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Patient Shares Story: Hep C Heart Transplant

Posted in Aortic Valve: TAVR, TAVI vs Open Heart Surgery, Cancer Surgery of the Heart, Heart Transplant, Patient Experience, Patient Experience: Personal Memories of Invasive Medical Intervantion, Patient’s Voice: Personal Experience with Invasive Medical Procedures, Uncategorized, tagged heart, Heart disease, Heart transplantation, Patient, physician-patient relationship on March 10, 2019| Leave a Comment »

Reporter: Gail S. Thornton

This article appeared on the website of Cardiovascular Business.

‘Patient No. 1’ from a Hep C heart transplant study shares his story

March 07, 2019

Daniel Allar

Tom Giangiulio Jr. and his wife, Carin, celebrate his 58th birthday on June 6, 2017, two weeks before Giangiulio received a transplant with a Hepatitis C-infected heart.

By the time three transplant physicians approached Tom Giangiulio Jr. about being the first patient in a new clinical trial to accept a heart from a Hepatitis C-positive donor, Giangiulio didn’t have much of a choice.

He had already been on the heart transplant waitlist for more than two years, he was a live-in at the Hospital of the University of Pennsylvania and he had a body size (6-foot-2, 220 pounds) and blood type (O-positive) that was difficult to match to a donor.

It took Giangiulio less than 24 hours to speak to his previous cardiologist and his family and decide to enroll in the program. The doctors at Penn explained to him that because of new medications that can cure Hepatitis C, they were confident the virus could be eradicated post-transplant.

“There was no hesitation at all, not with me,” said Carin Giangiulio, Tom’s wife of 33 years. “Because I knew what the alternative was and we didn’t have too much choice except for going on a VAD (ventricular assist device) … and he didn’t want to do that. I said, ‘If they have a cure, then it’s a no-brainer. Let’s just do it.’ And I’m glad we did because I don’t think he would’ve been here today.”

Tom, 59, is set to celebrate his second anniversary with his new heart in June. He received the heart the day after Father’s Day in 2017 and subsequently contracted Hepatitis C, which was promptly wiped out with a 12-week regimen of elbasvir/grazoprevir (Zepatier).

Some of Giangiulio’s doctors at Penn published in February their experience with the first 10 patients in the clinical trial, called USHER, in the American Journal of Transplantation. All nine patients who survived were cured of Hepatitis C thanks to the antiviral therapy.

The implications of the research are massive, said Rhondalyn McLean, MD, MHS, the medical director of Penn’s heart transplant program and lead author of the recently published study. For the past two decades, the U.S. has struggled to increase the number of heart transplants above about 3,000 per year. And every year, patients die waiting for a heart transplant or become too sick to handle a transplant surgery.

McLean estimated 700 hearts from donors with Hepatitis C are discarded each year in the U.S. If even half of those are suitable for transplant, it would increase by 10 percent the number of organs that are available for implantation.

“There are so many people who have end-stage heart failure who die waiting for transplant, so anytime that we can increase our access to organs then I think we’re all going to be happy about that,” McLean said. “I think the people believe in the medicine, they believe that Hepatitis C is curable, so the risk to these folks is low. With the results of the study, I think we’ve proven that we can do this safely and the medications have great efficacy.”

Transplanting Hepatitis C-positive hearts isn’t a new idea, McLean explained.

“We used to do this all the time (with) the thinking that Hepatitis C usually doesn’t cause a problem for many, many years, so if hearts are only going to last 13 years or so and Hepatitis C doesn’t usually cause a problem for 30 years in someone, it should be an OK thing to do,” she said.

But then a study published in the 1990s found Hepatitis C-negative patients who accepted a heart from a donor with Hepatitis C actually had an increased risk of death compared to those who received normal hearts, and the practice of using these organs ceased.

However, with the new medications—the first commercially available treatment for Hepatitis C was approved by the FDA in 2014—McLean and her team are systematically studying the safety of implanting these hearts and then wiping out the virus once it’s contracted. And they’re optimistic about the program, which showed the first 10 patients had no evidence of the virus after their 12-week medication regimens.

“That met the criteria for sustained virologic response and those patients are deemed to be cured,” she said. “There’s no reason to think that this population would be any different than your normal, nontransplant population (in terms of Hepatitis C reappearing) so I think it was a pretty successful study.”

Penn researchers are also studying a similar approach in kidney and lung transplant candidates, which could help patients stuck on waitlists for those organs as well.

McLean described the increasing availability of these organs as an “unfortunate benefit” of the opioid epidemic. Through sharing needles, many opioid users are contracting Hepatitis C and dying young. Organs from young donors tend to perform better and often have no other problems, so solving the Hep C issue through medication could have a huge impact if this strategy is eventually rolled out on a broader scale.

“It’s hard when you have single-center studies,” McLean said. “They’re always promising, but in order to get a better assessment of what we’re doing and how the drug is doing I think you need to combine numbers so there has to be a registry that looks at all of the patients who have received these drugs and then using numbers to determine whether this is a successful strategy for us. And I believe that it will be.”

Those are the large-scale implications of this research. Tom Giangiulio can share the personal side.

Patient No. 1

Giangiulio said he feels “extremely gifted” to be Patient No. 1 in the USHER program. He knows he may not be alive if he wasn’t.

He recalls going into ventricular tachycardia about a week before his transplant and said it “scared the daylights” out of him.

“The amount of red tape, meetings and research, technology, and things that had to happen at a very precise moment in time for me to be the first … it’s mind-boggling to think about it,” he said. “But for all that to happen and for it to happen when it happened—and for me to get the heart when I got it—there was a lot of divine intervention along with a lot of people that were involved.”

Giangiulio has also experienced some powerful moments since receiving the transplant. After a bit of written correspondence with his donor’s family, he met the young man’s family one weekend in December of 2018.

He said riding over to the meeting was probably the most tense he’s ever been, but once he arrived the experience far exceeded his expectations.

“We were there for 2 ½ hours and nobody wanted to leave,” Giangiulio said.

The donor’s mother got Giangiulio a gift, a ceramic heart with a photograph of her son. A fellow transplant patient had told Giangiulio about a product called Enso, a kidney-shaped object you can hold in your hand which plays a recording of a user’s heartbeat.

Giangiulio decided to give it to her.

“I was very cautious at the advice of the people here at Penn,” he said. “Nobody knew how she would react to it. It might bother her, she could be thrilled to death. And she was, she was thrilled to death with it and she sleeps with it every night. She boots up the app and she listens to my heartbeat on that app every night.”

Another moment that sticks out to Giangiulio is meeting Patient No. 7 in the USHER program, who he remains in touch with. They ran into each other while waiting to get blood work done, and began talking about their shared experience as transplant recipients.

The clinical trial came up and Giangiulio slow-played his involvement, asking Patient No. 7 about the trial and not letting on that he was ultra-familiar with the program.

When Giangiulio finally told him he was Patient No. 1, Patient No. 7 “came launching out of his chair” to hug him.

“He said, ‘I owe you my life,’” Giangiulio recalled.

After Giangiulio responded that it was the doctors he really owed, Patient No. 7 said he had specifically asked how Patient No. 1 was doing when McLean first offered the program to him.

“She explained that I was going to be No. 7. … I didn’t care about 6, 5, 4, 3 or 2. I wanted to know how No. 1 was doing,” Giangiulio recalled of the conversation. “He said, ‘That was you. … They told me how well you were doing and that if I wanted you’d come here and talk to me, so I owe you.’”

Giangiulio feels strongly about giving back and reciprocating the good fortune he’s had. That’s why he talks to fellow patients and the media to share his story—because it could save other people’s lives, too.

He can’t do as much physical labor as he used to, but he remains involved in the excavating company he owns with his brothers and is the Emergency Management Coordinator for Waterford Township, New Jersey. He also serves on the township’s planning board and was previously Director of Public Safety.

“To me, he’s Superman,” Carin Giangiulio said. “It was insane, completely insane what the human body can endure and still survive.”

That now includes being given a heart with Hepatitis C and then wiping out the virus with the help of modern medicine.

“I would tell (other transplant candidates) to not fear it, especially if you’re here at Penn,” Giangiulio said. “I know there’s a lot of good hospitals across the country, but my loyalty kind of lies here for understandable reasons.”

Editor’s note:

I wish to encourage the e-Reader of this Interview to consider reading and comparing the experiences of other Open Heart Surgery Patients, voicing their private-life episodes in the ER that are included in this recently published volume, The VOICES of Patients, Hospital CEOs, Health Care Providers, Caregivers and Families: Personal Experience with Critical Care and Invasive Medical Procedures.

https://pharmaceuticalintelligence.com/2017/11/21/the-voices-of-patients-hospital-ceos-health-care-providers-caregivers-and-families-personal-experience-with-critical-care-and-invasive-medical-procedures/

I also wish to encourage the e-Reader to consider, if interested, reviewing additional e-Books on Cardiovascular Diseases from the same Publisher, Leaders in Pharmaceutical Business Intelligence (LPBI) Group, on Amazon.com.

Perspectives on Nitric Oxide in Disease Mechanisms, on Amazon since 6/2/12013

http://www.amazon.com/dp/B00DINFFYC

Cardiovascular, Volume Two: Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation, on Amazon since 11/30/2015

http://www.amazon.com/dp/B018Q5MCN8

Cardiovascular Diseases, Volume Three: Etiologies of Cardiovascular Diseases: Epigenetics, Genetics and Genomics, on Amazon since 11/29/2015

http://www.amazon.com/dp/B018PNHJ84

Cardiovascular Diseases, Volume Four: Regenerative and Translational Medicine: The Therapeutics Promise for Cardiovascular Diseases, on Amazon since 12/26/2015

http://www.amazon.com/dp/B019UM909A

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What is the key method to harness Inflammation to close the doors for many complex diseases?

Posted in Biomarkers & Medical Diagnostics, Calcium Signaling, CANCER BIOLOGY & Innovations in Cancer Therapy, Genome Biology, Human Immune System in Health and in Disease, Human Sensation and Cellular Transduction: Physiology and Therapeutics, Immunodiagnostics, Molecular Genetics & Pharmaceutical, Personalized and Precision Medicine & Genomic Research, Pharmaceutical Analytics, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Pharmacogenomics, Systemic Inflammatory Response Related Disorders, Translational Research, Translational Science, tagged angiogenesis, Blood, Cancer - General, coagulationi, complement activation, Dee Sag, Demet Sag, diagnostics, endothelial cell activation, fibrinolysis, genomics screen, heart, immunomodulation, inflammation, inflammation disorders, inflammatory diseases, kinin system, liver, lymphatic system, Plasma, review, Therapy on March 21, 2014| Leave a Comment »

What is the key method to harness Inflammation to close the doors for many complex diseases?

Author and Curator: Larry H Bernstein, MD, FCAP

The main goal is to have a quality of a healthy life.

When we look at the picture 90% of main fluid of life, blood, carried by cardiovascular system with two main pumping mechanisms, lung with gas exchange and systemic with complex scavenger actions, collection of waste, distribution of nutrition and clean gases etc. Yet without lymphatic system body can’t make up the 100% fluid. Therefore, 10% balance is completed by lymphatic system as a counter clockwise direction so that not only the fluid balance but also mass balance is maintained. Finally, the immune system patches the remaining mechanism by providing cellular support to protect the body because it contains 99% of white cells to fight against any kinds of invasion, attack, trauma.

These three musketeers, ccardiovascular, lyphatic and immune systems, create the core mechanism of survival during human life.

However, there is a cellular balance between immune and cardiovascular system since blood that made up off 99% red cells and 1% white blood cells that are used to scavenger hunt circulating foreign materials. These three systems are acting with a harmony not only defend the body but provide basic needs of life. Thus, controlling angiogenesis and working mechanisms in blood not only helps to develop new diagnostic tools but more importantly establishes long lasting treatments that can harness Immunomodulation.

The word inflammation comes from the Latin “inflammo”, meaning “I set alight, I ignite”.

Medical Dictionary description is:

“A fundamental pathologic process consisting of a dynamic complex of histologically apparent cytologic changes, cellular infiltration, and mediator release that occurs in the affected blood vessels and adjacent tissues in response to an injury or abnormal stimulation caused by a physical, chemical, or biologic agent, including the local reactions and resulting morphologic changes; the destruction or removal of the injurious material; and the responses that lead to repair and healing.”

The five elements makes up the signature of inflammation: rubor, redness; calor, heat (or warmth); tumor swelling; and dolor, pain; a fifth sign, functio laesa, inhibited or lost function. However, these indications may not be present at once.

Please click on to the following link for genetic association of autoimmune diseases (Cho Et al selected major association signals in autoimmune diseases) from Cho JH, Gregersen PK. N Engl J Med 2011;365:1612-1623.

Inflammatory diseases grouped under two classification: the immune system related due to inflammatory disorders, such as both allergic reactions and some myopathies, with many immune system disorders. The examples of inflammatory disorders include Acne vulgaris, asthma, autoimmune disorders, celiac disease, chronic prostatitis, glomerulonepritis, hypersensitivities, inflammatory bowel diseases, pelvic inflammatory diseases, reperfusion diseases, rheumatoid arthritis, sarcoidosis, transplant rejection, vasculitis, interstitial cyctitis, The second kind of inflammation are related to non-immune diseases such as cancer, atherosclerosis, and ischaemic heart disease.

This seems simple yet at molecular physiology and gene activation levels this is a complex response as an innate immune response from body. There can be acute lasting few days after exposure to bacterial pathogens, injured tissues or chronic inflammation continuing few months to years after unresolved acute responses such as non-degradable pathogens, viral infection, antigens or any foreignmaterials, or autoimmune responses.

As the system responses arise from plasma fluid, blood vessels, blood plasma through vasciular changes, differentiation in plasma cascade systems like coagulation system, fibrinolysis, complement system and kinin system. Some of the various mediators include bradykinin produced by kinin system, C3, C5, membrane attack system (endothelial cell activation or endothelial coagulation activation mechanism) created by the complement system; factor XII that can activate kinin, fibrinolysys and coagulation systems at the same time produced in liver; plasmin from fibrinolysis system to inactivate factor Xii and C3 formation, and thrombin of coagulation system with a reaction through protein activated receptor 1 (PAR1), which is a seven spanning membrane protein-GPCR. This system is quite fragile and well regulated. For example activation of inactive Factor XII by collagen, platelets, trauma such as cut, wound, surgery that results in basement membrane changes since it usually circulate in inactive form in plasma automatically initiates and alerts kinin, fibrinolysis and coagulation systems.

Furthermore, the changes reflected through receptors and create gene activation by cellular mediators to establish system wide unified mechanisms. These factors (such as IFN-gamma, IL-1, IL-8, prostaglandins, leukotrene B4, nitric oxide, histamines,TNFa) target immune cells and redesign their responses, mast cells, macrophages, granulocytes, leukocytes, B cells, T cells) platelets, some neuron cells and endothelial cells. Therefore, immune system can react with non-specific or specific mechanisms either for a short or a long term.

As a result, controlling of mechanisms in blood and prevention of angiogenesis answer to cure/treat many diseases Description of angiogenesis is simply formation of new blood vessels without using or changing pre-existing capillaries. This involves serial numbers of events play a central role during physiologic and pathologic processes such as normal tissue growth, such as in embryonic development, wound healing, and the menstrual cycle. However this system requires three main elements: oxygen, nutrients and getting rid of waste or end products.

Genome Wide Gene Association Studies, Genomics and Metabolomics, on the other hand, development of new technologies for diagnostics and non-invasive technologies provided better targeting systems.

In this token recent genomewide association studies showed a clear view on a disease mechanism, or that suggest a new diagnostic or therapeutic approach particularly these disorders are related to genes within the major histocompatibility complex (MHC) that predisposes the most significant genetic effect. Presumably, these genes are reflecting the immunoregulatory effects of the HLA molecules themselves. As a result, the working mechanism of pathological conditions are revisited or created new assumptions to develop new targets for diagnosis and treatments.

Even though B and T cells are reactive to initiate responses there are several level of mechanisms control the cell differentiation for designing rules during health or diseases. These regulators are in check for both T and B cells. For example, during Type 1 diabetes there are presence of more limited defects in selection against reactivity with self-antigens like insulin, thus, T cell differentiation is in jeopardy. In addition, B cells have many active checkpoints to modulate the immune responses like pre-B cells in the bone marrow are highly autoreactive yet they prefer to stay in naïve-B cell forms in the periphery through tyrosine phosphatase nonreceptor type 22 (PTPN22) along with many genes play a role in autoimmunity. In a nut shell this is just peeling the first layer of the onion at the level of Mendelian Genetics.

There is a great work to be done but if one can harness the blood and immune responses many complex diseases patients may have a big relief and have a quality of life. When we look at the picture 90% of main fluid of life, blood, carried by cardiovascular system with two main pumping mechanisms, lung with gas exchange and systemic with complex scavenger actions, collection of waste, distribution of nutrition and clean gases. Yet, without lymphatic system body can’t make up the 100% fluid. Therefore, 10% balance is completed by lymphatic system as a counter clockwise direction so that not only the fluid balance but also mass balance is maintained. Finally, the immune system patches the remaining mechanism by providing cellular support to protect the body because it contains 99% of white cells to fight against any kinds of invasion, attack, trauma.

FURTHER READINGS AND REFERENCES:

Arap W, Pasqualini R, Ruoslahti E (1998) Cancer treatment by targeted drug delivery to tumor vasculature in a mouse model. Science (Wash DC)279:377–380.

Brouty BD, Zetter BR (1980) Inhibition of cell motility by interferon.Science (Wash DC) 208:516–518.

Ferrara N, Alitalo K (1999) Clinical Applications of angiogenic growth factors and their inhibitors. Nat Med 5:1359–1364.

Ferrara N (1999) Role of vascular endothelial growth factor in the regulation of angiogenesis. Kidney Int 56:794–814.

Ferrara N (1995) Leukocyte adhesion: Missing link in angiogenesis. Nature (Lond) 376:467.

Kohn EC, Alessandro R, Spoonster J, Wersto RP, Liotta LA (1995) Angiogenesis: Role of calcium-mediated signal transduction. Proc Natl Acad Sci U S A 92:1307–1311

Meijer DKF, Molema G (1995) Targeting of drugs to the liver. Semin Liver Dis 15:202–256.

Sidky YA, Borden EC (1987) Inhibition of angiogenesis by interferons: Effects on tumor- and lymphocyte-induced vascular responses. Cancer Res47:5155–5161.

Anonymous (1999a) Genentech takes VEGF back to lab. SCRIP 2493:24.

Ziche M, Morbidelli L, Choudhuri R, Zhang HT, Donnini S, Granger HJ,Bicknell R (1997) Nitric oxide synthase lies downstream from vascular endothelial growth factor-induced but not basic fibroblast growth factor-induced angiogenesis. J Clin Invest 99:2625–2634.

Yoshida S, Ono M, Shono T, Izumi H, Ishibashi T, Suzuki H, Kuwano M(1997) Involvement of interleukin-8, vascular endothelial growth factor, and basic fibroblast growth factor in tumor necrosis factor α-dependent angiogenesis. Mol Cell Biol 17:4015–4023.

Vittet D, Prandini MH, Berthier R, Schweitzer A, Martin SH, Uzan G,Dejana E (1996) Embryonic stem cells differentiate in vitro to endothelial cells through successive maturation steps. Blood 88:3424–3431.

Ruegg C, Yilmaz A, Bieler G, Bamat J, Chaubert P, Lejeune FJ (1998) Evidence for the involvement of endothelial cell integrin αvβ3 in the disruption of the tumor vasculature induced by TNF and IFN. Nat Med4:408–414

Patey N, Vazeux R, Canioni D, Potter T, Gallatin WM, Brousse N (1996) Intercellular adhesion molecule-3 on endothelial cells. Expression in tumors but not in inflammatory responses. Am J Pathol 148:465–472.

Oliver SJ, Banquerigo ML, Brahn E (1994) Supression of collagen-induced arthritis using an angiogenesis inhibitor AGM-1470 and microtubule stabilizer taxol. Cell Immunol 157:291–299

Molema G, Griffioen AW (1998) Rocking the foundations of solid tumor growth by attacking the tumor’s blood supply. Immunol Today 19:392–394.

Losordo DW, Vale PR, Symes JF, Dunnington CH, Esakof DD, Maysky M,Ashare AB, Lathi K, Isner JM (1998) Gene therapy for myocardial angiogenesis: Initial clinical results with direct myocardial injection of PhVEGF165 as sole therapy for myocardial ischemia. Circulation98:2800–2804.

Jain RK, Schlenger K, Hockel M, Yuan F (1997) Quantitative angiogenesis assays: Progress and problems. Nat Med 3:1203–1208.

Jain RK (1996) 1995 Whitaker Lecture: Delivery of molecules, particles and cells to solid tumors. Ann Biomed Eng 24:457–473.

Giraudo E, Primo L, Audero E, Gerber H, Koolwijk P, Soker S,Klagsbrun M, Ferrara N, Bussolino F (1998) Tumor necrosis factor-alpha regulates expression of vascular endothelial growth factor receptor-2 and of its co-receptor neuropilin-1 in human vascular endothelial cells. J Biol Chem273:22128–22135.

Inflammation Genomics

Kocarnik JM, Pendergrass SA, Carty CL, Pankow JS, Schumacher FR, Cheng I, Durda P, Ambite JL, Deelman E, Cook NR, Liu S, Wactawski-Wende J, Hutter C, Brown-Gentry K, Wilson S, Best LG, Pankratz N, Hong CP, Cole SA, Voruganti VS, Bůžkova P, Jorgensen NW, Jenny NS, Wilkens LR, Haiman CA, Kolonel LN, Lacroix A, North K, Jackson R, Le Marchand L, Hindorff LA, Crawford DC, Gross M, Peters U. Multi-Ancestral Analysis of Inflammation-Related Genetic Variants and C-Reactive Protein in the Population Architecture using Genomics and Epidemiology (PAGE) Study. Circ Cardiovasc Genet. 2014 Mar 12

Ellis J, Lange EM, Li J, Dupuis J, Baumert J, Walston JD, Keating BJ, Durda P, Fox ER, Palmer CD, Meng YA, Young T, Farlow DN, Schnabel RB, Marzi CS, Larkin E, Martin LW, Bis JC, Auer P, Ramachandran VS, Gabriel SB, Willis MS, Pankow JS, Papanicolaou GJ, Rotter JI, Ballantyne CM, Gross MD, Lettre G, Wilson JG, Peters U, Koenig W, Tracy RP, Redline S, Reiner AP, Benjamin EJ, Lange LA. Large multiethnic Candidate Gene Study for C-reactive protein levels: identification of a novelassociation at CD36 in African Americans. Hum Genet. 2014 Mar 19.

Ricaño-Ponce I, Wijmenga C. Mapping of immune-mediated disease genes. Annu Rev Genomics Hum Genet. 2013;14:325-53. doi: 10.1146/annurev-genom-091212-153450. Epub 2013 Jul 3. Review.

McKillop AM, Flatt PR. Emerging applications of metabolomic and genomic profiling in diabetic clinical medicine. Diabetes Care. 2011 Dec;34(12):2624-30. doi: 10.2337/dc11-0837. Review.

Ricaño-Ponce I, Wijmenga C. Mapping of immune-mediated disease genes. Annu Rev Genomics Hum Genet. 2013;14:325-53. doi: 10.1146/annurev-genom-091212-153450. Epub 2013 Jul 3.Review.

Chen YB, Cutler CS. Biomarkers for acute GVHD: can we predict the unpredictable? Bone Marrow Transplant. 2013 Jun;48(6):755-60. doi: 10.1038/bmt.2012.143. Epub 2012 Aug 6. Review.

Cho JH, Gregersen PK. Genomics and the multifactorial nature of human autoimmune disease. N Engl J Med. 2011 Oct 27;365(17):1612-23. doi: 10.1056/NEJMra1100030. Review.

Shikama N, Nusspaumer G, Hollander GA. Clearing the AIRE: on the pathophysiological basis of the autoimmune polyendocrinopathy syndrome type-1. Endocrinol Metab Clin North Am2009;38:273-288

Concannon P, Rich SS, Nepom GT. Genetics of type 1A diabetes. N Engl J Med 2009;360:1646-1654

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Leptin signaling in mediating the cardiac hypertrophy associated with obesity

Posted in Cardiomyopathy, Cell Biology, Signaling & Cell Circuits, Frontiers in Cardiology and Cardiovascular Disorders, Metabolomics, Myocardial metabolism, Myocardial ischemia, myocardial perfusion, Myocardial adenine nucleotide metabolism, Nutrition, Population Health Management, Genetics & Pharmaceutical, Population Health Management, Nutrition and Phytochemistry, Proteomics, Systemic Inflammatory Response Related Disorders, tagged heart, Hypertrophy, leptin, Obesity, Signal transduction, STAT3 on November 3, 2013| Leave a Comment »

Leptin signaling in mediating the cardiac hypertrophy associated with obesity

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

There has been a lot of interest in leptins and both insulin resistance and obesity for the last decade. The association between obesity and cardiac hypertrophy is also known, but what drives this association. We have covered heart disease from many aspects in a long series of articles. The next is a pleasure to take in.

Importance of leptin signaling and signal transducer and activator of transcription-3 activation in mediating the cardiac hypertrophy associated with obesity

Maren Leifheit-Nestler¹², Nana-Maria Wagner¹³, Rajinikanth Gogiraju¹,Michael Didié¹⁴, Stavros Konstantinides¹⁵, Gerd Hasenfuss¹ and Katrin Schäfer¹^*

¹Department of Cardiology and Pulmonary Medicine, Heart Research Center, Georg August University Medicine Goettingen, Robert Koch Strasse 40, D-37075, Göttingen, Germany

²Current address: Department of Pediatric Kidney, Liver and Metabolic Diseases, Hannover Medical School, Hannover, Germany

³Current address: Clinic for Anesthesiology and Intensive Care Medicine, University Medicine Rostock, Rostock, Germany

⁴Department of Pharmacology, Georg August University Medicine Goettingen, Goettingen, Germany

⁵Current address: Center for Thrombosis and Hemostasis, University Medicine Mainz, Mainz, Germany

J Translational Medicine: Cardiovascular, Metabolic and Lipoprotein Translation. 2013; 11:170. http://www.translational-medicine.com/content/11/1/170

h ttp://dx.doi.org/10.1186/1479-5876-11-170

This is an Open Access article distributed under the terms of the Creative Commons Attribution License
http://creativecommons.org/licenses/by/2.0

Abstract

Background

The adipokine leptin and its receptor are expressed in the heart, and

leptin has been shown to promote cardiomyocyte hypertrophy in vitro.

Obesity is associated with

hyperleptinemia

hypothalamic leptin resistance and

an increased risk to develop cardiac hypertrophy and heart failure.

However, the role of cardiac leptin signaling in mediating the cardiomyopathy associated with increased body weight is unclear, in particular, whether it develops subsequently to cardiac leptin resistance or overactivation of hypertrophic signaling pathways via elevated leptin levels.

Methods

The cardiac phenotype of high-fat diet (HFD)-induced obese wildtype (WT) mice was examined and compared to age-matched genetically obese leptin receptor (LepR)-deficient (LepR^db/db) or lean WT mice. To study the role of leptin-mediated STAT3 activation during obesity-induced cardiac remodeling,

mice in which tyrosine residue 1138 within LepR had been replaced with a serine (LepR^S1138) were also analyzed.

Results

Obesity was associated with hyperleptinemia and elevated cardiac leptin expression in both diet-induced and genetically obese mice.

Enhanced LepR and STAT3 phosphorylation levels were detected in hearts of obese WT mice, but not in those with LepR mutations, and
exogenous leptin continued to induce cardiac STAT3 activation in diet-induced obese mice.

Although echocardiography revealed signs of cardiac hypertrophy in all obese mice,

the increase in left ventricular (LV) mass and diameter was significantly more pronounced in LepR^S1138 animals.

LepR^S1138 mice also exhibited an increased activation of signaling proteins downstream of LepR, including Jak2 (1.8-fold), Src kinase (1.7-fold), protein kinase B (1.3-fold) or C (1.6-fold). Histological analysis of hearts revealed that the inability of leptin to activate STAT3 in LepR^db/db and LepR^S1138 mice

was associated with reduced cardiac angiogenesis as well as increased apoptosis and fibrosis.

Conclusions

Our findings suggest that hearts from obese mice continue to respond to elevated circulating or cardiac leptin, which

may mediate cardioprotection via LepR-induced STAT3 activation, whereas
signals distinct from LepR-Tyr1138 promote cardiac hypertrophy.

On the other hand, the presence of cardiac hypertrophy in obese mice with complete LepR signal disruption indicates that additional pathways also play a role.

Keywords:

Heart; Hypertrophy; Leptin; Obesity; Signal transduction; STAT3

Background

Obesity is frequently associated with elevated circulating leptin levels [1] and an increased risk to develop cardiac hypertrophy [2,3] or heart failure [4]. Clinical studies demonstrated a positive correlation between serum leptin levels and left ventricular (LV) mass or wall thickness [5,6], independent of blood pressure levels, suggesting a direct role for leptin in the pathogenesis of obesity-associated cardiomyopathy. Furthermore, leptin was shown to promote hypertrophy of isolated rat or human ventricular cardiomyocytes [7,8], and

this effect could be prevented using neutralizing antibodies [9].

Cardiac hypertrophy also develops in obese rodents fed high-fat diet (HFD)[10,11], and

studies in mice with (functional) leptin deficiency suggested that the cardiac hypertrophy developing in states of chronic hyperleptinemia
may result from the inability to transduce anti-hypertrophic and/or cardioprotective effects of the adipokine [12,13].

The effects of leptin on cell shortening and intracellular Ca²⁺ transients were abrogated in cardiomyocytes isolated from HFD-fed obese rats [14], but then others found

a preserved signal transduction in response to leptin in hyperleptinemic obese mice [15,16] or rats[17].

The leptin receptor (LepR) belongs to the family of cytokine type I receptors that signal via activation of

Janus kinase (Jak)-2 and
signal transducer and
activator of transcription (STAT)-3 [18].

Analysis of cardiomyocytes ex vivo revealed leptin promotes hypertrophy via activation of p38 and p42/44 MAP kinases as well as protein kinase B (Akt) [19,20]. But it is unknown whether STAT-3 activation downstream of LepR is required to transmit the cardiac effects of leptin and whether it may be involved in mediating protective (i.e. anti-apoptotic, anti-fibrotic or pro-angiogenic) signals, as previously reported in mice with cardiomyocyte-specific STAT-3 deletion [21,22].

In this study, we examined the cardiac phenotype of diet-induced (i.e. with hypothalamic leptin resistance) and genetically obese (i.e. with systemic leptin receptor deficiency) hyperleptinemic mice, developing with age or after continuous β-adrenergic stimulation. Moreover, we determined

the importance of leptin-mediated STAT-3 activation

for the development of cardiac hypertrophy in obesity

by analyzing mice with targeted mutation of the STAT3 binding site within LepR.

Methods

Animals

C57Bl6/J leptin receptor-deficient db/db (LepR^db/db; BKS.Cg Lepr^db/Lepr^db) mice and C57Bl6/J wildtype (WT) controls were obtained from Harlan Winkelmann, Germany. Mice heterozygous mutant for the LepR^S1138 allele (on the congenic B6.129/J background; 98- > 99% homozygous for C57Bl/6; [23]) were obtained from Professor Martin Myers (University of Michigan Medical School, Ann Arbor, USA) and bred at the animal facility of the University of Goettingen, Germany, to generate homozygous mutant obese LepR^S1138 mice. Age- and gender-matched WT (LepR^+/+) and heterozygous (LepR^S/+) littermates were used as controls. To induce obesity, 3 months-old mice were switched to high-fat diet (HFD; D12451) for 4 months, while controls were maintained on normal rodent chow (D12450B; both Research Diets Inc.). The composition of both diets is shown in Additional file 1: Table S1. To examine the cardiac response to hypertrophic stimuli other than leptin, osmotic minipumps (Alzet®; model 2002; Charles River Laboratories) were filled with isoprenaline hydrochloride (Sigma; 20 mg/kg body weight [BW] per day) and implanted for 14 days under the dorsal skinfold of 2 months-old, 2% isoflurane anesthetized mice. At the time of tissue harvest, mice were weighed followed by intraperitoneal anesthesia with a mixture of 2% xylazine (6 mg/kg BW) and 10% ketamine hydrochloride (100 mg/kg BW), and blood was drawn by cardiac puncture. Hearts were rapidly excised, the atria removed and ventricles immediately processed for protein isolation or cryoembedding, respectively. All animal care and experimental procedures had been approved by the institutional Animal Research Committee and complied with national guidelines for the care and use of laboratory animals.

Additional file 1: Table S1. Diet composition.
Format: DOC Size: 33KB

Serum analysis

Freshly drawn blood was allowed to clot at room temperature (RT) for 30 min, followed by centrifugation for 10 min at 3,000 rpm. The supernatant was stored at -80°C pending analysis of serum leptin levels using specific enzyme-linked immunoassays (ELISA; R&D Systems).

Echocardiography

Echocardiography was performed by a blinded examiner at the day before tissue harvest in mice under 1.5% isoflurane anesthesia using the VisualSonics Vevo 2100 system (Visualsonics) equipped with a 30 MHz center frequency ultrasound transducer, as previously described [24]. M-mode echocardiographical recordings were used to determine the end-diastolic and end-systolic LV diameter (EDD and ESD, respectively) and the ventricular wall thickness (WTh), corresponding to the mean of the anterior and posterior WTh. LV mass was calculated using the formula: 1.055 × ([AWTh + EDD + PWTh]³ – EDD³). Fractional shortening (FS) was calculated as (EDD – ESD)/EDD × 100. B-mode echocardiography images were used to calculate the heart weight, using the equation: 1.05 × (5/6) × ((Epi_syst × (L_syst + ((AWTh_syst + PWTh_syst)/2))) – (Area_syst × L_syst)).

Histology and immunohistochemistry

Histochemical analyses were performed on 5 μm-thick frozen cross sections through the LV. For each mouse, 4 sections (approx. 500 μm apart) and 4 randomly selected viewing fields (at 200-fold magnification) per section were analyzed and findings averaged. Cardiac fibrosis was determined after overnight incubation in Bouin’s fixative followed by Masson’s trichrome (MTC) stain. Monoclonal rat antibodies against mouse CD31 (Santa Cruz Biotechnology) were used to detect endothelial cells [24,25]. Their number was manually counted by a person blinded to the mouse genotype and expressed per mm² or cardiomyocyte, respectively.

Single cardiomyocytes were visualized by incubation with fluorescein-labeled wheat germ agglutinin (WGA; Molecular Probes), followed by determination of the cardiomyocyte cross-sectional area (CSA) using image analysis software (Image ProPlus). Per cross section, at least 10 randomly selected cardiomyocytes were evaluated and results averaged. Apoptosis was analyzed using the ‘In Situ Cell Death Detection kit’ (Roche). Cell nuclei were visualized using 4′,6-diamidino-2-phenylindole (DAPI; Sigma).

Immunoprecipitation and western blot analysis

Membranes were blocked in 1% bovine serum albumine (in TBS, containing 0.1% Tween-20) prior to incubation with antibodies against phosphorylated (p)-Akt (S473) and total Akt, p-Jak2 (Y1007/1008) and total Jak2, p-p38 (T180/Y182) and total p38, p-p42/44 (T202/Y204) and total p42/44, p-Src (Y416) and total Src, p-STAT3 (Y705) and total STAT3, or p-PKC (pan), respectively (all Cell Signaling Technologies), or against leptin (R&D Systems) and GAPDH (Biotrend), respectively. Protein bands were visualized using HRP-conjugated secondary antibodies (Amersham Biosciences), followed by detection with SuperSignal® West Pico Substrate (Pierce). For the analysis of LepR phosphorylation, 100 μg total heart tissue lysates were immunoprecipitated under rotation at 4°C with 2 μg anti-LepR antibody (against an internal domain present in the short and long isoforms of murine LepR; Santa Cruz Biotechnology) plus 50 μL nProtein A Sepharose™ 4 Fast Flow beads (GE Healthcare) followed by detection of phosphorylated tyrosines (p-Tyr [PY20]; Santa Cruz Biotechnology) or LepR. For the analysis of STAT3 phosphorylation in response to acute elevations of circulating leptin, mice were fasted overnight, injected with recombinant murine leptin (1 mg/kg BW i.p.) and hearts harvested 30 min later.

Statistical analysis

Quantitative data are presented as mean ± standard error of the mean (SEM). Normal data distribution was tested using the D’Agostino & Pearson omnibus normality test. When three or more groups were compared, ANOVA was employed, if samples were normally distributed, or Kruskal-Wallis test, if not. For post-hoc comparisons, ANOVA was followed by Bonferroni’s and Kruskal-Wallis by Dunn’s multiple comparison test. Differences before and after isoprenaline infusion were tested using Student’s t-test for paired means. Statistical significance was assumed when P reached a value less than 0.05. All statistical analyses were performed using GraphPad PRISM software, version 4.01 (GraphPad Software Inc).

Results

Clinical and experimental studies revealed that obesity is associated with LV hypertrophy [10,11], an important risk factor for the development of heart failure. As shown in Tables 1 and 2, WT mice fed HFD for 4 months (WT + HFD; mean body weight [BW], 44±1.9 g) to induce obesity exhibited a non-significant trend towards an increased mean heart weight, LV mass and WTh compared to age-matched lean controls fed normal chow (BW, 29±1.0 g). Marked LV hypertrophy was observed in 7 months-old obese LepR^db/db mice (Table 1 and 2), consistent with a previous report [12]. Longitudinal sections through hearts of WT, WT + HFD and LepR^db/db mice are shown in Figure 1A, representative M-mode echocardiography recordings in Figure 1B and cardiac cross-sections after WGA staining to delineate cardiomyocyte borders in Figure 1C. Of note, adiposity in mice with LepR deficiency was more pronounced compared to age-matched WT + HFD mice (Table 1; P < 0.001), in which obesity develops as result of hypothalamic resistance to chronically elevated leptin levels[26].

Figure 1.Cardiac phenotype of lean and obese WT, WT + HFD, LepR^S1138and LepR^db/dbmice.
(A)Representative H&E-stained longitudinal sections through hearts of 7 months-old mice are shown. Magnification, ×10.(B) Representative M-mode echocardiographic recordings.(C) Representative images of wheat germ agglutinin (WGA)-stained myocardial cross sections. The mean cardiomyocyte cross-sectional areas are given in Table 1.

Table 1.Body, visceral fat and heart weights in 7 months-old mice

Table 2.Echocardiographic parameter in 7 months-old mice

The presence of cardiac hypertrophy in LepR-deficient and, to a lesser extent also in diet-induced obese mice, suggests that it develops as a result of the heart’s inability to respond to elevated systemic (Table 1) and/or cardiac (Figure 2A) leptin levels. In this regard, Western blot analysis revealed increased levels of phosphorylated (p-) LepR (Figure 2B) and STAT-3 (Figure 2C) protein in hearts of HFD-induced obese mice (P < 0.05 vs. WT for both), whereas findings in LepR^db/db mice did not differ from those in lean controls or were reduced compared to those in WT + HFD mice (P < 0.05 for differences in LepR phosphorylation). Moreover, both lean and HFD-induced obese WT mice responded to a single i.p. injection of recombinant murine leptin with a significant increase in the cardiac STAT-3 phosphorylation (Figure 2D), suggesting a preserved cardiac leptin signal transduction in hyperleptinemic, diet-induced obese mice.

Figure 2.Cardiac leptin expression and signal transduction in lean and obese mice.
Protein was extracted from hearts of 7 months-old mice (n = 8 per group) and analyzed for the expression of (A) leptin, (B)phosphorylated LepR (using immunoprecipitation of LepR, followed by the detection of total phosphotyrosines and LepR) and (C) phosphorylated STAT3. (D) Cardiac STAT3 phosphorylation in response (30 min later) to a single injection of recombinant murine leptin (1 mg/kg BW i.p.) was examined in WT (n = 4) and WT + HFD (n = 6) mice. Results are expressed as -fold increase of controls (black bars) after normalization for total protein and GAPDH expression. The mean ± SEM as well as representative Western blot results are shown. *P < 0.05 and **P < 0.01 vs. WT mice; #P < 0.05 vs. WT + HFD mice.

To further study the role of leptin signaling in the development of cardiac hypertrophy and also to determine, whether the inability of leptin to activate STAT3 contributes to the cardiac maladaptation in obesity, we examined mice in which tyrosine (Tyr)1138 within LepR had been replaced by a serine (LepR^S1138). In these mice, leptin cannot signal via STAT3, but continues to be able to activate Jak2 and SH2 domain-containing adapter proteins. Western blot analysis revealed that p-LepR (Figure 2B) and p-STAT3 (Figure 2C) levels in hearts of LepR^S1138 mice did not significantly differ from those in WT and LepR^db/db mice. Similar to mice with complete LepR deficiency, lack of LepR-mediated STAT3 activation resulted in severe adiposity, although serum leptin levels were lower than those in LepR^db/db mice (P < 0.001; Table 1). Interestingly, obese LepR^S1138 exhibited a more pronounced increase in mean heart weights not only compared to lean or diet-induced obese WT mice, but also compared to LepR^db/db mice (P < 0.001 for all comparisons; Table 1), and differences persisted after normalization for body weight (P < 0.001) or tibia length (P < 0.001). Echocardiography confirmed increased LV mass (P < 0.01) or heart weights (P < 0.001) in LepR^S1138 mice compared to their LepR^db/dbcounterparts (Table 2; please also see Figure 1A-C). Moreover, hearts of LepR^S1138 mice exhibited elevated levels of phosphorylated Jak2 (P < 0.001 vs. WT; Figure 3A), Src kinase (P < 0.05 vs. WT, WT + HFD and LepR^db/db; Figure 3B), Akt (P < 0.001 vs. LepR^db/db; Figure 3C), PKC (P < 0.05 vs. WT and LepR^db/db, P < 0.01 vs. WT + HFD; Figure 3D) and p38 MAPK (P < 0.01 vs. LepR^db/db; Figure 3E), suggesting that an intact, Tyr1138-independent LepR activation in the presence of elevated leptin levels may have contributed to the pronounced cardiac hypertrophy present in these mice. On the other hand, cardiac levels of p-p42/44 MAPK did not significantly differ between the mouse groups (Figure 3F).

Figure 3.Hypertrophic signal transduction in hearts of lean and obese mice. Protein was isolated from hearts of 7 months-old WT (n = 15), WT + HFD (n = 12), LepR^S1138(n = 15) and LepR^db/db (n = 15) mice and analyzed for the expression of phosphorylated Jak2 (A), Src kinase (B), Akt (C), PKC (D), p38 (E) and p42/44 MAPK (F). Results are expressed as -fold increase of lean control mice (after normalization for total protein [with the exception of PKC] and GAPDH expression). The mean ± SEM as well as representative findings are shown. *P < 0.05, **P < 0.01 and ***P < 0.001 vs. WT mice; #P < 0.05 and ##P < 0.01 vs. WT + HFD mice; §P < 0.05, §§P < 0.01 and §§§P < 0.001 for the difference between LepR^db/db and LepR^S1138 mice.

M-mode echocardiography also revealed significantly increased enddiastolic LV diameters in LepR^S1138 mice (P < 0.01 vs. WT and LepR^db/db mice; Table 2; representative findings are shown in Figure 1B), suggesting that the observed (over-)activation of LepR signaling together with the inability to induce STAT3 may result in augmented hypertrophy and maladaptive cardiac remodeling. Of note, fractional shortening (FS) was not significantly altered in HFD-induced obese WT mice (P = n.s. vs. WT mice), but found to be increased in both LepR^db/db (P < 0.01 vs. WT and P < 0.001 vs. WT + HFD mice) and LepR^S1138mice (P < 0.05 vs. WT and P < 0.001 vs. WT + HFD mice). Histological analyses revealed significantly reduced numbers of CD31-positive capillary endothelial cells in LepR^db/db, and to a lesser extent also in LepR^S1138 mice (Figure 4A), whereas the number of TUNEL-positive apoptotic cells (Figure 4B) and the fibrotic tissue area (Figure 4C) were found to be significantly increased in hearts of both LepR^S1138 and LepR^db/db mice compared to lean and diet-induced obese WT mice.

Figure 4.Histological analysis of angiogenesis, apoptosis and fibrosis in hearts of lean and obese mice.
Serial cross sections through the LV of WT, WT + HFD, LepR^S1138 and LepR^db/db mice (n = 10 per group) were immunostained and the number of (A) CD31-positive endothelial cells and (B) TUNEL-positive apoptotic cell nuclei determined. Results are expressed per cardiomyocyte and/or mm².(C) The degree of cardiac fibrosis was quantified after Masson’s trichrome (MTC) staining. Results are expressed as % of total tissue area (at 200-fold magnification). The mean ± SEM as well as representative findings are shown. **P < 0.01 and ***P < 0.001 vs. WT; #P < 0.05, ##P < 0.01 and ###P < 0.001 vs. WT + HFD mice.

Figure 4.Histological analysis (unable to post)

To examine the specificity of leptin’s hypertrophic action in obesity, the cardiac response of young, i.e. 2 months-old WT (n = 12; body weight, 22 ± 0.9 g), LepR^S1138 (n = 9; 34 ± 1.1 g, P < 0.001 vs. WT) and LepR^db/db mice (n = 7; 40 ± 1.3 g; P < 0.001 vs. WT and P < 0.01 vs. LepR^S1138) to chronic isoprenaline infusion (20 mg/kg BW per day) was examined. Under basal conditions, similar findings as those in 7 months-old mice were observed, i.e. LepR^S1138 mice exhibited an increased heart weight (P < 0.05 vs. LepR^db/db; Figure 5A), LV mass (P < 0.01 vs. WT; Figure 5B) and mean WTh (P < 0.05 vs. WT; Figure 5C), whereas other changes, such as differences in fractional shortening (Figure 5D), ESD (Figure 5E) and EDD (Figure 5F) were not (yet) detected. On the other hand, all mouse groups responded to chronic β-adrenergic stimulation with significant cardiac hypertrophy, and no differences (with the exception of heart weight; Figure 5A) were observed between LepR^S1138 and LepR^db/db mice. Representative M-mode echocardiography tracings are shown in Figure 6 and summarized in Additional file 2: Table S2.

Figure 5.Echocardiography findings in young lean and obese mice before and after chronic β-adrenergic stimulation.
Isoprenaline-filled osmotic minipumps were subcutaneously implanted into 2 months-old WT (n = 12), LepR^S1138 (n = 9) and LepR^db/db (n = 7) mice to examine the cardiac response to a hypertrophic stimulus other than leptin. Echocardiography (A-F) was performed immediately before (open bars) as well as at the time of tissue harvest 14 days later (dotted bars). *P < 0.05, *P < 0.01 and ***P < 0.001 for differences vs. WT mice; §P < 0.05 for differences between LepR^db/db and LepR^S1138 mice. Significance levels for differences before and after isoprenaline stimulation (as determined using Student’s t test for paired means) are indicated within the graph.

Figure 6.Representative M-mode echocardiography recordings.

Additional file 2: Table S2. Echocardiographic parameter in 2 months-old mice before and 14 days after isoprenaline infusion.

Format: DOCX Size: 22KB

Discussion

The adipocytokine leptin may link obesity with cardiac hypertrophy, an important risk factor for the development of heart failure. Studies in humans [2,3] and rodents [10,11] have shown that obesity is associated with LV hypertrophy, and body mass index was identified as a strong and independent predictor of LV mass [2,3]. Importantly, cardiac hypertrophy is also observed in normotensive obese subjects [6], and plasma leptin levels are associated with increased myocardial wall thickness independent of BW or blood pressure elevations [5], suggesting a causal role for leptin in the pathogenesis of cardiac hypertrophy.

Although the major source of leptin is adipose tissue, cardiomyocytes are also capable of synthesizing leptin [27], and increased cardiac leptin levels have been reported in mice or rats following coronary ligation [13,18] or in patients with heart failure [28]. In this study, elevated circulating as well as cardiac leptin levels were detected in both diet-induced and genetically obese mice, which may have acted on cardiomyocytes as well as other, non-cardiomyocyte cells expressing leptin receptors [29]. Although leptin serum levels were higher than in previous publications [30], we explain this findings with the higher age of the mice, a factor previously found to be associated with increased circulating leptin levels [31]. Leptin has been shown to stimulate the hypertrophy of cardiomyocytes isolated from rats [7,20] or humans [8,19]. Moreover, chronic leptin infusion increased cardiac ANP expression after myocardial infarction (MI) in mice [32], whereas neutralizing LepR antibodies abrogated the hypertrophy of the surviving myocardium after coronary artery ligation in rats [33]. On the other hand and as confirmed in our analysis, cardiac hypertrophy also develops in leptin- and LepR-deficient mice and may be reversed by leptin substitution[12]. Caloric restriction experiments suggested that the anti-hypertrophic effects of leptin had occurred in addition to weight loss [12], which itself may preserve heart function and attenuate LV remodeling [34]. Thus, it is unclear whether the cardiac hypertrophy in obesity is the consequence of pro-hypertrophic effects of the adipokine or rather the result of a resistance towards leptin’s preventive effects on hypertrophic cardiac remodeling. Of note, since body weight is markedly elevated in the diet-induced and particularly, the genetically obese mice, the heart-to-body weight ratio decreases, even though the absolute heart weight is increased (but to a relatively lesser extent).

Obesity is associated with elevated circulating leptin levels and hypothalamic resistance to the weight-reducing effects of the adipokine, whereas the existence of a peripheral (e.g. cardiac) leptin resistance is controversial. For example, reduced cardiac LepR expression has been reported in HFD-fed rats[14], whereas others demonstrated unaltered cardiac STAT3 phosphorylation in diet-induced obese rodents following acute leptin administration [15–17]. Our findings also suggest that hearts from diet-induced obese mice continue to respond to leptin in the presence of chronically elevated leptin levels and that the observed elevation of serum and cardiac leptin may thus contribute to the development of cardiac hypertrophy in obesity. For example, hearts of hyperleptinemic obese WT mice (i.e. those with intact leptin receptors) exhibited signs of activated leptin signaling, including elevated levels of phosphorylated LepR and STAT3, while they were unchanged or reduced in mice with mutated or truncated forms of LepR (i.e. LepR^S1138 or LepR^db/db mice). Moreover, both lean and obese WT mice responded to a single leptin injection with increased cardiac STAT3 phosphorylation. Of note, we could not spatially dissect the cardiac responsiveness to leptin, since whole heart homogenates were examined. Possible explanations underlying the discrepancy between the present and some previous studies include the animal species, as the absence of a response to leptin in obesity has been so far primarily observed in rats[14]. In addition, age, sex and feeding status of the animals or the time of recombinant leptin administration may have influenced the results. Of note, previous studies in humans have reported the existence of individuals (up to 40%) exhibiting a blunted response to leptin [35], although it is unknown, whether such phenomenon also occurs in rodents.

Interestingly, hearts from LepR^S1138 mice exhibited a marked overactivation of STAT3-independent leptin signaling pathways, including Jak2, Src kinase, Akt or p38 MAPK, i.e. factors previously shown to mediate the pro-hypertrophic effects of the adipokine in cardiomyocytes [19,20]. Importantly, overactivation of leptin signaling in hearts of LepR^S1138 mice was accompanied by a pronounced cardiac hypertrophy, both at the organ and the single cardiomyocyte level, despite similar adiposity. Although leptin levels were found to be lower in LepR^S1138compared to LepR^db/db mice, as previously reported [23], leptin continues to be able to activate LepR signal transduction in these mice, for example via LepR-Tyr985. Similar echocardiographical findings were obtained in young (i.e. 2 months-old) and older (i.e. 7 months-old) mice, arguing against the development of cardiac hypertrophy secondary to hemodynamic or other metabolic changes associated with obesity, although we cannot exclude the possible contribution of a more pronounced hyperinsulinemia [23] to the development of cardiac hypertrophy in LepR^S1138 mice. On the other hand, hypertension had not been observed in ob/ob mice [12], and heart weight increase and concentric LV hypertrophy in obese mice and humans also occurs without systolic and diastolic blood pressure elevations [5,6,36].

Although a predominant cardiac expression of the short (i.e. without STAT3 binding site) over the long LepR isoform has been reported [7,29], previous studies have shown that stimulation of neonatal rat cardiomyocytes with leptin increased STAT3 phosphorylation, nuclear translocation and DNA binding activity [32]. Also, cardiac STAT3 activation after MI was blunted in leptin-deficient mice [13]. The observation that increased cardiac STAT3 phosphorylation in hyperleptinemic, diet-induced obese mice was reduced or almost completely abolished in LepR^S1138 or LepR^db/db mice suggests that cardiac STAT3 activation in obesity largely occurs downstream of elevated leptin levels and that other cytokines, also elevated in obesity and known to signal via Jak2-STAT3, may be of minor importance. On the other hand, the importance of leptin-mediated STAT3 activation in the heart and its contribution to cardioprotective signaling pathways in vivo have not been directly examined so far.

STAT3 has been implicated in cardioprotection after various injuries. For example, cardiomyocyte-specific STAT3 deletion results in dilatative cardiomyopathy, characterized by increased apoptosis and interstitial fibrosis as well as reduced myocardial capillary density [21,22]. Previous studies suggested that leptin may exert beneficial effects on the heart. For example, administration of leptin was associated with smaller infarct size after ischemia/reperfusion injury [37], whereas ischemic postconditioning failed to induce cardioprotection in mice lacking leptin or its receptor [38]. Also, leptin deficiency was associated with a worsened cardiac function and survival after coronary artery ligation, which could be improved by leptin repletion [13]. Regarding possible mechanisms, increased cardiac myocyte apoptosis was observed in hearts from leptin (receptor)-deficient mice [39,40]. Similar findings were obtained in vitro, showing that leptin protects cardiomyocytes against apoptotic cell death induced by serum starvation [41]. Our analyses also revealed significantly elevated numbers of apoptotic cells in hearts of obese LepR^S1138 and LepR^db/dbmice, consistent with a reduced activation of STAT3-responsive anti-apoptotic genes [40]. Although findings in mice with systemic defects in leptin signal transduction may have been confounded by the concomitant presence of obesity and associated metabolic and inflammatory alterations, adverse cardiac remodeling after MI [42] or lethal heart failure [43] were recently reported in mice with cardiomyocyte-specific LepR deletion. On the other hand, the beneficial effects of leptin-mediated STAT3 activation may not be restricted to cardiomyocytes. For example, we and others have shown that leptin promotes the angiogenic properties of endothelial (progenitor) cells [25,44], and cardiac angiogenesis was reduced in LepR^S1138 and LepR^db/db mice. In addition, hearts of obese LepR^S1138 and LepR^db/db mice exhibited increased interstitial fibrosis, which may have occurred secondary to increased cardiomyocyte loss, although previous studies have shown that leptin may also directly influence myocardial matrix metabolism [45]. On the functional level, the enhanced activation of pro-hypertrophic signaling pathways in the absence of STAT3-mediated cardioprotection may have contributed to the echocardiographic finding of LV cavity dilation in LepR^S1138 compared to LepR^db/db mice.

Conclusions

Taken together, our findings suggest that hearts from diet-induced obese mice continue to respond to chronically elevated leptin levels and that increased systemic and/or local leptin and enhanced cardiac LepR activation contribute the development of cardiac hypertrophy. On the other hand, chronic overactivation of hypertrophic signaling mediators together with an inabilitity to activate STAT3-dependent cardioprotective pathways may promote maladaptive cardiac remodeling. Of note, our findings also indicate that leptin signaling is not a prerequisite to develop cardiac hypertrophy in obesity and that additional pathways also contribute to the increase in LV mass associated with higher body weight.

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Ca2+ Signaling: Transcriptional Control

Posted in Biological Networks, Cell Biology, Chemical Biology and its relations to Metabolic Disease, Computational Biology/Systems and Bioinformatics, Frontiers in Cardiology and Cardiovascular Disorders, Gene Regulation and Evolution, International Global Work in Pharmaceutical, Metabolomics, Molecular Genetics & Pharmaceutical, Origins of Cardiovascular Disease, Pharmaceutical Industry Competitive Intelligence, Proteomics, Signaling & Cell Circuits, Statistical Methods for Research Evaluation, Technology Transfer: Biotech and Pharmaceutical, tagged Ca2+/calmodulin-dependent protein kinase, Cornell University, data integration, Domínguez-Rodríguez, Excitation-contraction coupling, genome scale network reconstruction, heart, Messenger RNA, metabolic network reconstruction, Mount Sinai School of Medicine, predictive models, tissue-specific metabolism, United States on March 6, 2013| 7 Comments »

Ca2+ Signaling: Transcriptional Control

Reporter: Larry H. Bernstein, MD, FCAP

Cardiac Physiology (excitation-transcription coupling)(transient receptor potential channels canonical; TRPCs)
The other side of cardiac Ca2+ signaling: transcriptional control
Domínguez-Rodríguez A, Ruiz-Hurtado G, Benitah J-P and Gómez AM
Front. Physio.2012; 3:452. http://dx.doi.org/10.3389/fphys.2012.00452
http://www.FrontPhysiol.com/The_other_side_of_cardiac_Ca2+_signaling:_transcriptional_control
http://www.frontiersin.org/Computational_Physiology_and_Medicine/10.3389/fphys.2012.00299/full

Integration of expression data in genome-scale metabolic network reconstructions
Anna S. Blazier and Jason A. Papin*
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
Front. Physiol., 06 August 2012 | http://dx.doi.org/10.3389/fphys.2012.00299
http://

The other side of cardiac Ca2+ signaling: transcriptional control
Alejandro Domínguez-Rodríguez1, Gema Ruiz-Hurtado2, Jean-Pierre Benitah1 and Ana M. Gómez1*
Ca2+ is probably the most versatile signal transduction element used by all cell types. In the heart, it is essential to activate cellular contraction in each heartbeat. Nevertheless Ca2+ is not only a key element in excitation-contraction coupling (EC coupling), but it is also

a pivotal second messenger in cardiac signal transduction, being able to control processes such as
- excitability, metabolism, and transcriptional regulation.

Regarding the latter, Ca2+ activates Ca2+-dependent transcription factors by a process called excitation-transcription coupling (ET coupling). ET coupling is an integrated process by which

the common signaling pathways that regulate EC coupling
- activate transcription factors.

In studies on the development of cardiac hypertrophy, two Ca2+-dependent enzymes are key actors:

Ca2+/Calmodulin kinase II (CaMKII) and
phosphatase calcineurin,

both of which are activated by the complex Ca2+/Calmodulin.

The question now is how ET coupling occurs in cardiomyocytes, where intracellular Ca2+ is continuously oscillating. We draw attention to location of Ca2+ signaling:

intranuclear ([Ca2+]n) or cytoplasmic ([Ca2+]c), and
the specific ionic channels involved in the activation of cardiac ET coupling.

We highlight the role of the 1,4,5 inositol triphosphate receptors (IP3Rs) in the elevation of [Ca2+]n levels, which are important to

locally activate CaMKII, and
the role of transient receptor potential channels canonical (TRPCs) in [Ca2+]c,
- needed to activate calcineurin (Cn).

Keywords: heart, calcium, excitation-transcription coupling, TRPC, nuclear calcium
Citation: Domínguez-Rodríguez A, Ruiz-Hurtado G, Benitah J-P and Gómez AM (2012) The other side of cardiac Ca2+ signaling: transcriptional control.
Front. Physio. 3:452. http://dx.doi.org/10.3389/fphys.2012.00452 Published online: 28 November 2012.
Edited by:Eric A. Sobie, Mount Sinai School of Medicine, USA; Reviewed by: Jeffrey Varner, Cornell University, USA; Ravi Radhakrishnan, University of Pennsylvania, USA

Integration of expression data in genome-scale metabolic network reconstructions
Anna S. Blazier and Jason A. Papin*
Front. Physiol., 06 August 2012 | doi: 10.3389/fphys.2012.00299

With the advent of high-throughput technologies, the field of systems biology has amassed an abundance of “omics” data,

quantifying thousands of cellular components across a variety of scales,
- ranging from mRNA transcript levels to metabolite quantities.

Methods are needed to not only

integrate this omics data but to also
use this data to heighten the predictive capabilities of computational models.

Several recent studies have successfully demonstrated how flux balance analysis (FBA), a constraint-based modeling approach, can be used

to integrate transcriptomic data into genome-scale metabolic network reconstructions
- to generate predictive computational models.

We summarize such FBA-based methods for integrating expression data into genome-scale metabolic network reconstructions, highlighting their advantages as well as their limitations.

Introduction

Genomics provides data on a cell’s DNA sequence,
transcriptomics on the mRNA expression of cells,
proteomics on a cell’s protein composition, and
metabolomics on a cell’s metabolite abundance.

Computational methods are needed to reduce this dimensionality across the wide spectrum of omics data to improve understanding of the underlying biological processes (Cakir et al., 2006; Pfau et al., 2011).

Metabolic network reconstructions are an advantageous platform for the integration of omics data (Palsson, 2002). Assembled in part from

annotated genomes as well as
- biochemical, genetic, and cell phenotype data,
a metabolic network reconstruction is a manually-curated, computational framework that
- enables the description of gene-protein-reaction relationships (Chavali et al., 2012).

Numerous studies have demonstrated how such reconstructions of metabolism can guide the development of biological hypotheses and discoveries (Oberhardt et al., 2010; Sigurdsson et al., 2010; Chang et al., 2011).

Flux balance analysis (FBA), a constraint-based modeling approach, can be used to probe these network reconstructions by

predicting physiologically relevant growth rates as a function of the underlying biochemical networks (Gianchandani et al., 2009).

To do so, FBA involves delineating constraints on the network according to

physicochemical,
environmental,
regulatory, and
thermodynamic principles
- (Kauffman et al., 2003; Price et al., 2003).

After applying constraints, the solution space of possible phenotypes narrows, allowing for more accurate characterization of the reconstructed metabolic network,

Omics data can be used to further constrain the possible solution space and
enhance the model’s predictive powers
- (Palsson, 2002; Lewis et al., 2012).

Given the wealth of transcriptomic data, efforts to integrate mRNA expression data with metabolic network reconstructions, have, in particular, made significant progress when using FBA as an analytical platform (Covert and Palsson, 2002; Akesson et al., 2004; Covert et al., 2004). However, despite this abundance of data, the integration of expression data faces unique challenges such as

experimental and inherent biological noise,
variation among experimental platforms,
detection bias, and the
unclear relationship between gene expression and reaction flux
- (Zhang et al., 2010).

The past few years have witnessed several advances in the integration of transcriptomic data with genome-scale metabolic network reconstructions. Specifically, numerous FBA-driven algorithms have been introduced that use experimentally derived mRNA transcript levels to modify the network’s reactions either by

inactivating them entirely or
by constraining their activity levels.

Such algorithms have demonstrated their applicability by, for example,

classifying tissue-specific metabolic activity in the human network and
by identifying novel drug targets in Mycobacterium tuberculosis
- (Shlomi et al., 2008; Colijn et al., 2009).

We give an overview of the formulation of FBA.
We summarize various FBA-driven methods for integrating expression data into genome-scale metabolic network reconstructions.
We survey the limitations of these algorithms as well as look to the future of

multi-omics data integration using genome-scale metabolic network reconstructions as the scaffold.

Flux balance analysis

FBA is a constraint-based modeling approach that characterizes and predicts aspects of an organism’s metabolism (Gianchandani et al., 2009) To use FBA, the user supplies a metabolic network reconstruction in the form of a stoichiometric matrix, S, where

the rows in S correspond to the metabolites of the reconstruction and
the columns in S represent reactions in the reconstruction.
a stoichiometric coefficient s_ijconveys the molecularity of a certain metabolite in a particular reaction, with
- s_ij ≥ 1 indicating that the metabolite is a product of the reaction,
- s_ij ≤ −1 a reactant, and
- s_ij = 0 signifies that the metabolite is not involved.

A system of linear equations is established by multiplying the S matrix by a column vector, v, which contains the unknown fluxes through each of the reactions of the S matrix. Under the assumption that the system operates at steady-state, that is to say there is no net production or consumption of mass within the system, the product of this matrix multiplication must equal zero, S · v = 0 (Gianchandani et al., 2009). Because the resulting system is underdetermined (i.e., too few equations, too many unknowns), linear programming (LP) is used to optimize for a particular flux,Z, the objective function, subject to underlying constraints. The objective function typically takes on the form of: Z = c ⋅ v
where c is a row vector of weights for each of the fluxes in column vector v, indicating how much each reaction in v contributes to the objective function,Z (Lee et al., 2006; Orth et al., 2010). Examples of objective functions include maximizing biomass, ATP production, and the production of a metabolite of interest (Lewis et al., 2012).

(1)

subject to

S ⋅ v = 0

(2)

lb ≤ v ≤ ub

(3)

(1) outlines the objective function to be optimized,

(2) the steady state assumption, and

(3) describes the upper and lower bounds, ub and lb, of each of the fluxes in v according to such constraints as

enzyme capacities,
maximum uptake and secretion rates, and
thermodynamic constraints
- (Price et al., 2003; Jensen and Papin, 2011).

Through this application of constraints, the solution space of physiologically feasible flux distributions for v shrinks. Thus, the task of FBA is to find a solution to v that lies within the bounded solution space and that optimizes the objective function at the same time.

Several recently developed algorithms have demonstrated how expression data can be incorporated into FBA models to further constrain the flux distribution solution space in genome-scale metabolic network reconstructions .
Summary of the algorithms for the integration of expression data. Table 1 image URL http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3429070/table/T1/?report=thumb

List of Methods:

GIMME guarantees to both produce a functioning metabolic model based on gene expression levels and quantify the agreement between the model and the data is called the Gene Inactivity Moderated by Metabolism and Expression (GIMME) algorithm (Becker and Palsson, 2008).

iMAT Similar to GIMME, the Integrative Metabolic Analysis Tool (iMAT) results in a functioning model in which the fluxes of reactions correlated with high mRNA levels are maximized and the fluxes of reactions associated with low mRNA levels are minimized (Shlomi et al., 2008; Zur et al., 2010). A key difference is that iMAT does not require a priori knowledge of a defined metabolic functionality. Briefly, this method establishes a tri-valued gene-to-reaction mapping for each reaction in the model according to the level of gene expression in the data. iMAT requires that reactions catalyzed by the products of highly expressed genes are able to carry a minimum flux. By removing this need for user-specified objective functions, iMAT bypasses assumptions about metabolic functionalities of a particular network, which proves advantageous for models where there is no clear objective function, as in models of mammalian cells.

MADE While both GIMME and iMAT rely on user-specified threshold values to determine which reactions are highly expressed and which reactions are lowly expressed, Metabolic Adjustment by Differential Expression (MADE) uses statistically significant changes in gene expression measurements to determine sequences of highly and lowly expressed reactions (Jensen and Papin, 2011). The lack of correlation between mRNA levels and protein levels makes it difficult to accurately determine when genes are “turned on,” and when they are “turned off.” Therefore, in eliminating this need for thresholding, MADE removes significant user-bias from the system.

E-Flux Whereas GIMME, iMAT, and MADE incorporate gene expression data into their models by reducing gene expression levels to binary states, the method E-Flux attempts to more directly incorporate gene expression data into FBA optimization problems by constraining the maximum possible flux through the reactions (Colijn et al., 2009). Rather than setting the upper bounds of a reaction to some large constant or 0, mirroring the implementation of binary-based algorithms, E-Flux constrains the upper bound of a reaction according to its respective gene expression level relative to a particular threshold. In cases where the gene expression data is below a certain threshold, tight constraints are placed on the flux through the corresponding reactions in the reconstruction; conversely, in cases where the gene expression is above a certain threshold, loose constraints are placed on the flux through the corresponding reactions.

PROM In contrast to the other methods discussed, which focused solely on integrating gene expression data into genome-scale metabolic network reconstructions, Probabilistic Regulation of Metabolism (PROM) aims to fuse together metabolic networks and transcription regulatory networks with expression data (Chandrasekaran and Price, 2010). To run PROM, the user supplies a genome-scale metabolic network reconstruction, a regulatory network structure describing transcription factors and their targets, and a range of expression data from various environmental and genetic perturbations. Given this expression data, PROM binarizes the genes with respect to a user-supplied threshold to evaluate the likelihood of the expression of a target gene given the expression of that gene’s transcription factor.

Challenges facing the integration of expression data

Each of the methods discussed hinges on the assumption that mRNA transcript levels are a strong indicator for the level of protein activity. For instance, GIMME and iMAT assume that mRNA levels below a certain threshold suggest that the corresponding reactions are inactive. MADE follows a similar logic, turning reactions on and off depending on the changes in mRNA transcript levels. E-Flux and PROM assume that transcript levels indicate the degree to which reactions are active, evident in the constraining of the upper bounds in the FBA optimization problems associated with these methods.

Rather than requiring that the reconstruction mirror the expression data exactly, the methods allow for deviations in the FBA flux solution space in order to generate a functioning model that adheres to the specified constraints. In the case of GIMME, highly expressed reactions are prioritized relative to lowly expressed reactions; however, in the event that an optimal, functioning solution cannot be found, the assumption can be violated and lowly expressed reactions can be added back into the reconstruction. Thus, this assumption that mRNA transcript levels correlate to protein levels serves as a cue rather than a mandate.

Conclusion

The above methods have been used to not only integrate expression data from a variety of sources but to also make progress toward overcoming key challenges in the field of systems biology. For instance, iMAT, highlighting its applicability in multi-cellular organisms, was used to curate the human metabolic network reconstruction and predict tissue-specific gene activity levels in ten human tissues (Duarte et al., 2007; Shlomi et al., 2008). Additionally, both E-Flux and PROM have been used to discover novel drug targets in Mycobacterium tuberculosis (Colijn et al., 2009; Chandrasekaran and Price, 2010).

Given the recent success with using genome-scale metabolic network reconstructions as a platform for integrating expression data, efforts should focus on multi-omics data integration. A handful of methods have already been introduced that integrate two or more types of omics data into genome-scale metabolic network reconstructions. For example, despite the current dearth of quantitative metabolomics data, a method has been developed that demonstrates how semi-quantitative metabolomics data can be used with transcriptomic data to curate genome-scale metabolic network reconstructions and identify key reactions involved in the production of certain metabolites (Cakir et al., 2006). Another algorithm, called Integrative Omics-Metabolic Analysis (IOMA), integrates metabolomics data and proteomics data into a genome-scale metabolic network reconstruction by evaluating kinetic rate equations subject to quantitative omics measurements (Yizhak et al., 2010). Furthermore, Mass Action Stoichiometric Simulation (MASS) uses metabolomic, fluxomic, and proteomic data to transform a static stoichiometric reconstruction of an organism into a large-scale dynamic network model (Jamshidi and Palsson, 2010). And finally, building off of iMAT, the Model-Building Algorithm (MBA) utilizes literature-based knowledge, transcriptomic, proteomic, metabolomic, and phenotypic data to curate the human metabolic network reconstruction to derive a more complete picture of tissue-specific metabolism (Jerby et al., 2010). Such algorithms show promise in their ability to easily integrate high-throughput data into genome-scale metabolic network reconstructions to generate phenotypically accurate and predictive computational models.

cardiac Ca2+ signaling: transcriptional control (pharmaceuticalintelligence.com)
AKT signaling variable effects (pharmaceuticalintelligence.com)
long noncoding RNA network regulates PTEN transcription (pharmaceuticalintelligence.com)
Transcript Dynamics of Proinflammatory Genes (pharmaceuticalintelligence.com)
Nuclear Translocation of Cardiac G Protein-Coupled Receptor Kinase 5 Downstream of Select Gq-Activating Hypertrophic Ligands Is a Calmodulin-Dependent Process (plosone.org)
Study finds possible link between diabetes and increased risk of heart attack death (eurekalert.org)
Ca2+/Calmodulin-Dependent Protein Kinase II (CaMKII) Activity and Sinoatrial Nodal Pacemaker Cell Energetics (plosone.org)

calcium release calmodulin

Ca(2+) and contraction

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Cardiac Ca2+ Signaling: Transcriptional Control

Posted in Biological Networks, Cardiovascular Pharmacogenomics, Cell Biology, Disease Biology, Gene Regulation and Evolution, Genetics & Pharmaceutical, Genome Biology, Molecular Genetics & Pharmaceutical, Nutrition and Phytochemistry, Pharmaceutical Analytics, Pharmaceutical Industry Competitive Intelligence, Population Health Management, Signaling & Cell Circuits, Small Molecules in Development of Therapeutic Drugs, tagged Ca2+/calmodulin-dependent protein kinase, Calcium in biology, Green fluorescent protein, health, heart, Open source intelligence, Protocols, Second messenger system, Signal transduction, Transcription (genetics) on March 2, 2013| 2 Comments »

Cardiac Ca2+ Signaling: Transcriptional Control

Reporter: Larry H Bernstein, MD, FCAP

The other side of cardiac Ca2+ signaling: transcriptional control

A Domínguez-Rodríguez, G Ruiz-Hurtado, Jean-Pierre Benitah and AM Gómez

Ca2+ is not only a key element in excitation-contraction coupling (EC coupling), but
it is also a pivotal second messenger in cardiac signal transduction,
being able to control processes such as
- excitability,
- metabolism, and
- transcriptional regulation.

Front. Physio. 2012; 3:452. http://dx.doi.org/fphys.2012.00452/
http://www.fphys.com/The other side of cardiac Ca2+ signaling: transcriptional control

calcium release calmodulin

English: A rendition of the CaMKII holoenzyme in the (A) Closed and the (B) Open conformation (Photo credit: Wikipedia)

Ca2+/Calmodulin-Dependent Protein Kinase II (CaMKII) Activity and Sinoatrial Nodal Pacemaker Cell Energetics (plosone.org)
The L-type Ca2+ Channels Blocker Nifedipine Represses Mesodermal Fate Determination in Murine Embryonic Stem Cells (plosone.org)
Calreticulin Induces Dilated Cardiomyopathy (plosone.org)
Phosphorylation and Feedback Regulation of Metabotropic Glutamate Receptor 1 by Calcium/Calmodulin-Dependent Protein Kinase II (jneurosci.org)
Watching Neurons in action (greenfluorescentblog.wordpress.com)

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Mitochondrial Dynamics and Cardiovascular Diseases

Posted in Cardiovascular Pharmacogenomics, Cell Biology, Signaling & Cell Circuits, Disease Biology, Small Molecules in Development of Therapeutic Drugs, Origins of Cardiovascular Disease, Pharmacotherapy of Cardiovascular Disease, tagged Aging, cardiomyocyte, cardiomyocyte differentiation, Cardiomyopathy, cardiovascular, DLP1, Drp1, filamentous network, GTP hydrolysis, GTPase, heart, Heart Failure, IF, interfibrillar filaments, Mfn1, Mfn2, mitochondria, mitochondrial dynamics, mitochondrial fission, mitochondrial fusion, myocytes, neurodegeneration, Opa1, type 2 diabtes, ventricular myocytes on November 14, 2012| 18 Comments »

Mitochondrial Dynamics and Cardiovascular Diseases

Author and Curator: Ritu Saxena, Ph.D.

Morphological changes in mitochondria have been observed in several human diseases including myopathies, diabetes mellitus, liver diseases, neurodegeneration, aging, and cancer. Ong et al (2010) studied neonatal rat ventricular myocytes as an experimental model of aging and concluded that the interplay between mitochondrial fission and autophagy controls the rate of mitochondrial turnover. A disturbance in the balance is observed in aging heart cells resulting in giant mitochondria. This observation is an indication that mitochondrial morphology is connected to pathogenesis of cardiac disease. http://www.ncbi.nlm.nih.gov/pubmed/20631158 Thus, it is important to understand the mechanism of mitochondrial dynamics in order to correlate it with the development of cardiovascular diseases.

Mitochondrial dynamics

The shape of mitochondria is very dynamic in living cells, constantly interchanging between thread-like and grain-like morphology through what we know now as the fusion and fission processes, respectively. The fusion and fission processes together with the mitochondrial movement have been termed “mitochondrial dynamics”. Nucleoids, the assemblies of mitochondrial DNA (mtDNA) with its associated proteins, are distributed during fission in such a way that each mitochondrion contains at least one nucleoid.

Mitochondrial fusion is a complex process that involves the fusing together of four lipid bilayers. Proteins involved in the mitochondrial fission and fusion have been discussed in an earlier post published on October 31, 2012. Mitochondrial fusion requires two 85kD-GTPase isoforms mitofusin1 (Mfn1) and mitofusin2 (Mfn2). Mfn1 and Mfn1 are both anchored to the outer mitochondrial membrane. They contain – two transmembrane domains connected by a small intermembrane-space loop, a cytosolic N-terminal GTPase domain and two cytosolic hydrophobic heptad-repeat coiled-coil domains. The coiled-coil domains of Mfn1 and Mfn2 help in tethering adjacent mitochondria in both homo-oligomeric and hetero-oligomeic fashion. The fusion process requires GTP hydrolysis and the cells where Mfn2 had a GTPase mutation; mitochondria were not able to undergo fusion even after tethering. Mitochondrial fission and fusion have been illustrated in Figure 1.

Mitochondrial fission is opposite of the fusion process. Mammalian mitochondria undergo fission by the interaction of two proteins: dynamin-like protein 1 or dynamin-related protein 1 (DLP1/Drp1), an 80–85-kD cytosolic GTPase, and human fission protein 1 (hFis1), a 17-kD outer mitochondrial membrane anchored protein. Mitochondrial fission too requires GTP hydrolysis. DLP1 mainly localizes in the cytosol and with the help of hFis1, DLP1 is recruited to the constriction sites of the membrane. DLP1 translocation depends on actin and microtubules and once inside, DLP1 oligomerizes into a ring around the mitochondrion. The self-assembly of DLP1 stimulates the final step of fission which is disassembly and it requires GTP hydrolysis.

Figure 1: Model of mammalian mitochondrial fission and fusion (Hom et al, J Mol Cell Cardiol, 2009)

http://www.ncbi.nlm.nih.gov/pubmed?term=19281816

Additional information on different aspects of mitochondria could be found articles published earlier in the Pharmaceutical Intelligence webpage.

Mitochondrial dynamics in the heart

In cultured cardiovascular cell line the mitochondria are arranged in a filamentous network and are highly dynamic, constantly undergoing fusion and fission. Similar mitochondrial network is observed in vascular smooth muscle cells, cardiac stem cells, and neonatal cardiomyocytes. Thus, these cell types have been used to study mitochondrial dynamics.

However, in the adult cardiomyocyte, there are three distinct populations of mitochondria:

(i) peri-nuclear mitochondria,

(ii) subsarcolemmal (SSC) mitochondria, and

(iii) interfibrillar (IF) mitochondria

Electron micrographs of adult cardiac muscle cells, especially ventricular myocytes, show that mitochondria are numerous, making up about 35% of the cell volume, and that mitochondria are highly organized and compacted between contractile filaments and next to T-tubules. This crystal-like pattern of mitochondria in adult ventricular myocytes raises an interesting question- Do the mitochondria in these cells also undergo physiological fission, fusion, and movement just like other cell types? Whether the crystal-like lattice arrangement restricts their movements and prevents them from undergoing fusion or fission is unclear. It has been speculated that the fission and fusion processes might occur at a slower rate because of the tight packing. A four-dimensional (x, y, z axis and time) live-cell imaging is needed to detect possible movements like mitochondria winding slowly through the myofibrils in the third dimension.

Figure 2. Representative electron micrograph of adult murine heart depicting the three subpopulations of mitochondria: perinuclear (PN) mitochondria; interfibrillar (IF) mitochondria; and subsarcolemmal mitochondria (SSM). Photo credit: Ong et al, Cardiovascular Research (2010).

Expression of fission/fusion proteins in adult heart: Interestingly, it has been observed that proteins required for mitochondrial dynamics including fission and fusion proteins is abundantly present in the adult heart and would have been active during cardiomyocyte differentiation to ensure the unique spatial organization of the three different subpopulations of cardiac mitochondria.

Several studies suggest the existence of fission and fusion proteins in the adult heart.

Mfn1 and Mfn2 fusion proteins have been found to be expressed in highest amounts in the heart compared to that in human tissues of pancreas, skeletal muscle, brain, liver, placenta, lung, and kidney using both Northern and Western blot analysis. Infact, Mfn2 mRNA was found to be abundantly expressed in heart and muscle tissue but expressed only at low levels in other tissues. Mfn1 and Mfn2 expression has also been confirmed in heart tissue of rat and mouse by RT-PCR.
hFis1, a fission protein, has been shown to be ubiquitously expressed in isolated rat mitochondria in heart tissue apart from several other tissues.
DLP1 mRNA, coding for a fusion protein, have been detected in high levels in several adult tissues including heart, skeletal muscle, kidney and brain.
OPA1 codes for another fusion protein and four transcripts of OPA1 have been detected in adult mouse hearts.

Mitochondria in cardiac diseases:

Abnormal mitochondrial morphology corresponding to various cardiac diseases has been listed as follows:

Abnormally small and disorganized mitochondria – observed in endstage dilated cardiomyopathy, myocardial hibernation, cardiac rhabdomyoma, and ventricular-associated congenital heart diseases.
Disorganized clusters of fragmented mitochondria – observed in Tetralogy of Fallot and are located away from contractile filaments, along with having a very small diameter measured to be 0.1 μm as observed in the electron micrographs.
Big and defective mitochondria – observed in senescent cardiomyocytes.

http://www.ncbi.nlm.nih.gov/pubmed?term=19281816

Condition	Cell type	Change in mitochondrial morphology	Other findings	Study
Ischemia-perfusion injury	HL-1 cells	Fission	P38 inhibition at reperfusion allows mitochondrial re-fusion	Brady et al
β – Adrenergic stimulation by isoproterenol or exercise	Adult murine heart	Not investigated	Phosphorylation and inhibiton of Drp1 at Ser656	Cribbs and Strack et al
Cardiac differentiation	Embryonic stem cells	Fusion	Fusion is required to support Oxidative phosphorylation	Chung et al
Hyperglycemia	H9C2 rat myoblast	Fission		Yu et al
Post-MI heart failure and dilated cardiomyopathy	Adult rat and human heart	Fragmentation	Decrease in OPA1	Chen et al
Diabetes	Murine coronary endothelial cell	Fission	Decreased OPA1, increased Drp1	Makino et al
Diabetes	Adult murine diabetic heart	Fission	Lower mitochondrial membrane potential	Williamson et al
Ischaemia-reperfusion injury and cardioprotection	HL-1 cells, adult heart	Fission	Inhibiting fission cardioprotective	Ong et al
Cytosolic calcium overload	Neonatal cardiomyocytes and adult heart	Fission		Hom et al

Table 1: Studies implicating changes in mitochondrial morphology in cardiovascular diseases, Adapted from Ong et al, Cardiovascular Research (2010).

Mitochondrial dynamics in heart failure

Fission and Fusion in Heart Failure

Mutation or abnormal expression of fission and fusion proteins have been implicated in several diseases including neuropathies, Parkinson’s disease, type 2 diabetes and so on. However, few studies have addressed the involvement of mitochondrial dynamics in heart failure. Research groups have used cardiac-like cell lines, neonatal and adult cardiomyocytes, and animal models to demonstrate the importance of fission and fusion proteins. Observations from some studies have been listed below:

Mitochondria are highly organized and compacted between contractile filaments (interfibrillar) or adjacent to the sarcolemma (subsarcolemmal) in adult mammalian cardiomyocytes. However, during heart failure, interfibrillar mitochondria may lose their normal organization.
There is also a reduction in size and density of interfibrillar mitochondria in rodent models of heart failure.
It was recently reported that OPA1 is decreased in both human and rat heart failure.
Electron microscopic data showed an increase in the number and decrease in the size of the mitochondria in a coronary artery ligation rat heart failure model.
Inhibition of fission in cultured neonatal ventricular myocytes by overexpression of dominant negative mutant form of Drp1, Drp1-K38A, prevents overproduction of ROS, mitochondrial permeability transient pore formation and ultimately cell death under high glucose conditions.
In cultured neonatal and adult cardiomyocytes, cytosolic Ca2+ overload induced by thapsigargin (Tg) or potassium chloride (KCl) resulted in rapid mitochondrial fragmentation. Calcium overload is a common feature in heart failure, which might lead to increase in fission contributing to decrease in energy production in the failing heart.
In H9c2 cells, reduction in OPA1 increased apoptosis both at baseline and after simulated ischemia, via cytochrome c release from mitochondria.
Drosophila heart tube-specific silencing of OPA1 and mitochondrial assembly regulatory factor (MARF) increased mitochondrial morphometric heterogeneity and induced heart tube dilation with profound contractile impairment. In this model, human MFN1/2 was rescued MARF RNAi induced cardiomyopathy.
MFN-2-deficient mice have mild cardiac hypertrophy and mild depression of cardiac function. Also, mitochondria of cardiac myocytes lacking MFN-2 are pleiotropic and larger.
In rat hearts, decreased MFN2, increased Fis1 and no change in OPA1 expression was observed 12–18 weeks after myocardial infarction. http://www.ncbi.nlm.nih.gov/pubmed/22848903

However, further research is needed to accurately and fully define the role of abnormal mitochondrial morphology in heart failure. Those researches might lead to developing new interventions for treating abnormal mitochondrial function based diseases.

Reference: