Posts Tagged ‘NMR’

Dynamic Protein Profiling

Larry H. Bernstein, MD, FCAP, Curator


Dynamic profiling of the protein life cycle in response to pathogens

The protein lifecycle is regulated by mRNA expression, translation,
and degradation. Image courtesy of Broad Communications.

Cellular protein levels are dictated by the net balance of mRNA expression (the type of RNA that provides genetic information for proteins), protein synthesis, and protein degradation. While changes in protein levels are commonly inferred from measuring changes in mRNA levels (due to the difficulties involved in measuring protein levels), it’s not often clear whether determining RNA levels is actually a good proxy for measuring protein levels.

In their recent article in the journal Science, Broad Institute researchers working in core member Aviv Regev’s and institute member Nir Hacohen’s laboratories, along with the Broad’s Proteomics Platform led by Steve Carr describe a quantitative genomic model that lets them explain the abundance of proteins in cells based on mRNA expression, translation, and degradation. They performed their study in mouse dendritic cells stimulated with LPS, a component of bacteria.

While previous studies had looked at global levels of regulation in rapidly-dividing, unstimulated cells, this work focuses on understanding how much of the change in protein levels is due to a change in mRNA expression, translation, and degradation in specific genes and classes of genes in response to a stimulus – in this case, LPS. For example, would the changes in levels of one class of proteins be mostly driven by changes in the levels of the mRNAs that encode them? On the other hand, would changes in the levels of other groups of proteins occur without changes in mRNA, but rather due to faster translation or slower degradation of the protein? These were the type of questions the scientists were interested in.

Explains co-first author Marko Jovanovic, “Can we, in a dynamic system, integrate RNA and protein life cycle data? People rarely do this, and never systematically. Can we really make a global model of gene expression where we know, in the end, how much each type of regulatory layer is contributing to each gene? You can get a global answer too, but straight percentages of global contribution of RNA levels and the protein life cycle to final protein levels was not my goal. My question was really, do we see that certain classes of genes are controlled one way and certain other classes another way and therefore gain new regulatory insight?”

Since changes in protein levels are not as dramatic and fast as changes in RNA levels, one of the greatest challenges they faced in their study was distinguishing actual signal from noise. Co-first author Michael Rooney explains how they tackled this problem: “While the quantitative accuracy of mass spectrometry has grown tremendously, we realized that statistical strategies for handling stochastic and systematic errors in the data would still be critical to getting correct results. As a first step, we developed a generative statistical model for the data. This allowed us to leverage the entire time course in a manner that was robust to missing values and stochastic variation. Second, we saw that the contribution of translation might be over-estimated if we allowed translation rates and protein levels to be calculated from the same experimental system, because in such a case they would both be confounded by the same systematic errors, making them appear more similar than they actually are. This led us to the novel strategy of creating biological replicates prepared by distinct peptide library protocols.”

In this way, the team was able to robustly build a dynamic model in which the mRNA synthesis rate, the translation rate and the protein degradation rate change over time. Based on this model, it was possible to predict how much of each of the three types of regulation are contributing to the change in the level of each protein and from that measure both globally, per gene class, and per gene, the relative contributions of each type of regulation.

Analyzing the LPS-stimulated dendritic cells, the researchers found that overall mRNA expression dominates the regulation strategies, accounting for up to 90% of the fold changes in protein level variation. This is a significant increase from their pre-stimulation measurements showing regulation of mRNA expression contributing 60-70%, translation 15-25%, and degradation also 10-20%.

What appeared to be regulated more substantially by the protein lifecycle (translation, degradation) were highly expressed genes. And, looking at changes in the number of protein molecules rather than just the relative fold changes in pre- versus post-stimulated cells, what emerges is that post-stimulation, regulation at the level of the protein lifecycle begins to dominate.

The findings lead to a model for the LPS-stimulated system in which protein expression associated with functions critical for a dendritic cell-specific functions is taken care of by regulation at the level of RNA expression. However, the readjustment of the pre-existing proteome when the cells enter a new state (for example, in response to pathogen stimulation) is controlled via regulation of the protein life cycle (translation, degradation) rather than RNA expression.

“We termed this the ‘cupcake model’,” says Jovanovic. “You have to forgive me, this is my European view on how I see people buy cupcakes. They go into the store and choose the cupcake based on the icing, so the icing is kind of the identity of the cupcake. So from one cupcake to another you are basically changing the icing. In our model, the identity of cell states is adjusted by mRNA regulation so mRNA regulation is basically contributing to the icing. However, there’s also the cake part. The cake part is often specifically adjusted to the icing on top of the cupcake. The cake part, analogous to “housekeeping genes’, also needs to change and this is mainly through the protein life cycle. I’m very biased because I don’t like the icing on cupcakes, just the cake part, and so in the same vein, I wanted to know more about how the protein lifecycle contributes to gene expression. I think people have focused too much on the icing. “

So, mRNA changes drive new cell state identity. Protein lifecycle regulation drives readjustment of preexisting “housekeeping genes” such as those encoding ribosomes and factors involved in metabolism to adjust the cell to its new state.

This approach is extensible to test the regulation of gene expression in other perturbed systems as well, and allows researchers for the first time to assess the relative contributions of each of the three levels of protein level regulation – mRNA expression, translation, and degradation – in any perturbed system.

Paper cited: Jovanovic, M et al. Dynamic profiling of the protein life cycle in response to pathogens.Science. Feb. 12, 2015.

More Dynamic Protein Profiling

To Capture Fleeting Expressions, Go High-Throughput

  • One of the unexpected findings of the Human Genome Project was that human chromosomes contain only 20,000–25,000 protein-encoding genes, fewer than had been anticipated, …

Transitioning from Traditional Assay Formats to HTRF Technology 
Sensitivity of Fluorescence Coupled to Low Background of Time Resolution

Researchers are working on novel adaptations of HTRF-based assays, as well as their combination with other types of assays, to characterize complex disease pathways that may present multiple drug targets for disease therapy. [iStock/ponsulak]

  • At the 6th Cisbio HTRF symposium, “Charting the Course of Drug Discovery” held recently in Brewster, MA, investigators described how homogeneous time-resolved fluorescence (HTRF®) continues to expand and improve upon the repertoire of available bioassay formats for basic research and drug discovery. Participants described applications of these assays as integral components in studies ranging from identification of allosteric modulators as potential drugs to determination of critical components in protein-modifying biochemical pathways as new drug targets.

    A form of time-resolved fluorescence energy transfer (TR-FRET) technology, HTRF brings together the sensitivity of fluorescence with the homogeneous nature of FRET and the low background of time resolution. As in other FRET systems, HTRF uses two fluorophores—a donor and an acceptor that transfer energy when in close proximity to each other. Excitation of the donor molecule by an energy source such as a laser causes the emission of light waves at donor-specific wave lengths.

    If the donor and acceptor are not within proximity to each other, the donor is excited but no energy transfer occurs and no acceptor emission occurs. Dual-wavelength detection reduces buffer and media interference, and the final signal is proportional to the extent of product formation.

    The HTRF assay can be miniaturized into 384- and 1536-well plate formats, which proponents say, can save reagent costs and minimize quantities of limited target and compound material used in the assay. This assay technology has been applied to many antibody-based assays, including GPCR signaling (cAMP and IP-One), kinase, cytokine, biomarker, and bioprocess (antibody and protein production), as well as assays for protein-protein, protein-peptide, and protein-DNA/RNA interactions.

    Unlike traditional TR-FRET systems that employ fluorophores such as fluorescein and rhodamine that are characterized by immediate and transient emissions, HTRF-specific donors such as europium and terbium cryptate emit relatively long-lived fluorescence upon excitation. Conversely, acceptor molecules rapidly emit fluorescence.

    Thus, the nonspecific short-lived background fluorescence that occurs in FRET assays can be reduced by introducing a time delay ranging from 50-150 microseconds between the initial donor excitation and measurement. In HTRF, therefore, if the donor and acceptor molecules are not within proximity, only donor emissions are detected following a time delay.

    Participants at the symposium focused on novel adaptations of HTRF-based assays, as well as their combination with other types of assays, to characterize convoluted disease pathways that may present multiple drug targets for disease therapy, especially neurodegenerative disorders. In particular, several presenters noted its use in addressing what the conference keynote speaker, Terrance Kenakin, Ph.D., of the University of North Carolina, characterized as “The Perfect Storm” of pharmacology, receptor allostery, and biased signaling. Strictly defined, allosteric molecules regulate proteins by binding to the molecule at a site other than the protein’s active site.

    With regard to the seven transmembrane receptors (7TMRs) also known as G protein-coupled receptors, Dr. Kenakin noted that GPCRs comprise the largest class of receptors in the human genome and are common targets for therapeutics. Originally identified as mediators of 7TMR desensitization, β-arrestins (arrestin 2 and arrestin 3), for example, are now recognized as true adaptor proteins that transduce signals to multiple effector pathways. The introduction of molecular dynamics coupled with new assays, including HTRF, he said, opened new vistas for 7TMRs as therapeutic entities. Specifically, probe-dependent allosteric vectors oriented toward the cell cytosol provided fertile ground for new 7TMR drugs in the form of ligand-producing biased signaling.

    Discovering and Characterizing Allosteric Modulators  

    Positive and negative allosteric modulators (PAMs and NAMs) of GPCRs have emerged as a novel and highly desirable class of compounds, particularly in potential treatment for mental disorders, and for metabolic, neurodegenerative, and neuromuscular diseases. Advocates say they offer some distinct advantages over conventional competitive compounds, including the potential for fine-tuning of GPCR signaling and the promise to address formerly intractable targets.

    Introduced to the market in 2010 for the treatment of secondary hyperparathyroidism in adult patients with chronic kidney disease on dialysis, Cinacalcet, a PAM, activates the calcium-sensing receptor that functions as the principal regulator of parathyroid hormone secretion. Cinacalcet is the first clinically administered allosteric modulator acting on a GPCR, and provided a proof-of-concept for future development of allosteric modulators on other GPCR drug targets..

    Hayley Jones and Jeff Jerman, both of Medical Research Council Technology (MRCT) in the U.K., talked about the characterization of novel PAMs for the dopamine 1 receptor. Although preclinical and clinical data have validated this receptor as a target for drugs to improve cognitive impairment in schizophrenia, Jones noted that, to date, attempts to clinically develop agonists have failed.

    She and her colleagues have approached this problem by targeting D1R via PAM saying that in contrast to “direct” orthosteric D1R agonists, PAMS potentially offer advantages, including physiological spatiotemporal control of dopamine function by enhancing the effect of its endogenous ligand and avoiding over stimulation by self-limiting effects.

    The investigators said they had configured a cell-based HTRF assay to screen a subset of an MRCT compound library using CHO cells that transiently express the human receptor. Inclusion of a submaximal concentration of dopamine in the assays facilitated simultaneous detection of both PAMs and agonists, allowing them to identify novel D1R activators.

    Michelle Arkin, Ph.D., of the University of California, San Francisco, focuses her research on developing small molecule modulators of allosterically regulated enzymes and protein complexes as potential drug leads. Neurodegenerative diseases such as Alzheimer’s and other “taopathies” are characterized by formation of intracellular tangles comprised of aggregated tau proteins. Previous studies have shown that the protein actyltransferase p300 acetylates tau at several sites, competing with ubiquitination and thereby inhibiting tau degradation.

    Dr. Arkin and colleagues developed a high-throughput screen using HTRF to identify p300 inhibitors, designing a suite of counter screens and secondary assays to validate hits. Based on previous findings that the protease caspase-6 clips tau at specific sites and that truncated tau forms are associated with disease progression, the investigators developed selective caspase-6 inhibitors.

    HTRF assays demonstrated, she said, that small molecule compounds inhibit caspase-6 mediated cleavage of tau in cell lysates, concluding that the combination of HTRF enzymatic and biophysical assay formats allow characterization of inhibitors of proteins that may be involved in tauopathy progression.

  • Lack of Suitable Assays

    Martha Kimos, biochemist at the Lieber Institute, noted that the discovery of novel catehechol-o methyltransferase (COMT) inhibitors for use in the treatment of Parkinson ’s disease has been limited due to lack of suitable assays for high-throughput screening. COMT inhibitors like entacapone and tolcapone prolong the action of levodopa by preventing its demethylation by COMT.

    Kimos and her colleagues developed an HTRF assay involving an enzymatic step that uses membrane-bound human COMT as an enzyme substrate and an assay step that measures s-adenosyl-L-homcysteine (SAH) as an enzymatic reaction product. To directly measure SAH release, an anti- SAH antibody labeled with terbium cryptate and a SAH-d2 tracer were used. The SAH released by the enzymatic reaction competes with the SAH-d2 labeled leading to a decrease of the HTRF signal. The assay, the researchers said, showed good potency for tolcapone, with a high degree of translation between data in fluorescence ratio and data in terms of SAH produced, and suitable for kinetic studies, including Km determination.

    At Pfizer USA, Richard Frisbee, a scientist in the hit discovery and lead profiling (HDLP) department, and colleagues have focused on the development of HTS whole blood assays using HTRF, particularly to monitor anti-inflammatory drug potency. They noted that traditional whole-blood formats such as ELISAs for detecting cytokines require multiple assay plate manipulations, including wash steps and incubation steps, have limited throughput, and are relatively time consuming.

    They reported that they had developed a sandwich immunoassay protocol that measures cytokine production in human whole blood in a 384-well format, describing key elements of the assay, including nanoliter spotting of test compounds, miniaturized blood/reagent transfer, and optimized assay incubations. Development of a relatively convenient assay to monitor compound potency in whole blood can facilitate they said, the prediction of compound doses required for therapeutic efficacy.

    Inhibiting the enzyme γ-secretase, which converts amyloid precursor protein to β-amyloid , thus preventing its accumulation in the brain, has been a goal of drug developers.

    Most recently, Bristol-Myers Squibb elected to discontinue development of its inhibitor candidate avagacestat into Phase III trials after disappointing Phase II results. BMS remains in the hunt for drugs to treat Alzheimer’s disease. Despite clinical failures of its and other companies’ other gamma secretase inhibitors, researchers continue to search for next-generation compounds they believe may succeed.

    At BMS, Dave Harden, Ph.D., principal scientist and team leader, biochemical screening in the leads discovery and optimization group, has developed novel assays to identify molecules that inhibit secretase by measuring multiple amyloid beta species in cell supernatant. He and his team have capitalized on terbium cryptate’s properties as a donor fluorophore in HTRF, that has different photophysical properties compared to the donor fluor europium. These properties afford the opportunity to measure more than one interaction within a well due to the multiple emission spectra observed upon excitation. It can therefore serve as a donor fluorophore to green-emitting fluors because it has multiple emission peaks including one at 490 nm as well as the typically used 665 nm (red) emission.

    Dr. Harden and colleagues, in order to “enhance” their screening practices by expanding well information content, enabled two color multiplexed HTRF in multiple settings in large (>1 MM well) screening campaigns. This approach, they reported, successfully identified mechanistically distinct gamma secretase inhibitors by measuring multiple amyloid beta peptide species in cell supernatants. This, and several other examples, the presenters said, demonstrated the power of multiplexed HTRF in maximizing screening outcomes.

    Across the board, meeting presenters demonstrated the flexibility of HTRF assays and their adaptability to multiple research settings. The scientists pointed out that the assays yielded values consistent with other assay results using less versatile and convenient assays formats.


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Metabolomic analysis of two leukemia cell lines. I.

Larry H. Bernstein, MD, FCAP, Reviewer and Curator

Leaders in Pharmaceutical Intelligence


I have just posted a review of metabolomics.  In the last few weeks, the Human Metabolome was published.  I am hopeful that my decision has taken the right path to prepare my readers adequately if they will have read the articles that preceded this.  I pondered how I would present this massive piece of work, a study using two leukemia cell lines and mapping the features and differences that drive the carcinogenesis pathways, and identify key metabolic signatures in these differentiated cell types and subtypes.  It is a culmination of a large collaborative effort that required cell culture, enzymatic assays, mass spectrometry, the full measure of which I need not present here, and a very superb validation of the model with a description of method limitations or conflicts.  This is a beautiful piece of work carried out by a small group by today’s standards.

I shall begin this by asking a few questions that will be addressed in the article, which I need to beak up into parts, to draw the readers in more effectively.

Q 1. What metabolic pathways do you expect to have the largest role in the study about to be presented?

Q2. What are the largest metabolic differences that one expects to see in compairing the two lymphoblastic cell lines?

Q3. What methods would be used to extract the information based on external metabolites, enzymes, substrates, etc., to create the model for the cell internal metabolome?




Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used

  • to investigate metabolic alternations in human diseases.

An expression of the altered metabolic pathway utilization is

  • the selection of metabolites consumed and released by cells.

However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models

  • remain underdeveloped compared to methods for other omics data.

Herein, we describe a workflow for such an integrative analysis

  • extracting the information from extracellular metabolomics data.

We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how

  • our methods can reveal differences in cell metabolism.

Our models explain metabolite uptake and secretion by

  • predicting a more glycolytic phenotype for the CCRF-CEM model and
  • a more oxidative phenotype for the Molt-4 model, which
  • was supported by our experimental data.

Gene expression analysis revealed altered expression of gene products at

  • key regulatory steps in those central metabolic pathways,

and literature query emphasized

  • the role of these genes in cancer metabolism.

Moreover, in silico gene knock-outs identified

  • unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model.

Thus, our workflow is well suited to the characterization of cellular metabolic traits based on

  • extracellular metabolomic data, and
  • it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.

Keywords Constraint-based modeling _ Metabolomics _Multi-omics _ Metabolic network _ Transcriptomics


Reviewer Summary:

  1. A model is introduced to demonstrate a lymphocytic integrated data set using to cell lines.
  2. The method is required to integrate extracted data sets from extracellular metabolites to an intracellular picture of cellular metabolism for each cell line.
  3. The method predicts a more glycolytic or a more oxidative metabolic framework for one or the othe cell line.
  4. The genetic phenotypes differ with a unique control point for each cell line.
  5. The model presents an integration of omics data sets into a cohesive picture based on the model context.

Without having seen the full presentation –

  1. Is the method a snapshot of the neoplastic processes described?
  2. Does the model give insight into the cellular metabolism of an initial cell state for either one or both cell lines?
  3. Would one be able to predict a therapeutic strategy based on the model for either or both cell lines?

Before proceeding further into the study, I would conjecture that there is no way of knowing the initial state ( consistent with what is described by Ilya Prigogine for a self-organizing system) because the model is based on the study of cultured cells that had an unknown metabolic control profile in a host proliferating bone marrow that is likely B-cell origin.  So this is a snapshot of a stable state of two incubated cell lines.  Then the question that is raised is whether there is not only a genetic-phenotypic relationship between the cells in culture and the external metabolites produced, but also whether differences can be discerned between the  internal metabolic constructions that would fit into a family tree.



Modern high-throughput techniques

  • have increased the pace of biological data generation.

Also referred to as the ‘‘omics avalanche’’, this wealth of data

  • provides great opportunities for metabolic discovery.

Omics data sets contain a snapshot of almost the entire repertoire of

  • mRNA, protein, or metabolites at a given time point or
  • under a particular set of experimental conditions.

Because of the high complexity of the data sets,

  • computational modeling is essential for their integrative analysis.

Currently, such data analysis

  • is a bottleneck in the research process and
  • methods are needed to facilitate the use of these data sets, e.g.,
  1. through meta-analysis of data available in public databases
    [e.g., the human protein atlas (Uhlen et al. 2010)
  2. or the gene expression omnibus (Barrett  et al.  2011)], and
  3. to increase the accessibility of valuable information
    for the biomedical research community.

Constraint-based modeling and analysis (COBRA) is

  • a computational approach that has been successfully used
  • to investigate and engineer microbial metabolism through
    the prediction of steady-states (Durot et al.2009).

The basis of COBRA is network reconstruction: networks are assembled

  1. in a bottom-up fashion based on genomic data and
  2. extensive organism-specific information from the literature.

Metabolic reconstructions

  1. capture information on the known biochemical transformations
    taking place in a target organism
  2. to generate a biochemical, genetic and genomic knowledge base
    (Reed et al. 2006).

Once assembled, a metabolic reconstruction

  • can be converted into a mathematical model
    (Thiele and Palsson 2010), and
  • model properties can be interrogated using a great variety of methods
    (Schellenberger et al. 2011).

The ability of COBRA models to represent

  • genotype–phenotype and environment–phenotype relationships
  • arises through the imposition of constraints,
  • which limit the system to a subset of possible network states
    (Lewis et al. 2012).

Currently, COBRA models exist for more than 100 organisms, including humans
(Duarte et al. 2007; Thiele et al. 2013).

Since the first human metabolic reconstruction was described
[Recon 1 (Duarte et al. 2007)],

  • biomedical applications of COBRA have increased
    (Bordbar and Palsson 2012).

One way to contextualize networks is to

  • define their system boundaries
  • according to the metabolic states of the system,
    e.g., disease or dietary regimes.

The consequences of the applied constraints

  • can then be assessed for the entire network
    (Sahoo and Thiele 2013).

Additionally, omics data sets have frequently been used

  • to generate cell-type or condition-specific metabolic models.

Models exist for specific cell types, such as

  • enterocytes (Sahoo and Thiele2013),
  • macrophages (Bordbar et al. 2010), and
  • adipocytes (Mardinoglu et al. 2013), and
  • even multi-cell assemblies that represent
    the interactions of brain cells (Lewis et al. 2010).

All of these cell type specific models,

  • except the enterocyte reconstruction
  • were generated based on omics data sets.

Cell-type-specific models have been used

  • to study diverse human disease conditions.

For example, an adipocyte model was generated using

  • transcriptomic,
  • proteomic, and
  • metabolomics data.

This model was subsequently used to investigate

  • metabolic alternations in adipocytes
  • that would allow for the stratification of obese patients
    (Mardinoglu et al. 2013).

One highly active field within the biomedical applications of COBRA is

  • cancer metabolism (Jerby and Ruppin, 2012).

Omics-driven large-scale models have been used

  • to predict drug targets (Folger et al. 2011; Jerby et al. 2012).

A cancer model was generated using

  • multiple gene expression data sets and
  • subsequently used to predict synthetic lethal gene pairs
  • as potential drug targets selective for the cancer model,
  • but non-toxic to the global model (Recon 1),
  • a consequence of the reduced redundancy in the
    cancer specific model (Folger et al. 2011).

In a follow up study, lethal synergy between

  • FH and enzymes of the heme metabolic pathway
    were experimentally validated and
  • resolved the mechanism by which FH deficient cells,
    e.g., in renal-cell cancer cells
  • survive a non-functional TCA cycle (Frezza et al. 2011).

Contextualized models, which contain only 

  • the subset of reactions active in 
  • a particular tissue (or cell-) type,
  • can be generated in different ways
    (Becker and Palsson, 2008; Jerby et al. 2010).

However, the existing algorithms mainly consider

  • gene expression and proteomic data to define the reaction sets
  • that comprise the contextualized metabolic models.

These subset of reactions are usually defined based on

  • the expression or absence of expression of the genes or proteins
    (present and absent calls), or
  • inferred from expression values or differential gene expression.

Comprehensive reviews of the methods are available
(Blazier and Papin, 2012; Hyduke et al. 2013).

Only the compilation of a large set of omics data sets

  • can result in a tissue (or cell-type) specific metabolic model, whereas

the representation of one particular experimental condition is achieved through

  • the integration of omics data set generated from one experiment only
    (condition-specific cell line model).

Recently, metabolomic data sets

  • have become more comprehensive and using these data sets allow
  • direct determination of the metabolic network components (the metabolites).

Additionally, metabolomics has proven to be

  1. stable,
  2. relatively inexpensive, and
  3. highly reproducible
    (Antonucci et al. 2012).

These factors make metabolomic data sets

  •  particularly valuable for interrogation of metabolic phenotypes. 

Thus, the integration of these data sets is now an active field of research
(Li et al. 2013; Mo et al. 2009; Paglia et al. 2012b; Schmidt et al. 2013).

Generally, metabolomic data can be incorporated into metabolic networks as

  1. qualitative,
  2. quantitative, and
  3. thermodynamic constraints
    (Fleming et al. 2009; Mo et al. 2009).

Mo et al. used metabolites detected in the spent medium
of yeast cells to determine

  • intracellular flux states through a sampling analysis (Mo et al. 2009),
  • which allowed unbiased interrogation of the possible network states
    (Schellenberger and Palsson 2009)
  • and prediction of internal pathway use.

Such analyses have also been used

  • to reveal the effects of enzymopathies on red blood cells (Price et al. 2004),
  • to study effects of diet on diabetes (Thiele et al. 2005) and
  • to define macrophage metabolic states (Bordbar et al. 2010).

This type of analysis is available as a function in the COBRA toolbox
(Schellenberger et al. 2011).




In this study, we established a workflow for the generation and analysis of

  • condition-specific metabolic cell line models that
  • can facilitate the interpretation of metabolomic data.

Our modeling yields meaningful predictions regarding

  • metabolic differences between two lymphoblastic leukemia cell lines
    (Fig. 1A).
Differences in the use of the TCA cycle by the CCRF-CEM

Differences in the use of the TCA cycle by the CCRF-CEM


Fig. 1

A  Combined experimental and computational pipeline to study human metabolism.
Experimental work and omics data analysis steps precede computational modeling. Model

  • predictions are validated based on targeted experimental data.

Metabolomic and transcriptomic data are used for

  • model refinement and submodel extraction.

Functional analysis methods are used to characterize

  • the metabolism of the cell-line models and compare it to additional experimental

The validated models are subsequently 

  • used for the prediction of drug targets.

B Uptake and secretion pattern of model.
All metabolite uptakes and secretions that were mapped during model
generation are shown.
Metabolite uptakes are depicted on the left, and

  • secreted metabolites are shown on the right.

A number of metabolite exchanges mapped to the model

  • were unique to one cell line.

Differences between cell lines were used to set

  • quantitative constraints for the sampling analysis.

C Statistics about the cell line-specific network generation.

 Quantitative constraints.
For the sampling analysis, an additional

  • set of constraints was imposed on the cell line specific models,
  • emphasizing the differences in metabolite uptake and secretion between cell lines.

Higher uptake of a metabolite was allowed in the model of the cell line

  • that consumed more of the metabolite in vitro, whereas
  • the supply was restricted for the model with lower in vitro uptake.

This was done by establishing the same ratio between the models bounds as detected in vitro.
X denotes the factor(slope ratio) that

  1. distinguishes the bounds, and
  2. which was individual for each metabolite.
  • (a) The uptake of a metabolite could be x times higher in CCRF-CEM cells,
    (b) the metabolite uptake could be x times higher in Molt-4,
    (c) metabolite secretion could be x times higher in CCRF-CEM, or
    (d) metabolite secretion could be x times higher in Molt-4 cells. LOD limit of detection.

The consequence of the adjustment was, in case of uptake, that  one model

  1. was constrained to a lower metabolite uptake (A, B), and the difference
  2. depended on the ratio detected in vitro.

In case of secretion,

  • one model had to secrete more of the metabolite, and again

the difference depended on

  • the experimental difference detected between the cell lines.

Q5. What is your expectation that this type of integrative approach could be used for facilitating medical data interpretations?

The most inventive approach was made years ago by using data constructions from the medical literature by a pioneer in the medical record development, but the technology was  not what it is today, and the cost of data input was high.  Nevertheless, the data acquisition would not be uniform across institutions, except for those that belong to a consolidated network with all of the data in the cloud, and the calculations would be carried out with a separate engine.  However, whether the uniform capture of the massive amount of data needed is not possible in the near foreseeable future.  There is no accurate way of assessing the system cost, and predicting the benefits.  In carrying this model forward there has to be a minimal amount of insufficient data.  The developments in the regulatory sphere have created a high barrier.

This concludes a first portion of this presentation.


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Larry H Bernstein, MD, FCAP Biomarkers for Companion Diagnostics

Scientists from around the world gathered to share some of their newest biomarker research at the “Oncology Biomarkers Conference”.

Honing in on Cancer Biomarkers

Caitlin Smith
EN  15 Nov 2013; 33(20)

Introduction and Goals

Some of the newest cancer treatments aim to individualize the therapy to the specific type of cancer and patient. The large and growing number of different genetic alterations that researchers observe in cancer cells have made it unfeasible to test for only a handful of targets. Instead, clinical testing is moving toward testing for many targets simultaneously.

“This approach of multiplexed tumor genotyping allows for the simultaneous evaluation of a broad range of common and rare tumor alterations,” said Darrell Borger, Ph.D., director of biomarker and co-director of translational research laboratories at the Massachusetts General Hospital Cancer Center. “This is important for expanding the application of targeted therapy across a greater number of patients who undergo testing, and directing those patients into the most relevant clinical trials.”

Dr. Borger and colleagues are uncovering “molecular signatures of tumors,” or collections of targets present in specific tumor types. “A molecular signature of a tumor is in essence a map of the abnormalities within a particular tumor that are thought to be critical in driving the disease process,” said Dr. Borger. “We know that each tumor will have a unique combination of genetic alterations.”

These signatures are useful because the ability to genotype a certain kind of cancer can help find the most effective treatment possible. “The more comprehensive the tumor profiling, the more detailed the roadmap we can draw for directing that patient’s care,” Dr. Borger said.

Uncovering the molecular signatures of tumors has another important role—to better understand the differences among cells within the same tumor. “Tumor heterogeneity is an important mechanism of emerging drug resistance,” said Dr. Borger. “Broad-based tumor profiling and the use of sensitive testing platforms are essential in identifying these potential mechanisms of disease resistance, so that targeted approaches can be aimed at circumventing those mechanisms.”

Target Signaling

Also working to help physicians figure out which treatments among many might work best for individual patients is Selventa. Focusing on gene expression biomarkers, Selventa researchers correlate gene expression patterns from patient data with changes in target signaling mechanisms.

“We operate on the hypothesis that patients with high or low levels of target (or downstream target) pathway signaling correspond to potential responders or nonresponders to target therapy, respectively,” said Renée Deehan Kenney, Ph.D., vp of research. “If we know who responded and who did not respond to treatment, then we can use that information to hone the biomarker using machine-learning approaches.”

Selventa is using its Systems Diagnostics (SysDx) platform to identify biomarkers used in diagnosing immune disorders such as rheumatoid arthritis (RA). Their product Clarify-RA is based on the SysDx approach using a blood biomarker. It is designed to aid clinicians in matching RA patients with those RA drugs that will be most beneficial to them. Such matching is valuable because RA is a heterogeneous disease, but different patients respond differently to the over 15 RA drugs that are available. Moreover, RA is a debilitating disease that cannot wait for a trial-and-error treatment approach.

“To compound this clinical challenge, drugs approved for RA offer about 50% improvement for only 40% of the patients,” said Dr. Deehan Kenney. For example, one biomarker Selventa found can identify RA patients who are likely to respond to anti-TNF therapy. Similarly, Selventa’s SysDx approach also found a biomarker from tumor biopsy tissue that identifies ER+ breast cancer patients whose cancer tends to progress with tamoxifen treatment.

IHC-Based Testing

President and CEO of Precision Biologics, Philip Arlen, M.D., discussed his company’s research on a new monoclonal antibody (NPC-1C), which targets tumors in both pancreatic and colorectal cancer. The antibody’s target is specific to tumors, and the antibody has negligible reactions with normal tissue, he said.

Precision Biologics took an unconventional tack to making NPC-1C, using a cancer vaccine that had been developed from colorectal cancer tissue removed from patients with varying stages of disease. They screened for antibodies that were specific for tumors, but nonreactive with normal tissue.

In both cell cultures and in animal models, they found that NPC-1C destroyed pancreatic cancer cells. “Furthermore, we had very encouraging Phase I/IIa data demonstrating prolongation in overall survival in patients that had exhausted all standards of therapy,” said Dr. Arlen.

Precision Biologics has developed an immunohistochemistry-based diagnostic test for expression of NPC-1C’s target. “Patients’ tumors are tested, and if the target is present, the patients can receive treatment with NPC-1C,” said Dr. Arlen. “We are also developing a diagnostic assay with NPC-1C for early detection and prognosis of colorectal and pancreatic cancer.”

NMR Technology

LipoScience researchers using NMR technology to look for cancer biomarkers expect that panels of metabolites covering a range biochemical processes will need to be analyzed. They produced these 1H NMR spectra of unprocessed serum focusing on (A) macromolecular signals and (B) the small molecule metabolome.

LipoScience is also developing new ways to search for biomarkers. Specifically, to find biomarkers of clinical value, they are using NMR technology. “We take advantage of two of the key features of the NMR platform,” explained Thomas O’Connell, Ph.D., senior director of research and development. “These are the lack of required sample preparation for routine biofluids and the inherently quantitative signals.” This means that they can profile large sample sets very quickly.

LipoScience researchers are now using NMR to look for cancer biomarkers. “Given the heterogeneity of most cancers, it is not likely that a single biomarker will provide the necessary clinical performance,” said Dr. O’Connell, “so we are examining panels of metabolites that cover a range of biochemical processes, including lipid and lipoprotein metabolism, energy perturbations, inflammatory processes, and others.”

They plan to use NMR and metabolomic profiling to develop clinical assays that help to choose patient-specific therapies. “We are hopeful that one day in the near future, panels of biomarkers could provide clinicians with much more objective, quantifiable, and personalized information regarding the diagnosis and management of their patients,” added Dr. O’Connell.

Single Molecule Arrays

The Simoa (for single molecule array) instrument from Quanterix uses a digital ELISA technique, trapping fluorescent reaction product in indiv-idual wells, to speed blood testing for HIV.

Researchers at Quanterix have developed a method of testing for a different type of biomarker—one that indicates the early and acute (and most contagious) stage of HIV infection. Their method is faster, cheaper, and more sensitive than previous tests.

Previously, the gold standard HIV test with the highest sensitivity was nucleic acid testing, which detects viral genetic material. The new test from Quanterix, called Simoa for “single molecule arrays,” is a digital ELISA technique. Simoa works by preventing the sensitivity loss that can occur in conventional ELISAs because of the dilution of reaction product into the reaction volume. Simoa essentially miniaturizes the ELISA principle, trapping fluorescent reaction product in individual wells to prevent dilution.

“The technology basically supercharges a standard ELISA to give 1,000-times greater sensitivity,” said David Wilson, Ph.D., vp of product development. “Due to this extreme sensitivity of Simoa to enzyme label, label molecules can be reduced, which lowers nonspecific interactions and improves signal background. This drives the sensitivity of Simoa digital immunoassays down to the level of nucleic acid testing.”

Simoa assays are easily amenable to high-throughput fluidics instrumentation and automation. So Dr. Wilson hopes Simoa will be applied to HIV screening in blood banks, as well as other blood-borne viruses to which Quanterix is developing new Simoa assays. “A key need in many blood banking centers is high throughput,” Dr. Wilson said. “Blood units are screened for a number of pathogens, so effective throughput is measured in number of units processed in a given period of time.”

Simoa immunoassays can be multiplexed to test for up to 10 different target proteins simultaneously, which may benefit blood banks. However, blood banking is highly regulated, so introducing Simoa assays may take time. “As with any new test used to ensure a blood unit is pathogen-free,” explained Dr. Wilson, “a substantial amount of data is needed to prove to regulatory bodies that the test exhibits the claimed performance, and that the manufacturing processes are fully validated and controlled.”

Perhaps one day, it will be possible to detect biomarkers of viral infection, cancer, and other diseases for many people very quickly. Then, armed with the relevant information, healthcare providers will be able to fight disease more effectively.


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Reporter and Curator: Dr. Sudipta Saha, Ph.D.

Molecular biomarkers could detect biochemical changes associated with disease processes. The key metabolites have become an important part for improving the diagnosis, prognosis, and therapy of diseases. Because of the chemical diversity and dynamic concentration range, the analysis of metabolites remains a challenge. Assessment of fluctuations on the levels of endogenous metabolites by advanced NMR spectroscopy technique combined with multivariate statistics, the so-called metabolomics approach, has proved to be exquisitely valuable in human disease diagnosis. Because of its ability to detect a large number of metabolites in intact biological samples with isotope labeling of metabolites using nuclei such as H, C, N, and P, NMR has emerged as one of the most powerful analytical techniques in metabolomics and has dramatically improved the ability to identify low concentration metabolites and trace important metabolic pathways. Multivariate statistical methods or pattern recognition programs have been developed to handle the acquired data and to search for the discriminating features from biosample sets. Furthermore, the combination of NMR with pattern recognition methods has proven highly effective at identifying unknown metabolites that correlate with changes in genotype or phenotype. The research and clinical results achieved through NMR investigations during the first 13 years of the 21st century illustrate areas where this technology can be best translated into clinical practice.

In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, “omics” platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease.

Marginal deficiency of vitamin B-6 is common among segments of the population worldwide. Because pyridoxal 5′-phosphate serves as a coenzyme in the metabolism of amino acids, carbohydrates, organic acids, and neurotransmitters, as well as in aspects of one-carbon metabolism, vitamin B-6 deficiency could have many effects. NMR spectral features of selected metabolites indicated that vitamin B-6 restriction significantly increased the ratios of glutamine/glutamate and 2-oxoglutarate/glutamate and tended to increase concentrations of acetate, pyruvate, and trimethylamine-N-oxide. Tandem MS showed significantly greater plasma proline after vitamin B-6 restriction, but there were no effects on the profile of 14 other amino acids and 45 acylcarnitines. These findings demonstrate that marginal vitamin B-6 deficiency has widespread metabolic perturbations and illustrate the utility of metabolomics in evaluating complex effects of altered vitamin B-6 intake.

Hepatocellular carcinoma is one of the most common malignancies worldwide, and it has a poor prognosis due to its rapid development and early metastasis. An understanding of tumor metabolism would be helpful for the clinical diagnosis and therapy of hepatocellular carcinoma. To investigate the metabolic features of hepatocellular carcinoma, a non-targeted metabolic profiling strategy based on liquid chromatography-mass spectrometry was performed. The results revealed multiple metabolic changes in the tumor, and the principal changes included elevated glycolysis, inhibition of the tricarboxylic acid cycle, accelerated gluconeogenesis and β-oxidation for energy supply and down-regulated Δ-12 desaturase. Furthermore, increased levels of anti-oxidative molecules, such as glutathione, and decreased levels of inflammatory-related polyunsaturated fatty acids and the phospholipase A2 enzyme were also observed. The differential metabolites found in the tissue were tested in serum samples from the chronic hepatitis, cirrhosis and hepatocellular carcinoma patients. The combination of betaine and propionylcarnitine was confirmed to have a good diagnostic potential to distinguish hepatocellular carcinoma from chronic hepatitis and cirrhosis. External validation of cirrhosis and hepatocellular carcinoma serum samples further shows the combination biomarker is useful for hepatocellular carcinoma diagnosis.

Current diagnostic techniques have increased the detection of prostate cancer; however, these tools inadequately stratify patients to minimize mortality. Recent studies have identified a biochemical signature of prostate cancer metastasis, including increased sarcosine abundance. Prostate tumors had significantly altered metabolite profiles compared to cancer-free prostate tissues, including biochemicals associated with cell growth, energetics, stress, and loss of prostate-specific biochemistry. Many metabolites were further associated with clinical findings of aggressive disease. Aggressiveness-associated metabolites stratified prostate tumor tissues with high abundances of compounds associated with normal prostate function (e.g., citrate and polyamines) from more clinically advanced prostate tumors. These aggressive prostate tumors were further subdivided by abundance profiles of metabolites including NAD+ and kynurenine. When added to multiparametric nomograms, metabolites improved prediction of organ confinement and 5-year recurrence. These findings support and extend earlier metabolomic studies in prostate cancer and studies where metabolic enzymes have been associated with carcinogenesis and/or outcome. Furthermore, it suggests that panels of analytes may be valuable to translate metabolomic findings to clinically useful diagnostic tests.

Source References:

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