruption of hubs therefore leads to widespread changes within the network. This concept has powerful applications in the study of biology, genetics, and disease.29,30 Although mutations of peripheral genes can certainly lead to disease, gene network changes are more likely to be motivated by changes in hub genes, making them more biologically interesting targets for further study.17,29,31 Indeed,
the hub genes of the salmon and tan modules accounted for the vast majority of the variation in their respective module eigengenes, signaling their importance in driving gene module behavior.
The hub genes identified in the salmon and tan modules were significantly associated with AF phenotype overall. It was noted that this association was statistically weaker for the lower-ranked hub genes in the tan module. This highlights an important aspect and strength of WGCNA—to be able to capture module-wide changes with respect to disease despite potentially weaker associations among individual genes.
The implementation of WGCNA necessitated the selection of a soft-thresholding parameter 13. Unlike hard-thresholding (where gene correlations below a certain value are shrunk to zero), the soft-thresholding approach gives greater weight to stronger correlations while maintaining the continuous nature of gene–gene relationships. We selected a 13 value of 3 based on the criteria outlined by Zhang and Horvath.17 His team and other investigators have demonstrated that module identification is robust with respect to the 13 parameter.17,19–21 In our data, we were also able to reproduce the key findings reported with a different, larger 13 value, thereby verifying the stability of our results relating to 13.
The salmon module (124 genes) was associated with both AF phenotypes; furthermore, IPA analysis of its gene contents suggested enrichment in cardiovascular development as well as disease. Its eigengene increased with worsening AF severity, with the largest stepwise change occurring between the paroxysmal AF and persistent AF categories (Figure 3). Hence,
the gene expression changes within the salmon module may reflect the later stages of AF pathophysiology.
The top hub gene of the salmon module was RCAN1 (regulator of calcineurin 1). Calcineurin is a cytoplasmic Ca2+/ calmodulin-dependent protein phosphatase that stimulates cardiac hypertrophy via its interactions with NFAT and L-type Ca2+ channels.32,33RCAN1 is known to inhibit calcineurin and its associated pathways.32,34 However, some data suggest that RCAN1 may instead function as a calcineurin activator when highly expressed and consequently potentiate hypertrophic signaling.35 Thus,
perturbations in RCAN1 levels (attributable to genetic variants or mutations) may cause an aberrant switching in function, which in turn triggers atrial remodeling and arrhythmogenesis.
Other hub genes found in the salmon module are also involved in cardiovascular development and function and may be potential targets for further study.
DNAJA4 (DnaJ homolog, subfamily A, member 4) regulates the trafficking and maturation of KCNH2 potassium channels, which have a prominent role in cardiac repolarization and are implicated in the long-QT syndromes.36
FHL2 (four-and-a-half LIM domain protein 2) interacts with numerous cellular components, including
actin cytoskeleton,
transcription machinery, and
ion channels.37
FHL2 was shown to enhance the hypertrophic effects of isoproterenol, indicating that
FHL2 may modulate the effect of environmental stress on cardiomyocyte growth.38
FHL2 also interacts with several potassium channels in the heart, such as KCNQ1, KCNE1, and KCNA5.37,39
Additionally, blood vessel epicardial substance (BVES) and other members of its family were shown to be highly expressed in cardiac pacemaker cells. BVES knockout mice exhibited sinus nodal dysfunction, suggesting that BVES regulates the development of the cardiac pacemaking and conduction system40 and may therefore be involved in the early phase of AF development.
The tan module (679 genes) eigengene was negatively correlated with atrial rhythm in the MV and CAD groups (Figure 4); this may indicate a general decrease in gene expression of its members in fibrillating atrial tissue. IPA analysis revealed enrichment in genes involved in cell signaling as well as apoptosis. The top-ranked hub gene, cytoplasmic polyade-nylation element binding protein 3 (CPEB3), regulates mRNA translation and has been associated with synaptic plasticity and memory formation.41 The role of CPEB3 in the heart is currently unknown, so further exploration via animal model studies may be warranted.
Natriuretic peptide-precursor B (NPPB), another highly interconnected hub gene, produces a precursor peptide of brain natriuretic peptide, which
regulates blood pressure through natriuresis and vasodilation.42
(NPPB) gene variants have been linked with diabetes mellitus, although associations with cardiac phenotypes are less clear.42 TBX5 and GATA4, which play important roles in the embryonic heart development,43 were members of the tan module. Although not hub genes, they may also contribute toward developmental susceptibility of AF. In addition, TBX5 was previously reported to be near an SNP associated with PR interval and AF in separate large-scale GWAS studies.12,28 MYOZ1, another candidate gene identified in the recent AF GWAS meta-analysis, was found to be a member as well; it associates with proteins found in the Z-disc of skeletal and cardiac muscle and may suppress calcineurin-dependent hypertrophic signaling.12
Some, but not all, of the candidate genes found in previous GWAS studies were located in the AF-associated modules. One possible explanation for this could be the difference in sample sizes. The meta-analysis involved thousands of individuals, whereas the current study had <100 in each group of data set, which limited the power to detect significant differences between levels of AF phenotype even with the module-wise approach. Additionally, transcription factors like PITX2 are most highly expressed during the fetal phase of development. Perturbations in these genes (attributable to genetic variants or mutations) may therefore initiate the development of AF at this stage and play no significant role in adults (when we obtained their tissue samples).
Limitations in Study
We noted several limitations in this study. First, no human left atrial mRNA data set of adequate size currently exists publicly. Hence, we were unable to validate our results with an external, independent data set. However, the network preservation assessment performed within our data set showed strong preservation in all modules, indicating that our findings are robust and reproducible.
Although the module eigengenes captured a significant proportion of module variance, a large fraction of variability did remain unaccounted for, which may limit their use as representative summary measures.
We extracted RNA from human left atrial appendage tissue, which consists primarily of cardiomyocytes and fibroblasts. Atrial fibrosis is known to occur with AF-associated remodeling.44 As such, the cardiomyocyte to fibroblast ratio is likely to change with different levels of AF severity, which in turn influences the amount of RNA extracted from each cell type. Hence, true differences in gene expression (and coexpression) within cardiomyocytes may be confounded by changes in cellular composition attributable to atrial remodeling. Also, there may be significant regional heterogeneity in the left atrium with respect to structure, cellular composition, and gene expression,45 which may limit the generaliz-ability of our results to other parts of the left atrium.
All subjects in the study were whites to minimize the effects of population stratification. However, it is recognized that the genetic basis of AF may differ among ethnic groups.9 Thus, our results may not be generalizable to other ethnicities.
Finally, it is possible for genes to be involved in multiple processes and functions that require different sets of genes. However, WGCNA does not allow for overlapping modules to be formed. Thus,
this limits the method’s ability to characterize such gene interactions.
Conclusions
In summary, we constructed a weighted gene coexpression network based on RNA expression data from the largest collection of human left atrial appendage tissue specimens to date. We identified 2 gene modules significantly associated with AF severity or atrial rhythm at surgery. Hub genes within these modules may be involved in the initiation or progression of AF and may therefore be candidates for functional studies.
Refererences
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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.
This article is about the measure of central tendancy and dispersion of values around the center (mean or median), the underpinning of parametric methods of comparison of 2 or more sets of data. More importantly, it is the beginning of a statistical journey. The clinical laboratory deals with large volumes of patient data. The use of a parametric approach is limited and is prone to problems introduced in the clinical domain. Consequently, Galen and Gambino introduced the concept of predictive value and the effect of prevalence in a Bayesian context in Beyond Normality. These calculations work off of tables, the same tables that are used for sensitivity and specificity, and are used to calculate a chi squared probability. The subsequent influence of epidemiology went further in introducing odds and odds ratios. The third and last article will address the more recent advances beyond, beyond normality. These improvements have all come about by the development of a powerful statistical methodology that is not constraint by the parametric paradigm and is well developed for hypothesis generation and validation, not just testing of simple hypotheses.
We have grown up with the normal curve and have incorporated it in our thinking, not just our work. Even the use of the term Six Sigma for reduction of errors has reference to the classical “normal curve” introduced by Johann Carl Friedrich Gauss (1777–1855). The normal or “bell shaped” curve is a plot of numerical values along the x-axis and the frequency of the occurrence on the y-axis. If the set of measurements occurs as a random and independent event, we refer to this as parametric, and the distribution of the values is a bell shaped curve with all but 2.5% of the values included within both ends, with the mean or arithmetic average at the center, and with 67% of the sample contained within 1 standard deviation of the mean. The reference to normality has been used with respect to student test scores, with respect to coin flipping and games of chance, with respect to investment, and in our experience with respect to errors of quality controlled measurements. The expected value we refer to as the mean (closest to the true value), and the distance from the mean (or scatter) we refer to as dispersion, measured as the standard deviation. Viewed in this light, we can convert the curve from a standard curve with an actual mean to a standard normal curve with a mean at the center of “0”, and with distances from “0” in standard deviations. A bad example of this is the distribution of serum AST measurements of a large unselected population enrolled in a clinical trial. The AST values tend to have many high values, which we call skewness to the right of the curve, so the behavior we are looking for is better described by a log transformation of the values to minimize nonlinearities in the measurement. This is illustrated by the comparison of AST and log(AST) in Figure 1.
What has not been said is that we view a reference range in terms of a homogeneous population. This means that while all values might not be the same, the values are scattered within a distance from the mean that becomes less frequent as the distance is larger so that we can describe a mean and a 95% confidence interval around the mean. In mineralogy we can measure physical elements that have structure defined by a relationship of structure to spectral lines. Hence, the scatter about the mean is very small because of the precise measurements, even though the quantity may be very small. This is not necessarily the case with clinical laboratory measurement because of hidden variables, such as – age, diurnal variation, racial factors, and disease. One way to level the playing field is to compose uniform specimens for quality control that are representative of a population for comparison of laboratory measurements among many laboratories, which is established practice. What is assumed is that a “normal” population is that population that is found after we remove bias, or contamination of the population by the hidden variable effects mentioned above. Therefore, parametric statistics is actually a comparison of one or more populations that are to be compared with the hypothetical normal population. The test of significance is a comparison of A and B with the assumption that they are sampled from the same population, but when they are found to have different means and confidence intervals by a t-test or an analysis of variance, we reject the “null hypothesis” and conclude that they are different based on a p (significance) less than 5%. There are basic assumptions that are required when we use the parametric paradigm. The distributions of the samples are the same, normality, the variances are the same, and errors are independent. Consequently, when comparing 2 samples, as for a placebo and a test drug, these assumptions must hold (which is inherent in the logistic regression). When we run quality control material, the confidence lines that we use are equivalent to a normal curve turned on its side. When doing the t-test, the parametric limitations have to be followed. A result of this is that a minimum of 40 samples are required because as N approaches 40 and over the fit of the data to a normal distribution is more likely. This is a daily phenomenon in laboratories globally – it takes about 10 – 14 days to be confident about the reference range for a new lot of quality control material, regardless of high, low or normal. Nevertheless, we have to ask whether we can use a small sample size to validate the reference range of a population sample. The answer is not so simple. One can minimize sampling bias by taking a sample of blood donors who are prescreened for serious medical conditions. The use of laboratory staff donors historically introduced selection bias when the staff was uniformly younger. On the other hand, the amount of computing power readily available to the average practitioner has substantially improved in the last 5 years, and middleware may offer a further opportunity for improvement. One can download a file with two weeks of results for any test and review and exclude outliers to the established values for the method. The substantial remaining sample has at least 1,000 patients to work with. Another method would use a nonparametric adjustment of the data by randomly removing a patient at a time and recalculating. We are not here concerned with distributional assumptions and population parameters. We work only with the data, and we observe the effects of recalculation. That is an uncommon and unfamiliar approach.
We proceed to the important problem of comparing 2 variables. Figure 1 is a bivariate plot of data with log(AST) and log(ALT) on each axis. The result is a scattergram with 95 and 99 percent confidence limits for a reference range formed from two liver tests that meet the parametric constraints. The scattergram shown in Figure 2 may show correlation, method A and method B, distinctly different, but having a linear association between them. The parametric assumption holds, and the confidence interval along the so called regression line is determined by ordinary least square regression (OLS). The subject of regression is a subject worthy of a separate topic.
The next topic is comparing two classes of subjects that we expect to be different because of effects on each group. This can be represented by the plot of means and standard deviations between patients with ovarian cancer who underwent chemotherapy and either had no or short remission, or had a remission of 20 months, defining treatment success (Figure 2). The result of means comparison is significant at p < 0.01 using the t-test (Figure 3). But what if we were to take the same data and compare the patients with no remission, small remission, and complete remission? One would do the one-way analysis of variance (ANOVA1), which uses the F test (Fisher’s variance ratio). F is the same as t squared, or t is the square root of F. The result would again be significant at p < 0.01.
This is a light review of very important methods used in both clinical and research laboratory studies. They have a history of widespread use going back at least 5 decades, and certainly in experimental physics before biology, although it is from biological observations that we have Fisher’s discriminant function, which gives a linear distance between classified variable, i.e., petal length and petal width. The discussion to follow will be concerned with tables and the chi squared distribution.
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