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Metabolomic analysis of two leukemia cell lines. II.

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

Leaders in Pharmaceutical Intelligence

 

In Part I of metabolomics of two leukemia cell lines, we have established a major premise for the study, an insight into the use of an experimental model, and some insight into questions raised.

I here return to examine these before pursuing more detail in the study.

Q1. What strong metabolic pathways come into focus in this study?

Answer – The aerobic and anaerobic glycolytic pathways, with a difference measured in the extent of participation of mitochondrial oxidative phosphorylation.

Q2. Would we expect to also gain insight into the effect, on balance, played by a suppressed ubiquitin pathway?

Answer – lets look into this in Part II.

Q3. Would the synthesis of phospholipid and the maintenance of membrane structures requires availability of NADPH, which would be a reversal of the TCA cycle at the cost of delta G in catabolic energy, be consistent with increased dependence of anaerobic glycolysis  with unchecked replication?

Answer: Part II might show this, as the direction and the difference between the cell lines is consistent with a Warburg (Pasteur) effect.

Recall the observation that the model is based on experimental results from  lymphocytic leukemia cell lines in cell culture.  The internal metabolic state is inferred from measurement of external metabolites.

The classification of the lymphocytic leukemias in humans is based on T-cell and B-cell lineages, but actually uses cell differentiation (CD) markers on the cytoskeleton for recognition.  It is only a conjecture that if the cells line were highly anaplastic, they might not be sustainable in cell culture in perpetuity.
The analogue of these cells to what I would expect to see in humans is the SLL having the characteristic marking: CD5, see http://www.pathologyoutlines.com/topic/lymphomaSLL.html

Micro description
=======================================================

● Effacement of nodal architecture by pale staining pseudofollicles or proliferation centers with ill-defined borders, containing small round mature lymphocytes, prolymphocytes (larger than small lymphocytes, abundant basophilic cytoplasm, prominent nucleoli), paraimmunoblasts (larger cells with distinct nucleoli) and many smudge cells
● Pseudofollicular centers are highlighted by decreasing light through the condenser at low power; cells have pale cytoplasm but resemble soccer balls or smudge cells on peripheral smear (cytoplasm is bubbly in mantle cell lymphoma); may have plasmacytoid features
● May have marginal zone, perifollicular or interfollicular patterns, but these cases also have proliferation centers (Mod Pathol 2000;13:1161)
● Interfollicular pattern: large, reactive germinal centers; resembles follicular lymphoma but germinal centers are bcl2 negative and tumor cells resemble SLL by morphology and immunostains
(Am J Clin Path 2000;114:41)
● Paraimmunoblastic variant: diffuse proliferation of paraimmunoblasts (normally just in pseudoproliferation centers); rare, <30 reported cases; usually multiple lymphadenopathies and rapid disease progression; case report in 69 year old man (Hum Pathol 2002;33:1145); consider as mantile cell lymphoma if t(11;14)(q13;q32) is present; may also represent CD5+ diffuse large B cell lymphoma
Bone marrow: small focal aggregates of variable size with irregular, poorly circumscribed outlines; lymphocytes are well differentiated, small, round with minimal atypia; may have foci of transformation; rarely has granulomas (J Clin Pathol 2005;58:815)
● Marrow infiltrative patterns are also described as diffuse (unmutated IgH genes, ZAP-70+, more aggressive), nodular (associated with IgH hypermutation, ZAP-70 negative) or mixed (variable mutation of IgH, variable ZAP-70, Hum Pathol 2006;37:1153)

 

Positive stains
=======================================================

● CD5, CD19, CD20 (dim), CD23, surface Ig light chain, surface IgM (dim)
● Also CD43, CD79a, CD79b (dim in 20%, Arch Pathol Lab Med 2003;127:561), bcl2
● Variable CD11c, FMC7 (42%)
Negative stains
=======================================================

● CD10, cyclin D1
Molecular
=======================================================

● Trisomy 12 (30%, associated with atypical CLL and CD79b), deletion 13q14 (25-50%),
deletion of 11q23 (worse prognosis, 10-20%)

 

Results

We set up a pipeline that could be used to

  • infer intracellular metabolic states from semi-quantitative data
  • regarding metabolites exchanged between cells and their environment.

Our pipeline combined the following four steps:

  1. data acquisition,
  2. data analysis,
  3. metabolic modeling and
  4.  experimental validation of
  • the model predictions (Fig. 1A).

We demonstrated the pipeline and the predictive potential

  • to predict metabolic alternations in diseases such as cancer
  • based on two lymphoblastic leukemia cell lines.

The resulting Molt-4 and CCRF-CEM condition-specific cell line models were able

  • to explain metabolite uptake and secretion
  •  by predicting the distinct utilization of central metabolic pathways by the two cell lines.

Whereas the CCRF-CEM model

  • resembled more a glycolytic, commonly referred to as ‘Warburg’ phenotype,
  • our predictions suggested  a more respiratory phenotype for the Molt-4  model.

We found these predictions to be in agreement with measured gene expression differences

  • at key regulatory steps in the central metabolic pathways, and
  • they were also consistent with  data regarding the energy and redox states of the cells.

After a brief discussion of the data generation and analysis steps, the results

  • derived from model generation and analysis will be described in detail.

 

2.1 Pipeline for generation of condition-specific metabolic cell line models

2.1.1 Generation of experimental data

We monitored the growth and viability of lymphoblastic leukemia cell lines in
serum- free medium (File S2, Fig. S1). Multiple omics  data sets  were derived  from these cells.

Extracellular metabolomics (exo-metabolomic) data,

  • comprising measurements of the metabolites in the spent medium of the cell cultures
    (Paglia et al. 2012a),
  • were collected along with transcriptomic data, and
  • these data sets were used to construct the models.

 

2.1.4 Condition-specific models for CCRF-CEM and Molt-4 cells

To determine whether we had obtained two distinct models,

  • we evaluated the reactions, metabolites, and genes of the two models.

Both the Molt-4 and CCRF-CEM models contained approximately

  • half of the reactions and metabolites present in the global model (Fig. 1C).

They were very similar to each other in terms of their

  • reactions,
  • metabolites, and
  • genes (File S1, Table S5A–C).

The Molt– 4 model contained

  • seven reactions that were not present in the CCRF-CEM model
    (Co-A biosynthesis pathway and exchange reactions).

In contrast, the CCRF-CEM  contained

31 unique reactions

  • arginine and proline metabolism,
  • vitamin B6  metabolism,
  • fatty acid activation,
  • transport, and exchange reaction.
  • There  were 2 and 15 unique metabolites in the Molt-4 and CCRF-CEM models,  respectively
    (File S1, Table S5B).
    Approximately three quarters of the global  model  genesremained in the condition-specific cell line models  (Fig. 1C).

The Molt-4 model contained

  • 15 unique genes, and

the CCRF-CEM model had

  • 4 unique genes (File S1, Table S5C).

Both models lacked NADH dehydrogenase
(complex I of the electron transport chain—ETC),

  •  determined by  the  absence of expression of a mandatory subunit
    (NDUFB3, Entrez gene ID 4709).

The ETC was fueled by FADH2 originating from

  1. succinate dehydrogenase and
  2. from fatty acid oxidation, which
  • through flavoprotein electron transfer
  • could contribute to the same ubiquinone pool as
  • complex I and complex II (succinate dehydrogenase).

Despite their different in vitro growth rates
(which differed by 11 %, see File S2, Fig. S1) and

  • differences in exo-metabolomic data (Fig. 1B) and
  • transcriptomic data,
  • the internal networks were largely conserved
  • in the two condition-specific cell line models.

 

2.1.5 Condition-specific cell line models predict distinct metabolic strategies

Despite the overall similarity of the metabolic models,

  • differences in their cellular uptake and secretion patterns suggested
  • distinct metabolic states in the two cell lines
    (Fig. 1B and see “Materials and methods” section for more detail).

To interrogate the metabolic differences, we sampled the solution space

  • of each model  using an Artificial Centering Hit-and-Run (ACHR) sampler (Thiele et al. 2005).

For this  analysis, additional constraints were applied, emphasizing

  • the  quantitative differences in commonly uptaken and secreted metabolites.

The  maximum possible uptake and maximum possible secretion flux rates were

  • reduced according to the measured relative differences between the cell lines
    (Fig. 1D, see “Materials and methods” section).

We plotted the number of sample points containing a particular flux rate for each reaction. The resulting

  • binned histograms can be understood as representing the probability that
  • a particular reaction can have a certain flux value.

A comparison of the sample points obtained for the Molt-4 and CCRF-CEM models revealed

  • a  considerable shift in the distributions, suggesting
  • a higher utilization of  glycolysis by the CCRF-CEM model (File S2, Fig. S2).

This result  was further  supported by differences

  • in medians calculated from sampling points (File S1,  Table S6).

The shift persisted throughout all reactions of the pathway and

  • was  induced by the higher glucose uptake (35 %) from
  • the extracellular medium in CCRF-CEM cells.

The sampling median for glucose uptake was 34 % higher

  • in the  CCRF-CEM model than in Molt-4 model (File S2, Fig. S2).

The usage of the  TCA cycle was also distinct in the two condition-specific cell-line models (Fig. 2).

  • the models used succinate dehydrogenase differently (Figs. 23).

The Molt-4 model utilized an associated reaction to generate FADH2, whereas

  • in  the CCRF-CEM model, the histogram was shifted in the opposite direction,
  • toward  the generation of succinate.

Additionally, there was a higher efflux of  citrate toward

  • amino acid and lipid metabolism in the CCRF-CEM model (Fig. 2).

There was higher flux through anaplerotic and cataplerotic reactions

  • in the CCRF-CEM model than in the Molt-4 model (Fig. 2);
  • these reactions include the efflux  of citrate through

 

  1. ATP-citrate lyase,
  2. uptake of glutamine,
  3. generation of  glutamate from glutamine,
  4. transamination of pyruvate and
  5.  glutamate to alanine  and to 2-oxoglutarate,
  6. secretion of nitrogen, and
  7. secretion of alanine.

The Molt-4 model showed higher utilization of oxidative phosphorylation (Fig. 3),

  • supported by elevated median flux through ATP synthase (36 %) and other  enzymes,
  • which contributed to higher oxidative metabolism.

The sampling  analysis therefore revealed different usage of

  • central metabolic pathways by the condition-specific models.

 

Fig. 2

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

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

Differences in the use of the TCA cycle by the CCRF-CEM model (red) and the Molt-4 model (blue).
The table provides the median values of the sampling results. Negative values in histograms and Table

  • describe reversible  reactions with flux in the reverse direction.

There are multiple reversible  reactions for the transformation of

  1. isocitrate and α-ketoglutarate,
  2. malate and  fumarate, and
  3. succinyl-CoA and succinate.

These reactions are  unbounded,  and therefore histograms are not shown.
The details of participating cofactors  have been removed.

Atp ATP, cit citrate, adp ADP, pi phosphate, oaa oxaloacetate, accoa acetyl-CoAcoa coenzyme-A,
icit isocitrate, αkg α-ketoglutarate, succcoa succinyl-CoAsucc succinate, fumfumarate, mal malate,
oxa oxaloacetate,  pyr pyruvate, lac lactate, ala alanine, gln glutamine, ETC electron transport  chain.

 

Electronic supplementary material The online version of this article
http://dx.doi.org:/10.1007/s11306-014-0721-3 
contains supplementary material,  which  is available to authorized users.

  1.  K. Aurich _ G. Paglia _ O ´ . Rolfsson _ S. Hrafnsdo´ ttir _
  2. Magnu´sdo´ ttir _ B. Ø. Palsson _ R. M. T. Fleming _ I. Thiele. Center for Systems Biology,
    University of Iceland, Reykjavik, Iceland
  3.  K. Aurich _ R. M. T. Fleming _ I. Thiele (&). Luxembourg Centre for Systems Biomedicine,
    University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
    e-mail: ines.thiele@uni.lu
  4. M. Stefaniak. School of Health Science, Faculty of Food Science and Nutrition,
    University of Iceland, Reykjavik, Iceland
  5. Ø. Palsson. Department of Bioengineering, University of California San Diego, La Jolla, CA, USA

http://link.springer.com/static-content/images/404/art%253A10.1007%252
Fs11306-014-0721-3/MediaObjects/11306_2014_721_Fig3_HTML.gif

 

Fig. 3

Fatty acid oxidation and ETC _Fig3

Fatty acid oxidation and ETC _Fig3

 

Sampling reveals different utilization of oxidative phosphorylation by the

  • generated models.

Different distributions are observed for the CCRF-CEM model (red) and the Molt-4 model (blue).

  • Molt-4 has higher  median  flux through ETC reactions II–IV.

The table provides the median values  of the sampling results. Negative values in the histograms and in the table describe

  • reversible reactions with flux in the reverse direction.

Both models lack Complex I of the ETC because of constraints

  • arising from the mapping of transcriptomic data.

Electron transfer flavoprotein and

  • electron transfer flavoprotein–ubiquinone oxidoreductase
  •  both also carry higher flux in the Molt-4 model

 

2.1.6 Experimental validation of energy and redox status of CCRF-CEM and Molt-4 cells

Cancer cells have to balance their needs

  •  for energy and biosynthetic precursors, and they have
  • to maintain redox homeostasis to proliferate (Cairns et al. 2011).

We conducted enzymatic assays of cell lysates to measure levels and/or ratios of

  • ATP,
  • NADPH + NADP,
  • NADH + NAD, and
  • glutathione.

These measurements were used to provide support for

  • the in silico predicted metabolic differences (Fig. 4).

Additionally, an Oxygen Radical Absorbance Capacity (ORAC) assay was used

  • to evaluate the cellular antioxidant status (Fig. 4B).

Total concentrations of NADH + NAD, GSH + GSSG, NADPH + NADP and ATP, were higher in Molt-4 cells  (Fig. 4A).

The higher ATP concentration in Molt-4 cells could either result from

  • high production rates, or intracellular  accumulation connected to high or
  • low reactions fluxes (Fig. 4A).

Our simplified view that oxidative Molt-4 produces less ATP and was contradicted by

  • the higher ATP concentrations measured (Fig. 4L).

Yet we want to emphasize that concentrations

  • cannot be compared to flux values,
  • since we are modeling at steady-state.

NADH/NAD+ ratios for both cell lines were shifted toward NADH (Fig. 4D, E), but

  • the shift toward NADH was more pronounced in CCRF-CEM (Fig. 4E),
  • which matched  our expectation based on the higher utilization of
  • glycolysis and 2-oxoglutarate  dehydrogenase in the CCRF-CEM model (Fig. 4L).

 

Fig. 4 (not shown)

A–K  Experimentally determined ATP, NADH + NAD, NADPH + NADP, and GSH + GSSG concentrations, and ROS detoxification in the CCRF-CEM and Molt-4 cells.

L Expectations for cellular energy and redox states. Expectations are based on predicted metabolic differences of the Molt-4 and CCRF-CEM models

2.1.7 Comparison of network utilization and alteration in gene expression

With the assumption that

  • differential expression of particular genes would cause reaction flux changes,

we determined how the differences in gene expression (between CCRF-CEM and Molt-4)

  • compared to the flux differences observed in the  models.

Specifically, we checked whether the reactions associated with genes upregulated
(significantly more expressed in CCRF-CEM cells compared to Molt-4  cells)

  • were indeed more utilized by the CCRF-CEM model,

and we  checked  whether downregulated genes

  • were associated with reactions more utilized by the Molt-4 model.

The set of downregulated genes was associated with 15 reactions, and

  • the set of 49 upregulated genes was associated with 113 reactions in the models.

Reactions were defined as differently utilized

  • if the difference in flux exceeded 10 % (considering only non-loop reactions).

Of the reactions associated with upregulated genes,

  • 72.57 % were more utilized by the CCRF-CEM model, and
  • 2.65 % were more utilized by the Molt-4 model (File S1, Table S7).

In contrast, all 15 reactions associated with the 12 downregulated genes

  • were more utilized in the CCRF-CEM model (File S1, Table S8).

After this initial analysis, we approached the question from a different angle, asking

  • whether the majority of the reactions associated with each individual gene
  • upregulated in CCRF-CEM were more utilized by the CCRF-CEM model.
  •  this was the case for 77.55 % of the upregulated genes.

The majority of reactions associated with two (16.67 %) downregulated genes

  • were more utilized by the Molt-4 model.

Taken together, our comparisons of the

  • direction of gene expression with the fluxes of the two cancer cell-line models
  • confirmed that reactions associated with upregulated genes in the CCRF-CEM
    cells were generally more utilized by the CCRF-CEM model.

2.1.8 Accumulation of DEGs and AS genes at key metabolic steps

After we confirmed that most reactions associated with upregulated genes

  • were more utilized by the CCRF-CEM model,

we checked the locations of DEGs within the network. In this analysis, we paid special attention to

  • the central metabolic pathways that we had found
  • to be distinctively utilized by the two models.

Several DEGs and AS events were associated with

  • glycolysis,
  • the ETC,
  • pyruvate metabolism, and
  • the PPP (Table 1).

 

Table 1

DEGs and AS events of central metabolic and cancer-related pathways

Full lists of DEGs and AS are provided in the supplementary material.

Upregulated significantly more expressed in CCRF-CEM compared to Molt-4 cells

PPP pentose phosphate pathway, OxPhos oxidative phosphorylation, Glycolysis/gluconglycolysis/gluconeogenesis, Pyruvate met. pyruvate metabolism

Moreover, in glycolysis, the DEGs and/or AS genes

  • were associated with all three rate-limiting steps, i.e., the steps mediated by
  1. hexokinase,
  2. pyruvate kinase, and
  3. phosphofructokinase.

Of these key enzymes,

  • hexokinase 1 (Entrez Gene ID: 3098) was alternatively spliced,
  • pyruvate kinase (PKM, Entrez gene ID: 5315) was significantly more
    expressed in the CCRF-CEM cells (Table 1),

in agreement with the higher in silico predicted flux.

However, in contrast to the observed

  • higher utilization of glycolysis in the CCRF-CEM model,
  • the gene associated with the rate-limiting glycolysis step, phosphofructokinase (Entrez Gene ID: 5213),
  • was significantly upregulated in Molt-4 cells relative to CCRF-CEM cells.

This higher expression was detected for only a single isozyme, however. Two of
the three genes associated with phosphofructokinase were also subject to
alternative splicing (Table 1). In addition to the key enzymes, fructose
bisphosphate aldolase (Entrez Gene ID: 230) was also significantly

  • upregulated in Molt-4 cells relative to CCRF-CEM cells,
  • in contrast to the predicted higher utilization of glycolysis in the CCRF-CEM model.

Additionally, glucose-6P-dehydrogenase (G6PD), which catalyzes

  • the first reaction and committed step of the PPP,
  • was an AS gene (Table 1).

A second AS gene associated with

  •  the PPP reaction of the deoxyribokinase
  • was RBKS (Entrez Gene ID: 64080).

This gene is also associated with ribokinase, but ribokinase was removed

  • because of the lack of ribose uptake or secretion.

Single AS genes were associated with different complexes of the ETC (Table 1).

Literature query revealed that at least 13 genes associated with alternative

  • splicing events were mentioned previously in connection with both alternative
    splicing and cancer (File S1, Table S14), and
  • 37 genes were associated with cancer, e.g., upregulated, downregulated at the
    level of mRNA or protein, or otherwise
  • connected to cancer metabolism and signaling.

One general observation was that there was a surprising

  • accumulation of metabolite transporters among the AS.

Overall, the high incidence of

  • differential gene expression events at metabolic control points
  • increases the plausibility of the in silico predictions.

 

2.1.9 Single gene deletion

Analyses of essential genes in metabolic models have been used

  • to predict candidate drug targets for cancer cells (Folger et al. 2011).

Here, we conducted an in silico gene deletion study for all model genes to identify

  • a unique set of knock-out (KO) genes
  • for each condition-specific cell line model.

The analysis yielded 63 shared lethal KO genes and

  • distinct sets of KO genes for the CCRF-CEM model (11 genes) and the Molt-4 model (3 genes).

For three of the unique CCRF-CEM KO genes,

  • the genes were only present in the CCRF-CEM model (File S1, Table S9).

 

The essential genes for both models were then

  • related to the cell-line-specific differences in metabolite uptake and secretion (Fig. 1B).

The CCRF-CEM model

  1. needed to generate putrescine from ornithine
    (ORNDC, Entrez Gene ID: 4953)
  2. to subsequently produce 5-methylthioadenosine for secretion (Fig. 1B).
  3. S-adenosylmethioninamine produced by adenosylmethionine decarboxylase
    (arginine and proline metabolism, associated with Entrez Gene ID: 262)
  • is a substrate required for generation of 5-methylthioadenosine.

Another example of a KO gene connected to an enforced exchange reaction was

  • glutamic-oxaloacetic transaminase 1 (GOT1, Entrez Gene ID: 2805).

Without GOT1, the CCRF-CEM model was forced to secrete

  • 4-hydroxyphenylpyruvate (Fig. 1B),
  • the second product of tyrosine transaminase,
  • which is produced only by that enzyme.

 

One KO gene in the Molt-4 model (Entrez Gene ID: 26227) was associated with

  • phosphoglycerate dehydrogenase (PGDH),
  • which catalyzes the conversion of 3-phospho-d-glycerate to 3-phosphohydroxypyruvate
  • while generating NADH from NAD+.

This KO gene is particularly interesting, given

  • the involvement of this reaction in a novel pathway for ATP generation in rapidly proliferating cells
    (Locasale et al. 2011; Vander Heiden 2011; Vazquez et al. 2011).

Reactions associated with unique KO genes were in many cases utilized more by the model, in which

  • the gene KO was lethal,
  • underlining the potential importance of these reactions for the models.

Thus, single gene deletion provided unique sets of lethal genes that could be

  • specifically targeted to kill these cells.

 

3 Discussion

In the current study, we explored the possibility of

  • semi-quantitatively integrating metabolomic data with
  • the human genome-scale reconstruction to facilitate analysis.

By constructing condition-specific cell line models

  • to provide a structured framework,
  • we derived insights that could not have been obtained from data analysis alone.

We derived condition-specific cell line models

  • for CCRF-CEM and
  • Molt-4 cells

that were able to explain the observed exo-metabolomic differences (Fig. 1B).

Despite the overall similarities between the models, the analysis revealed

  • distinct usage of central metabolic pathways (Figs. 234),
  • which we validated based on experimental data and
  • differential gene expression.

The additional data sufficiently supported

  • metabolic differences in the cell lines,
  • providing confidence in the generated models and the model-based predictions.

We used the validated models

  • to predict unique sets of lethal genes
  • to identify weak links in each model.

These weak links may represent potential drug targets.

Integrating omics data with the human genome-scale reconstruction

  • provides a structured framework (i.e., pathways)
  • that is based on careful consideration of the available biochemical literature
    (Thiele and Palsson2010).

This network context can simplify omics data analysis, and

  • it allows even non-biochemical experts
  • to gain fast and comprehensive insights
  • into the metabolic aspects of omics data sets.

Compared to transcriptomic data,

  • methods for the integration and analysis of metabolomic data
  • in the context of metabolic models are less well established,

although it is an active field of research (Li et al. 2013; Paglia et al. 2012b).
In contrast to other studies, our approach emphasizes

  • the representation of experimental conditions rather than
  • the reconstruction of a generic, cell-line-specific network,
  • which would require the combination of data sets from
  • many experimental conditions and extensive manual curation.

Rather, our way of model construction allowed us to efficiently

  • assess the metabolic characteristics of cells.

Despite the fact, that only a limited number of exchanged metabolites can be

  • measured by available metabolomics platforms and
  • at reasonable time-scale,

and that pathways of measured metabolites might still be unknown to date
(File S1, Tables S2–S3), our methods have the potential

  • to reveal metabolic characteristics of cells
  • which could be useful for biomedicine and personalized health.

The reasons why some cancers respond to certain treatments and not others
remain unclear, and choosing a treatment for a specific patient is often difficult
(Vander Heiden 2011). One potential application of our approach could be the
characterization of cancer phenotypes to explore how cancer cells or other cell
types

  • with particular metabolic characteristics respond to drugs.

The generation of our condition-specific cell line models involved

  • only limited manual curation,
  • making this approach a fast way to place metabolomic data
  • into a network context.

Model building mainly involves

  • the rigid reduction of metabolite exchanges
  • to match the observed metabolite exchange pattern
  • with as few additional metabolite exchanges as possible.

It should be noted that this reduction determines,

  • which pathways can be utilized by the model.

Our approach mostly conserved the internal network redundancy. However, a

  • more significant reduction may be achieved using different data.

Generally, a trade-off exists between the reduction of the internal network and

  • the increasing number of network gaps that need to be curated
  • by using additional omics data, such as transcriptomics and proteomics.

One way to prevent the emergence of network gaps would be

  • to use mapping algorithms that conserve network functionality,
    such as GIMME (Becker and Palsson 2008).

However, several additional methods exist for the integration of
transcriptomic data (Blazier and Papin 2012), and

  • which model-building method is best depends on the available data.

Interestingly, the lack of a significant contribution of our

  • gene expression data to the reduction of network size
  • suggests that the use of transcriptomic data is not necessary
  • to identify distinct metabolic strategies;
  • rather, the integration of exo-metabolomic data alone
    may provide sufficient insight.

However, sampling of the cell line models constrained

  • according to the exo-metabolomic profiles only, or
  • increasing the cutoff for the generation of absent and present calls (p < 0.01),
  • did not yield the same insights as presented herein (File S1, Table S18).

Only recently Gene Inactivation Moderated by Metabolism, Metabolomics and
Expression (GIM(3)E) became available, which

  • enforces minimum turnover of detected metabolites
  • based on intracellular metabolomics data as well as
  • gene expression microarray data (Schmidt et al. 2013).

In contrast to this approach, we emphasized our analysis on the

  • relative differences in the exo-metabolomic data of two cell lines.

GIM(3)E constitutes another integration method when the analysis should be

  • emphasized on intracellular metabolomics data (Schmidt et al. 2013).

The metabolic differences predicted by the models are generally plausible.
Cancers are known to be heterogeneous (Cairns et al. 2011), and

  • the contribution of oxidative phosphorylation to cellular ATP production
    may vary (Zu and Guppy 2004).

Moreover, leukemia cell lines have been shown

  • to depend on glucose, glutamine, and fatty acids to varying extents
  • to support proliferation.

Such dependence may cause the cells to adapt their metabolism

  • to the environmental conditions (Suganuma et al. 2010).

In addition to identifying supporting data in the literature, we performed

  • several analyses to validate the models and model predictions.

Our expectations regarding the levels and ratios of metabolites

  • relevant to energy and redox state were largely met (Fig. 4L).

The more pronounced shift of the NADH/NAD+ ratio

  • toward NADH in the CCRF-CEM cells
  • was in agreement with the predicted Warburg phenotype (Fig. 4),
  • and the higher lactate secretion in the CCRF-CEM cells (File S2, Fig. S2)
  • implies an increase in NADH relative to NAD+
    (Chiarugi et al. 2012; Nikiforov et al. 2011), again
  • matching the known Warburg phenotype.

ROS production is enhanced in certain types of cancer (Droge 2002; Ha et al. 2000), and

  • the generation of ROS is thought to contribute to
  1. mutagenesis,
  2. tumor promotion, and
  3. tumor progression (Dreher and Junod1996; Ha et al. 2000).

However, decreased mitochondrial glucose oxidation and

  • a transition to aerobic glycolysis
  • protect cells against ROS damage during biosynthesis and cell division
    (Brand and Hermfisse1997).

The higher ROS detoxification capability in Molt-4 cells, in combination with

  • higher spermidine dismutase utilization by the Molt-4 model (Fig. 4),
  • provided a consistent picture of the predicted respiratory phenotype (Fig. 4L).

Control of NADPH maintains the redox potential through GSH and

  • protects against oxidative stress, yet
  • changes in the NADPH ratio in response to oxidative damage
  • are not well understood (Ogasawara et al.2009).

Under stress conditions, as assumed for Molt-4 cells,

  • the NADPH/NADP+ ratio is expected to decrease because of
  • the continuous reduction of GSSG (Fig. 4L), and
  • this was confirmed in the Molt-4 cells (Fig. 4).

The higher amounts of GSH found in Molt-4 cells in vitro may demonstrate

  • an additional need for ROS scavengers because of
  • a greater reliance on oxidative metabolism.

Cancer is related to metabolic reprogramming, which results from

  • alterations of gene expression and
  • the expression of specific isoforms or
  • splice forms to support proliferation
    (Cortes-Cros et al. 2013; Marin-Hernandez et al. 2009).

The gene expression differences detected between the two cell lines in this study
supported the existence of

  • metabolic differences in these cell lines, particularly because
  • key steps of the metabolic pathways central to cancer metabolism
  • seemed to be differentially regulated (Table 1).

The detailed analysis of the respective

  • differences on the pathway fluxes exceeds the scope of this study, which was to
  • demonstrate the potential of the integration of exo-metabolomic data into the network context.

We found discrepancies between differential gene regulation and

  • the flux differences between the two models as well as
  • the utilization AS gene-associated reaction.

This is not surprising, since analysis of the detailed system is required

  • to make any further assumptions on the impact that
  • the differential regulation or splicing might have on the reaction flux,
  • given that for many of the concerned enzymes isozymes exist, or
  • only one of multiple subunits of a protein complex was concerned.

Additionally, reaction fluxes are regulated by numerous post-translational factors, e.g.,

  • protein modification,
  • inhibition through proteins or metabolites,
  • alter reaction fluxes (Lenzen 2014),

which are out of the scope of constraint-based steady-state modeling.

Rather, the results of the presented  approach

  • demonstrate how the models can be used to generate
  • informed hypothesis that can guide experimental work.

The combination of our tailored metabolic models and

  • differential gene expression analysis seems well-suited
  • to determine the potential drivers
  • involved in metabolic differences between cells.

Such information could be valuable for drug discovery, especially when more

  • peripheral metabolic pathways are considered.

Statistical comparisons of gene expression data with sampling-derived flux data

  • could be useful in future studies (Mardinoglu et al. 2013).

A single-gene-deletion analysis revealed that PGDH was

  • a lethal KO gene for the Molt-4 model only.

Differences in PGDH protein levels

  • correspond to the amount of glycolytic carbon
  • diverted into glycine biosynthesis.

Rapidly proliferating cells may use an

  • alternative glycolytic pathway for ATP generation,
  • which may provide an advantage in the case of
  • extensive oxidative phosphorylation and proliferation
    (Locasale et al.2011; Vander Heiden 2011; Vazquez et al. 2011).

For breast cancer cell lines, variable dependency on

  • the expression of PGDH has already been demonstrated
    (Locasale et al. 2011).

This example of a unique KO gene demonstrates how

  • in silico gene deletion in metabolomics-driven models
  • can identify the metabolic pathways used by cancer cells.

This approach can provide valuable information for drug discovery.

In conclusion, our contextualization method produced

  • metabolic models that agreed in many ways with the validation data sets.

The analyses described in this study have great potential to reveal

  • the mechanisms of metabolic reprogramming,
  • not only in cancer cells but also in other cells affected by diseases, and
  • for drug discovery in general.

 

4.3 Analysis of the extracellular metabolome

Mass spectrometry analysis of the exo-metabolome was performed by
Metabolon®, Inc. (Durham, NC, USA) using a standardized analytical platform.
In total, 75 extracellular metabolites were detected in the initial data set for at
least 1 of the 2 cell lines (Paglia et al. 2012a). Of these metabolites, 15 were not
part of our global model and were discarded. Apart from being absent in our
global model, an independent search in HMDB (Wishart et al. 2013) revealed no
pathway information was available for most of these metabolites (File S1, Tables S2–S3).
It should be noted that metabolites e.g.,

  • N-acetylisoleucine,
  • N-acetyl-methionine or pseudouridine,

constitute protein and RNA degradation products, which were out of the scope
of the metabolic network.

Thiamin (Vitamin B1) was part of the minimal medium of essential compounds
supplied to both models.Riboflavin (Vitamin B2) and Trehalose were excluded
since these compounds cannot be produced by human cells. Erythrose and
fructose were also excluded. In contrast 46 metabolites that were part of the
global model. The data set included two different time points, which allowed us
to treat the increase/decrease of a metabolite signal between time points as

  • evidence for uptake or secretion when the change was greater than 5 %
    from what was observed in the control (File S1, Tables S2–S3).

We found 12 metabolites that were taken up by both cell lines and
10 metabolites that were commonly secreted by both cell lines over
the course of the experiment.

Molt-4 cells took up three metabolites not taken up by CCRF-CEM cells, and
secreted one metabolite not secreted by CCRF-CEM cells. Two of the three
uniquely uptaken metabolites were essential amino acids:

  1. valine and
  2. methionine.

It is unlikely that these metabolites were not taken up by the CCRF-CEM cells,
and the CCRF-CEM model was allowed to take up this metabolite. Therefore,
no quantitative constraints were applied for the sampling analysis either.
CCRF-CEM cells had

  • four unique uptaken
  • and seven unique secreted metabolites
    (exchange not detected in Molt-4 cells).

 

4.4 Network refinement based on exo-metabolic data

Despite its comprehensiveness, the human metabolic reconstruction is

  • not complete with respect to extracellular metabolite transporters
    (Sahoo et al. 2014; Thiele et al. 2013).

Accordingly, we identified metabolite transport systems

  • from the literature for metabolites that were already part of the global model,
  • but whose extracellular transport was not yet accounted for.

Diffusion reactions were included whenever a respective transporter could not be identified.

In total, 34 reactions [11 exchange reactions, 16 transport reactions and 7 demand reactions
(File S1, Table S11)] were added to Recon 2 (Thiele et al. 2013), and 2 additional reactions
were added to the global model (File S1, Table S10).

4.5 Expression profiling

Molt-4 and CCRF-CEM cells were grown in advanced RPMI 1640 and 2 mM
GlutaMax, and the cells were resuspended in medium containing DMSO
(0.67 %) at a concentration of 5 × 105 cells/mL. The cell suspension (2 mL)
was seeded in 12-well plates in triplicate. After 48 h of growth, the cells
were collected by centrifugation at 201×g for 5 min. Cell pellets were snap-frozen
in liquid N2 and kept frozen until RNA extraction and analysis by Aros
(Aarhus, Denmark).

4.6 Analysis of transcriptomic data

We used the Affymetrix GeneChip Human Exon 1.0 ST Array to measure whole
genome exon expression. We generated detection above background (DABG) calls
using ROOT (version 22) and the XPS package for R (version 11.1), with Robust
Multi-array Analysis summarization. Calls for data mapping were assigned based
on p < 0.05 as the cutoff probability to distinguish presence versus absence for
the 1,278 model genes (File S1, Table S12).

Differential gene expression and alternative splicing analyses were performed by
using AltAnalyse software (v2.02beta) with default options on the raw data files
(CEL files). The Homo sapiens Ensemble 65 database was used, probe set filtering
was kept as DABG p < 0.05, and non-log expression < 70 was used for
constitutive probe sets to determine gene expression levels. For the comparison,
CCRF-CEM was the experimental group and Molt-4 was the baseline group. The
set of DEGs between cell lines was identified based on a p < 0.05 FDR cutoff
(File S1, Table S13A–B). Alternative splicing analysis was performed on core probe sets
with a minimum alternative exon score of 2 and a maximum absolute gene
expression change of 3 because alternative splicing is a less critical factor among
highly DEGs (File S1, Table S14). Gene expression data, complete lists of DABG p-values,
DEGs and alternative splicing events have been deposited in the Gene
Expression Omnibus
 (GEO) database (Accession number: GSE53123).

 

4.7 Deriving cell-type-specific subnetworks

Transcriptomic data were mapped to the model in a manual fashion (COBRA
function: deleteModelGenes). Specifically, reactions dependent on gene products
that were called as “absent” were constrained to zero, such that fluxes through
these reactions were disabled. Submodels were extracted based on the set of
reactions carrying flux (network pruning) by running fastFVA
(Gudmundsson and Thiele 2010) after mapping the metabolomic and
transcriptomic data using the COBRA toolbox (Schellenberger et al. 2011).

 

…..

 

Electronic supplementary material

Below is the link to the electronic supplementary material.

File S1. Supplementary material 1 (XLSX 915 kb)

File S2. Supplementary material 2 (DOCX 448 kb)

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Summary – Volume 4, Part 2: Translational Medicine in Cardiovascular Diseases


Summary – Volume 4, Part 2:  Translational Medicine in Cardiovascular Diseases

Author and Curator: Larry H Bernstein, MD, FCAP

 

We have covered a large amount of material that involves

  • the development,
  • application, and
  • validation of outcomes of medical and surgical procedures

that are based on translation of science from the laboratory to the bedside, improving the standards of medical practice at an accelerated pace in the last quarter century, and in the last decade.  Encouraging enabling developments have been:

1. The establishment of national and international outcomes databases for procedures by specialist medical societies

Stent Design and Thrombosis: Bifurcation Intervention, Drug Eluting Stents (DES) and Biodegrable Stents
Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/06/stent-design-and-thrombosis-bifurcation-intervention-drug-eluting-stents-des-and-biodegrable-stents/

On Devices and On Algorithms: Prediction of Arrhythmia after Cardiac Surgery and ECG Prediction of an Onset of Paroxysmal Atrial Fibrillation
Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC
https://pharmaceuticalintelligence.com/2013/05/07/on-devices-and-on-algorithms-arrhythmia-after-cardiac-surgery-prediction-and-ecg-prediction-of-paroxysmal-atrial-fibrillation-onset/

Mitral Valve Repair: Who is a Patient Candidate for a Non-Ablative Fully Non-Invasive Procedure?
Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC and Article Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/11/04/mitral-valve-repair-who-is-a-candidate-for-a-non-ablative-fully-non-invasive-procedure/

Cardiovascular Complications: Death from Reoperative Sternotomy after prior CABG, MVR, AVR, or Radiation; Complications of PCI; Sepsis from Cardiovascular Interventions
Author, Introduction and Summary: Justin D Pearlman, MD, PhD, FACC and Article Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/07/23/cardiovascular-complications-of-multiple-etiologies-repeat-sternotomy-post-cabg-or-avr-post-pci-pad-endoscopy-andor-resultant-of-systemic-sepsis/

Survivals Comparison of Coronary Artery Bypass Graft (CABG) and Percutaneous Coronary Intervention (PCI) /Coronary Angioplasty
Larry H. Bernstein, MD, Writer And Aviva Lev-Ari, PhD, RN, Curator
https://pharmaceuticalintelligence.com/2013/06/23/comparison-of-cardiothoracic-bypass-and-percutaneous-interventional-catheterization-survivals/

Revascularization: PCI, Prior History of PCI vs CABG
Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/04/25/revascularization-pci-prior-history-of-pci-vs-cabg/

Outcomes in High Cardiovascular Risk Patients: Prasugrel (Effient) vs. Clopidogrel (Plavix); Aliskiren (Tekturna) added to ACE or added to ARB
Reporter and Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2012/08/27/outcomes-in-high-cardiovascular-risk-patients-prasugrel-effient-vs-clopidogrel-plavix-aliskiren-tekturna-added-to-ace-or-added-to-arb/

Endovascular Lower-extremity Revascularization Effectiveness: Vascular Surgeons (VSs), Interventional Cardiologists (ICs) and Interventional Radiologists (IRs)
Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2012/08/13/coronary-artery-disease-medical-devices-solutions-from-first-in-man-stent-implantation-via-medical-ethical-dilemmas-to-drug-eluting-stents/

and more

2. The identification of problem areas, particularly in activation of the prothrombotic pathways, infection control to an extent, and targeting of pathways leading to progression or to arrythmogenic complications.

Cardiovascular Complications: Death from Reoperative Sternotomy after prior CABG, MVR, AVR, or Radiation; Complications of PCI; Sepsis from Cardiovascular Interventions Author, Introduction and Summary: Justin D Pearlman, MD, PhD, FACC and Article Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/07/23/cardiovascular-complications-of-multiple-etiologies-repeat-sternotomy-post-cabg-or-avr-post-pci-pad-endoscopy-andor-resultant-of-systemic-sepsis/

Anticoagulation genotype guided dosing
Larry H. Bernstein, MD, FCAP, Author and Curator
https://pharmaceuticalintelligence.com/2013/12/08/anticoagulation-genotype-guided-dosing/

Stent Design and Thrombosis: Bifurcation Intervention, Drug Eluting Stents (DES) and Biodegrable Stents
Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/06/stent-design-and-thrombosis-bifurcation-intervention-drug-eluting-stents-des-and-biodegrable-stents/

The Effects of Aprotinin on Endothelial Cell Coagulant Biology
Co-Author (Kamran Baig, MBBS, James Jaggers, MD, Jeffrey H. Lawson, MD, PhD) and Curator
https://pharmaceuticalintelligence.com/2013/07/20/the-effects-of-aprotinin-on-endothelial-cell-coagulant-biology/

Outcomes in High Cardiovascular Risk Patients: Prasugrel (Effient) vs. Clopidogrel (Plavix); Aliskiren (Tekturna) added to ACE or added to ARB
Reporter and Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2012/08/27/outcomes-in-high-cardiovascular-risk-patients-prasugrel-effient-vs-clopidogrel-plavix-aliskiren-tekturna-added-to-ace-or-added-to-arb/

Pharmacogenomics – A New Method for Druggability  Author and Curator: Demet Sag, PhD
https://pharmaceuticalintelligence.com/2014/04/28/pharmacogenomics-a-new-method-for-druggability/

Advanced Topics in Sepsis and the Cardiovascular System at its End Stage    Author: Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2013/08/18/advanced-topics-in-Sepsis-and-the-Cardiovascular-System-at-its-End-Stage/

3. Development of procedures that use a safer materials in vascular management.

Stent Design and Thrombosis: Bifurcation Intervention, Drug Eluting Stents (DES) and Biodegrable Stents
Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/08/06/stent-design-and-thrombosis-bifurcation-intervention-drug-eluting-stents-des-and-biodegrable-stents/

Biomaterials Technology: Models of Tissue Engineering for Reperfusion and Implantable Devices for Revascularization
Author and Curator: Larry H Bernstein, MD, FACP and Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/05/05/bioengineering-of-vascular-and-tissue-models/

Vascular Repair: Stents and Biologically Active Implants
Author and Curator: Larry H Bernstein, MD, FACP and Curator: Aviva Lev-Ari, RN, PhD
https://pharmaceuticalintelligence.com/2013/05/04/stents-biologically-active-implants-and-vascular-repair/

Drug Eluting Stents: On MIT’s Edelman Lab’s Contributions to Vascular Biology and its Pioneering Research on DES
Author: Larry H Bernstein, MD, FACP and Curator: Aviva Lev-Ari, PhD, RN
http://PharmaceuticalIntelligence.com/2013/04/25/Contributions-to-vascular-biology/

MedTech & Medical Devices for Cardiovascular Repair – Curations by Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2014/04/17/medtech-medical-devices-for-cardiovascular-repair-curation-by-aviva-lev-ari-phd-rn/

4. Discrimination of cases presenting for treatment based on qualifications for medical versus surgical intervention.

Treatment Options for Left Ventricular Failure – Temporary Circulatory Support: Intra-aortic balloon pump (IABP) – Impella Recover LD/LP 5.0 and 2.5, Pump Catheters (Non-surgical) vs Bridge Therapy: Percutaneous Left Ventricular Assist Devices (pLVADs) and LVADs (Surgical)
Author: Larry H Bernstein, MD, FCAP And Curator: Justin D Pearlman, MD, PhD, FACC
https://pharmaceuticalintelligence.com/2013/07/17/treatment-options-for-left-ventricular-failure-temporary-circulatory-support-intra-aortic-balloon-pump-iabp-impella-recover-ldlp-5-0-and-2-5-pump-catheters-non-surgical-vs-bridge-therapy/

Coronary Reperfusion Therapies: CABG vs PCI – Mayo Clinic preprocedure Risk Score (MCRS) for Prediction of in-Hospital Mortality after CABG or PCI
Writer and Curator: Larry H. Bernstein, MD, FCAP and Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/06/30/mayo-risk-score-for-percutaneous-coronary-intervention/

ACC/AHA Guidelines for Coronary Artery Bypass Graft Surgery Reporter: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/11/05/accaha-guidelines-for-coronary-artery-bypass-graft-surgery/

Mitral Valve Repair: Who is a Patient Candidate for a Non-Ablative Fully Non-Invasive Procedure?
Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC and Article Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/11/04/mitral-valve-repair-who-is-a-candidate-for-a-non-ablative-fully-non-invasive-procedure/ 

5.  This has become possible because of the advances in our knowledge of key related pathogenetic mechanisms involving gene expression and cellular regulation of complex mechanisms.

What is the key method to harness Inflammation to close the doors for many complex diseases?
Author and Curator: Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2014/03/21/what-is-the-key-method-to-harness-inflammation-to-close-the-doors-for-many-complex-diseases/

CVD Prevention and Evaluation of Cardiovascular Imaging Modalities: Coronary Calcium Score by CT Scan Screening to justify or not the Use of Statin
Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2014/03/03/cvd-prevention-and-evaluation-of-cardiovascular-imaging-modalities-coronary-calcium-score-by-ct-scan-screening-to-justify-or-not-the-use-of-statin/

Richard Lifton, MD, PhD of Yale University and Howard Hughes Medical Institute: Recipient of 2014 Breakthrough Prizes Awarded in Life Sciences for the Discovery of Genes and Biochemical Mechanisms that cause Hypertension
Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2014/03/03/richard-lifton-md-phd-of-yale-university-and-howard-hughes-medical-institute-recipient-of-2014-breakthrough-prizes-awarded-in-life-sciences-for-the-discovery-of-genes-and-biochemical-mechanisms-tha/

Pathophysiological Effects of Diabetes on Ischemic-Cardiovascular Disease and on Chronic Obstructive Pulmonary Disease (COPD)
Curator:  Larry H. Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2014/01/15/pathophysiological-effects-of-diabetes-on-ischemic-cardiovascular-disease-and-on-chronic-obstructive-pulmonary-disease-copd/

Atherosclerosis Independence: Genetic Polymorphisms of Ion Channels Role in the Pathogenesis of Coronary Microvascular Dysfunction and Myocardial Ischemia (Coronary Artery Disease (CAD))
Reviewer and Co-Curator: Larry H Bernstein, MD, CAP and Curator: Aviva Lev-Ari, PhD, RN
https://pharmaceuticalintelligence.com/2013/12/21/genetic-polymorphisms-of-ion-channels-have-a-role-in-the-pathogenesis-of-coronary-microvascular-dysfunction-and-ischemic-heart-disease/

Notable Contributions to Regenerative Cardiology  Author and Curator: Larry H Bernstein, MD, FCAP and Article Commissioner: Aviva Lev-Ari, PhD, RD
https://pharmaceuticalintelligence.com/2013/10/20/notable-contributions-to-regenerative-cardiology/

As noted in the introduction, any of the material can be found and reviewed by content, and the eTOC is identified in attached:

http://wp.me/p2xfv8-1W

 

This completes what has been presented in Part 2, Vol 4 , and supporting references for the main points that are found in the Leaders in Pharmaceutical Intelligence Cardiovascular book.  Part 1 was concerned with Posttranslational Modification of Proteins, vital for understanding cellular regulation and dysregulation.  Part 2 was concerned with Translational Medical Therapeutics, the efficacy of medical and surgical decisions based on bringing the knowledge gained from the laboratory, and from clinical trials into the realm opf best practice.  The time for this to occur in practice in the past has been through roughly a generation of physicians.  That was in part related to the busy workload of physicians, and inability to easily access specialty literature as the volume and complexity increased.  This had an effect of making access of a family to a primary care provider through a lifetime less likely than the period post WWII into the 1980s.

However, the growth of knowledge has accelerated in the specialties since the 1980’s so that the use of physician referral in time became a concern about the cost of medical care.  This is not the place for or a matter for discussion here.  It is also true that the scientific advances and improvements in available technology have had a great impact on medical outcomes.  The only unrelated issue is that of healthcare delivery, which is not up to the standard set by serial advances in therapeutics, accompanied by high cost due to development costs, marketing costs, and development of drug resistance.

I shall identify continuing developments in cardiovascular diagnostics, therapeutics, and bioengineering that is and has been emerging.

1. Mechanisms of disease

REPORT: Mapping the Cellular Response to Small Molecules Using Chemogenomic Fitness Signatures 

Science 11 April 2014:
Vol. 344 no. 6180 pp. 208-211
http://dx.doi.org/10.1126/science.1250217

Abstract: Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes.

Yeasty HIPHOP

Laura Zahn
Sci. Signal. 15 April 2014; 7(321): ec103.   http://dx.doi.org/10.1126/scisignal.2005362

In order to identify how chemical compounds target genes and affect the physiology of the cell, tests of the perturbations that occur when treated with a range of pharmacological chemicals are required. By examining the haploinsufficiency profiling (HIP) and homozygous profiling (HOP) chemogenomic platforms, Lee et al.(p. 208) analyzed the response of yeast to thousands of different small molecules, with genetic, proteomic, and bioinformatic analyses. Over 300 compounds were identified that targeted 121 genes within 45 cellular response signature networks. These networks were used to extrapolate the likely effects of related chemicals, their impact upon genetic pathways, and to identify putative gene functions

Key Heart Failure Culprit Discovered

A team of cardiovascular researchers from the Cardiovascular Research Center at Icahn School of Medicine at Mount Sinai, Sanford-Burnham Medical Research Institute, and University of California, San Diego have identified a small, but powerful, new player in thIe onset and progression of heart failure. Their findings, published in the journal Nature  on March 12, also show how they successfully blocked the newly discovered culprit.
Investigators identified a tiny piece of RNA called miR-25 that blocks a gene known as SERCA2a, which regulates the flow of calcium within heart muscle cells. Decreased SERCA2a activity is one of the main causes of poor contraction of the heart and enlargement of heart muscle cells leading to heart failure.

Using a functional screening system developed by researchers at Sanford-Burnham, the research team discovered miR-25 acts pathologically in patients suffering from heart failure, delaying proper calcium uptake in heart muscle cells. According to co-lead study authors Christine Wahlquist and Dr. Agustin Rojas Muñoz, developers of the approach and researchers in Mercola’s lab at Sanford-Burnham, they used high-throughput robotics to sift through the entire genome for microRNAs involved in heart muscle dysfunction.

Subsequently, the researchers at the Cardiovascular Research Center at Icahn School of Medicine at Mount Sinai found that injecting a small piece of RNA to inhibit the effects of miR-25 dramatically halted heart failure progression in mice. In addition, it also improved their cardiac function and survival.

“In this study, we have not only identified one of the key cellular processes leading to heart failure, but have also demonstrated the therapeutic potential of blocking this process,” says co-lead study author Dr. Dongtak Jeong, a post-doctoral fellow at the Cardiovascular Research Center at Icahn School of  Medicine at Mount Sinai in the laboratory of the study’s co-senior author Dr. Roger J. Hajjar.

Publication: Inhibition of miR-25 improves cardiac contractility in the failing heart.Christine Wahlquist, Dongtak Jeong, Agustin Rojas-Muñoz, Changwon Kho, Ahyoung Lee, Shinichi Mitsuyama, Alain Van Mil, Woo Jin Park, Joost P. G. Sluijter, Pieter A. F. Doevendans, Roger J. :  Hajjar & Mark Mercola.     Nature (March 2014)    http://www.nature.com/nature/journal/vaop/ncurrent/full/nature13073.html

 

“Junk” DNA Tied to Heart Failure

Deep RNA Sequencing Reveals Dynamic Regulation of Myocardial Noncoding RNAs in Failing Human Heart and Remodeling With Mechanical Circulatory Support

Yang KC, Yamada KA, Patel AY, Topkara VK, George I, et al.
Circulation 2014;  129(9):1009-21.
http://dx.doi.org/10.1161/CIRCULATIONAHA.113.003863              http://circ.ahajournals.org/…/CIRCULATIONAHA.113.003863.full

The myocardial transcriptome is dynamically regulated in advanced heart failure and after LVAD support. The expression profiles of lncRNAs, but not mRNAs or miRNAs, can discriminate failing hearts of different pathologies and are markedly altered in response to LVAD support. These results suggest an important role for lncRNAs in the pathogenesis of heart failure and in reverse remodeling observed with mechanical support.

Junk DNA was long thought to have no important role in heredity or disease because it doesn’t code for proteins. But emerging research in recent years has revealed that many of these sections of the genome produce noncoding RNA molecules that still have important functions in the body. They come in a variety of forms, some more widely studied than others. Of these, about 90% are called long noncoding RNAs (lncRNAs), and exploration of their roles in health and disease is just beginning.

The Washington University group performed a comprehensive analysis of all RNA molecules expressed in the human heart. The researchers studied nonfailing hearts and failing hearts before and after patients received pump support from left ventricular assist devices (LVAD). The LVADs increased each heart’s pumping capacity while patients waited for heart transplants.

In their study, the researchers found that unlike other RNA molecules, expression patterns of long noncoding RNAs could distinguish between two major types of heart failure and between failing hearts before and after they received LVAD support.

“The myocardial transcriptome is dynamically regulated in advanced heart failure and after LVAD support. The expression profiles of lncRNAs, but not mRNAs or miRNAs, can discriminate failing hearts of different pathologies and are markedly altered in response to LVAD support,” wrote the researchers. “These results suggest an important role for lncRNAs in the pathogenesis of heart failure and in reverse remodeling observed with mechanical support.”

‘Junk’ Genome Regions Linked to Heart Failure

In a recent issue of the journal Circulation, Washington University investigators report results from the first comprehensive analysis of all RNA molecules expressed in the human heart. The researchers studied nonfailing hearts and failing hearts before and after patients received pump support from left ventricular assist devices (LVAD). The LVADs increased each heart’s pumping capacity while patients waited for heart transplants.

“We took an unbiased approach to investigating which types of RNA might be linked to heart failure,” said senior author Jeanne Nerbonne, the Alumni Endowed Professor of Molecular Biology and Pharmacology. “We were surprised to find that long noncoding RNAs stood out.

In the new study, the investigators found that unlike other RNA molecules, expression patterns of long noncoding RNAs could distinguish between two major types of heart failure and between failing hearts before and after they received LVAD support.

“We don’t know whether these changes in long noncoding RNAs are a cause or an effect of heart failure,” Nerbonne said. “But it seems likely they play some role in coordinating the regulation of multiple genes involved in heart function.”

Nerbonne pointed out that all types of RNA molecules they examined could make the obvious distinction: telling the difference between failing and nonfailing hearts. But only expression of the long noncoding RNAs was measurably different between heart failure associated with a heart attack (ischemic) and heart failure without the obvious trigger of blocked arteries (nonischemic). Similarly, only long noncoding RNAs significantly changed expression patterns after implantation of left ventricular assist devices.

Comment

Decoding the noncoding transcripts in human heart failure

Xiao XG, Touma M, Wang Y
Circulation. 2014; 129(9): 958960,  http://dx.doi.org/10.1161/CIRCULATIONAHA.114.007548 

Heart failure is a complex disease with a broad spectrum of pathological features. Despite significant advancement in clinical diagnosis through improved imaging modalities and hemodynamic approaches, reliable molecular signatures for better differential diagnosis and better monitoring of heart failure progression remain elusive. The few known clinical biomarkers for heart failure, such as plasma brain natriuretic peptide and troponin, have been shown to have limited use in defining the cause or prognosis of the disease.1,2 Consequently, current clinical identification and classification of heart failure remain descriptive, mostly based on functional and morphological parameters. Therefore, defining the pathogenic mechanisms for hypertrophic versus dilated or ischemic versus nonischemic cardiomyopathies in the failing heart remain a major challenge to both basic science and clinic researchers. In recent years, mechanical circulatory support using left ventricular assist devices (LVADs) has assumed a growing role in the care of patients with end-stage heart failure.3 During the earlier years of LVAD application as a bridge to transplant, it became evident that some patients exhibit substantial recovery of ventricular function, structure, and electric properties.4 This led to the recognition that reverse remodeling is potentially an achievable therapeutic goal using LVADs. However, the underlying mechanism for the reverse remodeling in the LVAD-treated hearts is unclear, and its discovery would likely hold great promise to halt or even reverse the progression of heart failure.

 

Efficacy and Safety of Dabigatran Compared With Warfarin in Relation to Baseline Renal Function in Patients With Atrial Fibrillation: A RE-LY (Randomized Evaluation of Long-term Anticoagulation Therapy) Trial Analysis

Circulation. 2014; 129: 951-952     http://dx.doi.org/10.1161/​CIR.0000000000000022

In patients with atrial fibrillation, impaired renal function is associated with a higher risk of thromboembolic events and major bleeding. Oral anticoagulation with vitamin K antagonists reduces thromboembolic events but raises the risk of bleeding. The new oral anticoagulant dabigatran has 80% renal elimination, and its efficacy and safety might, therefore, be related to renal function. In this prespecified analysis from the Randomized Evaluation of Long-Term Anticoagulant Therapy (RELY) trial, outcomes with dabigatran versus warfarin were evaluated in relation to 4 estimates of renal function, that is, equations based on creatinine levels (Cockcroft-Gault, Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI]) and cystatin C. The rates of stroke or systemic embolism were lower with dabigatran 150 mg and similar with 110 mg twice daily irrespective of renal function. Rates of major bleeding were lower with dabigatran 110 mg and similar with 150 mg twice daily across the entire range of renal function. However, when the CKD-EPI or MDRD equations were used, there was a significantly greater relative reduction in major bleeding with both doses of dabigatran than with warfarin in patients with estimated glomerular filtration rate ≥80 mL/min. These findings show that dabigatran can be used with the same efficacy and adequate safety in patients with a wide range of renal function and that a more accurate estimate of renal function might be useful for improved tailoring of anticoagulant treatment in patients with atrial fibrillation and an increased risk of stroke.

Aldosterone Regulates MicroRNAs in the Cortical Collecting Duct to Alter Sodium Transport.

Robert S Edinger, Claudia Coronnello, Andrew J Bodnar, William A Laframboise, Panayiotis V Benos, Jacqueline Ho, John P Johnson, Michael B Butterworth

Journal of the American Society of Nephrology (Impact Factor: 8.99). 04/2014;     http://dx. DO.org/I:10.1681/ASN.2013090931

Source: PubMed

ABSTRACT A role for microRNAs (miRs) in the physiologic regulation of sodium transport in the kidney has not been established. In this study, we investigated the potential of aldosterone to alter miR expression in mouse cortical collecting duct (mCCD) epithelial cells. Microarray studies demonstrated the regulation of miR expression by aldosterone in both cultured mCCD and isolated primary distal nephron principal cells.

Aldosterone regulation of the most significantly downregulated miRs, mmu-miR-335-3p, mmu-miR-290-5p, and mmu-miR-1983 was confirmed by quantitative RT-PCR. Reducing the expression of these miRs separately or in combination increased epithelial sodium channel (ENaC)-mediated sodium transport in mCCD cells, without mineralocorticoid supplementation. Artificially increasing the expression of these miRs by transfection with plasmid precursors or miR mimic constructs blunted aldosterone stimulation of ENaC transport.

Using a newly developed computational approach, termed ComiR, we predicted potential gene targets for the aldosterone-regulated miRs and confirmed ankyrin 3 (Ank3) as a novel aldosterone and miR-regulated protein.

A dual-luciferase assay demonstrated direct binding of the miRs with the Ank3-3′ untranslated region. Overexpression of Ank3 increased and depletion of Ank3 decreased ENaC-mediated sodium transport in mCCD cells. These findings implicate miRs as intermediaries in aldosterone signaling in principal cells of the distal kidney nephron.

 

2. Diagnostic Biomarker Status

A prospective study of the impact of serial troponin measurements on the diagnosis of myocardial infarction and hospital and 6-month mortality in patients admitted to ICU with non-cardiac diagnoses.

Marlies Ostermann, Jessica Lo, Michael Toolan, Emma Tuddenham, Barnaby Sanderson, Katie Lei, John Smith, Anna Griffiths, Ian Webb, James Coutts, John hambers, Paul Collinson, Janet Peacock, David Bennett, David Treacher

Critical care (London, England) (Impact Factor: 4.72). 04/2014; 18(2):R62.   http://dx.doi.org/:10.1186/cc13818

Source: PubMed

ABSTRACT Troponin T (cTnT) elevation is common in patients in the Intensive Care Unit (ICU) and associated with morbidity and mortality. Our aim was to determine the epidemiology of raised cTnT levels and contemporaneous electrocardiogram (ECG) changes suggesting myocardial infarction (MI) in ICU patients admitted for non-cardiac reasons.
cTnT and ECGs were recorded daily during week 1 and on alternate days during week 2 until discharge from ICU or death. ECGs were interpreted independently for the presence of ischaemic changes. Patients were classified into 4 groups: (i) definite MI (cTnT >=15 ng/L and contemporaneous changes of MI on ECG), (ii) possible MI (cTnT >=15 ng/L and contemporaneous ischaemic changes on ECG), (iii) troponin rise alone (cTnT >=15 ng/L), or (iv) normal. Medical notes were screened independently by two ICU clinicians for evidence that the clinical teams had considered a cardiac event.
Data from 144 patients were analysed [42% female; mean age 61.9 (SD 16.9)]. 121 patients (84%) had at least one cTnT level >=15 ng/L. A total of 20 patients (14%) had a definite MI, 27% had a possible MI, 43% had a cTNT rise without contemporaneous ECG changes, and 16% had no cTNT rise. ICU, hospital and 180 day mortality were significantly higher in patients with a definite or possible MI.Only 20% of definite MIs were recognised by the clinical team. There was no significant difference in mortality between recognised and non-recognised events.At time of cTNT rise, 100 patients (70%) were septic and 58% were on vasopressors. Patients who were septic when cTNT was elevated had an ICU mortality of 28% compared to 9% in patients without sepsis. ICU mortality of patients who were on vasopressors at time of cTNT elevation was 37% compared to 1.7% in patients not on vasopressors.
The majority of critically ill patients (84%) had a cTnT rise and 41% met criteria for a possible or definite MI of whom only 20% were recognised clinically. Mortality up to 180 days was higher in patients with a cTnT rise.

 

Prognostic performance of high-sensitivity cardiac troponin T kinetic changes adjusted for elevated admission values and the GRACE score in an unselected emergency department population.

Moritz BienerMatthias MuellerMehrshad VafaieAllan S JaffeHugo A Katus,Evangelos Giannitsis

Clinica chimica acta; international journal of clinical chemistry (Impact Factor: 2.54). 04/2014;   http://dx.doi.org/10.1016/j.cca.2014.04.007

Source: PubMed

ABSTRACT To test the prognostic performance of rising and falling kinetic changes of high-sensitivity cardiac troponin T (hs-cTnT) and the GRACE score.
Rising and falling hs-cTnT changes in an unselected emergency department population were compared.
635 patients with a hs-cTnT >99th percentile admission value were enrolled. Of these, 572 patients qualified for evaluation with rising patterns (n=254, 44.4%), falling patterns (n=224, 39.2%), or falling patterns following an initial rise (n=94, 16.4%). During 407days of follow-up, we observed 74 deaths, 17 recurrent AMI, and 79 subjects with a composite of death/AMI. Admission values >14ng/L were associated with a higher rate of adverse outcomes (OR, 95%CI:death:12.6, 1.8-92.1, p=0.01, death/AMI:6.7, 1.6-27.9, p=0.01). Neither rising nor falling changes increased the AUC of baseline values (AUC: rising 0.562 vs 0.561, p=ns, falling: 0.533 vs 0.575, p=ns). A GRACE score ≥140 points indicated a higher risk of death (OR, 95%CI: 3.14, 1.84-5.36), AMI (OR,95%CI: 1.56, 0.59-4.17), or death/AMI (OR, 95%CI: 2.49, 1.51-4.11). Hs-cTnT changes did not improve prognostic performance of a GRACE score ≥140 points (AUC, 95%CI: death: 0.635, 0.570-0.701 vs. 0.560, 0.470-0.649 p=ns, AMI: 0.555, 0.418-0.693 vs. 0.603, 0.424-0.782, p=ns, death/AMI: 0.610, 0.545-0.676 vs. 0.538, 0.454-0.622, p=ns). Coronary angiography was performed earlier in patients with rising than with falling kinetics (median, IQR [hours]:13.7, 5.5-28.0 vs. 20.8, 6.3-59.0, p=0.01).
Neither rising nor falling hs-cTnT changes improve prognostic performance of elevated hs-cTnT admission values or the GRACE score. However, rising values are more likely associated with the decision for earlier invasive strategy.

 

Troponin assays for the diagnosis of myocardial infarction and acute coronary syndrome: where do we stand?

Arie Eisenman

ABSTRACT: Under normal circumstances, most intracellular troponin is part of the muscle contractile apparatus, and only a small percentage (< 2-8%) is free in the cytoplasm. The presence of a cardiac-specific troponin in the circulation at levels above normal is good evidence of damage to cardiac muscle cells, such as myocardial infarction, myocarditis, trauma, unstable angina, cardiac surgery or other cardiac procedures. Troponins are released as complexes leading to various cut-off values depending on the assay used. This makes them very sensitive and specific indicators of cardiac injury. As with other cardiac markers, observation of a rise and fall in troponin levels in the appropriate time-frame increases the diagnostic specificity for acute myocardial infarction. They start to rise approximately 4-6 h after the onset of acute myocardial infarction and peak at approximately 24 h, as is the case with creatine kinase-MB. They remain elevated for 7-10 days giving a longer diagnostic window than creatine kinase. Although the diagnosis of various types of acute coronary syndrome remains a clinical-based diagnosis, the use of troponin levels contributes to their classification. This Editorial elaborates on the nature of troponin, its classification, clinical use and importance, as well as comparing it with other currently available cardiac markers.

Expert Review of Cardiovascular Therapy 07/2006; 4(4):509-14.   http://dx.doi.org/:10.1586/14779072.4.4.509 

 

Impact of redefining acute myocardial infarction on incidence, management and reimbursement rate of acute coronary syndromes.

Carísi A Polanczyk, Samir Schneid, Betina V Imhof, Mariana Furtado, Carolina Pithan, Luis E Rohde, Jorge P Ribeiro

ABSTRACT: Although redefinition for acute myocardial infarction (AMI) has been proposed few years ago, to date it has not been universally adopted by many institutions. The purpose of this study is to evaluate the diagnostic, prognostic and economical impact of the new diagnostic criteria for AMI. Patients consecutively admitted to the emergency department with suspected acute coronary syndromes were enrolled in this study. Troponin T (cTnT) was measured in samples collected for routine CK-MB analyses and results were not available to physicians. Patients without AMI by traditional criteria and cTnT > or = 0.035 ng/mL were coded as redefined AMI. Clinical outcomes were hospital death, major cardiac events and revascularization procedures. In-hospital management and reimbursement rates were also analyzed. Among 363 patients, 59 (16%) patients had AMI by conventional criteria, whereas additional 75 (21%) had redefined AMI, an increase of 127% in the incidence. Patients with redefined AMI were significantly older, more frequently male, with atypical chest pain and more risk factors. In multivariate analysis, redefined AMI was associated with 3.1 fold higher hospital death (95% CI: 0.6-14) and a 5.6 fold more cardiac events (95% CI: 2.1-15) compared to those without AMI. From hospital perspective, based on DRGs payment system, adoption of AMI redefinition would increase 12% the reimbursement rate [3552 Int dollars per 100 patients evaluated]. The redefined criteria result in a substantial increase in AMI cases, and allow identification of high-risk patients. Efforts should be made to reinforce the adoption of AMI redefinition, which may result in more qualified and efficient management of ACS.

International Journal of Cardiology 03/2006; 107(2):180-7. · 5.51 Impact Factor   http://www.sciencedirect.com/science/article/pii/S0167527305005279

 

3. Biomedical Engineerin3g

Safety and Efficacy of an Injectable Extracellular Matrix Hydrogel for Treating Myocardial Infarction 

Sonya B. Seif-Naraghi, Jennifer M. Singelyn, Michael A. Salvatore,  et al.
Sci Transl Med 20 February 2013 5:173ra25  http://dx.doi.org/10.1126/scitranslmed.3005503

Acellular biomaterials can stimulate the local environment to repair tissues without the regulatory and scientific challenges of cell-based therapies. A greater understanding of the mechanisms of such endogenous tissue repair is furthering the design and application of these biomaterials. We discuss recent progress in acellular materials for tissue repair, using cartilage and cardiac tissues as examples of application with substantial intrinsic hurdles, but where human translation is now occurring.

 Acellular Biomaterials: An Evolving Alternative to Cell-Based Therapies

J. A. Burdick, R. L. Mauck, J. H. Gorman, R. C. Gorman,
Sci. Transl. Med. 2013; 5, (176): 176 ps4    http://stm.sciencemag.org/content/5/176/176ps4

Acellular biomaterials can stimulate the local environment to repair tissues without the regulatory and scientific challenges of cell-based therapies. A greater understanding of the mechanisms of such endogenous tissue repair is furthering the design and application of these biomaterials. We discuss recent progress in acellular materials for tissue repair, using cartilage and cardiac tissues as examples of applications with substantial intrinsic hurdles, but where human translation is now occurring.


Instructive Nanofiber Scaffolds with VEGF Create a Microenvironment for Arteriogenesis and Cardiac Repair

Yi-Dong Lin, Chwan-Yau Luo, Yu-Ning Hu, Ming-Long Yeh, Ying-Chang Hsueh, Min-Yao Chang, et al.
Sci Transl Med 8 August 2012; 4(146):ra109.   http://dx.doi.org/ 10.1126/scitranslmed.3003841

Angiogenic therapy is a promising approach for tissue repair and regeneration. However, recent clinical trials with protein delivery or gene therapy to promote angiogenesis have failed to provide therapeutic effects. A key factor for achieving effective revascularization is the durability of the microvasculature and the formation of new arterial vessels. Accordingly, we carried out experiments to test whether intramyocardial injection of self-assembling peptide nanofibers (NFs) combined with vascular endothelial growth factor (VEGF) could create an intramyocardial microenvironment with prolonged VEGF release to improve post-infarct neovascularization in rats. Our data showed that when injected with NF, VEGF delivery was sustained within the myocardium for up to 14 days, and the side effects of systemic edema and proteinuria were significantly reduced to the same level as that of control. NF/VEGF injection significantly improved angiogenesis, arteriogenesis, and cardiac performance 28 days after myocardial infarction. NF/VEGF injection not only allowed controlled local delivery but also transformed the injected site into a favorable microenvironment that recruited endogenous myofibroblasts and helped achieve effective revascularization. The engineered vascular niche further attracted a new population of cardiomyocyte-like cells to home to the injected sites, suggesting cardiomyocyte regeneration. Follow-up studies in pigs also revealed healing benefits consistent with observations in rats. In summary, this study demonstrates a new strategy for cardiovascular repair with potential for future clinical translation.

Manufacturing Challenges in Regenerative Medicine

I. Martin, P. J. Simmons, D. F. Williams.
Sci. Transl. Med. 2014; 6(232): fs16.   http://dx.doi.org/10.1126/scitranslmed.3008558

Along with scientific and regulatory issues, the translation of cell and tissue therapies in the routine clinical practice needs to address standardization and cost-effectiveness through the definition of suitable manufacturing paradigms.

 

 

 

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Expanding the Genetic Alphabet and Linking the Genome to the Metabolome


English: The citric acid cycle, also known as ...

English: The citric acid cycle, also known as the tricarboxylic acid cycle (TCA cycle) or the Krebs cycle. Produced at WikiPathways. (Photo credit: Wikipedia)

Expanding the Genetic Alphabet and Linking the Genome to the Metabolome

 

Reporter& Curator:  Larry Bernstein, MD, FCAP

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Unlocking the diversity of genomic expression within tumorigenesis and “tailoring” of therapeutic options

1. Reshaping the DNA landscape between diseases and within diseases by the linking of DNA to treatments

In the NEW York Times of 9/24,2012 Gina Kolata reports on four types of breast cancer and the reshaping of breast cancer DNA treatment based on the findings of the genetically distinct types, which each have common “cluster” features that are driving many cancers.  The discoveries were published online in the journal Nature on Sunday (9/23).  The study is considered the first comprehensive genetic analysis of breast cancer and called a roadmap to future breast cancer treatments.  I consider that if this is a landmark study in cancer genomics leading to personalized drug management of patients, it is also a fitting of the treatment to measurable “combinatorial feature sets” that tie into population biodiversity with respect to known conditions.   The researchers caution that it will take years to establish transformative treatments, and this is clearly because in the genetic types, there are subsets that have a bearing on treatment “tailoring”.   In addition, there is growing evidence that the Watson-Crick model of the gene is itself being modified by an expansion of the alphabet used to construct the DNA library, which itself will open opportunities to explain some of what has been considered junk DNA, and which may carry essential information with respect to metabolic pathways and pathway regulation.  The breast cancer study is tied to the  “Cancer Genome Atlas” Project, already reported.  It is expected that this work will tie into building maps of genetic changes in common cancers, such as, breast, colon, and lung.  What is not explicit I presume is a closely related concept, that the translational challenge is closely related to the suppression of key proteomic processes tied into manipulating the metabolome.

Saha S. Impact of evolutionary selection on functional regions: The imprint of evolutionary selection on ENCODE regulatory elements is manifested between species and within human populations. 9/12/2012. PharmaceuticalIntelligence.Wordpress.com

Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature  Sept 14-20, 2012

Sarkar A. Prediction of Nucleosome Positioning and Occupancy Using a Statistical Mechanics Model. 9/12/2012. PharmaceuticalIntelligence.WordPress.com

Heijden et al.   Connecting nucleosome positions with free energy landscapes. (Proc Natl Acad Sci U S A. 2012, Aug 20 [Epub ahead of print]).  http://www.ncbi.nlm.nih.gov/pubmed/22908247

2. Fiddling with an expanded genetic alphabet – greater flexibility in design of treatment (pharmaneogenesis?)

Diagram of DNA polymerase extending a DNA stra...

Diagram of DNA polymerase extending a DNA strand and proof-reading. (Photo credit: Wikipedia)

A clear indication of this emerging remodeling of the genetic alphabet is a new
study led by scientists at The Scripps Research Institute appeared in the
June 3, 2012 issue of Nature Chemical Biology that indicates the genetic code as
we know it may be expanded to include synthetic and unnatural sequence pairing (Study Suggests Expanding the Genetic Alphabet May Be Easier than Previously Thought, Genome). They infer that the genetic instructions for living organisms
that is composed of four bases (C, G, A and T)— is open to unnatural letters. An expanded “DNA alphabet” could carry more information than natural DNA, potentially coding for a much wider range of molecules and enabling a variety of powerful applications. The implications of the application of this would further expand the translation of portions of DNA to new transciptional proteins that are heretofore unknown, but have metabolic relavence and therapeutic potential. The existence of such pairing in nature has been studied in Eukariotes for at least a decade, and may have a role in biodiversity. The investigators show how a previously identified pair of artificial DNA bases can go through the DNA replication process almost as efficiently as the four natural bases.  This could as well be translated into human diversity, and human diseases.

The Romesberg laboratory collaborated on the new study and his lab have been trying to find a way to extend the DNA alphabet since the late 1990s. In 2008, they developed the efficiently replicating bases NaM and 5SICS, which come together as a complementary base pair within the DNA helix, much as, in normal DNA, the base adenine (A) pairs with thymine (T), and cytosine (C) pairs with guanine (G). It had been clear that their chemical structures lack the ability to form the hydrogen bonds that join natural base pairs in DNA. Such bonds had been thought to be an absolute requirement for successful DNA replication, but that is not the case because other bonds can be in play.

The data strongly suggested that NaM and 5SICS do not even approximate the edge-to-edge geometry of natural base pairs—termed the Watson-Crick geometry, after the co-discoverers of the DNA double-helix. Instead, they join in a looser, overlapping, “intercalated” fashion that resembles a ‘mispair.’ In test after test, the NaM-5SICS pair was efficiently replicable even though it appeared that the DNA polymerase didn’t recognize it. Their structural data showed that the NaM-5SICS pair maintain an abnormal, intercalated structure within double-helix DNA—but remarkably adopt the normal, edge-to-edge, “Watson-Crick” positioning when gripped by the polymerase during the crucial moments of DNA replication. NaM and 5SICS, lacking hydrogen bonds, are held together in the DNA double-helix by “hydrophobic” forces, which cause certain molecular structures (like those found in oil) to be repelled by water molecules, and thus to cling together in a watery medium.

The finding suggests that NaM-5SICS and potentially other, hydrophobically bound base pairs could be used to extend the DNA alphabet and that Evolution’s choice of the existing four-letter DNA alphabet—on this planet—may have been developed allowing for life based on other genetic systems.

3.  Studies that consider a DNA triplet model that includes one or more NATURAL nucleosides and looks closely allied to the formation of the disulfide bond and oxidation reduction reaction.

This independent work is being conducted based on a similar concep. John Berger, founder of Triplex DNA has commented on this. He emphasizes Sulfur as the most important element for understanding evolution of metabolic pathways in the human transcriptome. It is a combination of sulfur 34 and sulphur 32 ATMU. S34 is element 16 + flourine, while S32 is element 16 + phosphorous. The cysteine-cystine bond is the bridge and controller between inorganic chemistry (flourine) and organic chemistry (phosphorous). He uses a dual spelling, using  sulfphur to combine the two referring to the master catalyst of oxidation-reduction reactions. Various isotopic alleles (please note the duality principle which is natures most important pattern). Sulfphur is Methionine, S adenosylmethionine, cysteine, cystine, taurine, gluthionine, acetyl Coenzyme A, Biotin, Linoic acid, H2S, H2SO4, HSO3-, cytochromes, thioredoxin, ferredoxins, purple sulfphur anerobic bacteria prokaroytes, hydrocarbons, green sulfphur bacteria, garlic, penicillin and many antibiotics; hundreds of CSN drugs for parasites and fungi antagonists. These are but a few names which come to mind. It is at the heart of the Krebs cycle of oxidative phosphorylation, i.e. ATP. It is also a second pathway to purine metabolism and nucleic acids. It literally is the key enzymes between RNA and DNA, ie, SH thiol bond oxidized to SS (dna) cysteine through thioredoxins, ferredoxins, and nitrogenase. The immune system is founded upon sulfphur compounds and processes. Photosynthesis Fe4S4 to Fe2S3 absorbs the entire electromagnetic spectrum which is filtered by the Allen belt some 75 miles above earth. Look up chromatium vinosum or allochromatium species.  There is reasonable evidence it is the first symbiotic species of sulfphur anerobic bacteria (Fe4S4) with high potential mvolts which drives photosynthesis while making glucose with H2S.
He envisions a sulfphur control map to automate human metabolism with exact timing sequences, at specific three dimensional coordinates on Bravais crystalline lattices. He proposes adding the inosine-xanthosine family to the current 5 nucleotide genetic code. Finally, he adds, the expanded genetic code is populated with “synthetic nucleosides and nucleotides” with all kinds of customized functional side groups, which often reshape nature’s allosteric and physiochemical properties. The inosine family is nature’s natural evolutionary partner with the adenosine and guanosine families in purine synthesis de novo, salvage, and catabolic degradation. Inosine has three major enzymes (IMPDH1,2&3 for purine ring closure, HPGRT for purine salvage, and xanthine oxidase and xanthine dehydrogenase.

English: DNA replication or DNA synthesis is t...

English: DNA replication or DNA synthesis is the process of copying a double-stranded DNA molecule. This process is paramount to all life as we know it. (Photo credit: Wikipedia)

3. Nutritional regulation of gene expression,  an essential role of sulfur, and metabolic control 

Finally, the research carried out for decades by Yves Ingenbleek and the late Vernon Young warrants mention. According to their work, sulfur is again tagged as essential for health. Sulfur (S) is the seventh most abundant element measurable in human tissues and its provision is mainly insured by the intake of methionine (Met) found in plant and animal proteins. Met is endowed with unique functional properties as it controls the ribosomal initiation of protein syntheses, governs a myriad of major metabolic and catalytic activities and may be subjected to reversible redox processes contributing to safeguard protein integrity.

Consuming diets with inadequate amounts of methionine (Met) are characterized by overt or subclinical protein malnutrition, and it has serious morbid consequences. The result is reduction in size of their lean body mass (LBM), best identified by the serial measurement of plasma transthyretin (TTR), which is seen with unachieved replenishment (chronic malnutrition, strict veganism) or excessive losses (trauma, burns, inflammatory diseases).  This status is accompanied by a rise in homocysteine, and a concomitant fall in methionine.  The ratio of S to N is quite invariant, but dependent on source.  The S:N ratio is typical 1:20 for plant sources and 1:14.5 for animal protein sources.  The key enzyme involved with the control of Met in man is the enzyme cystathionine-b-synthase, which declines with inadequate dietary provision of S, and the loss is not compensated by cobalamine for CH3- transfer.

As a result of the disordered metabolic state from inadequate sulfur intake (the S:N ratio is lower in plants than in animals), the transsulfuration pathway is depressed at cystathionine-β-synthase (CβS) level triggering the upstream sequestration of homocysteine (Hcy) in biological fluids and promoting its conversion to Met. They both stimulate comparable remethylation reactions from homocysteine (Hcy), indicating that Met homeostasis benefits from high metabolic priority. Maintenance of beneficial Met homeostasis is counterpoised by the drop of cysteine (Cys) and glutathione (GSH) values downstream to CβS causing reducing molecules implicated in the regulation of the 3 desulfuration pathways

4. The effect on accretion of LBM of protein malnutrition and/or the inflammatory state: in closer focus

Hepatic synthesis is influenced by nutritional and inflammatory circumstances working concomitantly and liver production of  TTR integrates the dietary and stressful components of any disease spectrum. Thus we have a depletion of visceral transport proteins made by the liver and fat-free weight loss secondary to protein catabolism. This is most accurately reflected by TTR, which is a rapid turnover protein, but it is involved in transport and is essential for thyroid function (thyroxine-binding prealbumin) and tied to retinol-binding protein. Furthermore, protein accretion is dependent on a sulfonation reaction with 2 ATP.  Consequently, Kwashiorkor is associated with thyroid goiter, as the pituitary-thyroid axis is a major sulfonation target. With this in mind, it is not surprising why TTR is the sole plasma protein whose evolutionary patterns closely follow the shape outlined by LBM fluctuations. Serial measurement of TTR therefore provides unequaled information on the alterations affecting overall protein nutritional status. Recent advances in TTR physiopathology emphasize the detecting power and preventive role played by the protein in hyper-homocysteinemic states.

Individuals submitted to N-restricted regimens are basically able to maintain N homeostasis until very late in the starvation processes. But the N balance study only provides an overall estimate of N gains and losses but fails to identify the tissue sites and specific interorgan fluxes involved. Using vastly improved methods the LBM has been measured in its components. The LBM of the reference man contains 98% of total body potassium (TBK) and the bulk of total body sulfur (TBS). TBK and TBS reach equal intracellular amounts (140 g each) and share distribution patterns (half in SM and half in the rest of cell mass). The body content of K and S largely exceeds that of magnesium (19 g), iron (4.2 g) and zinc (2.3 g).

TBN and TBK are highly correlated in healthy subjects and both parameters manifest an age-dependent curvilinear decline with an accelerated decrease after 65 years. Sulfur Methylation (SM) undergoes a 15% reduction in size per decade, an involutive process. The trend toward sarcopenia is more marked and rapid in elderly men than in elderly women decreasing strength and functional capacity. The downward SM slope may be somewhat prevented by physical training or accelerated by supranormal cytokine status as reported in apparently healthy aged persons suffering low-grade inflammation or in critically ill patients whose muscle mass undergoes proteolysis.

5.  The results of the events described are:

  • Declining generation of hydrogen sulfide (H2S) from enzymatic sources and in the non-enzymatic reduction of elemental S to H2S.
  • The biogenesis of H2S via non-enzymatic reduction is further inhibited in areas where earth’s crust is depleted in elemental sulfur (S8) and sulfate oxyanions.
  • Elemental S operates as co-factor of several (apo)enzymes critically involved in the control of oxidative processes.

Combination of protein and sulfur dietary deficiencies constitute a novel clinical entity threatening plant-eating population groups. They have a defective production of Cys, GSH and H2S reductants, explaining persistence of an oxidative burden.

6. The clinical entity increases the risk of developing:

  • cardiovascular diseases (CVD) and
  • stroke

in plant-eating populations regardless of Framingham criteria and vitamin-B status.
Met molecules supplied by dietary proteins are submitted to transmethylation processes resulting in the release of Hcy which:

  • either undergoes Hcy — Met RM pathways or
  • is committed to transsulfuration decay.

Impairment of CβS activity, as described in protein malnutrition, entails supranormal accumulation of Hcy in body fluids, stimulation of activity and maintenance of Met homeostasis. The data show that combined protein- and S-deficiencies work in concert to deplete Cys, GSH and H2S from their body reserves, hence impeding these reducing molecules to properly face the oxidative stress imposed by hyperhomocysteinemia.

Although unrecognized up to now, the nutritional disorder is one of the commonest worldwide, reaching top prevalence in populated regions of Southeastern Asia. Increased risk of hyperhomocysteinemia and oxidative stress may also affect individuals suffering from intestinal malabsorption or westernized communities having adopted vegan dietary lifestyles.

Ingenbleek Y. Hyperhomocysteinemia is a biomarker of sulfur-deficiency in human morbidities. Open Clin. Chem. J. 2009 ; 2 : 49-60.

7. The dysfunctional metabolism in transitional cell transformation

A third development is also important and possibly related. The transition a cell goes through in becoming cancerous tends to be driven by changes to the cell’s DNA. But that is not the whole story. Large-scale techniques to the study of metabolic processes going on in cancer cells is being carried out at Oxford, UK in collaboration with Japanese workers. This thread will extend our insight into the metabolome. Otto Warburg, the pioneer in respiration studies, pointed out in the early 1900s that most cancer cells get the energy they need predominantly through a high utilization of glucose with lower respiration (the metabolic process that breaks down glucose to release energy). It helps the cancer cells deal with the low oxygen levels that tend to be present in a tumor. The tissue reverts to a metabolic profile of anaerobiosis.  Studies of the genetic basis of cancer and dysfunctional metabolism in cancer cells are complementary. Tomoyoshi Soga’s large lab in Japan has been at the forefront of developing the technology for metabolomics research over the past couple of decades (metabolomics being the ugly-sounding term used to describe research that studies all metabolic processes at once, like genomics is the study of the entire genome).

Their results have led to the idea that some metabolic compounds, or metabolites, when they accumulate in cells, can cause changes to metabolic processes and set cells off on a path towards cancer. The collaborators have published a perspective article in the journal Frontiers in Molecular and Cellular Oncology that proposes fumarate as such an ‘oncometabolite’. Fumarate is a standard compound involved in cellular metabolism. The researchers summarize that shows how accumulation of fumarate when an enzyme goes wrong affects various biological pathways in the cell. It shifts the balance of metabolic processes and disrupts the cell in ways that could favor development of cancer.  This is of particular interest because “fumarate” is the intermediate in the TCA cycle that is converted to malate.

Animation of the structure of a section of DNA...

Animation of the structure of a section of DNA. The bases lie horizontally between the two spiraling strands. (Photo credit: Wikipedia)

The Keio group is able to label glucose or glutamine, basic biological sources of fuel for cells, and track the pathways cells use to burn up the fuel.  As these studies proceed, they could profile the metabolites in a cohort of tumor samples and matched normal tissue. This would produce a dataset of the concentrations of hundreds of different metabolites in each group. Statistical approaches could suggest which metabolic pathways were abnormal. These would then be the subject of experiments targeting the pathways to confirm the relationship between changed metabolism and uncontrolled growth of the cancer cells.

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