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Biomarkers and risk factors for cardiovascular events, endothelial dysfunction, and thromboembolic complications

Curator: Larry H Bernstein, MD, FCAP

 

 

Acute Coronary Syndrome

Predictive Cardiovascular and Circulation Biomarkers

Biomarkers are chemistry analytes measured in plasma, serum or whole blood that potentially identify injury or risk for injury.  They may be measured in the laboratory or at the bedside (point of care technology).  They may be measured as an enzyme (CK isoenzyme MB), a protein (troponins I & T), or as a micro RNA (miRNA).  In the last decade the discovery and use of cardiac biomarkers has moved toward very small quantities, even 100 times below the picogram range using Quanterix Simoa, compared with an enzyme immunoassay.

The time of sampling was based on time to appearance from time of damage, and the release of the biomarker is a stochastic process. The earliest studies of CK-MB appearance, peak height, and disappearance was by Burton Sobel and associates related to measuring the extent of damage, and determined that reperfusion had an effect.

There has been a nonlinear introduction of new biomarkers in that period, with an explosion of methods discovery and large studies to validate them in concert with clinical trials. The improvement of interventional methods, imaging methods, and the unraveling of patient characteristics associated with emerging cardiovascular disease is both cause for alarm (technology costs) and for raised expectations for both prevention, risk reduction, and treatment. What is strikingly missing is the kind of data analyses on the population database that could alleviate the burden of physician overload. It is an urgent requirement for the EHR, and it needs to be put in place to facilitate patient care.

 

Biomarkers: Diagnosis and Management, Present and Future

Curator: Larry H Bernstein, MD, FCAP
Biomarkers of Cardiovascular Disease : Molecular Basis and Practical Considerations.
RS Vasan .
Circulation. 2006;113:2335-2362. http://dx.doi.org/10.1161/CIRCULATIONAHA.104.482570
https://pharmaceuticalintelligence.com/2013/11/10/biomarkers-diagnosis-and-management/

sCD40L indicates soluble CD40 ligand; Fbg, fibrinogen; FFA, free fatty acid; ICAM, intercellular adhesion molecule; IL, interleukin; IMA, ischemia modified albumin; MMP, matrix metalloproteinases; MPO, myeloperoxidase; Myg, myoglobin; NT-proBNP, N-terminal proBNP; Ox-LDL, oxidized low-density lipoprotein; PAI-1, plasminogen activator inhibitor; PAPP-A, pregnancy-associated plasma protein-A; PlGF, placental growth factor; TF, tissue factor; TNF, tumor necrosis factor; TNI, troponin I; TNT, troponin T; VCAM, vascular cell adhesion molecule; and VWF, von Willebrand factor.

 

Accurate Identification and Treatment of Emergent Cardiac Events  

Author: Larry H Bernstein, MD, FCAP
https://pharmaceuticalintelligence.com/2013/03/15/accurate-identification-and-treatment-of-emergent-cardiac-events/

The main issue that we have a consensus agreement that PLAQUE RUPTURE is not the only basis for a cardiac ischemic event. The introduction of  high sensitivity troponin tests has made it no less difficult after throwing out the receiver-operator characteristic curve (ROC) and assuming that any amount of cardiac troponin released from the heart is pathognomonic of an acute ischemic event.  This has resulted in a consensus agreement that

  • ctn measurement at a coefficient of variant (CV) measurement in excess of 2 Std dev of the upper limit of normal is a “red flag” signaling AMI? or other cardiomyopathic disorder

This is the catch.  The ROC curve established AMI in ctn(s) that were accurate for NSTEMI – (and probably not needed with STEMI or new Q-wave, not previously seen) –

  1. ST-depression
  2. T-wave inversion
  3. in the presence of other findings
  • suspicious for AMI

Wouldn’t it be nice if it was like seeing a robin on your lawn after a harsh winter?  Life isn’t like that.  When acute illness hits the patient may well present with ambiguous findings.   We are accustomed to relying on

  • clinical history
  • family history
  • co-morbidities, eg., diabetes, obesity, limited activity?, diet?
  • stroke and/or peripheral vascular disease
  • hypertension and/or renal vascular disease
  • aortic atherosclerosis or valvular heart disease

these are evidence, and they make up syndromic classes

  • Electrocardiogram – 12 lead EKG (as above)
  • Laboratory tests
  • isoenzyme MB of creatine kinase (CK)… which declines after 12-18 hours
  • isoenzyme-1 of LD if the time of appearance is > day-1 after initial symptoms (no longer used)
  1. cardiac troponin cTnI or cTnT
  • genome testing
  • advanced analysis of EKG

This may result in more consults for cardiologists, but it lays the ground for better evaluation of the patient, in the long run.

Perspectives on the Value of Biomarkers in Acute Cardiac Care and Implications for Strategic Management
Antoine Kossaify, … STAR-P Consortium
Biomarker Insights 2013:8 115–126.
http://dx.doi.org:/10.4137/BMI.S12703

In addition to the conventional use of natriuretic peptides, cardiac troponin, and C-reactive protein, other biomarkers are outlined in variable critical conditions that may be related to acute cardiac illness. These include ST2 and chromogranin A in acute dyspnea and acute heart failure, matrix metalloproteinase in acute chest pain, heart-type fatty acid binding protein in acute coronary syndrome, CD40 ligand and interleukin-6 in acute myocardial infarction, blood ammonia and lactate in cardiac arrest, as well as tumor necrosis factor-alpha in atrial fibrillation. Endothelial dysfunction, oxidative stress and inflammation are involved in the physiopathology of most cardiac diseases, whether acute or chronic. In summary, natriuretic peptides, cardiac troponin, C-reactive protein are currently the most relevant biomarkers in acute cardiac care.

 Inverse Association between Cardiac Troponin-I and Soluble Receptor for Advanced Glycation End Products in Patients with Non-ST-Segment Elevation Myocardial Infarction

ED. McNair, CR. Wells, A.M. Qureshi, C Pearce, G Caspar-Bell, and K Prasad
Int J Angiol 2011;20:49–54
http://dx.doi.org/10.1055/s-0031-1272552

Interaction of advanced glycation end products (AGEs) with the receptor for advanced AGEs (RAGE) results in activation of nuclear factor kappa-B, release of cytokines, expression of adhesion molecules, and induction of oxidative stress. Oxygen radicals are involved in plaque rupture contributing to thromboembolism, resulting in acute coronary syndrome (ACS). Thromboembolism and the direct effect of oxygen radicals on myocardial cells cause cardiac damage that results in the release of cardiac troponin-I (cTnI) and other biochemical markers. The soluble RAGE (sRAGE) compete with RAGE for binding with AGE, thus functioning as a decoy and exerting a cytoprotective effect. Low levels of serum sRAGE would allow unopposed serum AGE availability for binding with RAGE, resulting in the generation of oxygen radicals and proinflammatory molecules that have deleterious consequences and promote myocardial damage. sRAGE may stabilize atherosclerotic plaques. It is hypothesized that low levels of sRAGE are associated with high levels of serum cTnI in patients with ACS.
The levels of cTnI were higher in NSTEMI patients (2.180.33 mg/mL) as compared with control subjects (0.0120.001 mg/mL). Serum sRAGE levels were negatively correlated with the levels of cTnI. In conclusion, the data suggest that low levels of serum sRAGE are associated with high serum levels of cTnI and that there is a negative correlation between sRAGE and cTnI.

Correlation of soluble receptor for advanced glycation end products (sRAGE) with cardiac troponin-I

Correlation of soluble receptor for advanced glycation end products (sRAGE) with cardiac troponin-I

 

Figure 1 Serum levels of soluble receptor for advanced glycation end products (sRAGE) in control subjects and in patients with non-ST-elevation myocardial infarction (NSTEMI). Results are expressed as meanstandard error. *p<0.05, control versus NSTEMI.

 

Serum levels of soluble receptor for advanced glycation end products

Serum levels of soluble receptor for advanced glycation end products

Figure 3 Correlation of soluble receptor for advanced glycation end products (sRAGE) with cardiac troponin-I (cTnI) in patients with non-ST-segment elevation myocardial infarction.

 

Heart Failure Complicating Non–ST-Segment Elevation Acute Coronary Syndrome

MC Bahit, RD. Lopes, RM. Clare, et al.
JACC: HtFail 2013; 1(3):223–9 .
http://dx.doi.org/10.1016/j.jchf.2013.02.007

This study sought to describe the occurrence and timing of heart failure (HF), associated clinical factors, and 30-day outcomes in patients with non–ST-segment elevation acute coronary syndromes (NSTE-ACS). Of 46,519 NSTE-ACS patients, 4,910 (10.6%) had HF at presentation. Of the 41,609 with no HF at presentation, 1,194 (2.9%) developed HF during hospitalization. A total of 40,415 (86.9%) had no HF at any time. Patients presenting with or developing HF during hospitalization were older, more often female, and had a higher risk of death at 30 days than patients without HF (adjusted odds ratio [OR]: 1.74; 95% confidence interval: 1.35 to 2.26). Older age, higher presenting heart rate, diabetes, prior myocardial infarction (MI), and enrolling MI were significantly associated with HF during hospitalization.

Other risk factors

Additive influence of genetic predisposition and conventional risk factors in the incidence of coronary heart disease: a population-based study in Greece
N Yiannakouris, M Katsoulis, A Trichopoulou, JM Ordovas, DTrichopoulos
BMJ Open 2014;4:e004387.
http://dx.doi.org:/10.1136/bmjopen-2013-004387

Genetic predisposition to CHD, operationalised through a multilocus GRS, and ConvRFs have essentially additive effects on CHD risk.

PTX3, A Prototypical Long Pentraxin, Is an Early Indicator of Acute Myocardial Infarction

G Peri, M Introna, D Corradi, G Iacuitti, S Signorini, et al.
Circulation. 2000;102:636-641
http://circ.ahajournals.org/content/102/6/636
http://dx.doi.org:/10.1161/01.CIR.102.6.636

PTX3 is a long pentraxin whose expression is induced by cytokines in endothelial cells, mononuclear phagocytes, and myocardium. PTX3 is present in the intact myocardium, increases in the blood of patients with AMI, and disappears from damaged myocytes. We suggest that PTX3 is an early indicator of myocyte irreversible injury in ischemic cardiomyopathy.

Early release of glycogen phosphorylase inpatients with unstable angina and transient ST-T alterations

J Mair, B Puschendorf, J Smidt, P Lechleitner, F Dienstl, et al.
BrHeartJ 1994;72:125-127.
http://www.ncbi.nlm.nih.gov/pubmed/7917682

Glycogen phosphorylase BB (molecular weight 96000 kDa as a monomer) is the predominant isotype in human myocardium where it occurs alongside the MM subtype. The release of glycogen phosphorylase from injured myocardium may reflect the burst in glycogenolysis initiated during acute myocardial ischaemia. This is supported by a rapid increase in serum concentrations of glycogen phosphorylase BB in patients with acute myocardial infarction before concentrations of creatine kinase, creatine kinase MB, myoglobin, and cardiac troponin T increase. Unstable angina, however, ranges from no myocardial cell damage to non-Q wave myocardial infarction.
All variables except for creatine kinase and creatine kinase MB activities were significantly higher on admission in patients with unstable angina and transient ST-T alterations than in patients without. However, glycogen phosphorylase BB concentration was the only marker that was significantly (p = 0-0001) increased above its discriminator value in most patients.

Endothelium and Vascular

Endothelial Dysfunction: An Early Cardiovascular Risk Marker in Asymptomatic Obese Individuals with Prediabetes
AK. Gupta, E Ravussin, DL. Johannsen, AJ. Stull, WT. Cefalu and WD. Johnson
Br J Med Med Res 2012; 2(3): 413-423.
http://www.ncbi.nlm.nih.gov/pubmed/22905340

Adults with desirable weight [n=12] and overweight [n=8] state, had normal fasting plasma glucose [Mean(SD)]: FPG [91.1(4.5), 94.8(5.8) mg/dL], insulin [INS, 2.3(4.4), 3.1(4.8) μU/ml], insulin sensitivity by homeostasis model assessment [HOMA-IR, 0.62(1.2), 0.80(1.2)] and desirable resting clinic blood pressure [SBP/DBP, 118(12)/74(5), 118(13)/76(8) mmHg]. Obese adults [n=22] had prediabetes [FPG, 106.5(3.5) mg/dL], hyperinsulinemia [INS 18.0(5.2) μU/ml], insulin resistance [HOMA-IR 4.59(2.3)], prehypertension [PreHTN; SBP/DBP 127(13)/81(7) mmHg] and endothelial dysfunction [ED; reduced RHI 1.7(0.3) vs. 2.4(0.3); all p<0.05]. Age-adjusted RHI correlated with BMI [r=-0.53; p<0.001]; however, BMI-adjusted RHI was not correlated with age [r=-0.01; p=0.89].

Association of digital vascular function with cardiovascular risk factors: a population study.
T Kuznetsova, E Van Vlierberghe, J Knez, G Szczesny, L Thijs, et al.
BMJ Open 2014; 4:e004399.
http://dx.doi.org:/10.1136/bmjopen-2013-004399

Our study is the first to implement the new photoplethysmography (PPG) technique to measure digital pulse amplitude hyperemic in a sample of a general population. The correlates of hyperaemic response were as expected and constitute an internal validation of the PPG technique in assessment of digital vascular function.

Thrombotic/Embolic Events

Risk marker associations with venous thrombotic events: a cross-sectional analysis 
BA Golomb, VT Chan, JO Denenberg, S Koperski,  & MH Criqui.
BMJ Open 2014;4:e003208.
http://dx.doi.org:/10.1136/bmjopen-2013-003208

To examine the interrelations among, and risk marker associations for, superficial and deep venous events—superficial venous thrombosis (SVT), deep venous thrombosis (DVT) and pulmonary embolism (PE). Significant correlates on multivariable analysis were, for SVT: female sex, ethnicity (African-American=protective), lower educational attainment, immobility and family history of varicose veins. For DVT and DVE, significant correlates included: heavy smoking, immobility and family history of DVEs (borderline for DVE). For PE, significant predictors included immobility and, in contrast to DVT, blood pressure (BP, systolic or diastolic). In women, estrogen use duration for hormone replacement therapy, in all and among estrogen users, predicted PE and DVE, respectively.

Endothelium and hemorheology
T Gori, S Dragoni, G Di Stolfo and S Forconi
Ann Ist Super Sanità 2007 | Vol. 43, No. 2: 124-129
http://www.ncbi.nlm.nih.gov/pubmed/22951621

The mechanisms underlying the regulation of its function are extremely complex, and are principally determined by physical forces imposed on the endothelium by the flowing blood. In the present paper, we describe the interactions between the rheological properties of blood and the vascular endothelium.The role of shear stress, viscosity, cell-cell interactions, as well as the molecular mechanisms that are important for the transduction of these signals are discussed both in physiology and in pathology, with a particular attention to the role of reactive oxygen species. In the final conclusions, we propose an hypothesis regarding the implications of changes in blood viscosity, and particularly on the significance of secondary hyperviscosity syndromes..

Fig. 1 | Endothelial “function” (i.e.,the production of protective autacoids by the vascular endothelium) and “dysfunction” (i.e., the involvement of the endothelium in vascular pathology). EDHF: En d o t h e l i um-De r i v e d Hyperpolarizing Factor; LDL:Low-Density Lipoprotein

Fig. 2 | Endothelial production of nitric oxide (NO) is stimulated by oscillatory shear stress, transmitted by the endothelial surface layer to the endothelial cells. NO: Nitric Oxide; NOS: Nitrous Oxide Systems; ESL: Endothelial Surface Layer

 

 

 

 

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Metabolomic analysis of two leukemia cell lines. II.

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

Leaders in Pharmaceutical Intelligence

 

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

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

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

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

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

Answer – lets look into this in Part II.

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

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

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

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

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

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

 

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

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

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

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

 

Results

We set up a pipeline that could be used to

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

Our pipeline combined the following four steps:

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

We demonstrated the pipeline and the predictive potential

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

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

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

Whereas the CCRF-CEM model

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

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

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

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

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

 

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

2.1.1 Generation of experimental data

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

Extracellular metabolomics (exo-metabolomic) data,

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

 

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

To determine whether we had obtained two distinct models,

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

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

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

They were very similar to each other in terms of their

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

The Molt– 4 model contained

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

In contrast, the CCRF-CEM  contained

31 unique reactions

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

The Molt-4 model contained

  • 15 unique genes, and

the CCRF-CEM model had

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

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

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

The ETC was fueled by FADH2 originating from

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

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

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

 

2.1.5 Condition-specific cell line models predict distinct metabolic strategies

Despite the overall similarity of the metabolic models,

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

To interrogate the metabolic differences, we sampled the solution space

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

For this  analysis, additional constraints were applied, emphasizing

  • the  quantitative differences in commonly uptaken and secreted metabolites.

The  maximum possible uptake and maximum possible secretion flux rates were

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

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

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

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

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

This result  was further  supported by differences

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

The shift persisted throughout all reactions of the pathway and

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

The sampling median for glucose uptake was 34 % higher

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

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

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

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

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

Additionally, there was a higher efflux of  citrate toward

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

There was higher flux through anaplerotic and cataplerotic reactions

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

 

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

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

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

The sampling  analysis therefore revealed different usage of

  • central metabolic pathways by the condition-specific models.

 

Fig. 2

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

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

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

  • describe reversible  reactions with flux in the reverse direction.

There are multiple reversible  reactions for the transformation of

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

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

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

 

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

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

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

 

Fig. 3

Fatty acid oxidation and ETC _Fig3

Fatty acid oxidation and ETC _Fig3

 

Sampling reveals different utilization of oxidative phosphorylation by the

  • generated models.

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

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

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

  • reversible reactions with flux in the reverse direction.

Both models lack Complex I of the ETC because of constraints

  • arising from the mapping of transcriptomic data.

Electron transfer flavoprotein and

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

 

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

Cancer cells have to balance their needs

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

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

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

These measurements were used to provide support for

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

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

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

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

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

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

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

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

Yet we want to emphasize that concentrations

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

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

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

 

Fig. 4 (not shown)

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

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

2.1.7 Comparison of network utilization and alteration in gene expression

With the assumption that

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

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

  • compared to the flux differences observed in the  models.

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

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

and we  checked  whether downregulated genes

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

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

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

Reactions were defined as differently utilized

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

Of the reactions associated with upregulated genes,

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

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

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

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

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

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

  • were more utilized by the Molt-4 model.

Taken together, our comparisons of the

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

2.1.8 Accumulation of DEGs and AS genes at key metabolic steps

After we confirmed that most reactions associated with upregulated genes

  • were more utilized by the CCRF-CEM model,

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

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

Several DEGs and AS events were associated with

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

 

Table 1

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

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

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

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

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

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

Of these key enzymes,

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

in agreement with the higher in silico predicted flux.

However, in contrast to the observed

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

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

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

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

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

A second AS gene associated with

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

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

  • because of the lack of ribose uptake or secretion.

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

Literature query revealed that at least 13 genes associated with alternative

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

One general observation was that there was a surprising

  • accumulation of metabolite transporters among the AS.

Overall, the high incidence of

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

 

2.1.9 Single gene deletion

Analyses of essential genes in metabolic models have been used

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

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

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

The analysis yielded 63 shared lethal KO genes and

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

For three of the unique CCRF-CEM KO genes,

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

 

The essential genes for both models were then

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

The CCRF-CEM model

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

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

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

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

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

 

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

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

This KO gene is particularly interesting, given

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

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

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

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

  • specifically targeted to kill these cells.

 

3 Discussion

In the current study, we explored the possibility of

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

By constructing condition-specific cell line models

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

We derived condition-specific cell line models

  • for CCRF-CEM and
  • Molt-4 cells

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

Despite the overall similarities between the models, the analysis revealed

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

The additional data sufficiently supported

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

We used the validated models

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

These weak links may represent potential drug targets.

Integrating omics data with the human genome-scale reconstruction

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

This network context can simplify omics data analysis, and

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

Compared to transcriptomic data,

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

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

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

Rather, our way of model construction allowed us to efficiently

  • assess the metabolic characteristics of cells.

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

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

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

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

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

  • with particular metabolic characteristics respond to drugs.

The generation of our condition-specific cell line models involved

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

Model building mainly involves

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

It should be noted that this reduction determines,

  • which pathways can be utilized by the model.

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

  • more significant reduction may be achieved using different data.

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

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

One way to prevent the emergence of network gaps would be

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

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

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

Interestingly, the lack of a significant contribution of our

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

However, sampling of the cell line models constrained

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

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

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

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

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

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

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

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

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

Moreover, leukemia cell lines have been shown

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

Such dependence may cause the cells to adapt their metabolism

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

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

  • several analyses to validate the models and model predictions.

Our expectations regarding the levels and ratios of metabolites

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

The more pronounced shift of the NADH/NAD+ ratio

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

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

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

However, decreased mitochondrial glucose oxidation and

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

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

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

Control of NADPH maintains the redox potential through GSH and

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

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

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

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

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

Cancer is related to metabolic reprogramming, which results from

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

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

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

The detailed analysis of the respective

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

We found discrepancies between differential gene regulation and

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

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

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

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

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

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

Rather, the results of the presented  approach

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

The combination of our tailored metabolic models and

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

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

  • peripheral metabolic pathways are considered.

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

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

A single-gene-deletion analysis revealed that PGDH was

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

Differences in PGDH protein levels

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

Rapidly proliferating cells may use an

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

For breast cancer cell lines, variable dependency on

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

This example of a unique KO gene demonstrates how

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

This approach can provide valuable information for drug discovery.

In conclusion, our contextualization method produced

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

The analyses described in this study have great potential to reveal

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

 

4.3 Analysis of the extracellular metabolome

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

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

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

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

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

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

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

  1. valine and
  2. methionine.

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

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

 

4.4 Network refinement based on exo-metabolic data

Despite its comprehensiveness, the human metabolic reconstruction is

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

Accordingly, we identified metabolite transport systems

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

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

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

4.5 Expression profiling

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

4.6 Analysis of transcriptomic data

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

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

 

4.7 Deriving cell-type-specific subnetworks

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

 

…..

 

Electronic supplementary material

Below is the link to the electronic supplementary material.

File S1. Supplementary material 1 (XLSX 915 kb)

File S2. Supplementary material 2 (DOCX 448 kb)

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Larry H Bernstein, MD, FCAP, Author and Curator

https://pharmaceuticalintelligence.com/2014/06/22/Proteomics – The Pathway to Understanding and Decision-making in Medicine

This dialogue is a series of discussions introducing several perspective on proteomics discovery, an emerging scientific enterprise in the -OMICS- family of disciplines that aim to clarify many of the challenges toward the understanding of disease and aiding in the diagnosis as well as guiding treatment decisions. Beyond that focus, it will contribute to personalized medical treatment in facilitating the identification of treatment targets for the pharmaceutical industry. Despite enormous advances in genomics research over the last two decades, there is a still a problem in reaching anticipated goals for introducing new targeted treatments that has seen repeated failures in stage III of clinical trials, and even when success has been achieved, it is temporal.  The other problem has been toxicity of agents widely used in chemotherapy.  Even though the genomic approach brings relieve to the issues of toxicity found in organic chemistry derivative blocking reactions, the specificity for the target cell without an effect on normal cells has been elusive.

This is not confined to cancer chemotherapy, but can also be seen in pain medication, and has been a growing problem in antimicrobial therapy.  The stumbling block has been inability to manage a multiplicity of reactions that also have to be modulated in a changing environment based on 3-dimension structure of proteins, pH changes, ionic balance, micro- and macrovascular circulation, and protein-protein and protein- membrane interactions. There is reason to consider that the present problems can be overcome through a much better modification of target cellular metabolism as we peel away the confounding and blinding factors with a multivariable control of these imbalances, like removing the skin of an onion.

This is the first of a series of articles, and for convenience we shall here  only emphasize the progress of application of proteomics to cardiovascular disease.

growth in funding proteomics 1990-2010

growth in funding proteomics 1990-2010

Part I.

Panomics: Decoding Biological Networks  (Clinical OMICs 2014; 5)

Technological advances such as high-throughput sequencing are transforming medicine from symptom-based diagnosis and treatment to personalized medicine as scientists employ novel rapid genomic methodologies to gain a broader comprehension of disease and disease progression. As next-generation sequencing becomes more rapid, researchers are turning toward large-scale pan-omics, the collective use of all omics such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics and lipoprotein proteomics, to better understand, identify, and treat complex disease.

Genomics has been a cornerstone in understanding disease, and the sequencing of the human genome has led to the identification of numerous disease biomarkers through genome-wide association studies (GWAS). It was the goal of these studies that these biomarkers would serve to predict individual disease risk, enable early detection of disease, help make treatment decisions, and identify new therapeutic targets. In reality, however, only a few have gone on to become established in clinical practice. For example in human GWAS studies for heart failure at least 35 biomarkers have been identified but only natriuretic peptides have moved into clinical practice, where they are limited primarily for use as a diagnostic tool.

Proteomics Advances Will Rival the Genetics Advances of the Last Ten Years

Seventy percent of the decisions made by physicians today are influenced by results of diagnostic tests, according to N. Leigh Anderson, founder of the Plasma Proteome Institute and CEO of SISCAPA Assay Technologies. Imagine the changes that will come about when future diagnostics tests are more accurate, more useful, more economical, and more accessible to healthcare practitioners. For Dr. Anderson, that’s the promise of proteomics, the study of the structure and function of proteins, the principal constituents of the protoplasm of all cells.

In explaining why proteomics is likely to have such a major impact, Dr. Anderson starts with a major difference between the genetic testing common today, and the proteomic testing that is fast coming on the scene. “Most genetic tests are aimed at measuring something that’s constant in a person over his or her entire lifetime. These tests provide information on the probability of something happening, and they can help us understand the basis of various diseases and their potential risks. What’s missing is, a genetic test is not going to tell you what’s happening to you right now.”

Mass Spec-Based Multiplexed Protein Biomarkers

Clinical proteomics applications rely on the translation of targeted protein quantitation technologies and methods to develop robust assays that can guide diagnostic, prognostic, and therapeutic decision-making. The development of a clinical proteomics-based test begins with the discovery of disease-relevant biomarkers, followed by validation of those biomarkers.

“In common practice, the discovery stage is performed on a MS-based platform for global unbiased sampling of the proteome, while biomarker qualification and clinical implementation generally involve the development of an antibody-based protocol, such as the commonly used enzyme linked ELISA assays,” state López et al. in Proteome Science (2012; 10: 35–45). “Although this process is potentially capable of delivering clinically important biomarkers, it is not the most efficient process as the latter is low-throughput, very costly, and time-consuming.”

Part II.  Proteomics for Clinical and Research Use: Combining Protein Chips, 2D Gels and Mass Spectrometry in 

The next Step: Exploring the Proteome: Translation and Beyond

N. Leigh Anderson, Ph.D., Chief Scientific Officer, Large Scale Proteomics Corporation

Three streams of technology will play major roles in quantitative (expression) proteomics over the coming decade. Two-dimensional electrophoresis and mass spectrometry represent well-established methods for, respectively, resolving and characterizing proteins, and both have now been automated to enable the high-throughput generation of data from large numbers of samples.

These methods can be powerfully applied to discover proteins of interest as diagnostics, small molecule therapeutic targets, and protein therapeutics. However, neither offers a simple, rapid, routine way to measure many proteins in common samples like blood or tissue homogenates.

Protein chips do offer this possibility, and thus complete the triumvirate of technologies that will deliver the benefits of proteomics to both research and clinical users. Integration of efforts in all three approaches are discussed, highlighting the application of the Human Protein Index® database as a source of protein leads.

leighAnderson

leighAnderson

N. Leigh Anderson, Ph D. is Chief Scientific Officer of the Proteomics subsidiary of Large Scale Biology Corporation (LSBC).
Dr. Anderson obtained his B.A. in Physics with honors from Yale and a Ph.D. in Molecular Biology from Cambridge University
(England) where he worked with M. F. Perutz as a Churchill Fellow at the MRC Laboratory of Molecular Biology. Subsequently
he co-founded the Molecular Anatomy Program at the Argonne National Laboratory (Chicago) where his work in the development
of 2D electrophoresis and molecular database technology earned him, among other distinctions, the American Association for
Clinical Chemistry’s Young Investigator Award for 1982, the 1983 Pittsburgh Analytical Chemistry Award, 2008 AACC Outstanding
Research Award, and 2013 National Science Medal..

In 1985 Dr. Anderson co-founded LSBC in order to pursue commercial development and large scale applications of 2-D electro-
phoretic protein mapping technology. This effort has resulted in a large-scale proteomics analytical facility supporting research
work for LSBC and its pharmaceutical industry partners. Dr. Anderson’s current primary interests are in the automation of proteomics
technologies, and the expansion of LSBC’s proteomics databases describing drug effects and disease processes in vivo and in vitro.
Large Scale Biology went public in August 2000.

Part II. Plasma Proteomics: Lessons in Biomarkers and Diagnostics

Exposome Workshop
N Leigh Anderson
Washington 8 Dec 2011

QUESTIONS AND LESSONS:

CLINICAL DIAGNOSTICS AS A MODEL FOR EXPOSOME INDICATORS
TECHNOLOGY OPTIONS FOR MEASURING PROTEIN RESPONSES TO EXPOSURES
SCALE OF THE PROBLEM: EXPOSURE SIGNALS VS POPULATION NOISE

The Clinical Plasma Proteome
• Plasma and serum are the dominant non-invasive clinical sample types
– standard materials for in vitro diagnostics (IVD)
• Proteins measured in clinically-available tests in the US
– 109 proteins via FDA-cleared or approved tests
• Clinical test costs range from $9 (albumin) to $122 (Her2)
• 90% of those ever approved are still in use
– 96 additional proteins via laboratory-developed tests (not FDA
cleared or approved)
– Total 205 proteins (≅ products of 211genes, excluding Ig’s)
• Clinically applied proteins thus account for
– About 1% of the baseline human proteome (1 gene :1 protein)
– About 10% of the 2,000+ proteins observed in deep discovery
plasma proteome datasets

“New” Protein Diagnostics Are FDA-Cleared at a Rate of ~1.5/yr:
Insufficient to Meet Dx or Rx Development Needs

FDA clearance of protein diagnostics

FDA clearance of protein diagnostics

A  Major Technology Gulf Exists Between Discovery

Proteomics and Routine Diagnostic Platforms

Two Streams of Proteomics
A.  Problem Technology
Basic biology: maximum proteome coverage (including PTM’s, splices) to
provide unbiased discovery of mechanistic information
• Critical: Depth and breadth
• Not critical: Cost, throughput, quant precision

B.  Discovery proteomics
Specialized proteomics field,
large groups,
complex workflows and informatics

Part III.  Addressing the Clinical Proteome with Mass Spectrometric Assays

N. Leigh Anderson, PhD, SISCAPA Assay Technologies, Inc.

protein changes in biological mechanisms

protein changes in biological mechanisms

No Increase in FDA Cleared Protein Tests in 20 yr

“New” Protein Tests in Plasma Are FDA-Cleared at a Rate of ~1.5/yr:
Insufficient to Meet Dx or Rx Development Needs

See figure above

An Explanation: the Biomarker Pipeline is Blocked at the Verification Step

Immunoassay Weaknesses Impact Biomarker Verification

1) Specificity: what actually forms the immunoassay sandwich – or prevents its
formation – is not directly visualized

2) Cost: an assay developed to FDA approvable quality costs $2-5M per
protein

Major_Plasma_Proteins

Major_Plasma_Proteins

Immunoassay vs Hybrid MS-based assays

Immunoassay vs Hybrid MS-based assays

MASS SPECTROMETRY: MRM’s provide what is missing in..IMMUNOASSAYS:

– SPECIFICITY
– INTERNAL STANDARDIZATION
– MULTIPLEXING
– RAPID CONFIGURATION PROVIDED A PROTEIN CAN ACT LIKE A SMALL
MOLECULE

MRM of Proteotypic Tryptic Peptides Provides Highly Specific Assays for Proteins > 1ug/ml in Plasma

Peptide-Level MS Provides High Structural Specificity
Multiple Reaction Monitoring (MRM) Quantitation

ADDRESSING MRM LIMITATIONS VIA SPECIFIC ENRICHMENT OF ANALYTE  PEPTIDES: SISCAPA

– SENSITIVITY
– THROUGHPUT (LC-MS/MS CYCLE TIME)

SISCAPA combines best features of immuno and MS

SISCAPA combines best features of immuno and MS

SISCAPA Process Schematic Diagram
Stable Isotope-labeled Standards with Capture on Anti-Peptide Antibodies

An automated process for SISCAPA targeted protein quantitation utilizes high affinity capture antibodies that are immobilized on magnetic beads

An automated process for SISCAPA targeted protein quantitation utilizes high affinity capture antibodies that are immobilized on magnetic beads

Antibodies sequence specific peptide binding

Antibodies sequence specific peptide binding

SISCAP target enrichmant

SISCAP target enrichmant

Multiple reaction monitoring (MRM) quantitation

Multiple reaction monitoring (MRM) quantitation

protein-quantitation-via-signature-peptides.png

protein-quantitation-via-signature-peptides.png

First SISCAP Assay - thyroglobulin

First SISCAP Assay – thyroglobulin

personalized reference range within population range

Glycemic control in DM

Glycemic control in DM

Part IV. National Heart, Lung, and Blood Institute Clinical

Proteomics Working Group Report
Christopher B. Granger, MD; Jennifer E. Van Eyk, PhD; Stephen C. Mockrin, PhD;
N. Leigh Anderson, PhD; on behalf of the Working Group Members*
Circulation. 2004;109:1697-1703 doi: 10.1161/01.CIR.0000121563.47232.2A
http://circ.ahajournals.org/content/109/14/1697

Abstract—The National Heart, Lung, and Blood Institute (NHLBI) Clinical Proteomics Working Group
was charged with identifying opportunities and challenges in clinical proteomics and using these as a
basis for recommendations aimed at directly improving patient care. The group included representatives
of clinical and translational research, proteomic technologies, laboratory medicine, bioinformatics, and
2 of the NHLBI Proteomics Centers, which form part of a program focused on innovative technology development.

This report represents the results from a one-and-a-half-day meeting on May 8 and 9, 2003. For the purposes
of this report, clinical proteomics is defined as the systematic, comprehensive, large-scale identification of
protein patterns (“fingerprints”) of disease and the application of this knowledge to improve patient care
and public health through better assessment of disease susceptibility, prevention of disease, selection of
therapy for the individual, and monitoring of treatment response. (Circulation. 2004;109:1697-1703.)
Key Words: proteins diagnosis prognosis genetics plasma

Part V.  Overview: The Maturing of Proteomics in Cardiovascular Research

Jennifer E. Van Eyk
Circ Res. 2011;108:490-498  doi: 10.1161/CIRCRESAHA.110.226894
http://circres.ahajournals.org/content/108/4/490

Abstract: Proteomic technologies are used to study the complexity of proteins, their roles, and biological functions.
It is based on the premise that the diversity of proteins, comprising their isoforms, and posttranslational modifications
(PTMs) underlies biology.

Based on an annotated human cardiac protein database, 62% have at least one PTM (phosphorylation currently dominating),
whereas 25% have more than one type of modification.

The field of proteomics strives to observe and quantify this protein diversity. It represents a broad group of technologies
and methods arising from analytic protein biochemistry, analytic separation, mass spectrometry, and bioinformatics.
Since the 1990s, the application of proteomic analysis has been increasingly used in cardiovascular research.

prevalence-of-cardiovascular-diseases-in-adults-by-age-and-sex-u-s-2007-2010.

prevalence-of-cardiovascular-diseases-in-adults-by-age-and-sex-u-s-2007-2010.

Technology development and adaptation have been at the heart of this progress. Technology undergoes a maturation,

becoming routine and ultimately obsolete, being replaced by newer methods. Because of extensive methodological
improvements, many proteomic studies today observe 1000 to 5000 proteins.

Only 5 years ago, this was not feasible. Even so, there are still road blocks. Nowadays, there is a focus on obtaining
better characterization of protein isoforms and specific PTMs. Consequentl, new techniques for identification and
quantification of modified amino acid residues are required, as is the assessment of single-nucleotide polymorphisms
in addition to determination of the structural and functional consequences.

In this series, 4 articles provide concrete examples of how proteomics can be incorporated into cardiovascular
research and address specific biological questions. They also illustrate how novel discoveries can be made and
how proteomic technology has continued to evolve. (Circ Res. 2011;108:490-498.)
Key Words: proteomics technology protein isoform posttranslational modification polymorphism

Part VI.   The -omics era: Proteomics and lipidomics in vascular research

Athanasios Didangelos, Christin Stegemann, Manuel Mayr∗

King’s British Heart Foundation Centre, King’s College London, UK

Atherosclerosis 2012; 221: 12– 17     http://dx.doi.org/10.1016/j.atherosclerosis.2011.09.043

a b s t r a c t

A main limitation of the current approaches to atherosclerosis research is the focus on the investigation of individual
factors, which are presumed to be involved in the pathophysiology and whose biological functions are, at least in part, understood.

These molecules are investigated extensively while others are not studied at all. In comparison to our detailed
knowledge about the role of inflammation in atherosclerosis, little is known about extracellular matrix remodelling
and the retention of individual lipid species rather than lipid classes in early and advanced atherosclerotic lesions.

The recent development of mass spectrometry-based methods and advanced analytical tools are transforming
our ability to profile extracellular proteins and lipid species in animal models and clinical specimen with the goal
of illuminating pathological processes and discovering new biomarkers.

Fig. 1. ECM in atherosclerosis

Fig. 1. ECM in atherosclerosis. The bulk of the vascular ECM is synthesised by smooth muscle cells and composed primarily of collagens, proteoglycans and glycoproteins.During the early stages of atherosclerosis, LDL binds to the proteoglycans of the vessel wall, becomes modified, i.e. by oxidation (ox-LDL), and sustains a proinflammatory cascade that is proatherogenic

Lipidomics of atherosclerotic plaques

Lipidomics of atherosclerotic plaques

Fig. 2. Lipidomics of atherosclerotic plaques. Lipids were separated by ultra performance reverse phase
liquid chromatography on a Waters® ACQUITY UPLC® (HSS T3 Column, 100 mm × 2.1 mm i.d., 1.8 _m
particle size, 55 ◦C, flow rate 400 _L/min, Waters, Milford MA, USA) and analyzed on a quadrupole time-of-flight
mass spectrometer (Waters® SYNAPTTM HDMSTM system) in both positive (A) and negative ion mode (C).
In positive MS mode, lysophosphatidyl-cholines (lPCs) and lysophosphatidylethanolamines (lPEs) eluted first;
followed by phosphatidylcholines (PCs), sphingomyelin (SMs), phosphatidylethanol-amines (PEs) and cholesteryl
esters (CEs); diacylglycerols (DAGs) and triacylglycerols (TAGs) had the longest retention times. In negative MS mode,
fatty acids (FA) were followed by phosphatidyl-glycerols (PGs), phosphatidyl-inositols (PIs), phosphatidylserines (PS)
and PEs. The chromatographic peaks corresponding to the different classes were detected as retention time-mass to
charge ratio (m/z) pairs and their areas were recorded. Principal component analyses on 629 variables from triplicate
analysis (C1, 2, 3 = control 1, 2, 3; P1, 2, 3 = endarterectomy patient 1, 2, 3) demonstrated a clear separation of
atherosclerotic plaques and control radial arteries in positive (B) and negative (D) ion mode. The clustering of the
technical replicates and the central projection of the pooled sample within the scores plot confirm the reproducibility
of the analyses, and the Goodness of Fit test returned a chi-squared of 0.4 and a R-squared value of 0.6.

Challenges in mass spectrometry

Mass spectrometry is an evolving technology and the technological advances facilitate the detection and quantification
of scarce proteins. Nonetheless, the enrichment of specific subproteomes using differential solubilityor isolation of cellular
organelleswill remain important to increase coverage and, at least partially, overcome the inhomogeneity of diseased tissue,
one of the major factors affecting sample-to-sample variation.

Proteomics is also the method of choice for the identification of post-translational modifications, which play an essential
role in protein function, i.e. enzymatic activation, binding ability and formation of ECM structures. Again, efficient enrichment
is essential to increase the likelihood of identifying modified peptides in complex mixtures. Lipidomics faces similar challenges.
While the extraction of lipids is more selective, new enrichment methods are needed for scarce lipids as well as labile lipid
metabolites, that may have important bioactivity. Another pressing issue in lipidomics is data analysis, in particular the lack
of automated search engines that can analyze mass spectra obtained from instruments of different vendors. Efforts to
overcome this issue are currently underway.

Conclusions

Proteomics and lipidomics offer an unbiased platform for the investigation of ECM and lipids within atherosclerosis. In
combination, these innovative technologies will reveal key differences in proteolytic processes responsible for plaque rupture
and advance our understanding of ECM – lipoprotein interactions in atherosclerosis.

references

Virtualization in Proteomics: ‘Sakshat’ in India, at IIT Bombay(tginnovations.wordpress.com)

Proteome Portraits (the-scientist.com)

A Protease for ‘Middle-down’ Proteomics(pharmaceuticalintelligence.com)

Intrinsic Disorder in the Human Spliceosomal Proteome(ploscompbiol.org)

proteome

proteome

active site of eNOS (PDB_1P6L) and nNOS (PDB_1P6H).

active site of eNOS (PDB_1P6L) and nNOS (PDB_1P6H).

Table - metabolic  targets

Table – metabolic targets

HK-II Phosphorylation

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