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Extracellular evaluation of intracellular flux in yeast cells

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

Leaders in Pharmaceutical Intelligence

This is the fourth article in a series on metabolomics, which is a major development in -omics, integrating transcriptomics, proteomics,  genomics, metabolic pathways analysis, metabolic and genomic regulatory control using computational mapping.  In the previous two part presentation, flux analysis was not a topic for evaluation, but here it is the major focus.  It is a study of yeast cells, and bears some relationship to the comparison of glycemia, oxidative phosphorylation, TCA cycle, and ETC in leukemia cell lines.  In the previous study – system flux was beyond the scope of analysis, and explicitly stated.  The inferences made in comparing the two lymphocytic leukemia cells was of intracellular metabolism from extracellular measurements.  The study of yeast cells is aimed at looking at cellular effluxes, which is also an important method for studying pharmacological effects and drug resistance.

Metabolomic series

1.  Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics

https://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/

2.  Metabolomic analysis of two leukemia cell lines. I

https://pharmaceuticalintelligence.com/2014/08/23/metabolomic-analysis-of-two-leukemia-cell-lines-_i/

3.  Metabolomic analysis of two leukemia cell lines. II.

 https://pharmaceuticalintelligence.com/2014/08/24/metabolomic-analysis-of-two-leukemia-cell-lines-ii/

4.  Extracellular evaluation of intracellular flux in yeast cells

Q1. What is efflux?

Q2. What measurements were excluded from the previous study that would not allow inference about fluxes?

Q3. Would this study bear any relationship to the Pasteur effect?

Q4 What is a genome scale network reconstruction?

Q5 What type of information is required for a network prediction model?

Q6. Is there a difference between the metabolites profiles for yeast grown under aerobic and anaerobuc conditions – under the constrainsts?

Q7.  If there is a difference in the S metabolism, would there be an effect on ATP production?

 

 

Connecting extracellular metabolomic measurements to intracellular flux
states in yeast

Monica L Mo1Bernhard Ø Palsson1 and Markus J Herrgård12*

Author Affiliations

1 Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA

2 Current address: Synthetic Genomics, Inc, 11149 N Torrey Pines Rd, La Jolla, CA 92037, USA

For all author emails, please log on.

BMC Systems Biology 2009, 3:37  doi:10.1186/1752-0509-3-37

 

The electronic version of this article is the complete one and can be found online at: http://www.biomedcentral.com/1752-0509/3/37

 

Received: 15 December 2008
Accepted: 25 March 2009
Published: 25 March 2009

© 2009 Mo et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Metabolomics has emerged as a powerful tool in the

  • quantitative identification of physiological and disease-induced biological states.

Extracellular metabolome or metabolic profiling data, in particular,

  • can provide an insightful view of intracellular physiological states in a noninvasive manner.

Results

We used an updated genome-scale

  • metabolic network model of Saccharomyces cerevisiae, iMM904, to investigate
  1. how changes in the extracellular metabolome can be used
  2. to study systemic changes in intracellular metabolic states.

The iMM904 metabolic network was reconstructed based on

  • an existing genome-scale network, iND750,
  • and includes 904 genes and 1,412 reactions.

The network model was first validated by

  • comparing 2,888 in silico single-gene deletion strain growth phenotype predictions
  • to published experimental data.

Extracellular metabolome data measured

  • of ammonium assimilation pathways 
  • in response to environmental and genetic perturbations

was then integrated with the iMM904 network

  • in the form of relative overflow secretion constraints and
  • a flux sampling approach was used to characterize candidate flux distributions allowed by these constraints.

Predicted intracellular flux changes were

  • consistent with published measurements
  • on intracellular metabolite levels and fluxes.

Patterns of predicted intracellular flux changes

  • could also be used to correctly identify the regions of
  • the metabolic network that were perturbed.

Conclusion

Our results indicate that

  • integrating quantitative extracellular metabolomic profiles
  • in a constraint-based framework
  • enables inferring changes in intracellular metabolic flux states.

Similar methods could potentially be applied

  • towards analyzing biofluid metabolome variations
  • related to human physiological and disease states.

Background

“Omics” technologies are rapidly generating high amounts of data

  • at varying levels of biological detail.

In addition, there is a rapidly growing literature and

  • accompanying databases that compile this information.

This has provided the basis for the assembly of

  • genome-scale metabolic networks for various microbial and eukaryotic organisms [111].

These network reconstructions serve

  • as manually curated knowledge bases of
  • biological information as well as
  • mathematical representations of biochemical components and
  • interactions specific to each organism.

genome-scale network reconstruction is

  • structured collection of genes, proteins, biochemical reactions, and metabolites
  • determined to exist and operate within a particular organism.

This network can be converted into a predictive model

  • that enables in silico simulations of allowable network states based on
  • governing physico-chemical and genetic constraints [12,13].

A wide range of constraint-based methods have been developed and applied

  • to analyze network metabolic capabilities under
  • different environmental and genetic conditions [13].

These methods have been extensively used to

  • study genome-scale metabolic networks and have successfully predicted, for example,
  1. optimal metabolic states,
  2. gene deletion lethality, and
  3. adaptive evolutionary endpoints [1416].

Most of these applications utilize

  • optimization-based methods such as flux balance analysis (FBA)
  • to explore the metabolic flux space.

However, the behavior of genome-scale metabolic networks can also be studied

  • using unbiased approaches such as
  • uniform random sampling of steady-state flux distributions [17].

Instead of identifying a single optimal flux distribution based on

  • a given optimization criterion (e.g. biomass production),

these methods allow statistical analysis of

  • a large range of possible alternative flux solutions determined by
  • constraints imposed on the network.

Sampling methods have been previously used to study

  1. global organization of E. coli metabolism [18] as well as
  2. to identify candidate disease states in the cardiomyocyte mitochondria [19].

Network reconstructions provide a structured framework

  • to systematically integrate and analyze disparate datasets
  • including transcriptomic, proteomic, metabolomic, and fluxomic data.

Metabolomic data is one of the more relevant data types for this type of analysis as

  1. network reconstructions define the biochemical links between metabolites, and
  2. recent advancements in analytical technologies have allowed increasingly comprehensive
  • intracellular and extracellular metabolite level measurements [20,21].

The metabolome is

  1. the set of metabolites present under a given physiological condition
  2. at a particular time and is the culminating phenotype resulting from
  • various “upstream” control mechanisms of metabolic processes.

Of particular interest to this present study are

  • the quantitative profiles of metabolites that are secreted into the extracellular environment
  • by cells under different conditions.

Recent advances in profiling the extracellular metabolome (EM) have allowed

  • obtaining insightful biological information on cellular metabolism
  • without disrupting the cell itself.

This information can be obtained through various

  • analytical detection,
  • identification, and
  • quantization techniques

for a variety of systems ranging from

  • unicellular model organisms to human biofluids [2023].

Metabolite secretion by a cell reflects its internal metabolic state, and

  • its composition varies in response to
  • genetic or experimental perturbations
  • due to changes in intracellular pathway activities
  • involved in the production and utilization of extracellular metabolites [21].

Variations in metabolic fluxes can be reflected in EM changes which can

  • provide insight into the intracellular pathway activities related to metabolite secretion.

The extracellular metabolomic approach has already shown promise

  • in a variety of applications, including
  1. capturing detailed metabolite biomarker variations related to disease and
  2. drug-induced states and
  3. characterizing gene functions in yeast [2427].

However, interpreting changes in the extracellular metabolome can be challenging

  • due to the indirect relationship between the proximal cause of the change
    (e.g. a mutation)
  • and metabolite secretion.

Since metabolic networks describe

  • mechanistic,
  • biochemical links between metabolites,

integrating such data can allow a systematic approach

  • to identifying altered pathways linked to
  • quantitative changes in secretion profiles.

Measured secretion rates of major byproduct metabolites

  • can be applied as additional exchange flux constraints
  • that define observed metabolic behavior.

For example, a recent study integrating small-scale EM data

  • with a genome-scale yeast model
  • correctly predicted oxygen consumption and ethanol production capacities
  • in mutant strains with respiratory deficiencies [28].

The respiratory deficient mutant study

  • used high accuracy measurements for a small number of
  • major byproduct secretion rates
  • together with an optimization-based method well suited for such data.

Here, we expand the application range of the model-based method used in [28]

  • to extracellular metabolome profiles,
  • which represent a temporal snapshot of the relative abundance
  • for a larger number of secreted metabolites.

Our approach is complementary to

  • statistical (i.e. “top-down”) approaches to metabolome analysis [29]
  • and can potentially be used in applications such as biofluid-based diagnostics or
  • large-scale characterization of mutants strains using metabolite profiles.

This study implements a constraint-based sampling approach on

  • an updated genome-scale network of yeast metabolism
  • to systematically determine how EM level variations

are linked to global changes in intracellular metabolic flux states.

By using a sampling-based network approach and statistical methods (Figure 1),

  • EM changes were linked to systemic intracellular flux perturbations
    in an unbiased manner
  • without relying on defining single optimal flux distributions
  • used in the previously mentioned study [28].

The inferred perturbations in intracellular reaction fluxes were further analyzed

  • using reporter metabolite and subsystem (i.e., metabolic pathway) approaches [30]
  • in order to identify dominant metabolic features that are collectively perturbed (Figure 2).

The sampling-based approach also has the additional benefit of

  • being less sensitive to inaccuracies in metabolite secretion profiles than
  • optimization-based methods and can effectively be used – in biofluid metabolome analysis.
integration of exometabolomic (EM) data

integration of exometabolomic (EM) data

Figure 1. Schematic illustrating the integration of exometabolomic (EM) data with the constraint-based framework.

(A) Cells are subjected to genetic and/or environmental perturbations to secrete metabolite patterns unique to that condition.
(B) EM is detected, identified, and quantified.
(C) EM data is integrated as required secretion flux constraints to define allowable solution space.
(D) Random sampling of solution space yields the range of feasible flux distributions for intracellular reactions.
(E) Sampled fluxes were compared to sampled fluxes of another condition to determine

  • which metabolic regions were altered between the two conditions (see Figure 2).

(F) Significantly altered metabolic regions were identified.

http://www.biomedcentral.com/content/figures/1752-0509-3-37-1.jpg

 

sampling and scoring analysis to determine intracellular flux changes

sampling and scoring analysis to determine intracellular flux changes

Figure 2. Schematic of sampling and scoring analysis to determine intracellular flux changes.

(A) Reaction fluxes are sampled for two conditions.
(B & C) Sample of flux differences is calculated by selecting random flux values from each condition

  • to obtain a distribution of flux differences for each reaction.

(D) Standardized reaction Z-scores are determined, which represent

  • how far the sampled flux differences deviates from a zero flux change.

Reaction scores can be used in

  1. visualizing perturbation subnetworks and
  2. analyzing reporter metabolites and subsystems.

http://www.biomedcentral.com/content/figures/1752-0509-3-37-2.jpg

This study was divided into two parts and describes:

(i) the reconstruction and validation of an expanded S. cerevisiae metabolic network, iMM904; and
(ii) the systematic inference of intracellular metabolic states from

  • two yeast EM data sets using a constraint-based sampling approach.

The first EM data set compares wild type yeast to the gdh1/GDH2 (glutamate dehydrogenase) strain [31],

  • which indicated good agreement between predicted metabolic changes
  • of intracellular metabolite levels and fluxes [31,32].

The second EM data set focused on secreted amino acid measurements

  • from a separate study of yeast cultured in different
    ammonium and potassium concentrations [33].

We analyzed the EM data to gain further insight into

  • perturbed ammonium assimilation processes as well as
  1. metabolic states relating potassium limitation and
  2. ammonium excess conditions to one another.

The model-based analysis of both

  • separately published extracellular metabolome datasets
  • suggests a relationship between
  1. glutamate,
  2. threonine and
  3. folate metabolism,
  • which are collectively perturbed when
    ammonium assimilation processes are broadly disrupted
  1. either by environmental (excess ammonia) or
  2. genetic (gene deletion/overexpression) perturbations.

The methods herein present an approach to

  • interpreting extracellular metabolome data and
  • associating these measured secreted metabolite variations
  • to changes in intracellular metabolic network states.

Additional file 1. iMM904 network content.

The data provided represent the content description of the iMM904 metabolic network and
detailed information on the expanded content.

Format: XLS Size: 2.7MB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional file 2. iMM904 model files.

The data provided are the model text files of the iMM904 metabolic network
that is compatible with the available COBRA Toolbox [13]. The model structure
can be loaded into Matlab using the ‘SimPhenyPlus’ format with GPR and compound information.

Format: ZIP Size: 163KB Download file

Conversion of the network to a predictive model

The network reconstruction was converted to a constraint-based model using established procedures [13].

Network reactions and metabolites were assembled into a stoichiometric matrix 

  • containing the stoichiometric coefficients of the reactions in the network.

The steady-state solution space containing possible flux distributions

  • is determined by calculating the null space of S= 0,

where is the reaction flux vector.

Minimal media conditions were set through constraints on exchange fluxes

  • corresponding to the experimental measured substrate uptake rates.

All the model-based calculations were done using the Matlab COBRA Toolbox [13]

  • utilizing the glpk or Tomlab/CPLEX (Tomopt, Inc.) optimization solvers.

Chemostat growth simulations

The iMM904 model was initially validated by

  1. simulating wild type yeast growth in aerobic and anaerobic
    carbon-limited chemostat conditions
  2. and comparing the simulation results to published experimental data

on substrate uptake and byproduct secretion in these conditions [34].

The study was performed following the approach taken to validate the iFF708 model in a previous study [35].

The predicted glucose uptake rates were determined

  1. by setting the in silico growth rate to the measured dilution rate,
    – equivalent under continuous culture growth,
  2. and minimizing the glucose uptake rate.

The accuracy of in silico predictions of

  • substrate uptake and byproduct secretion by the iMM904 model
  • was similar to the accuracy obtained using the iFF708 model
  • and results are shown in Figure S1 [see Additional file 3].

Additional file 3. Supplemental figures. 

The file provides the supplemental figures and descriptions of S1, S2, S3, and S4.

Format: PDF Size: 513KB Download file

This file can be viewed with: Adobe Acrobat Reader

Genome-scale gene deletion phenotype predictions

The iMM904 network was further validated by

  • performing genome-scale gene lethality computations
  • following established procedures to determine growth phenotypes
  1. under minimal medium conditions and
  2. compared to published data.

A modified version of the biomass function used in previous iND750 studies

  1. was set as the objective to be maximized and
  2. gene deletions were simulated by

setting the flux through the corresponding reaction(s) to zero.

The biomass function was based on the experimentally measured

  1. composition of major cellular constituents
  2. during exponential growth of yeast cells and
  3. was reformulated to include trace amounts of
  4. additional cofactors and metabolites
  5. with the assumed fractional contribution of 10-.

These additional biomass compounds were included

according to the biomass formulation used in the iLL672 study

  • to improve lethality predictions through
  • the inclusion of additional essential biomass components [3].

The model was constrained by limiting

  1. the carbon source uptake to 10 mmol/h/gDW
  2. and oxygen uptake to 2 mmol/h/gDW.

Ammonia, phosphate, and sulfate were assumed to be non-limiting.

The experimental phenotyping data was obtained

  • using strains that were auxotrophic for
  1. methionine,
  2. leucine,
  3. histidine, and
  4. uracil.

These auxotrophies were simulated

  1. by deleting the appropriate genes from the model and
  2. supplementing the in silico strain with the appropriate supplements
  3. at non-limiting, but low levels.

Furthermore, trace amounts of essential nutrients that are present

  • in the experimental minimal media formulation
  1. 4-aminobenzoate,
  2. biotin,
  3. inositol,
  4. nicotinate,
  5. panthothenate,
  6. thiamin)
  • were supplied in the simulations [3].

Three distinct methods to simulate the outcome of gene deletions were utilized:

  1. Flux-balance analysis (FBA) [36-38],
  2. Minimization of Metabolic Adjustment (MoMA) [39], and
  3. a linear version of MoMA (linearMoMA).

In the linearMoMA method, minimization of the quadratic objective function
of the original MoMA algorithm

  • was replaced by minimization of the corresponding 1-norm objective function
    (i.e. sum of the absolute values of the differences of wild type FBA solution
    and the knockout strain flux solution).

The computed results were then compared to growth phenotype data
(viable/lethal) from a previously published experimental gene deletion study [3].

The comparison between experimental and in silico deletion phenotypes involved

  • choosing a threshold for the predicted relative growth rate of
  • a deletion strain that is considered to be viable.

We used standard ROC curve analysis

  • to assess the accuracy of different prediction methods and models
  • across the full range of the viability threshold parameter,
    results shown in Figure S2 [see Additional file 3].

The ROC curve plots the true viable rate against the false viable rate

  • allowing comparison of different models and methods
  • without requiring arbitrarily choosing this parameter a priori [40].

The optimal prediction performance corresponds to

  • the point closest to the top left corner of the ROC plot
    (i.e. 100% true viable rate, 0% false viable rate).

Table 1

Table 1 Comparison of iMM904 and iLL672 gene deletion predictions and experimental data under minimal media conditions
Media Model Method True viable False viable False lethal True lethal True viable % False viable % MCC
Glucose iMM904 full FBA 647 10 32 33 95.29 23.26 0.6
iMM904 full linMOMA 644 10 35 33 94.85 23.26 0.58
iMM904 full MOMA 644 10 35 33 94.85 23.26 0.58
iMM904 red FBA 440 9 28 33 94.02 21.43 0.61
iMM904 red linMOMA 437 9 31 33 93.38 21.43 0.6
iMM904 red MOMA 437 9 31 33 93.38 21.43 0.6
iLL672 full MOMA 433 9 35 33 92.52 21.43 0.57
Galactose iMM904 full FBA 595 32 36 59 94.29 35.16 0.58
iMM904 full linMOMA 595 32 36 59 94.29 35.16 0.58
iMM904 full MOMA 595 32 36 59 94.29 35.16 0.58
iMM904 red FBA 409 12 33 56 92.53 17.65 0.67
iMM904 red linMOMA 409 12 33 56 92.53 17.65 0.67
iMM904 red MOMA 409 12 33 56 92.53 17.65 0.67
iLL672 full MOMA 411 19 31 49 92.99 27.94 0.61
Glycerol iMM904 full FBA 596 43 36 47 94.3 47.78 0.48
iMM904 full linMOMA 595 44 37 46 94.15 48.89 0.47
iMM904 full MOMA 598 44 34 46 94.62 48.89 0.48
iMM904 red FBA 410 20 34 46 92.34 30.3 0.57
iMM904 red linMOMA 409 21 35 45 92.12 31.82 0.56
iMM904 red MOMA 412 21 32 45 92.79 31.82 0.57
iLL672 full MOMA 406 20 38 46 91.44 30.3 0.55
Ethanol iMM904 full FBA 593 45 29 55 95.34 45 0.54
iMM904 full linMOMA 592 45 30 55 95.18 45 0.54
iMM904 full MOMA 592 44 30 56 95.18 44 0.55
iMM904 red FBA 408 21 27 54 93.79 28 0.64
iMM904 red linMOMA 407 21 28 54 93.56 28 0.63
iMM904 red MOMA 407 20 28 55 93.56 26.67 0.64
iLL672 full MOMA 401 13 34 62 92.18 17.33 0.68
MCC, Matthews correlation coefficient (see Methods). Note that the iLL672 predictions were obtained directly from [3] and thus the viability threshold was not optimized using the maximum MCC approach.
Mo et al. BMC Systems Biology 2009 3:37  http://dx.doi.org:/10.1186/1752-0509-3-37

 

The values reported in Table 1 correspond to selecting

  • the optimal viability threshold based on this criterion.

We summarized the overall prediction accuracy of a model and method

  • using the Matthews Correlation Coefficient (MCC) [40].

The MCC ranges from -1 (all predictions incorrect) to +1 (all predictions correct) and

  • is suitable for summarizing overall prediction performance

in our case where there are substantially more viable than lethal gene deletions.

ROC plots were produced in Matlab (Mathworks, Inc.).

 

Table 1. Comparison of iMM904 and iLL672

  • gene deletion predictions and
  • experimental data

Inferring perturbed metabolic regions based on EM profiles

The method implemented in this study is shown schematically in Figures 1 and 2

Constraining the iMM904 network 

Relative levels of quantitative EM data were incorporated into the constraint-based framework

  • as overflow secretion exchange fluxes to simulate the required low-level production of
  • experimentally observed excreted metabolites.

The primary objective of this study is to associate

  • relative metabolite levels that are generally measured for metabonomic or biofluid analyses
  • to the quantitative ranges of intracellular reaction fluxes required to produce them.

However, without detailed kinetic information or dynamic metabolite measurements available,

  • we approximated EM datasets of relative quantitative metabolite levels
  • to be proportional to the rate in which they are secreted and detected
  • (at a steady state) – into the extracellular media.

This approach is analogous to approximating uptake rates based

  • on metabolite concentrations from a previous study performing sampling analysis
  • on a cardiomyocyte mitochondrial network
  • to identify differential flux distribution ranges

for various environmental (i.e. substrate uptake) conditions [19].

The raw data was normalized by the raw maximum value of the dataset
(thus the maximum secretion flux was 1 mmol/hr/gDW) with

  • an assumed error of 10%
  • to set the lower and upper bounds and thus
  • inherently accounting for sampling calculation sensitivity.

The gdh1/GDH2 strains were flask cultured under minimal glucose media conditions; thus,

  • glucose and oxygen uptake rates were set at 15 and 2 mmol/hr/gDW, respectively,
  • for the gdh1/GDH2 strain study.

In the anaerobic case the oxygen uptake rate was set to zero, and

  • sterols and fatty acids were provided as in silico supplements as described in [35].

For the potassium limitation/ammonium toxicity study

  • the growth rate was set at 0.17 1/h, and
  • the glucose uptake rate was minimized
  • to mimic experimental chemostat cultivation conditions.

These input constraints were constant for each perturbation and comparative wild-type condition

  • such that the calculated solution spaces between the conditions
  • differed based only on variations in the output secretion constraints.

FBA optimization of EM-constrained networks

A modified FBA method with minimization of the 1-norm objective function

  • between two optimal flux distributions was used
  • to determine optimal intracellular fluxes
  • based on the EM-constrained metabolic models.

This method determines two optimal flux distributions simultaneously

  • for two differently constrained models (e.g. wild type vs. mutant) –
  • these flux distributions maximize biomass production in each case and
  • the 1-norm distance between the distributions is as small as possible
  • given the two sets of constraints.

This approach avoids problems with

  • alternative optimal solutions when comparing two FBA-computed flux distributions
  • by assuming minimal rerouting of flux distibution between a perturbed network and its reference network.

Reaction flux changes from the FBA optimization results were determined

  • by computing the relative percentage fold change for each reaction
  • between the mutant and wild-type flux distributions.

Random sampling of the steady-state solution space

We utilized artificial centering hit-and-run (ACHR) Monte Carlo sampling [19,41]

  • to uniformly sample the metabolic flux solution space
  • defined by the constraints described above.

Reactions, and their participating metabolites, found to participate in intracellular loops [42]

  • were discarded from further analysis as these reactions can have arbitrary flux values.

The following sections describe the approaches used for the analysis of the different datasets.

Sampling approach used in the gdh1/GDH2 study

Due to the overall shape of the metabolic flux solution space,

  • most of the sampled flux distributions resided close to the minimally allowed growth rate
    (i.e. biomass production) and
  • corresponded to various futile cycles that utilized substrates but
  • did not produce significant biomass.

In order to study more physiologically relevant portions of the flux space

  • we restricted the sampling to the part of the solution space
  • where the growth rate was at least 50% of the maximum growth rate
  • for the condition as determined by FBA.

This assumes that cellular growth remains an important overall objective by the yeast cells

  • even in batch cultivation conditions, but
  • that the intracellular flux distributions
  • may not correspond to maximum biomass production [43].

To test the sensitivity of the results to the minimum growth rate threshold,

  • separate Monte Carlo samples were created for each minimum threshold
  • ranging from 50% to 100% at 5% increments.

We also tested the sensitivity of the results

  • to the relative magnitude of the extracellular metabolite secretion rates
  • by performing the sampling at three different relative levels

(0 corresponding to no extracellular metabolite secretion, maximum rate of 0.5 mmol/hr/gDW,
and maximum rate of 1.0 mmol/hr/gDW).

For each minimum growth rate threshold and extracellular metabolite secretion rate,

  • the ACHR sampler was run for 5 million steps and
  • a flux distribution was stored every 5000 steps.

The sensitivity analysis results are presented in Figures S3 and S4 [see Additional File 3], and

  • the results indicate that the reaction Z-scores (see below) are not significantly affected by
  1. either the portion of the solution space sampled or
  2. the exact scaling of secretion rates.

The final overall sample used was created by combining the samples for all minimum growth rate thresholds

  • for the highest extracellular metabolite secretion rate (maximum 1 mmol/hr/gDW).

This approach allowed biasing the sampling towards

  • physiologically relevant parts of the solution space
  • without imposing the requirement of strictly maximizing a predetermined objective function.

The samples obtained with no EM data were used as control samples

  • to filter reporter metabolites/subsystems whose scores were significantly high
  • due to only random differences between sampling runs.

Sampling approach used in the potassium limitation/ammonium toxicity study

Since the experimental data used in this study was generated in chemostat conditions, and

  • previous studies have indicated that chemostat flux patterns predicted by FBA are
  • close to the experimentally measured ones [43],
  • we assumed that sampling of the optimal solution space was appropriate for this study.

In order to sample a physiologically reasonable range of flux distributions,

  • samples for four different oxygen uptake rates
    (1, 2, 3, and 4 mmol/hr/gDW with 5 million steps each)
  • were combined in the final analysis.

Standardized scoring of flux differences between perturbation and control conditions

Z-score based approach was implemented to quantify differences in flux samples between two conditions (Figure 2).
First, two flux vectors were chosen randomly,

  • one from each of the two samples to be compared and
  • the difference between the flux vectors was computed.

This approach was repeated to create a sample of 10,000 (n) flux difference vectors

  • for each pair of conditions considered (e.g. mutant or perturbed environment vs. wild type).

Based on this flux difference sample, the sample mean (μdiff,i) and standard deviation (σdiff,i)

  • between the two conditions was calculated for each reaction i. The reaction Z-score was calculated as:

 

reaction Z-score

reaction Z-score

which describes the sampled mean difference deviation

  • from a population mean change of zero (i.e. no flux difference
    between perturbation and wild type).

Note that this approach allows accounting for uncertainty in the

  • flux distributions inferred based on the extracellular metabolite secretion constraints.

This is in contrast to approaches such as FBA or MoMA that would predict

  • a single flux distribution for each condition and thus potentially
  • overestimate differences between conditions.

The reaction Z-scores can then be further used in analysis

  • to identify significantly perturbed regions of the metabolic network
  • based on reporter metabolite [44] or subsystem [30] Z-scores.

These reporter regions indicate, or “report”, dominant perturbation features

  • at the metabolite and pathway levels for a particular condition.

The reporter metabolite Z-score for any metabolite can be derived from the reaction Z-scores

  • of the reactions consuming or producing j (set of reactions denoted as Rj) as:

 

reporter z-score for any metabolite j

reporter z-score for any metabolite j

where Nis the number of reactions in Rand mmet,is calculated as

 

distributional correction for m_met,j SQRT

distributional correction for m_met,j SQRT

To account and correct for background distribution, the metabolite Z-score was normalized

  • by computing μmet,Nj and σmet,,Nj corresponding to the mean mmet and
  • its standard deviation for 1,000 randomly generated reaction sets of size Nj.

Z-scores for subsystems were calculated similarly by considering the set of reactions R

  • that belongs to each subsystem k.

Hence, positive metabolite and subsystem scores indicate a significantly perturbed metabolic region

  • relative to other regions, whereas
  • a negative score indicate regions that are not perturbed
  • more significantly than what is expected by random chance.

Perturbation subnetworks of reactions and connecting metabolites were visualized using Cytoscape [45].

Results and discussion

  1. Reconstruction and validation of iMM904 network iMM904 network content 

A previously reconstructed S. cerevisiae network, iND750,

  • was used as the basis for the construction of the expanded iMM904 network.
  • Prior to its presentation here, the
    iMM904 network content was the basis for a consensus jamboree network that was recently published
  • but has not yet been adapted for FBA calculations [46].

The majority of iND750 content was carried over and

  • further expanded on to construct iMM904, which accounts for
  1. 904 genes,
  2. 1,228 individual metabolites, and
  3. 1,412 reactions of which
  •                       395 are transport reactions.

Both the number of gene-associated reactions and the number of metabolites

  • increased in iMM904 compared with the iND750 network.

Additional genes and reactions included in the network primarily expanded the

  • lipid,
  • transport, and
  • carbohydrate subsystems.

The lipid subsystem includes

  • new genes and
  • reactions involving the degradation of sphingolipids and glycerolipids.

Sterol metabolism was also expanded to include

  • the formation and degradation of steryl esters, the
  •                      storage form of sterols.

The majority of the new transport reactions were added

  • to connect network gaps between intracellular compartments
  • to enable the completion of known physiological functions.

We also added a number of new secretion pathways

  • based on experimentally observed secreted metabolites [31].

A number of gene-protein-reaction (GPR) relationships were modified

  • to include additional gene products that are required to catalyze a reaction.

For example, the protein compounds

  • thioredoxin and
  • ferricytochrome C

were explicitly represented as compounds in iND750 reactions, but

  • the genes encoding these proteins were not associated with their corresponding GPRs.

Other examples include glycogenin and NADPH cytochrome p450 reductases (CPRs),

  1. which are required in the assembly of glycogen and
  2. to sustain catalytic activity in cytochromes p450, respectively.

These additional proteins were included in iMM904 as

  • part of protein complexes to provide a more complete
  • representation of the genes and
  • their corresponding products necessary for a catalytic activity to occur.

Major modifications to existing reactions were in cofactor biosynthesis, namely in

  • quinone,
  • beta-alanine, and
  • riboflavin biosynthetic pathways.

Reactions from previous S. cerevisiae networks associated with

  • quinone,
  • beta-alanine, and
  • riboflavin biosynthetic pathways

were essentially inferred from known reaction mechanisms based on

  • reactions in previous network reconstructions of E. coli [2,47].

These pathways were manually reviewed

  • based on current literature and subsequently replaced by
  • reactions and metabolites specific to yeast.

Additional changes in other subsystems were also made, such as

  1. changes to the compartmental location of a gene and
  2. its corresponding reaction(s),
  3. changes in reaction reversibility and cofactor specificity, and
  4. the elucidation of particular transport mechanisms.

A comprehensive listing of iMM904 network contents as well as

  • a detailed list of changes between iND750 and iMM904 is included
    [see Additional file 1].

Predicting deletion growth phenotypes

The updated genome-scale iMM904 metabolic network was validated

  • by comparing in silico single-gene deletion predictions to
  • in vivo results from a previous study used
  • to analyze another S. cerevisiae metabolic model, iLL672 [3].

This network was constructed based on the iFF708 network [22],

  • which was also the starting point for
  • reconstructing the iND750 network [2].

The experimental data used to validate the iLL672 model consisted of

3,360 single-gene knockout strain phenotypes evaluated

  • under minimal media growth conditions with
  1. glucose,
  2. galactose,
  3. glycerol, and
  4. ethanol

as sole carbon sources. Growth phenotypes for the iMM904 network were predictedusing

  1. FBA [3234],
  2. MoMA [35], and
  3. linear MoMA methods

as described in Methods and subsequently compared to the experimental data (Table 1).

Each deleted gene growth prediction comparison was classified as

  1. true lethal,
  2. true viable,
  3. false lethal, or
  4. false viable.

The growth rate threshold for considering a prediction viable was chosen

  • for each condition and method separately
  • to optimize the tradeoff between true viable and false viable predictions
    (maximum Matthews correlation coefficient, see Methods).

Since iMM904 has 212 more genes than iLL672 with experimental data, we also present results

  • for the subset of iMM904 predictions with genes included in iLL672 (reduced iMM904 set).

When the same gene sets are compared, iMM904 improves gene lethality predictions under

  • glucose,
  • galactose, and
  • glycerol conditions

over iLL672 somewhat, but is less accurate

  • at predicting growth phenotypes under the ethanol condition.

It should be noted that the iLL672 predictions were obtained directly from [3]

  • thus the growth rate threshold was not optimized similarly to iMM904 predictions.

Overall, when viability cutoff is chosen

  • as indicated above for each method separately,
  • the three prediction methods perform similarly
  1. FBA,
  2. MOMA, and
  3. linear MOMA) .

While the full gene complement in iMM904 greatly increased

  • the number of true viable predictions,
  • the full model also made significantly more false viable predictions
  • compared with reduced iMM904 and iLL672 predictions.

However, it is important to note that 143 reactions involved in dead-end biosynthetic pathways were actually

  • removed from iFF708 to build the iLL672 reconstruction [3].

These dead-ends are considered “knowledge gaps” in pathways

  • that have not been fully characterized and, as a result,
  • lead to false viable predictions when determining gene essentiality
  • if the pathway is in fact required for growth under a certain condition [2,26].

As more of these pathways are elucidated and

  • included in the model to
  • fill in existing network gaps,
  • we can expect false viable prediction rates to consequently decrease.

Thus, while a larger network has a temporarily reduced capacity to accurately predict gene deletion phenotypes,

  • it captures a more complete picture of currently known metabolic functions and
  • provides a framework for network expansion as new pathways are elucidated [48].

 

Inferring intracellular perturbation states from metabolic profiles – Aerobic and anaerobic gdh1/GDH2 mutant behavior

The gdh1/GDH2 mutant strain was previously developed [49,50]

  • to lower NADPH consumption in ammonia assimilation, which would
  • favor the NADPH-dependent fermentation of xylose.

In this strain, the NADPH-dependent glutamate dehydrogenase, Gdh1, was

  • deleted and the NADH-dependent form of the enzyme, Gdh2,
  •                     was overexpressed.

The net effect is to allow efficient assimilation of ammonia

  • into glutamate using NADH instead of NADPH as a cofactor.

While growth characteristics remained unaffected,

  • relative quantities of secreted metabolites differed between the wild-type and mutant strain
  • under aerobic and anaerobic conditions.

We analyzed EM data for the gdh1/GDH2 and wild-type strains reported

  • in [31] under aerobic and anaerobic conditions separately using
  • both FBA optimization and
  • sampling-based approaches as described in Methods.

43 measured extracellular and intracellular metabolites from the original dataset [31],

  • primarily of central carbon and amino acid metabolism,
  • were explicitly represented in the iMM904 network [see Additional file 4].

Extracellular metabolite levels were used

  • to formulate secretion constraints and
  • differential intracellular metabolites were used
  • to compare and validate the intracellular flux predictions.

Perturbed reactions from the FBA results were

  • determined by calculating relative flux changes, and
  • reaction Z-scores were calculated from the sampling analysis
  • to quantify flux changes between the mutant and wild-type strains,
  • with Z reaction > 1.96 corresponding to a two-tailed p-value < 0.05 and
  • considered to be significantly perturbed [see Additional file 4].

Additional file 4. Gdh mutant aerobic and anaerobic analysis results. 

The data provided are the full results for the exometabolomic analysis of aerobic and anerobic gdh1/GDH2 mutant.

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To validate the predicted results, reaction flux changes from both FBA and sampling methods were compared to differential intracellular metabolite level data measured from the same study. Intracellular metabolites involved in highly perturbed reactions (i.e. reactants and products) predicted from FBA and sampling analyses were identified and
compared to metabolites that were experimentally identified as significantly changed (< 0.05) between mutant and wild-type. Statistical measures of recall, accuracy, and
precision were calculated and represent the predictive sensitivity, exactness, and reproducibility respectively. From the sampling analysis, a considerably larger number of
significantly perturbed reactions are predicted in the anaerobic case (505 reactions, or 70.7% of active reactions) than in aerobic (394 reactions, or 49.8% of active reactions). The top percentile of FBA flux changes equivalent to the percentage of significantly perturbed sampling reactions were compared to the intracellular data. Results from both analyses are summarized in Table 2. Sampling predictions were considerably higher in recall than FBA predictions for both conditions, with respective ranges of 0.83–1
compared to 0.48–0.96. Accuracy was also higher in sampling predictions; however, precision was slightly better in the FBA predictions as expected due to the smaller
number of predicted changes. Overall, the sampling predictions of perturbed intracellular metabolites are strongly consistent with the experimental data and significantly
outperforms that of FBA optimization predictions in accurately predicting differential metabolites involved in perturbed intracellular fluxes.

Table 2. Statistical comparison of the differential intracellular metabolite data set (< 0.05) with metabolites involved in perturbed reactions predicted by FBA optimization and sampling analyses for aerobic and anaerobic gdh1/GDH2 mutant.

 

Table 2 Statistical comparison of the differential intracellular metabolite data set (p < 0.05)
with metabolites involved in perturbed reactions predicted by FBA optimization and
sampling analyses for aerobic and anaerobic gdh1/GDH2 mutant.
                           Aerobic                         Anaerobic                             Overall
FBA Sampling FBA Sampling FBA
Recall 0.48 0.83 0.96 1 0.71 0.91
Accuracy 0.55 0.62 0.64 0.64 0.6 0.63
Precision 0.78 0.69 0.64 0.63 0.68 0.66
Overall statistics indicate combined results of both conditions.
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37


Figure 3.
 Perturbation reaction subnetwork of gdh1/GDH2 mutant under aerobic conditions.

The network illustrates a simplified subset of highly perturbedPerturbation subnetworks can be drawn to visualize predicted significantly perturbed intracellular reactions and illustrate their connection to the observed secreted metabolites in the aerobic and anaerobic gdh1/GDH2 mutants.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under aerobic conditions.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under aerobic conditions.

Figure 3 shows an example of a simplified aerobic perturbation subnetwork consisting primarily of proximal pathways connected directly to a subset of major secreted
metabolites

  • glutamate,
  • proline,
  • D-lactate, and
  • 2-hydroxybuturate.

Figure 4 displays anaerobic reactions with Z-scores of similar magnitude to the perturbed reactions in Figure 3. The same subset of metabolites is also present in the
larger anaerobic perturbation network and indicates that the NADPH/NADH balance perturbation induced by the gdh1/GDH2 manipulation has widespread effects
beyond just altering glutamate metabolism anaerobically.

Interestingly, it is clear that the majority of the secreted metabolite pathways involve connected perturbed reactions that broadly converge on glutamate.

Note that Figures 3 and 4 only show the subnetworks that consisted of two or more connected reactions  for a number of secreted metabolites no contiguous perturbed pathway could be identified by the sampling approach. This indicates that the secreted metabolite pattern alone is not sufficient to determine which specific
production and secretion pathways are used by the cell for these metabolites.

Reactions connected to aerobically-secreted metabolites predicted from the sampling analysis of the gdh1/GDH2 mutant strain.
The major secreted metabolites

  • glutamate,
  • proline,
  • D-lactate, and
  • 2-hydroxybuturate

were also detected in the anaerobic condition. Metabolite abbreviations are found in Additional file 1.

Figure 4.

Perturbation reaction subnetwork of gdh1/GDH2 mutant under anaerobic conditions.

Perturbation reaction subnetwork of gdh1.GDH2 mutant under anaerobic conditions

Perturbation reaction subnetwork of gdh1.GDH2 mutant under anaerobic conditions

Subnetwork illustrates the highly perturbed anaerobic reactions of similar Z-reaction magnitude to the reactions in Figure 3.

A significantly larger number of reactions indicates mutant metabolic effects are more widespread in the anaerobic environment.
The network shows that perturbed pathways converge on glutamate, the main site in which the gdh1/GDH2 modification was introduced, which
suggests that the direct genetic perturbation effects are amplified under this environment. Metabolite abbreviations are found in Additional file 1.

To further highlight metabolic regions that have been systemically affected by the gdh1/GDH2 modification, reporter metabolite and subsystem methods [30] were used to
summarize reaction scores around specific metabolites and in specific metabolic subsystems. The top ten significant scores for metabolites/subsystems associated with more
than three reactions are summarized in Tables 3 (aerobic) and 4 (anaerobic), with Z > 1.64 corresponding to < 0.05 for a one-tailed distribution. Full data for all reactions,
reporter metabolites, and reporter subsystems is included [see Additional file 4].

Table 3. List of the top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in aerobic conditions.

Table 3
List of the top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in aerobic conditions.
Reporter metabolite Z-score No of reactions*
L-proline [c] 2.71 4
Carbon dioxide [m] 2.51 15
Proton [m] 2.19 51
Glyceraldehyde 3-phosphate [c] 1.93 7
Ubiquinone-6 [m] 1.82 5
Ubiquinol-6 [m] 1.82 5
Ribulose-5-phosphate [c] 1.8 4
Uracil [c] 1.74 4
L-homoserine [c] 1.72 4
Alpha-ketoglutarate [m] 1.71 8
Reporter subsystem Z-score No of reactions
Citric Acid Cycle 4.58 7
Pentose Phosphate Pathway 3.29 12
Glycine and Serine Metabolism 2.69 17
Alanine and Aspartate Metabolism 2.65 6
Oxidative Phosphorylation 1.79 8
Thiamine Metabolism 1.54 8
Arginine and Proline Metabolism 1.44 20
Other Amino Acid Metabolism 1.28 5
Glycolysis/Gluconeogenesis 0.58 14
Anaplerotic reactions 0.19 9
*Number of reactions categorized in a subsystem or found to be neighboring each metabolite
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37

Table 4. List of top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in anaerobic conditions.

 

Table 4
List of top ten significant reporter metabolite and subsystem scores for the gdh1/GDH2 vs. wild type comparison in anaerobic conditions.
Reporter metabolite Z-score No of reactions
Glutamate [c] 4.52 35
Aspartate [c] 3.21 11
Alpha-ketoglutarate [c] 2.66 17
Glycine [c] 2.65 7
Pyruvate [m] 2.56 7
Ribulose-5-phosphate [c] 2.43 4
Threonine [c] 2.28 6
10-formyltetrahydrofolate [c] 2.27 5
Fumarate [c] 2.27 5
L-proline [c] 2.04 4
Reporter subsystem Z-score No of reactions
Valine, Leucine, and Isoleucine Metabolism 3.97 15
Tyrosine, Tryptophan, and Phenylalanine Metabolism 3.39 23
Pentose Phosphate Pathway 3.29 11
Purine and Pyrimidine Biosynthesis 3.08 40
Arginine and Proline Metabolism 2.96 19
Threonine and Lysine Metabolism 2.74 14
NAD Biosynthesis 2.66 7
Alanine and Aspartate Metabolism 2.65 6
Histidine Metabolism 2.24 10
Cysteine Metabolism 1.85 10
Mo et al. BMC Systems Biology 2009 3:37   http://dx.doi.org:/10.1186/1752-0509-3-37
Open Data

Perturbations under aerobic conditions largely consisted of pathways involved in mediating the NADH and NADPH balance. Among the highest scoring aerobic subsystems
are TCA cycle and pentose phosphate pathway – key pathways directly involved in the generation of NADH and NADPH. Reporter metabolites involved in these
subsystems –

  • glyceraldehyde-3-phosphate,
  • ribulose-5-phosphate, and
  • alpha-ketoglutarate – were also identified.

These results are consistent with flux and enzyme activity measurements

  • of the gdh1/GDH2 strain under aerobic conditions [32],
  1. which reported significant reduction in the pentose phosphate pathway flux
  2. with concomitant changes in other central metabolic pathways.

Levels of several TCA cycle intermediates (e.g. fumarate, succinate, malate) were also elevated

  • in the gdh1/GDH2 mutant according to the differential intracellular metabolite data.

Altered energy metabolism, as indicated by

  • reporter metabolites (i.e. ubiquinone- , ubiquinol, mitochondrial proton)
  • and subsystem (oxidative phosphorylation),

is certainly feasible as NADH is a primary reducing agent for ATP production.

Pentose phosphate pathway and NAD biosynthesis also appears

  • among the most perturbed anaerobic subsystems, further suggesting
  • perturbed cofactor balance as a common, dominant effect under both conditions.

Glutamate dehydrogenase is a critical enzyme of amino acid biosynthesis as it acts as

  • the entry point for ammonium assimilation via glutamate.

Consequently, metabolic subsystems involved in amino acid biosynthesis were broadly perturbed

  • as a result of the gdh1/GDH2 modification in both aerobic and anaerobic conditions.

For example, the proline biosynthesis pathway that uses glutamate as a precursor

  • was significantly perturbed in both conditions,
  • with significantly changed intracellular and extracellular levels.

There were differences, however, in that more amino acid related subsystems were

  • significantly affected in the anaerobic case (Table 4),
  • further highlighting that altered ammonium assimilation in the mutant
  • has a more widespread effect under anaerobic conditions.

This effect is especially pronounced for

  • threonine and nucleotide metabolism,
  • which were predicted to be significantly perturbed only in anaerobic conditions.

Intracellular threonine levels were amongst the most significantly reduced

  • relative to other differential intracellular metabolites in the anaerobically grown gdh1/GDH2 strain
    (see [31] and Additional file 4), and
  • the relationship between threonine and nucleotide biosynthesis is further supported

by threonine’s recently discovered role as a key precursor in yeast nucleotide biosynthesis [51].

Other key anaerobic reporter metabolites are

  • glycine and 10-formyltetrahydrofolate,
  • both of which are involved in the cytosolic folate cycle (one-carbon metabolism).

Folate is intimately linked to biosynthetic pathways of

  • glycine (with threonine as its precursor) and purines
  • by mediating one-carbon reaction transfers necessary in their metabolism and
  • is a key cofactor in cellular growth [52].

Thus, the anaerobic perturbations identified in the analysis emphasize the close relationship

  • between threonine, folate, and nucleotide metabolic pathways as well as
  • their potential connection to perturbed ammonium assimilation processes.

Interestingly, this association has been previously demonstrated at the transcriptional level

  • as yeast ammonium assimilation (via glutamine synthesis) was found to be
  • co-regulated with genes involved in glycine, folate, and purine synthesis [53].

In summary, the overall differences in predicted gdh1/GDH2 mutant behavior

  • under aerobic and anaerobic conditions show that changes in flux states
  • directly related to modified ammonium assimilation pathway
  1. are amplified anaerobically whereas the
  2. indirect effects through NADH/NADPH balance are more significant aerobically.

Perturbed metabolic regions under aerobic conditions were predominantly

  • in central metabolic pathways involved in responding to the changed NADH/NADPH demand
  • and did not necessarily emphasize that glutamate dehydrogenase was the site of the genetic modification.

The majority of affected anaerobic pathways were involved directly

  • in modified ammonium assimilation as evidenced by

1) significantly perturbed amino acid subsystems,

2) a broad perturbation subnetwork converging on glutamate (Figure 4), and

3) glutamate as the most significant reporter metabolite (Table 4).

Potassium-limited and excess ammonium environments

A recent study reported that potassium limitation resulted in significant

  • growth retardation effect in yeast due to excess ammonium uptake
  • when ammonium was provided as the sole nitrogen source [33].

The proposed mechanism for this effect was that ammonium

  • could to be freely transported through potassium channels
  • when potassium concentrations were low in the media environment, thereby
  • resulting in excess ammonium uptake [33].

As a result, yeast incurred a significant metabolic cost

  • in assimilating ammonia to glutamate and
  • secreting significant amounts of glutamate and other amino acids
  • in potassium-limited conditions as a means to detoxify the excess ammonium.

A similar effect was observed when yeast was grown

  • with no potassium limitation,
  • but with excess ammonia in the environment.

While the observed effect of both environments (low potassium or excess ammonia) was similar,

  • quantitatively unique amino acid secretion profiles suggested that
  • internal metabolic states in these conditions are potentially different.

In order to elucidate the differences in internal metabolic states, we utilized

  • the iMM904 model and the EM profile analysis method to analyze amino acid secretion profiles
  • for a range of low potassium and high ammonia conditions reported in [33].

As before, we utilized amino acid secretion patterns as constraints to the iMM904 model,

  1. sampled the allowable solution space,
  2. computed reaction Z-scores for changes from a reference condition (normal potassium and ammonia), and
  3. finally summarized the resulting changes using reporter metabolites.

Figure 5 shows a clustering of the most significant reporter metabolites (Z ≥ 1.96 in any of the four conditions studied)

  • obtained from this analysis across the four conditions studied.

Interestingly, the potassium-limited environment perturbed only a subset of

  • the significant reporter metabolites identified in the high ammonia environments.

Both low potassium environments shared a consistent pattern of

  • highly perturbed amino acids and related precursor biosynthesis metabolites
    (e.g. pyruvate, PRPP, alpha-ketoglutarate)
  • with high ammonium environments.

The amino acid perturbation pattern (indicated by red labels in Figure 5) was present in

  • the ammonium-toxic environments, although the pattern was
  • slightly weaker for the lower ammonium concentration.

Nevertheless, the results clearly indicate that a similar

  • ammonium detoxifying mechanism that primarily perturbs pathways
  • directly related to amino acid metabolism
  • exists under both types of media conditions.

Figure 5.

Clustergram of top reporter metabolites - y in ammonium-toxic and potassium-limited conditions

Clustergram of top reporter metabolites – y in ammonium-toxic and potassium-limited conditions

Clustergram of top reporter metabolites (i.e. in yellow) in ammonium-toxic and potassium-limited conditions.

Amino acid perturbation patterns (shown in red) were shown to be consistently scored across conditions, indicating that potassium-limited environments K1 (lowest
concentration) and K2 (low concentration) elicited a similar ammonium detoxification response as ammonium-toxic environments N1 (high concentration) and N2
(highest concentration). Metabolites associated with folate metabolism (highlighted in green) are also highly perturbed in ammonium-toxic conditions. Metabolite
abbreviations are found in Additional file 1.

In addition to perturbed amino acids, a secondary effect notably appears at high ammonia levels in which metabolic regions related to folate metabolism are significantly affected. As highlighted in green in Figure 3, we predicted significantly perturbed key metabolites involved in the cytosolic folate cycle. These include tetrahydrofolate derivatives and other metabolites connected to the folate pathway, namely glycine and the methionine-derived methylation cofactors S-adenosylmethionine and S-adenosyl-homocysteine. Additionally, threonine was identified to be a key perturbed metabolite in excess ammonium conditions. These results further illustrate the close
connection between threonine biosynthesis, folate metabolism involving glycine derived from its threonine precursor, and nucleotide biosynthesis [51] that was discussed in
conjunction with the gdh1/GDH2 strain data. Taken together with the anaerobic gdh1/GDH2 data, the results consistently suggest highly perturbed threonine and folate
metabolism when amino acid-related pathways are broadly affected.

In both ammonium-toxic and potassium-limited environments, impaired cellular growth was observed, which can be attributed to high energetic costs of increased
ammonium assimilation to synthesize and excrete amino acids. However, under high ammonium environments, reporter metabolites related to threonine and folate
metabolism indicated that their perturbation, and thus purine supply, may be an additional factor in decreasing cellular viability as there is a direct relationship between
intracellular folate levels and growth rate [54]. Based on these results, we concluded that while potassiumlimited growth in yeast indeed shares physiological features with
growth in ammonium excess, its effects are not as detrimental as actual ammonium excess. The effects on proximal amino acid metabolic pathways are similar in both
environments as indicated by the secretion of the majority of amino acids. However, when our method was applied to analyze the physiological basis behind differences in
secretion profiles between low potassium and high ammonium conditions, ammonium excess was predicted to likely disrupt physiological ammonium assimilation processes,
which in turn potentially impacts folate metabolism and associated cellular growth.

Conclusion

The method presented in this study presents an approach to connecting intracellular flux states to metabolites that are excreted under various physiological conditions. We
showed that well-curated genome-scale metabolic networks can be used to integrate and analyze quantitative EM data by systematically identifying altered intracellular
pathways related to measured changes in the extracellular metabolome. We were able to identify statistically significant metabolic regions that were altered as a result of
genetic (gdh1/GD2 mutant) and environmental (excess ammonium and limited potassium) perturbations, and the predicted intracellular metabolic changes were consistent
with previously published experimental data including measurements of intracellular metabolite levels and metabolic fluxes. Our reanalysis of previously published EM data
on ammonium assimilation-related genetic and environmental perturbations also resulted in testable hypotheses about the role of threonine and folate pathways in mediating
broad responses to changes in ammonium utilization. These studies also demonstrated that the samplingbased method can be readily applied when only partial secreted
metabolite profiles (e.g. only amino acids) are available.

With the emergence of metabolite biofluid biomarkers as a diagnostic tool in human disease [55,56] and the availability of genome-scale human metabolic networks [1],
extensions of the present method would allow identifying potential pathway changes linked to these biomarkers. Employing such a method for studying yeast metabolism was possible as the metabolomic data was measured under controllable environmental conditions where the inputs and outputs of the system were defined. Measured metabolite biomarkers in a clinical setting, however, is far from a controlled environment with significant variations in genetic, nutritional, and environmental factors between different
patients. While there are certainly limitations for clinical applications, the method introduced here is a progressive step towards applying genome-scale metabolic networks
towards analyzing biofluid metabolome data as it 1) avoids the need to only study optimal metabolic states based on a predetermined objective function, 2) allows dealing with noisy experimental data through the sampling approach, and 3) enables analysis even with limited identification of metabolites in the data. The ability to establish potential
connections between extracellular markers and intracellular pathways would be valuable in delineating the genetic and environmental factors associated with a particular
disease.

Authors’ contributions

Conceived and designed the experiments: MLM MJH BOP. Performed experiments: MLM MJH. Analyzed the data: MLM MJH. Wrote the paper: MLM MJH BOP. All authors have read and approved the final manuscript.

Acknowledgements

We thank Jens Nielsen for providing the raw metabolome data for the mutant strain, and Jan Schellenberger and Ines Thiele for valuable discussions. This work was supported by NIH grant R01 GM071808. BOP serves on the scientific advisory board of Genomatica Inc.

 

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Larry H Bernstein, MD, FCAP, Curator

http://pharmaceuticalintelligence/7/8/2014/Proteins and cellular adaptation to stress

There are two recent articles that are, if not interesting, possibly important in the direction of cellular regulation, adaptation, and decline.  One deals with apoptosis, or cell death, which is synchronized with recovery of membrane and protein breakdown for reuse in synthesis and maintenance.  The other is a new perspective to Alzhemier’s Disease, for which there is no effective pharmacotherapy. In both cases, the stresses of the cell are critical to the responce to the environment.  This is not just about the classical transcriptomics story. This is a perfect followup to the just posted research on the regulatory role of a small RNA that is related to, but distinct from silencing RNA, and also the revelations about lncRNA.

Protein Helps Cells Adapt—or Die

Scientists show how cell stress both prevents and promotes cell suicide in a study that’s equally divisive.

By Ruth Williams | July 3, 2014

A cellular stress pathway called the unfolded-protein-response (UPR) both activates and degrades death receptor 5 protein (DR5), which can promote or prevent cell suicide, according to a paper published in Science today (July 3). The theory is that initial stress blocks cell suicide, or apoptosis, to give the cell a chance to adapt, but that if the stress persists, it eventually triggers apoptosis.

“This work has made the most beautiful simplification of all this big complex mess. Basically, they identified and pinpointed the specific protein involved in the switching decision and explain how the decision is made,” said Alexei Korennykh, a professor of molecular biology at Princeton University, who was not involved in the work.

But Randal Kaufman of the Sanford-Burnham Medical Research Institute in La Jolla, California, was not impressed. He questioned the physiological relevance of the experiments supporting the authors’ main conclusions about this key cellular process.

Protein folding in a cell takes place largely in the endoplasmic reticulum (ER), but if the process goes awry, unfolded proteins accumulate, stressing the ER. This triggers the UPR, which shuts down translation, degrades unfolded proteins, and increases production of protein-folding machinery. If ER stress is not resolved, however, the UPR can also induce apoptosis.

Two main factors control the UPR—IRE1a and PERK. IRE1a promotes cell survival by activating the transcription factor XBP1, which drives expression of cell-survival genes. PERK, on the other hand, activates a transcription factor called CHOP, which in turn drives expression of the proapoptotic factor DR5.

Peter Walter of the University of California, San Francisco, and his colleagues have now confirmed that CHOP activates DR5, showing that it is a cell-autonomous process. But they have also found that IRE1a suppresses DR5, directly degrading its mRNA through a process called regulated IRE1a-dependent degradation (RIDD). Inhibition of IRE1a in a human cancer cell line undergoing ER stress both prevented DR5 mRNA decay and increased apoptosis.

However, in an e-mail to The Scientist, Kaufman expressed concern that “the significance of RIDD has not been demonstrated in a physiologically-relevant context.”

Walter insisted that the evidence for RIDD’s existence is “crystal clear.” His only concession was that “the effects aren’t 100 percent,” he said, because “RIDD degrades mRNA by a few-fold,” making it difficult to measure.

This RIDD debate aside, the researchers have also sparked a rumpus with their finding that IRE1a expression switches off just 24 hours after ER stress initiation, leaving PERK to drive the cell toward apoptosis. “We and others have evidence that suggests another model,” said Scott Oakes, a professor of pathology at the University of California, San Francisco, “which is that both PERK and IRE1a under high stress will send out death signals.”

Whether IRE1a promotes or inhibits apoptosis under extreme stress “is controversial,” said Ira Tabas, a professor at Columbia University in New York City. But it’s essential that scientists figure it out. Cell death from ER stress is a pathological process in many major diseases, Tabas said, and there are IRE1a inhibitors in pharmaceutical development. “It is very important because under high stress you have two different views here,” said Oakes. “One is that you want to keep IRE1a on, the other is that you want to shut it off.”

Because ER stress is central to many diseases, “a lot of people are passionate about it,” said Tabas, explaining the polemic views. “Who’s right? . . . I think it depends on the context in which the experiments are done—one pathway may be important in some settings, and another pathway may be important in different settings,” he suggested. What might help to resolve the issues, he said, will be “in vivo causation studies using actual disease models.”

Researchers will continue to debate. So, said Walter, “we’ll have to see what holds-up five years from now.”

M. Lu et al., “Opposing unfolded-protein-response signals converge on death receptor 5 to control apoptosis,” Science, 345:98-101, 2014.

Tags stress responseprotein foldingdisease/medicinecell & molecular biology and apoptosis

 

Protein May Hold the Key to Who Gets Alzheimer’s

 

By PAM BELLUCK     MARCH 19, 2014

 

It is one of the big scientific mysteries of Alzheimer’s disease: Why do some people whose brains accumulate the plaques and tangles so strongly associated with Alzheimer’s not develop the disease?

 

Now, a series of studies by Harvard scientists suggests a possible answer, one that could lead to new treatments if confirmed by other research.

 

The memory and thinking problems of Alzheimer’s disease and other dementias, which affect an estimated seven million Americans, may be related to a failure in the brain’s stress response system, the new research suggests. If this system is working well, it can protect the brain from abnormal Alzheimer’s proteins; if it gets derailed, critical areas of the brain start degenerating.

“This is an extremely important study,” said Li-Huei Tsai, director of the Picower Institute for Learning and Memory at the Massachusetts Institute of Technology, who was not involved in the research but wrote a commentary accompanying the study. “This is the first study that is really starting to provide a plausible pathway to explain why some people are more vulnerable to Alzheimer’s than other people.”

An image of tau tangles in the brain, often a hallmark of Alzheimer’s disease.

An image of tau tangles in the brain, often a hallmark of Alzheimer’s disease.

 

 

 

The research, published on Wednesday in the journal Nature, focuses on a protein previously thought to act mostly in the brains of developing fetuses. The scientists found that the protein also appears to protect neurons in healthy older people from aging-related stresses. But in people with Alzheimer’s and other dementias, the protein is sharply depleted in key brain regions.

Experts said if other scientists could replicate and expand upon the findings, the role of the protein, called REST, could spur development of new drugs for dementia, which has so far been virtually impossible to treat. But they cautioned that much more needed to be determined, including whether the decline of REST was a cause, or an effect, of brain deterioration, and whether it was specific enough to neurological diseases that it could lead to effective therapies.

“You’re going to see a lot of papers now following up on it,” said Dr. Eric M. Reiman, executive director of the Banner Alzheimer’s Institute in Phoenix, who was not involved in the study. “While it’s a preliminary finding, it raises an avenue that hasn’t been considered before. And if this provides a handle on which to understand normal brain aging, that will be great, too.”

REST, a regulator that switches off certain genes, is primarily known to keep fetal neurons in an immature state until they develop to perform brain functions, said Dr. Bruce A. Yankner, a professor of genetics at Harvard Medical School and the lead author of the new study. By the time babies are born, REST becomes inactive, he said, except in some areas outside the brain like the colon, where it seems to suppress cancer.

While investigating how different genes in the brain change as people age, Dr. Yankner’s team was startled to find that REST was the most active gene regulator in older brains. The researchers have found that this protein, normally active in fetuses, may also protect the neurons in older people.  It is not yet possible to measure the levels of this protein that is a gene regulator called REST, in living people.

“Why should a fetal gene be coming on in an aging brain?” he wondered. He hypothesized that it was because in aging, as in birth, brains encounter great stress, threatening neurons that cannot regenerate if harmed.

His team discovered that REST appears to switch off genes that promote cell death, protecting neurons from normal aging processes like energy decrease, inflammation and oxidative stress.

Analyzing brains from brain banks and dementia studies, the researchers found that brains of young adults ages 20 to 35 contained little REST, while healthy adults between the ages of 73 and 106 had plenty. REST levels grew the older people got, so long as they did not develop dementia, suggesting that REST is related to longevity.

But in people with Alzheimer’s, mild cognitive impairment, frontotemporal dementia and Lewy body dementia, the brain areas affected by these diseases contained much less REST than healthy brains.

This was true only in people who actually had memory and thinking problems. People who remained cognitively healthy, but whose brains had the same accumulation of amyloid plaques and tau tangles as people with Alzheimer’s, had three times more REST than those suffering Alzheimer’s symptoms. About a third of people who have such plaques will not develop Alzheimer’s symptoms, studies show.

REST levels dropped as symptoms worsened, so people with mild cognitive impairment had more REST than Alzheimer’s patients. And only key brain regions were affected. In Alzheimer’s, REST steeply declined in the prefrontal cortex and hippocampus, areas critical to learning, memory and planning. Other areas of the brain not involved in Alzheimer’s showed no REST drop-off.

It is not yet possible to analyze REST levels in the brains of living people, and several Alzheimer’s experts said that fact limited what the new research could prove.

John Hardy, an Alzheimer’s researcher at University College London, cautioned in an email that information from post-mortem brains could not prove that a decline in REST caused dementia because death might produce unrelated damage to brain cells.

To investigate further, the team conducted what both Dr. Tsai and Dr. Reiman called a “tour de force” of research, examining REST in mice, roundworms and cells in the lab.

“We wanted to make sure the story was right,” Dr. Yankner said. “It was difficult to believe at first, to be honest with you.”

Especially persuasive was that mice genetically engineered to lack REST lost neurons as they aged in brain areas afflicted in Alzheimer’s.

Dr. Yankner said REST appeared to work by traveling to a neuron’s nucleus when the brain was stressed. In dementia, though, REST somehow gets diverted, traveling with toxic dementia-related proteins to another part of the neuron where it is eventually destroyed.

Experts said the research, while intriguing, left many unanswered questions. Bradley Wise of the National Institute on Aging’s neuroscience division, which helped finance the studies, said REST’s role needed further clarification. “I don’t think you can really say if it’s a cause of Alzheimer’s or a consequence of Alzheimer’s” yet, he said.

Dr. Samuel E. Gandy, an Alzheimer’s researcher at Mount Sinai Medical Center, wondered if REST figured only in neurodegenerative diseases or in other diseases, too, which could make it difficult to use REST to develop specific treatments or diagnostic tests for dementia.

“My ambivalence is, is this really a way that advances our understanding of the disease or does this just tell us this is even more complicated than we thought?” he said.

Dr. Yankner’s team is looking at REST in other neurological diseases, like Parkinson’s. He also has thoughts about a potential treatment, lithium, which he said appears to stimulate REST function, and is considered relatively safe.

But he and other experts said it was too early. “I would hesitate to start rushing into lithium treatment” unless rigorous studies showed that it could forestall dementia, said Dr. John C. Morris, an Alzheimer’s researcher at Washington University in St. Louis.

Still, Dr. Morris said, the REST research the team conducted so far is “very well done, and certainly helps support this idea that we’ve all tried to understand about why Alzheimer’s is age-associated and why, while amyloid is necessary for the development of Alzheimer’s disease, it certainly is not sufficient.”

He added, “There have to be some other processes and triggers that result in Alzheimer’s.”

Correction: March 19, 2014 
Because of an editing error, an earlier version of this article misstated the gender of Dr. Li-Huei Tsai. Dr. Tsai is a woman.

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Larry Bernstein, MD, FCAP

https://pharmaceuticalintelligence.com/6-19-3014/larryhbern/Activation of Efficient and Multiple Site-specific Nonstandard Amino Acid Incorporation

 

Cell-free Protein Synthesis from a Release Factor 1 Deficient Escherichia coli Activates Efficient and Multiple Site-specific Nonstandard Amino Acid Incorporation

Seok Hoon Hong Ioanna Ntai §Adrian D. Haimovich #, Neil L. Kelleher §Farren J. Isaacs #, and Michael C. Jewett *

Department of Chemical and Biological Engineering,Chemistry of Life Processes Institute, §Department of Chemistry, and Department of Molecular Biosciences,Northwestern University, Evanston, Illinois 60208,United States of America

Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, Connecticut 06520, United States of America

# Systems Biology Institute, Yale University, West Haven, Connecticut 06516, United States of America

Member, Robert H. Lurie Comprehensive Cancer Center, Northwestern University, Chicago, Illinois 60611, United States of America

Institute of Bionanotechnology in Medicine, Northwestern University, Chicago, Illinois 60611, United States of America

ACS Synth. Biol.20143 (6), pp 398–409

DOI: 10.1021/sb400140t

Publication Date (Web): December 13, 2013

Copyright © 2013 American Chemical Society

*Tel: +1 847 467 5007. Fax (+1) 847 491 3728. E-mail: m-jewett@northwestern.edu

Site-specific incorporation of nonstandard amino acids (NSAAs) into proteins

Site-specific incorporation of nonstandard amino acids (NSAAs) into proteins

 

 

 

 

 

 

 

 

 

Site-specific incorporation of nonstandard amino acids (NSAAs) into proteins enables the creation of biopolymers, proteins, and enzymes with new chemical properties, new structures, and new functions. To achieve this, amber (TAG codon) suppression has been widely applied. However, the suppression efficiency is limited due to the competition with translation termination by release factor 1 (RF1), which leads to truncated products. Recently, we constructed a genomically recoded Escherichia coli strain lacking RF1 where 13 occurrences of the amber stop codon have been reassigned to the synonymous TAA codon (rEc.E13.ΔprfA). Here, we assessed and characterized cell-free protein synthesis (CFPS) in crude S30 cell lysates derived from this strain. We observed the synthesis of 190 ± 20 μg/mL of modified soluble superfolder green fluorescent protein (sfGFP) containing a single p-propargyloxy-l-phenylalanine (pPaF) or p-acetyl-l-phenylalanine. As compared to the parentrEc.E13 strain with RF1, this results in a modified sfGFP synthesis improvement of more than 250%. Beyond introducing a single NSAA, we further demonstrated benefits of CFPS from the RF1-deficient strains for incorporating pPaF at two- and five-sites per sfGFP protein. Finally, we compared our crude S30 extract system to the PURE translation system lacking RF1. We observed that our S30 extract based approach is more cost-effective and high yielding than the PURE translation system lacking RF1, 1000 times on a milligram protein produced/$ basis. Looking forward, using RF1-deficient strains for extract-based CFPS will aid in the synthesis of proteins and biopolymers with site-specifically incorporated NSAAs.

Keywords: 

cell-free protein synthesisPURE translationnonstandard amino acid;release factor 1genomically recoded organisms

 

 

 

 

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Introduction to e-Series A: Cardiovascular Diseases, Volume Four Part 2: Regenerative Medicine


Introduction to e-Series A: Cardiovascular Diseases, Volume Four Part 2: Regenerative Medicine

Author and Curator: Larry H Bernstein, MD, FCAP

and

Curator: Aviva Lev-Ari, PhD, RN

This document is entirely devoted to medical and surgical therapies that have made huge strides in

  • simplification of interventional procedures,
  • reduced complexity, resulting in procedures previously requiring surgery are now done, circumstances permitting, by medical intervention.

This revolution in cardiovascular interventional therapy is regenerative medicine.  It is regenerative because it is largely driven by

  • the introduction into the impaired vasculature of an induced pleuripotent cell, called a stem cell, although
  • the level of differentiation may not be a most primitive cell line.

There is also a very closely aligned development in cell biology that extends beyond and including vascular regeneration that is called synthetic biology.  These developments have occurred at an accelerated rate in the last 15 years. The methods of interventional cardiology were already well developed in the mid 1980s.  This was at the peak of cardiothoracic bypass surgery.

Research on the endothelial cell,

  • endothelial cell proliferation,
  • shear flow in small arteries, especially at branch points, and
  • endothelial-platelet interactions

led to insights about plaque formation and vessel thrombosis.

Much was learned in biomechanics about the shear flow stresses on the luminal surface of the vasculature, and there was also

  • the concomitant discovery of nitric oxide,
  • oxidative stress, and
  • the isoenzymes of nitric oxide synthase (eNOS, iNOS, and nNOS).

It became a fundamental tenet of vascular biology that

  • atherogenesis is a maladjustment to oxidative stress not only through genetic, but also
  • non-genetic nutritional factors that could be related to the balance of omega (ω)-3 and omega (ω)-6 fatty acids,
  • a pro-inflammatory state that elicits inflammatory cytokines, such as, interleukin-6 (IL6) and c-reactive protein(CRP),
  • insulin resistance with excess carbohydrate associated with type 2 diabetes and beta (β) cell stress,
  • excess trans- and saturated fats, and perhaps
  • the now plausible colonic microbial population of the gastrointestinal tract (GIT).

There is also an association of abdominal adiposity,

  • including the visceral peritoneum, with both T2DM and with arteriosclerotic vessel disease,
  • which is presenting at a young age, and has ties to
  • the effects of an adipokine, adiponectin.

Much important work has already been discussed in the domain of cardiac catheterization and research done to

  • prevent atheroembolization.and beyond that,
  • research done to implant an endothelial growth matrix.

Even then, dramatic work had already been done on

  • the platelet structure and metabolism, and
  • this has transformed our knowledge of platelet biology.

The coagulation process has been discussed in detailed in a previous document.  The result was the development of a

  • new class of platelet aggregation inhibitors designed to block the activation of protein on the platelet surface that
  • is critical in the coagulation cascade.

In addition, the term long used to describe atherosclerosis, atheroma notwithstanding, is “hardening of the arteries”.  This is particularly notable with respect to mid-size arteries and arterioles that feed the heart and kidneys. Whether it is preceded by or develops concurrently with chronic renal insufficiency and lowered glomerular filtration rate is perhaps arguable.  However, there is now a body of evidence that points to

  • a change in the vascular muscularis and vessel stiffness, in addition to the endothelial features already mentioned.

This has provided a basis for

  • targeted pharmaceutical intervention, and
  • reduction in salt intake.

So we have a  group of metabolic disorders, which may alone or in combination,

  • lead to and be associated with the long term effects of cardiovascular disease, including
  • congestive heart failure.

This has been classically broken down into forward and backward failure,

  • depending on decrease outflow through the aorta (ejection fraction), or
  • decreased venous return through the vena cava,

which involves increased pulmonary vascular resistance and decreased return into the left atrium.

This also has ties to several causes, which may be cardiac or vascular. This document, as the previous, has four pats.  They are broadly:

  1. Stem Cells in Cardiovascular Diseases
  2. Regenerative Cell and Molecular Biology
  3. Therapeutics Levels In Molecular Cardiology
  4. Research Proposals for Endogenous Augmentation of circulating Endothelial Progenitor Cells (cEPCs)

As in the previous section, we start with the biology of the stem cell and the degeneration in cardiovascular diseases, then proceed to regeneration, then therapeutics, and finally – proposals for augmenting therapy with circulating endogenous endothelial progenitor cells (cEPCs).

 

context

stem cells

 

theme

regeneration

 

 

 

 

theme

Therapeutics

 

theme

augmentation

 

 

 

 

 

 

 

 

 

 

Key pathways involving NO

Key pathways involving NO

 

 

 

 

stem cell lin28

stem cellLlin28

1479-5876-10-175-1-l  translational research with feedback loops

Tranlational Research -Lab to Bedside

 

 

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A Synthesis of the Beauty and Complexity of How We View Cancer


A Synthesis of the Beauty and Complexity of How We View Cancer

Author: Larry H. Bernstein, MD, FCAP

Cancer Volume One – Summary

A Synthesis of the Beauty and Complexity of How We View Cancer

 

This document has covered a broad spectrum of the research, translational biology, diagnostics (both laboratory and imaging methodologies), and treatments for a variety of cancers, mainly by organs, and selectively by the most common cancers seen in human populations. A number of observations stand out on review of all the material presented. 1. The most common cancers affecting humans is spread worldwide, with some variation by region. 2. Cancers within geographic regions may be expressed differently in relationship to population migrations, the incidence of specific environmental pollutants, occurrence of insect transmitted and sexually transmitted diseases (HIV, HCV, HPV), and possibly according to age, or relationship to ultraviolet or high dose radiation exposure. 3. Cancers are expressed within generally recognized age timelines. For example, acute lymphocytic leukemia and neuroblastoma in children under 10 years age; malignant giant cell tumor and osteosarcoma in the third and fourth decade; prostate cancer and breast cancer over age 40, and are more aggressive at an earlier age, both having a strong sex hormone dependence. 4. There is dispute about the effectiveness of screening for cancer with respect to what age, excessive risk in treatment modality, and the duration of progression free survival. Despite the evidence of several years potential life extension, a long term survival of 10 years is not the expected outcome. However, the quality of life in the remaining years is a valid point in favor of progress. 5. There has been a significant reduction in toxicity of treatment, but attention has been focused on a patient-centric decision process. 6. There has been a dramatic improvement in surgical approaches, post-surgical surveillance, and in diagnosis by invasive and noninvasive methods, especially in the combination of needle biopsy and imaging techniques. 7. There is significant variation within cancer cell types with respect to disease-free survival.

The work presented has several main components: First, there is the biology and mechanisms involved in carcinogenesis related to (1) mutations; (2) carcinogenesis; (3) cell regulatory mechanisms; (4) cell signaling pathways; (5) apoptosis (6) ubitination (7) mitochondrial dysfunction; (8) cell-cell interactions; (9) cell migration; (10) metastasis. Then there are large portions covering (1) imaging; (2) specific targeted therapy; (3) nanotechology-based therapy; (4) specific organ-type cancers; (5) genomics-based testing; (6) circulating cancer cells; (7) miRNAs; (8) siRNAs; (9) cancer immunology and (10) immunotherapy.

Classically, we refer to cancer development in terms of the germ cell layers – ectoderm, mesoderm, and endoderm. These are formative in embryonic development. The most active development occurs during embryonic development, with a high growth rate of cells and also a high utilization of energy. The cells utilize oxidation for energy in this period characterized by movement of cells in differentiation and organogenesis. This was observed to be unlike the cell metabolism in carcinogenesis, which is characterized by impaired mitochondrial function and reliance on lactate production for energy – termed anaerobic glycolysis, as investigated by Meyerhof, Embden, Warburg, Szent-Gyorgy, H. Krebs, Theorell, AV Hill, B Chance, P Mitchell, P Boyer, F Lippman, and others.

In addition, the body economy has been divided into two major metabolic compartments: fat and lean body mass (LBM), which is further denoted as visceral and structural. This denotes the gut, kidneys, liver, lung, pancreas, sexual organs, endocrines, brain and fat cells in one compartment, and skeletal muscle, bone and cardiovascular in another. LBM is calculated as fat free mass. Further, brown fat is distinguished from white fat. But this was a first layer of construction of the human body. One peels away this layer to find a second layer. For example, the gut viscera have an inner (outer) epithelial layer, a muscularis, and a deep epithelium, which has circulation and fat. There is also an interstitium between the gut epithelium and muscularis. The lung has an epithelium exposed to the airspaces, then capillaries, and then epithelium, designed for exchange of O2 and CO2, the source of heat generation. The pancreas has an endocrine portion in the islets that are embedded in an exocrine secretory organ. The sexual organs have a combination of glandular structures embedded in a mesothelium.

The structural compartment is entirely accounted for by the force of contraction. If this is purely anatomical, that is not really the case when one goes into the functioning substructures of these tissues – cytoplasm, endoplasmic reticulum (ribosomal), mitochondria, liposomes, chromatin apparatus, cell membrane and vesicles. Within and between these structures are the working and interacting mechanisms of the cell in its unique role. What ties these together was first thought to be found in the dogma following the discovery of the genetic code in 1953 that begat DNA to RNA to protein.

This led to many other discoveries that made it clear that it was only a first approximation. It did not account for noncoding DNA, which became unmasked with the culmination of the Human Genome Project and concurrent advances in genomics (mtDNA, mtRNA, siRNA, exosomes, proteomics, synthetic biology, predictive analytics, and regulatory pathways directed by signaling molecules. Here is a list of signaling pathways: 1. JAK-STAT 2. GPCR 3. Endocrine 4. Cytochemical 5. RTK 6. P13K 7. NF-KB 8. MAPK 9. Ubiquitin 10. TGF-beta 11. Stem cell These signaling pathways have become the basis for the discovery of inhibitors of signaling pathways (suppressors), as well as activators, as these have been considered as specific targets for selective therapy. (.See Figure below) Of course, extensive examination of these pathways has required that all such findings are validated based on the STRENGTH of their effect on the target and in the impact of suppression.

inhibitors of signaling pathways-1

http://www.SelleckChem.com

 

Let us continue this discussion elucidating several major points.  While the early observations that drove the interest in biochemical behavior of cancer cells has been displaced, it has not faded from view.

Bioenergetics of Cancer cells

Michael J. Gonzalez (Bioenergetic_Theory_of_Carcinigenesis. http://www.academia.edu/2224071/ Bioenergetic_Theory_of_Carcinigenesis) maintains that the altered energy metabolism of tumor cells provides a viable target for a non-toxic chemotherapeutic approach.  An increased glucose consumption rate  has been observed in malignant cells. Warburg (NobelLaureate in medicine) postulated that the respiratory process of malignant cells was impaired in the malignant transformation. Szent-Györgyi (Nobel in medicine) also viewed cancer as originating from insufficient oxygen utilization. Oxygen inhibits anaerobic  metabolism (fermentation and lactic acid production). Interestingly, during cell differentiation (where cell energy level is high) there is an increased cellular production of oxidation products that appear to provide physiological stimulation for changes in gene expression that may lead to a terminal differentiated state. The failure to maintain high ATP production (high cell energy levels) may be a consequence of inactivation of key enzymes, especially those related to the Krebs cycle and the electron transport system. A distorted mitochondrial function (transmembrane potential) may result.  This  aspect could be suggestive of an important mitochondrial involvement in the carcinogenic process in addition to presenting it as a possible therapeutic target for cancer. Intermediate metabolic correction of the mitochondria is postulated as a possible non-toxic therapeutic approach for cancer.

Fermentation is the anaerobic metabolic breakdown of glucose without net oxidation. Fermentation does not release all the available energy of glucose or need oxygen as part of its biochemical reactions ;  it merely allows glycolysis  (a process that yields two ATP per mole of glucose) to continue by replenishing reduced coenzymes and yields lactate as its final product. The first step in aerobic and anaerobic energy producing pathways, it occurs in the cytoplasm of cells, not in specialized organelles, and is found in all living organisms.  Cancer cells have a fundamentally different energy metabolism compared to normal cells, that  are obligate aerobes (oxygen-requiring cells)  meeting their energy needs with oxidative metabolic processes., while cancer cells do not  require oxygen for their survival. This increase in glycolytic  flux is a metabolic strategy of tumor cells to ensure growth and    survival  in  environments  with  low   oxygen concentrations.

Radoslav Bozov has commented that the process of genomic evolution cannot be fully revealed through comparative genomicsHe states that DNA would be entropic- favorable stable state going towards absolute ZERO temp. Themodynamics measurement in subnano discrete space would go negative towards negativity. DNA is like a cold melting/growing crystal, quite stable as it appears not due to hydrogen bonding , but due to interference of C-N-O. That force is contradicted via proteins onto which we now know large amount of negative quantum redox state carbon attaches. The more locally one attempts to observe, the more hidden variables would emerge as a consequence of discrete energy spaces opposing continuity of matter/time. But stability emerges out of non-stable states, and never reaches absolute stability, for there would be neither feelings nor freedom.

Membrane potential(Vm)

Membrane potential (Vm), the voltage across the plasma membrane, arises because of the presence of differention channels/transporters with specific ion selectivity and permeability. Vm is a key biophysical signal in non-excitable cells, modulating important cellular activities, such as proliferation and differentiation. Therefore, the multiplicities of various ion channels/transporters expressed on different cells are finely tuned in order to regulate the Vm. (M Yang and WJ Brackenbury.

Membrane potential and cancer progression. Frontiers in Physiol.  2013(4); 185: 1.  http://dx.doi.org/10.3389/fphys.2013.00185)

It is well-established that cancer cells possess distinct bioelectrical properties. Notably, electrophysiological analyses in many cancer cell types have revealed a depolarized Vm that favors cell proliferation. Ion channels/transporters control cell volume and migration, and emerging data also suggest that the level of Vm has functional roles in cancer cell migration. In addition, yperpolarization is necessary for stem cell differentiation. For example, both osteogenesis and adipogenesis are hindered in human mesenchymal stem cells (hMSCs) under depolarizing conditions. Therefore, in the context of cancer, membrane depolarization might be important for the emergence and maintenance of cancer stem cells (CSCs), giving rise to sustained tumor growth. This review aims to provide a broad understanding of the Vm as a bioelectrical signal in cancer cells by examining several key types of ion channels that contribute to its regulation. The mechanisms by which Vm regulates cancer cell proliferation, migration, and differentiation will be discussed. In the long term, Vm might be avaluable clinical marker for tumor detection with prognostic value, and could even be artificially modified in order to inhibit tumor growth and metastasis.

Perspective beyond Cancer Genomics: Bioenergetics of Cancer Stem Cells

Hideshi Ishii, Yuichiro Doki, and Masaki Mori
Yonsei Med J 2010; 51(5):617-621.  http://dx.doi.org/10.3349/ymj.2010.51.5.617   pISSN: 0513-5796, eISSN: 1976-2437

Although the notion that cancer is a disease caused by genetic and epigenetic alterations is now widely accepted, perhaps more emphasis has been given to the fact that cancr is a genetic disease. It should be noted that in the post-genome sequencing project period of the 21st century, the underlined phenomenon nevertheless could not be discarded towards the complete control of cancer disaster as the whole strategy, and in depth investigation of the factors associated with tumorigenesis is required for achieving it. Otto Warburg has won a Nobel Prize in 1931 for the discovery of tumor bioenergetics, which is now commonly used as the basis of positron emission tomography (PET), a highly sensitive noninvasive technique used in cancer diagnosis. Furthermore, the importance of the cancer stem cell (CSC) hypothesis in therapy-related resistance and metastasis has been recognized during the past 2 decades. Accumulating evidence suggests that tumor bioenergetics plays a critical role in CSC regulation; this finding has opened up a new era of cancer medicine, which goes beyond cancer genomics.

Efficient execution of cell death in non-glycolytic cells requires the generation of ROS controlled by the activity of mitochondrial H+-ATP synthase.

Gema Santamaría1,#, Marta Martínez-Diez1,#, Isabel Fabregat2 and José M. Cuezva1,*
Carcinogenesis 2006 27(5):925-935      http://dx.doi.org/10.1093/carcin/bgi315

There is a large body of clinical data documenting that most human carcinomas contain reduced levels of the catalytic subunit of the mitochondrial H+-ATP synthase. In colon and lung cancer this alteration correlates with a poor patient prognosis. Furthermore, recent findings in colon cancer cells indicate that down-regulation of the H+-ATP synthase is linked to the resistance of the cells to chemotherapy. However, the mechanism by which the H+-ATP synthase participates in cancer progression is unknown. In this work, we show that inhibitors of the H+-ATP synthase delay

staurosporine-induced cell death in liver cells that are dependent on oxidative phosphorylation for energy provision whereas it has no effect on glycolytic cells. Efficient execution of cell death requires the generation of reactive oxygen species (ROS) controlled by the activity of the H+-ATP synthase in a process that is concurrent with the rapid disorganization of the cellular mitochondrial network. The generation of ROS after staurosporine treatment is highly dependent on the mitochondrial membrane potential and most likely caused by reverse electron flow to Complex I. The generated ROS promote the carbonylation and covalent modification of cellular and mitochondrial proteins. Inhibition of the activity of the H+-ATP synthase blunted ROS production, prevented the oxidation of cellular proteins and the modification of mitochondrial proteins, delaying the release of cyt c and the execution of cell death. The results in this work establish the down-regulation of the H+-ATP synthase, and thus of oxidative phosphorylation, as part of the molecular strategy adapted by cancer cells to avoid reactive oxygen species-mediated cell death. Furthermore, the results provide a mechanistic explanation to understand chemotherapeutic resistance of cancer cells that rely on glycolysis as main energy provision pathway.

see also –

The tumor suppressor function of mitochondria: Translation into the clinics

José M. CuezvaÁlvaro D. OrtegaImke Willers, et al.  
Biochimica et Biophysica Acta (BBA) – Molecular Basis of Disease  Dec 2009;  1792(12): 1145–1158  http://dx.doi.org/10.1016/j.bbadis.2009.01.006

Recently, the inevitable metabolic reprogramming experienced by cancer cells as a result of the onset of cellular proliferation has been added to the list of hallmarks of the cancer cell phenotype. Proliferation is bound to the synchronous fluctuation of cycles of an increased glycolysis concurrent with a restrained oxidative phosphorylation. Mitochondria are key players in the metabolic cycling experienced during proliferation because of their essential roles in the transduction of biological energy and in defining the life–death fate of the cell. These two activities are molecularly and functionally integrated and are both targets of commonly altered cancer genes. Moreover, energetic metabolism of the cancer cell also affords a target to develop new therapies because the activity of mitochondria has an unquestionable tumor suppressor function. In this review, we summarize most of these findings paying special attention to the opportunity that translation of energetic metabolism into the clinics could afford for the management of cancer patients. More specifically, we emphasize the role that mitochondrial β-F1-ATPase has as a marker for the prognosis of different cancer patients as well as in predicting the tumor response to therapy.

Self-Destructive Behavior in Cells May Hold Key to a Longer Life

Carl Zimmer, MY Times  October 5, 2009

In recent years, scientists have found evidence of autophagy in preventing a much wider range of diseases. Many disorders, like Alzheimer’s disease, are the result of certain kinds of proteins forming clumps. Lysosomes can devour these clumps before they cause damage, slowing the onset of diseases.

Lysosomes may also protect against cancer. As mitochondria get old, they cast off charged molecules that can wreak havoc in a cell and lead to potentially cancerous mutations. By gobbling up defective mitochondria, lysosomes may make cells less likely to damage their DNA. Many scientists suspect it is no coincidence that breast cancer cells are often missing autophagy-related genes. The genes may have been deleted by mistake as a breast cell divided. Unable to clear away defective mitochondria, the cell’s descendants become more vulnerable to mutations.

Unfortunately, as we get older, our cells lose their cannibalistic prowess. The decline of autophagy may be an important factor in the rise of cancer, Alzheimer’s disease and other disorders that become common in old age. Unable to clear away the cellular garbage, our bodies start to fail.

If this hypothesis turns out to be right, then it may be possible to slow the aging process by raising autophagy. It has long been known, for example, that animals that are put on a strict low-calorie diet can live much longer than animals that eat all they can. Recent research has shown that caloric restriction raises autophagy in animals and keeps it high. The animals seem to be responding to their low-calorie diet by feeding on their own cells, as they do during famines. In the process, their cells may also be clearing away more defective molecules, so that the animals age more slowly.

Some scientists are investigating how to manipulate autophagy directly. Dr. Cuervo and her colleagues, for example, have observed that in the livers of old mice, lysosomes produce fewer portals on their surface for taking in defective proteins. So they engineered mice to produce lysosomes with more portals. They found that the altered lysosomes of the old experimental mice could clear away more defective proteins. This change allowed the livers to work better.

 

Essentiality of pyruvate kinase, oxidation, and phosphorylation

We can move to the next level with greater clarity. Yu et al. reported an important relationship between Pyruvate kinase M2 (PKM2) and the Warburg effect of cancer cells ( M Yu, et al. PIM2 phosphorylates PKM2 and promotes Glycolysis in Cancer Cells. J Biol Chem (PMID: 24142698) http://dx.doi.org10.1074/jbc.M113.508226 ).  They found that PIM2 could directly phosphorylate PKM2 on the Thr454 residue, which resulted in an increase of PKM2 protein levels. PKM2 with a phosphorylation-defective mutation displayed a reduced effect on glycolysis compared to the wild-type, thereby co-activating HIF-1α and β-catenin, and enhanced mitochondria respiration and chemotherapeutic sensitivity of cancer cells. This indicated that PIM2-dependent phosphorylation of PKM2 is critical for regulating the Warburg effect in cancer, highlighting PIM2 as a potential therapeutic target.

In another study of the effect of 3 homoplastic mtDNA mutations on oxidative metabolism of osteosarcoma cells, there was a difference proportional to the magnitude of the defect. (Iommarini L, et al. Different mtDNA mutations modify tumor progression in dependence of the degree of respiratory complex I impairment. Hum Mol Genet. 2013 Nov 11. [Epub ahead of print]; PMID: 24163135 ).   Osteosarcoma cells carrying the most marked impairment of the gene encoding mitochondrial complex I  (CI) of oxidative phosphorylation displayed a reduced tumorigenic potential both in vitro and in vivo, when compared with cells with mild CI dysfunction. The severe CI dysfunction was an energetic defect associated with a compensatory increase in glycolytic metabolism and AMP-activated protein kinase activation.  The result suggested that mtDNA mutations may display diverse impact on tumorigenic potential depending on the type and severity of the resulting oxidative phosphorylation dysfunction. The modulation of tumor growth was independent from reactive oxygen species production but correlated with hypoxia-inducible factor 1α stabilization, indicating that structural and functional integrity of CI and oxidative phosphorylation are required for hypoxic adaptation and tumor progression.

An unrelated finding shares some agreement with what has been identified (Systematic isolation of context-dependent vulnerabilities in NSCLC. Cell, 24 Oct 2013; 155 (3): 552-566, http://dx.doi.org/10.1016/ j.cell.2013.09.041). They report  three distinct target/response-indicator pairings that are represented with significant frequencies (6%–16%) in the patient population. These include NLRP3 mutation/inflammasome activation-dependent FLIP addiction, co-occurring KRAS and LKB1 mutation-driven COPI addiction, and selective sensitivity to a synthetic indolotriazine that is specified by a seven-gene expression signature.   This is depicted in the Figure below.  The authors noted a frequency and diversity of somatic lesions detected among lung tumors can confound efforts to identify these targets.

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The forging of a cancer-metabolism link and twists in the chain (Biome 19th April 2013)

Ten years ago, Grahame Hardie and Dario Alessi discovered that the elusive upstream kinase required for the activation of AMP-activated protein kinase (AMPK) by metabolic stress that the Hardie lab had been pursuing in their research on the metabolic regulator AMPK was the tumor suppressor, LKB1, that the neighbouring Alessi lab was working on at the time. This finding represented the first clear link between AMPK and cancer.

The resulting paper [1], published in 2003 in what was then Journal of Biology (now BMC Biology), was one [1] of three [2, 3] connecting these two kinases and that helped to swell of a surge of interest in the metabolism of tumor cells that was just beginning at about that time and is still growing. (LKB1 and AMPK and the cancer-metabolism link – ten years after.  D Grahame Hardie, and Dario R Alessi.  BMC Biology 2013, 11:36.   http://dx doi.org.10.1186/1741-7007-11-36.)

 

In September 2003, both groups published a joint paper [1] in Journal of Biology (now BMC Biology) that identified the long-sought and elusive upstream kinase acting on AMP-activated protein kinase (AMPK) as a complex containing LKB1, a known tumor suppressor. Similar findings were reported at about the same time by David Carling and Marian Carlson [2] and by Reuben Shaw and Lew Cantley [3]; at the time of writing these three papers have received between them a total of over 2,000 citations. These findings provided a direct link between a protein kinase, AMPK, which at the time was mainly associated with regulation of metabolism, and another protein kinase, LKB1, which was known from genetic studies to be a tumor suppressor. While the idea that cancer is in part a metabolic disorder (first suggested by Warburg in the 1920s [4]) is well recognized today [5], this was not the case in 2003, and our paper perhaps contributed towards its renaissance.

The distinctive metabolic feature of tumor cells that enables them to meet the demands of unrestrained growth is the switch from oxidative generation of ATP to aerobic glycolysis – a phenomenon now well known as the Warburg effect. Operating this switch is one of the central functions of the AMP-activated protein kinase (AMPK) that has long been the focus of research in the Hardie lab. AMPK is an energy sensor that is allosterically tuned by competitive binding of ATP, ADP and AMP to sites on its g regulatory subunit (its portrait here, with AMP bound at two sites, was kindly provided by Bing Xiao and Stephen Gamblin). When phosphorylated by LKB1, AMPK responds to depletion of ATP by turning off anabolic reactions required for growth, and turning on catabolic reactions and oxidative phosphorylation – the reverse of the Warburg effect. In this light, it is not surprising that LKB1  is inactivated in some proportion of many different types of tumors.

AMPK as an energy sensor and metabolic switch

AMPK was discovered as a protein kinase activity that phosphorylated and inactivated two key enzymes of fatty acid and sterol biosynthesis: acetyl-CoA carboxylase (ACC) and 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR). The ACC kinase activity was reported to be activated by 5’-AMP, and the HMGR kinase activity by reversible phosphorylation, but for many years the two activities were thought to be due to distinct enzymes. However, in 1987 the DGH laboratory showed that both were functions of a single protein kinase, which we renamed AMPK after its allosteric activator, 5’-AMP. It was subsequently found that AMPK regulated not only lipid biosynthesis, but also many other metabolic pathways, both by direct phosphorylation of metabolic enzymes, and through longer-term effects mediated by phosphorylation of transcription factors and co-activators. In general, AMPK switches off anabolic pathways that consume ATP and NADPH, while switching on catabolic pathways that generate ATP (Figure 1).

 

target proteins and metabolic pathways regulated by AMPK 1741-7007-11-36-1_1

 

Summary of a selection of target proteins and metabolic pathways regulated by AMPK. Anabolic pathways switched off by AMPK are shown in the top half of the ‘wheel’ and catabolic pathways switched on by AMPK in the bottom half. Where a protein target for AMPK responsible for the effect is known, it is shown in the inner wheel; a question mark indicates that it is not yet certain that the protein is directly phosphorylated. For original references see [54].

Key to acronyms: ACC1/ACC2, acetyl-CoA carboxylases-1/-2; HMGR, HMG-CoA reductase; SREBP1c, sterol response element binding protein-1c; CHREBP, carbohydrate response element binding protein; TIF-1A, transcription initiation factor-1A; mTORC1, mechanistic target-of-rapamycin complex-1; PFKFB2/3, 6-phosphofructo-2-kinase, cardiac and inducible isoforms; TBC1D1, TBC1 domain protein-1; SIRT1, sirtuin-1; PGC-1α, PPAR-γ coactivator-1α; ULK1, Unc51-like kinase-1.

Regulation of AMPK  1741-7007-11-36-3

 

Regulation of AMPK. AMPK can be activated by increases in cellular AMP:ATP or ADP:ATP ratio, or Ca2+ concentration. AMPK is activated >100-fold on conversion from a dephosphorylated form (AMPK) to a form phosphorylated at Thr172 (AMPK-P) catalyzed by at least two upstream kinases: LKB1, which appears to be constitutively active, and CaMKKβ, which is only active when intracellular Ca2+ increases. Increases in AMP or ADP activate AMPK by three mechanisms: (1) binding of AMP or ADP to AMPK, causing a conformational change that promotes phosphorylation by upstream kinases (usually this will be LKB1, unless [Ca2+] is elevated); (2) binding of AMP or ADP, causing a conformational change that inhibits dephosphorylation by protein phosphatases; (3) binding of AMP (and not ADP), causing allosteric activation of AMPK-P. All three effects are antagonized by ATP, allowing AMPK to act as an energy sensor.

AMPK and AMPK-related kinase (ARK) family  1741-7007-11-36-4

 

Members of the AMPK and AMPK-related kinase (ARK) family. All the kinases named in the figure are phosphorylated and activated by LKB1, although what regulates this phosphorylation is known only for AMPK. Alternative names are shown, where applicable.

AMPK-activating drugs metformin or phenformin might provide protection against cancer 1741-7007-11-36-5

 

 

Three possible mechanisms to explain how the AMPK-activating drugs metformin or phenformin might provide protection against cancer. (a) Metformin acts on the liver and other insulin target tissues by activating AMPK (and probably via other targets), normalizing blood glucose; this reduces insulin secretion from pancreatic β cells, reducing the growth-promoting effects of insulin (and high glucose) on tumor cells. Since metformin does not reduce glucose levels in normoglycemic individuals, this mechanism would only operate in insulin-resistant subjects. (b) Metformin or phenformin activates AMPK in pre-neoplastic cells, restraining their growth and proliferation and thus delaying the onset of tumorigenesis; this mechanism would only operate in cells where the LKB1-AMPK pathway was intact. (c) Metformin or phenformin inhibits mitochondrial ATP synthesis in tumor cells, promoting cell death. If the LKB1-AMPK pathway was down-regulated in the tumor cells, they would be more sensitive to cell death induced by the biguanides than surrounding normal cells.

Metformin and phenformin are biguanides that inhibit mitochondrial function and so deplete ATP by inhibiting its production . AMPK is activated by any metabolic stress that depletes ATP, either by inhibiting its production (as do hypoxia, glucose deprivation, and treatment with biguanides) or by accelerating its consumption (as does muscle contraction). By switching off anabolism and other ATP-consuming processes and switching on alternative ATP-producing catabolic pathways, AMPK acts to restore cellular energy homeostasis.

Findings that AMPK is activated in skeletal muscle during exercise and that it increases muscle glucose uptake and fatty acid oxidation led to the suggestion that AMPK-activating drugs might be useful for treating type 2 diabetes. Indeed, it turned out that AMPK is activated by metformin, a drug that had at that time been used to treat type 2 diabetes for over 40 years, and by phenformin , a closely related drug that had been withdrawn for treatment of diabetes due to side effects of lactic acidosis.

If only it were so simple. Effects of metformin on cancer in type 2 diabetics could be secondary to reduction in insulin levels, and although there is evidence for direct effects of AMPK activation on the development of tumors in mice, there is also recent evidence that tumors that become established without down-regulating LKB1 survive metformin better than those that have lost it – probably because metformin poisons the mitochondrial respiratory chain, depressing ATP levels, and cells in which AMPK can still be activated in response to the challenge do better than those in which it can’t.

In their review, Hardie and Alessi chart these  twists and turns, and point to the explosion of further possibilities opened up by the discovery, since their 2003 publication, of at least one other class of kinase upstream of AMPK (the CaM kinases), and at least a dozen other downstream targets of LKB1 (AMPK-related kinases, or ARKs) – not to mention the innumerable downstream targets of AMPK; all which make half their schematic illustrations look like hedgehogs.

Analysis of respiration  in human cancer

Bioenergetic profiling of cancer cells is of great potential because it can bring forward new and effective

Therapeutic  strategies along with early diagnosis. Metabolic Control Analysis (MCA) is a methodology that enables quantification of the flux control exerted by different enzymatic steps in a metabolic network thus assessing their contribution to the system‘s function.

(T Kaambre,V Chekulayev, I Shevchuk, et al. Metabolic control analysis of respiration  in human cancer tissue.  Frontiers Physiol 2013 (4); 151:  1. http://dx.doi.org/10.3389/fphys.2013.00151)

Our main goal is to demonstrate the applicability of MCA for in situ studies of energy

Metabolism in human breast and colorectal cancer cells as well as in normal tissues .We seek to determine the metabolic conditions leading to energy flux redirection in cancer cells. A main result obtained is that the adenine nucleotide translocator exhibits the highest control of respiration in human breast cancer thus becoming a prospective therapeutic target. Additionally, we present evidence suggesting the existence of mitochondrial respiratory supercomplexes that may represent a way by which cancer cells avoid apoptosis. The data obtained show that MCA applied in situ can be insightful in cancer cell energetic research.

Metabolic control analysis of respiration in human cancer tissue. fphys-04-00151-g001

Metabolic control analysis of respiration in human cancer tissue.

Representative traces of change in the rate of oxygen consumption by permeabilized human colorectal cancer (HCC) fibers after their titration with increasing concentrations of mersalyl, an inhibitor of inorganic phosphate carrier (panel A). The values of respiration rate obtained were plotted vs. mersalyl concentration (panel B) and from the plot the corresponding flux control coefficient was calculated. Bars are ±SEM.

Oncologic diseases such as breast and colorectal cancers are still one of the main causes of premature death. The low efficiency of contemporary medicine in the treatment of these malignancies is largely mediated by a poor understanding of the processes involved in metastatic dissemination of cancer cells as well as the unique energetic properties of mitochondria from tumors. Current knowledge supports the idea that human breast and colorectal cancer cells exhibit increased rates of glucose consumption displaying Warburg phenotype,i.e.,elevated glycolysis even in the presence of oxygen (Warburg and Dickens, 1930; Warburg, 1956 ;Izuishietal., 2012). Notwithstanding,  there are some evidences that in these malignancies mitochondrial oxidative phosphorylation (OXPHOS) is the main source of ATP rather than glycolysis. Cancer cells have been classified according to their pattern of metabolic remodeling depending of the relative balance between aerobic glycolysis and OXPHOS (Bellanceetal.,2012). The first type of tumor cells is highly glycolytic, the second OXPHOS deficient and the third type of tumors dislay enhanced OXPHOS. Recent studies strongly sug gest  that cancer cells can utilize lactate, free fatty acids, ketone bodies, butyrate and glutamine as key respiratory substrate selic iting metabolic remodeling of normal surrounding cells toward aerobic glycolysis—“reverse Warburg”effect (Whitaker-Menezes et al.,2011;Salem et al.,2012;Sotgia et al.,2012;Witkiewicz et al., 2012).

In normal cells,the OXPHOS system is usually closely linked to phosphotransfer systems, including various creatine kinase(CK) isotypes,which ensure a safe operation of energetics over a broad functional range of cellular activities (Dzejaand Terzic,2003).  However, our current knowledge about the function of CK/creatine (Cr) system in human breast and colorectal cancer is insufficient. In some malignancies, for example sarcomas the CK/Cr system was shown to be strongly downregulated (Beraetal.,2008;Patraetal.,2008).  Our previous studies showed  that the mitochondrial-bound CK (MtCK) activity was significantly decreased in HL-1 tumor cells (Mongeetal.,2009), as compared to normal parent cardiac cells where the OXPHOS is the main ATP source of and the CK system is a main energy carrier. In the present study,we estimated the role of MtCK in maintaining energy homeostasis in human colorectal cancer cells. Understanding the control and regulation of energy metabolism requires analytical tools that take into account  the existing interactions between individual network components and their impact on systemic network function. Metabolic Control Analysis(MCA) is a theoretical framework relating the properties of metabolic systems to the kinetic characteristics of their individual enzymatic components (Fell,2005). An experimental approach of MCA has been already successfully applied to the studies of OXPHOS in isolated mitochondria (Tageretal.,1983; Kunzetal.,1999; Rossignoletal.,2000)  and in skinned muscle fibers (Kuznetsovetal.,1997;Teppetal.,2010).

Metabolic control analysis of respiration in human cancer tissue

Values of basal (Vo) and maximal respiration rate (Vmax, in the presence of 2 mM ADP) and apparent Michaelis Menten constant (Km) for ADP in permeabilized human breast and colorectal cancer samples as well as health tissue. – See more at: http://journal.frontiersin.org/Journal/10.3389/fphys.2013.00151/full#sthash.VBXPdodj.dpuf

Role of Uncoupling Proteins in Cancer

Adamo Valle, Jordi Oliver and Pilar Roca *
Cancers 2010; 2: 567-591;   http://dx.doi.org/10.3390/cancers2020567

Since Otto Warburg discovered that most cancer cells predominantly produce energy by glycolysis rather than by oxidative phosphorylation in mitochondria, much interest has been focused on the alterations of these organelles in cancer cells. Mitochondria have been shown to be key players in numerous cellular events tightly related with the biology of cancer. Although energy production relies on the glycolytic pathway in cancer cells, these organelles also participate in many other processes essential for cell survival and proliferation such as ROS production, apoptotic and necrotic cell death, modulation of oxygen concentration, calcium and iron homeostasis, and certain metabolic and biosynthetic pathways. Many of these mitochondrial-dependent processes are altered in cancer cells, leading to a phenotype characterized, among others, by higher oxidative stress, inhibition of apoptosis, enhanced cell proliferation, chemoresistance, induction of angiogenic genes and aggressive fatty acid oxidation. Uncoupling proteins, a family of inner mitochondrial membrane proteins specialized in energy-dissipation, has aroused enormous interest in cancer due to their relevant impact on such processes and their potential for the development of novel therapeutic strategies.

Uncoupling proteins (UCPs) are a family of inner mitochondrial membrane proteins whose function is to allow the re-entry of protons to the mitochondrial matrix, by dissipating the proton gradient and, subsequently, decreasing membrane potential and production of reactive oxygen species (ROS). Due to their pivotal role in the intersection between energy efficiency and oxidative stress UCPs are being investigated for a potential role in cancer. In this review we compile the latest evidence showing a link between uncoupling and the carcinogenic process, paying special attention to their involvement in cancer initiation, progression and drug chemoresistance.

The Warburg Effect

Uncoupling the Warburg effect from cancer

A Najafov and DR Alessi
Proc Nat Acad Sci                                      www.pnas.org/cgi/doi/10.1073/pnas.1014047107
A remarkable trademark of most tumors is their ability to break down glucose by glycolysis at a vastly higher rate than in normal tissues, even when oxygen is copious. This phenomenon, known as the Warburg effect, enables rapidly dividing tumor cells to generate essential biosynthetic building blocks such as nucleic acids, amino acids, and lipids from glycolytic intermediates to permit growth and duplication of cellular components during  division (1). An assumption dominating research in this area is that the Warburg effect is specific to cancer. Thus, much of the focus has been on uncovering mechanisms by which cancer-causing mutations influence metabolism to stimulate glycolysis.

This has lead to many exciting discoveries. For example, the p53 tumor suppressor can suppress glycolysis through its ability to control expression of key metabolic genes, such as phosphoglycerate mutase (2), synthesis of cytochrome C oxidase-2 (3), and TP53-induced glycolysis and apoptosis regulator (TIGAR) (4). Many cancer-causing mutations lead to activation of the Akt and mammalian target of rapamycin (mTOR) pathway that profoundly influences metabolism and expression of metabolic enzymes to promoteglycolysis (5).

Strikingly, all cancer cells but not nontransformed cells express a specific splice variant of pyruvate kinase, termed M2-PK, that is less active, leading to the build up of phosphoenolpyruvate (6). Recent work has revealed that reduced activity of M2-PK promotes a unique glycolytic pathway in which phosphoenolpyruvate is converted to pyruvate by a histidine-dependent phosphorylation of phosphoglycerate mutase, promoting assimilation of glycolytic products into biomass (7). However, despite these observations, one might imagine that the Warburg effect need not be specific for cancer and that any normal cell would need to stimulate glycolysis to generate sufficient biosynthetic materials to fuel expansion and division.

Recent work by Salvador Moncada’s group published in PNAS (8) and other recent work from the same group (9, 10) provides exciting evidence supporting the idea that the Warburg effect is also required for the proliferation of noncancer cells.

The key discovery was that the anaphase promoting complex/cyclosome-Cdh1(APC/C-Cdh1), a master regulator of the transition of G1 to S phase of the cell cycle, inhibits glycolysis in proliferating noncancer cells by mediating the degradation of two key metabolic enzymes, namely 6-phosphofructo-2-kinase/ fructose-2,6-bisphosphatase isoform3 (PFKFB3) (9, 10) and glutaminase-(Fig. 1) (8).

Fig. 1. Mechanism by which APC_C-Cdh1 inhibits glycolysis and glutaminolysis to suppress cell proliferation

 

Fig.  Mechanism by which APC/C-Cdh1 inhibits glycolysis and glutaminolysis to suppress cell proliferation.

APC/C-Cdh1 E3 ligase recognizes KEN-box–containing metabolic enzymes, such as PFKFB3 and glutaminase-1 (GLS1), and ubiquitinates and targets them for proteasomal degradation. This inhibits glycolysis and glutaminolysis, leading to decrease in metabolites that can be assimilated into biomass, thereby suppressing proliferation.

PFKFB3 potently stimulates glycolysis by catalyzing the formation of fructose-2,6-bisphosphate, the allosteric activatorof 6-phosphofructo-1-kinase (11). Glutaminase-1 is the first enzyme in glutaminolysis, converting glutamine to lactate, yielding biosyntheticintermediates required for cell proliferation (12).

APC/C is a cell cycle-regulated E3 ubiquitin ligase that promotes ubiquitination of a distinct set of cell cycle proteins containing either a D-box (destruction box) or a KEN-box, named after the essential Lys-Glu-Asn motif required for APC recognition (13). Among its well-known substrates are crucial cell cycle proteins, such as cyclin B1, securin, and Plk1. By ubiquitinating and targeting its substrates to 26S proteasome-mediated degradation, APC/C regulates processes in late mitotic stage, exit  from mitosis, and several events in G1 (14). The Cdh1 subunit is the KENbox binding adaptor of the APC/C ligase and is essential for G1/S transition.

Importantly, APC/C-Cdh1 is inactivated at the initiation of the S-phase of the cell cycle when DNA and cellular organelles are replicated at the time of the greatest need for generation of biosynthetic materials. APC/C-Cdh1 is reactivated later at the mitosis/G1 phase of the cell cycle when there is a lower requirement for biomassgeneration.

Both PFKFB3 (9, 10) and glutaminase-1 (8) possess a KEN-box and are rapidly degraded in nonneoplastic lymphocytes during the cell cycle when APC/C-Cdh1 is active. Consistent with destruction being mediated by APC-C-Cdh1, ablation of the KEN-box prevents degradation of PFKFB3 (9, 10) and glutaminase-1 (8). Inhibiting the proteasomal-dependent degradation with the MG132 inhibitor

markedly increases levels of ubiquitinated PFKFB3 and glutaminase-1 (8). Moreover, overexpression of Cdh1 to activate APC/C-Cdh1 decreases levels of PFKFB3 as well as glutmaninase-1 and concomitantly inhibited glycolysis, as judged by decrease in lactate production. This effect is also observed when cells were treated with a glutaminase-1 inhibitor (6-diazo-5- oxo-L-norleucine) (8). The final evidence supporting the authors’ hypothesis is that proliferation and glycolysis is inhibited after shRNA-mediated silencing of either PFKFB3 or glutaminase-1 (8).

These results are interesting, because unlike most recent work in this area, Colombo et al. (8) link the Warburg effect to the machinery of the cell cycle that is present in all cells rather than to cancer driving mutations. Further work is required to properly define the overall importance of this pathway, which has thus far only been studied in a limited number of cells. It would also be of value to undertake a more detailed analysis of how the rate of glycolysis and other metabolic pathways vary during the cell cycle of normal and cancer cells…(see full 2 page article) at PNAS.

 

The Warburg Effect Suppresses Oxidative Stress Induced Apoptosis in a Yeast Model for Cancer

C Ruckenstuhl, S Buttner, D Carmona-Gutierre, et al.
PLoS ONE 2009; 4(2): e4592.  http://dx.doi.org/10.1371/journal.pone.0004592

Colonies of Saccharomyces cerevisiae, suitable for manipulation of mitochondrial respiration and shows mitochondria-mediated cell death, were used as a model. Repression of respiration as well as ROS-scavenging via glutathione inhibited apoptosis, conferred a survival advantage during seeding and early development of this fast proliferating solid cell population. In contrast, enhancement of respiration triggered cell death.

Conclusion/Significance: The Warburg effect might directly contribute to the initiation of cancer formation – not only by enhanced glycolysis – but also via decreased respiration in the presence of oxygen, which suppresses apoptosis.

 

PIM2 phosphorylates PKM2 and promotes Glycolysis in Cancer Cells
Z Yu, L Huang, T Zhang, et al.
J Biol Chem 2013;                               http://dx.doi.org/10.1074/jbc.M113.508226

http://www.jbc.org/cgi/doi/10.1074/jbc.M113.508226

Serine/threonine protein kinase PIM2, a known oncogene is a binding partner of pyruvate kinase M2 (PKM2), a key player in the Warburg effect of cancer cells.   PIM2 interacts with PKM2 and phosphorylates PKM2 on the Thr454 residue.

The phosphorylation of PKM2 increases glycolysis and proliferation in cancer cells.

The PIM2-dependent phosphoirylation of ZPKM2 is critical for regulating the Warburg effect in cancer.

 

Genome-Scale Metabolic Modeling Elucidates the Role of Proliferative Adaptation in Causing the Warburg Effect

Shlomi T, Benyamini T, Gottlieb E, Sharan R, Ruppin E
PLoS Comput Biol 2011; 7(3): e1002018.    http://dx.doi.org/10.1371/journal.pcbi.1002018
The Warburg effect – a classical hallmark of cancer metabolism – is a counter-intuitive phenomenon in which rapidly proliferating cancer cells resort to inefficient ATP production via glycolysis leading to lactate secretion, instead of relying primarily on more efficient energy production through mitochondrial oxidative phosphorylation, as most normal cells do.

The causes for the Warburg effect have remained a subject of considerable controversy since its discovery over 80 years ago, with several competing hypotheses. Here, utilizing a genome-scale human metabolic network model accounting for stoichiometric and enzyme solvent capacity considerations, we show that the Warburg effect is a direct consequence of the metabolic adaptation of cancer cells to increase biomass production rate. The analysis is shown to accurately capture a three phase metabolic behavior that is observed experimentally during oncogenic progression, as well as a prominent characteristic of cancer cells involving their preference for glutamine uptake over other amino acids.

 

The metabolic advantage of tumor cells

Maurice Israël and Laurent Schwartz

Additional article information

Abstract

1- Oncogenes express proteins of “Tyrosine kinase receptor pathways”, a receptor family including insulin or IGF-Growth Hormone receptors. Other oncogenes alter the PP2A phosphatase brake over these kinases.

2- Experiments on pancreatectomized animals; treated with pure insulin or total pancreatic extracts, showed that choline in the extract, preserved them from hepatomas.

Since choline is a methyle donor, and since methylation regulates PP2A, the choline protection may result from PP2A methylation, which then attenuates kinases.

3- Moreover, kinases activated by the boosted signaling pathway inactivate pyruvate kinase and pyruvate dehydrogenase. In addition, demethylated PP2A would no longer dephosphorylate these enzymes. A “bottleneck” between glycolysis and the oxidative-citrate cycle interrupts the glycolytic pyruvate supply now provided via proteolysis and alanine transamination. This pyruvate forms lactate (Warburg effect) and NAD+ for glycolysis. Lipolysis and fatty acids provide acetyl CoA; the citrate condensation increases, unusual oxaloacetate sources are available. ATP citrate lyase follows, supporting aberrant transaminations with glutaminolysis and tumor lipogenesis. Truncated urea cycles, increased polyamine synthesis, consume the methyl donor SAM favoring carcinogenesis.

4- The decrease of butyrate, a histone deacetylase inhibitor, elicits epigenic changes (PETEN, P53, IGFBP decrease; hexokinase, fetal-genes-M2, increase)

5- IGFBP stops binding the IGF – IGFR complex, it is perhaps no longer inherited by a single mitotic daughter cell; leading to two daughter cells with a mitotic capability.

6- An excess of IGF induces a decrease of the major histocompatibility complex MHC1, Natural killer lymphocytes should eliminate such cells that start the tumor, unless the fever prostaglandin PGE2 or inflammation, inhibit them…

Introduction

The metabolic network of biochemical pathways forms a system controlled by a few switches, changing the finality of this system. Specific substrates and hormones control such switches. If for example, glycemia is elevated, the pancreas releases insulin, activating anabolism and oxidative glycolysis, energy being required to form new substance or refill stores. If starvation decreases glycemia, glucagon and epinephrine activate gluconeogenesis and ketogenesis to form nutriments, mobilizing body stores. The different finalities of the system are or oriented by switches sensing the NADH/NAD+, the ATP/AMP, the cAMP/AMP ratios or the O2 supply… We will not describe here these metabolic finalities and their controls found in biochemistry books.

Many of the switches depend of the phosphorylation of key enzymes that are active or not. Evidently, there is some coordination closing or opening the different pathways. Take for example gluconeogenesis, the citrate condensation slows down, sparing OAA, which starts the gluconeogenic pathway. In parallel, one also has to close pyruvate kinase (PK); if not, phosphoenolpyruvate would give back pyruvate, interrupting the pathway. Hence, the properties of key enzymes acting like switches on the pathway specify the finality of the system. Our aim is to show that tumor cells invent a new specific finality, with mixed glycolysis and gluconeogenesis features. This very special metabolism gives to tumor cells a selective advantage over normal cells, helping the tumor to develop at the detriment of the rest of the body.

I Abnormal metabolism of tumors, a selective advantage

The initial observation of Warburg 1956 on tumor glycolysis with lactate production is still a crucial observation [1]. Two fundamental findings complete the metabolic picture: the discovery of the M2 pyruvate kinase (PK) typical of tumors [2] and the implication of tyrosine kinase signals and subsequent phosphorylations in the M2 PK blockade [35].

A typical feature of tumor cells is a glycolysis associated to an inhibition of apoptosis. Tumors over-express the high affinity hexokinase 2, which strongly interacts with the mitochondrial ANT-VDAC-PTP complex. In this position, close to the ATP/ADP exchanger (ANT), the hexokinase receives efficiently its ATP substrate [6,7]. As long as hexokinase occupies this mitochondria site, glycolysis is efficient. However, this has another consequence, hexokinase pushes away from the mitochondria site the permeability transition pore (PTP), which inhibits the release of cytochrome C, the apoptotic trigger [8]. The site also contains a voltage dependent anion channel (VDAC) and other proteins. The repulsion of PTP by hexokinase would reduce the pore size and the release of cytochrome C. Thus, the apoptosome-caspase proteolytic structure does not assemble in the cytoplasm. The liver hexokinase or glucokinase, is different it has less interaction with the site, has a lower affinity for glucose; because of this difference, glucose goes preferentially to the brain.

Further, phosphofructokinase gives fructose 1-6 bis phosphate; glycolysis is stimulated if an allosteric analogue, fructose 2-6 bis phosphate increases in response to a decrease of cAMP. The activation of insulin receptors in tumors has multiple effects, among them; a decrease of cAMP, which will stimulate glycolysis.

Another control point is glyceraldehyde P dehydrogenase that requires NAD+ in the glycolytic direction. If the oxygen supply is normal, the mitochondria malate/aspartate (MAL/ASP) shuttle forms the required NAD+ in the cytosol and NADH in the mitochondria. In hypoxic conditions, the NAD+ will essentially come via lactate dehydrogenase converting pyruvate into lactate. This reaction is prominent in tumor cells; it is the first discovery of Warburg on cancer.

At the last step of glycolysis, pyruvate kinase (PK) converts phosphoenolpyruvate (PEP) into pyruvate, which enters in the mitochondria as acetyl CoA, starting the citric acid cycle and oxidative metabolism. To explain the PK situation in tumors we must recall that PK only works in the glycolytic direction, from PEP to pyruvate, which implies that gluconeogenesis uses other enzymes for converting pyruvate into PEP. In starvation, when cells need glucose, one switches from glycolysis to gluconeogenesis and ketogenesis; PK and pyruvate dehydrogenase (PDH) are off, in a phosphorylated form, presumably following a cAMP-glucagon-adrenergic signal. In parallel, pyruvate carboxylase (Pcarb) becomes active. Moreover, in starvation, much alanine comes from muscle protein proteolysis, and is transaminated into pyruvate. Pyruvate carboxylase first converts pyruvate to OAA and then, PEP carboxykinase converts OAA to PEP etc…, until glucose. The inhibition of PK is necessary, if not one would go back to pyruvate. Phosphorylation of PK, and alanine, inhibit the enzyme.

Well, tumors have a PK and a PDH inhibited by phosphorylation and alanine, like for gluconeogenesis, in spite of an increased glycolysis! Moreover, in tumors, one finds a particular PK, the M2 embryonic enzyme [2,9,10] the dimeric, phosphorylated form is inactive, leading to a “bottleneck “. The M2 PK has to be activated by fructose 1-6 bis P its allosteric activator, whereas the M1 adult enzyme is a constitutive active form. The M2 PK bottleneck between glycolysis and the citric acid cycle is a typical feature of tumor cell glycolysis.

We also know that starvation mobilizes lipid stores from adipocyte to form ketone bodies, they are like glucose, nutriments for cells. Growth hormone, cAMP, AMP, activate a lipase, which provides fatty acids; their β oxidation cuts them into acetyl CoA in mitochondria and in peroxisomes for very long fatty acids; forming ketone bodies. Normally, citrate synthase slows down, to spare acetyl CoA for the ketogenic route, and OAA for the gluconeogenic pathway. Like for starvation, tumors mobilize lipid stores. But here, citrate synthase activity is elevated, condensing acetyl CoA and OAA [1113]; citrate increases, ketone bodies decrease. Consequently, ketone bodies will stop stimulating Pcarb. In tumors, the OAA needed for citrate synthase will presumably come from PEP, via reversible PEP carboxykinase or other sources. The quiescent Pcarb will not process the pyruvate produced by alanine transamination after proteolysis, leaving even more pyruvate to lactate dehydrogenase, increasing the lactate released by the tumor, and the NAD+ required for glycolysis.

Above the bottleneck, the massive entry of glucose accumulates PEP, which converts to OAA via mitochondria PEP carboxykinase, an enzyme requiring biotine-CO2-GDP. This source of OAA is abnormal, since Pcarb, another biotin-requiring enzyme, should have provided OAA. Tumors may indeed contain “morule inclusions” of biotin-enzyme [14] suggesting an inhibition of Pcarb, presumably a consequence of the maintained citrate synthase activity, and decrease of ketone bodies that normally stimulate Pcarb. The OAA coming via PEP carboxykinase and OAA coming from aspartate transamination or via malate dehydrogenase condenses with acetyl CoA, feeding the elevated tumoral citric acid condensation starting the Krebs cycle. Thus, tumors have to find large amounts of acetyl CoA for their condensation reaction; it comes essentially from lipolysis and β oxidation of fatty acids, and enters in the mitochondria via the carnitine transporter. This is the major source of acetyl CoA; since PDH that might have provided acetyl CoA remains in tumors, like PK, in the inactive phosphorylated form. The blockade of PDH [15] was recently reversed by inhibiting its kinase [16,17].

The key question is then to find out why NADH, a natural citrate synthase inhibitor did not switch off the enzyme in tumor cells. Probably, the synthesis of NADH by the dehydrogenases of the Krebs cycle and malate/aspartate shuttle, was too low, or the oxidation of NADH via the respiratory electron transport chain and mitochondrial complex1 (NADH dehydrogenase) was abnormally elevated. Another important point concerns PDH and α ketoglutarate dehydrogenase that are homologous enzymes, they might be regulated in a concerted way; when PDH is off, α ketoglutarate dehydrogenase might be also be slowed. Moreover, this could be associated to an upstream inhibition of aconinase by NO, or more probably to a blockade of isocitrate dehydrogenase, which favors in tumor cells, the citrate efflux from mitochondria, and the ATP citrate lyase route.

Normally, an increase of NADH inhibits the citrate condensation, favoring the ketogenic route associated to gluconeogenesis, which turns off glycolysis. Apparently, this regulation does not occur in tumors, since citrate synthase remains active. Moreover, in tumor cells, the α ketoglutarate not processed by
α ketoglutarate dehydrogenase converts to glutamate, via glutamate dehydrogenase, in this direction the reaction forms NAD+, backing up the LDH production. Other sources of glutamate are glutaminolysis, which increases in tumors [2].

The Figure Figure11 shows how tumors bypass the PK and PDH bottlenecks and evidently, the increase of glucose influx above the bottleneck, favors the supply of substrates to the pentose shunt, as pentose is needed for synthesizing ribonucleotides, RNA and DNA. The Figure Figure11 represents the stop below the citrate condensation. Hence, citrate quits the mitochondria to give via ATP citrate lyase, acetyl CoA and OAA in the cytosol of tumor cells. Acetyl CoA supports the synthesis of fatty acids and the formation of triglycerides. The other product of the ATP citrate lyase reaction, OAA, drives the transaminase cascade (ALAT and GOT transaminases) in a direction that consumes GLU and glutamine and converts in fine alanine into pyruvate and lactate plus NAD+. This consumes protein body stores that provide amino acids and much alanine (like in starvation).

The Figure Figure11 indicates that malate dehydrogenase is a source of NAD+ converting OAA into malate, which backs-up LDH. Part of the malate converts to pyruvate (malic enzyme) and processed by LDH. Moreover, malate enters in mitochondria via the shuttle and gives back OAA to feed the citrate condensation. Glutamine will also provide amino groups for the “de novo” synthesis of purine and pyrimidine bases particularly needed by tumor cells. The Figure Figure11 indicates that ASP shuttled out of the mitochondrial, joins the ASP formed by cytosolic transaminases, to feed the synthesis of pyrimidine bases via ASP transcarbamylase, a process also enhanced in tumor cells. In tumors, this silences the argininosuccinate synthetase step of the urea cycle [1820].

This blockade also limits the supply of fumarate to the Krebs cycle. The latter, utilizes the α ketoglutarate provided by the transaminase reaction, since α ketoglutarate coming via aconitase slows down. Indeed, NO and peroxynitrite increase in tumors and probably block aconitase. The Figure Figure11 indicates the cleavage of arginine into urea and ornithine. In tumors, the ornithine production increases, following the polyamine pathway. Ornithine is decarboxylated into putrescine by ornithine decarboxylase, then it captures the backbone of S adenosyl methionine (SAM) to form polyamines spermine then spermidine, the enzyme controlling the process is SAM decarboxylase. The other reaction product, 5-methlthioribose is then decomposed into methylthioribose and adenine, providing purine bases to the tumor. We shall analyze below the role of SAM in the carcinogenic mechanism, its destruction aggravates the process.

metabolic pathways 1476-4598-10-70-1
Cancer metabolism. Glycolysis is elevated in tumors, but a pyruvate kinase (PK) “bottleneck” interrupts phosphoenol pyruvate (PEP) to pyruvate conversion. Thus, alanine following muscle proteolysis transaminates to pyruvate, feeding lactate dehydrogenase,

In summary, it is like if the mechanism switching from gluconeogenesis to glycolysis was jammed in tumors, PK and PDH are at rest, like for gluconeogenesis, but citrate synthase is on. Thus, citric acid condensation pulls the glucose flux in the glycolytic direction, which needs NAD+; it will come from the pyruvate to lactate conversion by lactate dehydrogenase (LDH) no longer in competition with a quiescent Pcarb. Since the citrate condensation consumes acetyl CoA, ketone bodies do not form; while citrate will support the synthesis of triglycerides via ATP citrate lyase and fatty acid synthesis… The cytosolic OAA drives the transaminases in a direction consuming amino acid. The result of these metabolic changes is that tumors burn glucose while consuming muscle protein and lipid stores of the organism. In a normal physiological situation, one mobilizes stores for making glucose or ketone bodies, but not while burning glucose! Tumor cell metabolism gives them a selective advantage over normal cells. However, one may attack some vulnerable points.

Cancer metabolism. Glycolysis is elevated in tumors, but a pyruvate kinase (PK) “bottleneck” interrupts phosphoenol pyruvate (PEP) to pyruvate conversion. Thus, alanine following muscle proteolysis transaminates to pyruvate, feeding lactate dehydrogenase, converting pyruvate to lactate, (Warburg effect) and NAD+ required for glycolysis. Cytosolic malate dehydrogenase also provides NAD+ (in OAA to MAL direction). Malate moves through the shuttle giving back OAA in the mitochondria. Below the PK-bottleneck, pyruvate dehydrogenase (PDH) is phosphorylated (second bottleneck). However, citrate condensation increases: acetyl-CoA, will thus come from fatty acids β-oxydation and lipolysis, while OAA sources are via PEP carboxy kinase, and malate dehydrogenase, (pyruvate carboxylase is inactive). Citrate quits the mitochondria, (note interrupted Krebs cycle). In the cytosol, ATPcitrate lyase cleaves citrate into acetyl CoA and OAA. Acetyl CoA will make fatty acids-triglycerides. Above all, OAA pushes transaminases in a direction usually associated to gluconeogenesis! This consumes protein stores, providing alanine (ALA); like glutamine, it is essential for tumors. The transaminases output is aspartate (ASP) it joins with ASP from the shuttle and feeds ASP transcarbamylase, starting pyrimidine synthesis. ASP in not processed by argininosuccinate synthetase, which is blocked, interrupting the urea cycle. Arginine gives ornithine via arginase, ornithine is decarboxylated into putrescine by ornithine decarboxylase. Putrescine and SAM form polyamines (spermine spermidine) via SAM decarboxylase. The other product 5-methylthioadenosine provides adenine. Arginine deprivation should affect tumors. The SAM destruction impairs methylations, particularly of PP2A, removing the “signaling kinase brake”, PP2A also fails to dephosphorylate PK and PDH, forming the “bottlenecks”. (Black arrows = interrupted pathways).

 II Starters for cancer metabolic anomaly

1. Lessons from oncogenes

Following the discovery of Rous sarcoma virus transmitting cancer [21], we have to wait the work of Stehelin [22] to realize that this retrovirus only transmitted a gene captured from a previous host. When one finds that the transmitted gene encodes the Src tyrosine kinase, we are back again to the tyrosine kinase signals, similar to those activated by insulin or IGF, which control carbohydrate metabolism, anabolism and mitosis.

An up regulation of the gene product, now under viral control causes tumors. However, the captured viral oncogene (v-oncogene) derives from a normal host gene the proto-oncogene. The virus only perturbs the expression of a cellular gene the proto-oncogene. It may modify its expression, or its regulation, or transmit a mutated form of the proto-oncogene. Independently of any viral infection, a similar tumorigenic process takes place, if the proto-oncogene is translocated in another chromosome; and transcribed under the control of stronger promoters. In this case, the proto-oncogene becomes an oncogene of cellular origin (c-oncogene). The third mode for converting a prot-oncogene into an oncogene occurs if a retrovirus simply inserts its strong promoters in front of the proto-oncogene enhancing its expression.

It is impressive to find that retroviral oncogenes and cellular oncogenes disturb this major signaling pathway: the MAP kinases mitogenic pathways. At the ligand level we find tumors such Wilm’s kidney cancer, resulting from an increased expression of insulin like growth factor; we have also the erbB or V-int-2 oncogenes expressing respectively NGF and FGF growth factor receptors. The receptors for these ligands activate tyrosine kinase signals, similarly to insulin receptors. The Rous sarcoma virus transmits the src tyrosine kinase, which activates these signals, leading to a chicken leukemia. Similarly, in murine leukemia, a virus captures and retransmits the tyrosine kinase abl. Moreover, abl is also stimulated if translocated and expressed with the bcr gene of chromosome 22, as a fusion protein (Philadelphia chromosome). Further, ahead Ras exchanging protein for GTP/GDP, and then the Raf serine-threonine kinases proto-oncogenes are known targets for oncogenes. Finally, at the level of transcription factors activated by MAP kinases, one finds cjun, cfos or cmyc. An avian leucosis virus stimulates cmyc, by inserting its strong viral promoter. The retroviral attacks boost the mitogenic MAP kinases similarly to inflammatory cytokins, or to insulin signals, that control glucose transport and gycolysis.

In addition to the MAP kinase mitogenic pathway, tyrosine kinase receptors activate PI3 kinase pathways; PTEN phosphatase counteracts this effect, thus acting as a tumor suppressor. Recall that a DNA virus, the Epstein-Barr virus of infectious mononucleose, gives also the Burkitt lymphoma; the effect of the virus is to enhance PI3 kinase. Down stream, we find mTOR (the target of rapamycine, an immune-suppressor) mTOR, inhibits PP2A phosphatase, which is also a target for the simian SV40 and Polyoma viruses. Schematically, one may consider that the different steps of MAP kinase pathways are targets for retroviruses, while the different steps of PI3 kinase pathway are targets for DNA viruses. The viral-driven enhanced function of these pathways mimics the effects of their prolonged activation by their usual triggers, such as insulin or IGF; one then expects to find an associated increase of glycolysis. The insulin or IGF actions boost the cellular influx of glucose and glycolysis. However, if the signaling pathway gets out of control, the tyrosine kinase phosphorylations may lead to a parallel PK blockade [35] explaining the tumor bottleneck at the end of glycolysis. Since an activation of enyme kinases may indeed block essential enzymes (PK, PDH and others); in principle, the inactivation of phosphatases may also keep these enzymes in a phosphorylated form and lead to a similar bottleneck and we do know that oncogenes bind and affect PP2A phosphatase. In sum, a perturbed MAP kinase pathway, elicits metabolic features that would give to tumor cells their metabolic advantage.

2. The methylation hypothesis and the role of PP2A phosphatase

In a remarkable comment, Newberne [23] highlights interesting observations on the carcinogenicity of diethanolamine [24] showing that diethanolamine decreased choline derivatives and methyl donors in the liver, like does a choline deficient diet. Such conditions trigger tumors in mice, particularly in the B6C3F1 strain. Again, the historical perspective recalled by Newberne’s comment brings us back to insulin. Indeed, after the discovery of insulin in 1922, Banting and Best were able to keep alive for several months depancreatized dogs, treated with pure insulin. However, these dogs developed a fatty liver and died. Unlike pure insulin, the total pancreatic extract contained a substance that prevented fatty liver: a lipotropic substance identified later as being choline [25]. Like other lipotropes, (methionine, folate, B12) choline supports transmethylation reactions, of a variety of substrates, that would change their cellular fate, or action, after methylation. In the particular case concerned here, the removal of triglycerides from the liver, as very low-density lipoprotein particles (VLDL), requires the synthesis of lecithin, which might decrease if choline and S-adenosyl methionine (SAM) are missing. Hence, a choline deficient diet decreases the removal of triglycerides from the liver; a fatty liver and tumors may then form. In sum, we have seen that pathways exemplified by the insulin-tyrosine kinase signaling pathway, which control anabolic processes, mitosis, growth and cell death, are at each step targets for oncogenes; we now find that insulin may also provoke fatty liver and cancer, when choline is not associated to insulin.

We must now find how the lipotropic methyl donor controls the signaling pathway. We know that after the tyrosine kinase reaction, serine-threonine kinases take over along the signaling route. It is thus highly probable that serine-threonine phosphatases will counteract the kinases and limit the intensity of the insulin or insulin like signals. One of the phosphatases involved is PP2A, itself the target of DNA viral oncogenes (Polyoma or SV40 antigens react with PP2A subunits and cause tumors). We found a possible link between the PP2A phosphatase brake and choline in works on Alzheimer’s disease [26]. Indeed, the catalytic C subunit of PP2A is associated to a structural subunit A. When C receives a methyle, the dimer recruits a regulatory subunit B. The trimer then targets specific proteins that are dephosphorylated [27].

In Alzheimer’s disease, the poor methylation of PP2A is associated to an increase of homocysteine in the blood [26]. The result of the PP2A methylation failure is a hyperphosphorylation of Tau protein and the formation of tangles in the brain. Tau protein is involved in tubulin polymerization, controlling axonal flow but also the mitotic spindle. It is thus possible that choline, via SAM, methylates PP2A, which is targeted toward the serine-threonine kinases that are counteracted along the insulin-signaling pathway. The choline dependent methylation of PP2A is the brake, the “antidote”, which limits “the poison” resulting from an excess of insulin signaling. Moreover, it seems that choline deficiency is involved in the L to M2 transition of PK isoenzymes [28].

3. Cellular distribution of PP2A

In fact, the negative regulation of Ras/MAP kinase signals mediated by PP2A phosphatase seems to be complex. The serine-threonine phosphatase does more than simply counteracting kinases; it binds to the intermediate Shc protein on the signaling cascade, which is inhibited [29]. The targeting of PP2A towards proteins of the signaling pathway depends of the assembly of the different holoenzymes. The carboxyl methylation of C-terminal leucine 309 of the catalytic C unit, permits to a dimeric form made of C and a structural unit A, to recruit one of the many regulatory units B, giving a great diversity of possible enzymes and effects. The different methylated ABC trimers would then find specific targets. It is consequently essential to have more information on methyl transferases and methyl esterases that control the assembly or disassembly of PP2A trimeric forms.

A specific carboxyl methyltransferase for PP2A [30] was purified and shown to be essential for normal progression through mitosis [31]. In addition, a specific methylesterase that demethylates PP2A has been purified [32]. Is seems that the methyl esterase cancels the action of PP2A, on signaling kinases that increase in glioma [33]. Evidently, the cellular localization of the methyl transferase (LCMT-1) and the phosphatase methyl esterase (PME-1) are crucial for controlling PP2A methylation and targeting. Apparently, LCMT-1 mainly localizes to the cytoplasm and not in the nucleus, where PME-1 is present, and the latter harbors a nuclear localization signal [34]. From these observations, one may suggest that PP2A gets its methyles in the cytoplasm and regulates the tyrosine kinase-signaling pathway, attenuating its effects.

A methylation deficit should then decrease the methylation of PP2A and boost the mitotic insulin signals as discussed above for choline deficiency, steatosis and hepatoma. At the nucleus, where PME-1 is present, it will remove the methyl, from PP2A, favoring the formation of dimeric AC species that have different targets, presumably proteins involved in the cell cycle. It is interesting to quote here the structural mechanism associated to the demethylation of PP2A. The crystal structures of PME-1 alone or in complex with PP2A dimeric core was reported [35] PME-1 binds directly to the active site of PP2A and this rearranges the catalytic triad of PME-1 into an active conformation that should demethylate PP2A, but this also seems to evict a manganese required for the phosphatase activity. Hence, demethylation and inactivation would take place in parallel, blocking mitotic actions.

However, another player is here involved, the so-called PTPA protein, which is a PP2A phosphatase activator. Apparently, this activator is a new type of cis/trans of prolyl isomerase, acting on Pro190 of the catalytic C unit isomerized in presence of Mg-ATP [36], which would then cancel the inactivation mediated by PME-1. Following the PTPA action, the demethylated phosphatase would become active again in the nucleus, and stimulate cell cycle proteins [37,38] inducing mitosis. Unfortunately, the ligand of this new prolyl isomerase is still unknown. Moreover, we have to consider that other enzymes such as cytochrome P450 have also demethylation properties.

In spite of deficient methylations and choline dehydrogenase pathway, tumor cells display an enhanced choline kinase activity, associated to a parallel synthesis of lecithin and triglycerides.

The hypothesis to consider is that triglycerides change the fate of methylated PP2A, by targeting it to the nucleus, there a methylesterase demethylates it; the phosphatase attacks new targets such as cell cycle proteins, inducing mitosis. Moreover, the phosphatase action on nuclear membrane proteins may render the nuclear membrane permeable to SAM the general methyl donor; promoters get methylated inducing epigenetic changes.

The relative decrease of methylated PP2A in the cytosol, not only cancels the brake over the signaling kinases, but also favors the inactivation of PK and PDH, which remain phosphorylated, contributing to the metabolic anomaly of tumor cells.

In order to prevent tumors, one should then favor the methylation route rather than the phosphorylation route for choline metabolism. This would decrease triglycerides, promote the methylation of PP2A and keep it in the cytosol, reestablishing the brake over signaling kinases.

Hypoxia is an essential issue to discuss

Many adequate “adult proteins” replace their fetal isoform: muscle proteins utrophine, switches to dystrophine; enzymes such as embryonic M2 PK [39] is replaced by M1. Hypoxic conditions seem to trigger back the expression of the fetal gene packet via HIF1-Von-Hippel signals. The mechanism would depend of a double switch since not all fetal genes become active after hypoxia. First, the histones have to be in an acetylated form, opening the way to transcription factors, this depends either of histone deacetylase (HDAC) inhibition or of histone acetyltransferase (HAT) activation, and represents the main switch. Second, a more specific switch must be open, indicating the adult/fetal gene couple concerned, or more generally the isoform of a given gene that is more adapted to the specific situation. When the adult gene mutates, an unbound ligand may indeed indicate, directly or indirectly, the particular fetal copy gene to reactivate [40]. In anoxia, lactate is more difficult to release against its external gradient, leading to a cytosolic increase of up-stream glycolytic products, 3P glycerate or others. These products may then be a second signal controlling the specific switch for triggering the expression of fetal genes, such as fetal hemoglobin or the embryonic M2 PK; this takes place if histones (main switch) are in an acetylated form.

Growth hormone-IGF actions, the control of asymmetrical mitosis

When IGF – Growth hormone operate, the fatty acid source of acetyl CoA takes over. Indeed, GH stimulates a triglyceride lipase in adipocytes, increasing the release of fatty acids and their β oxidation. In parallel, GH would close the glycolytic source of acetyl CoA, perhaps inhibiting the hexokinase interaction with the mitochondrial ANT site. This effect, which renders apoptosis possible, does not occur in tumor cells. GH mobilizes the fatty acid source of acetyl CoA from adipocytes, which should help the formation of ketone bodies, but since citrate synthase activity is elevated in tumors, ketone bodies do not form.

Compounds for correcting tumor metabolism

The figure figure1 indicates interrupted and enhanced metabolic pathways in tumor cells.

In table table1,1, the numbered pathways represent possible therapeutic targets; they cover several enzymes. When the activity of the pathway is increased, one may give inhibitors; when the activity of the pathway decreases, we propose possible activators

Table - metabolic  targets

Table 1 Mol Cancer. 2011; 10 70. Published online Jun 7, 2011. doi  10.1186_1476-4598-10-70

The origin of Cancers by means of metabolic selection

The disruption of cells by internal or external compounds, releases substrates stimulating the tyrosine kinase signals for anabolism proliferation and stem cell repair, like for most oncogenes. If such signals are not limited, there is a parallel blockade of key metabolic enzymes by activated kinases or inhibited phosphatases. The result is a metabolism typical of tumor cells, which gives them a selective advantage; stabilized by epigenetic changes. A proliferation process, in which the two daughter cells divide, increases the tumor mass at the detriment of the body. Inevitable mutations follow.

Maurice Israël, et al. Mol Cancer. 2011;10:70-70.
Transcriptomics and Regulatory Processes

What are lncRNAs?

It was traditionally thought that the transcriptome would be mostly comprised of mRNAs, however advances in high-throughput RNA sequencing technologies have revealed the complexity of our genome. Non-coding RNA is now known to make up the majority of transcribed RNAs and in addition to those that carry out well-known housekeeping functions (e.g. tRNA, rRNA etc), many different types of regulatory RNAs have been and continue to be discovered.

Long noncoding RNAs (lncRNAs) are a large and diverse class of transcribed RNA molecules with a length of more than 200 nucleotides that do not encode proteins. Their expression is developmentally regulated and lncRNAs can be tissue- and cell-type specific. A significant proportion of lncRNAs are located exclusively in the nucleus. They are comprised of many types of transcripts that can structurally resemble mRNAs, and are sometimes transcribed as whole or partial antisense transcripts to coding genes. LncRNAs are thought to carry out important regulatory functions, adding yet another layer of complexity to our understanding of genomic regulation.

lncRNA-s   A summary of the various functions described for lncRNA

 

The evolution of genome-scale models of cancer metabolism
The importance of metabolism in cancer is becoming increasingly apparent with the identification of metabolic enzyme mutations and the growing awareness of the influence of metabolism on signaling, epigenetic markers, and transcription. However, the complexity of these processes has challenged our ability to make sense of the metabolic changes in cancer. Fortunately, constraint-based modeling, a systems biology approach, now enables one to study the entirety of cancer metabolism and simulate basic phenotypes. With the newness of this field, there has been a rapid evolution of both the scope of these models and their applications. (NE Lewis and AM.Abdel-Haleem. frontiers physiol  2013;4(237): 1   http://dx.doi.org/10.3389/fphys.2013.00237)

Here we review the various constraint-based models built for cancer metabolism and how their predictions are shedding new light on basic cancer phenotypes, elucidating pathway differences between tumors, and discovering putative anti-cancer targets. As the field continues to evolve, the scope of these genome-scale cancer models must expand beyond central metabolism to address questions related to the diverse processes contributing to tumor development and metastasis.

“One of the goals of cancer research is to ascertain the mechanisms of cancer.”These words, penned by Dulbecco (1986), began a treatise on how a mechanistic understanding of cancer requires a sequenced human genome. Now with the abundance of sequence data, we are finding diverse genetic changes among different cancers (Vogelstein et al.,2013). While we are cataloging these mutations, the associated mechanisms leading to phenotypic changes are often unclear since mutations occur in the context of complex biological networks. For example, mutations to isocitrate dehydrogenase lead to oncometabolite synthesis, which alters DNA methylation and ultimately changes gene expression and the balance of normal cell processes (Sasakietal.,2012). Furthermore, many different combinations of mutations can lead to cancer. Since the genetic heterogeneity between tumors can be large, the biomolecular mechanisms underlying tumor physiology can vary substantially.

This is apparent in metabolism, where tumors can differ in serine metabolism  dependence (Possematoetal., 2011) or TCA cycle function (Frezzaetal., 2011b). In addition, diverse mutations can alter NADPH synthesis by differentially regulat ing  signaling pathways, such as the AMPK pathway (Cairnsetal., 2011; Jeonetal., 2012). The challenges regarding complexity and heterogeneity in cancer metabolism are beginning to be addressed with the COnstraint-Based Reconstruction and Analysis (COBRA) approach (Hernández Patiñoetal., 2012; Sharma and König,  2013), an emerging field in systems biology.Specifically, it accounts for the complexity of the perturbed biochemical processes by using genome-scale metabolic network reconstructions (Duarteetal., 2007; Maetal., 2007;Thieleetal., 2013).

In a reconstruction, the stoichiometric chemical reactions in a cell are carefully annotated and stitched together into a large network, often containing thousands of reactions. Genes and enzymes associated with each reaction are also delineated. The networks are converted into computational models and analyzed using many algorithms (Lewisetal., 2012). COBRA approaches are also beginning to address heterogeneity in cancer by integrating experimental data with the reconstructions (Blazier and Papin, 2012; Hydukeetal., 2013)  to tailor the models to the unique gene expression profiles of general cancer tissue, and even individual cell lines and tumors. Here we describe the recent conceptual evolution that has occurred for constraint-based cancer modeling.

Targeting of  gene expression

Tumor Suppressor Genes and its Implications in Human Cancer

Gain-of-function mutations in oncogenes and loss-of-function mutations in tumor suppressor genes (TSG) lead to cancer. In most human cancers, these mutations occur in somatic tissues. However, hereditary forms of cancer exist for which individuals are heterozygous for a germline mutation in a TSG locus at birth. The second allele is frequently inactivated by gene deletion, point mutation, or promoter methylation in classical TSGs that meet Knudson’s two-hit hypothesis. Conversely, the second allele remains as wild-type, even in tumors in which the gene is haplo-insufficient for tumor suppression. (K Inoue, EA Fry and Pj Taneja. Recent Progress in Mouse Models for Tumor Suppressor Genes and its Implications in Human Cancer. Clinical Medicine Insights: Oncology2013:7 103–122). This article highlights the importance of PTEN, APC, and other tumor suppressors for counteracting aberrant PI3K, β-catenin, and other oncogenic signaling pathways. We discuss the use of gene-engineered mouse models (GEMM) of human cancer focusing on Pten and Apc knockout mice that recapitulate key genetic events involved in initiation and progression of human neoplasia.

Targeting cancer metabolism – aiming at a tumour’s sweet-spot
Neil P. Jones and Almut Schulze
Drug Discovery Today   January 2012

Targeting cancer metabolism has emerged as a hot topic for drug discovery. Most cancers have a high demand for metabolic inputs (i.e. glucose/glutamine), which aid proliferation and survival. Interest in targeting cancer metabolism has been renewed in recent years with the discovery that many cancer related (e.g. oncogenic and tumor suppressor) pathways have a profound effect on metabolism and that many tumors become dependent on specific metabolic processes. Considering the recent increase in our understanding of cancer metabolism and the increasing knowledge of the enzymes and pathways involved, the question arises: could metabolism be cancer’s Achilles heel?
During recent years, interest into the possible therapeutic benefit of targeting metabolic pathways in cancer has increased dramatically with academic and pharmaceutical groups actively pursuing this aspect of tumor physiology. Therefore, what has fuelled this revived interest in targeting cancer metabolism and what are the major advances and potential challenges faced in the race to develop new therapeutics in this area? This review will attempt to answer these questions and illustrate why we, and others, believe that targeting metabolism in cancer presents such a promising therapeutic rationale.

Oncogenes and cancer metabolism
Glycolysis  TCA cycle  Pentose phosphate pathway

 FIGURE 1

Schematic representation of the regulation of cancer metabolism pathways. Metabolic enzymes are regulated by signaling pathways involving oncogenes and tumor suppressors. Complex regulatory mechanisms, key pathway interactions and enzymes are shown along with key metabolic endpoints (shown in purple) necessary for proliferation and survival (biosynthetic intermediates and NADPH). Key oncogenic pathways are shown in green and key tumor suppressor pathways are shown in red. Mutant IDH (mIDH) pathway is listed but is only functional in cancers containing mIDH.

FIGURE 2

Schematic representation of key components of the pentose phosphate pathway (PPP). Key enzymes are shown in blue boxes and key intermediates in purple text/box outline. DNA damage can activate ATM which in turn activates G6PDH to upregulate nucleotide synthesis for DNA repair and NAPDH to combat reactive oxygen species. PPP is also regulated by the tumour suppressor p53. The PPP can function as two separate branches (oxidative and non-oxidative) or be coupled into a recycling pathway – the pentose phosphate shunt – for maximum NADPH production.

Serine biosynthesis

Another branch diverting from glycolysis recently implicated in cancer is the serine biosynthesis pathway which converts the glycolytic intermediate 3-phosphoglycerate into serine (Fig. 3). Serine is an amino acid and an important neurotransmitter but can also provide fuel for the synthesis of other amino acids and nucleotides. The serine biosynthesis pathway also provides another key metabolic intermediate, a-KG, from glutamate breakdown via the action of phosphoserine aminotransferase (PSAT1). This pathway couples glycolysis (via 3-phosphoglycerate) with glutaminolysis (via glutamate), thereby linking two metabolic pathways known to be activated in many cancers.

FIGURE 3

Schematic representation of the serine biosynthesis pathway. Synthesis of serine involves integration of metabolites from glycolysis and  glutaminolysis pathways  and generates a-ketoglutarate, a key biosynthetic intermediate, and serine. Serine has many essential uses in the cell including amino acid, phospholipid and nucleotide synthesis.

 

Silencing of tumor suppressor genes by recruiting DNA methyltransferase 1 (DNMT1)

Ubiquitin-like containing PHD and Ring finger 1 (UHRF1) contributes to silencing of tumor suppressorgenes by recruiting DNA methyltransferase 1 (DNMT1) to their hemi-methylated promoters. Conversely,demethylation of these promoters has been ascribed to the natural anti-cancer drug, epigallocatechin-3-gallate (EGCG). The aim of the present study was to investigate whether the UHRF1/DNMT1 pair is an important target of EGCG action.  (Mayada Achour, et al. Epigallocatechin-3-gallate up-regulates tumor suppressor gene expression via a reactive oxygen species-dependent down-regulation of UHRF1.  Biochemical and Biophysical Research Communications 430 (2013) 208–212.    http://dx.doi.org/10.1016/j.bbrc.2012.11.087)

Here, we show that EGCG down-regulates UHRF1 and DNMT1 expression in Jurkat cells, with subsequent up-regulation of p73 and p16INK4A genes. The down-regulation of UHRF1 is dependent upon the generation of reactive oxygen species by EGCG. Up-regulation of p16INK4A  is strongly correlated with decreased promoter binding by UHRF1. UHRF1 over-expression counteracted EGCG-induced G1-arrested cells, apoptosis, and up-regulation of p16INK4A and p73. Mutants of the Set and Ring Associated (SRA) domain of UHRF1 were unable to down-regulate p16INK4A and p73, either in the presence or absence of EGCG. Our results show that down-regulation of UHRF1 is upstream to many cellular events, including G1 cell arrest, up-regulation of tumor suppressor genes and apoptosis.

Tumor Suppressor Activity of a Constitutively-Active ErbB4 Mutant

ErbB4 (HER4) is a member of the ErbB family of receptor tyrosine kinases, which includes the Epidermal Growth Factor Receptor (EGFR/ErbB1), ErbB2 (HER2/Neu), and ErbB3 (HER3). Mounting evidence indicates that ErbB4, unlike EGFR or ErbB2, functions as a tumor suppressor in many human malignancies. Previous analyses of the constitutively-dimerized and –active ErbB4 Q646C mutant indicate that ErbB4 kinase activity and phosphorylation of ErbB4 Tyr1056 are both required for the tumor suppressor activity of this mutant in human breast, prostate, and pancreatic cancer cell lines. However, the cytoplasmic region of ErbB4 possesses additional putative functional motifs, and the contributions of these functional motifs to ErbB4 tumor suppressor activity have been largely underexplored.  (Citation: Richard M. Gallo, et al. (2013) Multiple Functional Motifs Are Required for the Tumor Suppressor Activity of a Constitutively-Active ErbB4 Mutant. J Cancer Res Therap Oncol 1: 1-10)

Here we demonstrate that ErbB4 BH3 and LXXLL motifs, which are thought to mediate interactions with Bcl family proteins and steroid hormone receptors, respectively, are required for the tumor suppressor activity of the ErbB4 Q646C mutant. Furthermore, abrogation of the site of ErbB4 cleavage by gamma-secretase also disrupts the tumor suppressor activity of the ErbB4 Q646C mutant. This last result suggests that ErbB4 cleavage and subcellular trafficking of the ErbB4 cytoplasmic domain may be required for the tumor suppressor activity of the ErbB4 Q646C mutant. Indeed, here we demonstrate that mutants that disrupt ErbB4 kinase activity, ErbB4 phosphorylation at Tyr1056, or ErbB4 cleavage by gamma-secretase also disrupt ErbB4 trafficking away from the plasma membrane and to the cytoplasm. This supports a model for ErbB4 function in which ErbB4 tumor suppressor activity is dependent on ErbB4 trafficking away from the plasma membrane and to the cytoplasm, mitochondria, and/or the nucleus.

EGF Receptor

 Initiation of pancreatic ductal adenocarcinoma (PDA) is definitively linked to activating mutations in the KRAS oncogene. However, PDA mouse models show that mutant Kras expression early in development gives rise to a normal pancreas, with tumors forming only after a long latency or pancreatitis induction.

(CM Ardito,BM Gruner. ,EGF Receptor Is Required for KRAS-Induced Pancreatic Tumorigenesis.  http://dx.doi.org/10.1016/j.ccr.2012.07.024)

Here, we show that oncogenic KRAS upregulates endogenous EGFR expression and activation, the latter being dependent on the EGFR ligand sheddase, ADAM17. Genetic ablation or pharmacological inhibition of EGFR or ADAM17 effectively eliminates KRAS-driven tumorigenesis in vivo. Without EGFR activity, active RAS levels are not sufficient to induce robust MEK/ERK activity, a requirement for epithelial transformation

The almost universal lethality of PDA has led to the intense study of genetic mutations responsible for its formation and progression. The most common oncogenic mutations associated with all PDA stages are found in the KRAS gene, suggesting it as the primary initiator of pancreatic neoplasia. However, mutant Kras expression throughout the mouse pancreatic parenchyma shows that the oncogene remains largely indolent until secondary events, such as pancreatitis, unlock its transforming potential. We find KRAS requires an inside-outside-in signaling axis that involves ligand-dependent EGFR activation to initiate the signal transduction and cell biological changes that link PDA and pancreatitis. (Cancer Cell (2012); 22: 304–317).

HER4 (EGFR/ErbB, HER2/Neu, HER3)

 ErbB4 (HER4) is a member of the ErbB family of receptor tyrosine kinases, which includes the Epidermal Growth Factor Receptor (EGFR/ErbB1), ErbB2 (HER2/Neu), and ErbB3 (HER3). Mounting evidence indicates that ErbB4, unlike EGFR or ErbB2, functions as a tumor suppressor in many human malignancies. Previous analyses of the constitutively-dimerized and –active ErbB4 Q646C mutant indicate that ErbB4 kinase activity and phosphorylation of ErbB4 Tyr1056 are both required for the tumor suppressor activity of this mutant in human breast, prostate, and pancreatic cancer cell lines. However, the cytoplasmic region of ErbB4 possesses additional putative functional motifs, and the contributions of these functional motifs to ErbB4 tumor suppressor activity have been largely underexplored.

ErbB4 Possesses Multiple Functional Motifs and Mutations Have Been Engineered to Target These Motifs.

The organization of ErbB4 is as indicated in this schematic. The extracellular ligand-binding motifs reside in the amino-terminal region upstream of amino acid residue 651. The singlepass transmembrane domain consists of amino acid residues 652-675. The cytoplasmic tyrosine kinase domain consists of amino acid residues 713-989. The majority of cytoplasmic sites of tyrosine phosphorylation reside in amino acid residues 990-1308, most notably Tyr1056. Additional putative functional motifs include a TACE cleavage site, a gamma-secretase cleavage site, two LXXLL (steroid hormone receptor binding) motifs, a BH3 domain, three WW domain binding motifs, and a PDZ domain binding motif. Mutations that disrupt these motifs are noted. Finally, note the two locations of alternative transcriptional splicing, resulting in a total of four different splicing isoforms.

 

 

 

Here we demonstrate that ErbB4 BH3 and LXXLL motifs, which are thought to mediate interactions with Bcl family proteins and steroid hormone receptors, respectively, are required for the tumor suppressor activity of the ErbB4 Q646C mutant. Furthermore, abrogation of the site of ErbB4 cleavageby gamma-secretase also disrupts the tumor suppressor activity of the ErbB4 Q646C mutant. This last result suggests that ErbB4 cleavage and subcellular trafficking of the ErbB4 cytoplasmic domain may be required for the tumor suppressor activity of the ErbB4 Q646C mutant. Indeed, here we demonstrate that mutants that disrupt ErbB4 kinase activity, ErbB4 phosphorylation at Tyr1056, or ErbB4 cleavage by gamma-secretase also disrupt ErbB4 trafficking away from the plasma membrane and to the cytoplasm. This supports a model for ErbB4 function in which ErbB4 tumor suppressor activity is dependent on ErbB4 trafficking away from the plasma membrane and to the cytoplasm, mitochondria, and/or the nucleus.

(Richard M. Gallo, et al. (2013) Multiple Functional Motifs Are Required for the Tumor Suppressor Activity of a Constitutively-Active ErbB4 Mutant. J Cancer Res Therap Oncol 1: 1-10)

Resistance to Receptor Tyrosine Kinase Inhibition

Receptor tyrosine kinases (RTKs) are activated by somatic genetic alterations in a subset of cancers, and such cancers are often sensitive to specific inhibitors of the activated kinase. Two well-established examples of this paradigm include lung cancers with either EGFR mutations or ALK translocations. In these cancers, inhibition of the corresponding RTK leads to suppression of key downstream signaling pathways, such as the PI3K (phosphatidylinositol 3-kinase)/AKT and MEK (mitogen-activated protein kinase kinase)/ERK (extracellular signal–regulated kinase) pathways, resulting in cell growth arrest and death. Despite the initial clinical efficacy of ALK (anaplastic lymphoma kinase) and EGFR (epidermal growth factor receptor) inhibitors in these cancers, resistance invariably develops, typically within 1 to 2 years. (MJ Niederst and JA Engelman. Sci Signal, 24 Sep 2013; 6(294), p. re6 .  http://dx.doi.org/10.1126/scisignal.2004652)

Over the past several years, multiple molecular mechanisms of resistance have been identified, and some common themes have emerged. One is the development of resistance mutations in the drug target that prevent the drug from effectively inhibiting the respective RTK. A second is activation of alternative RTKs that maintain the signaling of key downstream pathways despite sustained inhibition of the original drug target. Indeed, several different RTKs have been implicated in promoting resistance to EGFR and ALK inhibitors in both laboratory studies and patient samples. In this mini-review, we summarize the concepts underlying RTK-mediated resistance, the specific examples known to date, and the challenges of applying this knowledge to develop improved therapeutic strategies to prevent or overcome resistance.

The TGF-β Pathway

Aberrations in the enzymes that modify ubiquitin moieties have been observed to cause a myriad of diseases, including cancer. Therefore a better understanding of these enzymes and their substrates will lead to the identification of prospective druggable targets. Here we discuss the role of ubiquitin modifying enzymes in the canonical TGF-β pathway highlighting the ubiquitin regulating enzymes, which may potentially be targeted by small molecule inhibitors. (Pieter Eichhorn. (DE) -Ubiquitination in The TGF-β Pathway. J Cancer Res Therap Oncol 2013; 1: 1-6).

TGF-β is a multifunctional cytokine that plays a key role in embryogenesis and adult tissue homoeostasis. TGF-β is secreted by a myriad of cell types triggering a varied array of cellular functions including apoptosis, proliferation, migration, endothelial and mesenchymal transition, and extracellular matrix production. Downstream TGFβ responses can also be modulated by other signalling pathways (i.e. PI3K, ERK, WNT, etc.) resulting in a complex web of TGF-β pathway activation or repression depending on the nature of the signal and cellular context. Apart from TGF-β mediated cell autonomous effects TGF-β can further play an important function in regulating tumour microenvironments effecting the interaction between stromal fibroblasts and tumour cells.
Due to the central role of TGF-β in cellular processes it is therefore unsurprising that loss of TGF-β pathway integrity is frequently observed in a variety of human diseases, including cancer. However, the TGF-β pathway plays a complex dual role in cancer. In normal epithelial cells and premalignant cells TGF-β acts a potent tumor suppressor eliciting a cytostatic response inhibiting tumor progression. Supporting this notion, inactivating mutations in members of the TGF-βpathway have been observed in a variety of cancers including pancreatic, colorectal, and head and neck cancer.

In contrast, during tumor progression the TGF-β antiproliferative function is lost, and in certain advanced cancers TGF-β becomes an oncogenic factor inducing cellular proliferation, invasion, angiogenesis, and immune suppression. As a consequence, the TGFβ pathway is currently considered a therapeutic target in advanced cancers and several anti- TGF-β agents in clinical trials have shown promising results. However, due to the complex dichotomous role of TGF-β in oncogenesis a detailed understanding of TGF-β biology is required in order to design successful therapeutic strategies to identify patient populations that will benefit most from these compounds.

G protein receptor

 G protein-coupled receptors (GPCRs) modulate a vast array of cellular processes. The current review gives an overview of the general characteristics of GPCRs and their role in physiological conditions. In addition, it describes the current knowledge of the physiological and pathophysiological functions of GPR55, an orphan GPCR, and how it can be exploited as a therapeutic target to combat various cancers.

(D Leyva-Illades, S DeMorrow . Orphan G protein receptor GPR55 as an emerging target in cancer therapy and management.  Cancer Management and Research 2013:5 147–155)

Signal transduction is essential for maintaining cellular homeostasis and to coordinate the activity of cells in all organisms. Proteins localized in the cell membrane serve as the interface between the outside and inside of the cell. G protein-coupled receptors (GPCRs) are the largest and most diverse group of membrane receptors in eukaryotes and are encoded by at least 800 genes in the human genome. GPCRs are also known as seven-transmembrane domain receptors, 7TM receptors, heptahelical receptors, serpentine receptors, and G protein-linked receptors. GPCRs can detect an expansive array of extracellular signals or ligands that include photons, ions, odors, pheromones, hormones, and neurotransmitters. Nonsensory GPCRs (excluding light, odor, and taste receptors) have been classified into four families: class A rhodopsin-like, class B secretin-like, class C metabotropic glutamate/pheromone, and frizzled receptors. They have a peculiar structure that has been highly conserved over the course of evolution and are made up of an amino acid chain, the N-terminal of which is localized outside of the cellular membrane and the C-terminal in the cytoplasm. The amino acid chain spans the cellular membrane seven times and has three intracellular and three extracellular loops.

GPCRs are called that because they exert their actions by associating with a family of heterotrimeric proteins (made up of α, β, and γ subunits) that are capable of binding and hydrolyzing guanosine triphosphate (GTP).To date, 16 different α subunits, five β subunits, and 11 γ subunits have been described in mammalian tissues. When activated, these receptors undergo conformational changes that are mechanically transduced to the G proteins, which then initiate a cycle of activation and inactivationassociated with the binding and hydrolysis of GTP. Activated G proteins can then positively or negatively modulate ion channels (mainly potassium and calcium) or the second messenger generating enzymes (ie, adenylate cyclase and phospholipase C [PLC]) that allow the signal to be propagated to the interior of the cell to ultimately affect cell function.

 Matrix Metalloproteinases

Degradation of extracellular matrix is crucial for malignant tumour growth, invasion, metastasis and angiogenesis. Matrix metalloproteinases (MMPs) are a family of zinc-dependent neutral endopeptidases collectively capable of degrading essentially all  components of the ECM. Elevated levels of distinct MMPs can be detected in tumour tissue or serumof patients with advanced cancer and their role as prognostic indicators in cancer is studied. In addition, therapeutic intervention of tumour growth and invasion based on inhibition of MMP activity is under intensive investigation and several MMP inhibitors are in clinical trials in cancer. In this review, we discuss the current view on the feasibility of MMPs as prognostic markers and as targets for therapeutic intervention in cancer.

(MATRIX METALLOPROTEINASES IN CANCER: PROGNOSTIC MARKERS AND THERAPEUTIC TARGETS.

Pia Vihinen and Veli-Matti Kahari.  Int. J. Cancer 2002;99: 157–166. http://dx.doi.org/10.1002/ijc.10329

Common properties of the MMPs include the requirement of zinc in their catalytic site for activity and their synthesis as inactive zymogens that generally need to be proteolytically cleaved to be active. Normally the MMPs are expressed only when and where needed for tissue remodeling accompanies various processes such as during embryonic development, wound healing, uterine and mammary involution, cartilage-to-bone transition during ossification, and trophoblast invasion into the endometrial stoma during placenta development. However, aberrant expression of various MMPs has been correlated with pathological conditions, such as periodontitis, rheumatoid arthritis, and tumor cell invasion and metastasis .

There are now over 20 members of the MMP family, and they can be subgrouped based on their structures. The minimal domain structure consists of a signal peptide, prodomain, and catalytic domain. The propeptide domain contains a conserved cysteine residue (the “cysteine switch”) that coordinates to the catalytic zinc to maintain inactivity. MMPs with only the minimal domain are referred to as matrilysins (MMP-7 and -26). The most common structures for secreted MMPs, including collagenases and stromelysins, have an additional hemopexin-like domain connected by a hinge region to the catalytic domain (MMP-1, -3, -8, -10, -12, -13, -19, and -20).

Terms: 1FN, fibronectin; 2M, 2-macroglobulin; 1PI, 1-proteinase inhibitor; COMP, cartilage oligomeric matrix protein; ND, not determined; TACE, TNF-converting enzyme; OP, osteopontin

FIGURE 1 – Structure of human matrix metalloproteinases

 

FIGURE 1 – Structure of human matrix metalloproteinases. The signal peptide directs the proenzyme for secretion. The propeptide contains a conserved sequence (PRCGxPD), in which the cysteine forms a covalent bond (cysteine switch), with the catalytic zinc (Zn2_) to maintain the latency of proMMPs. Catalytic domain contains the highly conserved zinc binding site (HExGHxxGxxHS) in which Zn2_is coordinated by 3 histidines. The proline-rich hinge region links the catalytic domain to the hemopexin domain, which determines the substrate specificity of specific MMPs. The hemopexin domain is absent in matrilysin (MMP-7) and matrilysin-2 (endometase, MMP-26). Gelatinases  A and B (MMP-2 and MMP-9, respectively) contain 3 repeats of the fibronectin-type II domain inserted in the catalytic domain. MT1-, MT2-, MT3- and MT5-MMP contain a transmembrane domain and MT4- and MT6-MMPs contain a glycosylphosphatidylinositol (GPI) anchor in the C-terminus of the molecule, which attach these MMPs to the cell surface. MT-MMPs, MMP-11, MMP-23 and MMP-28 contain a furin cleavage site (RxKR) between the propeptide and catalytic domain, making these proenzymes susceptible to activation by intracellular furin convertases. MMP-23 contains an N-terminal signal anchor, which anchors proMMP-23 to the Golgi complex and has a different C-terminal domain instead of hemopexin-like domain.

The physiologic expression of MMP-13 in vivo is limited to situations, such as fetal bone development and fetal wound repair, in which rapid remodeling of collagenous ECM is required. MMP-13 is expressed in pathologic conditions, such as arthritis, chronic dermal and intestinal ulcers, chronic periodontal inflammation and atherosclerotic plaques. The expression of MMP-13 is detected in vivo in invasive malignant tumours, breast carcinomas, squamous cell carcinomas (SCCs) of the head and neck and vulva, malignant melanomas, chondrosarcomas and urinary bladder carcinomas.

Table I. Human MMPS, their chromosomal localization, substrates, exogenous activators, and activating capacity1
Enzyme Chromosomal location Substrates Activated by Activator of
  • FN, fibronectin; 2M, 2-macroglobulin; 1PI, 1-proteinase inhibitor; COMP, cartilage oligomeric matrix protein; ND, not determined; TACE, TNF-converting enzyme; OP, osteopontin.

    …………..

Collagenases
 Collagenase-1 (MMP-1) 11q22.2-22.3 Collagen I, II, III, VII, VIII, X, aggregan, serpins, 2M MMP-3, -7, -10, plasmin kallikrein, chymase MMP-2
 Collagenase-2 (MMP-8) 11q22.2-22.3 Collagen I, II, III, aggregan, serpins, 2M MMP-3, -10, plasmin ND
 Collagenase-3 (MMP-13) 11q22.2-22.3 Collagen I, II, III, IV, IX, X, XIV, gelatin, FN, laminin, large tenascin aggrecan, fibrillin, osteonectin, serpins MMP-2, -3, -10, -14, -15, plasmin MMP-2, -9
Stromelysins
 Stromelysin-1 (MMP-3) 11q22.2-22.3 Collagen IV, V, IX, X, FN, elastin, gelatin, laminin, aggrecan, nidoge fibrillin*, osteonectin*, 1PI*, myelin basic protein*, OP, E-cadherin Plasmin, kallikrein, chymas tryptase MMP-1, -8, -9, -13
 Stromelysin-2 (MMP-10) 11q22.2-3 As MMP-3, except * Elastase, cathepsin G MMP-1, -7, -8, -9, -13
Stromelysin-like MMPs
 Stromelysin-3 (MMP-11) 22q11.2 Serine proteinase inhibitors, 1PI Furin ND
 Metalloelastase (MMP-12) 11q22.2-22.3 Collagen IV, gelatin, FN, laminin, vitronectin, elastin, fibrillin, 1-PI, myelin basic protein, apolipoprotein A ND ND
Matrilysins
 Matrilysin (MMP-7) 11q22.2-22.3 Elastin, FN, laminin, nidogen, collagen IV, tenascin, versican, 1PI, O E-cadherin, TNF- MMP-3, plasmin MMP-9
 Matrilysin-2 (MMP-26) 11q22.2 Gelatin, 1PI, synthetic MMP-substrates, TACE-substrate ND ND
Gelatinases
 Gelatinase A (MMP-2) 16q13 Gelatin, collagen I, IV, V, VII, X, FN, tenascin, fibrillin, osteonectin, Monocyte chemoattractant protein 3 MMP-1, -13, -14, -15, -16, -tryptase? MMP-9, -13
 Gelatinase B (MMP-9) 20q12-13 Gelatin, collagen IV, V, VII, XI, XIV, elastin, fibrillin, osteonectin 2 MMP-2, -3, 7, -13, plasmin, trypsin, chymotrypsin, cathepsin G ND
Membrane-type MMPs
 MT1-MMP (MMP-14) 14q12.2 Collagen I, II, III, gelatin, FN, laminin, vitronectin, aggrecan, tenasci nidogen, perlecan, fibrillin, 1PI, 2M, fibrin Plasmin, furin MMP-2, -13
 MT2-MMP (MMP-15) 16q12.2 FN, laminin, aggrecan, tenascin, nidogen, perlecan ND MMP-2, -13

 

MMP expression and activity are regulated at several levels. In most cases, MMPs are not synthesized until needed. Transcription can be induced by various signals including cytokines, growth factors, and mechanical stress. In certain cases, regulation of mRNA stability and translational efficiencyhave been reported. Because most MMPs are secreted as inactive zymogens, they need to be activated, usually by proteolytic cleavage of their NH2-terminal prodomains. Some MMPs are activated by other serine proteases such as plasmin and furin, whereas some of the MMPs can activate other members of their family. The most well characterized is the activation of pro-MMP-2 by MT1-MMP.

A number of MMPs have been strongly implicated in multiple stages of cancer progression including the acquisition of invasive and metastatic properties. Thus, efforts have been made for the past 20 years to develop MMPIs that can be used to halt the spread of cancer, which is what ultimately kills the person. However, initial clinical trials using first generation MMPIs proved to be disappointing . In the ensuing years, much has been learned about the roles of specific MMPs in the different processes of carcinogenesis and more specific MMPIs are being developed and brought to clinical trials.

However, the dosing and scheduling for optimal efficacy is not the same as required for conventional cytotoxic drugs because the MMPIs do not directly kill cancer cells, but instead target such processes as angiogenesis (the development of new blood vessels), invasion, and metastatic spread. (Matrix Metalloproteinases, Angiogenesis, and Cancer. Joyce E. Rundhaug.  Commentary re: A. C. Lockhart et al., Reduction of Wound Angiogenesis in Patients Treated with BMS-275291, a Broad Spectrum Matrix Metalloproteinase Inhibitor. Clin. Cancer Res., 2003; 9551–554).

 Role of p38 MAP Kinase Signal Transduction in Solid Tumors

HK Koul, M Pal, and S Koul. Genes & Cancer  2013 ; 4(9-10) 342–359.  http://dx.doi.org/10.1177/ 1947601913507951

Mitogen-activated protein kinases (MAPKs) mediate a wide variety of cellular behaviors in response to extracellular stimuli. One of the main subgroups, the p38 MAP kinases, has been implicated in a wide range of complex biologic processes, such as cell proliferation, cell differentiation, cell death, cell migration, and invasion. Dysregulation of p38 MAPK levels in patients are associated with advanced stages and short survival in cancer patients (e.g., prostate, breast, bladder, liver, and lung cancer). p38 MAPK plays a dual role as a regulator of cell death, and it can either mediate cell survival or cell death depending not only on the type of stimulus but also in a cell type specific manner. In addition to modulating cell survival, an essential role of p38 MAPK in modulation of cell migration and invasion offers a distinct opportunity to target this pathway with respect to tumor metastasis. The specific function of p38 MAPK appears to depend not only on the cell type but also on the stimuli and/or the isoform that is activated.

Mitogen-activated protein kinase (MAPK) signal transduction pathways are evolutionarily conserved among eukaryotes and have been implicated to play key roles in a number of biological processes, including cell growth, differentiation, apoptosis, inflammation, and responses to environmental stresses.

They are typically organized in 3-tiered architecture consisting of a MAPK, a MAPK activator (MAPK kinase), and a MAPKK activator (MAPKK kinase). The MAPK pathways can be regulated at multiple levels as well as via multiple mechanisms, of which the regulation of mitogen-activated protein kinase kinase kinase (MAPKKK/MAP3K) has been proved to be the most challenging due to the great diversity and versatility between different modules at this level. The complex array of growth factors and other ligands that can initiate intracellular cell signaling requires a very high level of coordination among the different proteins involved.

GTP cyclohydrolase (GCH1)

GTP cyclohydrolase (GCH1) is the key-enzyme to produce the essential enzyme cofactor, tetrahydrobiopterin. The byproduct, neopterin is increased in advanced human cancer and used as cancer-biomarker, suggesting that pathologically increased GCH1 activity may promote tumor growth.

(G Picker, Hee-Young Lim, et al. Inhibition of GTP cyclohydrolase attenuates tumor growth by reducing angiogenesis and M2-like polarization of tumor associated macrophages. Int. J. Cancer 2003; 132: 591–604 (2013)  http://dx.doi.org/10.1002/ijc.27706 )

We found that inhibition or silencing of GCH1 reduced tumor cell proliferation and survival and the tube formation of human umbilical vein endothelial cells, which upon hypoxia increased GCH1 and

endothelial NOS expression, the latter prevented by inhibition of GCH1. In nude mice xenografted with HT29-Luc colon cancer cells GCH1 inhibition reduced tumor growth and angiogenesis, determined by in vivo luciferase and near-infrared imaging of newly formed blood vessels. The treatment with the GCH1 inhibitor shifted the phenotype of tumor associated macrophages from the proangiogenic M2 towards M1, accompanied with a shift of plasma chemokine profiles towards tumor-attacking chemokines including CXCL10 and RANTES. GCH1 expression was increased in mouse AOM/DSS-induced colon tumors and in high grade human colon and skin cancer and oppositely, the growth of GCH1-deficient HT29-Luc tumor cells in mice was strongly reduced. The data suggest that GCH1 inhibition reduces tumor growth by (i) direct killing of tumor cells, (ii) by inhibiting angiogenesis, and (iii) by enhancing the antitumoral immune response.

The Role of Stroma in Tumour-Host Co-Existence

Molnár et al.,  The Role of Stroma in Tumour-Host Co-Existence: Some Perspectives in Stroma-Targeted Therapy of Cancer   Biochem Pharmacol 2013, 2:1    http://dx.doi.org/10.4172/2167-0501.1000107

 Cancer grows at the expense of the host as a parasite or superparasite following the second law of thermodynamics (conservation of energy). When the cancer cell progresses via replication to the special state called “spheroid”, a new phase begins with its intimate interaction and development of responses from the stroma which together assist in the formation of a full blown cancer. Among the processes involved are the development of blood vessels and lymphatic channels which are essential for maintenance and further growth of the cancer mass. In this way the condition of “parasitism” is completed with simultaneous suppression of the immune response of the host to the histo-incompatability of the tumor mass. Stroma/parenchyma promotes cancer invasion by feeding cancer cells and inducing immune tolerance. The dynamic changes in composition of stroma and biological consequences as feeder of cancer cells and immune tolerance can give a perspective for rational drug design in anti-stromal therapy. There are differences between normal and cancer cells at subcellular level such as compartmentalzation and structure of cytoskeleton and energy distribution (that is low generally, but locally high in normal cells). In cancer cannibalism of normal cells, the growing cancer mass is a factor for progression and invasion.

Cancer cells have been shown to kill normal cells and the products of cell death used for progression of growth of the cancer cell. Serum and growth factors produced by tumor stroma also provide the needed nutrients and conditions for further tumor growth. Cancer cannot feed off other cancer cells and therefore grow poorly. Probably, although not yet proven, the inability of cancer to “parasitise” other cancer cell types is probably due to some kind of competition or interference. The tumor is in charge of its own development due to its induction proteinases, lipid mobilization factors and angiogenetic factors as well as its ability to negate immune responses of the host response to what is in essence a foreign body.

In our review co-existence of normal and cancer cells in tumor with the growth promoting factors, and the immune tolerance mediating factors produced in the stromal and cancer cells/tissues will be discussed with perspective of stroma targeted therapy.

The clinical significance of cell cannibalism is well defined and described in a large number of publications. The direction of process of cancer development is defined as the tumor invades the normal tissue which never occurs in the reverse direction. This suggests that the cancer cell strives to achieve the lowest energy level possible. Therefore the first of the development of a full blown cancer can be considered as the 2nd Thermodynamic principle  that explains, describes and drives the invading cancer into normal surrounding tissue.

From the normal living state, under particular conditions such as hypoxia, where ATP synthesis is decreased resulting in a switch to glycolytic pathways, cancer cells are selected from a fraction of the population [4]. Energetically, in the presence of electron transfer, by using high energy from respiration, the proliferating state is more stable than resting cells where a higher degree of protein stabilization occurs such as that needed for maintainance of the cytoskeleton of the cell. It was proposed that tumor-promotion might be controlled or modulated by small electronic currents originating from reactive oxygen species and transported through the cytoskeletal microfilament network of the cancer cell.

Aerobic glycolysis is the main energy producing process in cancer cells. Among many other aspects, recently the mitochondria have also been regarded as potential targets in the therapy of cancer. Several small molecules have been tested to restore their dysfunctional functions either by direct or indirect effects. Because of poorly functioning mitochondria, the electron transfer component of the respiration cycle is inefficient; therefore, cancer cells have smaller Gibbs energy than healthy cells. This means, that these cancer cells exists in a metastable state and are not able maintain normal cell structure.

Therefore, the cytoskeleton system is collapsed and dielectric bilayers are formed as a lower grade of cellular structure with decreased electron conductivity. Consequently, to halt cancer growth, one has to evaluate the process of cancer cell development in situ, where the primary tumor is growing as well as that of the metastatic cell that is invading surrounding or distal tissues. This affords one to suggest that the stroma is formed first during long term repeated oxidative stress, a process that is initially accompanied with inflammation due to an active immune response to the histoincompatability antigens present on the surface of the cancer cell. If the cancer cell evades the activity of killer T cells (Treg cells) by either secreting agents that reduce the response of the Treg cells or the immune system for whatever reason is ineffective (immunosuppressed states such as HIV/AIDS, pregnancy, transplantation  therapy, etc.), the formed cancer cells have the opportunity to initiate tumor development. Because of the limited capacity of its electron transfer cycle, cancer cells are essentially starving cells that require glycolytically useful substrates. These substrates are obtained from the killing of normal cells by agents secreted by the cancer cell and the products yielded from dead normal cells “eaten” (phagocytosed) by the starving cancer cell which is digested by the cancer cells lysosomal system. This autophagic process of cannibalism keeps the cancer cell alive and thriving and is known as cytophagy, i.e., cannibalism of normal cells. This type of autophagocytosis  results in a parasitic co-existence of tumor cells with normal cells and will determine the main pathway of interaction between the growing cancer tissue (tumor) and normal tissue where the cancer tissue gradually destroys normal tissues. This process obeys the second law of thermodynamics-conservation of energy within a defined system.

Treatments for Cancer

 Bosutinib: a SRC–ABL tyrosine kinase inhibitor for treatment of chronic myeloid leukemia. 

FE Rassi, HJ Khoury. Pharmacogenomics and Personalized Medicine  2013:6 57–62.

Bosutinib is one of five tyrosine kinase inhibitors commercially available in the United States for the treatment of chronic myeloid leukemia. This review of bosutinib summarizes the mode of action, pharmacokinetics, efficacy and safety data, as well as the patient-focused perspective through quality-of-life data. Bosutinib has shown considerable and sustained efficacy in chronic myeloid leukemia, especially in the chronic phase, with resistance or intolerance to prior tyrosine kinase inhibitors. Bosutinib has distinct but manageable adverse events. In the absence of T315I and V299L mutations, there are no absolute contraindications for the use of bosutinib in this patient population

Chronic myeloid leukemia (CML) is a clonal myeloproliferative stem cell disorder characterized by the presence of a signature hybrid oncogene, the BCR–ABL. The Philadelphia chromosome (Ph+) results from a reciprocal translocation between chromosome 9 and chromosome 22 that juxtaposes the two genes BCR and ABL and drives the leukemogenesis in CML. The ABL gene encodes for a nonreceptor tyrosine kinase that becomes deregulated and constitutively active after the juxtaposition of BCR. BCR–ABL is central in controlling downstream pathways involved in cell proliferation, regulation of cellular adhesion, and apoptosis.The understanding of the importance of this kinase activity in the pathophysiology of CML led to the development of tyrosine kinase inhibitors (TKI) that specifically target BCR–ABL. These agents became the mainstay of modern therapy in CML. CML has a triphasic clinical course, and the majority of patients (∼80%) are diagnosed during the early phase or the chronic phase (CP). However, and without effective treatment, CML invariably progresses to the advanced phases of the disease – the accelerated phase (AP) and the blast phase (BP). BP CML is a lethal refractory secondary leukemia with a short predicted survival.

Comprehensive molecular portraits of human breast tumors

 The Cancer Genome Atlas Network

Nature. 2012 October 4; 490(7418): 61–70. http://dx.doi.org/10.1038/nature11412.

We analyzed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, mRNA arrays, microRNA sequencing and reverse phase protein arrays. Our ability to integrate information across platforms provided key insights into previously-defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity.

Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at  > 10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the Luminal A subtype. We identified two novel protein expression-defined subgroups, possibly contributed by stromal/microenvironmental elements, and integrated analyses identified specific signaling pathways dominant in each molecular subtype including a HER2/p-HER2/HER1/p-HER1 signature within the HER2-Enriched expression subtype. Comparison of Basal-like breast tumors with high-grade Serous Ovarian tumors showed many molecular commonalities, suggesting a related etiology and similar therapeutic opportunities. The biologic finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biologic subtypes of breast cancer.

Most molecular studies of breast cancer have focused on just one or two high information content platforms, most frequently mRNA expression profiling or DNA copy number analysis, and more recently massively parallel sequencing. Supervised clustering of mRNA expression data has reproducibly established that breast cancers encompass several distinct disease entities, often referred to as the intrinsic subtypes of breast cancer. The recent development of additional high information content assays focused on abnormalities in DNA methylation, microRNA expression and protein expression, provide further opportunities to more completely characterize the molecular architecture of breast cancer.

Synbiology contribution and Nanotechnology

Synthetic RNAs Designed to Fight Cancer

Xiaowei Wang and his colleagues at  Washington University School of Medicine in St. Louis have designed synthetic molecules that combine the advantages of two experimental RNA therapies against cancer.  They have designed synthetic molecules that combine the advantages of two experimental RNA therapies against cancer.  RNA plays an important role in how genes are turned on and off in the body. Both siRNAs and microRNAs are snippets of RNA known to modulate a gene’s signal or shut it down entirely. Separately, siRNA and microRNA treatment strategies are in early clinical trials against cancer, but few groups have attempted to marry the two.

“We are trying to merge two largely separate fields of RNA research and harness the advantages of both,” said Xiaowei Wang, assistant professor of radiation oncology and a research member of the Siteman Cancer Center.  The study appears in the December issue of the journal RNA.

“We designed an artificial RNA that is a combination of siRNA and microRNA,” Wang said “our artificial RNA simultaneously inhibits both cell migration and proliferation.”  For therapeutic purposes, “small interfering” RNAs, or siRNAs, are designed and assembled in a lab and can be made to shut down– or interfere with– a single specific gene that drives cancer.  The siRNA molecules work extremely well at silencing a gene target because the siRNA sequence is made to perfectly complement the target sequence, thereby silencing a gene’s expression.

Though siRNAs are great at turning off the gene target, they also have potentially dangerous side effects: siRNAs inadvertently can shut down other genes that need to be expressed to carry out tasks that keep the body healthy.  The siRNAs interfere with off-target genesthat closely complement their “seed region,” a section of the siRNA  that governs binding to a gene target. “In the past, we tried to block the seed region in an attempt to reduce the side effects. Until now, we never tried to replace the seed region completely.”

Wang and his colleagues asked whether they could replace the siRNA’s seed region with the seed region from microRNA. Unlike siRNA, microRNA is a natural part of the body’s gene expression. And it can also shut down genes. As such, the microRNA seed region (with its natural targets) might reduce the toxic side effects caused by the artificial siRNA seed region. Plus, the microRNA seed region would add a new tool to shut down other genes that also may be driving cancer.

Wang’s group started with a bioinformatics approach, using a computer algorithm to design siRNA sequences against a common driver of cancer, a gene called AKT1 that encourages uncontrolled cell division. The program also selected siRNAs against AKT1 that had a seed region highly similar to the seed region of a microRNA known to inhibit a cell’s ability to move, thus potentially reducing the cancer’s ability to spread.

A Neutralizing RNA Aptamer

 Nucleic acid aptamers have been developed as high-affinity ligands that may act as antagonists of disease-associated proteins. Aptamers are non immunogenic and characterised by high specificity and low toxicity thus representing a valid alternative to antibodies or soluble ligand receptor traps/decoys to target specific cancer cell surface proteins in clinical diagnosis and therapy. The epidermal growth factor receptor (EGFR) has been implicated in the development of a wide range of human cancers including breast, glioma and lung. The observation that its inhibition can interfere with the growth of such tumors has led to the design of new drugs including monoclonal antibodies and tyrosine kinase inhibitors currently used in clinic. However, some of these molecules can result in toxicity and acquired resistance, hence the need to develop novel kinds of EGFR-targeting drugs with high specificity and low toxicity.

(CL Esposito, D Passaro, et al. A Neutralizing RNA Aptamer against EGFR Causes Selective Apoptotic Cell Death. PLoS ONE 6(9): e24071. http://dx.doi.org/10.1371/journal.pone.0024071)

Here we generated, by a cell-Systematic Evolution of  Ligands by EXponential enrichment (SELEX) approach, a nuclease resistant RNA-aptamer that specifically binds to EGFR with a binding constant of 10 nM. When applied to EGFR-expressing cancer cells the aptamer inhibits EGFR-mediated signal pathways causing selective cell death. Furthermore, at low doses it induces apoptosis even of cells that are resistant to the most frequently used EGFR-inhibitors, such as gefitinib and cetuximab, and inhibits tumor growth in a mouse xenograft model of human non-small-cell lung cancer (NSCLC). Interestingly, combined treatment with cetuximab and the aptamer shows clear synergy in inducing apoptosis in vitro and in vivo. In conclusion, we demonstrate that this neutralizing RNA aptamer is a promising bio-molecule that can be developed as a more effective alternative to the repertoire of already existing EGFR-inhibitors.

In-Silico Molecular Docking Analysis of Cancer Biomarkers

Currently, in the research scenario for cancer, the identification of anti-cancer drugs using immuno-modulatory proteins and other molecular agents to initiate apoptosis in cancer cells and to inhibit the signaling pathways of cancer biomarkers as a drug targeted therapy, for cancer cell proliferation assays by the researchers. In-Silico analysis is used to recognize anticancer compounds as a future prospective for In-Vitro and In-Vivo analysis. A large number of herbal remedies (e.g. garlic, mistletoe) are used by cancer patients for treating the cancer and/or reducing the toxicities of chemotherapeutic drugs. Some herbal medicines have shown potentially beneficial effects on cancer progression and may ameliorate chemotherapy-induced toxicities.  (K. Gowri Shankar et al., In-Silico Molecular Docking Analysis of Cancer Biomarkers with Bioactive Compounds of Tribulus terrestris. Intl J NOVEL TRENDS PHARMAL SCI. 2013; 3(4).

Tribulus terrestris is mentioned in ancient Indian Ayurvedic medical texts dating back thousands of years. Tribulus terrestris has been widely used in the Ayurvedic system of medicine for the treatment of sexualdysfunction and various urinary disorders. The aim of the present study is to evaluate the interactions of some bioactive compounds of Tribulus terrestris for In-Silico anticancer analysis with cancer biomarkers as targets. The targeted biomarkers for analysis include NSE-Lung cancer, Follistatin-Prostrate cancer, GGT Hepatocellular carcinoma, Human Prostasin-Ovarian cancer.

GC-MS analysis of Tribulus terrestris whole plant methanol extract revealed the existence of the major compound like 3,7,11,15-tetramethylhexadec-2-en-1-ol, 1,2-Benzenedicarboxylic acid, disooctyl ester, 9,12,15-Octadecatrienoic acid, (z,z,z)-, 9,12-Octadecadienoic acid (z,z)-, Hexadecadienoic acid, ethyl ester, n-Hexadecadienoic acid, Octadecanoic acid, Phytol, α-Amyrin are chosen as ligands. Hence, by analyzing the minimum binding energy of the ligand binding complex with the receptors by dockinganalysis using AutoDock tools will show effective nature of inhibition of these receptors by the unique ligands. Based on the results low minimum binding energy ligands are identified and used as a future studies can be done for specific receptors  docking.

Anti-Cancerous Effect of4,4′-Dihydroxychalcone ((2E,2′E)-3,3′-(1,4-Phenylene) Bis (1-(4-hydroxyphenyl) Prop-2-en-1-one)) on T47D Breast Cancer Cell Line

Narges Mahmoodi, T Besharati-Seidani, N Motamed, and NO Mahmoodi*
Annual Research & Review in Biology 2014; 4(12): 2045-2052
SCIENCEDOMAIN international    www.sciencedomain.org

Aims: The majority of human breast tumors are estrogen receptor α (ERα) positive. However, not all of the ERα+ breast cancers respond to anti-estrogens drugs for those women who do respond, initial positive responses can be of short duration. Thus, more effective drugs are needed to enhance the efficacy of anti-estrogens drugs or to be used separately in a period of time. In view of potential cytotoxicity associated with silybin as polyhydroxy compounds a synthetic 4-hydroxychalcones (bis-phenol) was considered to explore its anti-carcinogenic effects in comparison to silybin on ERα+ breast cancer cell line.

Methodology: We have studied the inhibitory effect of 4,4′-dihydroxychalcone on the T47D breast cancer cell line by MTT test and the IC50s were estimated using Pharm PCS.

Results: The 4,4′-dihydroxychalcone showed significant dose- and time-dependent cell growth inhibitory effects on T47D breast cancer cells. The IC50 of 4,4′-dihydroxychalcone on T47D cells after 24 and 48 hours was 160.88+/1 μM, 62.20+/1 μM and for silybin was 373.42+/-1 μM,176.98+/1 μM respectively.

Conclusion: Our results strongly suggests that this premade synthetic 4,4′-dihydroxychalcone can promote anti carcinogenic actions on T47D cell line. All 4,4′-dihydroxychalcone doses had a much larger inhibitory effect on cell viability than silybin doses in T47D cells. The ratio of the IC50 of 4,4′-dihydroxychalcone to silybin after 24 and 48 hours was 1: 2.3 and 1: 2.8 respectively.

Anticancer and multidrug resistance-reversal effects of solanidine analogs synthetized from pregnadienolone acetate.

István Zupkó, Judit Molnár, Borbála Réthy, Renáta Minorics, Eva Frank, et al.
Molecules (Impact Factor: 2.43). 01/2014; 19(2):2061-76.  http://dx.doi.org/10.3390/molecules19022061
Source: PubMed

ABSTRACT A set of solanidine analogs  with antiproliferative properties were recently synthetized from pregnadienolone acetate, which occurs in Nature. The aim of the present study was an in vitro characterization of their antiproliferative action and an investigation of their multidrug resistance-reversal activity on cancer cells. Six of the compounds elicited the accumulation of a hypodiploid population of HeLa cells, indicating their apoptosis-inducing character, and another one caused cell cycle arrest at the G2/M phase. The most effective agents inhibited the activity of topoisomerase I, as evidenced by plasmid supercoil relaxation assays. One of the most potent analogs down-regulated the expression of cell-cycle related genes at the mRNA level, including tumor necrosis factor alpha and S-phase kinase-associated protein 2, and induced growth arrest and DNA damage protein 45 alpha. Some of the investigated compounds inhibited the ABCB1 transporter and caused rhodamine-123 accumulation in murine lymphoma cells transfected by human MDR1 gene, expressing the efflux pump (L5178). One of the most active agents in this aspect potentiated the antiproliferative action of doxorubicin without substantial intrinsic cytostatic capacity. The current results indicate that the modified solanidine skeleton is a suitable substrate for the rational design and synthesis of further innovative drug candidates with anticancer activities.

Nutrition and Cancer

 Ascorbic Acid and Selenium Interaction: Its Relevance in Carcinogenesis

 Michael J. Gonzalez
Journal of Orthomolecular Medicine 1990; 5(2)

Ascorbic acid and selenium are two nutrients that seem to have a preventive potential in the process of carcinogenesis; because of a possible synergistic action that may produce an enhanced anticarcinogenic effect. Interaction between these nutrients have been reported. Results indicate that the protective effect of the inorganic form of selenium (Na Selenite) was nullified by ascorbic acid, whereas the chemopreventive action of the organic form (seleno-DL-methionine) was not affected.

A possibility exists that Selenite is reduced by ascorbic acid to elemental selenium and is therefore not available for tissue uptake. In experiments using Selenite; plasma and erythrocyte glutathione peroxidase enzyme activity was directly related to the level of ascorbic acid fed.

Complementary RNA and Protein Profiling Identifies Iron as a Key Regulator of Mitochondrial Biogenesis

J W. Rensvold, Shao-En On, A Jeevananthan, et al.
Cell Rep. 2013 January 31; 3(1): .   http://dx.doi.org/10.1016/j.celrep.2012.11.029

Mitochondria are centers of metabolism and signaling whose content and function must adapt to
changing cellular environments. The biological signals that initiate mitochondrial restructuring
and the cellular processes that drive this adaptive response are largely obscure. To better define
these systems, we performed matched quantitative genomic and proteomic analyses of mouse
muscle cells as they performed mitochondrial biogenesis. We find that proteins involved in
cellular iron homeostasis are highly coordinated with this process and that depletion of cellular
iron results in a rapid, dose-dependent decrease of select mitochondrial protein levels and
oxidative capacity. We further show that this process is universal across a broad range of cell
types and fully reversed when iron is reintroduced. Collectively, our work reveals that cellular iron
is a key regulator of mitochondrial biogenesis, and provides quantitative data sets that can be
leveraged to explore posttranscriptional and posttranslational processes that are essential for
mitochondrial adaptation.

Avemar outshines new cancer ‘breakthrough’ drug

by Michael Traub
Townsend Letter / Oct, 2010

Many of us in the cancer research community were happy to hear about progress against metastatic melanoma reported this June at the annual meeting of the American Society of Clinical
Oncology (ASCO). since there has not been an improvement in overall survival from chemotherapy in over three decades.
Data from a phase III clinical trial of the experimental monoclonal antibody ipilimumab (pronounced “ep-eh-lim-uemab”) showed that patients with melanoma survived longer if they were taking ipilimumab than if they were not, regardless of whether they also were taking the other drug in the study, an experimental cancer vaccine. (1)

A Closer Look: How Big an Improvement, at What Cost to Patients?

Overall Survival: the ‘Gold Standard’ for Judging Cancer Therapies

Overall survival (OS) is the length of time that a patient actuallysurvives a cancer after treatment. It can also be measured as the percentage of patients surviving a specific time. It is the gold
standard by which the usefulness of a cancer treatment should be determined. Many things can help a patient, but the most important goal of doctors and patients is for the cancer patient to live longer, with a decent quality of life (QOL).

Among patients taking ipilimumab with or without the experimental vaccine, median overall survival was about 10 months. That is compared with 6.4 months’ overall survival among patients receiving the vaccine by itself. About 45.6% of patients taking ipilimumab survived one year, an improvement of some 7% over the 38% seen in some earlier studies. This very modest improvement in survival comes at quite a price.

Severe Side Effects in More Than One in Four Ipilimumab Patients Ipilimumab has some side effects that can be “both severe and long-lasting,” according to the study report. Among patients taking ipilimumab by itself (without the vaccine), 19.1% had side effects requiring hospitalization or invasive intervention, 3.8% died from the effects of the drug, and another 33.8% had life-threatening or disabling side effects. All totaled, 26.7% of the patients taking ipilimumab by itself– more than 1 in 4-had side effects that were severe, very severe, or fatal. Severe side effects included diarrhea, nausea, constipation, vomiting, abdominal pain, fatigue, cough, and headache. Vernon Sondak, MD, of the H. Lee Moffitt Cancer and Research Institute, said that “using the drug requires the medical team to be on guard to manage toxicity at all times.” But even with its severe side effects, the researchers said that the drug should be welcomed because it can increase median survival from 6.4 months to 10.1 months. That is because any lengthening of lives is welcome in a disease that hasn’t seen a new drug that can do that in many years.

Fermented Wheat Germ (Avemar) Improves Melanoma Survival Without Harsh Side Effects

But what if there already were such a treatment available-not a drug, but a safe, natural substance shown in clinical trials to have a remarkably similar ability to lengthen the lives of melanoma patients, without the severe side effects of the new drug?
What if the other substance had no significant side effects at all?
What if, instead of causing severe and sometimes fatal side effects, that other substance actually helped prevent and reduce serious side effects caused by chemotherapy and radiotherapy?
In fact, there is just such a treatment available. It is known as fermented wheat germ extract (FWGE) and by its trade name Avemar. It has been approved as a medical nutriment for cancer
patients in Europe for years and is available in the US as a dietary supplement. It has been compared to dacarbazine (DTIC), standard melanoma therapy, in a clinical trial with longer
follow-up than the ipilimumab trial. And with better results.

In 2008, data were published in the research journal Cancer Biotherapy and Radiopharmaceuticals from seven years’ follow-up on a trial at the N. N. Blokhin Cancer Center in Moscow,
Russia, involving 52 patients who had taken or not taken Avemar while taking dacarbazine for the year following surgical removal of their stage III melanoma tumors. (2) Patients who got only dacarbazine survived 44.7 months. Those who got Avemar along with their dacarbazine survived 66.2 months. This is an improvement in overall survival time of over 48%. In the Russian study,
just as it has in other studies, Avemar reduced side effects of the chemotherapy. Among those taking only dacarbazine, 11 % experienced severe (grade 3 or grade 4) side effects that required hospitalization or invasive intervention. None of the Avemar patients had grade 3 or 4 side effects. Since it is difficult to compare length of survival between the recent ipilimumab study and the Avemar melanoma study, because the ipilimumab study tested mostly stage 4 melanoma patients and the Avemar study tested mostly stage 3 melanoma patients, it is most instructive to look at
the percentage improvement in overall survival from adding either treatment to the regimen. Ipilimumab and Avemar both produced very similar improvements in OS (56% vs. 48%, respectively),

Avemar Ameliorates Conventional Treatment Side Effects

The improvement of survival and the amelioration of chemotherapy side effects by Avemar seen in the Russian melanoma study is typical of Avemar’s effects when used in treating other cancers, including in combination with chemotherapy or radiotherapy. Among 170 colorectal cancer patients in a 2003 study published in the British journal of Cancer, Avemar improved overall survival
and reduced metastasis and recurrences after surgery, chemotherapy, and radiotherapy. (3) Taking Avemar for six months during and after those conventional treatments resulted in a 61.8% reduction in the death rate among those patients, compared with those who received only the conventional treatment. Those taking Avemar experienced lower rates of recurrences and metastases
as well, even though most patients in the Avemar group came into the study with more advanced disease, had more radiation earlier, and had been diagnosed longer. Side effects of Avemar, as in
other Avemar trials., were rare, mild, and transient, with no serious adverse events occurring.

In a 2004 study published in the journal of Pediatric Hematology and Oncology, childhood cancer patients taking Avemar during and after conventional therapies had a 42.8% reduction in the
low white blood cell counts and high fever known as febrile neutropenia, which can be a life-threatening consequence of chemotherapy and radiation. (4) This and similar results with
Avemar in other cancers are consistent with animal studies showing that Avemar helps the immune system recover a full white blood cell count after chemotherapy and radiation faster
than would otherwise happen. This study also demonstrated the safety of Avemar for children.

Why Avemar Works in Many Different Kinds of Cancer

Extensive studies in cells and animals have shown how Avemar works. Perhaps its most important action is to restrict cancer cells’ use of glucose. (5) Cancer cells use up to 50 times more glucose
than normal cells, a phenomenon known as the Warburg effect. (6) They use those enormous amounts of glucose to make ribose, the backbone sugar of DNA, much faster than normal cells can. To
do this, they must use a different series of biochemical reactions (“pathway”) than normal cells. Avemar makes this very difficult for cancer cells to do, because it inhibits the activity of the key enzyme in that pathway, transketolase (TK). (7) With the TK pathway blocked, cancer cells cannot use large amounts of glucose to make DNA fast enough to support the proliferation that makes them so dangerous.(8-10)

In experiments in the US and abroad, scientists have learned that Avemar has these additional effects. It:

* lowers the levels of a DNA repair enzyme known as poly (ADPribose) polymerase (PARP).” With this effect, cancer cells are forced to self-destruct, preventing them from proliferating and
producing a synergistic cancer-cell killing effect when given with chemotherapy, which also works to damage cancer cells’ DNA;
* reduces the number of molecules on cancer cells that identify them as originating within the body (MHC-1 molecules). (12) With cancer cells stripped of that protection, the immune system,
which recognizes the cancer cells as abnormal, no longer gives them the pass given to cells originating in the body. The cancer cells are attacked by the immune system’s natural killer (NK)
cells and destroyed;
* increases levels of molecules called intercellular adhesion molecule-1 (ICAM-1) on the blood vessels of cancer tumors. (13). The increase helps immune system cells pass through the walls of the blood vessels supplying the tumor blood flow, moving directly into the tumor to attack its cancer cells; increases the activity of the primary anticancer cytokine, tumor necrosis factor alpha (TNF-a), and produces a synergistic effect in interaction with other anticancer cytokines. (14) Cytokines are substances produced by cells to act directly on other cells. TNF-a helps force cancer cells into the programmed death known as apoptosis and inhibits tumorigenesis, the process through which new tumors are formed;
* inhibits the activity of ribonucleotide reductase (RR), a key enzyme that cells must have to make new DNA so that each cancer cell can divide to make two more like it. (15) With DNA
production slowed, increases in cancer cell growth and replication are inhibited.

Antimetastatic and Immune-Boosting Effects Are Key to Survival

Because the biochemical changes listed above have consistently been shown in both animal and human studies to be directly linked to reducing cancer’s ability to metastasize and to
improving the immune system’s ability to fight cancer, scientists count them as among the most likely main causes of improved survival seen in cancer patients when Avemar is used alone or,
more often, as an adjuvant in addition to standard-of-care therapies such as chemotherapy, radiotherapy, or the combination of the two. (16-23)

Extending Life: How Long, Exactly, and At What Cost in Quality of Life?

Any improvement in advanced melanoma survival, no matter how small, is certainly an achievement. But ipilimumab had severe side effects requiring hospitalization or invasive intervention in
over one-quarter of patients treated with it. And it increased median survival only by 3-plus months. On the other hand, Avemar added to dacarbazine improved survival very markedly, with no severe side effects. If actually improving overall survival substantially without significant side effects means that a drug should be considered as the new standard of care for first-line therapy, then there is no need to wait for further results. Avemar has already demonstrated very significant improvement in survival over chemotherapy alone and has a safety profile unmatched by
conventional therapies.

Michael Traub, ND, FABNO, is in private practice and serves as a member of Oncology Association of Naturopathic Physicians board of examiners.
Notes
(1.) Hodi FS, O’Day SJ, McDermott DF, et al. Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med. 2010 Jun 14.
(2.) Demidov LV. Manziuk LV, Kharkevitch GY, Pirogova NA,  Artamonova EV. Adjuvant fermented wheat germ extract (Avemar) nutraceutical improves survival of high-risk skin
melanoma patients; a randomized, pilot, phase ll clinical study with a 7-year follow-up. Cancer Biother Radiopharm. 2008 Aug. 23(4):477-482. Erratum in: Cancer Biother Radiopharm. 2008
Oct;2315):669.
(3.) Jakab F, Shoenfeld Y, Balogh A. et al. A medical nutriment has supportive value in the treatment of colorectal cancer. Br J Cancer. 2001 Aug 4;89(3):465-9.
(4.) Garami M, Schuler D, Babosa M, et al. Fermented wheat germ extract reduces chemotherapy-induced febrile neutropenia in pediatric cancer patients, J Pediatr Hematol Oncol. 2004
Oct;26(10):631-635.
(5.) Boros I.G, Lapis K, Szende B, et al. Wheat germ extract decreases glucose uptake and RNA ribose formation but increases fatty acid synthesis in MIA pancreatic adenocarcinoma
cells. Pancreas. 2001 Aug:23(2):141-147.
(6.) Warburg, O. On the origin of cancer cells. Science. 1956 Feb 24; 123(31 91):309-314.
(7.) Boros LG, Lee VVN, Go VL., A metabolic hypothesis of cell growth and death in pancreatic cancer, Pancreas. 2002 Jan;
24:(1):26 33.
(8.) Boros LG, Lapis K, Szende B, et al. Op cit.
(9.) Comin-Anduix B, Boros LG, Marin S, et al. Fermented wheat germ extract inhibits glycolysis/pentose cycle enzymes and induces apoptosis through poly(ADP-ribose) polymerase
activation in Jurkat T-cell leukemia tumor cells. J Biol Chem. 2002 Nov 29;277 (48):46408-46414. Epub 2002 Sep 25.
(23.) Garami M, Schuler D, Babosa M, et al. Fermented wheat germ extract reduces chemotherapy-induced febrile neutropenia in pediatric cancer patients. J Pediatr Hematol Oncol. 2004 Oct;
26(10):631-635.

by Michael Traub, ND, FABNO
COPYRIGHT 2010 The Townsend Letter Group
COPYRIGHT 2010 Gale, Cengage Learning

Nanotechnology in Cancer Drug Delivery and Selective Targeting

Nanoparticles are rapidly being developed and trialed to overcome several limitations of traditional drug delivery systems and are coming up as a distinct therapeutics for cancer treatment. Conventional chemotherapeutics possess some serious side effects including damage of the immune system and other organs with rapidly proliferating cells due to nonspecific targeting, lack of solubility, and inability to enter the core of the tumors resulting in impaired treatment with reduced dose and with low survival rate.

Nanotechnology has provided the opportunity to get direct access of the cancerous cells selectively with increased drug localization and cellular uptake. Nanoparticles can be programmed for recognizing the cancerous cells and giving selective and accurate drug delivery avoiding interaction with the healthy cells. This review focuses on cell recognizing ability of nanoparticles by various strategies having unique identifying properties that distinguish them from previous anticancer therapies. It also discusses specific drug delivery by nanoparticles inside the cells illustrating many successful researches and how nanoparticles remove the side effects of conventional therapies with tailored cancer treatment.

(Kumar Bishwajit Sutradhar and Md. Lutful Amin. Hindawi Publ. Corp.  2014, Article ID 939378, 12 pages

http://dx.doi.org/10.1155/2014/939378)

Cancer, the uncontrolled proliferation of cells where apoptosis is greatly disappeared, requires very complex process of treatment. Because of complexity in genetic and phenotypic levels, it shows clinical diversity and therapeutic resistance. A variety of approaches are being practiced for the treatment of cancer each of which has some significant limitations and side effects. Cancer treatment includes surgical removal, chemotherapy, radiation, and hormone therapy. Chemotherapy, a  very common treatment, delivers anticancer drugs systemically to patients for quenching the uncontrolled proliferation of cancerous cells. Unfortunately, due to nonspecific targeting by anticancer agents, many side effects occur and poor drug delivery of those agents cannot bring out the desired outcome in most of the cases. Cancer drug development involves a very complex procedure which is associated with advanced polymer chemistry and electronic engineering.

The main challenge of cancer therapeutics is to differentiate the cancerous cells and the normal body cells. That is why the main objective becomes engineering the drug in such a way as it can identify the cancer cells to diminish their growth and proliferation. Conventional chemotherapy fails to target the cancerous cells selectively without interacting with the normal body cells. Thus they cause serious side effects including organ damage resulting in impaired  treatment with lower dose and ultimately low survival rates.

Nanotechnology is the science that usually deals with the size range from a few nanometers (nm) to several hundrednm, depending on their intended use. It has been the area of interest over the last decade for developing precise drug delivery systems as it offers numerous benefits to overcome the limitations of conventional formulations . It is very promising both in cancer diagnosis and treatment since it can enter the tissues at molecular level.

Cisplatin-incorporated nanoparticles of poly(acrylic acid-co-methyl methacrylate) copolymer

K Dong Lee, Young-Il Jeong,  DH Kim,  Gyun-Taek Lim,  Ki-Choon Choi.  Intl J Nanomedicine 2013:8 2835–2845.

Although cisplatin is extensively used in the clinical field, its intrinsic toxicity limits its clinical use. We investigated nanoparticle formations of poly(acrylic acid-co-methyl methacrylate) (PAA-MMA) incorporating cisplatin and their antitumor activity in vitro and in vivo.

Methods: Cisplatin-incorporated nanoparticles were prepared through the ion-complex for­mation between acrylic acid and cisplatin. The anticancer activity of cisplatin-incorporated nanoparticles was assessed with CT26 colorectal carcinoma cells.

Results: Cisplatin-incorporated nanoparticles have small particle sizes of less than 200 nm with spherical shapes. Drug content was increased according to the increase of the feeding amount of cisplatin and acrylic acid content in the copolymer. The higher acrylic acid content in the copolymer induced increase of particle size and decrease of zeta potential. Cisplatin-incorporated nanoparticles showed a similar growth-inhibitory effect against CT26 tumor cells in vitro. However, cisplatin-incorporated nanoparticles showed improved antitumor activity against an animal tumor xenograft model.

Conclusion: We suggest that PAA-MMA nanoparticles incorporating cisplatin are promising carriers for an antitumor drug-delivery system.

Researchers Say Molecule May Help Overcome Cancer Drug Resistance
By Estel Grace Masangkay

A group of researchers from the University of Delaware has discovered that a deubiquitinase (DUB) complex, USP1-UAF1, may present a key target in helping fight resistance to platinum-based anticancer drugs. The research team’s findings were published online in Nature Chemical Biology.

Zhihao Zhuang, associate professor in the Department of Chemistry and Biochemistry at UD, and his team studied a DNA damage tolerance mechanism called translesion synthesis (TLS). Enzymes known as TLS polymerases synthesize DNA over damaged nucleotide bases, followed by replication after lesion. The enzymes have been linked with building cancer cell resistance to certain cancer drugs including cisplatin. Cisplatin is used in treatment of ovarian, bladder, and testicular cancers which have spread.

“Cancer drugs like cisplatin work by damaging DNA and thereby preventing cancer cells from replicating the genomic DNA and dividing. However, cancer cells quickly develop resistance to cisplatin, and we and other researchers suspect that a polymerase known as Pol η is involved in overcoming cisplatin-induced lesions,” Professor Zhuang said.

The team found that USP1-UAF1 may play a crucial role in regulating DNA damage response. A new molecule ML323 can be used to inhibit processes such as translesion synthesis. Zhuang said, “Using ML323, we studied the cellular response to DNA damage and revealed new insights into the role of deubiquitination in both the TLS pathway and another one called the Fanconi anemia, or FA, pathway. We’re very encouraged by the fact that a single molecule is effective at inhibiting the USP1-UAF1 DUB complex and disrupting two essential DNA damage tolerance pathways.”

A novel small peptide as an epidermal growth factor receptor targeting ligand for nanodelivery in vitro

Cui-yan Han,  Li-ling Yue, Ling-yu Tai,  Li Zhou  et al.  Intl J Nanomedicine 2013:8 1541–1549

The discovery of suitable ligands that bind to cancer cells is important for drug delivery specifically targeted to tumors. Monoclonal antibodies and fragments that serve as ligands have specific targets. Natural ligands have strong mitogenic and neoangiogenic activities. Currently, small pep­tides are pursued as targeting moieties because of their small size, low immunogenicity, and their ability to be incorporated into certain delivery vectors.

The epidermal growth factor receptor (EGFR) serves an important function in the proliferation of tumors in humans and is an effective target for the treatment of cancer. The epidermal growth factor receptor (EGFR) is a transmembrane protein on the cell surface that is overexpressed in a wide variety of human cancers. EGFR is an effective tumor-specific target because of its significant functions in tumor cell growth, differentiation, and migration. EGFR-targeted small molecule peptides such as YHWYGYTPQNVI have been successfully identified using phage display library screening; by contrast, the peptide LARLLT has been generated using computer-assisted design (CAD).

These peptides can be conjugated to the surfaces of liposomes that are then delivered selectively to tumors by the specific and efficient binding of these peptides to cancer cells that express high levels of EGFR.

In this paper, we studied the targeting characteristics of small peptides (AEYLR, EYINQ, and PDYQQD) These small peptides were labeled with fluorescein isothiocyanate (FITC) and used the peptide LARLLT as a positive control, which bound to putative EGFR selected from a virtual peptide library by computer-aided design, and the independent peptide RALEL as a negative control.

Analyses with flow cytometry and an internalization assay using NCI-H1299 and K562 with high EGFR and no EGFR expression, respectively, indicated that FITC-AEYLR had high EGFR targeting activity. Biotin-AEYLR that was specifically bound to human EGFR proteins demonstrated a high affinity for human non-small-cell lung tumors.

We found that AEYLR peptide-conjugated, nanostructured lipid carriers enhanced specific cellular uptake in vitro during a process that was apparently mediated by tumor cells with high-expression EGFR. Analysis of the MTT assay indicated that the AEYLR peptide did not significantly stimulate or inhibit the growth activity of the cells. These findings suggest that, when mediated by EGFR, AEYLR may be a potentially safe and efficient delivery ligand for targeted chemotherapy, radiotherapy, and gene therapy.

Arginine-based cationic liposomes for efficient in vitro plasmid DNA delivery with low cytotoxicity

SR Sarker  Y Aoshima,   R Hokama  T Inoue  et al. Intl J Nanomedicine 2013:8 1361–1375.

Currently available gene delivery vehicles have many limitations such as low gene delivery efficiency and high cytotoxicity. To overcome these drawbacks, we designed and synthesized two cationic lipids comprised of n-tetradecyl alcohol as the hydrophobic moiety, 3-hydrocarbon chain as the spacer, and different counterions (eg, hydrogen chloride [HCl] salt or trifluoroacetic acid [TFA] salt) in the arginine head group.

 Cationic lipids were hydrated in 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) buffer to prepare cationic liposomes and characterized in terms of their size, zeta potential, phase transition temperature, and morphology. Lipoplexes were then prepared and characterized in terms of their size and zeta potential in the absence or presence of serum. The morphology of the lipoplexes was determined using transmission electron microscopy and atomic force microscopy. The gene delivery efficiency was evaluated in neuronal cells and HeLa cells and compared with that of lysine-based cationic assemblies and Lipofectamine™ 2000. The cytotoxicity level of the cationic lipids was investigated and compared with that of Lipofectamine™ 2000.

 We synthesized arginine-based cationic lipids having different counterions (ie, HCl-salt or TFA-salt) that formed cationic liposomes of around 100 nm in size. In the absence of serum, lipoplexes prepared from the arginine-based cationic liposomes and plasmid (p) DNA formed large aggregates and attained a positive zeta potential. However, in the presence of serum, the lipoplexes were smaller in size and negative in zeta potential. The morphology of the lipoplexes was vesicular.

Arginine-based cationic liposomes with HCl-salt showed the highest transfection efficiency in PC-12 cells. However, arginine-based cationic liposomes with TFA salt showed the highest transfection efficiency in HeLa cells, regardless of the presence of serum, with very low associated cytotoxicity.

The gene delivery efficiency of amino acid-based cationic assemblies is influ­enced by the amino acids (ie, arginine or lysine) present as the hydrophilic head group and their associated counterions.

Molecularly targeted approaches herald a new era of non-small-cell lung cancer treatment

H Kaneda, T Yoshida,  I Okamoto.   Cancer Management and Research 2013:5 91–101.

The discovery of activating mutations in the epidermal growth-factor receptor (EGFR) gene in 2004 opened a new era of personalized treatment for non-small-cell lung cancer (NSCLC). EGFR mutations are associated with a high sensitivity to EGFR tyrosine kinase inhibitors, such as gefitinib and erlotinib. Treatment with these agents in EGFR-mutant NSCLC patients results in dramatically high response rates and prolonged progression-free survival compared with conventional standard chemotherapy. Subsequently, echinoderm microtubule-associated protein-like 4 (EML4)–anaplastic lymphoma kinase (ALK), a novel driver oncogene, has been found in 2007. Crizotinib, the first clinically available ALK tyrosine kinase inhibitor, appeared more effective compared with standard chemotherapy in NSCLC patients harboring EML4-ALK. The identification of EGFR mutations and ALK rearrangement in NSCLC has further accelerated the shift to personalized treatmentbased on the appropriate patient selection according to detailed molecular genetic characterization. This review summarizes these genetic biomarker-based approaches to NSCLC, which allow the instigation of individualized therapy to provide the desired clinical outcome.

Non-small-cell lung cancer (NSCLC) has a poor prognosis and remains the leading cause of death related to cancer worldwide. For most individuals with advanced, metastatic NSCLC, cytotoxic chemotherapy is the mainstay of treatment on the basis of the associated moderate improvement in survival and quality of life. However, the outcome of chemotherapy in such patients has reached a plateau in terms of overall response rate (25%–35%) and overall survival (OS; 8–10 months). This poor outcome, even for patients with advanced NSCLC who respond to such chemotherapy, has motivated a search for new therapeutic approaches.

Recent years have seen rapid progress in the development of new treatment strat­egies for advanced NSCLC, in particular the introduction of molecularly targeted therapiesand appropriate patient selection. First, the most important change has been customization of treatment according to patient selection based on the genetic profile of the tumor. Small-molecule tyrosine kinase inhibitors (TKIs) that target the epidermal growth-factor receptor (EGFR), such as gefitinib and erlotinib, are especially effective in the treatment of NSCLC patients who harbor activating EGFR mutations.

Surgical Nanorobotics using nanorobots made from advanced DNA origami and Synthetic Biology

Ido Bachelet’s moonshot to use nanorobotics for surgery has the potential to change lives globally. But who is the man behind the moonshot?

Ido graduated from the Hebrew University of Jerusalem with a PhD in pharmacology and experimental therapeutics. Afterwards he did two postdocs; one in engineering at MIT and one in synthetic biology in the lab of George Church at the Wyss Institute at Harvard.

Now, his group at Bar-Ilan University designs and studies diverse technologies inspired by nature.

They will deliver enzymes that break down cells via programmable nanoparticles.

Delivering insulin to tell cells to grow and regenerate tissue at the desired location.

Surgery would be performed by putting the programmable nanoparticles into saline and injecting them into the body to seek out remove bad cells and grow new cells and perform other medical work.

 

http://2.bp.blogspot.com/-bnAE6hL2RIE/Uy0wFB8pYPI/AAAAAAAAubM/BeSpFC4vLu0/s1600/screenshot-by-nimbus+(3).png

 

Robots killing and suppressing cancer cells

 

http://1.bp.blogspot.com/-LGsE1msGIrw/Uy0vKGoaQ3I/AAAAAAAAubE/2E1_lcAspao/s1600/screenshot-by-nimbus+(2).png

 

Robots delivering payload

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0

http://4.bp.blogspot.com/-kkfXlMyPRCI/Uy0wkYPMvBI/AAAAAAAAubU/0AQPpJpM5E4/s1600/screenshot-by-nimbus+(4).png

Molecular building blocks

 

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=236

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=283

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=287

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=292

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=333

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=397

http://2.bp.blogspot.com/-gCHiyZ2MBHg/Uy0ySRKw_II/AAAAAAAAubg/BeneEQ5bY-U/s1600/screenshot-by-nimbus+(5).png

 

Robot blocks neuron

http://4.bp.blogspot.com/-cbYNJnN_w7U/Uy0yrqyqebI/AAAAAAAAubo/b42r4WRMr8k/s1600/screenshot-by-nimbus+(6).png

 

automation of robotic surgery

 

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=470

Nanoparticles with computational logic has already been done

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=501

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=521

http://1.bp.blogspot.com/-rSyRzo7p50w/Uy0y5teQkDI/AAAAAAAAubw/8cxZ4t0WNHw/s1600/screenshot-by-nimbus+(7).png

 

 robotic algorithm

 

Load an ensemble of drugs into many particles for programmed release based on situation that is found in the body

http://1.bp.blogspot.com/-kc99CbOQYLs/Uy0zgUG13KI/AAAAAAAAub4/j6nM7hAVxUg/s1600/screenshot-by-nimbus+(8).png

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=572

http://www.youtube.com/watch?feature=player_embedded&v=aA-H0L3eEo0#t=577

 

robotic lung cancer Rx

 

chemotherapy regimen

 

Chemoprevention in Model Experiments

Effects of Two Disiloxanes ALIS-409 and ALIS-421 on Chemoprevention in Model Experiments

H TOKUDA,…. L AMARAL and J MOLNAR.ANTICANCER RESEARCH 33: 2021-2028 (2013).

ALIS

 

Figure 1. Chemical structures of ALIS-409 and ALIS-421.

Morpholino-disiloxane (ALIS-409) and piperazinodisiloxane (ALIS-421) compounds were developed as inhibitors of multidrug resistance of various types of cancer cells. In the present study, the effects of ALIS-409 and ALIS-421 compounds were investigated on cancer promotion and on co-existence of

tumor and normal cells. The two compounds were evaluated for their inhibitory effects on Epstein-Barr virus immediate early antigen (EBV-EA) expression induced by tetradecanoylphorbolacetate (TPA) in Raji cell cultures. The method is known as a primary screening test for antitumor effect, below the (IC50) concentration. ALIS-409 was more effective in inhibiting EBV-EA (100 μg/ml) and tumor promotion, than

ALIS-421, in the concentration range up to 1000 μg/ml. However, neither of the compounds were able to reduce tumor promotion significantly, expressed as inhibition of TPA-induced tumor antigen activation. Based on the in vitro results, the two disiloxanes were investigated in vivo for their effects on mouse skin tumors in a two-stage mouse skin carcinogenesis study.

 

 

 

 

 

 

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Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison

Curator: Aviva Lev-Ari, PhD, RN

 

UPDATED on 8/19/2018

This is the best curation on Music I read to date

Aviva Lev-Ari, PhD, RN

LEONARD BERNSTEIN AT 100 Celebrating the legendary composerconductor’s string works and unbridled loyalty to the music By Thomas May

https://memeteria.files.wordpress.com/2018/07/august-2018-st280.pdf

 

On 2/11/2014 I read The Hub Review: Concert with a key by Thomas Gravey. I was inspired to develop an Analogy between his Review and the work we do.

This article has three parts:

Part 1: Six Components in the Analogy 

Part 2: Equivalence in the Analogy 

Part 3: Curation in Music (Component #1) and Music Review as a Curation (Component #2)

 

Part 1

Six Components in the Analogy

Component #1: Curation in Music

Component #1: Detailed, below

Work of Original Music Curation and Performance: The Celebrity Series Concert on 1/31/2014 in Boston, MA, which I attended.

#Boston premiere of ‘Old Friend’ tonight at New England Conservatory’s Jordan Hall at Kirill’s recital, which includes works by #Haydn#Schumann (‘Carnaval’), and #Mussorgsky (‘Pictures at an Exhibition’).

Component #2: Music Review and Critique as a Curation

Component #2: Detailed, below

Music Review and Critique as a Curation it represents a very fine example of Music Critique as a Curation written by Thomas Garvey on 2/8/2014 for the 1/31/2014, Celebrity Series concert in Jordan Hall by Kirill Gerstein, Component #1, above

http://hubreview.blogspot.com/2014/02/concert-with-key.html#links

Component #3, #4, #5, #6 – Curations in Medical Research

Component #3: Detailed, here

Work of Original Expression what is the methodology of Curation in the context of Medical Research Findings Exposition of Synthesis and Interpretation of the significance of the results to Clinical Care

Dr. A. Lev-Ari‘s definition of the Methodology of Curation

conceived: NEW Definition for Co-Curation in Medical Research

Component #4: Detailed, here

Work of Original Expression of the function and use of Curation methodology for Medical Research Findings Exposition of Synthesis and Interpretation of the significance of the results to Clinical Care

Dr. JD Pearlman‘s metaphoric expression of the Curation Methodology

In the Summary to Volume Two 

Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation – The Art of Scientific & Medical Curation

Dr. Pearlman writes:

This volume introduces a fresh look at keeping abreast of cardiovascular disease. In particular it explains and exemplifies the how and why of curation as a methodology for discourse. Curation is designed to edify and facilitate awareness and cohesive access to biomedical knowledge otherwise buried in subspecialty scientific journals in the Life Sciences and Medicine. Particular themes of focus include discovery, innovation and translation to clinical care, including linkages and underpinnings that might otherwise be mislabeled as esoteric. Key components of curation include expert identification of data, ideas and innovations of interest, expert interpretation of the original research results, integration with context, digesting, highlighting, correlating and presenting in novel light.

The superstructure of curations includes multiple additional creative elements:

  • eTOCs stands for electronic Table of Contents: fresh thought-provoking organizing themes link a path to a diverse trail of publications (analogous to creating a path in the forest)
  • Extracts highlighting notable elements of publications that mark a path
  • Voice of Expert commentary providing context and direction

The Electronic Table of Contents (eTOCs) serves several functions:

  • eTOCs collates information from multiple sources into coherent themes
  • eTOCs enables multiple pathways to information, including both Longitudinal and cross-sectional organizational themes.
  • eTOCs presents nested pathways through the forest, including nesting of topics by overreaching theme, chapters, Curations, reports and references.
  • eTOCs assemblies of thought provide fresh vistas that promote innovation and rethinking

In ekistics (urban design) Francis Bacon emphasized the importance of pathways linked to purpose, recommending a landmark magnet as an attractor for pursuits along a created path. Analogously, if the continually expanding collective knowledge embodied in subspecialty publications represents a forest of data and ideas, then Curation creates pathways in that forest that serve not only to keep the reader from getting lost, but also, as recommended by Francis Bacon, creates pathways that serve attractive purposes, with special vistas, highlights, themes, coherence, motivations and purposes.

CONTEXT (for each, Causes, Risks, Biomarkers and Therapeutics): See Volumes 1,2,3,4,5,6 

Component #5: Detailed, here

Work of Original Expression of two examples for the Writing Tatent and the Curation Talent applied in Medical writings by a Surgeon and by a Pathologist

Dr. A. Lev-Ari’s Curation of an article that demonstrates the Art of Praise for the Physician as a Author and Writer of proze of high literary merit on subjects in Science and Medicine:

The Young Surgeon and The Retired Pathologist: On Science, Medicine and HealthCare Policy – The Best Writers Among the WRITERS

Component #6: Detailed, here

Music in the Service of Clinical Care

Dr. A. Lev-Ari’s Curation of an article on the Function of Music in Restoration of Wellness from a Disease Stage

The Role of the Harp and of Music in Medical Recovery

More Harp Music

http://www.youtube.com/watch?v=Tiye0BqxJS4 

Part 2

Equivalence in the Analogy

Analogy Defined

(from Greek ἀναλογία, analogia, “proportion”[1][2]) is a cognitive process of transferring information or meaning from a particular subject (the analogue or source) to another particular subject (the target), or a linguistic expression corresponding to such a process. In a narrower sense, analogy is an inference or anargument from one particular to another particular, as opposed to deductioninduction, and abduction, where at least one of the premises or the conclusion is general. The word analogy can also refer to the relation between the source and the target themselves, which is often, though not necessarily, a similarity, as in the biological notion of analogy.

Analogy has been studied and discussed since classical antiquity by philosophers, scientists and lawyers. The last few decades have shown a renewed interest in analogy, most notably in cognitive science.

SOURCE of the definition

http://en.wikipedia.org/wiki/Analogy

Equivalence in the Analogy

[Component #1] is analogous to [Component #6] = [Component #6] is analogous to [Component #1]

[Component #2] is analogous to [Component #4] = [Component #4] is analogous to [Component #2]

[Components #3, #5] are analogous to [Components #1, #4] and [Component #2]

 

Component #1: Work of Original Music Curation and Performance:

Component #2: Music Review and Critique as a Curation

Component #3: Work of Original Expression what is the methodology of Curation

Component #4: Work of Original Expression of the function and use of Curation methodology for Medical Research

Component #5:  Work of Original Expression of two examples for the Writing Tatent and the Curation Talent applied in Medical writings by a Surgeon and by a Pathologist

Component #6: Music in the Service of Clinical Care

 

Part 3

Curation in Music (Component #1) and

Music Review as  a Curation (Component #2)

Component #1: Curation in Music

Work of Original Music Curation and Performance: The Celebrity Series Concert on 1/31/2014 in Boston, MA, which I attended.

#Boston premiere of ‘Old Friend’ tonight at New England Conservatory’s Jordan Hall at Kirill’s recital, which includes works by #Haydn#Schumann (‘Carnaval’), and #Mussorgsky (‘Pictures at an Exhibition’).

The Boston Globe tells the tale of how ‘Old Friend’, a piece Kirill commissioned from composer Timo Andres, came into fruition.You can hear the #Boston premiere of ‘Old Friend’ tonight at New England Conservatory’s Jordan Hall at Kirill’s recital, which includes works by #Haydn#Schumann (‘Carnaval’), and #Mussorgsky (‘Pictures at an Exhibition’).Tickets: http://www.celebrityseries.org/CS_performers_2013_14/gerstein.htm

‘Old Friend’ is formed over a piano and some coffee – The Boston Globe
As Kirill Gerstein remembers it, it was over coffee early in 2011 that he asked composer Timothy Andres to write a…

SOURCE

https://www.facebook.com/pages/Kirill-Gerstein/101570384501

Component #2: Music Review as a Curation

Music Review and Critique which represents a very fine example of a Curation in Music Critique written by Thomas Garvey for the 1/31/2014, Celebrity Series concert in Jordan Hall by Kirill Gerstein, Component #1, above

http://hubreview.blogspot.com/2014/02/concert-with-key.html#links

Saturday, February 8, 2014

Concert with a key

Kirill Gerstein
Rarely has a performance been curated with such subtle thematic skill as Kirill Gerstein’s at Celebrity Series last weekend.
Its calling card was the Boston premiere of current wunderkind Timo Andres’ “Old Friend,” a kind of millennial fantasia on Chopin’s Third Scherzo.  You know the Chopin – it’s a dazzler split between two apparent emotional poles, one grumbling at the bottom of the keyboard, the other chiming at the top; the piece is perhaps most memorable for the sparkling arpeggios that rain over the conflicted theme that sounds at the point where the two modes meet in the middle.

Andres teases that opposition into a vast structure in “Old Friend” – but more on that later. The point I want to make now is that Andres’ title unlocks the design of Gerstein’s whole concert – or concert à clef, if you will. For the pianist had clearly taken Andres’ insight into Chopin’s scherzo as the key to his entire program, and had thought long and hard not only about the theme of “friendship” (particularly lost friendship) in life and art, but about the musical values that undergird its expression.Hence the opening choice of Haydn’s familiar Variations in F Minor. It too, of course, is a double variation: an initial melancholy voice in F minor is slowly entwined by a lighter song in F major; two “friends,” if you will, of opposed temperaments. The voices dance in ever more elaborate patterns until the second is abruptly cut off, and a coda of poignant force rings down the curtain on the piece. Legend has it that this shock was inspired by the unexpected death of Haydn’s friend Maria Anna von Genzinger, with whom he had struck up a passionate correspondence. And the Variations do have a sweetly epistolary quality; one voice seems to “reply” to the other almost by post. But Gerstein took that sense of distance a bit far; he played with a measured precision that came off as slightly dry – although the outpouring of emotion at the end of the affair, if you will, was genuine, and genuinely moving.In the next offering, Schumann’s Carnaval, the theme of friendship evoked in music was even more overt. For the program of Carnaval – now worked out by scholars from Schumann’s notes and titles – is a cavalcade of the composer’s friends, both real and imaginary, through which move two lovers, Ernestine von Fricken and Schumann’s eventual wife, Clara (along with real-life musical idols like Paganini). The piece seems structureless to the uninitiated (and, well, it is!) – but there is clearly some sort of romantic showdown at its core; many believe Schumann’s eventual rejection of Ernestine in favor of Clara is prefigured in its variations. But then Chopin shows up, and the party grinds on. I admit Carnaval never quite sustains my interest throughout its meandering length; but I also admit that Gerstein’s version was among the most compelling I’ve heard.  From its opening flourish, the pianist seemed in superb control of its many voices, and even the sense of their overlapping interpenetration, and the musical haze that surrounds them.  And Gerstein carried off the finale, in which the whole artsy crowd marches out to confront the Philistines, in very high style indeed.After intermission came the premiere from Andres, who delivered the most musically abstracted vision of friendship yet. Of course, this time the friend was itself a piece of music – Chopin’s scherzo (rather than the composer himself) – and music about music is almost always inherently abstract. Andres basically took the most famous feature of the scherzo – those cascades of arpeggios – and doubled them, so that “Old Friend” rippled up from the bottom of the keyboard as well as down from its top, in a series of interlocking minimalist cells drawn from Chopin’s harmonic material. The cells moved in and out of phase, and various points of intersection or inversion were constantly shifting – still, Andres seemed unable to transcend the limits of his schema, and the eventual emergence of the scherzo’s own phrases seemed like a slight anticlimax (as we could see them coming from so very far away).

Hartmann’s “Catacombs of Paris”

Thus “Old Friend” at times felt like pianism for pianists, a kind of giant tinkertoy – still, its construction was virtuosic, and its technical demands challenging indeed (the composer himself is an astonishingly facile pianist, and he clearly intended this as his own exploration of the grand manner – a kind of maximal minimalism!). For his part, Gerstein played its rumbling, chiming cadences for all they were worth; Andres wrote the piece for him, and he had to have been pleased with this performance.

Finally, galloping after the premiere came one of the great keyboard warhorses – Mussorgsky’sPictures at an Exhibition. If you’re wondering at the friendship connection here, recall that the paintings in question were by a close friend and artistic associate of the composer – the architect/artist Viktor Hartmann. And in keeping with the slightly funereal theme of much of the concert, these famous tone poems were intended as both valedictory and obituary; for the exhibition that Mussorgsky evokes (and which included works from his own collection) was, tragically, a posthumous one, as the artist died of an aneurysm at the early age of 39.

Hartmann’s “Great Gate” of Kiev was never realized.

I always make it a point to recommend that concertgoers who are only familiar with Ravel’s celebrated orchestration seek out a performance of the original score (preferably in its first form – as here – rather than Rimsky-Korsakov’s corrected edition). It’s not often heard, as its demands are punishing, particularly in the final two “pictures,” but it is an eye-opener. Perhaps inevitably, the dazzling color of the Ravel somehow spectacularizes, and perhaps even slightly de-personalizes, everything inPictures; certainly on the keyboard, for instance, it is far easier to limn the shifting response of the “Promenade” theme as it moves from vignette to vignette.

Although of course the viewer of these pictures eventually seems to step right into them; his voice first materializes deep within “Catacombs” – where perhaps he is calling to Hartmann himself – before later opening out into its own apotheosis in “The Great Gate of Kiev” (the artist’s sketch for the project, at left) – which in a way is both a gate to Heaven, through which we can imagine the artist’s spirit soaring, and a portal into the deeply Russian artistic consciousness that Mussorgsky and Hartmann dreamed of together.

To be honest, I felt that Gerstein was finally tiring a bit as the bells chimed their welcome in “Great Gate,” but it hardly mattered, as so much of his performance had proved so very exciting (perhaps it’s worth noting at this point the pianist’s own Russian roots). Just a few highlights were the subtly singing line of “The Old Castle,” the note of tragedy sounding beneath “Goldenberg and Schmuyle,” and the haunted murmur of “Con Mortuis in Lingua Mortua.” This was truly a masterly performance of a masterpiece, so no wonder the crowd called the pianist back for an encore. Gerstein chose Rachmaninoff’s Op. 3, No. 3, “Mélodie,” – a last nostalgic bouquet, simple and sweet – and perhaps meant for yet another friend cut down too soon.

Posted by at 3:54 PM 
Thomas Garvey – A local reviewer for several years, I was cast from my perch at the Boston Globesome time ago, but quickly learned I could write about my hometown’s culture with more freedom and accuracy on the Web than I ever could at the Globe. And as local reviews grow ever more watered-down (as the press becomes more and more desperate to hang onto advertising – and readers), it has become obvious this town needs an independent, unfettered critic who’s not interested in tossing softballs to the suburbs (or the academy), And I guess I’m just dumb enough to take the job. You can reach me with invites, praise, screeds, etc., at hubreview@hotmail.com.
SOURCE

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