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Biochemical Insights of Dr. Jose Eduardo de Salles Roselino
How is it that developments late in the 20th century diverted the attention of
biological processes from a dynamic construct involving interacting chemical
reactions under rapidly changing external conditions effecting tissues and cell
function to a rigid construct that is determined unilaterally by the genome
construct, diverting attention from mechanisms essential for seeing the complete
cellular construct?
Larry, I assume that in case you read the article titled Neo – Darwinism, The
Modern Synthesis and Selfish Genes that bares no relationship with Physiology
with Molecular Biology J. Physiol 2011; 589(5): 1007-11 by Denis Noble, you might
find that it was the key factor required in order to understand the dislodgment
of physiology as a foundation of medical reasoning. In the near unilateral emphasis
of genomic activity as a determinant of cellular activity all of the required general
support for the understanding of my reasoning. The DNA to protein link goes
from triplet sequence to amino acid sequence. That is the realm of genetics.
Further, protein conformation, activity and function requires that environmental
and micro-environmental factors should be considered (Biochemistry). If that
were not the case, we have no way to bridge the gap between the genetic
code and the evolution of cells, tissues, organs, and organisms.
Consider this example of hormonal function. I would like to stress in
the cAMP dependent hormonal response, the transfer of information
that occurs through conformation changes after protein interactions.
This mechanism therefore, requires that proteins must not have their
conformation determined by sequence alone.
Regulatory protein conformation is determined by its sequence plus
the interaction it has in its micro-environment. For instance, if your
scheme takes into account what happens inside the membrane and
that occurs before cAMP, then production is increased by hormone
action. A dynamic scheme will show an effect initially, over hormone
receptor (hormone binding causing change in its conformation) followed
by GTPase change in conformation caused by receptor interaction and
finally, Adenylate cyclase change in conformation and in activity after
GTPase protein binding in a complex system that is dependent on self-
assembly and also, on changes in their conformation in response to
hormonal signals (see R. A Kahn and A. G Gilman 1984 J. Biol. Chem.
v. 259,n 10 pp6235-6240. In this case, trimeric or dimeric G does not
matter). Furthermore, after the step of cAMP increased production we
also can see changes in protein conformation. The effect of increased
cAMP levels over (inhibitor protein and protein kinase protein complex)
also is an effect upon protein conformation. Increased cAMP levels led
to the separation of inhibitor protein (R ) from cAMP dependent protein
kinase (C ) causing removal of the inhibitor R and the increase in C activity.
R stands for regulatory subunit and C for catalytic subunit of the protein
complex.
This cAMP effect over the quaternary structure of the enzyme complex
(C protein kinase + R the inhibitor) may be better understood as an
environmental information producing an effect in opposition to
what may be considered as a tendency towards a conformation
“determined” by the genetic code. This “ideal” conformation
“determined” by the genome would be only seen in crystalline
protein. In carbohydrate metabolism in the liver the hormonal signal
causes a biochemical regulatory response that preserves homeostatic
levels of glucose (one function) and in the muscle, it is a biochemical
regulatory response that preserves intracellular levels of ATP (another
function).
Therefore, sequence alone does not explain conformation, activity
and function of regulatory proteins. If this important regulatory
mechanism was not ignored, the work of S. Prusiner (Prion diseases
and the BSE crisis Stanley B. Prusiner 1997 Science; 278: 245 – 251,
10 October) would be easily understood. We would be accustomed
to reason about changes in protein conformation caused by protein
interaction with other proteins, lipids, small molecules and even ions.
In case this wrong biochemical reasoning is used in microorganisms.
Still it is wrong but, it will cause a minor error most of the time, since
we may reduce almost all activity of microorganism´s proteins to a
single function – The production of another microorganism. However,
even microorganisms respond differently to their micro-environment
despite a single genome (See M. Rouxii dimorphic fungus works,
later). The reason for the reasoning error is, proteins are proteins
and DNA are DNA quite different in chemical terms. Proteins must
change their conformation to allow for fast regulatory responses and
DNA must preserve its sequence to allow for genetic inheritance.
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
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
how changes in the extracellular metabolome can be used
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 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
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.
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.
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
metabolic states relating potassium limitation and
ammonium excess conditions to one another.
The model-based analysis of both
separately published extracellular metabolome datasets
suggests a relationship between
glutamate,
threonine and
folate metabolism,
which are collectively perturbed when ammonium assimilation processes are broadly disrupted
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.
Minimization of Metabolic Adjustment (MoMA) [39], and
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).
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.
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
A 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
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 j 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
where Nj is the number of reactions in Rj and mmet,j is calculated as
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 Rk
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
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
904 genes,
1,228 individual metabolites, and
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),
which are required in the assembly of glycogen and
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
changes to the compartmental location of a gene and
its corresponding reaction(s),
changes in reaction reversibility and cofactor specificity, and
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],
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 (p < 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 (p < 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.
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.
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.
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 p < 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
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],
which reported significant reduction in the pentose phosphate pathway flux
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.
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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