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Posts Tagged ‘Excitation-contraction coupling’

Science Has A Systemic Problem, Not an Innovation Problem

Curator: Stephen J. Williams, Ph.D.

    A recent email, asking me to submit a survey, got me thinking about the malaise that scientists and industry professionals frequently bemoan: that innovation has been stymied for some reason and all sorts of convuluted processes must be altered to spur this mythical void of great new discoveries…..  and it got me thinking about our current state of science, and what is the perceived issue… and if this desert of innovation actually exists or is more a fundamental problem which we have created.

The email was from an NIH committee asking for opinions on recreating the grant review process …. now this on the same day someone complained to me about a shoddy and perplexing grant review they received.

The following email, which was sent out to multiple researchers, involved in either NIH grant review on both sides, as well as those who had been involved in previous questionnaires and studies on grant review and bias.  The email asked for researchers to fill out a survey on the grant review process, and how to best change it to increase innovation of ideas as well as inclusivity.  In recent years, there have been multiple survey requests on these matters, with multiple confusing procedural changes to grant format and content requirements, adding more administrative burden to scientists.

The email from Center for Scientific Review (one of the divisions a grant will go to before review {they set up review study sections and decide what section a grant should be  assigned to} was as follows:

Update on Simplifying Review Criteria: A Request for Information

https://www.csr.nih.gov/reviewmatters/2022/12/08/update-on-simplifying-review-criteria-a-request-for-information/

NIH has issued a request for information (RFI) seeking feedback on revising and simplifying the peer review framework for research project grant applications. The goal of this effort is to facilitate the mission of scientific peer review – identification of the strongest, highest-impact research. The proposed changes will allow peer reviewers to focus on scientific merit by evaluating 1) the scientific impact, research rigor, and feasibility of the proposed research without the distraction of administrative questions and 2) whether or not appropriate expertise and resources are available to conduct the research, thus mitigating the undue influence of the reputation of the institution or investigator.

Currently, applications for research project grants (RPGs, such as R01s, R03s, R15s, R21s, R34s) are evaluated based on five scored criteria: Significance, Investigators, Innovation, Approach, and Environment (derived from NIH peer review regulations 42 C.F.R. Part 52h.8; see Definitions of Criteria and Considerations for Research Project Grant Critiques for more detail) and a number of additional review criteria such as Human Subject Protections.

NIH gathered input from the community to identify potential revisions to the review framework. Given longstanding and often-heard concerns from diverse groups, CSR decided to form two working groups to the CSR Advisory Council—one on non-clinical trials and one on clinical trials. To inform these groups, CSR published a Review Matters blog, which was cross-posted on the Office of Extramural Research blog, Open Mike. The blog received more than 9,000 views by unique individuals and over 400 comments. Interim recommendations were presented to the CSR Advisory Council in a public forum (March 2020 videoslides; March 2021 videoslides). Final recommendations from the CSRAC (report) were considered by the major extramural committees of the NIH that included leadership from across NIH institutes and centers. Additional background information can be found here. This process produced many modifications and the final proposal presented below. Discussions are underway to incorporate consideration of a Plan for Enhancing Diverse Perspectives (PEDP) and rigorous review of clinical trials RPGs (~10% of RPGs are clinical trials) within the proposed framework.

Simplified Review Criteria

NIH proposes to reorganize the five review criteria into three factors, with Factors 1 and 2 receiving a numerical score. Reviewers will be instructed to consider all three factors (Factors 1, 2 and 3) in arriving at their Overall Impact Score (scored 1-9), reflecting the overall scientific and technical merit of the application.

  • Factor 1: Importance of the Research (Significance, Innovation), numerical score (1-9)
  • Factor 2: Rigor and Feasibility (Approach), numerical score (1-9)
  • Factor 3: Expertise and Resources (Investigator, Environment), assessed and considered in the Overall Impact Score, but not individually scored

Within Factor 3 (Expertise and Resources), Investigator and Environment will be assessed in the context of the research proposed. Investigator(s) will be rated as “fully capable” or “additional expertise/capability needed”. Environment will be rated as “appropriate” or “additional resources needed.” If a need for additional expertise or resources is identified, written justification must be provided. Detailed descriptions of the three factors can be found here.

Now looking at some of the Comments were very illuminating:

I strongly support streamlining the five current main review criteria into three, and the present five additional criteria into two. This will bring clarity to applicants and reduce the workload on both applicants and reviewers. Blinding reviewers to the applicants’ identities and institutions would be a helpful next step, and would do much to reduce the “rich-getting-richer” / “good ole girls and good ole boys” / “big science” elitism that plagues the present review system, wherein pedigree and connections often outweigh substance and creativity.

I support the proposed changes. The shift away from “innovation” will help reduce the tendency to create hype around a proposed research direction. The shift away from Investigator and Environment assessments will help reduce bias toward already funded investigators in large well-known institutions.

As a reviewer for 5 years, I believe that the proposed changes are a step in the right direction, refocusing the review on whether the science SHOULD be done and whether it CAN BE DONE WELL, while eliminating burdensome and unhelpful sections of review that are better handled administratively. I particularly believe that the de-emphasis of innovation (which typically focuses on technical innovation) will improve evaluation of the overall science, and de-emphasis of review of minor technical details will, if implemented correctly, reduce the “downward pull” on scores for approach. The above comments reference blinded reviews, but I did not see this in the proposed recommendations. I do not believe this is a good idea for several reasons: 1) Blinding of the applicant and institution is not likely feasible for many of the reasons others have described (e.g., self-referencing of prior work), 2) Blinding would eliminate the potential to review investigators’ biosketches and budget justifications, which are critically important in review, 3) Making review blinded would make determination of conflicts of interest harder to identify and avoid, 4) Evaluation of “Investigator and Environment” would be nearly impossible.

Most of the Comments were in favor of the proposed changes, however many admitted that it adds additional confusion on top of many administrative changes to formats and content of grant sections.

Being a Stephen Covey devotee, and just have listened to  The Four Principles of Execution, it became more apparent that issues that hinder many great ideas coming into fruition, especially in science, is a result of these systemic or problems in the process, not at the level of individual researchers or small companies trying to get their innovations funded or noticed.  In summary, Dr. Covey states most issues related to the success of any initiative is NOT in the strategic planning, but in the failure to adhere to a few EXECUTION principles.  Primary to these failures of strategic plans is lack of accounting of what Dr. Covey calls the ‘whirlwind’, or those important but recurring tasks that take us away from achieving the wildly important goals.  In addition, lack of  determining lead and lag measures of success hinder such plans.

In this case a lag measure in INNOVATION.  It appears we have created such a whirlwind and focus on lag measures that we are incapable of translating great discoveries into INNOVATION.

In the following post, I will focus on issues relating to Open Access, publishing and dissemination of scientific discovery may be costing us TIME to INNOVATION.  And it appears that there are systemic reasons why we appear stuck in a rut, so to speak.

The first indication is from a paper published by Johan Chu and James Evans in 2021 in PNAS:

 

Slowed canonical progress in large fields of science

Chu JSG, Evans JA. Slowed canonical progress in large fields of science. Proc Natl Acad Sci U S A. 2021 Oct 12;118(41):e2021636118. doi: 10.1073/pnas.2021636118. PMID: 34607941; PMCID: PMC8522281

 

Abstract

In many academic fields, the number of papers published each year has increased significantly over time. Policy measures aim to increase the quantity of scientists, research funding, and scientific output, which is measured by the number of papers produced. These quantitative metrics determine the career trajectories of scholars and evaluations of academic departments, institutions, and nations. Whether and how these increases in the numbers of scientists and papers translate into advances in knowledge is unclear, however. Here, we first lay out a theoretical argument for why too many papers published each year in a field can lead to stagnation rather than advance. The deluge of new papers may deprive reviewers and readers the cognitive slack required to fully recognize and understand novel ideas. Competition among many new ideas may prevent the gradual accumulation of focused attention on a promising new idea. Then, we show data supporting the predictions of this theory. When the number of papers published per year in a scientific field grows large, citations flow disproportionately to already well-cited papers; the list of most-cited papers ossifies; new papers are unlikely to ever become highly cited, and when they do, it is not through a gradual, cumulative process of attention gathering; and newly published papers become unlikely to disrupt existing work. These findings suggest that the progress of large scientific fields may be slowed, trapped in existing canon. Policy measures shifting how scientific work is produced, disseminated, consumed, and rewarded may be called for to push fields into new, more fertile areas of study.

So the Summary of this paper is

  • The authors examined 1.8 billion citations among 90 million papers over 241 subjects
  • found the corpus of papers do not lead to turnover of new ideas in a field, but rather the ossification or entrenchment of canonical (or older ideas)
  • this is mainly due to older paper cited more frequently than new papers with new ideas, potentially because authors are trying to get their own papers cited more frequently for funding and exposure purposes
  • The authors suggest that “fundamental progress may be stymied if quantitative growth of scientific endeavors is not balanced by structures fostering disruptive scholarship and focusing attention of novel ideas”

The authors note that, in most cases, science policy reinforces this “more is better” philosophy”,  where metrics of publication productivity are either number of publications or impact measured by citation rankings.  However, using an analysis of citation changes occurring in large versus smaller fields, it becomes apparent that this process is favoring the older, more established papers and a recirculating of older canonical ideas.

“Rather than resulting in faster turnover of field paradigms, the massive amounts of new publications entrenches the ideas of top-cited papers.”  New ideas are pushed down to the bottom of the citation list and potentially lost in the literature.  The authors suggest that this problem will intensify as the “annual mass” of new publications in each field grows, especially in large fields.  This issue is exacerbated by the deluge on new online ‘open access’ journals, in which authors would focus on citing the more highly cited literature. 

We maybe at a critical junction, where if many papers are published in a short time, new ideas will not be considered as carefully as the older ideas.  In addition,

with proliferation of journals and the blurring of journal hierarchies due to online articles-level access can exacerbate this problem

As a counterpoint, the authors do note that even though many molecular biology highly cited articles were done in 1976, there has been extremely much innovation since then however it may take a lot more in experiments and money to gain the level of citations that those papers produced, and hence a lower scientific productivity.

This issue is seen in the field of economics as well

Ellison, Glenn. “Is peer review in decline?” Economic Inquiry, vol. 49, no. 3, July 2011, pp. 635+. Gale Academic OneFile, link.gale.com/apps/doc/A261386330/AONE?u=temple_main&sid=bookmark-AONE&xid=f5891002. Accessed 12 Dec. 2022.

Abstract

Over the past decade, there has been a decline in the fraction of papers in top economics journals written by economists from the highest-ranked economics departments. This paper documents this fact and uses additional data on publications and citations to assess various potential explanations. Several observations are consistent with the hypothesis that the Internet improves the ability of high-profile authors to disseminate their research without going through the traditional peer-review process. (JEL A14, 030)

The facts part of this paper documents two main facts:

1. Economists in top-ranked departments now publish very few papers in top field journals. There is a marked decline in such publications between the early 1990s and early 2000s.

2. Comparing the early 2000s with the early 1990s, there is a decline in both the absolute number of papers and the share of papers in the top general interest journals written by Harvard economics department faculty.

Although the second fact just concerns one department, I see it as potentially important to understanding what is happening because it comes at a time when Harvard is widely regarded (I believe correctly) as having ascended to the top position in the profession.

The “decline-of-peer-review” theory I allude to in the title is that the necessity of going through the peer-review process has lessened for high-status authors: in the old days peer-reviewed journals were by far the most effective means of reaching readers, whereas with the growth of the Internet high-status authors can now post papers online and exploit their reputation to attract readers.

Many alternate explanations are possible. I focus on four theories: the decline-in-peer-review theory and three alternatives.

1. The trends could be a consequence of top-school authors’ being crowded out of the top journals by other researchers. Several such stories have an optimistic message, for example, there is more talent entering the profession, old pro-elite biases are being broken down, more schools are encouraging faculty to do cutting-edge research, and the Internet is enabling more cutting-edge research by breaking down informational barriers that had hampered researchers outside the top schools. (2)

2. The trends could be a consequence of the growth of revisions at economics journals discussed in Ellison (2002a, 2002b). In this more pessimistic theory, highly productive researchers must abandon some projects and/or seek out faster outlets to conserve the time now required to publish their most important works.

3. The trends could simply reflect that field journals have declined in quality in some relative sense and become a less attractive place to publish. This theory is meant to encompass also the rise of new journals, which is not obviously desirable or undesirable.

The majority of this paper is devoted to examining various data sources that provide additional details about how economics publishing has changed over the past decade. These are intended both to sharpen understanding of the facts to be explained and to provide tests of auxiliary predictions of the theories. Two main sources of information are used: data on publications and data on citations. The publication data include department-level counts of publications in various additional journals, an individual-level dataset containing records of publications in a subset of journals for thousands of economists, and a very small dataset containing complete data on a few authors’ publication records. The citation data include citations at the paper level for 9,000 published papers and less well-matched data that is used to construct measures of citations to authors’ unpublished works, to departments as a whole, and to various journals.

Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship

Josh Angrist, Pierre Azoulay, Glenn Ellison, Ryan Hill, Susan Feng Lu. Inside Job or Deep Impact? Extramural Citations and the Influence of Economic Scholarship.

JOURNAL OF ECONOMIC LITERATURE

VOL. 58, NO. 1, MARCH 2020

(pp. 3-52)

So if innovation is there but it may be buried under the massive amount of heavily cited older literature, do we see evidence of this in other fields like medicine?

Why Isn’t Innovation Helping Reduce Health Care Costs?

 
 

National health care expenditures (NHEs) in the United States continue to grow at rates outpacing the broader economy: Inflation- and population-adjusted NHEs have increased 1.6 percent faster than the gross domestic product (GDP) between 1990 and 2018. US national health expenditure growth as a share of GDP far outpaces comparable nations in the Organization for Economic Cooperation and Development (17.2 versus 8.9 percent).

Multiple recent analyses have proposed that growth in the prices and intensity of US health care services—rather than in utilization rates or demographic characteristics—is responsible for the disproportionate increases in NHEs relative to global counterparts. The consequences of ever-rising costs amid ubiquitous underinsurance in the US include price-induced deferral of care leading to excess morbidity relative to comparable nations.

These patterns exist despite a robust innovation ecosystem in US health care—implying that novel technologies, in isolation, are insufficient to bend the health care cost curve. Indeed, studies have documented that novel technologies directly increase expenditure growth.

Why is our prolific innovation ecosystem not helping reduce costs? The core issue relates to its apparent failure to enhance net productivity—the relative output generated per unit resource required. In this post, we decompose the concept of innovation to highlight situations in which inventions may not increase net productivity. We begin by describing how this issue has taken on increased urgency amid resource constraints magnified by the COVID-19 pandemic. In turn, we describe incentives for the pervasiveness of productivity-diminishing innovations. Finally, we provide recommendations to promote opportunities for low-cost innovation.

 

 

Net Productivity During The COVID-19 Pandemic

The issue of productivity-enhancing innovation is timely, as health care systems have been overwhelmed by COVID-19. Hospitals in Italy, New York City, and elsewhere have lacked adequate capital resources to care for patients with the disease, sufficient liquidity to invest in sorely needed resources, and enough staff to perform all of the necessary tasks.

The critical constraint in these settings is not technology: In fact, the most advanced technology required to routinely treat COVID-19—the mechanical ventilator—was invented nearly 100 years ago in response to polio (the so-called iron lung). Rather, the bottleneck relates to the total financial and human resources required to use the technology—the denominator of net productivity. The clinical implementation of ventilators has been illustrative: Health care workers are still required to operate ventilators on a nearly one-to-one basis, just like in the mid-twentieth century. 

High levels of resources required for implementation of health care technologies constrain the scalability of patient care—such as during respiratory disease outbreaks such as COVID-19. Thus, research to reduce health care costs is the same kind of research we urgently require to promote health care access for patients with COVID-19.

Types Of Innovation And Their Relationship To Expenditure Growth

The widespread use of novel medical technologies has been highlighted as a central driver of NHE growth in the US. We believe that the continued expansion of health care costs is largely the result of innovation that tends to have low productivity (exhibit 1). We argue that these archetypes—novel widgets tacked on to existing workflows to reinforce traditional care models—are exactly the wrong properties to reduce NHEs at the systemic level.

Exhibit 1: Relative productivity of innovation subtypes

Source: Authors’ analysis.

Content Versus Process Innovation

Content (also called technical) innovation refers to the creation of new widgets, such as biochemical agents, diagnostic tools, or therapeutic interventions. Contemporary examples of content innovation include specialty pharmaceuticalsmolecular diagnostics, and advanced interventions and imaging.

These may be contrasted with process innovations, which address the organized sequences of activities that implement content. Classically, these include clinical pathways and protocols. They can address the delivery of care for acute conditions, such as central line infections, sepsis, or natural disasters. Alternatively, they can target chronic conditions through initiatives such as team-based management of hypertension and hospital-at-home models for geriatric care. Other processes include hiring staffdelegating labor, and supply chain management.

Performance-Enhancing Versus Cost-Reducing Innovation

Performance-enhancing innovations frequently create incremental outcome gains in diagnostic characteristics, such as sensitivity or specificity, or in therapeutic characteristics, such as biomarkers for disease status. Their performance gains often lead to higher prices compared to existing alternatives.  

Performance-enhancing innovations can be compared to “non-inferior” innovations capable of achieving outcomes approximating those of existing alternatives, but at reduced cost. Industries outside of medicine, such as the computing industry, have relied heavily on the ability to reduce costs while retaining performance.

In health care though, this pattern of innovation is rare. Since passage of the 2010 “Biosimilars” Act aimed at stimulating non-inferior innovation and competition in therapeutics markets, only 17 agents have been approved, and only seven have made it to market. More than three-quarters of all drugs receiving new patents between 2005 and 2015 were “reissues,” meaning they had already been approved, and the new patent reflected changes to the previously approved formula. Meanwhile, the costs of approved drugs have increased over time, at rates between 4 percent and 7 percent annually.

Moreover, the preponderance of performance-enhancing diagnostic and therapeutic innovations tend to address narrow patient cohorts (such as rare diseases or cancer subtypes), with limited clear clinical utility in broader populations. For example, the recently approved eculizimab is a monoclonal antibody approved for paroxysmal nocturnal hemoglobinuria—which effects 1 in 10 million individuals. At the time of its launch, eculizimab was priced at more than $400,000 per year, making it the most expensive drug in modern history. For clinical populations with no available alternatives, drugs such as eculizimab may be cost-effective, pending society’s willingness to pay, and morally desirable, given a society’s values. But such drugs are certainly not cost-reducing.

Additive Versus Substitutive Innovation

Additive innovations are those that append to preexisting workflows, while substitutive innovations reconfigure preexisting workflows. In this way, additive innovations increase the use of precedent services, whereas substitutive innovations decrease precedent service use.

For example, previous analyses have found that novel imaging modalities are additive innovations, as they tend not to diminish use of preexisting modalities. Similarly, novel procedures tend to incompletely replace traditional procedures. In the case of therapeutics and devices, off-label uses in disease groups outside of the approved indication(s) can prompt innovation that is additive. This is especially true, given that off-label prescriptions classically occur after approved methods are exhausted.

Eculizimab once again provides an illustrative example. As of February 2019, the drug had been used for 39 indications (it had been approved for three of those, by that time), 69 percent of which lacked any form of evidence of real-world effectiveness. Meanwhile, the drug generated nearly $4 billion in sales in 2019. Again, these expenditures may be something for which society chooses to pay—but they are nonetheless additive, rather than substitutive.

Sustaining Versus Disruptive Innovation

Competitive market theory suggests that incumbents and disruptors innovate differently. Incumbents seek sustaining innovations capable of perpetuating their dominance, whereas disruptors pursue innovations capable of redefining traditional business models.

In health care, while disruptive innovations hold the potential to reduce overall health expenditures, often they run counter to the capabilities of market incumbents. For example, telemedicine can deliver care asynchronously, remotely, and virtually, but large-scale brick-and-mortar medical facilities invest enormous capital in the delivery of synchronous, in-house, in-person care (incentivized by facility fees).

The connection between incumbent business models and the innovation pipeline is particularly relevant given that 58 percent of total funding for biomedical research in the US is now derived from private entities, compared with 46 percent a decade prior. It follows that the growing influence of eminent private organizations may favor innovations supporting their market dominance—rather than innovations that are societally optimal.

Incentives And Repercussions Of High-Cost Innovation

Taken together, these observations suggest that innovation in health care is preferentially designed for revenue expansion rather than for cost reduction. While offering incremental improvements in patient outcomes, therefore creating theoretical value for society, these innovations rarely deliver incremental reductions in short- or long-term costs at the health system level.

For example, content-based, performance-enhancing, additive, sustaining innovations tend to add layers of complexity to the health care system—which in turn require additional administration to manage. The net result is employment growth in excess of outcome improvement, leading to productivity losses. This gap leads to continuously increasing overall expenditures in turn passed along to payers and consumers.

Nonetheless, high-cost innovations are incentivized across health care stakeholders (exhibit 2). From the supply side of innovation, for academic researchers, “breakthrough” and “groundbreaking” innovations constitute the basis for career advancement via funding and tenure. This is despite stakeholders’ frequent inability to generalize early successes to become cost-effective in the clinical setting. As previously discussed, the increasing influence of private entities in setting the medical research agenda is also likely to stimulate innovation benefitting single stakeholders rather than the system.

Exhibit 2: Incentives promoting low-value innovation

Source: Authors’ analysis adapted from Hofmann BM. Too much technology. BMJ. 2015 Feb 16.

From the demand side of innovation (providers and health systems), a combined allure (to provide “cutting-edge” patient care), imperative (to leave “no stone unturned” in patient care), and profit-motive (to amplify fee-for-service reimbursements) spur participation in a “technological arms-race.” The status quo thus remains as Clay Christensen has written: “Our major health care institutions…together overshoot the level of care actually needed or used by the vast majority of patients.”

Christensen’s observations have been validated during the COVID-19 epidemic, as treatment of the disease requires predominantly century-old technology. By continually adopting innovation that routinely overshoots the needs of most patients, layer by layer, health care institutions are accruing costs that quickly become the burden of society writ large.

Recommendations To Reduce The Costs Of Health Care Innovation

Henry Aaron wrote in 2002 that “…the forces that have driven up costs are, if anything, intensifying. The staggering fecundity of biomedical research is increasing…[and] always raises expenditures.” With NHEs spiraling ever-higher, urgency to “bend the cost curve” is mounting. Yet, since much biomedical innovation targets the “flat of the [productivity] curve,” alternative forms of innovation are necessary.

The shortcomings in net productivity revealed by the COVID-19 pandemic highlight the urgent need for redesign of health care delivery in this country, and reevaluation of the innovation needed to support it. Specifically, efforts supporting process redesign are critical to promote cost-reducing, substitutive innovations that can inaugurate new and disruptive business models.

Process redesign rarely involves novel gizmos, so much as rejiggering the wiring of, and connections between, existing gadgets. It targets operational changes capable of streamlining workflows, rather than technical advancements that complicate them. As described above, precisely these sorts of “frugal innovations” have led to productivity improvements yielding lower costs in other high-technology industries, such as the computing industry.

Shrank and colleagues recently estimated that nearly one-third of NHEs—almost $1 trillion—were due to preventable waste. Four of the six categories of waste enumerated by the authors—failure in care delivery, failure in care coordination, low-value care, and administrative complexity—represent ripe targets for process innovation, accounting for $610 billion in waste annually, according to Shrank.

Health systems adopting process redesign methods such as continuous improvement and value-based management have exhibited outcome enhancement and expense reduction simultaneously. Internal processes addressed have included supply chain reconfiguration, operational redesign, outlier reconciliation, and resource standardization.

Despite the potential of process innovation, focus on this area (often bundled into “health services” or “quality improvement” research) occupies only a minute fraction of wallet- or mind-share in the biomedical research landscape, accounting for 0.3 percent of research dollars in medicine. This may be due to a variety of barriers beyond minimal funding. One set of barriers is academic, relating to negative perceptions around rigor and a lack of outlets in which to publish quality improvement research. To achieve health care cost containment over the long term, this dimension of innovation must be destigmatized relative to more traditional manners of innovation by the funders and institutions determining the conditions of the research ecosystem.

Another set of barriers is financial: Innovations yielding cost reduction are less “reimbursable” than are innovations fashioned for revenue expansion. This is especially the case in a fee-for-service system where reimbursement is tethered to cost, which creates perverse incentives for health care institutions to overlook cost increases. However, institutions investing in low-cost innovation will be well-positioned in a rapidly approaching future of value-based care—in which the solvency of health care institutions will rely upon their ability to provide economically efficient care.

Innovating For Cost Control Necessitates Frugality Over Novelty

Restraining US NHEs represents a critical step toward health promotion. Innovation for innovation’s sake—that is content-based, incrementally effective, additive, and sustaining—is unlikely to constrain continually expanding NHEs.

In contrast, process innovation offers opportunities to reduce costs while maintaining high standards of patient care. As COVID-19 stress-tests health care systems across the world, the importance of cost control and productivity amplification for patient care has become apparent.

As such, frugality, rather than novelty, may hold the key to health care cost containment. Redesigning the innovation agenda to stem the tide of ever-rising NHEs is an essential strategy to promote widespread access to care—as well as high-value preventive care—in this country. In the words of investors across Silicon Valley: Cost-reducing innovation is no longer a “nice-to-have,” but a “need-to-have” for the future of health and overall well-being this country.

So Do We Need A New Way of Disseminating Scientific Information?  Can Curation Help?

We had high hopes for Science 2.0, in particular the smashing of data and knowledge silos. However the digital age along with 2.0 platforms seemed to excaccerbate this somehow. We still are critically short on analysis!



Old Science 1.0 is still the backbone of all scientific discourse, built on the massive amount of experimental and review literature. However this literature was in analog format, and we moved to a more accesible digital open access format for both publications as well as raw data. However as there was a structure for 1.0, like the Dewey decimal system and indexing, 2.0 made science more accesible and easier to search due to the newer digital formats. Yet both needed an organizing structure; for 1.0 that was the scientific method of data and literature organization with libraries as the indexers. In 2.0 this relied on an army mostly of volunteers who did not have much in the way of incentivization to co-curate and organize the findings and massive literature.



The Intenet and the Web is rapidly adopting a new “Web 3.0” format, with decentralized networks, enhanced virtual experiences, and greater interconnection between people. Here we start the discussion what will the move from Science 2.0, where dissemination of scientific findings was revolutionized and piggybacking on Web 2.0 or social media, to a Science 3.0 format. And what will it involve or what paradigms will be turned upside down?

We have discussed this in other posts such as

Will Web 3.0 Do Away With Science 2.0? Is Science Falling Behind?

and

Curation Methodology – Digital Communication Technology to mitigate Published Information Explosion and Obsolescence in Medicine and Life Sciences

For years the pharmaceutical industry has toyed with the idea of making innovation networks and innovation hubs

It has been the main focus of whole conferences

Tales from the Translational Frontier – Four Unique Approaches to Turning Novel Biology into Investable Innovations @BIOConvention #BIO2018

However it still seems these strategies have not worked

Is it because we did not have an Execution plan? Or we did not understand the lead measures for success?

Other Related Articles on this Open Access Scientific Journal Include:

Old Industrial Revolution Paradigm of Education Needs to End: How Scientific Curation Can Transform Education

Analysis of Utilizing LPBI Group’s Scientific Curation Platform as an Educational Tool: New Paradigm for Student Engagement

Global Alliance for Genomics and Health Issues Guidelines for Data Siloing and Sharing

Multiple Major Scientific Journals Will Fully Adopt Open Access Under Plan S

eScientific Publishing a Case in Point: Evolution of Platform Architecture Methodologies and of Intellectual Property Development (Content Creation by Curation) Business Model 

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Heart, Vascular Smooth Muscle, Excitation-Contraction Coupling (E-CC), Cytoskeleton, Cellular Dynamics and Ca2 Signaling

Heart, Vascular Smooth Muscle, Excitation-Contraction Coupling (E-CC), Cytoskeleton, Cellular Dynamics and Ca2 Signaling

Author and Curator: Larry H Bernstein, MD, FCAP

Author and Cardiovascular Three-volume Series, Editor: Justin Pearlman, MD, PhD, FACC, and

Curator: Aviva Lev-Ari, PhD, RN

Article V Heart, Vascular Smooth Muscle, Excitation-Contraction Coupling (E-CC), Cytoskeleton, Cellular Dynamics and Ca2 Signaling

Image created by Adina Hazan 06/30/2021

Abbreviations

AP, action potential; ARVD2, arrhythmogenic right ventricular cardiomyopathy type 2; CaMKII, Ca2+/calmodulim-dependent protein kinase II; CICR, Ca2+ induced Ca2+ release;CM, calmodulin; CPVT, catecholaminergic polymorphic ventricular tachycardia;  ECC, excitation–contraction coupling; FKBP12/12.6, FK506 binding protein; HF, heart failure; LCC, L-type Ca2+ channel;  P-1 or P-2, phosphatase inhibitor type-1 or type-2; PKA, protein kinase A; PLB, phosphoplamban; PP1, protein phosphatase 1; PP2A, protein phosphatase 2A; RyR1/2, ryanodine receptor type-1/type-2; SCD, sudden cardiac death; SERCA, sarcoplasmic reticulum Ca2+ ATPase; SL, sarcolemma; SR, sarcoplasmic reticulum.

This is Part V of a series on the cytoskeleton and structural shared thematics in cellular movement and cellular dynamics.

The Series consists of the following articles:

Part I: Identification of Biomarkers that are Related to the Actin Cytoskeleton

Larry H Bernstein, MD, FCAP

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

Part II: Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility

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

http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/

Part III: Renal Distal Tubular Ca2+ Exchange Mechanism in Health and Disease

Larry H. Bernstein, MD, FCAP, Stephen J. Williams, PhD
 and Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/09/02/renal-distal-tubular-ca2-exchange-mechanism-in-health-and-disease/

Part IV: The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets

 

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

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

Part V: Heart, Vascular Smooth Muscle, Excitation-Contraction Coupling (E-CC), Cytoskeleton, Cellular Dynamics and Ca2 Signaling

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

http://pharmaceuticalintelligence.com/2013/08/26/heart-smooth-muscle-excitation-contraction-coupling-cytoskeleton-cellular-dynamics-and-ca2-signaling/

Part VI: Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD

Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/08/01/calcium-molecule-in-cardiac-gene-therapy-inhalable-gene-therapy-for-pulmonary-arterial-hypertension-and-percutaneous-intra-coronary-artery-infusion-for-heart-failure-contributions-by-roger-j-hajjar/

Part VII: Cardiac Contractility & Myocardium Performance: Ventricular Arrhythmias and Non-ischemic Heart Failure – Therapeutic Implications for Cardiomyocyte Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses

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

http://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/

In the first part, we discussed common MOTIFs across cell-types that are essential for cell division, embryogenesis, cancer metastasis, osteogenesis, musculoskeletal function, vascular compliance, and cardiac contractility.   This second article concentrates on specific functionalities for cardiac contractility based on Ca++ signaling in excitation-contraction coupling.  The modifications discussed apply specifically to cardiac muscle and not to skeletal muscle.  Considering the observations described might raise additional questions specifically address to the unique requirements of smooth muscle, abundant in the GI tract and responsible for motility in organ function, and in blood vessel compliance or rigidity. Due to the distinctly different aspects of the cardiac contractility and contraction force, and the interactions with potential pharmaceutical targets, there are two separate articles on calcium signaling and cardiac arrhythmias or heart failure (Part 2 and Part 3).  Part 2 focuses on the RYANODINE role in cardiac Ca(2+) signaling and its effect in heart failure.  Part 3 takes up other aspects of heart failure and calcium signaling with respect to phosporylation/dephosphorylation. I add a single review and classification of genetic cardiac disorders of the same cardiac Ca(2+) signaling and the initiation and force of contraction. Keep in mind that the heart is a syncytium, and this makes a huge difference compared with skeletal muscle dynamics. In Part 1 there was some discussion of the importance of Ca2+ signaling on innate immune system, and the immunology will be further expanded in a fourth of the series.

SUMMARY:

This second article on the cardiomyocyte and the Ca(2+) cycling between the sarcomere and the cytoplasm, takes a little distance from the discussion of the ryanodine that precedes it.  In this discussion we found that there is a critical phosphorylation/dephosphorylation balance that exists between Ca(+) ion displacement and it occurs at a specific amino acid residue on the CaMKIId, specific for myocardium, and there is a 4-fold increase in contraction and calcium release associated with this CAM kinase (ser 2809) dependent exchange.  These events are discussed in depth, and the research holds promise for therapeutic application. We also learn that Ca(2+) ion channels are critically involved in the generation of arrhythmia as well as dilated and hypertrophic cardiomyopathy.  In the case of arrhythmiagenesis, there are two possible manners by which this occurs.  One trigger is Ca(2+) efflux instability.  The other is based on the finding that when the cellular instability is voltage driven, the steady-state wave­length (separation of nodes in space) depends on electrotonic coupling between cells and the steepness of APD and CV restitution. The last article is an in depth review of the genetic mutations that occur in cardiac diseases.  It is an attempt at classifying them into reasonable groupings. What are the therapeutic implications of this? We see that the molecular mechanism of cardiac function has been substantially elucidated, although there are contradictions in experimental findings that are unexplained.  However, for the first time, it appears that personalized medicine is on a course that will improve health in the population, and the findings will allow specific targets designed for the individual with a treatable impairment in cardiac function that is identifiable early in the course of illness. This article is a continuation to the following articles on tightly related topics: Part I: Identification of Biomarkers that are Related to the Actin Cytoskeleton     Larry H Bernstein, MD, FCAP http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/ Part II:  Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility    Larry H. Bernstein, MD, FCAP, Stephen Williams, PhD and Aviva Lev-Ari, PhD, RN  http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/ Part III: Renal Distal Tubular Ca2+ Exchange Mechanism in Health and Disease    Larry H. Bernstein, MD, FCAP, Stephen J. Williams, PhD
 and  Aviva Lev-Ari, PhD, RN http://pharmaceuticalintelligence.com/2013/09/02/renal-distal-tubular-ca2-exchange-mechanism-in-health-and-disease/ Part  IV:  The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN  http:/pharmaceuticalintelligence.com/2013.09.089/lhbern/The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets

Part V:  Heart Smooth Muscle and Cardiomyocyte Cells: Excitation-Contraction Coupling & Ryanodine Receptor (RyR) type-1/type-2 in Cytoskeleton Cellular Dynamics and Ca2+ Signaling

Larry H Bernstein, MD, FCAP, Justin Pearlman, MD, PhD, FACC and Aviva Lev-Ari, PhD, RN http://pharmaceuticalintelligence.com/2013/08/26/heart-smooth-muscle-excitation-contraction-coupling-cytoskeleton-cellular-dynamics-and-ca2-signaling/ Part VI:  Calcium Cycling (ATPase Pump) in Cardiac Gene Therapy: Inhalable Gene Therapy for Pulmonary Arterial Hypertension and Percutaneous Intra-coronary Artery Infusion for Heart Failure: Contributions by Roger J. Hajjar, MD Curator: Aviva Lev-Ari, PhD, RN http://pharmaceuticalintelligence.com/2013/08/01/calcium-molecule-in-cardiac-gene-therapy-inhalable-gene-therapy-for-pulmonary-arterial-hypertension-and-percutaneous-intra-coronary-artery-infusion-for-heart-failure-contributions-by-roger-j-hajjar/ and Advanced Topics in Sepsis and the Cardiovascular System at its End Stage Larry H Bernstein, MD, FCAP  http://pharmaceuticalintelligence.com/2013/08/18/advanced-topics-in-sepsis-and-the-cardiovascular-system-at-its-end-stage/

The Role of Protein Kinases and Protein Phosphatases in the Regulation of Cardiac Sarcoplasmic Reticulum Function

EG Kranias, RC Gupta, G Jakab, HW Kim, NAE Steenaart, ST Rapundalo Molecular and Cellular Biochemistry 06/1988; 82(1):37-44. · 2.06 Impact Factor http://www.researchgate.net/publication/6420466_Protein_phosphatases_decrease_sarcoplasmic_reticulum_calcium_content_by_stimulating_calcium_release_in_cardiac_myocytes Canine cardiac sarcoplasmic reticulum is phosphorylated by

  • adenosine 3,5-monophosphate (cAMP)-dependent and
  • calcium calmodulin-dependent protein kinases on
  • a proteolipid, called phospholamban.

Both types of phosphorylation are associated with

  •  an increase in the initial rates of Ca(2+) transport by SR vesicles
  • which reflects an increased turnover of elementary steps of the calcium ATPase reaction sequence.

The stimulatory effects of the protein kinases on the calcium pump may be reversed by an endogenous protein phosphatase, which

  • can dephosphorylate both the CAMP-dependent and the calcium calmodulin-dependent sites on phospholamban.

Thus, the calcium pump in cardiac sarcoplasmic reticulum appears to be under reversible regulation mediated by protein kinases and protein phosphatases. calcium release calmodulin + ER Ca(2+) and contraction

Regulation of the Cardiac Ryanodine Receptor Channel by Luminal Ca2+ involves Luminal Ca2+ Sensing Sites

I Györke, S Györke.   Biophysical Journal 01/1999; 75(6):2801-10. · 3.65 Impact factor  http:// www.researchgate.net/publication/13459335/Regulation_of_the_cardiac_ryanodine_receptor_channel_by_luminal_Ca2_involves_luminal_Ca2_sensing_sites The mechanism of activation of the cardiac calcium release channel/ryanodine receptor (RyR) by luminal Ca(2+) was investigated in native canine cardiac RyRs incorporated into lipid bilayers in the presence of 0.01 microM to 2 mM Ca(2+) (free) and 3 mM ATP (total) on the cytosolic (cis) side and 20 microM to 20 mM Ca(2+) on the luminal (trans) side of the channel and with Cs+ as the charge carrier. Under conditions of low [trans Ca(2+)] (20 microM), increasing [cis Ca(2+)] from 0.1 to 10 microM caused a gradual increase in channel open probability (Po). Elevating [cis Ca(2+)] [cytosolic] above 100 microM resulted in a gradual decrease in Po. Elevating trans [Ca(2+)] [luminal] enhanced channel activity (EC50 approximately 2.5 mM at 1 microM cis Ca2+) primarily by increasing the frequency of channel openings. The dependency of Po on trans [Ca2+] [luminal] was similar at negative and positive holding potentials and was not influenced by high cytosolic concentrations of the fast Ca(2+) chelator, 1,2-bis(2-aminophenoxy)ethane-N,N,N, N-tetraacetic acid. Elevated luminal Ca(2+)

  1. enhanced the sensitivity of the channel to activating cytosolic Ca(2+), and it
  2. essentially reversed the inhibition of the channel by high cytosolic Ca(2+).

Potentiation of Po by increased luminal Ca(2+) occurred irrespective of whether the electrochemical gradient for Ca(2+) supported a cytosolic-to-luminal or a luminal-to-cytosolic flow of Ca(2+) through the channel. These results rule out the possibility that under our experimental conditions, luminal Ca(2+) acts by interacting with the cytosolic activation site of the channel and suggest that the effects of luminal Ca2+ are mediated by distinct Ca(2+)-sensitive site(s) at the luminal face of the channel or associated protein. F1.large  calcium movement and RyR2 receptor

Protein phosphatases Decrease Sarcoplasmic Reticulum Calcium Content by Stimulating Calcium Release in Cardiac Myocytes

D Terentyev, S Viatchenko-Karpinski, I Gyorke, R Terentyeva and S Gyorke Texas Tech University Health Sciences Center, Lubbock, TX J Physiol 2003; 552(1), pp. 109–118.  http://dx.doi.org/10.1113/jphysiol.2003.046367 Phosphorylation/dephosphorylation of Ca2+ transport proteins by cellular kinases and phosphatases plays an important role in regulation of cardiac excitation–contraction coupling; furthermore,

  • abnormal protein kinase and phosphatase activities have been implicated in heart failure.

However, the precise mechanisms of action of these enzymes on intracellular Ca2+ handling in normal and diseased hearts remains poorly understood. We have investigated

  •   the effects of protein phosphatases PP1 and PP2A on spontaneous Ca(2+) sparks and SR Ca(2+) load in myocytes permeabilized with saponin.

Exposure of myocytes to PP1 or PP2A caused a dramatic increase in frequency of Ca(2+) sparks followed by a nearly complete disappearance of events, which were accompanied by depletion of the SR Ca(2+) stores, as determined by application of caffeine. These changes in

  •  Ca(2+) release and
  • SR Ca(2+) load

could be prevented by the inhibitors of PP1 and PP2A phosphatase activities okadaic acid and calyculin A. At the single channel level, PP1 increased the open probability of RyRs incorporated into lipid bilayers. PP1-medited RyR dephosphorylation in our permeabilized myocytes preparations was confirmed biochemically by quantitative immunoblotting using a phosphospecific anti-RyR antibody. Our results suggest that

  •  increased intracellular phosphatase activity stimulates
  • RyR mediated SR Ca(2+) release
    • leading to depleted SR Ca(2+) stores in cardiac myocytes.

In heart muscle cells, the process of excitation–contraction (EC) coupling is mediated by

  •  Ca(2+) influx through sarcolemmal L-type Ca(2+) channels
  • activating Ca(2+) release channels (ryanodine receptors, RyRs) in the sarcoplasmic reticulum (SR).

Once activated, the RyR channels allow Ca(2+) to be released from the SR into the cytosol to induce contraction. This mechanism is known as Ca(2+)-induced calcium release (CICR) (Fabiato, 1985; Bers, 2002).  During relaxation, most of the Ca(2+) is resequestered into the SR by the Ca(2+)-ATPase. The amount of Ca(2+) released and the force of contraction depend on

  •  the magnitude of the Ca(2+) trigger signal,
  • the functional state of the RyRs and
  • the amount of Ca(2+) stored in the SR.

F1.large  calcium movement and RyR2 receptor Ca(2+) and contraction calcium release calmodulin + ER Reversible phosphorylation of proteins composing the EC coupling machinery plays an important role in regulation of cardiac contractility (Bers, 2002). Thus, during stimulation of the b-adrenergic pathway, phosphorylation of several target proteins, including

  • the L-type Ca(2+) channels,
  • RyRs and
  • phospholamban,

by protein kinase A (PKA) leads to an overall increase in SR Ca2+ release and contractile force in heart cells (Callewaert et al. 1988, Spurgeon et al. 1990; Hussain & Orchard, 1997; Zhou et al. 1999; Song et al. 2001; Viatchenko-Karpinski & Gyorke, 2001). PKA-dependent phosphorylation of the L-type Ca(2+) channels increases the Ca2+ current (ICa), increasing both

  • the Ca2+ trigger for SR Ca2+ release and
  • the SR Ca(2+) content

(Callewaert et al. 1988; Hussain & Orchard, 1997; Del Principe et al. 2001). Phosphorylation of phospholamban (PLB) relieves the tonic inhibition dephosphorylated PLB exerts on the SR Ca(2+)-ATPase (SERCA) resulting in enhanced SR Ca(2+) accumulation and enlarged Ca(2+) release (Kranias et al. 1985; Simmermann & Jones, 1998). With regard to the RyR, despite clear demonstration of phosphorylation of the channel in biochemical studies (Takasago et al. 1989; Yoshida et al. 1992), the consequences of this reaction to channel function have not been clearly defined. RyR phosphorylation by PKA and Ca(2+)–calmodulin dependent protein kinase (CaMKII) has been reported to increase RyR activity in lipid bilayers (Hain et al. 1995; Marx et al. 2000; Uehara et al. 2002). Moreover, it has been reported that in heart failure (HF), hyperphosphorylation of RyR causes

  •  the release of FK-506 binding protein (FKBP12.6) from the RyR,
    • rendering the channel excessively leaky for Ca(2+) (Marx et al. 2000).

However, other studies have reported no functional effects (Li et al. 2002) or even found phosphorylation to reduce RyR channel steady-state open probability (Valdivia et al. 1995; Lokuta et al. 1995).  The action of protein kinases is opposed by dephosphorylating phosphatases. Three types of protein phosphatases (PPs), referred to as PP1, PP2A and PP2B (calcineurin), have been shown to influence cardiac performance (Neumann et al. 1993; Rusnak & Mertz, 2000). Overall, according to most studies phosphatases appear to downregulate SR Ca(2+) release and contractile performance (Neumann et al. 1993; duBell et al. 1996, 2002; Carr et al. 2002; Santana et al. 2002). Furthermore, PP1 and PP2A activities appear to be increased in heart failure (Neumann, 2002; Carr et al. 2002). However, again the precise mode of action of these enzymes on intracellular Ca(2+) handling in normal and diseased hearts remains poorly understood.  In the present study, we have investigated the effects of protein phosphatases PP1 and PP2A on local Ca(2+) release events, Ca(2+) sparks, in cardiac cells. Our results show that

  •  phosphatases activate RyR mediated SR Ca(2+) release
    • leading to depletion of SR Ca(2+) stores.

These results provide novel insights into the mechanisms and potential role of protein phosphorylation/dephosphorylation in regulation of Ca(2+) signaling in normal and diseased hearts. F2.large   RyR and calcium

RESULTS

Effects of PP1 and PP2A on Ca2+ sparks and SR Ca(2+) content.

[1]  PP1 caused an early transient potentiation of Ca2+ spark frequency followed by a delayed inhibition of event occurrence. [2]  PP1 produced similar biphasic effects on the magnitude and spatio-temporal characteristics of Ca(2+) sparks Specifically, during the potentiatory phase (1 min after addition of the enzyme), PP1 significantly increased

  • the amplitude,
  • rise-time,
  • duration and
  • width of Ca(2+) sparks;

during the inhibitory phase (5 min after addition of the enzyme),

  •  all these parameters were significantly suppressed by PP1.

The SR Ca(2+) content decreased by 35 % or 69 % following the exposure of myocytes to either 0.5 or 2Uml_1 PP1, respectively (Fig. 1C). Qualitatively similar results were obtained with phosphatase PP2A. Similar to the effects of PP1, PP2A (5Uml_1) produced a transient increase in Ca(2+) spark frequency (~4-fold) followed by a depression of event occurrence and decreased SR Ca(2+) content (by 82 % and 65 %, respectively). Also similar to the action of PP1, PP2A increased

  •  the amplitude and
  • spatio-temporal spread (i.e. rise-time, duration and width) of Ca(2+) sparks at 1 min
  • and suppressed the same parameters at 5 min of exposure to the enzyme (Table 1).

Together, these results suggest that phosphatases enhance spark-mediated SR Ca2+ release, leading to decreased SR Ca(2+) content. Preventive effects of calyculin A and okadaic acid Preventive effects of ryanodine

PP1-mediated RyR dephosphorylation

F3.large  cardiomyocyte SR F3.large  cardiomyocyte SR F2.large   RyR and calcium coupled receptors coupled receptors The cardiac RyR is phosphorylated at Ser-2809 (in the rabbit sequence) by both PKA and CAMKII (Witcher et al. 1991; Marx et al. 2000). Although additional phosphorylation sites may exist on the RyR (Rodriguez et al. 2003), but Ser-2809 is believed to be the only site that is phosphorylated by PKA, and RyR hyperphosphorylation at this site has been reported in heart failure (Marx et al. 2000).  To test whether indeed phosphatases dephosphorylated the RyR in our permeabilized myocyte experiments we performed quantitative immunoblotting using an antibody that specifically recognizes the phosphorylated form of the RyR at Ser-2809 (Rodriguez et al. 2003). Myocytes exhibited a significant level of phosphorylation under baseline conditions. Maximal phosphorylation was 201 % of control. When exposed to 2Uml_1 PP1, RyR phosphorylation was 58 % of the control basal condition. Exposing to a higher PP1 concentration (10Uml_1) further reduced RyR phosphorylation to 22% of control. Thus, consistent with the results of our functional measurements,

  •  PP1 decreased RyR phosphorylation in cardiac myocytes.

Figure 1. Effects of PP1 on properties of Ca(2+) sparks and SR Ca(2+) content in rat permeabilized myocytes    see .  http://dx.doi.org/10.1113/jphysiol.2003.046367 A, spontaneous Ca(2+) spark images recorded under reference conditions, and 1 or 5 min after exposure of the cell to 2Uml_1 PP1. Traces below the images are Ca(2+) transients induced by application of 10 mM caffeine immediately following the acquisition of sparks before (3 min) and after (5 min) application of PP1 in the same cell. The Ca(2+) transients were elicited by a whole bath application of 10 mM caffeine. B, averaged spark frequency at early (1 min) and late (5 min) times following the addition of either 0.5 or 2Uml_1 of PP1 to the bathing solution. C, averaged SR Ca(2+) content for 0.5 or 2Uml_1 of PP1 measured before and 5 min after exposure to the enzyme. Data are presented as means ± S.E.M. of 6 experiments in different cells. Figure 2. Effects of PP2A on properties of Ca2+ sparks and SR Ca2+ content in rat permeabilized myocytes   see .  http://dx.doi.org/10.1113/jphysiol.2003.046367 A, spontaneous Ca(2+) spark images recorded under reference conditions, and 1 or 5 min after exposure of the cell to 5Uml_1 PP2A. Traces below the images are Ca(2+) transients induced by application of 10 mM caffeine immediately following the acquisition of sparks before (3 min) and after (5 min) application of PP2A in the same cell. B and C, averaged spark frequency (B) and SR Ca(2+) content (C) for the same conditions as in A. Data are presented as means ± S.E.M. of 6 experiments in different cells.

 DISCUSSION

In the present study, we have investigated the impact of physiologically relevant exogenous protein phosphatases PP1 and PP2A on RyR-mediated SR Ca(2+) release (measured as Ca(2+) sparks) in permeabilized heart cells. Our principal finding is that

  • phosphatases stimulated RyR channels lead to depleted SR Ca(2+) stores.

These results have important ramifications for understanding the mechanisms and role of protein phosphorylation/dephosphorylation in

  •  modulation of Ca(2+) handling in normal and diseased heart.

Modulation of SR Ca2+ release by protein phosphorylation/dephophorylation

Since protein dephosphorylation clearly resulted in increased functional activity of the Ca(+)release channel, our results imply that a reverse, phosphorylation reaction should reduce RyR activity. If indeed such effects take place, why do they not manifest in inhibition of Ca(+)sparks? One possibility is that enhanced Ca(+) uptake by SERCA

  •  masks or overcomes the effects phosphorylation may have on RyRs.

In addition, the potential inhibitory influence of protein phosphorylation on RyR activity in myocytes could be countered by feedback mechanisms  involving changes in luminal Ca(2+)(Trafford et al. 2002; Gyorke et al. 2002). In particular, reduced open probability of RyRs would be expected to lead to

  •  increased Ca2+ accumulation in the SR;
  • and increased intra-SR [Ca(2+)], in turn would
  • increase activity of RyRs at their luminal Ca(2+) regulatory sites

as demonstrated for the RyR channel inhibitor tetracaine (Gyorke et al. 1997; Overend et al. 1997). Thus

  • potentiation of SERCA
  • combined with the intrinsic capacity of the release mechanism to self-regulate

could explain at least in part why PKA-mediated protein phoshorylation results in maintained potentiation of Ca(2+) sparks despite a potential initial decrease in RyR activity.

Role of altered RyR Phosphorylation in Heart Failure

Marx et al. (2000) have proposed that  enhanced levels of circulating catecholamines lead to increased phosphorylation of RyR in heart failure.  Based on biochemical observations as well as on studying properties of single RyRs incorporated into artificial lipid bilayers, these investigators have hypothesized that

  •  hyperphosphorylation of RyRs contributes to pathogenesis of heart failure
    • by making the channel excessively leaky due to dissociation of FKBP12.6 from the channel.

We show that the mode of modulation of RyRs by phosphatases does not support this hypothesis as

  • dephosphorylation caused activation instead of

Interestingly, our results provide the basis for a different possibility in which

  •  dephophosphorylation of RyR rather than its phosphorylation causes depletion of SR Ca(2+) stores by stimulating RyRs in failing hearts.

It has been reported that PP1 and PP2 activities are increased in heart failure (Huang et al. 1999; Neumann et al. 1997; Neuman, 2002). Furthermore,  overexpression of PP1 or ablation of the endogenous PP1 inhibitor, l-1, results in

  • depressed contractile performance and heart failure (Carr et al. 2002).

Our finding that PP1 causes depletion of SR Ca(2+) stores by activating RyRs could account for, or contribute to, these results.

References

1 DelPrincipe F, Egger M, Pignier C & Niggli E (2001). Enhanced E-C coupling efficiency after beta-stimulation of cardiac myocytes. Biophys J 80, 64a. 2 Gyorke I & Gyorke S (1998). Regulation of the cardiac ryanodine receptor channel by luminal Ca2+ involves luminal Ca2+ sensing sites. Biophys J 75, 2801–2810. 3 Gyorke S, Gyorke I, Lukyanenko V, Terentyev D, Viatchenko-Karpinski S & Wiesner TF (2002). Regulation of sarcoplasmic reticulum calcium release by luminal calcium in cardiac muscle. Front Biosci 7, d1454–d1463. 4 Gyorke I, Lukyanenko V & Gyorke S (1997). Dual effects of tetracaine on spontaneous calcium release in rat ventricular myocytes. J Physiol 500, 297–309. 5 MacDougall LK, Jones LR & Cohen P (1991). Identification of the major protein phosphatases in mammalian cardiac muscle which dephosphorylate phospholamban. Eur J Biochem 196, 725–734. 6 Marx SO, Reiken S, Hisamatsu Y, Jayaraman T, Burkhoff D, Rosemblit N & Marks AR (2000). PKA phosphorylation dissociates FKBP12.6 from the calcium release channel (ryanodine receptor): defective regulation in failing hearts. Cell 101, 365–376. 7 Rodriguez P, Bhogal MS & Colyer J (2003). Stoichiometric phosphorylation of cardiac ryanodine receptor on serine-2809 by calmodulin-dependent kinase II and protein kinase A. J Biol Chem (in press).

The δC Isoform of CaMKII Is Activated in Cardiac Hypertrophy and Induces Dilated Cardiomyopathy and Heart Failure

T Zhang, LS Maier, ND Dalton, S Miyamoto, J Ross, DM Bers, JH Brown.  University of California, San Diego, La Jolla, Calif; and Loyola University, Chicago, Ill. Circ Res. 2003;92:912-919.    http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5 Recent studies have demonstrated that transgenic (TG) expression of either Ca(2+)/calmodulin-dependent protein kinase IV (CaMKIV) or CaMKIIδB, both of which localize to the nucleus, induces cardiac hypertrophy. However,

  •  CaMKIV is not present in heart, and
  • cardiomyocytes express not only the nuclear CaMKIIδB
    • but also a cytoplasmic isoform, CaMKII δC.

In the present study, we demonstrate that

  1.  expression of the δC isoform of CaMKII is selectively increased and
  2. its phosphorylation elevated as early as 2 days and continuously for up to 7 days after pressure overload.

To determine whether enhanced activity of this cytoplasmic δC isoform of CaMKII can lead to phosphorylation of Ca(2+) regulatory proteins and induce hypertrophy, we generated TG mice that expressed the δC isoform of CaMKII.  Immunocytochemical staining demonstrated that the expressed transgene is confined to the cytoplasm of cardiomyocytes isolated from these mice. These mice develop a dilated cardiomyopathy with up to a 65% decrease in fractional shortening and die prematurely. Isolated myocytes are enlarged and exhibit reduced contractility and altered Ca2(2+) handling. Phosphorylation of the ryanodine receptor (RyR) at a CaMKII site is increased even before development of heart failure, and

  • CaMKII is found associated with the RyR  from the CaMKII TG mice.
  • Phosphorylation of phospholamban is increased specifically at the CaMKII but not at the PKA phosphorylation site.

These findings are the first to demonstrate that CaMKIIδC can mediate phosphorylation of Ca(2+) regulatory proteins in vivo and provide evidence for the involvement of CaMKIIδC activation in the pathogenesis of dilated cardiomyopathy and heart failure.  Multifunctional Ca(2+)/calmodulin-dependent protein kinases (CaM kinases or CaMKs) are transducers of Ca2+ signals that phosphorylate a wide range of substrates and thereby affect Ca(2+)-mediated cellular responses.1 The family includes CaMKI and CaMKIV, monomeric enzymes activated by CaM kinase kinase,2,3 and CaMKII, a multimer of 6 to 12 subunits activated by autophosphorylation.1 The CaMKII subunits α, β, γ, and δ show different tissue distributions,1 with

  • the δ isoform predominating in the heart.4–7
  • Splice variants of the δ isoform, characterized by the presence of a second variable domain,4,7 include δB, which contains a nuclear localization signal (NLS), and
  • δC, which does not. CaMKII composed of δB subunits localizes to the nucleus, whereas CaMKIIδC localizes to the cytoplasm.4,8,9

CaMKII has been implicated in several key aspects of acute cellular Ca(2+) regulation related to cardiac excitation-contraction (E-C) coupling. CaMKII

  • phosphorylates sarcoplasmic reticulum (SR) proteins including the ryanodine receptors (RyR2) and
  • phospholamban (PLB).10–14

Phosphorylation of RyR has been suggested to alter the channel open probability,14,15 whereas phosphorylation of PLB has been suggested to regulate SR Ca(2+) uptake.14 It is also likely that CaMKII phosphorylates the L-type Ca(2+) channel complex or an associated regulatory protein and thus

  1. mediates Ca(2+) current (ICa) facilitation.16-18 and
  2. the development of early after-depolarizations and arrhythmias.19

Thus, CaMKII has significant effects on E-C coupling and cellular Ca(2 +) regulation. Nothing is known about the CaMKII isoforms regulating these responses.  Contractile dysfunction develops with hypertrophy, characterizes heart failure, and is associated with changes in cardiomyocyte (Ca2+) homeostasis.20  CaMKII expression and activity are altered in the myocardium of rat models of hypertensive cardiac hypertrophy21,22 and heart failure,23 and

  • in cardiac tissue from patients with dilated cardiomyopathy.24,25

Several transgenic mouse models have confirmed a role for CaMK in the development of cardiac hypertrophy, as originally suggested by studies in isolated neonatal rat ventricular myocytes.9,26–28 Hypertrophy develops in transgenic mice that overexpress CaMKIV,27 but this isoform is not detectable in the heart,4,29 and CaMKIV knockout mice still develop hypertrophy after transverse aortic constriction (TAC).29  Transgenic mice overexpressing calmodulin developed severe cardiac hypertrophy,30 later shown to be associated with an increase in activated CaMKII31; the isoform of CaMKII involved in hypertrophy could not be determined from these studies. We recently reported that transgenic mice that overexpress CaMKIIδB, which is highly concentrated in cardiomyocyte nuclei, develop hypertrophy and dilated cardiomyopathy.32 To determine whether

  • in vivo expression of the cytoplasmic CaMKIIδC can phosphorylate cytoplasmic Ca(2+) regulatory proteins and
  • induce hypertrophy or heart failure,

we generated transgenic (TG) mice that expressed the δC isoform of CaMKII under the control of the cardiac specific α-myosin heavy chain (MHC) promoter. Our findings implicate CaMKIIδC in the pathogenesis of dilated cardiomyopathy and heart failure and suggest that

  • this occurs at least in part via alterations in Ca(2+) handling proteins.33

Ca(2+) and contraction RyR yuan_image3  Ca++ exchange yuan_image3  Ca++ exchange

Results

 Expression and Activation of CaMKIIδC Isoform After TAC

To determine whether CaMKII was regulated in pressure overload–induced hypertrophy, CaMKIIδ expression and phosphorylation were examined by Western blot analysis using left ventricular samples obtained at various times after TAC.  A selective increase (1.6-fold) in the lower band of CaMKIIδwas observed as early as 1 day and continuously for 4 days (2.3-fold) and 7 days (2-fold) after TAC (Figure 1A).  To confirm that CaMKIIδC was increased and determine whether this occurred at the transcriptional level, we performed semiquantitative RT-PCR using primers specific for the CaMKIIδC isoform. These experiments revealed that

  • mRNA levels for CaMKIIδC were increased 1 to 7 days after TAC (Figure 1B).

In addition to examining CaMKII expression, the activation state of CaMKII was monitored by its autophosphorylation, which confers Ca2-independent activity.

Figure 1. Expression and activation of CaMKII δC isoform after TAC.

see http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5 A, Western blot analysis of total CaMKII in left ventricular (LV) homogenates obtained at indicated times after TAC. Cardiomyocytes transfected with CaMKIIδB and δC (right) served as positive controls and molecular markers. Top band (58 kDa) represents CaMKIIδB plus δ9, and the bottom band (56 kDa) corresponds to CaMKIIδC. *P0.05 vs control. B, Semiquantitative RT-PCR using primers specific for CaMKIIδC isoform (24 cycles) and GAPDH (19 cycles) using total RNA isolated from the same LV samples. C, Western blot analysis of phospho-CaMKII in LV homogenates obtained at various times after TAC. Three bands seen for each sample represent CaMKIIγ subunit (uppermost), CaMKIIδB plus δ9 (58 kDa), and CaMKIIδC (56 kDa). Quantitation is based on the sum of all of the bands. *P0.05 vs control.

 Figure 2. Expression and activation of CaMKII in CaMKIIδC transgenic mice.

see  http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5 A, Transgene copy number based on Southern blots using genomic DNA isolated from mouse tails (digested with EcoRI). Probe (a 32P-labeled 1.7-kb EcoRI-SalI -MHC fragment) was hybridized to a 2.3-kb endogenous fragment (En) and a 3.9-kb transgenic fragment (TG). Transgene copy number was determined from the ratio of the 3.9-kb/2.3-kb multiplied by 2. B, Immunocytochemical staining of ventricular myocytes isolated from WT and CaMKIIδTG mice. Myocytes were cultured on laminin-coated slides overnight. Transgene was detected by indirect immunofluorescence staining using rabbit anti-HA antibody (1:100 dilution) followed by FITC-conjugated goat antirabbit IgG antibody (1:100 dilution). CaMKIIδB localization to the nucleus in CaMKIIδB TG mice (see Reference 32) is shown here for comparative purpose. C, Quantitation of the fold increase in CaMKIIδprotein expression in TGL and TGM lines. Different amounts of ventricular protein (numbers) from WT control, TG () and their littermates () were immunoblotted with an anti-CaMKIIδ antibody. Standard curve from the WT control was used to calculate fold increases in protein expression in TGL and TGM lines. D, Phosphorylated CaMKII in ventricular homogenates was measured by Western blot analysis (n5 for each group). **P0.01 vs WT.

 Generation and Identification of CaMKIIδC Transgenic Mice

TG mice expressing HA-tagged rat wild-type CaMKIIδC under the control of the cardiac-specific α-MHC promoter were generated as described in Materials and Methods. By Southern blot analysis, 3 independent TG founder lines carrying 3, 5, and 15 copies of the transgene were identified. They were designated as TGL (low copy number), TGM (medium copy number), and TGH (high copy number), The founder mice from the TGH line died at 5 weeks of age with marked cardiac enlargement.  The other two lines showed germline transmission of the transgene. The transgene was expressed only in the heart. Although CaMKII protein levels in TGL and TGM hearts were increased 12- and 17-fold over wild-type (WT) controls (Figure 2C), the amount of activated CaMKII was only increased 1.7- and 3-fold in TGL and TGM hearts (Figure 2D). The relatively small increase in CaMKII activity in the TG lines probably reflects the fact that the enzyme is not constitutively activated and that the availability of Ca2/CaM, necessary for activation of the overexpressed CaMKII, is limited. Importantly,

  • the extent of increase in active CaMKII in the TG lines was similar to that elicited by TAC.

 Cardiac Overexpression of CaMKIIδC Induces Cardiac Hypertrophy and Dilated Cardiomyopathy

There was significant enlargement of hearts from CaMKIIδC TGM mice by 8 to 10 weeks [see  http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5%5D  (Figure 3A) and from TGL mice by 12 to 16 weeks. Histological analysis showed ventricular dilation (Figure 3B), cardiomyocyte enlargement (Figure 3C), and mild fibrosis (Figure 3D) in CaMKIIδC TG mice. Quantitative analysis of cardiomyocyte cell volume from 12-week-old TGM mice gave values of 54.7 + 0.1 pL for TGM (n = 96) versus 28.6 + 0.1 pL for WT littermates (n=94; P0.001). Ventricular dilation and cardiac dysfunction developed over time in proportion to the extent of transgene expression. Left ventricular end diastolic diameter (LVEDD) was increased by 35% to 45%, left ventricular posterior wall thickness (LVPW) decreased by 26% to 29% and fractional shortening decreased by 50% to 60% at 8 weeks for TGM and at 16 weeks for TGL. None of these parameters were significantly altered at 4 weeks in TGM or up to 11 weeks in TGL mice, indicating that heart failure had not yet developed.  Contractile function was significantly decreased. Figure 6. Dilated cardiomyopathy and dysfunction in CaMKIIδC TG mice at both whole heart and single cell levels.  [see Fig 6:  http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5] C, Decreased contractile function in ventricular myocytes isolated from 12-week old TGM and WT controls presented as percent change of resting cell length (RCL) stimulated at 0.5 Hz. Representative trace and mean values are shown. *P0.05 vs WT. Figure 7. Phosphorylation of PLB in CaMKIIδC TG mice.  [see Fig 7: http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5] Thr17 and Ser16 phosphorylated PLB was measured by Western blots using specific anti-phospho antibodies. Ventricular homogenates were from 12- to 14-week-old WT and TGM mice (A) or 4 to 5-week-old WT and TGM mice (B). Data were normalized to total PLB examined by Western blots (data not shown here). n = 6 to 8 mice per group; *P0.05 vs WT.

 Cardiac Overexpression of CaMKIIδC Results in Changes in the Phosphorylation of Ca2 Handling Proteins

To assess the possible involvement of phosphorylation of Ca2cycling proteins in the phenotypic changes observed in the CaMKIIC TG mice, we first compared PLB phosphorylation state in homogenates from 12- to 14-week-old TGM and WT littermates. Western blots using antibodies specific for phosphorylated PLB showed a 2.3-fold increase in phosphorylation of Thr17 (the CaMKII site) in hearts from TGM versus WT (Figure 7A). Phosphorylation of PLB at the CaMKII site was also increased 2-fold in 4- to 5-week-old TGM mice (Figure 7B). Significantly, phosphorylation of the PKA site (Ser16) was unchanged in either the older or the younger TGM mice (Figures 7A and 7B). (see  http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5)  To demonstrate that the RyR2 phosphorylation changes observed in the CaMKII transgenic mice are not secondary to development of heart failure, we performed biochemical studies examining RyR2 phosphorylation in 4- to 5-week-old TGM mice. At this age, most mice showed no signs of hypertrophy or heart failure (see Figure 6B) and there was no significant increase in myocyte size (21.3 + 1.3 versus 27.7 + 4.6 pL; P0.14). Also, twitch Ca2 transient amplitude was not yet significantly depressed, and mean δ [Ca2+]i (1 Hz) was only 20% lower (192 + 36 versus 156 + 13 nmol/L; P0.47) versus 50% lower in TGM at 13 weeks.33  The in vivo phosphorylation of RyR2, determined by back phosphorylation, was significantly (2.10.3-fold; P0.05) increased in these 4- to 5-week-old TGM animals (Figure 8C), an increase equivalent to that seen in 12- to 14-week-old mice. We also performed the RyR2 back-phosphorylation assay using purified CaMKII rather than PKA. RyR2 phosphorylation at the CaMKII site was also significantly increased (2.2 + 0.3-fold; P0.05) in 4- to 5-week-old TGM mice (Figure 8C).  (http://dx.doi.org/10.1161/01.RES.0000069686.31472.C5) The association of CaMKII with the RyR2 is consistent with a physical interaction between this protein kinase and its substrate. The catalytic subunit of PKA and the phosphatases PP1 and PP2A were also present in the RyR2 immunoprecipitates, but not different in WT versus TG mouse hearts (Figure 8D). These data provide further evidence that

  • the increase in RyR2 phosphorylation, which precedes development of failure in the 4- to 5-week-old CaMKIIδC TG hearts, can be attributed to the increased activity of CaMKII.

 Discussion

  1. CaMKII is involved in the dynamic modulation of cellular
  2. Ca2 regulation and has been implicated in the development of cardiac hypertrophy and heart failure.14
  3. Published data from CaMK-expressing TG mice demonstrate that forced expression of CaMK can induce cardiac hypertrophy and lead to heart failure.27,32

However, the CaMK genes expressed in these mice are neither the endogenous isoforms of the enzyme nor the isoforms likely to regulate cytoplasmic Ca(2+) handling, because they localize to the nucleus.

  1.  the cytoplasmic cardiac isoform of CaMKII is upregulated at the expression level and is in the active state (based on autophosphorylation) after pressure overload induced by TAC.
  2.  two cytoplasmic CaMKII substrates (PLB and RyR) are phosphorylated in vivo when CaMKII is overexpressed and its activity increased to an extent seen under pathophysiological conditions.
  3. CaMKIIδ is found to associate physically with the RyR in the heart.
  4.  heart failure can result from activation of the cytoplasmic form of CaMKII and this may be due to altered Ca(2+) handling.

 Differential Regulation of CaMKIIδ Isoforms in Cardiac Hypertrophy

  1.  The isoform of CaMKII that predominates in the heart is the δ isoform.4–7 Neither the α nor the β isoforms are expressed and there is only a low level of expression of the γ isoforms.39
  2. Both δB and δC splice variants of CaMKIIδ are present in the adult mammalian myocardium36,40 and expressed in distinct cellular compartments.4,8,9

We suggest that the CaMKIIδ isoforms are differentially regulated in pressure-overload–induced hypertrophy, because the expression of CaMKIIδC is selectively increased as early as 1 day after TAC. Studies using RT-PCR confirm that

  • CaMKIIδC is regulated at the transcriptional level in response to TAC. In addition,
  • activation of both CaMKIIδB and CaMKIIδC, as indexed by autophosphorylation, increases as early as 2 days after TAC.
  • Activation of CaMKIIδB by TAC is relevant to our previous work indicating its role in hypertrophy.9,32
  • The increased expression, as well as activation of the CaMKIIδC isoform, suggests that it could also play a critical role in both the acute and longer responses to pressure overload.

In conclusion, we demonstrate here that CaMKIIδC can phosphorylate RyR2 and PLB when expressed in vivo at levels leading to 2- to 3-fold increases in its activity. Similar increases in CaMKII activity occur with TAC or in heart failure. Data presented in this study and in the accompanying article33 suggest that altered phosphorylation of Ca(2+) cycling proteins is a major component of the observed decrease in contractile function in CaMKIIδC TG mice. The occurrence of increased CaMKII activity after TAC, and of RyR and PLB phosphorylation in the CaMKIIδC TG mice suggest that

  • CaMKIIδC plays an important role in the pathogenesis of dilated cardiomyopathy and heart failure.

These results have major implications for considering CaMKII and its isoforms in exploring new treatment strategies for heart failure.

Cardiac Electrophysiological Dynamics From the Cellular Level to the Organ Level

Daisuke Sato and Colleen E. Clancy Department of Pharmacology, University of California – Davis, Davis, CA. Biomedical Engineering and Computational Biology 2013:5: 69–75 http://www.la-press.com.   http://dx.doi.org/10.4137/BECB.S10960 Abstract: Cardiac alternans describes contraction of the ventricles in a strong-weak-strong-weak sequence at a constant pacing fre­quency. Clinically, alternans manifests as alternation of the T-wave on the ECG and predisposes individuals to arrhythmia and sudden cardiac death. In this review, we focus on the fundamental dynamical mechanisms of alternans and show how alternans at the cellular level underlies alternans in the tissue and on the ECG. A clear picture of dynamical mechanisms underlying alternans is important to allow development of effective anti-arrhythmic strategies. The cardiac action potential is the single cellular level electrical signal that triggers contraction of the heart.1 Under normal conditions, the originating activation signal comes from a small bundle of tissue in the right atrium called the sinoatrial node (SAN). The action potentials generated by the SAN initiate an excitatory wave that, in healthy tissue, propagates smoothly through a well-defined path and causes excitation and contraction in the ventricles. In disease states, the normal excitation pathway is disrupted and a variety of abnormal rhythms can occur, including cardiac alternans, a well-known precursor to sudden cardiac death. Cardiac alternans was initially documented in 1872 by a German physician, Ludwig Traube.2 He observed contraction of the ventricles in a strong-weak-strong-weak sequence even though the pacing frequency was constant. Clinically, alternans mani­fests as alternation of the T-wave on the ECG, typi­cally in the microvolt range. It is well established that individuals with microvolt T-wave alternans are at much higher risk for arrhythmia and sudden cardiac death. A clear picture of physio­logical mechanisms underlying alternans is important to allow development of effective anti-arrhythmic drugs. It is also important to understand dynamical mechanisms because while the cardiac action poten­tial is composed of multiple currents, each of which confers specific properties, revelation of dynamical mechanisms provides a unified fundamental view of the emergent phenomena that holds independently of specific current interactions. The ventricular myocyte is an excitable cell pro­viding the cellular level electrical activity that under­lies cardiac contraction. Under resting conditions, the membrane potential is about -80 mV. When the cell is stimulated, sodium (Na) channels open and the membrane potential goes above 0 mV. Then, a few ms later, the inward current L-type calcium (Ca) current activates and maintains depolarization of the mem­brane potential. During this action potential plateau, several types of outward current potassium (K) chan­nels also activate. Depending on the balance between inward and outward currents, the action potential duration (APD) is determined.The diastolic interval (DI) that follows cellular repolarization describes the duration the cell resides in the resting state until the next excitation. During the DI, channels recover with kinetics determined by intrinsic time constants. APD restitution defines the relationship between the APD and the previous DI (Fig. 1 top panel). In most cases1, the APD becomes longer as the previous DI becomes longer due to recovery of the L-type Ca channel (Fig. 1, bottom panel), and thus the APD restitution curve has a positive slope. Figure 1. (Top): APD and DI. (Bottom): The physiological mechanism of APD alternans involves recovery from inactivation of ICaL.  [see  http://dx.doi.org/10.4137/BECB.S10960]

 Action Potential Duration Restitution

In 1968 Nolasco and Dahlen showed graphically that APD alternans occurs when the slope of the APD res­titution curve exceeds unity. Why is the steepness of the slope important? As shown graphically in Figure 2, APD alternans amplitude is multiplied by the slope of the APD restitution curve in each cycle. When the slope is larger than one, then the alternans amplitude will be amplified until the average slope reaches 1 or the cell shows a 2:1 stimulus to response ratio.  The one-dimensional mapping between APD and DI fails to explain quasi-periodic oscillation of the APD. Figure 2. APD restitution and dynamical mechanism of APD alternans.   [see  http://dx.doi.org/10.4137/BECB.S10960]

Calcium Driven Alternans

A strong-weak-strong-weak oscillation in contrac­tion implies that the Ca transient (CaT) is alternating. Until 1999 it was assumed that if the APD is alternat­ing then the CaT alternates because the CaT follows APD changes. However, Chudin et al showed that CaT can alternate even when APD is kept constant during pacing with a periodic AP clamp waveform.14 This implies that the intracellular Ca cycling has intrinsic nonlinear dynamics. A critical component in this process is the sarcoplasmic reticulum (SR), a subcellular organelle that stores Ca inside the cell. When Ca enters a cell through the L-type Ca channel (or reverse mode Na-Ca exchanger (NCX) ryanodine receptors open and large Ca releases occur from the SR (Ca induced Ca release). The amount of Ca release steeply depends on SR Ca load. This steep relation between Ca release and SR Ca load is the key to induce CaT alternans.  A one-dimensional map between Ca release and SR calcium load can be constructed to describe the relationship21 similar to the map used in APD restitution.

 Subcellular Alternans

A number of experimental and computational stud­ies have been undertaken to identify molecular mechanisms of CaT alternans by identifying the specific components in the calcium cycling process critical to formation of CaT alternans. These compo­nents include SR Ca leak and load, Ca spark frequency and amplitude, and rate of SR refilling. For example, experiments have shown that alternation in diastolic SR Ca is not required for CaT alternans.24 In addition, stochastic openings of ryanodine receptors (RyR) lead to Ca sparks that occur randomly, not in an alternating sequence that would be expected to underlie Ca altern-ans. So, how do local random sparks and constant dia­stolic SR calcium load lead to global CaT alternans? Mathematical models with detailed representations of subcellular Ca cycling have been developed in order to elucidate the underlying mechanisms. Model­ing studies have shown that even when SR Ca load is not changing, RyRs, which are analogous to ICaL in APD alternans, recover gradually from refractoriness. As RyR availability increases (for example during a long diastolic interval) a single Ca spark from a RyR will be larger in amplitude and recruit neighboring Ca release units to generate more sparks. The large resultant CaT causes depletion of the SR and when complete recovery of RyRs does not occur prior to the arrival of the next stimulus, the subsequent CaT will be small. This process results in an alternans of CaT amplitude from beat-to-beat.

 Coupling Between the Membrane Potential and Subcellular Calcium Dynamics

Importantly, the membrane voltage and intracellu­lar Ca cycling are coupled via Ca sensitive channels such as the L-type Ca channel and the sodium-calcium exchanger (NCX). The membrane voltage dynamics and the intracellular Ca dynamics are bi-directionally coupled. One direction is from voltage to Ca. As the DI becomes longer, the CaT usually becomes larger since the recovery time for the L-type Ca channel in increased and the SR Ca release becomes larger. The other direction is from Ca to voltage. Here we consider two major currents, NCX and ICaL. As the CaT becomes larger, forward mode NCX becomes larger and pro­longs APD. On the other hand, as the CaT becomes larger, ICaL becomes smaller due to Ca-induced inacti­vation, and thus, larger CaT shortens the APD. There­fore, depending on which current dominates, larger CaT can prolong or shorten APD. If a larger CaT pro­longs (shortens) the APD, then the coupling is positive (negative). The coupled dynamics of the membrane voltage and the intracellular Ca cycling can be cate­gorized by the instability of membrane voltage (steep APD restitution), instability of the intracellular Ca cycling (steep relation between Ca release versus SR Ca load), and the coupling (positive or negative). If the coupling is positive, alternans is electromechani­cally concordant (long-short-long-short APD cor­responds to large-small-large-small CaT sequence) regardless of the underlying instability mechanism. On the other hand, if the coupling is negative, alternans is electromechanically concordant in a voltage-driven regime. However, if alternans is Ca driven, alternans becomes electromechanically discordant (long-short-long-short APD corresponds to small-large-small-large CaT sequence). It is also possible to induce quasi- periodic oscillation of APD and CaT when volt­age and Ca instabilities contribute equally.

 Alternans in Higher Dimensions

Tissue level alternans in APD and CaT also occur and here we describe how the dynamical mechanism of alternans at the single cell level determines the phenomena in tissue. Spatially discordant alternans (SDA) where APDs in different regions of tissue alternate out-of-phase, is more arrhythmogenic since it causes large gradients of refractoriness and wave-break, which can initiate ventricular tachycardia and ventricular fibrillation. How is SDA induced? As the APD is a function of the previous DI, con­duction velocity (CV) is also function of the previ­ous DI (CV restitution) since the action potential propagation speed depends on the availability of the sodium channel. As the DI becomes shorter, sodium channels have less time to recover. Therefore, in general, as the DI becomes shorter, the CV becomes slower. When tissue is paced rapidly, action poten­tials propagate slowly near the stimulus, and thenac-celerate downstream as the DI becomes longer. This causes heterogeneity in APD (APD is shorter near the stimulus). During the following tissue excitation, APD becomes longer and the CV becomes faster at the pacing site then gradually APD becomes shorter and the CV becomes slower. The interaction between steep APD restitution and steep CV restitution creates SDA. This mechanism applies only when the cel­lular instability is voltage driven. When the cellular instability is Ca driven, the mechanism of SDA formation is different. If the volt­age-Ca coupling is negative, SDA can form without steep APD and CV restitution. The mechanism can be understood as follows. First, when cells are uncou­pled, alternans of APD and Ca are electromechanically discordant. If two cells are alternating in opposite phases, once these cells are coupled by voltage, due to electrotonic coupling, the membrane voltage of both cells is synchronized and thus APD becomes the same. This synchronization of APD amplifies the difference of CaT between two cells (Fig. 5 in). In other words it desynchronizes CaT. This instability mechanism is also found in subcellular SDA. In the case where the instability is Ca driven and the coupling is positive, there are several interest­ing distinctive phenomena that can occur. First, the profile of SDA of Ca contains a much steeper gra­dient at the node (point in space where no alternans occurs–cells downstream of the node are alternating out of phase with those upstream of the node) com­pared to the case of voltage driven SDA. Thus, the cellular mechanism of instability can be identified by evaluating the steepness of the alternans amplitude gradient in space around the node. When the cellular instability is voltage driven, the steady-state wave­length (separation of nodes in space) depends on electrotonic coupling between cells and the steepness of APD and CV restitution, regardless of the initial conditions. However, if the cellular instability is Ca driven, the location of nodes depends on the pacing history, which includes pacing cycle length and other parameters affected by pacing frequency. In this case, once the node is formed, the location of the node may be fixed, especially when Ca instability is strong. Such an explanation may apply to recent experimen­tal results. Summary In this review, we described how the origin of alternans at the cellular level (voltage driven, Ca drive, coupling between voltage and Ca) affects the formation of spatially discordant alternans at the tissue level. Cardiac alternans is a multi-scale emergent phenomenon. Channel properties determine the instability mechanism at the cellular level. Alternans mechanisms at cellular level determine SDA patterns at the tissue level. In order to understand alternans and develop anti-arrhythmic drug and therapy, multi-scale modeling of the heart is useful, which is increasingly enabled by emerging technologies such as general-purpose computing on graphics processing units (GPGPU) and cloud computing.

English: Diagram of contraction of smooth musc...

English: Diagram of contraction of smooth muscle fiber (Photo credit: Wikipedia)

Schematic representation of Calcium Cycling in Contractile and Proliferating VSMCs receptors voltage gated Ca(2) channel Marks-Wehrens Model and multiphosphorylation  site model ncpcardio0419-f4   calcium leak

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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:

  1. Ca2+/Calmodulin kinase II (CaMKII) and
  2. 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:

  1. intranuclear ([Ca2+]n) or cytoplasmic ([Ca2+]c), and
  2. 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
  1. Genomics provides data on a cell’s DNA sequence,
  2. transcriptomics on the mRNA expression of cells,
  3. proteomics on a cell’s protein composition, and
  4. 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., 2006Pfau 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

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

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

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, 2002Akesson et al., 2004Covert 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

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,

  1. We give an overview of the formulation of FBA.
  2. We summarize various FBA-driven methods for integrating expression data into genome-scale metabolic network reconstructions.
  3. 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

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

A system of linear equations is established by multiplying the 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).

equation M2     (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.

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