Posts Tagged ‘Domínguez-Rodríguez’

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

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

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.

  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
lb ≤ v ≤ ub                     

(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.


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.

calcium release calmodulin

calcium release calmodulin

Ca(2+) and contraction

Ca(2+) and contraction

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