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Larry H Bernstein, MD, Curator

Leaders in Pharmaceutical Intelligence

 

Natriuretic Peptides (BNP and Amino-terminal proBNP)

Author: Larry Bernstein, M.D.,
(see Reviewers/Authors page)
Revised: 12 December 2010, last major update December 2010
Copyright: (c) 2003-2010, PathologyOutlines.com, Inc.
http://dx.doi.org:/PathologyOutlines.com/cardiac

General
=========================================================================

  • Brain natriuretic peptide (BNP), now known as B-type natriuretic peptide (also BNP),
    is a 32 amino acid polypeptide secreted by the cardiac ventricles in response to
    excessive stretching of cardiomyocytes (Wikipedia)
  • BNP was originally identified in extracts of porcine brain, although in humans
    it is produced mainly in the cardiac ventricles
  • BNP is co-secreted with a 76 amino acid N-terminal fragment (NT-proBNP),
    which is biologically inactive Indications

=========================================================================

  • Evaluation of dyspneic patient with suspected congestive heart failure,
    regardless of renal function (J Am Coll Cardiol 2006;47:91)
  • B-type natriuretic peptide levels are higher in patients with congestive heart
    failure than in dyspnea from other causes (J Am Coll Cardiol 2002;39:202,
    N Engl J Med 2004;350:647)
  • NT-proBNP measurement is a valuable addition to standard clinical
    assessment for the identification and exclusion of acute CHF in the
    emergency department setting (Am J Cardiol 2005;95:9480)

Clinical features
=========================================================================

  • Reduces misdiagnosis of congestive heart failure, which occurs
    50% to 75% of the time
  • NT-proBNP is superior to BNP for predicting mortality and morbidity for heart
    failure (Clin Chem 2006;52:1528), and coexisting renal disease and heart failure
    (Clin Chem 2007;53:1511)

Reference ranges
=========================================================================

  • BNP levels below 100 pg/mL indicate no heart failure

Limitations
=========================================================================

  • Determination of endogenous BNP with the AxSYM assay using frozen
    plasma samples may not be valid after 1 day, but NT-proBNP as
    measured by the Elecsys assay may be stored at -20 degrees C for
    at least four months without a relevant loss of the immunoreactive
    analyte (Clin Chem Lab Med 2004;42:942)

Additional references
=========================================================================

  • Clin Chem 2007;53:1928, Am J Kidney Dis 2005;46:610,
    Hypertension 2005;46:118, Hypertension 2006;47:874,
    Eur J Heart Fail 2004;6:269

Natriuretic peptides for risk stratification of patients with acute
coronary
 syndromes  
M Galvani,  D Ferrini, F Ottani. Eur J Heart Fail 2004;  6: 327–333.
http://eurjhf.oxfordjournals.org

Both BNP and NT-proBNP possess several characteristics of the ideal biomarker,
showing independent and incremental prognostic value above traditional clinical,
electrocardiographic, and biochemical (particularly troponin) risk indicators. Specifically,
in ACS patients, BNP and NT-proBNP have powerful prognostic value both in patients
without a history of previous heart failure or without clinical or instrumental signs of
left ventricular dysfunction on admission or during hospital stay.

Our results show that the prognostic value of natriuretic peptides is similar:
(1) both at short- and long-term;
(2) when natriuretic peptides are measured at first patient contact or during hospital stay;
(3) for BNP or NT-proBNP; and
(4) in patients with ST elevation myocardial infarction or no ST elevation ACS.

 

Steady-State Levels of Troponin and Brain Natriuretic Peptide for Prediction
of Long-Term
 Outcome after Acute Heart Failure with or without Stage 3 to 4
Chronic Kidney Disease

Y Endo, S Kohsaka, T Nagai, K Koide, M Takahashi, et al.
Br J Med Med Res 2012; 2(4): 490-500.
http://dx.doi.org:/10.9734/BJMMR/2012/1384

The population was predominantly male (69.3%), and the mean age was 66.6±15.3 years.
Patients with higher BNP levels or detectable TnT had a worse prognosis (BNP45.0% vs.
18.8%, p<0.001; TnT 43.8% vs. 25.1%, p=0.002, respectively). The primary event rate
was additively worse among patients with both increased BNP levels and detectable TnT
compared to those with increased levels of BNP or detectable TnT alone (log-rank p<0.001).
A similar trend was observed in the subgroup of patients with CKD stage III–V (n=172).

The Effect of Correction of Mild Anemia in Severe, Resistant Congestive
Heart Failure
 Using Subcutaneous Erythropoietin and Intravenous Iron:
A Randomized Controlled Study

DS. Silverberg, D Wexler, D Sheps, M Blum, G Keren, et al.  JACC 2001; 37(7).
PII S0735-1097(01)01248-7  http://www.ncbi.nlm.nih.gov/pubmed/11401110

When anemia in CHF is treated with EPO and IV iron, a marked improvement in
cardiac and patient function is seen, associated with less hospitalization and renal
impairment and less need for diuretics. (J Am Coll Cardiol 2001;37:1775– 80)

 

 

 

Hemoglobin on NT proBNP

Hemoglobin on NT proBNP

 

 

 

 

What is the best approximation of reference normal for NT-proBNP?
Clinical levels for enhanced assessment
 of NT-proBNP (CLEAN) 

Larry H. Bernstein1*, Michael Y. Zions1,4, Mohammed E. Alam1,5, Salman A. Haq1,
John F. Heitner1, Stuart Zarich2, Bette Seamonds3 and Stanley Berger3
1New York Methodist Hospital, Brooklyn, NY; 2Bridgeport Hospital, Bridgeport, CT;
3Mercy Catholic Medical Center, Darby, Phila, PA;  4Touro College, &  5Medgar
Evers College, Brooklyn, NY
Journal of Medical Laboratory and Diagnosis 04/2011; 2:16-21.
http://www.academicjournals.org/jmld

The natriuretic peptides, B-type natriuretic peptide (BNP) and NT-proBNP that
have emerged as tools for diagnosing congestive heart failure (CHF) are affected
by age and renal insufficiency (RI).  NTproBNP is used in rejecting CHF and as a
marker of risk for patients with acute coronary syndromes. This observational study
was undertaken to evaluate the reference value for interpreting NT-proBNP
concentrations. The hypothesis is that increasing concentrations of NT-proBNP
are associated with the effects of multiple co-morbidities, not merely CHF,
resulting in altered volume status or myocardial filling pressures.

NT-proBNP was measured in a population with normal trans-thoracic echocardiograms
(TTE) and free of anemia or renal impairment. Exclusion conditions were the following
co-morbidities:

  • anemia as defined by WHO,
  • atrial fibrillation (AF),
  • elevated troponin T exceeding 0.070 mg/dl,
  • systolic or diastolic blood pressure exceeding 140 and 90 respectively,
  • ejection fraction less than 45%,
  • left ventricular hypertrophy (LVH),
  • left ventricular wall relaxation impairment, and
  • renal insufficiency (RI) defined by creatinine clearance < 60ml/min using
    the MDRD formula .

Study participants were seen in acute care for symptoms of shortness of breath
suspicious for CHF requiring evaluation with cardiac NTproBNP assay. The median
NT-proBNP for patients under 50 years is 60.5 pg/ml with an upper limit of 462 pg/ml,
and for patients over 50 years the median was 272.8 pg/ml with an upper limit of
998.2 pg/ml.
We suggest that NT-proBNP levels can be more accurately interpreted only after
removal of the major co-morbidities that affect an increase in this  peptide in serum.
The PRIDE study guidelines should be applied until presence or absence of
comorbidities is diagnosed. With no comorbidities, the reference range for normal
over 50 years of age remains steady at ~1000 pg/ml. The effect shown in previous
papers likely is due to increasing concurrent comorbidity with age.

NT-proBNP profile of combined population taken from 3 sites and donors.

Age    Under 50 years 50-69 years 70 and over
NT-proBNP

Mean   
95% CI of Mean
Median   
95% CI of median
2.5-97.5 percentile   
25-75 percentile
209
35.9
29.8-43.3
27.6
24.8-33.6
5.0-1364
14.9-55.8
126
182.4
132.1-251.9
142.3
92.3-219.0
10.8-11604
42.1-565
82
611.7
425.2-880.1
564.2
419.7-1007.7
28.8-14242
210.2-2062

 

We observe the following changes with respect to NTproBNP and age:

(i) Sharp increase in NT-proBNP at over age 50

(ii) Increase in NT-proBNP at 7% per decade over 50

(iii) Decrease in eGFR at 4% per decade over 50

(iv) Slope of NT-proBNP increase with age is related to proportion of patients with
eGFR less than 90

(v) NT-proBNP increase can be delayed or accelerated based on disease
comorbidities

NT-proBNP sensitivity and specificity with RI prevalence

NT-proBNP sensitivity and specificity with RI prevalence

Figure 1. Plot of NT-proBNP sensitivity and specificity with RI prevalence.
GFRe scale: 0, > 120; 1, 90- 119; 2, 60-89; 3, 40-59; 4, 15-39; 5, under 15 ml/min.

NKF staging by GFRe interval and NT-proBNP (CHF removed).

NKF staging by GFRe interval and NT-proBNP (CHF removed).

 

Figure 2  plots the mean and 95% CI of NTproBNP (CHF removed) by the National Kidney Foundation
staging for eGFR interval (eGFR scale: 0, > 120; 1, 90 to 119;2, 60 to 89; 3, 40 to 59; 4, 15 to 39; 5,
under 15 ml/min). We created a new variable to minimize the effects of age and eGFR variability by
correcting these large effects in the whole sample population.

Adjustment of the NT-proBNP for eGFR and for age over 50 differences. We have
carried out a normalization to adjust for both eGFR and for age over 50:

(i) Take Log of NT-proBNP and multiply by 1000

(ii) Divide the result by eGFR (using MDRD9 or Cockroft Gault10)

(iii) Compare results for age under 50, 50-70, and over 70 years

(iv) Adjust to age under 50 years by multiplying by 0.66 and 0.56.

The equation does not require weight because the results are reported normalized
to 1.73 m2 body surface area, which is an accepted average adult surface area.

 

fn.log-NT-proBNP vs age

fn.log-NT-proBNP vs age

Figure 3.  Plot of 1000*log (NT-proBNP)/GFR vs age at  eGFR over 90  and 60 ml/min

scatterplot and regression line with centroid and confidence interval for fn.logNTproBNP vs age

scatterplot and regression line with centroid and confidence interval for fn.logNTproBNP vs age

Figure 4. Superimposed scatterplot and regression line with centroid and
confidence interval for 1000*log(NT-proBNP)/eGFR vs age (anemia removed)
at eGFR over 40 and 90 ml/min. (Black: eGFR > 90, Blue:  eGFR > 40)  

 

Ref Range NTpro NKLogNTpro

Ref Range NTpro NKLogNTpro

 

Reference range for NT-proBNP before and after adjusting

 

Amino-Terminal Pro-Brain Natriuretic Peptide, Renal Function, and
Outcomes in Acute Heart Failure
RRJ. van Kimmenade,  JL. Januzzi, JR,  AL. Baggish, et al. JACC 2006; 48(8).: 1621-7.

We sought to study the individual and integrative role of amino-terminal pro-brain natriuretic
peptide (NT-proBNP) and parameters of renal function for prognosis in heart failure. The
combination of NT-proBNP with measures of renal function better predicts short-term outcome
in acute heart failure than either parameter alone. Among heart failure patients, the objective
parameter of NT-proBNP seems more useful to delineate the “cardiorenal syndrome” than the
previous criteria of a clinical diagnosis of heart failure.

 

NT-proBNP testing for diagnosis and short-term prognosis in acute destabilized
heart failure: an international pooled analysis of 1256 patients The International
Collaborative of NT-proBNP Study
Januzzi, R van Kimmenade, J Lainchbury, A Bayes-Genis, J Ordonez-Llanos, et al.
Eur Heart J 2006; 27, 330–337. http://dx.doi.org:/10.1093/eurheartj/ehi631

Differences in NT-proBNP levels among 1256 patients with and without acute HF and the relationship
between NT-proBNPlevels and HF symptomswere examined.Optimal cut-points for diagnosis and
prognosis were identified and verified using bootstrapping and multi-variable logistic regression techniques.

Seven hundred and twenty subjects (57.3%) had acute HF, whose median NT-proBNP was considerably
higher than those without (4639 vs. 108 pg/mL, P < 0.001), and levels of NT-proBNP correlated with HF
symptom severity (P < 0.008). An optimal strategy to identify acute HF was to use age-related cut-points
of 450, 900, and 1800 pg/mL for ages < 50, 50–75, and  > 75, which yielded 90% sensitivity and 84% specificity
for acute HF. An age-independent cut-point of 300 pg/mL had 98% negative predictive value to exclude acute
HF. Among those with acute HF, a presenting NT-proBNP concentration > 5180 pg/mL was strongly predictive
of death by 76 days [odds ratio = 5.2, 95% confidence interval (CI) =2.2 – 8.1, P < 0.001].

Effect of B-type natriuretic peptide-guided treatment of chronic heart failure on total mortality
and hospitalization: an individual patient meta-analysis
RW. Troughton, CM. Frampton, HP Brunner-La Rocca, M Pfisterer, LW.M. Eurlings, et al.
Eur Heart J Mar 2014; 35, 1559–1567.
http://dx.doi.org:/10.1093/eurheartj/ehu090

We sought to perform an individual patient data meta-analysis to evaluate the effect of NP-guided treatment
of heart failure on all-cause mortality.  The survival benefit from NP-guided therapy was seen in younger (< 75
years) patients [0.62 (0.45–0.85); P = 0.004] but not older (≥75 years) patients [0.98 (0.75–1.27); P = 0.96].
Hospitalization due to heart failure [0.80 (0.67–0.94); P = 0.009] or cardiovascular disease [0.82 (0.67–0.99);
P = 0.048] was significantly lower in NP-guided patients with no heterogeneity between studies and no interaction
with age or LVEF.

 

Diagnostic and prognostic evaluation of left ventricular systolic heart failure by plasma N-terminal
pro-brain natriuretic peptide concentrations in a large sample of the general population

BA Groenning, I Raymond, PR Hildebrandt, JC Nilsson, M Baumann, F Pedersen.
Heart 2004; 90:297–303.  http://dx.doi.org:/10.1136/hrt.2003.026021

Value of NT-proBNP in evaluating patients with symptoms of heart failure and impaired left ventricular (LV) systolic
function; prognostic value of NT-proBNP for mortality and hospital admissions. In 38 (5.6%) participants LV ejection
fraction (LVEF) was ( 40%. NT-proBNP identified patients with symptoms of heart failure and LVEF ( 40% with a
sensitivity of 0.92, a specificity of 0.86, AUC of 0.94.  NT-proBNP was the strongest independent predictor of mortality
(hazard ratio (HR) = 5.70, p , 0.0001), hospital admissions for heart failure (HR = 13.83, p , 0.0001), and other cardiac
admissions (HR = 3.69, p , 0.0001). Mortality (26 v 6, p = 0.0003), heart failure admissions (18 v 2, p = 0.0002), and
admissions for other cardiac causes (44 v 13, p , 0.0001) were significantly higher in patients with NTproBNP above the
study median (32.5 pmol/l).

 

Testing for BNP and NT-proBNP in the Diagnosis and Prognosis of Heart Failure
Evidence Report/Technology Assessment – Number 142. Agency for Healthcare Research and Quality.
Prepared by: McMaster University Evidence-based Practice Center, Hamilton, ON, Canada
C Balion, PL. Santaguida, S Hill, A Worster, M McQueen, et al.
http://archive.ahrq.gov/downloads/pub/evidence/pdf/bnp/bnp.pdf

Question 1: What are the determinants of both BNP and NT-proBNP?
Question 2a: What are the clinical performance characteristics of both BNP and NTproBNP
measurement in patients with symptoms suggestive of HF or with known HF?
Question 2b: Does measurement of BNP or NT-proBNP add independent diagnostic information
to the traditional diagnostic measures of HF in patients with suggestive HF?
Question 3a: Do BNP or NT-proBNP levels predict cardiac events in populations at risk of CAD,
with diagnosed CAD and HF?
Question 3b: What are the screening performance characteristics of BNP or NT-proBNP in
general asymptomatic populations?
Question 4: Can BNP or NT-proBNP measurement be used to monitor response to therapy?        

Diagnosis: In all settings both BNP and NT-proBNP show good diagnostic properties as a rule out test for HF.
Prognosis: BNP and NT-proBNP are consistent independent predictors of mortality and other cardiac composite
endpoints for populations with risk of CAD, diagnosed CAD, and diagnosed HF. There is insufficient evidence to
determine the value of B-type natriuretic peptides for screening of HF.
Monitoring Treatment: There is insufficient evidence to demonstrate that BNP and NT-proBNP levels
show change in response to therapies to manage stable chronic HF patients.

Guide-IT Trial

Biomarker-Guided HF Therapy: Is It Cost-Effective
www.medscape.org/viewarticle/764686_transcript

Jan 29, 2013 – Uploaded by DCLRI
Michael Felker, MD, MHS
Associate Professor in the Division of Cardiology
Duke University Medical Center
www.youtube.com/watch?v=AW0480EE2kw

GUIDE-IT will last five years and involve approximately 45 trial sites in the United States. The first group of
patients will be enrolled by the end of 2012.

The trial tests NT-proBNP guided therapy with a COMPANION diagnostic biomarker used to optimize already
available and effective therapies for heart failure. It may identify  patients who will benefit from intensified therapy,
and  who would not have been known using only signs and symptoms of heart failure as it is currently the practice.
The NT-proBNP biomarker would enable doctors to create personalized treatment plans for patients to substantially
reduce mortality and morbidity

 Risk stratification in acute heart failure: Rationale and design of the
STRATIFY and DECIDE studies 

SP. Collins, CJ. Lindsell, CA. Jenkins, FE. Harrell, et al.
Am Heart J 2012;164:825-34.
http://dx.doi.org/10.1016/j.ahj.2012.07.033

Two studies (STRATIFY and DECIDE) have been funded by the National Heart Lung and Blood Institute with
the goal of developing prediction rules to facilitate early decision making in AHF. Using prospectively gathered
evaluation and treatment data from the acute setting (STRATIFY) and early inpatient stay (DECIDE), rules will
be generated to predict risk for death and serious complications.
A rigorous analysis plan has been developed to construct the prediction rules that will maximally extract both the
statistical and clinical properties of every data element. Upon completion of this study we will subsequently externally
test the prediction rules in a heterogeneous patient cohort.

N-terminal pro-B-type natriuretic peptide and the prediction of primary cardiovascular
events: results from 15-year follow-up of WOSCOPS

P Welsh, O Doolin, P Willeit, C Packard, P Macfarlane, S Cobbe, et al.
Eur Heart J Aug  2012.
http://dx.doi.org:/10.1093/eurheartj/ehs239

To test whether N-terminal pro-B-type natriuretic peptide (NT-proBNP) was independently associated with, and
improved the prediction of, cardiovascular disease (CVD) in a primary prevention cohort. N-terminal pro-B-type
natriuretic peptide predicts CVD events in men without clinical evidence of CHD, angina, or history of stroke,
and appears related more strongly to the risk for fatal events.
NT-proBNP was associated with an increased risk of all CVD [HR: 1.17 (95% CI: 1.11–1.23) per standard deviation
increase in log NT-proBNP] after adjustment for classical and clinical cardiovascular risk factors plus C-reactive protein.
N-terminal pro-B-type natriuretic peptide was more strongly related to the rsk of fatal [HR: 1.34 (95% CI: 1.19–1.52)]
than non-fatal CVD [HR: 1.17 (95% CI: 1.10–1.24)] (P = 0.022). The addition of NT-proBNP to traditional risk factors
improved the C-index (+0.013; P = 0.001). The continuous net reclassification index improved with the addition of NT-
proBNP by 19.8% (95% CI: 13.6–25.9%) compared with 9.8% (95% CI: 4.2–15.6%) with the addition of C-reactive protein.

 

Utility of B-Natriuretic Peptide in Detecting Diastolic Dysfunction: Comparison With
Doppler Velocity Recordings
E Lubien, A DeMaria, P Krishnaswamy, P Clopton, J Koon…A Maisel.
http://circ.ahajournals.org/content/105/5/595
Circulation. 2002;105:595-601
http://dx.doi.org:/10.1161/hc0502.103010

Although Doppler echocardiography has been used to identify abnormal left ventricular (LV) diastolic filling dynamics,
inherent limitations suggest the need for additional measures of diastolic dysfunction. Because data suggest that B-natriuretic
peptide (BNP) partially reflects ventricular pressure, we hypothesized that BNP levels could predict diastolic abnormalities
in patients with normal systolic function. A rapid assay for BNP can reliably detect the presence of diastolic abnormalities
on echocardiography. In  patients with normal systolic function, elevated BNP levels and diastolic filling abnormalities might
help to reinforce the diagnosis diastolic dysfunction

Association of common variants in NPPA and NPPB with circulating natriuretic
peptides and blood pressure.
C Newton-Cheh, MG Larson, RS Vasan, D Levy, KD Bloch, et al.
Nat Genet. 2009 Mar; 41(3): 348–353.
http://dx.doi.org:/10.1038/ng.328

We examined the association of common variants at the NPPA-NPPB locus with circulating concentrations of the
natriuretic peptides, which have blood pressure–lowering properties. In 29,717 individuals, the alleles of rs5068
and rs198358 that showed association with increased circulating natriuretic peptide concentrations were also found
to be associated with lower systolic (P = 2 ×10−6 and 6 × 10−5, respectively) and diastolic blood pressure (P = 1 × 10−6
and 5 × 10−5), as well as reduced odds of hypertension (OR = 0.85, 95% CI = 0.79–0.92, P = 4 × 10−5; OR = 0.90, 95%
CI = 0.85–0.95, P = 2 × 10−4, respectively).

2013 ACC/AHA Guideline on the Assessment of Cardiovascular Risk
DC. Goff, Jr, DM. Lloyd-Jones, G Bennett, S Coady, RB. D’Agostino, Sr, et al.
Circulation. 2013;  http://circ.ahajournals.org/content/early/2013/11/11/01.cir.0000437741.48606.98.citation
http://dx.doi.org:/10.1161/01.cir.0000437741.48606.98

The ACC and AHA have collaborated with the National Heart, Lung, and Blood Institute (NHLBI) and stakeholder
and professional organizations to develop clinical practice guidelines for assessment of CV risk, lifestyle modifications
to reduce CV risk, and management of blood cholesterol, overweight and obesity in adults.
Although the Task Force led the final development of these prevention guidelines, they differ from other ACC/AHA
guidelines. First, as opposed to an extensive compendium of clinical information, these documents are significantly
more limited in scope and focus on selected CQs in each topic, based on the highest quality evidence available.
Recommendations were derived from randomized trials, meta-analyses, and observational studies evaluated for quality,
and were not formulated when sufficient evidence was not available. Second, the text accompanying each recommendation
is succinct, summarizing the evidence for each question. Third, the format of the recommendations differs from other
ACC/AHA guidelines. Each recommendation has been mapped from the NHLBI grading format to the ACC/AHA Class
of Recommendation/Level of Evidence (COR/LOE) construct (Table 1) and is expressed in both formats.

 

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Compilation of References in Leaders in Pharmaceutical Intelligence about proteomics, metabolomics, signaling pathways, and cell regulation

Compilation of References in Leaders in Pharmaceutical Intelligence about
proteomics, metabolomics, signaling pathways, and cell regulation

Curator: Larry H. Bernstein, MD, FCAP

 

Proteomics

  1. The Human Proteome Map Completed
    Reporter and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/28/the-human-proteome-map-completed/
  1. Proteomics – The Pathway to Understanding and Decision-making in Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/06/24/proteomics-the-pathway-to-understanding-and-decision-making-in-medicine/
  1. Advances in Separations Technology for the “OMICs” and Clarification of Therapeutic Targets
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/10/22/advances-in-separations-technology-for-the-omics-and-clarification-of-therapeutic-targets/
  1. Expanding the Genetic Alphabet and Linking the Genome to the Metabolome
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/09/24/expanding-the-genetic-alphabet-and-linking-the-genome-to-the-metabolome/
  1. Synthesizing Synthetic Biology: PLOS Collections
    Reporter: Aviva Lev-Ari
    http://pharmaceuticalintelligence.com/2012/08/17/synthesizing-synthetic-biology-plos-collections/

 

Metabolomics

  1. Extracellular evaluation of intracellular flux in yeast cells
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/25/extracellular-evaluation-of-intracellular-flux-in-yeast-cells/ 
  2. Metabolomic analysis of two leukemia cell lines. I.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/23/metabolomic-analysis-of-two-leukemia-cell-lines-_i/ 
  3. Metabolomic analysis of two leukemia cell lines. II.
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2014/08/24/metabolomic-analysis-of-two-leukemia-cell-lines-ii/ 
  4. Metabolomics, Metabonomics and Functional Nutrition: the next step in nutritional metabolism and biotherapeutics
    Reviewer and Curator, Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/22/metabolomics-metabonomics-and-functional-nutrition-the-next-step-in-nutritional-metabolism-and-biotherapeutics/ 
  5. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    http://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/

 

Metabolic Pathways

  1. Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief
    Reviewer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/21/pentose-shunt-electron-transfer-galactose-more-lipids-in-brief/
  2. Mitochondria: More than just the “powerhouse of the cell”
    Reviewer and Curator: Ritu Saxena
    http://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/
  3. Mitochondrial fission and fusion: potential therapeutic targets?
    Reviewer and Curator: Ritu saxena
    http://pharmaceuticalintelligence.com/2012/10/31/mitochondrial-fission-and-fusion-potential-therapeutic-target/ 
  4. Mitochondrial mutation analysis might be “1-step” away
    Reviewer and Curator: Ritu Saxena
    http://pharmaceuticalintelligence.com/2012/08/14/mitochondrial-mutation-analysis-might-be-1-step-away/
  5. Selected References to Signaling and Metabolic Pathways in PharmaceuticalIntelligence.com
    Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/14/selected-references-to-signaling-and-metabolic-pathways-in-leaders-in-pharmaceutical-intelligence/
  6. Metabolic drivers in aggressive brain tumors
    Prabodh Kandal, PhD
    http://pharmaceuticalintelligence.com/2012/11/11/metabolic-drivers-in-aggressive-brain-tumors/ 
  7. Metabolite Identification Combining Genetic and Metabolic Information: Genetic association links unknown metabolites to functionally related genes
    Author and Curator: Aviva Lev-Ari, PhD, RD
    http://pharmaceuticalintelligence.com/2012/10/22/metabolite-identification-combining-genetic-and-metabolic-information-genetic-association-links-unknown-metabolites-to-functionally-related-genes/
  8. Mitochondria: Origin from oxygen free environment, role in aerobic glycolysis, metabolic adaptation
    Author and curator:Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/09/26/mitochondria-origin-from-oxygen-free-environment-role-in-aerobic-glycolysis-metabolic-adaptation/
  9. Therapeutic Targets for Diabetes and Related Metabolic Disorders
    Reporter, Aviva Lev-Ari, PhD, RD
    http://pharmaceuticalintelligence.com/2012/08/20/therapeutic-targets-for-diabetes-and-related-metabolic-disorders/
  10. Buffering of genetic modules involved in tricarboxylic acid cycle metabolism provides homeomeostatic regulation
    Larry H. Bernstein, MD, FCAP, Reviewer and curator
    http://pharmaceuticalintelligence.com/2014/08/27/buffering-of-genetic-modules-involved-in-tricarboxylic-acid-cycle-metabolism-provides-homeomeostatic-regulation/
  11. The multi-step transfer of phosphate bond and hydrogen exchange energy
    Curator:Larry H. Bernstein, MD, FCAP,
    http://pharmaceuticalintelligence.com/2014/08/19/the-multi-step-transfer-of-phosphate-bond-and-hydrogen-exchange-energy/
  12. Studies of Respiration Lead to Acetyl CoA
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/
  13. Lipid Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/15/lipid-metabolism/
  14. Carbohydrate Metabolism
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/
  15. Prologue to Cancer – e-book Volume One – Where are we in this journey?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/04/13/prologue-to-cancer-ebook-4-where-are-we-in-this-journey/
  16. Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/04/04/introduction-the-evolution-of-cancer-therapy-and-cancer-research-how-we-got-here/
  17. Inhibition of the Cardiomyocyte-Specific Kinase TNNI3K
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/11/01/inhibition-of-the-cardiomyocyte-specific-kinase-tnni3k/
  18. The Binding of Oligonucleotides in DNA and 3-D Lattice Structures
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/05/15/the-binding-of-oligonucleotides-in-dna-and-3-d-lattice-structures/
  19. Mitochondrial Metabolism and Cardiac Function
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/
  20. How Methionine Imbalance with Sulfur-Insufficiency Leads to Hyperhomocysteinemia
    Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/04/04/sulfur-deficiency-leads_to_hyperhomocysteinemia/
  21. AMPK Is a Negative Regulator of the Warburg Effect and Suppresses Tumor Growth In Vivo
    Author and Curator: SJ. Williams
    http://pharmaceuticalintelligence.com/2013/03/12/ampk-is-a-negative-regulator-of-the-warburg-effect-and-suppresses-tumor-growth-in-vivo/
  22. A Second Look at the Transthyretin Nutrition Inflammatory Conundrum
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/12/03/a-second-look-at-the-transthyretin-nutrition-inflammatory-conundrum/
  23. Overview of Posttranslational Modification (PTM)
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/29/overview-of-posttranslational-modification-ptm/
  24. Malnutrition in India, high newborn death rate and stunting of children age under five years
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/15/malnutrition-in-india-high-newborn-death-rate-and-stunting-of-children-age-under-five-years/
  25. Update on mitochondrial function, respiration, and associated disorders
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/08/update-on-mitochondrial-function-respiration-and-associated-disorders/
  26. Omega-3 fatty acids, depleting the source, and protein insufficiency in renal disease
    Larry H. Bernstein, MD, FCAP, Curator
    http://pharmaceuticalintelligence.com/2014/07/06/omega-3-fatty-acids-depleting-the-source-and-protein-insufficiency-in-renal-disease/ 
  27. Late Onset of Alzheimer’s Disease and One-carbon Metabolism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    http://pharmaceuticalintelligence.com/2013/05/06/alzheimers-disease-and-one-carbon-metabolism/
  28. Problems of vegetarianism
    Reporter and Curator: Dr. Sudipta Saha, Ph.D.
    http://pharmaceuticalintelligence.com/2013/04/22/problems-of-vegetarianism/

 

Signaling Pathways

  1. Introduction to e-Series A: Cardiovascular Diseases, Volume Four Part 2: Regenerative Medicine
    Larry H. Bernstein, MD, FCAP, writer, and Aviva Lev- Ari, PhD, RN  http://pharmaceuticalintelligence.com/2014/04/27/larryhbernintroduction_to_cardiovascular_diseases-translational_medicine-part_2/
  2. Epilogue: Envisioning New Insights in Cancer Translational Biology
    Series C: e-Books on Cancer & Oncology
    Author & Curator: Larry H. Bernstein, MD, FCAP, Series C Content Consultant
    http://pharmaceuticalintelligence.com/2014/03/29/epilogue-envisioning-new-insights/
  3. Ca2+-Stimulated Exocytosis:  The Role of Calmodulin and Protein Kinase C in Ca2+ Regulation of Hormone and Neurotransmitter  Writer and Curator: Larry H Bernstein, MD, FCAP and Curator and Content Editor: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/12/23/calmodulin-and-protein-kinase-c-drive-the-ca2-regulation-of-hormone-and-neurotransmitter-release-that-triggers-ca2-stimulated-exocy
  4. Cardiac Contractility & Myocardial Performance: Therapeutic Implications of Ryanopathy (Calcium Release-related Contractile Dysfunction) and Catecholamine Responses
    Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC
    Author and Curator: Larry H Bernstein, MD, FCAP and Article Curator: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/08/28/cardiac-contractility-myocardium-performance-ventricular-arrhythmias-and-non-ischemic-heart-failure-therapeutic-implications-for-cardiomyocyte-ryanopathy-calcium-release-related-contractile/
  5. Role of Calcium, the Actin Skeleton, and Lipid Structures in Signaling and Cell Motility
    Author and Curator: Larry H Bernstein, MD, FCAP Author: Stephen Williams, PhD, and Curator: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/08/26/role-of-calcium-the-actin-skeleton-and-lipid-structures-in-signaling-and-cell-motility/
  6. Identification of Biomarkers that are Related to the Actin Cytoskeleton
    Larry H Bernstein, MD, FCAP, Author and Curator
    http://pharmaceuticalintelligence.com/2012/12/10/identification-of-biomarkers-that-are-related-to-the-actin-cytoskeleton/
  7. Advanced Topics in Sepsis and the Cardiovascular System at its End Stage
    Author and Curator: Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/08/18/advanced-topics-in-Sepsis-and-the-Cardiovascular-System-at-its-End-Stage/
  8. The Delicate Connection: IDO (Indolamine 2, 3 dehydrogenase) and Cancer Immunology
    Demet Sag, PhD, Author and Curator
    http://pharmaceuticalintelligence.com/2013/08/04/the-delicate-connection-ido-indolamine-2-3-dehydrogenase-and-immunology/
  9. IDO for Commitment of a Life Time: The Origins and Mechanisms of IDO, indolamine 2, 3-dioxygenase
    Demet Sag, PhD, Author and Curator
    http://pharmaceuticalintelligence.com/2013/08/04/ido-for-commitment-of-a-life-time-the-origins-and-mechanisms-of-ido-indolamine-2-3-dioxygenase/
  10. Confined Indolamine 2, 3 dioxygenase (IDO) Controls the Homeostasis of Immune Responses for Good and Bad
    Author and Curator: Demet Sag, PhD, CRA, GCP
    http://pharmaceuticalintelligence.com/2013/07/31/confined-indolamine-2-3-dehydrogenase-controls-the-hemostasis-of-immune-responses-for-good-and-bad/
  11. Signaling Pathway that Makes Young Neurons Connect was discovered @ Scripps Research Institute
    Reporter: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/06/26/signaling-pathway-that-makes-young-neurons-connect-was-discovered-scripps-research-institute/
  12. Naked Mole Rats Cancer-Free
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/06/20/naked-mole-rats-cancer-free/
  13. Amyloidosis with Cardiomyopathy
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/03/31/amyloidosis-with-cardiomyopathy/
  14. Liver endoplasmic reticulum stress and hepatosteatosis
    Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2013/03/10/liver-endoplasmic-reticulum-stress-and-hepatosteatosis/
  15. The Molecular Biology of Renal Disorders: Nitric Oxide – Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/the-molecular-biology-of-renal-disorders/
  16. Nitric Oxide Function in Coagulation – Part II
    Curator and Author: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-function-in-coagulation/
  17. Nitric Oxide, Platelets, Endothelium and Hemostasis
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/08/nitric-oxide-platelets-endothelium-and-hemostasis/
  18. Interaction of Nitric Oxide and Prostacyclin in Vascular Endothelium
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/09/14/interaction-of-nitric-oxide-and-prostacyclin-in-vascular-endothelium/
  19. Nitric Oxide and Immune Responses: Part 1
    Curator and Author:  Aviral Vatsa PhD, MBBS
    http://pharmaceuticalintelligence.com/2012/10/18/nitric-oxide-and-immune-responses-part-1/
  20. Nitric Oxide and Immune Responses: Part 2
    Curator and Author:  Aviral Vatsa PhD, MBBS
    http://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  21. Nitric Oxide and iNOS have Key Roles in Kidney Diseases – Part II
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/nitric-oxide-and-inos-have-key-roles-in-kidney-diseases/
  22. New Insights on Nitric Oxide donors – Part IV
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/new-insights-on-no-donors/
  23. Crucial role of Nitric Oxide in Cancer
    Curator and Author: Ritu Saxena, Ph.D.
    http://pharmaceuticalintelligence.com/2012/10/16/crucial-role-of-nitric-oxide-in-cancer/
  24. Nitric Oxide has a ubiquitous role in the regulation of glycolysis -with a concomitant influence on mitochondrial function
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/09/16/nitric-oxide-has-a-ubiquitous-role-in-the-regulation-of-glycolysis-with-a-concomitant-influence-on-mitochondrial-function/
  25. Nitric Oxide and Immune Responses: Part 2
    Author and Curator: Aviral Vatsa, PhD, MBBS
    http://pharmaceuticalintelligence.com/2012/10/28/nitric-oxide-and-immune-responses-part-2/
  26. Mitochondrial Damage and Repair under Oxidative Stress
    Author and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/10/28/mitochondrial-damage-and-repair-under-oxidative-stress/
  27. Is the Warburg Effect the cause or the effect of cancer: A 21st Century View?
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/
  28. Targeting Mitochondrial-bound Hexokinase for Cancer Therapy
    Curator and Author: Ziv Raviv, PhD, RN 04/06/2013
    http://pharmaceuticalintelligence.com/2013/04/06/targeting-mitochondrial-bound-hexokinase-for-cancer-therapy/
  29. Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/
  30. Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2013/02/14/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis-reconsidered/
  31. Biochemistry of the Coagulation Cascade and Platelet Aggregation – Part I
    Curator and Author: Larry H Bernstein, MD, FACP
    http://pharmaceuticalintelligence.com/2012/11/26/biochemistry-of-the-coagulation-cascade-and-platelet-aggregation/

 

Genomics, Transcriptomics, and Epigenetics

  1. What is the meaning of so many RNAs?
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/06/what-is-the-meaning-of-so-many-rnas/
  2. RNA and the transcription the genetic code
    Larry H. Bernstein, MD, FCAP, Writer and Curator
    http://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  3. A Primer on DNA and DNA Replication
    Writer and Curator: Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/29/a_primer_on_dna_and_dna_replication/
  4. Pathology Emergence in the 21st Century
    Author and Curator: Larry Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/03/pathology-emergence-in-the-21st-century/
  5. RNA and the transcription the genetic code
    Writer and Curator, Larry H. Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/02/rna-and-the-transcription-of-the-genetic-code/
  6. Commentary on Biomarkers for Genetics and Genomics of Cardiovascular Disease: Views by Larry H Bernstein, MD, FCAP
    Author: Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/07/16/commentary-on-biomarkers-for-genetics-and-genomics-of-cardiovascular-disease-views-by-larry-h-bernstein-md-fcap/
  7. Observations on Finding the Genetic Links in Common Disease: Whole Genomic Sequencing Studies
    Author an Curator: Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2013/05/18/observations-on-finding-the-genetic-links/
  8. Silencing Cancers with Synthetic siRNAs
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2013/12/09/silencing-cancers-with-synthetic-sirnas/
  9. Cardiometabolic Syndrome and the Genetics of Hypertension: The Neuroendocrine Transcriptome Control Points
    Reporter: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/12/12/cardiometabolic-syndrome-and-the-genetics-of-hypertension-the-neuroendocrine-transcriptome-control-points/
  10. Developments in the Genomics and Proteomics of Type 2 Diabetes Mellitus and Treatment Targets
    Larry H. Bernstein, MD, FCAP, Reviewer and Curator
    http://pharmaceuticalintelligence.com/2013/12/08/developments-in-the-genomics-and-proteomics-of-type-2-diabetes-mellitus-and-treatment-targets/
  11. CT Angiography & TrueVision™ Metabolomics (Genomic Phenotyping) for new Therapeutic Targets to Atherosclerosis
    Reporter: Aviva Lev-Ari, PhD, RN
    http://pharmaceuticalintelligence.com/2013/11/15/ct-angiography-truevision-metabolomics-genomic-phenotyping-for-new-therapeutic-targets-to-atherosclerosis/
  12. CRACKING THE CODE OF HUMAN LIFE: The Birth of BioInformatics & Computational Genomics
    Genomics Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/08/30/cracking-the-code-of-human-life-the-birth-of-bioinformatics-computational-genomics/
  13. Big Data in Genomic Medicine
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2012/12/17/big-data-in-genomic-medicine/
  14.  From Genomics of Microorganisms to Translational Medicine
    Author and Curator: Demet Sag, PhD
    http://pharmaceuticalintelligence.com/2014/03/20/without-the-past-no-future-but-learn-and-move-genomics-of-microorganisms-to-translational-medicine/
  15.  Summary of Genomics and Medicine: Role in Cardiovascular Diseases
    Author and Curator, Larry H Bernstein, MD, FCAP
    http://pharmaceuticalintelligence.com/2014/01/06/summary-of-genomics-and-medicine-role-in-cardiovascular-diseases/

Read Full Post »

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

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

2.  Metabolomic analysis of two leukemia cell lines. I

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

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

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

4.  Extracellular evaluation of intracellular flux in yeast cells

Q1. What is efflux?

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

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

Q4 What is a genome scale network reconstruction?

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

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

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

 

 

Connecting extracellular metabolomic measurements to intracellular flux
states in yeast

Monica L Mo1Bernhard Ø Palsson1 and Markus J Herrgård12*

Author Affiliations

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

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

For all author emails, please log on.

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

 

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

 

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

© 2009 Mo et al; licensee BioMed Central Ltd.

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

Abstract

Background

Metabolomics has emerged as a powerful tool in the

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

Extracellular metabolome or metabolic profiling data, in particular,

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

Results

We used an updated genome-scale

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

The iMM904 metabolic network was reconstructed based on

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

The network model was first validated by

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

Extracellular metabolome data measured

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

was then integrated with the iMM904 network

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

Predicted intracellular flux changes were

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

Patterns of predicted intracellular flux changes

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

Conclusion

Our results indicate that

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

Similar methods could potentially be applied

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

Background

“Omics” technologies are rapidly generating high amounts of data

  • at varying levels of biological detail.

In addition, there is a rapidly growing literature and

  • accompanying databases that compile this information.

This has provided the basis for the assembly of

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

These network reconstructions serve

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

genome-scale network reconstruction is

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

This network can be converted into a predictive model

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

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

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

These methods have been extensively used to

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

Most of these applications utilize

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

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

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

Instead of identifying a single optimal flux distribution based on

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

these methods allow statistical analysis of

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

Sampling methods have been previously used to study

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

Network reconstructions provide a structured framework

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

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

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

The metabolome is

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

Of particular interest to this present study are

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

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

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

This information can be obtained through various

  • analytical detection,
  • identification, and
  • quantization techniques

for a variety of systems ranging from

  • unicellular model organisms to human biofluids [2023].

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

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

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

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

The extracellular metabolomic approach has already shown promise

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

However, interpreting changes in the extracellular metabolome can be challenging

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

Since metabolic networks describe

  • mechanistic,
  • biochemical links between metabolites,

integrating such data can allow a systematic approach

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

Measured secretion rates of major byproduct metabolites

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

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

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

The respiratory deficient mutant study

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

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

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

Our approach is complementary to

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

This study implements a constraint-based sampling approach on

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

are linked to global changes in intracellular metabolic flux states.

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

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

The inferred perturbations in intracellular reaction fluxes were further analyzed

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

The sampling-based approach also has the additional benefit of

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

integration of exometabolomic (EM) data

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

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

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

(F) Significantly altered metabolic regions were identified.

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

 

sampling and scoring analysis to determine intracellular flux changes

sampling and scoring analysis to determine intracellular flux changes

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

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

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

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

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

Reaction scores can be used in

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

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

This study was divided into two parts and describes:

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

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

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

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

The second EM data set focused on secreted amino acid measurements

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

We analyzed the EM data to gain further insight into

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

The model-based analysis of both

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

The methods herein present an approach to

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

Additional file 1. iMM904 network content.

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

Format: XLS Size: 2.7MB Download file

This file can be viewed with: Microsoft Excel Viewer

Additional file 2. iMM904 model files.

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

Format: ZIP Size: 163KB Download file

Conversion of the network to a predictive model

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

Network reactions and metabolites were assembled into a stoichiometric matrix 

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

The steady-state solution space containing possible flux distributions

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

where is the reaction flux vector.

Minimal media conditions were set through constraints on exchange fluxes

  • corresponding to the experimental measured substrate uptake rates.

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

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

Chemostat growth simulations

The iMM904 model was initially validated by

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

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

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

The predicted glucose uptake rates were determined

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

The accuracy of in silico predictions of

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

Additional file 3. Supplemental figures. 

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

Format: PDF Size: 513KB Download file

This file can be viewed with: Adobe Acrobat Reader

Genome-scale gene deletion phenotype predictions

The iMM904 network was further validated by

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

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

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

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

The biomass function was based on the experimentally measured

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

These additional biomass compounds were included

according to the biomass formulation used in the iLL672 study

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

The model was constrained by limiting

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

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

The experimental phenotyping data was obtained

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

These auxotrophies were simulated

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

Furthermore, trace amounts of essential nutrients that are present

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

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

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

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

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

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

The comparison between experimental and in silico deletion phenotypes involved

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

We used standard ROC curve analysis

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

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

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

The optimal prediction performance corresponds to

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

Table 1

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

 

The values reported in Table 1 correspond to selecting

  • the optimal viability threshold based on this criterion.

We summarized the overall prediction accuracy of a model and method

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

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

  • is suitable for summarizing overall prediction performance

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

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

 

Table 1. Comparison of iMM904 and iLL672

  • gene deletion predictions and
  • experimental data

Inferring perturbed metabolic regions based on EM profiles

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

Constraining the iMM904 network 

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

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

The primary objective of this study is to associate

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

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

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

This approach is analogous to approximating uptake rates based

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

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

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

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

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

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

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

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

For the potassium limitation/ammonium toxicity study

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

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

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

FBA optimization of EM-constrained networks

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

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

This method determines two optimal flux distributions simultaneously

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

This approach avoids problems with

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

Reaction flux changes from the FBA optimization results were determined

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

Random sampling of the steady-state solution space

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

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

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

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

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

Sampling approach used in the gdh1/GDH2 study

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

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

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

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

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

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

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

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

We also tested the sensitivity of the results

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

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

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

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

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

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

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

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

This approach allowed biasing the sampling towards

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

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

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

Sampling approach used in the potassium limitation/ammonium toxicity study

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

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

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

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

Standardized scoring of flux differences between perturbation and control conditions

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

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

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

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

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

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

 

reaction Z-score

reaction Z-score

which describes the sampled mean difference deviation

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

Note that this approach allows accounting for uncertainty in the

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

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

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

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

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

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

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

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

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

 

reporter z-score for any metabolite j

reporter z-score for any metabolite j

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

 

distributional correction for m_met,j SQRT

distributional correction for m_met,j SQRT

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

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

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

  • that belongs to each subsystem k.

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

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

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

Results and discussion

  1. Reconstruction and validation of iMM904 network iMM904 network content 

A previously reconstructed S. cerevisiae network, iND750,

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

The majority of iND750 content was carried over and

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

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

  • increased in iMM904 compared with the iND750 network.

Additional genes and reactions included in the network primarily expanded the

  • lipid,
  • transport, and
  • carbohydrate subsystems.

The lipid subsystem includes

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

Sterol metabolism was also expanded to include

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

The majority of the new transport reactions were added

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

We also added a number of new secretion pathways

  • based on experimentally observed secreted metabolites [31].

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

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

For example, the protein compounds

  • thioredoxin and
  • ferricytochrome C

were explicitly represented as compounds in iND750 reactions, but

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

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

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

These additional proteins were included in iMM904 as

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

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

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

Reactions from previous S. cerevisiae networks associated with

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

were essentially inferred from known reaction mechanisms based on

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

These pathways were manually reviewed

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

Additional changes in other subsystems were also made, such as

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

A comprehensive listing of iMM904 network contents as well as

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

Predicting deletion growth phenotypes

The updated genome-scale iMM904 metabolic network was validated

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

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

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

The experimental data used to validate the iLL672 model consisted of

3,360 single-gene knockout strain phenotypes evaluated

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

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

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

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

Each deleted gene growth prediction comparison was classified as

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

The growth rate threshold for considering a prediction viable was chosen

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

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

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

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

  • glucose,
  • galactose, and
  • glycerol conditions

over iLL672 somewhat, but is less accurate

  • at predicting growth phenotypes under the ethanol condition.

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

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

Overall, when viability cutoff is chosen

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

While the full gene complement in iMM904 greatly increased

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

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

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

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

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

As more of these pathways are elucidated and

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

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

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

 

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

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

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

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

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

The net effect is to allow efficient assimilation of ammonia

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

While growth characteristics remained unaffected,

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

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

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

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

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

Extracellular metabolite levels were used

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

Perturbed reactions from the FBA results were

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

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

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

Format: XLS Size: 669KB Download file

This file can be viewed with: Microsoft Excel Viewer

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

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

 

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


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

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

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

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

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

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

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

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

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

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

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

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

Figure 4.

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

Perturbation reaction subnetwork of gdh1.GDH2 mutant under anaerobic conditions

Perturbation reaction subnetwork of gdh1.GDH2 mutant under anaerobic conditions

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

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

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

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

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

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

 

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

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

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

These results are consistent with flux and enzyme activity measurements

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

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

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

Altered energy metabolism, as indicated by

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

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

Pentose phosphate pathway and NAD biosynthesis also appears

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

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

  • the entry point for ammonium assimilation via glutamate.

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

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

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

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

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

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

This effect is especially pronounced for

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

Intracellular threonine levels were amongst the most significantly reduced

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

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

Other key anaerobic reporter metabolites are

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

Folate is intimately linked to biosynthetic pathways of

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

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

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

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

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

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

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

Perturbed metabolic regions under aerobic conditions were predominantly

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

The majority of affected anaerobic pathways were involved directly

  • in modified ammonium assimilation as evidenced by

1) significantly perturbed amino acid subsystems,

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

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

Potassium-limited and excess ammonium environments

A recent study reported that potassium limitation resulted in significant

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

The proposed mechanism for this effect was that ammonium

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

As a result, yeast incurred a significant metabolic cost

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

A similar effect was observed when yeast was grown

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

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

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

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

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

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

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

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

  • obtained from this analysis across the four conditions studied.

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

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

Both low potassium environments shared a consistent pattern of

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

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

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

Nevertheless, the results clearly indicate that a similar

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

Figure 5.

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

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

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

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

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

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

Conclusion

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

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

Authors’ contributions

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

Acknowledgements

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

 

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

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

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

 

The human genome is estimated to encode over 30,000 genes, and to be responsible for generating more than 100,000 functionally distinct proteins. Understanding the interrelationships among

  1. genes,
  2. gene products, and
  3. dietary habits

is fundamental to identifying those who will benefit most from or be placed at risk by intervention strategies.

Unraveling the multitude of

  • nutrigenomic,
  • proteomic, and
  • metabolomic patterns

that arise from the ingestion of foods or their

  • bioactive food components

will not be simple but is likely to provide insights into a tailored approach to diet and health. The use of new and innovative technologies, such as

  • microarrays,
  • RNA interference, and
  • nanotechnologies,

will provide needed insights into molecular targets for specific bioactive food components and

  • how they harmonize to influence individual phenotypes(1).

Nutrigenetics asks the question how individual genetic disposition, manifesting as

  • single nucleotide polymorphisms,
  • copy-number polymorphisms and
  • epigenetic phenomena,

affects susceptibility to diet.

Nutrigenomics addresses the inverse relationship, that is how diet influences

  • gene transcription,
  • protein expression and
  • metabolism.

A major methodological challenge and first pre-requisite of nutrigenomics is integrating

  • genomics (gene analysis),
  • transcriptomics (gene expression analysis),
  • proteomics (protein expression analysis) and
  • metabonomics (metabolite profiling)

to define a “healthy” phenotype. The long-term deliverable of nutrigenomics is personalised nutrition (2).

Science is beginning to understand how genetic variation and epigenetic events

  • alter requirements for, and responses to, nutrients (nutrigenomics).

At the same time, methods for profiling almost all of the products of metabolism in a single sample of blood or urine are being developed (metabolomics). Relations between

  • diet and nutrigenomic and metabolomic profiles and
  • between those profiles and health

have become important components of research that could change clinical practice in nutrition.

Most nutrition studies assume that all persons have average dietary requirements, and the studies often

  • do not plan for a large subset of subjects who differ in requirements for a nutrient.

Large variances in responses that occur when such a population exists

  • can result in statistical analyses that argue for a null effect.

If nutrition studies could better identify responders and differentiate them from nonresponders on the basis of nutrigenomic or metabolomic profiles,

  • the sensitivity to detect differences between groups could be greatly increased, and
  • the resulting dietary recommendations could be appropriately targeted (3).

In recent years, nutrition research has moved from classical epidemiology and physiology to molecular biology and genetics. Following this trend,

  • Nutrigenomics has emerged as a novel and multidisciplinary research field in nutritional science that
  • aims to elucidate how diet can influence human health.

It is already well known that bioactive food compounds can interact with genes affecting

  • transcription factors,
  • protein expression and
  • metabolite production.

The study of these complex interactions requires the development of

  • advanced analytical approaches combined with bioinformatics.

Thus, to carry out these studies

  • Transcriptomics,
  • Proteomics and
  • Metabolomics

approaches are employed together with an adequate integration of the information that they provide(4).

Metabonomics is a diagnostic tool for metabolic classification of individuals with the asset of quantitative, non-invasive analysis of easily accessible human body fluids such as urine, blood and saliva. This feature also applies to some extent to Proteomics, with the constraint that

  • the latter discipline is more complex in terms of composition and dynamic range of the sample.

Apart from addressing the most complex “Ome”, Proteomics represents

  • the only platform that delivers not only markers for disposition and efficacy
  • but also targets of intervention.

Application of integrated Omic technologies will drive the understanding of

  • interrelated pathways in healthy and pathological conditions and
  • will help to define molecular ‘switchboards’,
  • necessary to develop disease related biomarkers.

This will contribute to the development of new preventive and therapeutic strategies for both pharmacological and nutritional interventions (5).

Human health is affected by many factors. Diet and inherited genes play an important role. Food constituents,

  • including secondary metabolites of fruits and vegetables, may
  • interact directly with DNA via methylation and changes in expression profiles (mRNA, proteins)
  • which results in metabolite content changes.

Many studies have shown that

  • food constituents may affect human health and
  • the exact knowledge of genotypes and food constituent interactions with
  • both genes and proteins may delay or prevent the onset of diseases.

Many high throughput methods have been employed to get some insight into the whole process and several examples of successful research, namely in the field of genomics and transcriptomics, exist. Studies on epigenetics and RNome significance have been launched. Proteomics and metabolomics need to encompass large numbers of experiments and linked data. Due to the nature of the proteins, as well as due to the properties of various metabolites, experimental approaches require the use of

  • comprehensive high throughput methods and a sufficiency of analysed tissue or body fluids (6).

New experimental tools that investigate gene function at the subcellular, cellular, organ, organismal, and ecosystem level need to be developed. New bioinformatics tools to analyze and extract meaning

  • from increasingly systems-based datasets will need to be developed.

These will require, in part, creation of entirely new tools. An important and revolutionary aspect of “The 2010 Project”  is that it implicitly endorses

  • the allocation of resources to attempts to assign function to genes that have no known function.

This represents a significant departure from the common practice of defining and justifying a scientific goal based on the biological phenomena. The rationale for endorsing this radical change is that

  • for the first time it is feasible to envision a whole-systems approach to gene and protein function.

This whole-systems approach promises to be orders of magnitude more efficient than the conventional approach (7).

The Institute of Medicine recently convened a workshop to review the state of the various domains of nutritional genomics research and policy and to provide guidance for further development and translation of this knowledge into nutrition practice and policy (8). Nutritional genomics holds the promise to revolutionize both clinical and public health nutrition practice and facilitate the establishment of

(a) genome-informed nutrient and food-based dietary guidelines for disease prevention and healthful aging,

(b) individualized medical nutrition therapy for disease management, and

(c) better targeted public health nutrition interventions (including micronutrient fortification and supplementation) that

  • maximize benefit and minimize adverse outcomes within genetically diverse human populations.

As the field of nutritional genomics matures, which will include filling fundamental gaps in

  • knowledge of nutrient-genome interactions in health and disease and
  • demonstrating the potential benefits of customizing nutrition prescriptions based on genetics,
  • registered dietitians will be faced with the opportunity of making genetically driven dietary recommendations aimed at improving human health.

The new era of nutrition research translates empirical knowledge to evidence-based molecular science (9). Modern nutrition research focuses on

  • promoting health,
  • preventing or delaying the onset of disease,
  • optimizing performance, and
  • assessing risk.

Personalized nutrition is a conceptual analogue to personalized medicine and means adapting food to individual needs. Nutrigenomics and nutrigenetics

  • build the science foundation for understanding human variability in
  • preferences, requirements, and responses to diet and
  • may become the future tools for consumer assessment

motivated by personalized nutritional counseling for health maintenance and disease prevention.

The primary aim of ―omic‖ technologies is

  • the non-targeted identification of all gene products (transcripts, proteins, and metabolites) present in a specific biological sample.

By their nature, these technologies reveal unexpected properties of biological systems.

A second and more challenging aspect of ―omic‖ technologies is

  • the refined analysis of quantitative dynamics in biological systems (10).

For metabolomics, gas and liquid chromatography coupled to mass spectrometry are well suited for coping with

  • high sample numbers in reliable measurement times with respect to
  • both technical accuracy and the identification and quantitation of small-molecular-weight metabolites.

This potential is a prerequisite for the analysis of dynamic systems. Thus, metabolomics is a key technology for systems biology.

In modern nutrition research, mass spectrometry has developed into a tool

  • to assess health, sensory as well as quality and safety aspects of food.

In this review, we focus on health-related benefits of food components and, accordingly,

  • on biomarkers of exposure (bioavailability) and bioefficacy.

Current nutrition research focuses on unraveling the link between

  • dietary patterns,
  • individual foods or
  • food constituents and

the physiological effects at cellular, tissue and whole body level

  • after acute and chronic uptake.

The bioavailability of bioactive food constituents as well as dose-effect correlations are key information to understand

  • the impact of food on defined health outcomes.

Both strongly depend on appropriate analytical tools

  • to identify and quantify minute amounts of individual compounds in highly complex matrices–food or biological fluids–and
  • to monitor molecular changes in the body in a highly specific and sensitive manner.

Based on these requirements,

  • mass spectrometry has become the analytical method of choice
  • with broad applications throughout all areas of nutrition research (11).

Recent advances in high data-density analytical techniques offer unrivaled promise for improved medical diagnostics in the coming decade. Genomics, proteomics and metabonomics (as well as a whole slew of less well known ―omics‖ technologies) provide a detailed descriptor of each individual. Relating the large quantity of data on many different individuals to their current (and possibly even future) phenotype is a task not well suited to classical multivariate statistics. The datasets generated by ―omics‖ techniques very often violate the requirements for multiple regression. However, another statistical approach exists, which is already well established in areas such as medicinal chemistry and process control, but which is new to medical diagnostics, that can overcome these problems. This approach, called megavariate analysis (MVA),

  • has the potential to revolutionise medical diagnostics in a broad range of diseases.

It opens up the possibility of expert systems that can diagnose the presence of many different diseases simultaneously, and

  • even make exacting predictions about the future diseases an individual is likely to suffer from (12).

Cardiovascular diseases

Cardiovascular diseases are the leading cause of morbidity and mortality in Western countries. Although coronary thrombosis is the final event in acute coronary syndromes,

  • there is increasing evidence that inflammation also plays a role in development of atherosclerosis and its clinical manifestations, such as
  • myocardial infarction, stroke, and peripheral vascular disease.

The beneficial cardiovascular health effects of

  • diets rich in fruits and vegetables are in part mediated by their flavanol content.

This concept is supported by findings from small-scale intervention studies with surrogate endpoints including

  1. endothelium-dependent vasodilation,
  2. blood pressure,
  3. platelet function, and
  4. glucose tolerance.

Mechanistically, short term effects on endothelium-dependent vasodilation

  • following the consumption of flavanol-rich foods, as well as purified flavanols,
  • have been linked to an increased nitric oxide bioactivity.

The critical biological target(s) for flavanols have yet to be identified (13), but we are beginning to see over the horizon.

Nutritional sciences

Nutrition sciences apply

  1. transcriptomics,
  2. proteomics and
  3. metabolomics

to molecularly assess nutritional adaptations.

Transcriptomics can generate a

  • holistic overview on molecular changes to dietary interventions.

Proteomics is most challenging because of the higher complexity of proteomes as compared to transcriptomes and metabolomes. However, it delivers

  • not only markers but also
  • targets of intervention, such as
  • enzymes or transporters, and
  • it is the platform of choice for discovering bioactive food proteins and peptides.

Metabolomics is a tool for metabolic characterization of individuals and

  • can deliver metabolic endpoints possibly related to health or disease.

Omics in nutrition should be deployed in an integrated fashion to elucidate biomarkers

  • for defining an individual’s susceptibility to diet in nutritional interventions and
  • for assessing food ingredient efficacy (14).

The more elaborate tools offered by metabolomics opened the door to exploring an active role played by adipose tissue that is affected by diet, race, sex, and probably age and activity. When the multifactorial is brought into play, and the effect of changes in diet and activities studied we leave the study of metabolomics and enter the world of ―metabonomics‖. Adiponectin and adipokines arrive (15-22). We shall discuss ―adiposity‖ later.

Potential Applications of Metabolomics

Either individually or grouped as a profile, metabolites are detected by either

  • nuclear magnetic resonance spectroscopy or mass spectrometry.

There is potential for a multitude of uses of metabolome research, including

  1. the early detection and diagnosis of cancer and as
  2. both a predictive and pharmacodynamic marker of drug effect.

However, the knowledge regarding metabolomics, its technical challenges, and clinical applications is unappreciated

  • even though when used as a translational research tool,
  • it can provide a link between the laboratory and clinic.

Precise numbers of human metabolites is unknown, with estimates ranging from the thousands to tens of thousands. Metabolomics is a term that encompasses several types of analyses, including

(a) metabolic fingerprinting, which measures a subset of the whole profile with little differentiation or quantitation of metabolites;

(b) metabolic profiling, the quantitative study of a group of metabolites, known or unknown, within or associated with a particular metabolic pathway; and

(c) target isotope-based analysis, which focuses on a particular segment of the metabolome by analyzing

  • only a few selected metabolites that comprise a specific biochemical pathway.

 

Dynamic Construct of the –Omics

Dynamic Construct of the –Omics

 

Dynamic Construct of the –Omics

 

 

Iron metabolism – Anemia

Hepcidin is a key hormone governing mammalian iron homeostasis and may be directly or indirectly involved in the development of most iron deficiency/overload and inflammation-induced anemia. The anemia of chronic disease (ACD) is characterized by macrophage iron retention induced by cytokines and hepcidin regulation. Hepcidin controls cellular iron efflux on binding to the iron export protein ferroportin. While patients present with both ACD and iron deficiency anemia (ACD/IDA), the latter results from chronic blood loss. Iron retention during inflammation occurs in macrophages and the spleen, but not in the liver. In ACD, serum hepcidin concentrations are elevated, which is related to reduced duodenal and macrophage expression of ferroportin. Individuals with ACD/IDA have significantly lower hepcidin levels than ACD subjects. ACD/IDA patients, in contrast to ACD subjects, were able to absorb dietary iron from the gut and to mobilize iron from macrophages. Hepcidin elevation may affect iron transport in ACD and ACD/IDA and it is more responsive to iron demand with IDA than to inflammation. Hepcidin determination may aid in selecting appropriate therapy for these patients (23).

There is correlation between serum hepcidin, iron and inflammatory indicators associated with anemia of chronic disease (ACD), ACD, ACD concomitant iron-deficiency anemia (ACD/IDA), pure IDA and acute inflammation (AcI) patients. Hepcidin levels in anemia types were statistically different, from high to low: ACD, AcI > ACD/IDA > the control > IDA. Serum ferritin levels were significantly increased in ACD and AcI patients but were decreased significantly in ACD/IDA and IDA. Elevated serum EPO concentrations were found in ACD, ACD/IDA and IDA patients but not in AcI patients and the controls. A positive correlation exists between hepcidin and IL-6 levels only in ACD/IDA, AcI and the control groups. A positive correlation between hepcidin and ferritin was marked in the control group, while a negative correlation between hepcidin and ferritin was noted in IDA. The significant negative correlation between hepcidin expression and reticulocyte count was marked in both ACD/IDA and IDA groups. If the hepcidin role in pathogenesis of ACD, ACD/IDA and IDA, it could be a potential marker for detection and differentiation of these anemias (24).

Cancer

Because cancer cells are known to possess a highly unique metabolic phenotype, development of specific biomarkers in oncology is possible and might be used in identifying fingerprints, profiles, or signatures to detect the presence of cancer, determine prognosis, and/or assess the pharmacodynamic effects of therapy (25).

HDM2, a negative regulator of the tumor suppressor p53, is over-expressed in many cancers that retain wild-type p53. Consequently, the effectiveness of chemotherapies that induce p53 might be limited, and inhibitors of the HDM2–p53 interaction are being sought as tumor-selective drugs. A binding site within HDM2 has been dentified which can be blocked with peptides inducing p53 transcriptional activity. A recent report demonstrates the principle using drug-like small molecules that target HDM2 (26).

Obesity, CRP, interleukins, and chronic inflammatory disease

Elevated CRP levels and clinically raised CRP levels were present in 27.6% and 6.7% of the population, respectively. Both overweight (body mass index [BMI], 25-29.9 kg/m2) and obese (BMI, 30 kg/m2) persons were more likely to have elevated CRP levels than their normal-weight counterparts (BMI, <25 kg/m2). After adjusting for potential confounders, the odds ratio (OR) for elevated CRP was 2.13 for obese men and 6.21 for obese women. In addition, BMI was associated with clinically raised CRP levels in women, with an OR of 4.76 (95% CI, 3.42-6.61) for obese women. Waist-to-hip ratio was positively associated with both elevated and clinically raised CRP levels, independent of BMI. Restricting the analyses to young adults (aged 17-39 years) and excluding smokers, persons with inflammatory disease, cardiovascular disease, or diabetes mellitus and estrogen users did not change the main findings (27).

A study of C-reactive protein and interleukin-6 with measures of obesity and of chronic infection as their putative determinants related levels of C-reactive protein and interleukin-6 to markers of the insulin resistance syndrome and of endothelial dysfunction. Levels of C-reactive protein were significantly related to those of interleukin-6 (r=0.37, P<0.0005) and tumor necrosis factor-a (r=0.46, P<0.0001), and concentrations of C-reactive protein were related to insulin resistance as calculated from the homoeostasis model and to markers of endothelial dysfunction (plasma levels of von Willebrand factor, tissue plasminogen activator, and cellular fibronectin). A mean standard deviation score of levels of acute phase markers correlated closely with a similar score of insulin resistance syndrome variables (r=0.59, P<0.00005) and the data suggested that adipose tissue is an important determinant of a low level, chronic inflammatory state as reflected by levels of interleukin-6, tumor necrosis factor-a, and C-reactive protein (28).

A number of other studies have indicated the inflammatory ties of visceral obesity to adipose tissue metabolic profiles, suggesting a role in ―metabolic syndrome‖. There is now a concept of altered liver metabolism in ―non-alcoholic‖ fatty liver disease (NAFLD) progressing from steatosis to steatohepatitis (NASH) (31,32).

These unifying concepts were incomprehensible 50 years ago. It was only known that insulin is anabolic and that insulin deficiency (or resistance) would have consequences in the point of entry into the citric acid cycle, which generates 16 ATPs. In fat catabolism, triglycerides are hydrolyzed to break them into fatty acids and glycerol. In the liver the glycerol can be converted into glucose via dihydroxyacetone phosphate and glyceraldehyde-3-phosphate by way of gluconeogenesis. In the case of this cycle there is a tie in with both catabolism and anabolism.

 

TCA_reactions

TCA_reactions

 http://www.newworldencyclopedia.org/entry/Image:TCA_reactions.gif

 

For bypass of the Pyruvate Kinase reaction of Glycolysis, cleavage of 2 ~P bonds is required. The free energy change associated with cleavage of one ~P bond of ATP is insufficient to drive synthesis of phosphoenolpyruvate (PEP), since PEP has a higher negative G of phosphate hydrolysis than ATP.

The two enzymes that catalyze the reactions for bypass of the Pyruvate Kinase reaction are the following:

(a) Pyruvate Carboxylase (Gluconeogenesis) catalyzes:

pyruvate + HCO3 + ATP — oxaloacetate + ADP + Pi

(b) PEP Carboxykinase (Gluconeogenesis) catalyzes:

oxaloacetate + GTP — phosphoenolpyruvate + GDP + CO2

The concept of anomalies in the pathways with respect to diabetes was sketchy then, and there was much to be filled in. This has been substantially done, and is by no means complete. However, one can see how this comes into play with diabetic ketoacidosis accompanied by gluconeogenesis and in severe injury or sepsis with peripheral proteolysis to provide gluconeogenic precursors. The reprioritization of liver synthetic processes is also brought into play with the conundrum of protein-energy malnutrition.

The picture began to be filled in with the improvements in technology that emerged at the end of the 1980s with the ability to profile tissue and body fluids by NMR and by MS. There was already a good inkling of a relationship of type 2 diabetes to major indicators of CVD (29,30). And a long suspected relationship between obesity and type 2 diabetes was evident. But how did it tie together?

End Stage Renal Disease and Cardiovascular Risk

Mortality is markedly elevated in patients with end-stage renal disease. The leading cause of death is cardiovascular disease.

As renal function declines,

  • the prevalence of both malnutrition and cardiovascular disease increase.

Malnutrition and vascular disease correlate with the levels of

  • markers of inflammation in patients treated with dialysis and in those not yet on dialysis.

The causes of inflammation are likely to be multifactorial. CRP levels are associated with cardio-vascular risk in the general population.

The changes in endothelial cell function,

  • in plasma proteins, and
  • in lpiids in inflammation

are likely to be atherogenic.

That cardiovascular risk is inversely correlated with serum cholesterol in dialysis patients, suggests that

  • hyperlipidemia plays a minor role in the incidence of cardiovascular disease.

Hypoalbuminemia, ascribed to malnutrition, has been one of the most powerful risk factors that predict all-cause and cardiovascular mortality in dialysis patients. The presence of inflammation, as evidenced by increased levels of specific cytokines (interleukin-6 and tumor necrosis factor a) or acute-phase proteins (C-reactive protein and serum amyloid A), however, has been found to be associated with vascular disease in the general population as well as in dialysis patients. Patients have

  • loss of muscle mass and changes in plasma composition—decreases in serum albumin, prealbumin, and transferrin levels, also associated with malnutrition.

Inflammation alters

  • lipoprotein structure and function as well as
  • endothelial structure and function

to favor atherogenesis and increases

  • the concentration of atherogenic proteins in serum.

In addition, proinflammatory compounds, such as

  • advanced glycation end products, accumulate in renal failure, and
  • defense mechanisms against oxidative injury are reduced,

contributing to inflammation and to its effect on the vascular endothelium (33,34).

Endogenous copper can play an important role in postischemic reperfusion injury, a condition associated with endothelial cell activation and increased interleukin 8 (IL-8) production. Excessive endothelial IL-8 secreted during trauma, major surgery, and sepsis may contribute to the development of systemic inflammatory response syndrome (SIRS), adult respiratory distress syndrome (ARDS), and multiple organ failure (MOF). No previous reports have indicated that copper has a direct role in stimulating human endothelial IL-8 secretion. Copper did not stimulate secretion of other cytokines. Cu(II) appeared to be the primary copper ion responsible for the observed increase in IL-8 because a specific high-affinity Cu(II)-binding peptide, d-Asp-d-Ala-d-Hisd-Lys (d-DAHK), completely abolished this effect in a dose-dependent manner. These results suggest that Cu(II) may induce endothelial IL-8 by a mechanism independent of known Cu(I) generation of reactive oxygen species (35).

Blood coagulation plays a key role among numerous mediating systems that are activated in inflammation. Receptors of the PAR family serve as sensors of serine proteinases of the blood clotting system in the target cells involved in inflammation. Activation of PAR_1 by thrombin and of PAR_2 by factor Xa leads to a rapid expression and exposure on the membrane of endothelial cells of both adhesive proteins that mediate an acute inflammatory reaction and of the tissue factor that initiates the blood coagulation cascade. Other receptors that can modulate responses of the cells activated by proteinases through PAR receptors are also involved in the association of coagulation and inflammation together with the receptors of the PAR family. The presence of PAR receptors on mast cells is responsible for their reactivity to thrombin and factor Xa , essential to the inflammation and blood clotting processes (36).

The understanding of regulation of the inflammatory process in chronic inflammatory diseases is advancing.

Evidence consistently indicates that T-cells play a key role in initiating and perpetuating inflammation, not only via the production of soluble mediators but also via cell/cell contact interactions with a variety of cell types through membrane receptors and their ligands. Signalling through CD40 and CD40 ligand is a versatile pathway that is potently involved in all these processes. Many inflammatory genes relevant to atherosclerosis are influenced by the transcriptional regulator nuclear factor κ B (NFκB). In these events T-cells become activated by dendritic cells or inflammatory cytokines, and these T-cells activate, in turn, monocytes / macrophages, endothelial cells, smooth muscle cells and fibroblasts to produce pro-inflammatory cytokines, chemokines, the coagulation cascade in vivo, and finally matrix metalloproteinases, responsible for tissue destruction. Moreover, CD40 ligand at inflammatory sites stimulates fibroblasts and tissue monocyte/macrophage production of VEGF, leading to angiogenesis, which promotes and maintains the chronic inflammatory process.

NFκB plays a pivotal role in co-ordinating the expression of genes involved in the immune and inflammatory response, evoking tumor necrosis factor α (TNFα), chemokines such as monocyte chemoattractant protein-1 (MCP-1) and interleukin (IL)-8, matrix metalloproteinase enzymes (MMP), and genes involved in cell survival. A complex array of mechanisms, including T cell activation, leukocyte extravasation, tissue factor expression, MMP expression and activation, as well induction of cytokines and chemokines, implicated in atherosclerosis, are regulated by NFκB.

Expression of NFκB in the atherosclerotic milieu may have a number of potentially harmful consequences. IL-1 activates NFκB upregulating expression of MMP-1, -3, and -9. Oxidized LDL increases macrophage MMP-9, associated with increased nuclear binding of NFκB and AP-1. Expression of tissue factor, initiating the coagulation cascade, is regulated by NFκB. In atherosclerotic plaque cells, tissue factor antigen and activity were inhibited following over-expression of IκBα and dominant-negative IKK-2, but not by dominant negative IKK-1 or NIK. Tis supports the concept that activation of the ―canonical‖ pathway upregulates pro-thrombotic mediators involved in disease. Many of the cytokines and chemokines which have been detected in human atherosclerotic plaques are also regulated by NFκB. Over-expression of IκBα inhibits release of TNFα, IL-1, IL-6, and IL-8 in macrophages stimulated with LPS and CD40 ligand (CD40L). This report describes how NFκB activation upregulates major pro-inflammatory and pro-thrombotic mediators of atherosclerosis (37-41).

This review is both focused and comprehensive. The details of evolving methods are avoided in order to build the argument that a very rapid expansion of discovery has been evolving depicting disease, disease mechanisms, disease associations, metabolic biomarkers, study of effects of diet and diet modification, and opportunities for targeted drug development. The extent of future success will depend on the duration and strength of the developed interventions, and possibly the avoidance of dead end interventions that are unexpectedly bypassed. I anticipate the prospects for the interplay between genomics, metabolomics, metabonomics, and personalized medicine may be realized for several of the most common conditions worldwide within a few decades (42-44).

References

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  1. Zha JM, Di WJ, Zhu T, Xie T, et al. Comparison of gene transcription between subcutaneous and visceral adipose tissue in chinese adults. Endocr J 2009;56:934-44. [TLR4 signaling, 11 beta-HSD1 and GR levels in VAT];
  2. Albert L, Girola A, Gilardini L, Conti A, et al. Type 2 diabetes and metabolic syndrome are associated with increased expression of 11 beta-hydroxysteroid dehydrogenase 1 in obese subjects. Int J Obesity (Lond) 2007;31:1826-31;
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  4. Tong J, Boyko EJ, Utzschneider KM, McNeely MJ, et al. Intraabdominal fat accumulation predicts the development of the metabolic syndrome in non-diabetic Japanese-Americans. Diabetologia 2007;50:1156-60;
  5. Kim K, Valentine RJ, Shin Y, Gong K. Association of visceral adiposity and exercise participation with C- reactive protein, insulin resistance, and endothelial dysfunction in Korean healthy adults. Metabolism 2008;57:1181-9. [(VAT-EC exhibits a marked angiogenic and proinflammatory state];
  6. Villaret A, Galitzky J, Decaunes P, Exteve D, et al. Adipose tisue endothelial cells from obese human subjects: differences among depots in angiogenic, metabolic, and inflammatory gene expression and cellular senescence. Diabetes 2010;59:2755-63;
  7. van Dijk -, Feskens EJ, Bos MB, Hoelen DW, et al. A saturated fatty acid-rich diet induces an obesity-linked proinflammatory gene expression profile in adipose tissue of subjects at risk of metabolic syndrome. Am J Clin Nutr 2009;90:1656-64.[MUFA in LDL lowering].
  8. Theurl I, Aigner E, Theurl M, Nairz M, et al. Regulation of iron homeostasis in anemia of chronic disease and iron deficiency anemia: diagnostic and therapeutic implications. Blood. 2009;113(21):5277-86
  9. Cheng PP, Jiao XY, Wang XH, Lin JH, Cai YM. Hepcidin expression in anemia of chronic disease and concomitant iron-deficiency anemia. Clin Exp Med. 2010 May 25. [Epub ahead of print].
  10. Spratlin JL, Serkova NJ, and Eckhardt SG. Clinical Applications of Metabolomics in Oncology: A Review. Clin Cancer Res. 2009 ;15; 15(2): 431–440.
  11. Fischer PM, Lane DP. Small molecule inhibitors of thep53 suppressor HDM2: have protein-protein interactions come of age as drug targets? Trends in Pharm Sci 2004;25(7):343-346.
  12. Visser M, Bouter LM, McQuillan GM, Wener HM. Elevated C-Reactive Protein Levels in Overweight and Obese Adults. JAMA. 1999;282:2131-2135.
  13. Yudkin JS, Stehouwer CDA, Emeis JJ, Coppack SW. C-Reactive Protein in Healthy Subjects: Associations With Obesity, Insulin Resistance, and Endothelial Dysfunction : A Potential Role for Cytokines Originating From Adipose Tissue? Arterioscler. Thromb. Vasc. Biol. 1999; 19:972-978.
  14. Visvikis-Siest S, Siest G. The STANISLAS cohort: a 10-year followup of supposed healthy families. Gene-environment interactions, reference values and evaluation of biomarkers in prevention of cardiovascular diseases. Clin Chem Lab Med 2008;46:733-47.
  15. Schmidt MI, Duncan BB. Diabesity: an inflammatory metabolic condition. Clin Chem Lab Med 2003;41:1120-1130.
  16. Fenkci S, Rota S, Sabir N, Akdag B. Ultrasonographic and biochemical evaluation of visceral obesity in obese women with non-alcoholic fatty liver disease. Eur J Med Res 2007;12:68-73. (VAT, HOMA)
  17. Lee JW, Lee HR, Shim JY, Im JA, et al. Viscerally obese women with normal body weight have greater brachial-ankle pulse wave velocity than non viscerally obese women with excessive body weight. Clin Endocrinol (Oxf) 2007;66:572-8. [visceral obesity – high trigly, high baPWV, greater SFA and thigh SFA].
  18. Kaysen GE. The Microinflammatory State in Uremia: Causes and Potential Consequences. J Am Soc Nephrol 2001;12:1549–1557.
  19. Kaysen GE. Role of Inflammation and Its Treatment in ESRD Patients. Blood Purif 2002;20:70–80.
  20. Bar-Or D, Thomas GW, Yukl RL, Rael LT, et al. Copper stimulates the synthesis and release of interleukin-8 in human endothelial cells: a possible early role in systemic inflammatory responses. Shock 2003;20(2):154–158.
  21. Dugina TN, Kiseleva EV, Chistov IV, Umarova BA, and Strukova SM. Receptors of the PAR Family as a Link between Blood Coagulation and Inflammation. Biochemistry (Moscow), 2002; 67(1):65-74. [Translated from Biokhimiya 2002;67(1):77-87].
  22. Monaco C, Andreakos E, Kiriakidis S, Feldmann M, and and Ewa Paleolog. T-Cell-Mediated Signalling in Immune, Inflammatory and Angiogenic Processes: The Cascade of Events Leading to Inflammatory Diseases. Current Drug Targets – Inflammation & Allergy, 2004, 3, 35-42.
  23. Monaco C, Grosjean J, and Paleolog E. The role of the NFκB pathway in atherosclerosis. [E-mail: e.paleolog@imperial.ac.uk]
  24. Libby P, Ridker PM, and Maseri A. Inflammation and atherosclerosis. Circulation 2002;105:1135-43.
  25. Karin M, Yamamoto Y, Wang QM. The IKK NF-kappa B system: a treasure trove for drug development. Nat Rev Drug Discov 2004;3:17-26.
  26. Karin M, Ben-Neriah Y. Phosphorylation meets ubiquitination: the control of NF-[kappa]B activity. Annu Rev Immunol 2000;18:621-63.
  27. Lee DY, Bowen BP, and Northen TR. Mass spectrometry–based metabolomics, analysis of metabolite-protein interactions, and imaging. BioTechniques 2010;49:557-565.
  28. Faca V, Krasnoselsky A, and Hanash S. Innovative proteomic approaches for cancer biomarker discove.
  29. Sharp, P, and MIT faculty. ‘Convergence’ offers potential for revolutionary advance in biomedicine. The Third Revolution: Convergence of the Life Sciences, Physical Sciences and Engineering. White paper. Reported in Biotechnology Jan 5, 2011. [Convergence is a new paradigm that can yield critical advances in a broad array of sectors]

 

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Use of Subtyping for Presurgical Breast Cancer Treatment Use

Reporter, Reblog: Larry H Bernstein, MD, FCAP

 

 

More Accurate Identification of Molecular Subgroups May Better Guide Neo-adjuvant Treatment of Breast Cancer

By Susan Reckling
Posted: 8/19/2014 12:43:52 PM
Last Updated: 8/19/2014 12:43:52 PM

Key Points:
  • Although accurate classification of breast tumors by molecular subtype may guide the appropriate selection of therapy, conventional assessment methods lack standardization.
  • In the Neoadjuvant Breast Registry Symphony Trial of more than 400 women with breast cancer, standard assessment methods were compared with a novel 80-gene classifier known as BluePrint in combination with MammaPrint.
  • BluePrint molecular subtyping reclassified nearly one-fourth of tumors, with more responsive patients reassigned to the HER2 and basal categories and less responsive patients reassigned to the luminal category.

BluePrint in combination with MammaPrint molecular subtyping reclassified more than 20% of breast cancer patients into a different subgroup compared with conventional assessment, according to the results of the prospective Neoadjuvant Breast Registry Symphony Trial (NBRST). In Annals of Surgical Oncology, Whitworth et al reported that this reclassification of patients led to an improved distribution of response rates and a more accurate picture of which patients were likely to respond (or not respond) to neoadjuvant chemotherapy for breast cancer.

Selection of the appropriate therapy for a woman with breast cancer can be guided by accurate classification of the tumor by molecular subtype. Currently, however, conventional assessment methods such as immunohistochemistry and fluorescence in situ hybridization (FISH) lack standardization and the interpretation of test results differs among laboratories.

Thus, investigators have turned to other potentially more effective approaches to molecular subtyping. BluePrint, a novel molecular profile, is a multigene classifier, determining the mRNA levels of 80 genes. In combination with MammaPrint (risk stratification by multigene assays), BluePrint can classify patients with breast cancer into three subtypes based on functional molecular pathways: luminal (A or B), HER2, and basal.

Study Details

In the NBRST study, the investigators attempted to predict chemosensitivity in women with histologically proven breast cancer with the 80-gene BluePrint functional subtype profile vs conventional subtyping. Chemosensitivity was defined as pathologic complete response or the absence of invasive carcinoma in both the breast and axilla at microscopic examination of the resected specimen.

More than 400 women with breast cancer who had started or were scheduled to start neoadjuvant chemotherapy or hormone therapy took part in the multicenter NBRST study. All of them had definitive surgical resection. The age of study participants ranged from 22 to 80 years, with a median age of 52 years. Most of the patients (85%) had T2 or T3 tumors.

Patients who had undergone an excisional biopsy or axillary dissection or who had confirmed distant metastases were excluded from the study. Also, those who had received prior chemotherapy, radiotherapy, or endocrine therapy for breast cancer were ineligible for study participation.

Microarray analysis for the 80-gene BluePrint subtype and the 70-gene MammaPrint profiles was conducted at Agendia Laboratory, which was blinded to both clinical and pathologic data. BluePrint and MammaPrint analysis categorized the study patients as follows: 59 (14%) were luminal A, 153 (36%) were luminal B, 74 (17%) were HER2, and 140 (33%) were basal.

Reclassification to Different Molecular Subgroup

In total, 22% (94 of 426 patients) were reclassified in a different BluePrint/MammaPrint molecular subgroup compared with conventional subtyping. For instance, 37 of 211 patients (18%) of conventionally determined hormone receptor–positive/HER2-negative patients were reassigned by BluePrint as basal (35) or HER2-positive (2). In addition, 53 of 123 conventionally determined HER2-positive patients (43%) were reclassified as luminal (36) or basal (17).

As for response rates to neoadjuvant chemotherapy, the investigators reported an overall pathologic complete response rate of 25% (99 of 403 patients). Six percent of patients with luminal breast tumors had a pathologic complete response rate (2% for luminal A, 7% for luminal B).

More than half of the 74 patients with BluePrint-determined HER2-positive tumors had a pathologic complete response, which the investigators noted was significantly superior (P = .047) to the 38% of conventionally assigned HER2-positive patients.

Clinical Implications

Use of the multigene classifier BluePrint may assist oncologists in accurately identifying which patients with breast cancer may benefit from neoadjuvant chemotherapy and which ones are less likely to do so. According to the investigators, there are potential clinical implications for two particular groups of reassigned patients via BluePrint molecular subtyping: (1) those who were conventionally assigned as HER2-positive but not classified as such by BluePrint, and (2) those who were considered to have hormone receptor–positive/HER2-negative disease via conventional assessment but were reclassified to basal disease by BluePrint.

“This reclassification of patients leads to an improved distribution of response rates in the different subgroups of patients: a lower pathologic complete response rate for BluePrint luminal patients compared with [immumohistochemistry]/FISH-defined conventional luminal patients, with more responsive patients reassigned to the HER2 and basal categories,” concluded the investigators.

Pat Whitworth, MD, of the Department of Surgery, Nashville Breast Center, Nashville, Tennessee, is the corresponding author of the article in Annals of Surgical Oncology.

Lisette Stork-Sloots, MSc, and Femke A. de Snoo, MD, PhD, are employees of Agendia NV, Amsterdam, The Netherlands. The other authors disclosed no potential conflicts of interest.

The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.

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Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief

Pentose Shunt, Electron Transfer, Galactose, more Lipids in brief

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

Pentose Shunt, Electron Transfer, Galactose, and other Lipids in brief

This is a continuation of the series of articles that spans the horizon of the genetic
code and the progression in complexity from genomics to proteomics, which must
be completed before proceeding to metabolomics and multi-omics.  At this point
we have covered genomics, transcriptomics, signaling, and carbohydrate metabolism
with considerable detail.In carbohydrates. There are two topics that need some attention –
(1) pentose phosphate shunt;
(2) H+ transfer
(3) galactose.
(4) more lipids
Then we are to move on to proteins and proteomics.

Summary of this series:

The outline of what I am presenting in series is as follows:

  1. Signaling and Signaling Pathways
    http://pharmaceuticalintelligence.com/2014/08/12/signaling-and-signaling-pathways/
  2. Signaling transduction tutorial.
    http://pharmaceuticalintelligence.com/2014/08/12/signaling-transduction-tutorial/
  3. Carbohydrate metabolism
    http://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/

Selected References to Signaling and Metabolic Pathways published in this Open Access Online Scientific Journal, include the following: 

http://pharmaceuticalintelligence.com/2014/08/14/selected-references-to-signaling-
and-metabolic-pathways-in-leaders-in-pharmaceutical-intelligence/

  1. Lipid metabolism

4.1  Studies of respiration lead to Acetyl CoA
http://pharmaceuticalintelligence.com/2014/08/18/studies-of-respiration-lead-to-acetyl-coa/

4.2 The multi-step transfer of phosphate bond and hydrogen exchange energy
http://pharmaceuticalintelligence.com/2014/08/19/the-multi-step-transfer-of-phosphate-
bond-and-hydrogen-exchange-energy/

5.Pentose shunt, electron transfers, galactose, and other lipids in brief

6. Protein synthesis and degradation

7.  Subcellular structure

8. Impairments in pathological states: endocrine disorders; stress
hypermetabolism; cancer.

Section I. Pentose Shunt

Bernard L. Horecker’s Contributions to Elucidating the Pentose Phosphate Pathway

Nicole Kresge,     Robert D. Simoni and     Robert L. Hill

The Enzymatic Conversion of 6-Phosphogluconate to Ribulose-5-Phosphate
and Ribose-5-Phosphate (Horecker, B. L., Smyrniotis, P. Z., and Seegmiller,
J. E.      J. Biol. Chem. 1951; 193: 383–396

Bernard Horecker

Bernard Leonard Horecker (1914) began his training in enzymology in 1936 as a
graduate student at the University of Chicago in the laboratory of T. R. Hogness.
His initial project involved studying succinic dehydrogenase from beef heart using
the Warburg manometric apparatus. However, when Erwin Hass arrived from Otto
Warburg’s laboratory he asked Horecker to join him in the search for an enzyme
that would catalyze the reduction of cytochrome c by reduced NADP. This marked
the beginning of Horecker’s lifelong involvement with the pentose phosphate pathway.

During World War II, Horecker left Chicago and got a job at the National Institutes of
Health (NIH) in Frederick S. Brackett’s laboratory in the Division of Industrial Hygiene.
As part of the wartime effort, Horecker was assigned the task of developing a method
to determine the carbon monoxide hemoglobin content of the blood of Navy pilots
returning from combat missions. When the war ended, Horecker returned to research
in enzymology and began studying the reduction of cytochrome c by the succinic
dehydrogenase system.

Shortly after he began these investigation changes, Horecker was approached by
future Nobel laureate Arthur Kornberg, who was convinced that enzymes were the
key to understanding intracellular biochemical processes
. Kornberg suggested
they collaborate, and the two began to study the effect of cyanide on the succinic
dehydrogenase system. Cyanide had previously been found to inhibit enzymes
containing a heme group, with the exception of cytochrome c. However, Horecker
and Kornberg found that

  • cyanide did in fact react with cytochrome c and concluded that
  • previous groups had failed to perceive this interaction because
    • the shift in the absorption maximum was too small to be detected by
      visual examination.

Two years later, Kornberg invited Horecker and Leon Heppel to join him in setting up
a new Section on Enzymes in the Laboratory of Physiology at the NIH. Their Section on Enzymes eventually became part of the new Experimental Biology and Medicine
Institute and was later renamed the National Institute of Arthritis and Metabolic
Diseases.

Horecker and Kornberg continued to collaborate, this time on

  • the isolation of DPN and TPN.

By 1948 they had amassed a huge supply of the coenzymes and were able to
present Otto Warburg, the discoverer of TPN, with a gift of 25 mg of the enzyme
when he came to visit. Horecker also collaborated with Heppel on 

  • the isolation of cytochrome c reductase from yeast and 
  • eventually accomplished the first isolation of the flavoprotein from
    mammalian liver.

Along with his lab technician Pauline Smyrniotis, Horecker began to study

  • the enzymes involved in the oxidation of 6-phosphogluconate and the
    metabolic intermediates formed in the pentose phosphate pathway.

Joined by Horecker’s first postdoctoral student, J. E. Seegmiller, they worked
out a new method for the preparation of glucose 6-phosphate and 6-phosphogluconate, 
both of which were not yet commercially available.
As reported in the Journal of Biological Chemistry (JBC) Classic reprinted here, they

  • purified 6-phosphogluconate dehydrogenase from brewer’s yeast (1), and 
  • by coupling the reduction of TPN to its reoxidation by pyruvate in
    the presence of lactic dehydrogenase
    ,
  • they were able to show that the first product of 6-phosphogluconate oxidation,
  • in addition to carbon dioxide, was ribulose 5-phosphte.
  • This pentose ester was then converted to ribose 5-phosphate by a
    pentose-phosphate isomerase.

They were able to separate ribulose 5-phosphate from ribose 5- phosphate and demonstrate their interconversion using a recently developed nucleotide separation
technique called ion-exchange chromatography. Horecker and Seegmiller later
showed that 6-phosphogluconate metabolism by enzymes from mammalian
tissues also produced the same products
.8

Bernard Horecker

Bernard Horecker

http://www.jbc.org/content/280/29/e26/F1.small.gif

Over the next several years, Horecker played a key role in elucidating the

  • remaining steps of the pentose phosphate pathway.

His total contributions included the discovery of three new sugar phosphate esters,
ribulose 5-phosphate, sedoheptulose 7-phosphate, and erythrose 4-phosphate, and
three new enzymes, transketolase, transaldolase, and pentose-phosphate 3-epimerase.
The outline of the complete pentose phosphate cycle was published in 1955
(2). Horecker’s personal account of his work on the pentose phosphate pathway can
be found in his JBC Reflection (3).1

Horecker’s contributions to science were recognized with many awards and honors
including the Washington Academy of Sciences Award for Scientific Achievement in
Biological Sciences (1954) and his election to the National Academy of Sciences in
1961. Horecker also served as president of the American Society of Biological
Chemists (now the American Society for Biochemistry and Molecular Biology) in 1968.

Footnotes

  • 1 All biographical information on Bernard L. Horecker was taken from Ref. 3.
  • The American Society for Biochemistry and Molecular Biology, Inc.

References

  1. ↵Horecker, B. L., and Smyrniotis, P. Z. (1951) Phosphogluconic acid dehydrogenase
    from yeast. J. Biol. Chem. 193, 371–381FREE Full Text
  2. Gunsalus, I. C., Horecker, B. L., and Wood, W. A. (1955) Pathways of carbohydrate
    metabolism in microorganisms. Bacteriol. Rev. 19, 79–128  FREE Full Text
  3. Horecker, B. L. (2002) The pentose phosphate pathway. J. Biol. Chem. 277, 47965–
    47971 FREE Full Text

The Pentose Phosphate Pathway (also called Phosphogluconate Pathway, or Hexose
Monophosphate Shunt) is depicted with structures of intermediates in Fig. 23-25
p. 863 of Biochemistry, by Voet & Voet, 3rd Edition. The linear portion of the pathway
carries out oxidation and decarboxylation of glucose-6-phosphate, producing the
5-C sugar ribulose-5-phosphate.

Glucose-6-phosphate Dehydrogenase catalyzes oxidation of the aldehyde
(hemiacetal), at C1 of glucose-6-phosphate, to a carboxylic acid in ester linkage
(lactone). NADPserves as electron acceptor.

6-Phosphogluconolactonase catalyzes hydrolysis of the ester linkage (lactone)
resulting in ring opening. The product is 6-phosphogluconate. Although ring opening
occurs in the absence of a catalyst, 6-Phosphogluconolactonase speeds up the
reaction, decreasing the lifetime of the highly reactive, and thus potentially
toxic, 6-phosphogluconolactone.

Phosphogluconate Dehydrogenase catalyzes oxidative decarboxylation of
6-phosphogluconate, to yield the 5-C ketose ribulose-5-phosphate. The
hydroxyl at C(C2 of the product) is oxidized to a ketone. This promotes loss
of the carboxyl at C1 as CO2.  NADP+ again serves as oxidant (electron acceptor).

pglucose hd

pglucose hd

https://www.rpi.edu/dept/bcbp/molbiochem/MBWeb/mb2/part1/images/pglucd.gif

Reduction of NADP+ (as with NAD+) involves transfer of 2e- plus 1H+ to the
nicotinamide moiety.

nadp

NADPH, a product of the Pentose Phosphate Pathway, functions as a reductant in
various synthetic (anabolic) pathways, including fatty acid synthesis.

NAD+ serves as electron acceptor in catabolic pathways in which metabolites are
oxidized. The resultant NADH is reoxidized by the respiratory chain, producing ATP.

nadnadp

https://www.rpi.edu/dept/bcbp/molbiochem/MBWeb/mb2/part1/images/nadnadp.gif

Regulation: 
Glucose-6-phosphate Dehydrogenase is the committed step of the Pentose
Phosphate Pathway. This enzyme is regulated by availability of the substrate NADP+.
As NADPH is utilized in reductive synthetic pathways, the increasing concentration of
NADP+ stimulates the Pentose Phosphate Pathway, to replenish NADPH.

The remainder of the Pentose Phosphate Pathway accomplishes conversion of the
5-C ribulose-5-phosphate to the 5-C product ribose-5-phosphate, or to the 3-C
glyceraldehyde -3-phosphate and the 6-C fructose-6-phosphate (reactions 4 to 8
p. 863).

Transketolase utilizes as prosthetic group thiamine pyrophosphate (TPP), a
derivative of vitamin B1.

tpp

tpp

https://www.rpi.edu/dept/bcbp/molbiochem/MBWeb/mb2/part1/images/tpp.gif

Thiamine pyrophosphate binds at the active sites of enzymes in a “V” conformation.The amino group of the aminopyrimidine moiety is close to the dissociable proton,
and serves as the proton acceptor. This proton transfer is promoted by a glutamate
residue adjacent to the pyrimidine ring.

The positively charged N in the thiazole ring acts as an electron sink, promoting
C-C bond cleavage. The 3-C aldose glyceraldehyde-3-phosphate is released.
2-C fragment remains on TPP.

FASEB J. 1996 Mar;10(4):461-70.   http://www.ncbi.nlm.nih.gov/pubmed/8647345

Reviewer

The importance of this pathway can easily be underestimated.  The main source for
energy in respiration was considered to be tied to the

  • high energy phosphate bond in phosphorylation and utilizes NADPH, converting it to NADP+.

glycolysis n skeletal muscle in short term, dependent on muscle glycogen conversion
to glucose, and there is a buildup of lactic acid – used as fuel by the heart.  This
pathway accounts for roughly 5% of metabolic needs, varying between tissues,
depending on there priority for synthetic functions, such as endocrine or nucleic
acid production.

The mature erythrocyte and the ocular lens both are enucleate.  85% of their
metabolic energy needs are by anaerobic glycolysis.  Consider the erythrocyte
somewhat different than the lens because it has iron-based hemoglobin, which
exchanges O2 and CO2 in the pulmonary alveoli, and in that role, is a rapid
regulator of H+ and pH in the circulation (carbonic anhydrase reaction), and also to
a lesser extent in the kidney cortex, where H+ is removed  from the circulation to
the urine, making the blood less acidic, except when there is a reciprocal loss of K+.
This is how we need a nomogram to determine respiratory vs renal acidosis or
alkalosis.  In the case of chronic renal disease, there is substantial loss of
functioning nephrons, loss of countercurrent multiplier, and a reduced capacity to
remove H+.  So there is both a metabolic acidosis and a hyperkalemia, with increased
serum creatinine, but the creatinine is only from muscle mass – not accurately
reflecting total body mass, which includes visceral organs.  The only accurate
measure of lean body mass would be in the linear relationship between circulating
hepatic produced transthyretin (TTR).

The pentose phosphate shunt is essential for

  • the generation of nucleic acids, in regeneration of red cells and lens – requiring NADPH.

Insofar as the red blood cell is engaged in O2 exchange, the lactic dehydrogenase
isoenzyme composition is the same as the heart. What about the lens of and cornea the eye, and platelets?  The explanation does appear to be more complex than
has been proposed and is not discussed here.

Section II. Mitochondrial NADH – NADP+ Transhydrogenase Reaction

There is also another consideration for the balance of di- and tri- phospopyridine
nucleotides in their oxidized and reduced forms.  I have brought this into the
discussion because of the centrality of hydride tranfer to mitochondrial oxidative
phosphorylation and the energetics – for catabolism and synthesis.

The role of transhydrogenase in the energy-linked reduction of TPN 

Fritz HommesRonald W. Estabrook∗∗

The Wenner-Gren Institute, University of Stockholm
Stockholm, Sweden
Biochemical and Biophysical Research Communications 11, (1), 2 Apr 1963, Pp 1–6
http://dx.doi.org:/10.1016/0006-291X(63)90017-2

In 1959, Klingenberg and Slenczka (1) made the important observation that incubation of isolated

  • liver mitochondria with DPN-specific substrates or succinate in the absence of phosphate
    acceptor resulted in a rapid and almost complete reduction of the intramitochondrial TPN.

These and related findings led Klingenberg and co-workers (1-3) to postulate

  • the occurrence of an ATP-controlled transhydrogenase reaction catalyzing the reduction of
    mitochondrial TPN by DPNH. A similar conclusion was reached by Estabrook and Nissley (4).

The present paper describes the demonstration and some properties of an

  • energy-dependent reduction of TPN by DPNH, catalyzed by submitochondrial particles.

Preliminary reports of some of these results have already appeared (5, 6 ) , and a
complete account is being published elsewhere (7).We have studied the energy- dependent reduction of TPN by PNH with submitochondrial particles from both
rat liver and beef heart. Rat liver particles were prepared essentially according to
the method of Kielley and Bronk (8), and beef heart particles by the method of
Low and Vallin (9).

PYRIDINE NUCLEOTIDE TRANSHYDROGENASE  II. DIRECT EVIDENCE FOR
AND MECHANISM OF THE
 TRANSHYDROGENASE REACTION*

BY  NATHAN 0. KAPLAN, SIDNEY P. COLOWICK, AND ELIZABETH F. NEUFELD
(From the McCollum-Pratt Institute, The Johns Hopkins University, Baltimore,
Maryland)  J. Biol. Chem. 1952, 195:107-119.
http://www.jbc.org/content/195/1/107.citation

NO Kaplan

NO Kaplan

Sidney Colowick

Sidney Colowick

Elizabeth Neufeld

Elizabeth Neufeld

Kaplan studied carbohydrate metabolism in the liver under David M. Greenberg at the
University of California, Berkeley medical school. He earned his Ph.D. in 1943. From
1942 to 1944, Kaplan participated in the Manhattan Project. From 1945 to 1949,
Kaplan worked with Fritz Lipmann at Massachusetts General Hospital to study
coenzyme A. He worked at the McCollum-Pratt Institute of Johns Hopkins University
from 1950 to 957. In 1957, he was recruited to head a new graduate program in
biochemistry at Brandeis University. In 1968, Kaplan moved to the University of
California, San Diego
, where he studied the role of lactate dehydrogenase in cancer. He also founded a colony of nude mice, a strain of laboratory mice useful in the study
of cancer and other diseases. [1] He was a member of the National Academy of
Sciences.One of Kaplan’s students at the University of California was genomic
researcher Craig Venter.[2]3]  He was, with Sidney Colowick, a founding editor of the scientific book series Methods
in Enzymology
.[1]

http://books.nap.edu/books/0309049768/xhtml/images/img00009.jpg

Colowick became Carl Cori’s first graduate student and earned his Ph.D. at
Washington University St. Louis in 1942, continuing to work with the Coris (Nobel
Prize jointly) for 10 years. At the age of 21, he published his first paper on the
classical studies of glucose 1-phosphate (2), and a year later he was the sole author on a paper on the synthesis of mannose 1-phosphate and galactose 1-phosphate (3). Both papers were published in the JBC. During his time in the Cori lab,

Colowick was involved in many projects. Along with Herman Kalckar he discovered
myokinase (distinguished from adenylate kinase from liver), which is now known as
adenyl kinase. This discovery proved to be important in understanding transphos-phorylation reactions in yeast and animal cells. Colowick’s interest then turned to
the conversion of glucose to polysaccharides, and he and Earl Sutherland (who
will be featured in an upcoming JBC Classic) published an important paper on the
formation of glycogen from glucose using purified enzymes (4). In 1951, Colowick
and Nathan Kaplan were approached by Kurt Jacoby of Academic Press to do a
series comparable to Methodem der Ferment Forschung. Colowick and Kaplan
planned and edited the first 6 volumes of Methods in Enzymology, launching in 1955
what became a series of well known and useful handbooks. He continued as
Editor of the series until his death in 1985.

http://bioenergetics.jbc.org/highwire/filestream/9/field_highwire_fragment_image_s/0/F1.small.gif

The Structure of NADH: the Work of Sidney P. Colowick

Nicole KresgeRobert D. Simoni and Robert L. Hill

On the Structure of Reduced Diphosphopyridine Nucleotide

(Pullman, M. E., San Pietro, A., and Colowick, S. P. (1954)

J. Biol. Chem. 206, 129–141)

Elizabeth Neufeld
·  Born: September 27, 1928 (age 85), Paris, France
·  EducationQueens College, City University of New YorkUniversity of California,
Berkeley

http://fdb5.ctrl.ucla.edu/biological-chemistry/institution/photo?personnel%5fid=45290&max_width=155&max_height=225

In Paper I (l), indirect evidence was presented for the following transhydrogenase
reaction, catalyzed by an enzyme present in extracts of Pseudomonas
fluorescens:

TPNHz + DPN -+ TPN + DPNHz

The evidence was obtained by coupling TPN-specific dehydrogenases with the
transhydrogenase and observing the reduction of large amounts of diphosphopyridine nucleotide (DPN) in the presence of catalytic amounts of triphosphopyridine
nucleotide (TPN).

In this paper, data will be reported showing the direct

  • interaction between TPNHz and DPN, in thepresence of transhydrogenase alone,
  • to yield products having the propertiesof TPN and DPNHZ.

Information will be given indicating that the reaction involves

  • a transfer of electrons (or hydrogen) rather than a phosphate 

Experiments dealing with the kinetics and reversibility of the reaction, and with the
nature of the products, suggest that the reaction is a complex one, not fully described
by the above formulation.

Materials and Methods [edited]

The TPN and DPN used in these studies were preparations of approximately 75
percent purity and were prepared from sheep liver by the chromatographic procedure
of Kornberg and Horecker (unpublished). Reduced DPN was prepared enzymatically with alcohol dehydrogenase as described elsewhere (2). Reduced TPN was prepared by treating TPN with hydrosulfite. This treated mixture contained 2 pM of TPNHz per ml.
The preparations of desamino DPN and reduced desamino DPN have been
described previously (2, 3). Phosphogluconate was a barium salt which was kindly
supplied by Dr. B. F. Horecker. Cytochrome c was obtained from the Sigma Chemical Company.

Transhydrogenase preparations with an activity of 250 to 7000 units per mg. were
used in these studies. The DPNase was a purified enzyme, which was obtained
from zinc-deficient Neurospora and had an activity of 5500 units per mg. (4). The
alcohol dehydrogenase was a crystalline preparation isolated from yeast according to the procedure of Racker (5).

Phosphogluconate dehydrogenase from yeast and a 10 per cent pure preparation of the TPN-specific cytochrome c reductase from liver (6) were gifts of Dr. B. F.
Horecker.

DPN was assayed with alcohol and crystalline yeast alcohol dehydrogenase. TPN was determined By the specific phosphogluconic acid dehydrogenase from yeast and also by the specific isocitric dehydrogenase from pig heart. Reduced DPN was
determined by the use of acetaldehyde and the yeast alcohol dehydrogenase.
All of the above assays were based on the measurement of optical density changes
at 340 rnp. TPNHz was determined with the TPN-specific cytochrome c reductase system. The assay of the reaction followed increase in optical density at 550 rnp  as a measure of the reduction of the cytochrome c after cytochrome c
reductase was added to initiate the reaction. The changes at 550 rnp are plotted for different concentrations of TPNHz in Fig. 3, a. The method is an extremely sensitive and accurate assay for reduced TPN.

Results
[No Figures or Table shown]

Formation of DPNHz from TPNHz and DPN-Fig. 1, a illustrates the direct reaction between TPNHz and DPN to form DPNHZ. The reaction was carried out by incubating TPNHz with DPN in the presence of the
transhydrogenase, yeast alcohol dehydrogenase, and acetaldehyde. Since the yeast dehydrogenase is specific for DPN,

  • a decrease in absorption at340 rnp can only be due to the formation of reduced DPN. It can
    be seen from the curves in Fig. 1, a that a decrease in optical density occurs only in the
    presence of the complete system.

The Pseudomonas enzyme is essential for the formation of DPNH2. It is noteworthy
that, under the conditions of reaction in Fig. 1, a,

  • approximately 40 per cent of theTPNH, reacted with the DPN.

Fig. 1, a also indicates that magnesium is not required for transhydrogenase activity.  The reaction between TPNHz and DPN takes place in the absence of alcohol
dehydrogenase and acetaldehyde
. This can be demonstrated by incubating the
two pyridine nucleotides with the transhydrogenase for 4 8 12 16 20 24 28 32 36
minutes

FIG. 1. Evidence for enzymatic reaction of TPNHt with DPN.

  • Rate offormation of DPNH2.

(b) DPN disappearance and TPN formation.

(c) Identification of desamino DPNHz as product of reaction of TPNHz with desamino DPN.  (assaying for reduced DPN by the yeast alcohol dehydrogenase technique.

Table I (Experiment 1) summarizes the results of such experiments in which TPNHz was added with varying amounts of DPN.

  • In the absence of DPN, no DPNHz was formed. This eliminates the possibility that TPNH 2 is
    converted to DPNHz
  • by removal ofthe monoester phosphate grouping.

The data also show that the extent of the reaction is

  • dependent on the concentration of DPN.

Even with a large excess of DPN, only approximately 40 per cent of the TPNHzreacts to form reduced DPN. It is of importance to emphasize that in the above
experiments, which were carried out in phosphate buffer, the extent of  the reaction

  • is the same in the presence or absence of acetaldehyde andalcohol dehydrogenase.

With an excess of DPN and different  levels of TPNHZ,

  • the amount of reduced DPN which is formed is
  • dependent on the concentration of TPNHz(Table I, Experiment 2).
  • In all cases, the amount of DPNHz formed is approximately
    40 per cent of the added reduced TPN.

Formation of TPN-The reaction between TPNHz and DPN should yield TPN as well as DPNHz.
The formation of TPN is demonstrated in Table 1. in Fig. 1, b. In this experiment,
TPNHz was allowed to react with DPN in the presence of the transhydrogenase
(PS.), and then alcohol and alcohol dehydrogenase were added . This
would result in reduction of the residual DPN, and the sample incubated with the
transhydrogenase contained less DPN. After the completion of the alcohol
dehydrogenase reaction, phosphogluconate and phosphogluconic dehydrogenase (PGAD) were added to reduce the TPN. The addition of this TPN-specific
dehydrogenase results in an

  • increase inoptical density in the enzymatically treated sample.
  • This change represents the amount of TPN formed.

It is of interest to point out that, after addition of both dehydrogenases,

  • the total optical density change is the same in both

Therefore it is evident that

  • for every mole of DPN disappearing  a mole of TPN appears.

Balance of All Components of Reaction

Table II (Experiment 1) shows that,

  • if measurements for all components of the reaction are made, one can demonstrate
    that there is
  • a mole for mole disappearance of TPNH, and DPN, and
  • a stoichiometric appearance of TPN and DPNH2.
  1. The oxidized forms of the nucleotides were assayed as described
  2. the reduced form of TPN was determined by the TPNHz-specific cytochrome c reductase,
  3. the DPNHz by means of yeast alcohol dehydrogenase plus

This stoichiometric balance is true, however,

  • only when the analyses for the oxidized forms are determined directly on the reaction

When analyses are made after acidification of the incubated reaction mixture,

  • the values found forDPN and TPN are much lower than those obtained by direct analysis.

This discrepancy in the balance when analyses for the oxidized nucleotides are
carried out in acid is indicated in Table II (Experiment 2). The results, when
compared with the findings in Experiment 1, are quite striking.

Reaction of TPNHz with Desamino DPN

Desamino DPN

  • reacts with the transhydrogenase system at the same rate as does DPN (2).

This was of value in establishing the fact that

  • the transhydrogenase catalyzesa transfer of hydrogen rather than a phosphate transfer reaction.

The reaction between desamino DPN and TPNHz can be written in two ways.

TPN f desamino DPNHz

TPNH, + desamino DPN

DPNH2 + desamino TPN

If the reaction involved an electron transfer,

  • desamino DPNHz would be
  • Phosphate transfer would result in the production of reduced

Desamino DPNHz can be distinguished from DPNHz by its

  • slowerrate of reaction with yeast alcohol dehydrogenase (2, 3).

Fig. 1, c illustrates that, when desamino DPN reacts with TPNH2, 

  • the product of the reaction is desamino DPNHZ.

This is indicated by the slow rate of oxidation of the product by yeast alcohol
dehydrogenase and acetaldehyde.

From the above evidence phosphate transfer 

  • has been ruled out as a possible mechanism for the transhydrogenase reaction.

Inhibition by TPN

As mentioned in Paper I and as will be discussed later in this paper,

  • the transhydrogenase reaction does not appear to be readily reversible.

This is surprising, particularly since only approximately 

  • 40 per cent of the TPNHz undergoes reaction with DPN
    under the conditions described above. It was therefore thought that
  • the TPN formed might inhibit further transfer of electrons from TPNH2.

Table III summarizes data showing the

  • strong inhibitory effect of TPN on thereaction between TPNHz and DPN.

It is evident from the data that

  • TPN concentration is a factor in determining the extent of the reaction.

Effect of Removal of TPN on Extent of Reaction

A purified DPNase from Neurospora has been found

  • to cleave the nicotinamide riboside linkagesof the oxidized forms of both TPN and DPN
  • without acting on thereduced forms of both nucleotides (4).

It has been found, however, that

  • the DPNase hydrolyzes desamino DPN at a very slow rate (3).

In the reaction between TPNHz and desamino DPN, TPN and desamino DPNH:,

  • TPNis the only component of this reaction attacked by the Neurospora enzyme
    at an appreciable rate

It was  thought that addition of the DPNase to the TPNHZ-desamino DPN trans-
hydrogenase reaction mixture

  • would split the TPN formed andpermit the reaction to go to completion.

This, indeed, proved to be the case, as indicated in Table IV, where addition of
the DPNase with desamino DPN results in almost

  • a stoichiometric formation of desamino DPNHz
  • and a complete disappearance of TPNH2.

Extent of Reaction in Buffers Other Than Phosphate

All the reactions described above were carried out in phosphate buffer of pH 7.5.
If the transhydrogenase reaction between TPNHz and DPN is run at the same pH
in tris(hydroxymethyl)aminomethane buffer (TRIS buffer)

  • with acetaldehydeand alcohol dehydrogenase present,
  • the reaction proceeds muchfurther toward completion 
  • than is the case under the same conditions ina phosphate medium (Fig. 2, a).

The importance of phosphate concentration in governing the extent of the reaction
is illustrated in Fig. 2, b.

In the presence of TRIS the transfer reaction

  • seems to go further toward completion in the presence of acetaldehyde
    and 
    alcohol dehydrogenase
  • than when these two components are absent.

This is not true of the reaction in phosphate,

  • in which the extent is independent of the alcoholdehydrogenase system.

Removal of one of the products of the reaction (DPNHp) in TRIS thus

  • appears to permit the reaction to approach completion,whereas
  • in phosphate this removal is without effect on the finalcourse of the reaction.

The extent of the reaction in TRIS in the absence of alcohol dehydrogenase
and acetaldehyde
 is

  • somewhat greater than when the reaction is run in phosphate.

TPN also inhibits the reaction of TPNHz with DPN in TRIS medium, but the inhibition

  • is not as marked as when the reaction is carried out in phosphate buffer.

Reversibility of Transhydrogenase Reaction;

Reaction between DPNHz and TPN

In Paper I, it was mentioned that no reversal of the reaction could be achieved in a system containing alcohol, alcohol dehydrogenase, TPN, and catalytic amounts of
DPN.

When DPNH, and TPN are incubated with the purified transhydrogenase, there is
also

  • no evidence for reversibility.

This is indicated in Table V which shows that

  • there is no disappearance of DPNHz in such a system.

It was thought that removal of the TPNHz, which might be formed in the reaction,
could promote the reversal of the reaction. Hence,

  • by using the TPNHe-specific cytochrome c reductase, one could
  1. not only accomplishthe removal of any reduced TPN,
  2. but also follow the course of the reaction.

A system containing DPNH2, TPN, the transhydrogenase, the cytochrome c
reductase, and cytochrome c, however, gives

  • no reduction of the cytochrome

This is true for either TRIS or phosphate buffers.2

Some positive evidence for the reversibility has been obtained by using a system
containing

  • DPNH2, TPNH2, cytochrome c, and the cytochrome creductase in TRIS buffer.

In this case, there is, of course, reduction of cytochrome c by TPNHZ, but,

  • when the transhydrogenase is present.,there is
  • additional reduction over and above that due to the added TPNH2.

This additional reduction suggests that some reversibility of the reaction occurred
under these conditions. Fig. 3, b shows

  • the necessity of DPNHzfor this additional reduction.

Interaction of DPNHz with Desamino DPN-

If desamino DPN and DPNHz are incubated with the purified Pseudomonas enzyme,
there appears

  • to be a transfer of electrons to form desamino DPNHz.

This is illustrated in Fig. 4, a, which shows the

  • decreased rate of oxidation by thealcohol dehydrogenase system
  • after incubation with the transhydrogenase.
  • Incubation of desamino DPNHz with DPN results in the formation of DPNH2,
  • which is detected by the faster rate of oxidation by the alcohol dehydrogenase system
  • after reaction of the pyridine nucleotides with thetranshydrogenase (Fig. 4, b).

It is evident from the above experiments that

the transhydrogenase catalyzes an exchange of hydrogens between

  • the adenylic and inosinic pyridine nucleotides.

However, it is difficult to obtain any quantitative information on the rate or extent of
the reaction by the method used, because

  • desamino DPNHz also reacts with the alcohol dehydrogenase system,
  • although at a much slower rate than does DPNH2.

DISCUSSION

The results of the balance experiments seem to offer convincing evidence that
the transhydrogenase catalyzes the following reaction.

TPNHz + DPN -+ DPNHz + TPN

Since desamino DPNHz is formed from TPNHz and desamino DPN,

  • thereaction appears to involve an electron (or hydrogen) transfer
  • rather thana transfer of the monoester phosphate grouping of TPN.

A number of the findings reported in this paper are not readily understandable in
terms of the above simple formulation of the reaction. It is difficult to understand
the greater extent of the reaction in TRIS than in phosphate when acetaldehyde
and alcohol dehydrogenase are present.

One possibility is that an intermediate may be involved which is more easily converted
to reduced DPN in the TRIS medium. The existence of such an intermediate is also
suggested by the discrepancies noted in balance experiments, in which

  • analyses of the oxidized nucleotides after acidification showed
  • much lower values than those found by direct analysis.

These findings suggest that the reaction may involve

  • a 1 electron ratherthan a 2 electron transfer with
  • the formation of acid-labile free radicals as intermediates.

The transfer of hydrogens from DPNHz to desamino DPN

  • to yield desamino DPNHz and DPN and the reversal of this transfer
  • indicate the unique role of the transhydrogenase
  • in promoting electron exchange between the pyridine nucleotides.

In this connection, it is of interest that alcohol dehydrogenase and lactic
dehydrogenase cannot duplicate this exchange  between the DPN and
the desamino systems.3  If one assumes that desamino DPN behaves
like DPN,

  • one might predict that the transhydrogenase would catalyze an
    exchange of electrons (or hydrogen) 3.

Since alcohol dehydrogenase alone

  • does not catalyze an exchange of electrons between the adenylic
    and inosinic pyridine nucleotides, this rules out the possibility
  • that the dehydrogenase is converted to a reduced intermediate
  • during electron between DPNHz and added DPN.

It is hoped to investigate this possibility with isotopically labeled DPN.
Experiments to test the interaction between TPN and desamino TPN are
also now in progress.

It seems likely that the transhydrogenase will prove capable of

  • catalyzingan exchange between TPN and TPNH2, as well as between DPN and

The observed inhibition by TPN of the reaction between TPNHz and DPN may
therefore

  • be due to a competition between DPN and TPNfor the TPNH2.

SUMMARY

  1. Direct evidence for the following transhydrogenase reaction. catalyzedby an
    enzyme from Pseudomonas fluorescens, is presented.

TPNHz + DPN -+ TPN + DPNHz

Balance experiments have shown that for every mole of TPNHz disappearing
1 mole of TPN appears and that for each mole of DPNHz generated 1 mole of
DPN disappears. The oxidized nucleotides found at the end of the reaction,
however, show anomalous lability toward acid.

  1. The transhydrogenase also promotes the following reaction.

TPNHz + desamino DPN -+ TPN + desamino DPNH,

This rules out the possibility that the transhydrogenase reaction involves a
phosphate transfer and indicates that the

  • enzyme catalyzes a shift of electrons (or hydrogen atoms).

The reaction of TPNHz with DPN in 0.1 M phosphate buffer is strongly
inhibited by TPN; thus

  • it proceeds only to the extent of about40 per cent or less, even
  • when DPNHz is removed continuously by meansof acetaldehyde
    and alcohol dehydrogenase.
  • In other buffers, in whichTPN is less inhibitory, the reaction proceeds
    much further toward completion under these conditions.
  • The reaction in phosphate buffer proceedsto completion when TPN
    is removed as it is formed.
  1. DPNHz does not react with TPN to form TPNHz and DPN in the presence
    of transhydrogenase. Some evidence, however, has been obtained for
    the reversibility by using the following system:
  • DPNHZ, TPNHZ, cytochromec, the TPNHz-specific cytochrome c reductase,
    and the transhydrogenase.
  1. Evidence is cited for the following reversible reaction, which is catalyzed
    by the transhydrogenase.

DPNHz + desamino DPN fi DPN + desamino DPNHz

  1. The results are discussed with respect to the possibility that the
    transhydrogenase reaction may
  • involve a 1 electron transfer with theformation of free radicals as intermediates.

 

BIBLIOGRAPHY

  1. Coiowick, S. P., Kaplan, N. O., Neufeld, E. F., and Ciotti, M. M., J. Biol. Chem.,196, 95 (1952).
  2. Pullman, 111. E., Colowick, S. P., and Kaplan, N. O., J. Biol. Chem., 194, 593(1952).
  3. Kaplan, N. O., Colowick, S. P., and Ciotti, M. M., J. Biol. Chem., 194, 579 (1952).
  4. Kaplan, N. O., Colowick, S. P., and Nason, A., J. Biol. Chem., 191, 473 (1951).
  5. Racker, E., J. Biol. Chem., 184, 313 (1950).
  6. Horecker, B. F., J. Biol. Chem., 183, 593 (1950).

Section !II. 

Luis_Federico_Leloir_-_young

The Leloir pathway: a mechanistic imperative for three enzymes to change
the stereochemical configuration of a single carbon in galactose.

Frey PA.
FASEB J. 1996 Mar;10(4):461-70.    http://www.fasebj.org/content/10/4/461.full.pdf
PMID:8647345

The biological interconversion of galactose and glucose takes place only by way of
the Leloir pathway and requires the three enzymes galactokinase, galactose-1-P
uridylyltransferase, and UDP-galactose 4-epimerase.
The only biological importance of these enzymes appears to be to

  • provide for the interconversion of galactosyl and glucosyl groups.

Galactose mutarotase also participates by producing the galactokinase substrate
alpha-D-galactose from its beta-anomer. The galacto/gluco configurational change takes place at the level of the nucleotide sugar by an oxidation/reduction
mechanism in the active site of the epimerase NAD+ complex. The nucleotide portion
of UDP-galactose and UDP-glucose participates in the epimerization process in two ways:

1) by serving as a binding anchor that allows epimerization to take place at glycosyl-C-4 through weak binding of the sugar, and

2) by inducing a conformational change in the epimerase that destabilizes NAD+ and
increases its reactivity toward substrates.

Reversible hydride transfer is thereby facilitated between NAD+ and carbon-4
of the weakly bound sugars.

The structure of the enzyme reveals many details of the binding of NAD+ and
inhibitors at the active site
.

The essential roles of the kinase and transferase are to attach the UDP group
to galactose, allowing for its participation in catalysis by the epimerase. The
transferase is a Zn/Fe metalloprotein
, in which the metal ions stabilize the
structure rather than participating in catalysis. The structure is interesting
in that

  • it consists of single beta-sheet with 13 antiparallel strands and 1 parallel strand
    connected by 6 helices.

The mechanism of UMP attachment at the active site of the transferase is a double
displacement
, with the participation of a covalent UMP-His 166-enzyme intermediate
in the Escherichia coli enzyme. The evolution of this mechanism appears to have
been guided by the principle of economy in the evolution of binding sites.

PMID: 8647345 Free full text

Section IV.

More on Lipids – Role of lipids – classification

  • Energy
  • Energy Storage
  • Hormones
  • Vitamins
  • Digestion
  • Insulation
  • Membrane structure: Hydrophobic properties

Lipid types

lipid types

lipid types

nat occuring FAs in mammals

nat occuring FAs in mammals

Read Full Post »

Studies of Respiration Lead to Acetyl CoA

Curator: Larry H. Bernstein, MD, FCAP

In this series of discussions it has become clear that the studies of carbohydrate metabolism were highlighted by Meyerhof’s work on the glycolytic pathway, and the further elucidation of a tie between Warburg’s studies of impaired respiration for malignant aerobic cells relying on glycolysis, comparanle to Pasteur’s observations 60 years earlier by for yeast.   The mitochondrion was unknown at the time, and it took many years to discover the key role played by oxidative phosphorylation and Fritz Lipmann’s discovery of “acetyl coenzyme A, and the later explanation of electron transport.  This was crucial to understanding cellular energetics, which explains the high energy of fatty acid catabolism from stored adipose tissue.  I shall here embark on a journey to trace these important connected developments.

  1. Signaling and signaling pathways
  2. Signaling transduction tutorial.
  3. Carbohydrate metabolism

3.1  Selected References to Signaling and Metabolic Pathways in Leaders in Pharmaceutical Intelligence

  1. Lipid metabolism

4.1  Studies of respiration lead to Acetyl CoA

  1. Protein synthesis and degradation
  2. Subcellular structure
  3. Impairments in pathological states: endocrine disorders; stress hypermetabolism; cancer.

Phosphorylation

In some reactions, the purpose of phosphorylation is to “activate” or “volatize” a molecule, increasing its energy so it is able to participate in a subsequent reaction with a negative free-energy change. All kinases require a divalent metal ion such as Mg2+ or Mn2+ to be present, which stabilizes the high-energy bonds of the donor molecule (usually ATP or ATP derivative) and allows phosphorylation to occur.This is a major focus of this discussion.

In other reactions, phosphorylation of a protein substrate can inhibit its activity (as when AKT phosphorylates the enzyme GSK-3). When src is phosphorylated on a particular tyrosine, it folds on itself, and thus masks its own kinase domain, and is thus turned “off”. In still other reactions, phosphorylation of a protein causes it to be bound to other proteins which have “recognition domains” for a phosphorylated tyrosine, serine, or threonine motif. In the late 1990s it was recognized that phosphorylation of some proteins causes them to be degraded by the ATP-dependent ubiquitin/proteasome pathway. This is all that needs to be said at this time about proteins.

 

Oxidative Phosphorylation

ATP is the molecule that supplies energy to metabolism. Almost all aerobic organisms carry out oxidative phosphorylation. This pathway is probably so pervasive because it is a highly efficient way of releasing energy, compared to alternative fermentation processes such as anaerobic glycolysis.

During oxidative phosphorylation, electrons are transferred from electron donors to electron acceptors such as oxygen, in redox reactions. These redox reactions release energy, which is used to form ATP. In eukaryotes, these redox reactions are carried out by a series of protein complexes within the cell’s intermembrane wall mitochondria, whereas, in prokaryotes, these proteins are located in the cells’ intermembrane space.

O t to  W a r b u r g
Nobel Lecture, December 10, 1931

The oxygen-transferring ferment of respiration

The effects of iron are very great, and it follows that oxidation and reduction of the ferment iron must occur extremely rapidly. In fact, almost every molecule of oxygen that comes into contact with an atom of ferment iron reacts with it.  Complex-bound bivalent iron in compounds reacts, in vitro as well as in the cell, with molecular oxygen. tt is not yet possible to reduce in vitro trivalent iron with the cell fuel: it is always necessary to add a substance of unknown composition, a ferment, that activates the combustible material for the attack of the iron. It must, therefore, be concluded that activation of the combustible substance in the breathing cell precedes the attack of the ferment iron; this corresponds with “hydrogen activation” as postulated in the theory of Wieland and Thunberg. According to the results of a joint research with W. Christian, this is a cleavage comparable with those known as fermentation.

It is possible that the interplay of splitting ferment and oxygen-transferring ferment does not fully explain the mechanism of cellular respiration; that the iron that reacts with the molecular oxygen does not directly oxidize the activated combustible substances, but that it exerts its effects indirectly through still other iron compounds – the three non-auto-oxidizable cell hemes of MacMunn, which occur in living cells according to the spectroscopic observations of MacMunn and Keilin, and which are reduced in the cell under exclusion of oxygen. It is still not possible to answer the question whether the MacMunn hemes form part of the normal respiratory cycle, i.e., whether respiration is not a simple iron catalysis but a four-fold one. The available spectroscopic observations are also consistent with the view that the MacMunn hemes in the cell are only reduced when the concentration of activated combustible substance is physiologically above normal. This will suffice to indicate that oxygen transfer by the iron of the oxygen transferring ferment is not the whole story of respiration. Respiration requires not only oxygen-transferring ferment and combustible substance, but oxygen-transferring ferment and the living cell.

Inhibition of cellular respiration by prussic acid was discovered some 50 years ago by Claude Bernard, and has interested both chemists and biologists ever since. It takes place as the result of a reaction between the prussic acid and the oxygen-transferring ferment iron, that is, with the ferment iron in trivalent form. [In the prussic acid reaction] the oxidizing OH-group of the trivalent ferment-iron is replaced by the non-oxidizing CN-group, thus bringing transfer of oxygen to a standstill. Prussic acid inhibits reduction of the ferment iron. Inhibition of respiration by carbon monoxide was discovered only a few years ago. [Given] the initial reaction in respiration, then, in the presence of carbon monoxide, the competing reaction will also occur and, varying with the pressures of the carbon monoxide and of the oxygen, more or less of the ferment iron will be removed from the catalytic process on account of fixation of carbon monoxide to the ferment iron. Unlike prussic acid, therefore, carbon monoxide affects the bivalent iron of the ferment. Carbon monoxide inhibits oxidation of the ferment iron.

Thus inhibition of respiration by carbon monoxide, unlike that by prussic acid, depends upon the partial pressure of oxygen. The toxic action of prussic acid in the human subject is based on its inhibitory action on cellular respiration. The toxic effect of carbon monoxide on man has nothing to do with inhibition of cellular respiration by carbon monoxide but is based on the reaction of carbon monoxide with blood iron. For, the effect of carbon monoxide on blood iron occurs at pressures of carbon monoxide far from the level at which cellular respiration would be inhibited.

If carbon monoxide is added to the oxygen in which living cells breathe, respiration ceases, as has already been mentioned, but if exposure to ultraviolet or visible light is administered, respiration recurs. By alternate illumination and darkness it is possible to cause respiration and cessation of respiration in living, breathing cells in mixtures of carbon monoxide and oxygen. In the dark, the iron of the oxygen-transferring ferment becomes bound to carbon monoxide, whereas in the light the carbon monoxide is split off from the iron which is, thus, liberated for oxygen transfer. This fact was discovered in 1926 in collaboration with Fritz Kubowitz. Photochemical dissociation of iron carbonyl compounds was discovered in 1891 by Mond and Langer, by exposing iron pentacarbonyl. This reaction is specific for carbonyl compounds of iron, most of which appear to dissociate in the presence of light, e.g., carbon-monoxide hemoglobin (John Haldane, 1897) carbon-monoxide hemochromogen (Anson and Mirsky, 1925), carbon-monoxide pyridine hemochromogen (H. A. Krebs, 1928), and carbon-monoxide ferrocysteine (W. Cremer, 1929).

When the photochemical dissociation of iron carbonyl compounds is measured quantitatively (we followed hereby Emil Warburg’s photochemical experiments), by using monochromatic light and comparing the amount of light energy absorbed with the amount of carbon monoxide set free, it is found that Einstein’s law of photochemical equivalence is very exactly fulfilled. The number of FeCO-groups set free is equal to the number of light quanta absorbed, and this is independent of the wavelength employed.

Photochemical dissociation of iron carbonyl compounds can be used to determine the absorption spectrum of a catalytic oxygen-transferring iron compound. One combines the catalyst in the dark with carbon monoxide, and so abolishes the oxygen-transferring power of the iron. If then this is exposed to monochromatic light of various wavelengths and of measured quantum intensity, and the effect of light W measured the increase in the rate of catalysis – it is found that the effects of the light are proportional to the quanta absorbed. The arrangement becomes very simple if the catalyst is present, as is usually the case, in infinitesimally low concentration in the exposed system. Then the thickness of the layers related to the amount of absorption of light can be considered to be infinitely thin, the number of quanta absorbed is proportional to the number of quanta supplied by irradiation.

In collaboration with Erwin Negelein, this principle was employed to measure the relative absorption spectrum of the oxygen-transferring respiratory ferment. The respiration of living cells was inhibited by carbon monoxide which was mixed with the oxygen. We then irradiated with monochromatic light of various wavelengths and of measured quantum intensity, and [measured] the increase of respiration together with the relative absorption spectrum. Only practically colorless cells are suitable for this type of experiment, [which requires] a layer infinitely thin with regard to light absorption.

Imagine living cells whose respiration is inhibited by carbon monoxide. If these are irradiated, respiration does not increase suddenly from the dark to the light-value, but there is a definite, although very short, interval until the combination of carbon monoxide with the ferment is broken down by the light. Even without calculation, it is obvious that the rate of increase in the effect of light must be related to the depth of colour of the ferment. If the ferment absorbs strongly, the -monoxide compound will be rapidly broken down, and vice versa.

The time of increase of the action of light can be measured. The time taken for a given intensity of light to cause dissociation of approximately half the carbon-monoxide compound of the ferment can be measured and, from this time, and from the effective intensity of light, the absolute absorption coefficient of the ferment for every wavelength can be calculated. The absorption capacity of the ferment, measured in accordance with this principle, was found to be of the same order as the power of light absorption of our strongest pigments. If one imagines a ferment solution of molar concentration, a layer of 2 x 10-6 cm thickness would weaken the blue mercury line 436 µµ up by half. The fact that the ferment in spite of this cannot be seen in the cells is due to its low concentration.

Monochromators and color filters were used to isolate the lines from these sources of light. If the absorption coefficient is entered as a function of the wavelength, the absorption spectrum of the carbon-monoxide compound of the ferment is obtained. The principal absorption-band or y-band lies in the blue.
This is the spectrum of a heme compound, according to the position of the bands, the intensity state of the bands, and the absolute magnitude of the absorption coefficients.

It appeared essential to have a control to ascertain whether heme as an oxidation catalyst of carbon monoxide and prussic acid really behaves like the ferment. If cysteine is dissolved in water containing pyridine, and a trace of heme is added, and this is shaken with air, the cysteine is catalytically oxidized by the oxygen-transferring power of the heme. According to Krebs, the catalysis is inhibited by carbon monoxide in the dark, but the inhibition ceases when the mixture is illuminated. Prussic acid too acts on this model on cellular respiration, inasmuch as it combines with the trivalent heme and inhibits its reduction. Just as in life, inhibition by carbon monoxide is dependent on the oxygen pressure, while inhibition by prussic acid is independent of the oxygen pressure.

In conjunction with Negelein, this model was also used to test the ferment experiments quantitatively. Heme catalysis in the model was inhibited by carbon monoxide in the dark. Then monochromatic light of known quantum intensity was used to irradiate it, and the absorption spectrum of the catalyst calculated from the effect of the light which was known from direct measurements on the pure substance. The calculation gave the absorption spectrum of the heme that had been added as a catalyst, and so the method was verified as a technique for the determination of the ferment spectrum, both the calculation and the measurement method.

The positions of the principal band and a-band of the ferment are:

Principal band            α-band

433 µµ                    590 µµ

These will be referred to as the “ferment bands” because the ferment was the first for which they were determined. Hemes are the complex iron compounds of the porphyrins, in which two valencies of the iron are bound to nitrogen. The porphyrins, of which Hans Fischer determined the chemical structure, are tetrapyrrole compounds in which the four pyrrole nuclei are held together by four interposed methane groups in the cr-position. Green, red, and mixed shades of hemes are known. If magnesium is replaced by iron in chlorophyll, green hemes are obtained. Their color is due to a strong band in the red which is already recognized in chlorophyll. The ferment does not absorb in the red and cannot, therefore, be a green heme. Red hemes are the usual hemes in blood pigment and in its related substances, such as mesoheme and deuteroheme. Coproheme is also a red heme which is an iron compound of the coproporphyrin that H. Fischer recognized in the body. Other red hemes are 20 µµ further from the red than the ferment bands. It follows that the ferment is not a red heme.

The pheoporphyrins are closely related to blood pigment but, as H. Fischer showed, pheoporphyrin a is simply mesoporphyrin in which the one propionic acid has been oxidized so that ring closure with the porphyrin nucleus is made possible. Pheoporphyrin a is a reduction product of chlorophyll a or an oxidation product of blood pigment, and connects together, in an amazingly simple manner, the principal pigments of the organic world the blood pigment and the leaf pigment.

Chlorophyll b has, in general, bands of longer wavelength than chlorophyll a, and for this reason,

  1. Christian and I applied Fischer’s reduction method to it. In this way we obtained pheoheme b, which, when linked with protein, corresponds with the ferment in respect to the position of the principal band. The principal band of the carbon-monoxide compound of pheohemoglobin b is 435 µµ.
  2. However, while the principal band of pheohemoglobin corresponds with the ferment bands within the permitted limits, the α-band shifts so far beyond them because it lies too near the red. It is, nevertheless, interesting that
  3. when ‘chlorophyll b is reduced, one obtains a pheoporphyrin of which the heme of all the pheohemes that have been demonstrated up to the present time is the most like the ferment.

 

Still nearer the ferment in its spectrum, is a heme occurring in Nature. This is

  • spirographis heme, which has been isolated from chlorocruorin, the blood pigment of the bristle-worm Spirographis,

in collaboration with Negelein and Haas, the bands of spirographis heme, coupled to globin, are :

  • carbon-monoxyhemoglobin of spirographis:  principle band, 434 µµ; α-band, 594 µµ.

Spirographis heme differs from the red hemes by the surplus or ketone oxygen-atom, and is classified as pheoheme. Like Fischer’s pheohemes, spirographis heme is intermediate between chlorophyll and blood pigment in respect of

  • the degree of oxidation of the side-chains.

The two hemes with a spectrum most like that of the ferment – pheoheme b and spirographis heme – possess a remarkable property. If they are dissolved in dilute sodium-hydroxide solution, in the form of ferrous compounds,

  • the absorption bands slowly wander towards the blue, near the bands of blood heme. In this way,
  • mixed-color hemes have been converted into red hemes.

On acidification, the change reverts: the <<blood bands>> disappear and

  • the ferment bands appear.

This experiment shows that

  1. oxidation of the side-chains does not suffice to give rise to the ferment bands, but
  2. some process of the type of anhydride formation must also occur.

The unique intermediate status of the ferment-like hemes demonstrated by these simple experiments suggests

  1. the suspicion that blood pigment and leaf pigments have both arisen from the ferment –
  2. blood pigments by reduction, and leaf pigment by oxidation.
  • For evidently, the ferment existed earlier than hemoglobin and chlorophyll.

The investigations on the oxygen-transferring ferment have been supported from the start by the Notgemeinschaft der deutschen Wissenschaft and the Rockefeller Foundation, without whose help they could not have been carried out. I have to thank both organizations here.

A L B E R T S Z E N T- GY Ö R G Y I      Nobel Lecture, December 11, 1937

Oxidation, energy transfer, and vitamins

A living cell requires energy not only for all its functions, but also

  • for the maintenance of its structure.
  • The source of this energy is the sun’s radiation.

Energy from the sun’s rays is trapped by green plants, and

  • converted into a bound form, invested in a chemical reaction.

When sunlight falls on green-plants, they liberate oxygen from carbon dioxide, and

  1. store up carbon, bound to the elements of water, as carbohydrate.

The radiant energy is now locked up in this carbohydrate molecule. This molecule is our food. When energy is required,

  • the carbohydrate is again combined with oxygen to form carbon dioxide, oxidized, and energy released.

Investigations during the last few decades have brought hydrogen instead of carbon, and instead of CO2 water, the mother of all life, into the foreground. It is becoming increasingly probable that

  1. radiant energy is used primarily to break water down into its elements,
  2. while CO2, serves only to fix the elusive hydrogen thus released.

While this concept of energy fixation was still being developed, the importance of hydrogen in the reversal of this process, whereby energy is liberated by oxidation, had already been confirmed by H. Wieland’s experiments.

Our body really only knowns one fuel, hydrogen. The foodstuff, carbohydrate, is essentially a packet of hydrogen, a hydrogen supplier, a hydrogen donor, and the main event during its combustion is

  • the splitting off of hydrogen.

So the combustion of hydrogen is

  • the real energy-supplying reaction;

To the elucidation of reaction (6), which seems so simple, I have devoted all my energy for the last fifteen years.

When I first ventured into this territory, the foundations had already been laid by the two pioneers H. Wieland and
O. Warburg, and Wieland’s teaching had been applied by Th. Thunberg to the realm of animal physiology.Wieland and Thunberg showed, with regard to foodstuffs, how

  1. the first step in oxidation is the “activation” of hydrogen, whereby
  2. the bonds linking it to the food molecule are loosened, and
  3. hydrogen prepared for splitting off.

But at the same time oxygen is also, as Warburg showed,

  • activated for the reaction by an enzyme.
  • the hydrogen-activating enzymes are called dehydrases or dehydrogenases.

Warburg called his oxygen-activating catalyst, “respiratory enzyme”.These concepts of Wieland and Warburg were apparently contradictory, and

  1. my first task was to show that the two processes are complementary to one another, and that
  2. in muscle cells activated oxygen oxidizes activated hydrogen.

This picture was enriched by the English worker D. Keilin, who showed that

  • activated oxygen does not oxidize activated hydrogen directly, but
  • that a dye, cytochrome, is interposed between them.

In keeping with this function, the “respiratory enzyme” is now also called “cytochrome oxidase”.

About ten years ago, when I tried to construct this system of respiration artificially and added together the respiratory enzyme with cytochrome and some foodstuff together with its dehydrogenase, I could justifiably expect that this system would use up oxygen and oxidize the food. But the system remained inactive. I found that

  • the dehydrogenation of certain donors is linked to the presence of a co-enzyme.

Analysis of this co-enzyme showed it to be a nucleotide, identical with v. Euler’s co-zymase, which H. v. Euler and R. Nilsson had already shown to accelerate the process of dehydration. As a result of Warburg’s investigations,this co-dehydrogenase has recently come very much into the foreground. Warburg showed that

  • it contains a pyridine base, and that it accepts hydrogen directly
    [pyridine nucleotide, triphosphopyridine nucleotide, TPN]

from food when the latter is dehydrogenated. It is therefore, the primary H-acceptor.

While working on the isolation of the co-enzyme with Banga, I found a remarkable dye, which showed clearly by its reversible oxidation that it, too, played a part in the respiration. We called this new dye cytoflav. Later Warburg showed that

  • this substance exercised its function in combination with a protein.

He called this protein complex of the dye, “yellow enzyme”. R. Kuhn, to whom we owe the structural analysis of the dye, called the dye lactoflavin and, with Györgyi and Wagner-Jauregg, showed it to be identical with vitamin B,.But the respiratory system stayed inactive even

  • after the addition of both these new components, codehydrogenase and yellow enzyme.

The C4-dicarboxylic acids and their activators which Thunberg discovered are

  • interposed between cytochrome and the activation of hydrogen as intermediate hydrogen-carriers.

In the case of carbohydrate, hydrogen from the food is first taken up by oxaloacetic acid, which

  • is reacted with the cytoplasmic malic dehydrogenase (and pyridine nucleotide –
    reduced DPN[H])
    , and thereby activated.

By taking up two hydrogen atoms, oxaloacetic acid is changed into malic acid.

  • OAA + NADH – (MDH) – malate + NAD+ + H+

This malic acid now passes on the H-atoms, and thus reverts to oxaloacetic acid,

  • which can again take up new H-atoms.

Malate + NAD+ + H+ — MDH – OAA + NADH

The H-atoms released by malic acid are taken up by fumaric acid, which is similarly

  • activated by the so-called succinic dehydrogenase.

The uptake of two H-atoms

  • converts the fumarate to succinate, to succinic acid.

The two H-atoms of succinic acid are then

  • oxidized away by the cytochrome.

Finally the cytochrome is oxidized by the respiratory enzyme, and

  • the respiratory enzyme by oxygen.

The function of the C4-dicarboxylic acids is not to be pictured as consisting of a certain amount of C4-dicarboxylic acid in the cell which is alternately oxidized and reduced. Fig. 2 corresponds more to the real situation. The protoplasmic surface, which is represented by the semi-circle, has single molecules of oxaloacetate and fumarate attached to it as prosthetic groups. These fused, activated dicarboxylic molecules then temporarily bind the hydrogen from the food. The co-dehydrogenases and the yellow enzymes also take part in this system. I have attempted to add them in at the right place.

This diagram, which will probably still undergo many more modifications, states that the “foodstuff” – H-donor – starts by

  1. passing its hydrogen, which has been activated by dehydrase, to the co-dehydrogenase.
  2. The coenzyme passes it to the oxaloacetic acid*.
  3. The malic acid then passes it on again to a co-enzyme,
  4. which passes the hydrogen to the yellow enzyme.
  5. The yellow enzyme passes the hydrogen to the fumarate.
  6. The succinate so produced is then oxidized by cytochrome,
  7. the cytochrome by respiratory enzyme,
  8. the respiratory enzyme by oxygen.

So the reaction 2H + O – H2O, which seems such a simple one,

  • breaks down into a long series of separate reactions.

With each new step, with each transfer between substances,

  • the hydrogen loses some of its energy,
  • finally combining with oxygen in its lowest-energy compound.

So each hydrogen atom is gradually oxidized in a long series of reactions, and

  • its energy released in stages.

This oxidation of hydrogen in stages seems to be one of the basic principles of biological oxidation. The reason for it is probably mainly that

  • the cell would not be able to harness and transfer to other processes
  • the large amount of energy which would be released by direct oxidation.

The cell needs small change if it is to be able to

  • pay for its functions without losing too much in the process.

So it oxidizes the H-atom by stages, converting the large banknote into small change.

About half of all plants – contain a polyphenol, generally a pyrocatechol derivative, together with an enzyme, polyphenoloxidase, which oxidizes polyphenol with the help of oxygen. The current interpretation of the mode of action of this oxidase was a confused one. I succeeded in showing that the situation was simply this, that

the oxidase oxidizes the polyphenol to quinone with oxygen.

  • In the intact plant the quinone is reduced back again
  • with hydrogen made available from the foodstuff.

Phenol therefore acts as a hydrogen-carrier between oxygen and the H-donor, and we are here again faced with a probably still imperfectly understood system for

  • the stepwise combustion of hydrogen.

——————————————————————————————————————————–

Vitamin C

If benzidine is added to a peroxide in the presence of peroxidase, a deep-blue color appears immediately, which is caused by the oxidation of the benzidine. This reaction does not occur without peroxidase. I simply used some juice which had been squeezed from these plants instead of a purified peroxidase, and added benzidine and peroxide, and the blue pigment appeared, after a small delay of about a second. Analysis of this delay showed that it was due to the presence of a powerful reducing substance, which reduced the oxidized benzidine again, until it had itself been used up. Thanks to the invitation from F. G. Hopkins and the help of the Rockefeller Foundation, I was able ten years ago to transfer my workshop to Cambridge, where for the first time I was able to pay more serious attention to chemistry. Soon I succeeded in isolating the substance in question from adrenals and various plants, and in showing that it corresponded to the formula C6H8O6 and was related to the carbohydrates. This last circumstance induced me to apply to Prof. W. N. Haworth, who immediately recognized the chemical interest of the substance and asked me for a larger quantity to permit analysis of its structure.

The Mayo Foundation and Prof. Kendall came to my help on a large scale, and made it possible for me to work, regardless of expense, on the material from large American slaughter-houses. The result of a year’s

work-was 25 g of a crystalline substance, which was given the name “hexuronic acid”. I shared this amount of the substance with Prof. Haworth. He undertook to investigate the exact structural formula of the substance. I used the other half of my preparation to gain a deeper understanding of the substance’s function. The substance could not replace the adrenals, but caused the disappearance of pigmentation in patients with Addison’s disease.

In 1930 I settled down in my own country at the University of Szeged. I also received a first-rate young American collaborator, J. L. Svirbely, who had experience in vitamin research, but besides this experience brought only the conviction that my hexuronic acid was not identical with vitamin C. In the autumn of 1931 our first experiments were completed, and showed unmistakably that hexuronic acid was power- fully anti-scorbutic, and that the anti-scorbutic acitvity of plant juices corresponded to their hexuronic acid content. We did not publish our results till the following year after repeating our experiments. At this time Tillmans was already directing attention to the connection between the reducing strength and the vitamin activity of plant juices. At the same time King and Waugh also reported crystals obtained from lemon juice, which were active anti-scorbutically and resembled our hexuronic acid.

My town, Szeged, is the centre of the Hungarian paprika industry. Since this fruit travels badly, I had not had the chance of trying it earlier. The sight of this healthy fruit inspired me one evening with a last hope, and that same night investigation revealed that this fruit represented an unbelievably rich source of hexuronic acid, which, with Haworth, I re-baptized ascorbic acid. I also had the privilege of providing my two prize-winning colleagues P. Karrer and W. N. Haworth with abundant material, and making its structural analysis possible for them. I myself produced with Varga the mono-acetone derivative of ascorbic acid, which forms magnificent crystals; from which, after repeated dissolving and recrystallization, ascorbic acid can be separated again with undiminished activity. This was the first proof that ascorbic acid was identical with vitamin C.
————————————————————————————————————————————-

Returning to the processes of oxidation, I now tried to analyse further the system of respiration in plants, in which ascorbic acid and peroxidase played an important part. I had already found in Rochester that the peroxidase plants contain an enzyme which reversibly oxidizes ascorbic acid with two valencies in the presence of oxygen. Further analysis showed that here again a system of respiration was in question, in which hydrogen was oxidized by stages. I would like, in the interests of brevity, to summarize the end result of these experiments, which I carried out with St. Huszák. Ascorbic acid oxidase oxidizes the acid with oxygen to reversible dehydroascorbic acid, whereby the oxygen unites with the two labile H-atoms from the acid to form hydrogen peroxide. This peroxide reacts with peroxidase and oxidizes a second molecule of ascorbic acid. Both these molecules of dehydro-ascorbic acid again take up hydrogen from the foodstuff, possibly by means of SH-groups. But peroxidase does not oxidize ascorbic acid directly. Another substance is interposed between the two, which belongs to the large group of yellow, water-soluble phenol-benzol-r-pyran plant dyes (flavone, flavonol, flavanone). Here the peroxidase oxidizes the phenol group to the quinone, which then oxidizes the ascorbic acid directly, taking up both its H-atoms.

At the time that I had just detected the rich vitamin content of the paprika, I was asked by a colleague of mine for pure vitamin C. This colleague himself suffered from a serious haemorrhagic diathesis. Since I still did not have enough of this crystalline substance at my disposal then, I sent him paprikas. My colleague was cured. But later we tried in vain to obtain the same therapeutic effect with pure vitamin C. Guided by my earlier studies into the peroxidase system, I investigated with my friend St. Rusznyák and his collaborators Armentano and Bentsáth the effect of the other link in the chain, the flavones. Certain members of this group of substances, the flavanone hesperidin (Fig. 5) and the formerly unknown eriodictyolglycoside, a mixture of which we had isolated from lemons and named citrin, now had the same therapeutic effect as paprika itself.

H U G O T H E O R E L L          Nobel Lecture, December 12, 1955

The nature and mode of action of oxidation enzymes

 

Practically all chemical reactions in living nature are started and directed in their course by enzymes. This being the case, Man has of course since time immemorial seen examples of what we now call enzymatic reactions, e.g. fermentation and decay. It would thus be possible to trace the history of enzymes back to the ancient Greeks, or still further for that matter. But it would be rather pointless, since to observe a phenomenon is not the same thing as to explain it. It is more correct to say that our knowledge of enzymes is essentially a product of twentieth-century research.

Jöns Jacob Berzelius, wrote in his yearbook in 1835: “…The catalytic force appears actually to consist thought herein that through their mere presence, and not through their affinity, bodies are able to arouse affinities which at this temperature are slumbering…”  Enzymes are the catalyzers of the biological world, and Berzelius’ description of catalytic force is surprisingly far-sighted…  if one could once understand the mechanism it would doubtless prove that the forces of ordinary chemistry would suffice to explain also these as yet mysterious reactions.

The year 1926 was a memorable one. The German chemist Richard Willstitter gave a lecture then in Deutsche Chemische Gesellschaft, in which he summarized the experiences gained in his attempts over many years to produce pure enzymes. Willstätter drew the conclusion that the enzymes did not belong to any known class of chemical substances, and that the effects of the enzymes derived from a new natural force, thus taking the view that 90 years earlier Berzelius thought to be improbable. That same year, through an irony of fate, the American researcher J. B. Sumner published a work in which he claimed to have crystallized in pure form an enzyme, urease. In the ensuing years J. H. Northrop and his collaborators crystallized out a further three enzyme preparations, pepsin, trypsin, and chymotrypsin, like urease, hydrolytic enzymes that split linkages by introducing water. If these discoveries had been undisputed from the outset it would probably not have been 20 years before Sumner, together with Northrop and Stanley, received a Nobel Prize.

When in 1933 I went on a Rockefeller fellowship to Otto Warburg’s institute in Berlin, Warburg and Christian had in the previous year produced a yellow-coloured preparation of an oxidation enzyme from yeast. The yellow colour was of particular interest: it faded away on reduction and returned on oxidation with e.g. oxygen, so that it was evident that the yellow pigment had to do with the actual enzymatic process of oxido-reduction. It was possible to free the yellow pigment from the high-molecular carrier substance, whose nature was still unknown, for example by treatment with acid methyl alcohol, whereupon the enzyme effect disappeared. Through simultaneous works by Warburg in Berlin, Kuhn in Heidelberg and Karrer in Zurich the constitution of the yellow pigment (lactoflavin, later riboflavin or vitamin B,) was determined. It was here for the first time possible to localize the enzymatic effect to a definite atomic constellation: hydrogen freed from the substrate (hexose monophosphate) is, with the aid of a special enzyme system (TPN-Zwischenferment) whose nature was elucidated somewhat later, placed on the nitrogen atoms of the flavin (1) and (10), giving rise to the colourless leucoflavin. This is reoxidized by oxygen, hydrogen peroxide being formed, and may afterwards be reduced again, and so forth. This cyclic process then continues until the entire amount of substrate has been deprived of two hydrogen atoms and been transformed into phosphogluconic acid; and a corresponding amount of hydrogen peroxide has been formed. At the end of the process the yellow enzyme is still there in unchanged form, and has thus apparently, as Berzelius expressed himself, aroused a chemical affinity through its mere presence.

The polysaccharides, which constituted 80-90% of the entire weight, were completely removed, together with some inactive colourless proteins. After fractionated precipitations with ammonium sulphate I produced a crystalline preparation which on ultracentrifuging and electrophoresis appeared homogeneous. The enzyme was a protein with the molecular weight 75,000 and strongly yellow-colored by the flavin part. The result of the Flavin analysis was 1 mol flavin per 1 mol protein. With dialysis against diluted hydrochloric acid at low temperature the yellow pigment was separated from the protein, which then became colorless. In the enzyme test the flavin part and the protein separately were inactive, but if the flavin part and the protein were mixed at approximately neutral reaction the enzyme effect returned, and the original effect came back when one mixed them in the molecular proportions 1 : 1. That in this connection a combination between the pigment and the protein came about was obvious, moreover, for other reasons: the green-yellow colour of the flavin part changed to pure yellow,and its strong. yellow fluorescence disappeared with linking to the protein.

In my electrophoretic experiments lactoflavin behaved as a neutral body, while the pigment part separated from the yellow enzyme moved rapidly towards the anode and was thus an acid. An analysis for phosphorus showed 1 P per mol flavin, and when after a time (1934) I succeeded in isolating the natural pigment component this proved to be a lactoflavin phosphoric acid ester, thus a kind of nucleotide, and it was obvious that the phosphoric acid served to link the pigment part to the protein. I will now show some simple experiments with the yellow enzyme, its colored part, which we now generally refer to as FMN (flavin mononucleotide), and the colorless enzyme protein.

  • The ferment-solution is pure yellow, the FMN-solution green-yellow,owing to the 1st that the light-absorption band in the blue of the free FMN is displaced somewhat in the long-wave direction on being linked with the protein component. A reducing agent (Na2S2O4) is now added to the one cuvette, it is indifferent which. The colour disappears in consequence of the formation of leucoflavin. Oxygen-gas is bubbled through the solution: the colour comes back as soon as the excess of reducing agent has been consumed. The experiment demonstrates the reaction cycle of the yellow enzyme: reduction through hydrogen from the substrate side, reoxidation with oxygen-gas.
  • A flask containing FMN-solution so diluted that its yellow color is not descernible to the eye is placed on a lamp giving long-wave ultraviolet light. The solution gives a strong, yellow fluorescence which disappears on reduction and returns on bubbling with oxygen-gas.
  • Two flasks are placed on the fluorescence lamp. The one contains a diluted solution of the free protein in phosphate buffer (pH 7), the other phosphate buffer alone. An equal amount of FMN-solution is dripped into each flask. In the flask with protein the fluorescence is at once extinguished,

but in the flask with buffer-solution alone it remains. The experiment demonstrates the resynthesis of  yellow enzyme, and since the fluorescence is extinguished by the protein, one may draw the conclusion that some group in the protein is in this connection linked to the imino-group NH(3) of the flavin, which according to Kuhn must be free for the fluorescence to appear.

The significance of these investigations on the yellow enzyme may be summarized

as follows.

  1. The reversible splitting of the yellow enzyme to apo-enzyme + coenzyme in the simple molecular relation 1 : 1 proved that we had here to do with a pure enzyme; the experiments would have been incomprehensible if the enzyme itself had been only an impurity.
  2. This enzyme was thus demonstrably a protein. In the sequel all the enzymes which have been isolated have proved to be proteins.
  3. The first coenzyme, FMN, was isolated and found to be a vitamin phosphoric acid ester. This has since proved to be something occurring widely in nature: the vitamins nicotinic acid amide, thiamine and pyridoxine form in an analogous way nucleotide-like coenzymes, which like the nucleic acids

themselves combine reversibly with proteins.

During the past 20 years a large number of flavoproteins with various enzyme effects have been produced. Instead of FMN many of them contain a dinucleotide, FAD, which consists of FMN + adenylic acid.

We constructed a very sensitive apparatus to record changes in the intensity of the fluorescence, and were thus able to follow the rapidity with which the fluorescence diminishes when FMN and protein are combined, or increases when they are split. Under suitable conditions the speed of combination is very high. Thanks to the great sensitivity of the fluorescent method my Norwegian collaborator Agnar Nygaard and I were able to make accurate determinations of the speed-constant simply by working in extremely diluted solutions, where the speed of combination is low because an FMN molecule so seldom happens to collide with a protein-molecule. We then varied the degree of acidity, ionic milieu and temperature, and we treated the protein with a large number of different reagents which affect in a known way different groups in proteins. In this way we succeeded with a rather high degree of certainty in ascertaining that phosphoric acid in FMN is linked to primary amino-groups in the protein, and the imino-group (3) in FMN to the phenolic hydroxyl group in a tyrosine residue, whereby the fluorescence is extinguished.

We still do not quite understand how through its linkage to the coenzyme the enzyme-protein “activates” the latter to a rapid absorption and giving off of hydrogen. But something we do know. The so-called oxido-reduction potential of the enzyme is in any case of great importance, and it is determined by a simple relation to the dissociation constants for the oxidized and for the reduced coenzyme-enzyme complex. The dissociation constants are in their turn functions of the velocity constants for the combination between coenzyme and enzyme and for the reverse process, and these velocity constants we have been able to determine both in the yellow ferment and in a number of enzyme systems. Without going into any details I may mention that the linkage of coenzyme to enzyme was found to have surprisingly big effects upon the potential of the former.

Alcohol dehydrogenase

 

Alcohol dehydrogenases occur in both the animal and the vegetable kingdoms, e.g. in liver, in yeast, and in peas. They are colourless proteins which together with DPN may either oxidize alcohol to aldehyde, as occurs chiefly in the liver, or conversely reduce aldehyde to alcohol, as occurs in yeast.

The yeast enzyme was crystallized by Negelein & Wulf (1936) in Warburg’s institute, the liver enzyme (from horse liver) by Bonnichsen & Wassén at our institute in Stockholm in 1948. These two enzymes have come to play a certain general rôle in biochemistry on account of the fact that it has been possible to investigate their kinetics more accurately than is the case with other enzyme systems. The liver enzyme especially, we have on repeated occasions studied with particular thoroughness, since especially favourable experimental conditions here presented themselves. For all reactions with DPN-system it is possible to follow the reaction DPN+ + 2H =+ DPNH + H+ spectrophotometrically, since DPNH has an absorption-band in the more long-wave ultraviolet region, at 340 rnp, and thousands of such experiments have been performed all over the world. A couple of years ago, moreover, we began to apply our fluorescence method, which is based on the fact that DPNH but not DPN fluoresces, even if considerably more weakly than the flavins. Asregards the liver enzyme there is a further effect, which proved extremely useful for certain spectrophotometrical determinations of reaction speeds; together with Bonnichsen I found in 1950 that the 340 rnp band of the reduced coenzyme was displaced, on combination with liver alcohol dehydrogenase, to 325 rnp, and together with Britton Chance we were thus able with the help of his extremely refined rapid spectrophotometric methods to determine the velocity constant for this very rapid reaction. This reaction belongs to the 3 bost problem involving the enzyme, the coenzyme, and the substrate, and both the coenzyme and the substrate occur in both oxidized and reduced forms.

It is a curious whim of nature that the same coenzyme which in the yeast makes alcohol by attaching hydrogen to aldehyde also occurs in the liver to remove from alcohol the same hydrogen, so that the alcohol becomes aldehyde again, which is then oxidized further
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Heme proteins

In 1936 we had obtained cytochrome approximately 80% pure, and in 1939 close to 100%.It is a beautiful red, iron-porphyrin-containing protein which functions as a link in the chain of the cell-respiration enzymes, the iron atom now taking up and now giving off an electron, and the iron thus alternating valency between the 3-valent ferri and the 2-valent ferro stages. It is a very pleasant substance to work with, not merely because it is lovely to look at, but also because it is uncommonly stable and durable. From 100 kg horse heart one can produce 3-4 grams of pure cytochrome c. The molecule weighs about 12,000 and contains one mol iron porphyrin per-mol.

Exp. 4. Two cuvettes each contain a solution of ferricytochrome c. The colour is blood-red. To the one are added some grains of sodium hydrosulphite: the color is changed to violet-red (ferrocytochrome). Oxygen is now bubbled through the ferrocytochrome-solution: no visible change occurs. The ferrocyto-chrome can thus not be oxidized by oxygen. A small amount of cytochrome oxidase is now added: the ferricytochrome color returns.

From this experiment we can draw the conclusion that reduced cytochrome c cannot react with molecular oxygen. In a chain of oxidation enzymes it will thus not be able to be next to the oxygen. The incapacity of cytochrome to react with oxygen was a striking fact that required an explanation. Another peculiarity was the extremely firm linkage between the red heme pigment and the protein part; in contradistinction to the majority of other heme protides, the pigment cannot be split off by the addition of acetone acidified with hydrochloric acid. Further, there was a displacement of the light-absorption bands which indicated that the two unsaturated vinyl groups occurring in ordinary protohemin were saturated in the hematin of

the cytochrome. In 1938 we succeeded in showing that the porphyrin part of the cytochrome was linked to the protein by means of two sulphur bridges from cysteine residues in the protein of the porphyrin in such a way that the vinyl groups were saturated and were converted to α-thioether groups. The firmness of the linkage and the displacement of the spectral bands were herewith explained. This was the first time that it had been possible to show the nature of chemical linkages between a “prosthetic” group (in this case iron porphyrin) and the protein part in an enzyme.

The light-absorption bands of the cytochrome showed that it is a so-called hemochromogen, which means that two as a rule nitrogen-containing groups are linked to the iron, in addition to the four pyrrol-nitrogen atoms in the porphyrin. From magnetic measurements that I made at Linus Pauling’s institute in Pasadena and from amino-acid analyses, titration curves and spectrophotometry together with Å. Åkeson it emerged (1941) that the nitrogen-containing, hemochromogen-forming groups in cytochrome c were histidine residues, or to be more specific, their imidazole groups.   Recently we have got a bit farther. Tuppy & Bodo in Vienna began last year with Sanger’s method to elucidate the amino-acid sequence in the hemin-containing peptide fragment that one obtains with the proteolytic breaking down of cytochrome c, and succeeded in determining the sequence of the amino acids nearest the heme. The experiments were continued and supplemented by Tuppy, Paléus & Ehrenberg at our institute in Stockholm with the following result:

The peptide chain 1-12 (“Val”) = the amino acid valine, “Glu” = glutamine,”Lys” = lysine, and so forth) is by means of two cysteine-S-bridges and a linkage histidine-Fe linked to the heme. When in 1954 Linus Pauling delivered his Nobel Lecture in Stockholm he showed a new kind of models for the study of the steric configuration of peptide chains, which as we know may form helices or “pleated sheets” of various kinds. It struck me then that it would be extremely interesting to study the question as to which of these possibilities might be compatible with the sulphur bridges to the hemin part and with the linkage of nitrogen containing groups to the iron. Pauling was kind enough to make me a present of his peptide-model pieces, which I shall show presently. This is thus the second time they figure in a Lecture.

Anders Ehrenberg and I now made a hemin model on the same scale as the peptide pieces and constructed models of hemin peptides with every conceivable variant of hydrogen bonding. It proved that many variants could be definitely excluded on steric grounds, and others were improbable for other reasons. Of the original, at least 20 alternatives, finally only one remained – a left-twisting a-helix with the cysteine residue no. 4 linked to the porphyrin side-chain in 4-position, and cysteine no. 7 to the side-chain in 2-position. The imidazole residue fitted exactly to linkage with the iron atom. The peptide spiral becomes parallel with the plane of the heme disc.

Through calculations on the basis of the known partial specific volume of the cytochrome we now consider it extremely probable that the heme plate in cytochrome c is surrounded by peptide spirals on all sides in such a way that the heme iron is entirely screened off from contact with oxygen; here is the explanation of our experiment in which we were unable to oxidize reduced cytochrome c with oxygen-gas. The oxygen simply cannot get at the iron atom. There is, on the other hand, a possibility for electrons to pass in and out in the iron atom via the imidazole groups.  It strikes us as interesting that even at this stage the special mode of reacting of the cytochrome is beginning to be understood from what we know of its chemical constitution.

F r i t z  L i p m a n n           Nobel Lecture, December 11, 1953

Development of the acetylation problem: a personal account

 

In my development, the recognition of facts and the rationalization of these facts into a unified picture, have interplayed continuously. After my apprenticeship with Otto Meyerhof, a first interest on my own became the phenomenon we call the Pasteur effect, this peculiar depression of the wasteful fermentation in the respiring cell. By looking for a chemical explanation of this economy measure on the cellular level, I was prompted into a study of the mechanism of pyruvic acid oxidation, since it is at the pyruvic stage where respiration branches off from fermentation. For this study I chose as a promising system a relatively simple looking pyruvic acid oxidation enzyme in a certain strain of Lactobacillus delbrueckii1. The decision to explore this particular reaction started me on a rather continuous journey into partly virgin territory to meet with some unexpected discoveries, but also to encounter quite a few nagging disappointments

The most important event during this whole period, I now feel, was the accidental observation that in the L. delbrueckii system, pyruvic acid oxidation was completely dependent on the presence of inorganic phosphate. This observation was made in the course of attempts to replace oxygen by methylene blue. To measure the methylene blue reduction manometrically, I had to switch to a bicarbonate buffer instead of the otherwise routinely used phosphate. In bicarbonate, to my surprise, as shown in Fig. 1, pyruvate oxidation was very slow, but the addition of a little phosphate caused a  remarkable increase in rate. The next figure, Fig. 2, shows the phosphate effect more drastically, using a preparation from which all phosphate was removed by washing with acetate buffer. Then it appeared that the reaction was really fully dependent on phosphate. In spite of such a phosphate dependence, the phosphate balance measured by the ordinary Fiske-Subbarow procedure did not at first indicate any phosphorylative step. Nevertheless, the suspicion remained that phosphate in some manner was entering into the reaction and that a phosphorylated intermediary was formed. As a first approximation, a coupling of this pyruvate

oxidation with adenylic acid phosphorylation was attempted. And, indeed, addition of adenylic acid to the pyruvic oxidation system brought out a net disappearance of inorganic phosphate, accounted for as adenosine triphosphate (Table 11). In parallel with the then just developing fermentation now concluded that the missing link in the reaction chain was acetyl phosphate. In partial confirmation it was shown that a crude preparation of acetyl phosphate, synthesized by the old method of Kämmerer and Carius2

would transfer phosphate to adenylic acid (Table 2). However, it still took quite some time from then on to identify acetyl phosphate definitely as the initial product of the pyruvic oxidation in this system3,4.

At the time when these observations were made, about a dozen years ago, there was, to say the least, a tendency to believe that phosphorylation was rather specifically coupled with the glycolytic reaction. Here, however, we had found a coupling of phosphorylation with a respiratory system. This observation immediately suggested a rather sweeping biochemical significance, of transformations of electron transfer potential, respiratory or fermentative, to phosphate bond energy and therefrom to a wide range of biosynthetic reactions7.

There was a further unusual feature in this pyruvate oxidation system in that the product emerging from the process not only carried an energy-rich phosphoryl radical such as already known, but the acetyl phosphate was even more impressive through its energy-rich acetyl. It rather naturally became a contender for the role of “active” acetate, for the widespread existence of which the isotope experience had already furnished extensive evidence. I became, therefore, quite attracted by the possibility that acetyl phosphate could serve two rather different purposes, either to transfer its phosphoryl group into the phosphate pool, or to supply its active acetyl for biosynthesis of carbon structures. Thus acetyl phosphate should be able to serve as acetyl donor as well as phosphoryl donor, transferring, as shown in Fig. 3, on either side of the oxygen center, such as indicated by Bentley’s early experiments on cleavage7a of acetyl phosphate in H2 18O.

These two novel aspects of the energy problem, namely

(1) the emergence of an energy-rich phosphate bond from a purely respiratory reaction; and

(2) the presumed derivation of a metabolic building-block through this same there towards a general concept of transfer of activated groupings by carrier as the fundamental reaction in biosynthesis8,9.

Although in the related manner the appearance of acetyl phosphate as a metabolic intermediary first

focussed attention to possible mechanisms for the metabolic elaboration of group activation, it soon turned out that the relationship between acetyl phosphate and acetyl transfer was much more complicated than anticipated. reaction, prompted me to propose

  • not only the generalization of the phosphate bond as a versatile energy distributing system,
  • but also to aim there towards a general concept of transfer of activated groupings by carrier as the fundamental reaction in biosynthesis8,9.

Although in the related manner the appearance of acetyl phosphate as a metabolic intermediary first focussed attention to possible mechanisms for the metabolic elaboration of group activation, it soon turned out that the relationship between acetyl phosphate and acetyl transfer was much more complicated than anticipated.

It appeared that as an energy source the particle bound oxidative phosphorylation of the kind observed first by Herman Kalckar14 could be replaced by ATP, as had first been observed with the acetylation of choline in brain preparations by Nachmansohn and his group15,16. Using ATP and acetate as precursors, it was possible to set up a homogeneous particle-free acetylation system obtained by extraction of acetone pigeon liver. In this extract acetyl phosphate was unable to replace the ATP acetate as acetyl precursor.

In spite of this disappointment with acetyl phosphate, our decision to turn to a study of acetylation started then to be rewarding in another way. During these studies we became aware of the participation of a heat-stable factor which disappeared from our enzyme extracts on aging or dialysis. This cofactor was present in boiled extracts of all organs, as well as in microorganisms and yeast. It could not be replaced by any other known cofactor. Therefore, it was suspected that we were dealing with a new coenzyme. From then on, for a number of years, the isolation and identification of this coenzyme became the prominent task of our laboratory. The problem now increased in volume and I had the very good fortune that a group of exceedingly able people were attracted to the laboratory; first Constance Tuttle, then Nathan O. Kaplan and shortly afterwards, G. David Novelli, and then others.

Early data on the replacement of this heat-stable factor by boiled extracts are shown in the next table (Table 3). The pigeon liver acetylation system proved to be a very convenient assay system for the new coenzyme17 since on aging for 4 hours at room temperature, the cofactor was completely autolyzed.

Fortunately, on the other hand, the enzyme responsible for the decomposition of this factor was quite unstable and faded out during the aging, while the acetylation apoenzymes were unaffected.

The next figure, Fig. 4, shows coenzyme A (CoA) assay curves obtained with acetone pigeon liver extract. Finding pig liver a good source for the coenzyme, we set out to collect a reasonably large quantity of a highly purified preparation and then to concentrate on the chemistry with this material. In this analysis we paid particular attention to the possibility of finding in this obviously novel cofactor one of the vitamins.

The subsequence finding of a B-vitamin in the preparation gave us further confidence that we were dealing here with a key substance. We still felt, however, slightly dissatisfied with the proof for pantothenic acid. Therefore, to liberate the chemically rather unstable pantothenic acid from CoA, we made use of observations on enzymatic cleavage of the coenzyme. Two enzyme preparations, intestinal phosphatase and an enzyme in pigeon liver extract, had caused independent inactivation. It then was found that through combined action of these two enzymes, pantothenic acid was liberated18,19.

The two independent enzymatic cleavages indicated early that in CoA existed two independent sites of attachment to the pantothenic acid molecule. One of these obviously was a phosphate link, linking presumably to one of a hydroxyl group in pantothenic acid. The other moiety attached to pantothenic acid, which, cleaved off by liver enzyme, remained unidentified for a long time. In addition to pantothenic acid, our sample of 40 per cent purity had been found to contain about 2 per cent sulfur by elementary analysis and identified by cyanide-nitroprusside test as a potential SH grouping 20,21. Furthermore, the coenzyme preparation contained large amounts of adenylic acid21.

Units Coenzyme

Fig. 4. Concentration-activity curves for coenzyme A preparations of different purity. The arrow indicates the point of 1 unit on the curve. (o) crude coenzyme, 0.25 unit per mg; (x) purified coenzyme, 130 units per mg.

In the subsequent elaboration of the structure, the indications by enzyme analysis for the two sites of attachment to pantothenic acid have been most helpful. The phosphate link was soon identified as a pyrophosphate bridge22; 5-adenylic acid was identified by Novelli23 as enzymatic split product and by Baddiley 24, through chemical cleavage. At the same time, Novelli made observations which indicated the presence of a third phosphate in addition to the pyrophosphate bridge. These indications were confirmed by analysis of a nearly pure preparation which was obtained by Gregoryas from Streptomyces fradiae in collaboration with the research group at the Upjohn Company26.

It was at this period that we started to pay more and more attention to the sulfur in the coenzyme. As shown in Table 5, our purest preparation contained 4.13 per cent sulfur corresponding to one mole per mole of pantothenate. We also found26 that dephosphorylation of CoA yielded a compound containing pantothenic acid and the sulfur carrying moiety, which we suspected as bound through the carboxyl. Through the work of Snell and his group27, the sulfur-containing moiety proved to be attached to pantothenic acid through a link broken by our liver enzyme. It was identified as thioethanolamine by Snell and his group, linked peptidically to pantothenic acid.

Through analysis and synthesis, Baddiley now identified the point of attachment of the phosphate bridge to pantothenic acid in 4-position24 and Novelli et al.28 completed the structure analysis by enzymatic synthesis of “dephospho-CoA” from pantetheine-4’-phosphates and ATP. Furthermore, the attachment of the third phosphate was identified by Kaplan29 to attach in s-position on the ribose of the 5-adenylic acid (while in triphosphopyridine nucleotide it happens to be in 2-position). Therefore, the structure was now

established, as shown in Fig. 5.

Fig. 5. Structure of coenzyme A

 

The metabolic function of CoA


Parallel with this slow but steady elaboration of the structure, all the time we explored intensively metabolic mechanisms in the acetylation field. By use of the enzymatic assay, as shown in Tables 6, 7, 8, and 9, CoA was found present in all living cells, animals, plants and microorganisms17. Furthermore,

the finding that all cellular pantothenic acid could be accounted for by CoA17 made it clear that CoA represented the only functional form of this vitamin. The finding of the vitamin furnished great impetus; nevertheless, a temptation to connect the pantothenic acid with the acetyl transfer function has

blinded us for a long time to other possibilities.

The first attempts to further explore the function of CoA were made with pantothenic acid-deficient cells and tissues. A deficiency of pyruvate oxidation in pantothenic acid-deficient Proteus morganii, an early isolated observation by Dorfman30 and Hills31, now fitted rather well into the picture. We soon became quite interested in this effect, taking it as an indication for participation of CoA in citric acid synthesis. A parallel between CoA levels and pyruvate oxidation in Proteus morganii was demonstrated32. Using panto thenic aciddeficient yeast, Novelli et al.33 demonstrated a CoA-dependence of acetate oxidation (Fig. 5a) and Olson and Kaplan34 found with duck liver a striking parallel between CoA content and pyruvic utilization, which is shown in Fig. 6.

But more important information was being gathered on -the enzymatic level. The first example of a generality of function was obtained by comparing the activation of apoenzymes for choline- and sulfonamide-acetylation respectively, using our highly purified preparations9 of CoA. As shown in Fig. 7, similar activation curves obtained for the two respective enzymes. Through these experiments, the heat-stable factor for choline acetylation that had been found by Nachmansohn and Berman35 and by Feldberg and Mann36 was identified with CoA. The next most significant step toward a generalization of CoA function for acetyl transfer was made by demonstrating its functioning in the enzymatic synthesis of acetoacetate. The CoA effect in acetoacetate synthesis was studied by Morris Soodak37, who obtained for this reaction a reactivation curve quite similar to those for enzymatic acetylation, as shown in Fig. 8.

Soon afterwards Stern and Ochoa38 showed a CoA-dependent citrate synthesis with a pigeon liver fraction similar to the one used by Soodak for acetoacetate synthesis. In our laboratory, Novelli et al. confirmed and extended this observation with extracts of Escherichia coli39.

In the course of this work, which more and more clearly defined the acetyl transfer function of CoA, Novelli once more tried acetyl phosphate. To our surprise and satisfaction, it then appeared, as shown in Table 9, that in Escherichia coli extracts in contrast to the animal tissue, acetyl phosphate was more than twice as active as acetyl donor for citrate synthesis than ATP acetate 39. Acetyl phosphate, therefore, functioned as a patent microbial acetyl donor. Acetyl transfer from acetyl phosphate, like that from ATP-acetate, was CoA-dependent, as shown in Table 9. Furthermore, a small amount of “microbial conversion factor”, as we called it first, primed acetyl phosphate for activity with pigeon liver acetylation systems40, as shown in Table 10.

Eventually the microbial conversion factor was identified by Stadtman et al.40 with the transacetylase first encountered by Stadtman and Barker in extracts of Clostridium kluyveri41 and likewise, although not clearly defined as such, in extracts of Escherichia coli and Clostridium butylicum by Lipmann and Tuttle42. The definition of such a function was based on the work of Doudoroff et al.43 on transglucosidation with sucrose phosphorylase. Their imaginative use of isotope exchange for closer definition of enzyme mechanisms has been most influential. Like glucose-I-phosphate with sucrose phosphorylase, acetyl phosphate with these various microbial preparations equilibrates its phosphate rapidly with the inorganic phosphate of the solution. As in Doudoroff et al. experiments, first a covalent substrate enzyme derivative had been proposed 43. However, then Stadtman et al.40, with the new experience of CoA dependent acetyl transfer, could implicate CoA in this equilibration between acetyl- and inorganic phosphate and thus could define the transacetylase as an enzyme equilibrating acetyl between phosphate and CoA:

In the course of these various observations, it became quite clear that there existed in cellular metabolism an acetyl distribution system centering around CoA as the acetyl carrier which was rather similar to the ATP-centered phosphoryl distribution system. The general pattern of group transfer became recognizable, with donor and acceptor enzymes being connected through the CoA —- acetyl CoA shuttle. A clearer definition of the donor-acceptor enzyme scheme was obtained through acetone fractionation of our standard system for acetylation of sulfonamide into two separate enzyme fractions, which were inactive separately but showed the acetylation effect when combined. A fraction, A-40, separating out with 40 per cent acetone, was shown by Chou44 to contain the donor enzyme responsible for the ATP-CoA-acetate reaction, while with more acetone precipitated, the acceptor function, A-60, the acetoarylamine kinase as we propose to call this type of enzyme. The need for a combination of the two for overall acetyl transfer is shown in Fig. 9. This showed that a separate system was responsible for acetyl CoA formation through interaction of ATP, CoA and acetate (cf. below) and that the overall acetylation was a two-step reaction:

These observations crystallized into the definition of a metabolic acetyl transfer territory as pictured in Fig. 10. This picture had developed from the growing understanding of enzymatic interplay involving metabolic generation of acyl CoA and transfer of the active acyl to various acceptor systems. A most important, then still missing link in the picture was supplied through the brilliant work of Feodor Lynen45 who chemically identified acetyl CoA as the thioester of CoA. Therewith the thioester link was introduced as a new energy-rich bond and this discovery added a very novel facet to our understanding of the mechanisms of metabolic energy transformation.

Enzyme Localization In The  Anaerobic Mitochondria Of Ascaris L Umbricoides

 

Robert S. Rew And Howard J. Saz

From the Department of Biology, University of Notre Dame, Notre Dame, Indiana 46556

 

Mitochondria from the muscle of the parasitic nematode Ascaris lumbricoides   var. suum function anaerobically in electron transport-associated  phosphorylations under physiological conditions. These helminth organelles have been fractionated into inner and outer membrane, matrix, and intermembrane space fractions. The distributions of enzyme systems were determined and compared with corresponding distributions reported in mammalian mitochondria.  Succinate and pyruvate dehydrogenases as well as NADH  oxidase, Mg++-dependent ATPase, adenylate kinase, citrate synthase, and cytochrome c  reductases  were  determined to be distributed  as  in mammalian mitochondria.  In contrast  with  the  mammalian systems, fumarase and NAD-linked “malic” enzyme were isolated primarily from the intermembrane  space fraction of the worm mitochondria. These enzymes required for the anaerobic  energy-generating system in Ascaris and would be expected to give rise to NADH in the intermembrane space.  The need for and possible mechanism of a proton translocation system to obtain energy generation is suggested.                                Downloaded from jcb.rupress.org

                                                                                                                                                      

                          

                               

                               

                               

                               

David Keilin’s Respiratory Chain Concept and its Chemiosmotic Consequences

Peter Mitchell              Nobel Lecture, 8 December, 1978

Glynn Research Institute, Bodmin, Cornwall, U. K.
“for his contribution to the understanding of biological energy transfer through the formulation of the chemiosmotic theory”

Peter D. Mitchell (1920-1992) received the Nobel Prize in 1978 for developing the Chemiosmotic Theory to explain ATP synthesis resulting from membrane-associated electron transport [Ubiquinone and the Proton Pump].

Mitchell is the last of the gentleman scientists. He first proposed the chemiosmotic principle in a 1961 Nature article while he was at the University of Edinburgh. Shortly after that, ill health forced him to move to Cornwall where he renovated an old manor house and converted it into a research laboratory. From then on, he and his research colleague, Jennifer Moyle, continued to work on the chemiosmotic theory while being funded by his private research foundation. [Peter Mitchell: Wikipedia]

The Chemiosmotic Theory was controversial in 1978 and it still has not been fully integrated into some biochemistry textbooks in spite of the fact that it is now proven. The main reason for the resistance is that it overthrows much of traditional biochemistry and introduces a new way of thinking. It is a good example of a “paradigm shift” in biology.

Because he was such a private, and eccentric, scientist there are very few photos of Peter Mitchell or his research laboratory at Glynn House . The best description of him is in his biography Wandering in the Gardens of the Mind: Peter Mitchell and the Making of Glynn by John Prebble, and Bruce Weber. A Nature review by E.C. Slater [Metabolic Gardening] gives some of the flavor and mentions some of the controversy.

Wandering_in_the_Gardens_of_the_Mind_Peter_Mitchell

Wandering_in_the_Gardens_of_the_Mind_Peter_Mitchell

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Many scientists believe that the Chemiosmotic Theory was the second greatest contribution to biology in the 2oth century (after the discovery of the structure of DNA). Mitchell had to overcome many critics including Hans Krebs. The case is strong.

In the 1960s, ATP was known to be the energy currency of life, but the mechanism by which ATP was created in the mitochondria was assumed to be by substrate-level phosphorylation. Mitchell’s chemiosmotic hypothesis was the basis for understanding the actual process of oxidative phosphorylation. At the time, the biochemical mechanism of ATP synthesis by oxidative phosphorylation was unknown.

Mitchell realised that the movement of ions across an electrochemical potential difference could provide the energy needed to produce ATP. His hypothesis was derived from information that was well known in the 1960s. He knew that living cells had a membrane potential; interior negative to the environment. The movement of charged ions across a membrane is thus affected by the electrical forces (the attraction of positive to negative charges). Their movement is also affected by thermodynamic forces, the tendency of substances to diffuse from regions of higher concentration. He went on to show that ATP synthesis was coupled to this electrochemical gradient.[11]

His hypothesis was confirmed by the discovery of ATP synthase, a membrane-bound protein that uses the potential energy of the electrochemical gradient to make ATP.

Growth, development and metabolism are some of the central phenomena in the study of biological organisms. The role of energy is fundamental to such biological processes. The ability to harness energy from a variety of metabolic pathways is a property of all living organisms. Life is dependent on energy transformations; living organisms survive because of exchange of energy within and without.

In a living organism, chemical bonds are broken and made as part of the exchange and transformation of energy. Energy is available for work (such as mechanical work) or for other processes (such as chemical synthesis and anabolic processes in growth), when weak bonds are broken and stronger bonds are made. The production of stronger bonds allows release of usable energy.

One of the major triumphs of bioenergetics is Peter D. Mitchell‘s chemiosmotic theory of how protons in aqueous solution function in the production of ATP in cell organelles such as mitochondria.[5] This work earned Mitchell the 1978 Nobel Prize for Chemistry. Other cellular sources of ATP such as glycolysis were understood first, but such processes for direct coupling of enzyme activity to ATP production are not the major source of useful chemical energy in most cells. Chemiosmotic coupling is the major energy producing process in most cells, being utilized in chloroplasts and several single celled organisms in addition to mitochondria.

Cotransport

In August 1960, Robert K. Crane presented for the first time his discovery of the sodium-glucose cotransport as the mechanism for intestinal glucose absorption.[2] Crane’s discovery of cotransport was the first ever proposal of flux coupling in biology and was the most important event concerning carbohydrate absorption in the 20th century.[3][4]

The free energy (ΔG) gained or lost in a reaction can be calculated: ΔG = ΔH – TΔS
where G = Gibbs free energy, H = enthalpy, T = temperature, and S = entropy.

How inositol pyrophosphates control cellular phosphate homeostasis?

Adolfo Saiardi*

Cell Biology Unit, Medical Research Council Laboratory for Molecular Cell Biology, Department of Cell and Developmental Biology,

University College London, Gower Street, London WC1E 6BT, United Kingdom

Advances in Biological Regulation 52 (2012) 351–359

Phosphorus in his phosphate PO43_ configuration is an essential constituent of all life forms. Phosphate diesters are at the core of nucleic acid structure, while phosphate monoester transmits information under the control of protein kinases and phosphatases. Due to these fundamental roles in biology it is not a surprise that phosphate cellular homeostasis is under tight control.

Inositol pyrophosphates are organic molecules with the highest proportion of phosphate groups, and they are capable of regulating many biological processes, possibly by controlling energetic metabolism and adenosine triphosphate (ATP) production.

Furthermore, inositol pyrophosphates influence inorganic polyphosphates (polyP) synthesis. The polymer polyP is solely constituted by phosphate groups and beside other known functions, it also plays a role in buffering cellular free phosphate [Pi] levels, an event that is ultimately necessary to generate ATP and inositol pyrophosphate.

Two distinct classes of proteins the inositol hexakisphosphates kinases (IP6Ks) and the diphosphoinositol pentakisphosphate kinases (PP-IP5Ks or IP7Ks) are capable of synthesizing inositol pyrophosphates.

IP6Ks utilize ATP as a phosphate donor to phosphorylate IP6 to IP7, generation the isomer 5PP-IP5 (Fig. 1A), and inositol pentakisphosphate I(1,3,4,5,6)P5 to PP-IP4 (Saiardi et al., 1999, 2000; Losito et al., 2009). Furthermore, at least in vitro, IP6Ks generate more complex molecules containing two or more pyrophosphate moieties, or even three-phosphate species (Draskovic et al., 2008; Saiardi et al., 2001). Three IP6K isoforms referred to as IP6K1, 2, 3 exist in mammal; however, there is a single IP6K in the yeast Saccharomyces cerevisiae called Kcs1.

The PP-IP5Ks enzymes, synthesize inositol pyrophosphate from IP6, but not from IP5, (Losito et al., 2009) generating the isomer 1PP-IP5. Kinetic studies performed in vitro suggested that IP7, the 5PP-IP5 isomer generated by IP6Ks, is the primary substrate of this new enzyme, and this finding was confirmed in vivo by analysing PP-IP5K null yeast (vip1D) that accumulate the un-metabolized substrate IP7 (Azevedo et al., 2009; Onnebo and Saiardi, 2009). Thus PP-IP5K is responsible for IP8,

isomer 1,5PP2-IP4 synthesis (Fig. 1A). Two PP-IP5K isoforms referred to as PP-IP5Ka and b exist in mammal while a single PP-IP5K called Vip1 is present in S. cerevisiae.

Inositol pyrophosphates are hydrolysed by the diphosphoinositol-polyphosphate phosphohydrolases (DIPPs) (Safrany et al., 1998). Four mammalian enzymes DIPP1,2,3,4 have been identified, while only one DIPP protein exists in S. cerevisiae called Ddp1. These phosphatases are promiscuous enzymes able to hydrolyse inositol pyrophosphate as well as nucleotide analogues, such as diadenosine hexaphosphate (Ap6A) (Caffrey et al., 2000; Fisher et al., 2002). More recently, it has been shown that DIPPs also degrade polyP (Lonetti et al., 2011). Inositol pyrophosphates control the most disparate biological processes, from telomere length to vesicular trafficking. It is conceivable that all these function can be focused on the fact that inositol pyrophosphates are controlling cellular energy metabolism and consequently, ATP production. We have recently, demonstrated that inositol pyrophosphates control glycolysis and mitochondrial oxidative phosphorylation by both inhibiting the glycolytic flux and increasing mitochondrial activity (Szijgyarto et al., 2011).

Another important molecule to briefly introduce is polyP (Fig. 1B). The interested reader is encouraged to read the following comprehensive reviews (Kornberg et al., 1999; Rao et al., 2009). The polyP polymer likely represents a phosphate buffer that is synthesized and degraded in function of the phosphate needs of the cells. Furthermore, it also functions as a chelator of metal ions, thereby regulating cellular cation homeostasis. However, polyP also possesses more classical signalling roles.

In bacteria for example, it influences pathogenicity (Brown and Kornberg, 2008) and in mammalian cells it has been proposed to regulate fibrinolysis and platelet aggregation (Caen and Wu, 2010). In prokaryotes, polyP synthesis is carried out by a family of conserved polyP kinases (PPKs), whereas degradation is mediated by several polyP phosphatases (Rao et al., 2009). In higher eukaryotes polyP synthesis remains poorly characterized.

In humans alteration of phosphate metabolism is implicated in several pathological states. Higher serum phosphate leads to vascular calcification and cardiovascular complications. Although only very small amount of phosphate circulates in the serum, its concentration is tightly regulated and it is independent from dietary phosphorus intake (de Boer et al., 2009). Therefore, it is not surprising that intense research efforts are aimed to elucidate phosphate uptake and metabolism. IP6K2 was initially cloned while searching for a novel mammalian intestinal phosphate transporter that the group of Murer identified as PiUS (Phosphate inorganic Uptake Stimulator) (Norbis et al., 1997). Once transfected into Xenopus oocytes, PiUS stimulated the cellular uptake of radioactive phosphate.

Subsequently, two groups discovered that PiUS was capable of converting IP6 to IP7 and rename it to IP6K2 (Saiardi et al., 1999; Schell et al., 1999). The ability of inositol pyrophosphate to control the uptake of phosphate is an evolutionary conserved feature; in fact, kcs1D yeast with undetectable level of IP7 exhibits a reduced uptake of phosphate from the culture medium (Saiardi et al., 2004).

In mammals, regulation of phosphate homeostasis is not restricted to IP6K2, all three mammalian IP6Ks are likely to play a role. A genome-wide study aimed at identifying genetic variations associated with changes of serum phosphorus concentration identified IP6K3 (Kestenbaum et al., 2010). This human genetic study identified two independent single nucleotide polymorphisms (SNP) at locus 6p21.31, which are localised within the first intron of the IP6K3 gene. Interestingly, this study that analysed more than 16,000 humans identified SNP variant in only seven genes. Three of which, the sodium phosphate cotransporter type IIa, the calcium sensing receptor and the fibroblast growth factor 23, are well known regulators of phosphate homeostasis. These evidences support a role for IP6K3 in controlling serum phosphate levels in humans (Kestenbaum et al., 2010).

 

The hypothesis

 

Although, inositol pyrophosphate may have acquired unique organism-specific functions, the conserved ability of this class of molecules to regulate phosphate metabolism suggests an evolutionary ancient role. In this last paragraph, I will formulate few hypotheses that I hope will stimulate further research aimed at elucidating the biological link between phosphate, inositol pyrophosphates and polyP.

Inositol pyrophosphates regulate the entry of phosphate into the cells (Norbis et al., 1997), suggesting that they could affect phosphate uptake either directly (by stimulating a transporter, for example) or a indirectly by helping ‘fixing’ free phosphates in organic molecules. The cytosolic concentration of free phosphate [Pi] cannot fluctuate widely. Therefore, cellular entry of phosphates and its utilization may well be coupled. For example, the synthesis of polyP may be linked to phosphate entry in the cell. Inositol pyrophosphate control of energy metabolism (Szijgyarto et al., 2011) affects not only ATP levels but it can also alter the entire cellular balance of adenine nucleotides. Given that phosphate transfer reactions mainly use ATP as a vehicle for the phosphate groups, inositol pyrophosphate could affect phosphate metabolism by regulating the adenylate cellular pool. Moreover, it is tempting to speculate the existence of a feedback mechanism that coordinates the metabolic balance between ATP, phosphate and inositol pyrophosphates.

Inositol pyrophosphates could either contribute to the regulation of polyP synthesis, play a role in polyP degradation, or both. The yeast polyP polymerase has been identified with the subunit four (Vtc4) of the vacuolar membrane transporter chaperone (VTC) complex (Hothorn et al., 2009). Interestingly, pyrophosphates (Pi–Pi) dramatically accelerate the polyP polymerase reaction. It would therefore be interesting to determine whether the pyrophosphate moiety of IP7 can stimulate polyP vacuolar synthesis in a similar fashion. Similarly, it would be interesting to analyse the effect of inositol pyrophosphates on controlling the activity of the actin-like DdIPK2 enzyme. It should be noted however, that the existence even in yeast or Dictyostelium of other enzymes able to synthesize different polyP pools cannot be excluded. Thus, we will be able to validate and fully appreciate the role played by inositol pyrophosphates on polyP synthesis only after the identification of higher eukaryotes polyp synthesizing peptide/s.

The most abundant form of organic phosphate on earth is IP6, or phytic acid, a molecule that is highly abundant in plant seeds from which was originally characterised. In plant seeds, IP6 represents a phosphate storage molecule that it is hydrolysed during germination, releasing phosphates and cations. It will be an astonishing twist of event if inositol pyrophosphates were controlling the levels of their own precursor IP6 (Raboy, 2003), although due to the evolutionary conserved ability of inositol pyrophosphate to control phosphate homeostasis we should not be entirely surprised.

Although it is not yet clear how inositol pyrophosphates regulate cellular metabolism, understanding how inositol pyrophosphates influence phosphates homeostasis will help to clarify this important link.

Auesukaree C, Tochio H, Shirakawa M, Kaneko Y, Harashima S. Plc1p, Arg82p, and Kcs1p, enzymes involved in inositol pyrophosphate synthesis, are essential for phosphate regulation and polyphosphate accumulation in Saccharomyces cerevisiae. J Biol Chem 2005;280:25127–33.

Azevedo C, Burton A, Ruiz-Mateos E, Marsh M, Saiardi A. Inositol pyrophosphate mediated pyrophosphorylation of AP3B1 regulates HIV-1 Gag release. Proc Natl Acad Sci U S A 2009;106:21161–6.

Bennett M, Onnebo SM, Azevedo C, Saiardi A. Inositol pyrophosphates: metabolism and signaling. Cell Mol Life Sci 2006;63:552–64.

Boer VM, Crutchfield CA, Bradley PH, Botstein D, Rabinowitz JD. Growth-limiting intracellular metabolites in yeast growing under diverse nutrient limitations. Mol Biol Cell 2010;21:198–211.

Brown MR, Kornberg A. The long and short of it – polyphosphate, PPK and bacterial survival. Trends Biochem Sci 2008;33:284–90.

Burton A, Hu X, Saiardi A. Are inositol pyrophosphates signalling molecules? J Cell Physiol 2009;220:8–15.

Caen J, Wu Q. Hageman factor, platelets and polyphosphates: early history and recent connection. J Thromb Haemost 2010;8:1670–4.

Caffrey JJ, Safrany ST, Yang X, Shears SB. Discovery of molecular and catalytic diversity among human diphosphoinositol polyphosphate phosphohydrolases. An expanding Nudt family. J Biol Chem 2000;275:12730–6.

A Mitochondrial RNAi Screen Defines Cellular Bioenergetic Determinants and Identifies an Adenylate Kinase as a Key Regulator of ATP Levels

Nathan J. Lanning,1 Brendan D. Looyenga,1,2 Audra L. Kauffman,1 Natalie M. Niemi,1 Jessica Sudderth,3

Ralph J. DeBerardinis,3 and Jeffrey P. MacKeigan1,*

Cell Reports   http://dx.doi.org/10.1016/j.celrep.2014.03.065

Altered cellular bioenergetics and mitochondrial function are major features of several diseases, including cancer, diabetes, and neurodegenerative disorders. Given this important link to human health, we sought to define proteins within mitochondria that are critical for maintaining homeostatic ATP levels.

We screened an RNAi library targeting >1,000 nuclear-encoded genes whose protein products localize to the mitochondria in multiple metabolic conditions in order to examine their effects on cellular ATP levels. We identified a mechanism by which electron transport chain (ETC) perturbation under glycolytic conditions increased ATP production through enhanced glycolytic flux, thereby highlighting the cellular potential for metabolic plasticity.

Additionally, we identified a mitochondrial adenylate kinase (AK4) that regulates cellular ATP levels and AMPK signaling and whose expression significantly correlates with glioma patient survival. This study maps the bioenergetic landscape of >1,000 mitochondrial proteins in the context of varied metabolic substrates and begins to link key metabolic genes with clinical outcome.

Comments to be further addressed by JES Roselino

I will add some observations or at least one single observation.
Just at the beginning, when phosphorylation of proteins is presented, I assume you must mention that some proteins are activated by phosphorylation. This is fundamental in order to present self –organization reflex upon fast regulatory mechanisms. Even from an historical point of view. The first observation arrived from a sample due to be studied on the following day of glycogen synthetase. It was unintended left overnight out of the refrigerator. The result was it has changed from active form of the previous day to a non-active form. The story could have being finished here, if the researcher did not decide to spent this day increasing substrate levels (it could be a simple case of denaturation of proteins that changes its conformation despite the same order of amino acids). He kept on trying and found restoration of maximal activity. This assay was repeated with glycogen phosphorylase and the result was the opposite it increases its activity. This lead to the discovery of cAMP activated protein kinase and the assembly of a very complex system in the glycogen granule that is not a simple carbohydrate polymer. Instead it has several proteins assembled and preserves the capacity to receive from a single event (rise in cAMP) two opposing signals with maximal efficiency, stops glycogen synthesis, as long as levels of glucose 6 phosphate are low and increases glycogen phosphorylation as long as AMP levels are high).
I did everything I was able to do by the end of 1970 in order to repeat this assays with PK I, PKII and PKIII of M. Rouxii and Sutherland route to cAMP failed in this case. I ask Leloir to suggest to my chief (SP) the idea of AA, AB, BB subunits as was observed in lactic dehydrogenase (tetramer) indicating this as his idea. The reason was my “chief”(SP) more than once, have said it to me: “Leave these great ideas for the Houssay, Leloir etc…We must do our career with small things.” However, as she also have a faulty ability for recollection she also uses to arrive some time later, with the very same idea but in that case, as her idea.
Leloir, said to me: I will not offer your interpretation to her as mine. I think it is not phosphorylation, however I think it is glycosylation that explains the changes in the isoenzymes with the same molecular weight preserved. This dialogue explains why during “What is life” reading with him he asked me if from biochemist in exile, to biochemist I talked everything to him. Since I have considered that Schrödinger did not have confronted Darlington & Haldane for being in exile. Also, may explain why Leloir could have answered a bad telephone call from P. Boyer, Editor of The Enzymes in a way that suggest the the pattern could be of covalent changes over a protein. Our FEBS and Eur J. Biochemistry papers on pyruvate kinase of M. Rouxii is wrongly quoted in this way on his review about pyruvate kinase of that year(1971).

Another aspect I think you must call attention, in my opinion, is the following, show in detail with different colors what carbons belongs to CoA a huge molecule, in comparison with the single two carbons of acetate that will produce the enormous jump in energy yield in comparison with anaerobic glycolysis. The idea is how much must have being spent in DNA sequences to build that molecule in order to use only two atoms of carbon. Very limited aspects of biology could be explained in this way. In case we follow an alternative way of thinking, it becomes clearer that proteins were made more stable by interaction with other molecules (great and small). Afterwards, it rather easy to understand how the stability of protein-RNA complexes where transmitted to RNA (vibrational +solvational reactivity stability pair of conformational energy). Latter, millions of years, or as soon as, the information of interaction leading to activity and regulation could be found in RNA, proteins like reverse transcriptase move this information to a more stable form (DNA). In this way it is easier to understand the use of CoA to make two carbon molecules more reactive.

Yours,

JES Roselino

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Selected References to Signaling and Metabolic Pathways in PharmaceuticalIntelligence.com

Curator: Larry H. Bernstein, MD, FCAP

 

This is an added selection of articles in Leaders in Pharmaceutical Intelligence after the third portion of the discussion in a series of articles that began with signaling and signaling pathways. There are fine features on the functioning of enzymes and proteins, on sequential changes in a chain reaction, and on conformational changes that we shall return to.  These are critical to developing a more complete understanding of life processes.  I have indicated that many of the protein-protein interactions or protein-membrane interactions and associated regulatory features have been referred to previously, but the focus of the discussion or points made were different.

  1. Signaling and signaling pathways
  2. Signaling transduction tutorial.
  3. Carbohydrate metabolism3.1  Selected References to Signaling and Metabolic Pathways in Leaders in Pharmaceutical Intelligence
  4. Lipid metabolism
  5. Protein synthesis and degradation
  6. Subcellular structure
  7. Impairments in pathological states: endocrine disorders; stress hypermetabolism; cancer.

Selected References to Signaling and Metabolic Pathwayspublished in this Open Access Online Scientific Journal, include the following:

Update on mitochondrial function, respiration, and associated disorders

Curator and writer: Larry H. Benstein, MD, FCAP

http://pharmaceuticalintelligence.com/2014/07/08/update-on-mitochondrial-function-respiration-and-associated-disorders/

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


Cancer Volume One – Summary

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

Author: Larry H. Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/2014/03/26/a-synthesis-of-the-beauty-and-complexity-of-how-we-view-cancer/

Introduction – The Evolution of Cancer Therapy and Cancer Research: How We Got Here?

Author and Curator: Larry H Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/2014/04/04/introduction-the-evolution-of-cancer-therapy-and-cancer-research-how-we-got-here/

 The Centrality of Ca(2+) Signaling and Cytoskeleton Involving Calmodulin Kinases and Ryanodine Receptors in Cardiac Failure, Arterial Smooth Muscle, Post-ischemic Arrhythmia, Similarities and Differences, and Pharmaceutical Targets

Author and Curator: Larry H Bernstein, MD, FCAP, 
Author, and Content Consultant to e-SERIES A: Cardiovascular Diseases: Justin Pearlman, MD, PhD, FACC
And Curator: Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/09/08/the-centrality-of-ca2-signaling-and-cytoskeleton-involving-calmodulin-kinases-and-ryanodine-receptors-in-cardiac-failure

Renal Distal Tubular Ca2+ Exchange Mechanism in Health and Disease

Author and Curator: Larry H. Bernstein, MD, FCAP
Curator:  Stephen J. Williams, PhD
and Curator: Aviva Lev-Ari, PhD, RN

http://pharmaceuticalintelligence.com/2013/09/02/renal-distal-tubular-ca2-exchange-mechanism-in-health-and-disease/

Mitochondrial Metabolism and Cardiac Function

Curator: Larry H Bernstein, MD, FACP

http://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/

Mitochondrial Dysfunction and Cardiac Disorders

Curator: Larry H Bernstein, MD, FACP

http://pharmaceuticalintelligence.com/2013/04/14/mitochondrial-metabolism-and-cardiac-function/

Reversal of Cardiac mitochondrial dysfunction

Curator: Larry H Bernstein, MD, FACP

http://pharmaceuticalintelligence.com/2013/04/14/reversal-of-cardiac-mitochondrial-dysfunction/

Advanced Topics in Sepsis and the Cardiovascular System  at its End Stage

Author: Larry H Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/2013/08/18/advanced-topics-in-Sepsis-and-the-Cardiovascular-System-at-its-End-Stage/

Ubiquinin-Proteosome pathway, autophagy, the mitochondrion, proteolysis and cell apoptosis

Curator: Larry H Bernstein, MD, FACP

http://pharmaceuticalintelligence.com/2012/10/30/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis/

Ubiquitin-Proteosome pathway, Autophagy, the Mitochondrion, Proteolysis and Cell Apoptosis: Part III

Curator: Larry H Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/2013/02/14/ubiquinin-proteosome-pathway-autophagy-the-mitochondrion-proteolysis-and-cell-apoptosis-reconsidered/

 

Nitric Oxide, Platelets, Endothelium and Hemostasis (Coagulation Part II)

Curator: Larry H. Bernstein, MD, FCAP 

http://pharmaceuticalintelligence.com/2012/11/08/nitric-oxide-platelets-endothelium-and-hemostasis/


Mitochondrial Damage and Repair under Oxidative Stress

Curator: Larry H Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/2012/10/28/mitochondrial-damage-and-repair-under-oxidative-stress/

Mitochondria: Origin from oxygen free environment, role in aerobic glycolysis, metabolic adaptation

Reporter and Curator: Larry H Bernstein, MD, FACP

http://pharmaceuticalintelligence.com/2012/09/26/mitochondria-origin-from-oxygen-free-environment-role-in-aerobic-glycolysis-metabolic-adaptation/

 

Nitric Oxide has a Ubiquitous Role in the Regulation of Glycolysis – with a Concomitant Influence on Mitochondrial Function

Reporter, Editor, and Topic Co-Leader: Larry H. Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/2012/09/16/nitric-oxide-has-a-ubiquitous-role-in-the-regulation-of-glycolysis-with-a-concomitant-influence-on-mitochondrial-function/


Mitochondria and Cancer: An overview of mechanisms

Author and Curator: Ritu Saxena, Ph.D.

http://pharmaceuticalintelligence.com/2012/09/01/mitochondria-and-cancer-an-overview/

Mitochondria: More than just the “powerhouse of the cell”

Author and Curator: Ritu Saxena, Ph.D.

http://pharmaceuticalintelligence.com/2012/07/09/mitochondria-more-than-just-the-powerhouse-of-the-cell/

Overview of Posttranslational Modification (PTM)

Curator: Larry H. Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/2014/07/29/overview-of-posttranslational-modification-ptm/


Ubiquitin Pathway Involved in Neurodegenerative Diseases

Author and curator: Larry H Bernstein, MD,  FCAP

http://pharmaceuticalintelligence.com/2013/02/15/ubiquitin-pathway-involved-in-neurodegenerative-diseases/

Is the Warburg Effect the Cause or the Effect of Cancer: A 21st Century View?

Author: Larry H. Bernstein, MD, FCAP 

http://pharmaceuticalintelligence.com/2012/10/17/is-the-warburg-effect-the-cause-or-the-effect-of-cancer-a-21st-century-view/

New Insights on Nitric Oxide donors – Part IV

Curator and Author: Larry H. Bernstein, MD, FCAP

http://pharmaceuticalintelligence.com/2012/11/26/new-insights-on-no-donors/

Perspectives on Nitric Oxide in Disease Mechanisms [Kindle Edition]

Margaret Baker PhD (Author), Tilda Barliya PhD (Author), Anamika Sarkar PhD (Author), Ritu Saxena PhD (Author), Stephen J. Williams PhD (Author), Larry Bernstein MD FCAP (Editor), Aviva Lev-Ari PhD RN (Editor), Aviral Vatsa PhD (Editor)

http://pharmaceuticalintelligence.com/biomed-e-books/series-a-e-books-on-cardiovascular-diseases/perspectives-on-nitric-oxide-in-disease-mechanisms-v2/

 

Summary

Nitric oxide and its role in vascular biology

Signal transmission by a gas that is produced by one cell, penetrates through membranes and regulates the function of another cell represents an entirely new principle for signaling in biological systems.   All compounds that inhibit endothelium-derived relaxation-factor (EDRF) have one property in common, redox activity, which accounts for their inhibitory action on EDRF. One exception is hemoglobin, which inactivates EDRF by binding to it. Furchgott, Ignarro and Murad received the Nobel Prize in Physiology and Medicine for discovery of EDRF in 1998 and demonstrating that it might be nitric oxide (NO) based on a study of the transient relaxations of endothelium-denuded rings of rabbit aorta.  These investigators working independently demonstrated that NO is indeed produced by mammalian cells and that NO has specific biological roles in the human body. These studies highlighted the role of NO in cardiovascular, nervous and immune systems. In cardiovascular system NO was shown to cause relaxation of vascular smooth muscle cells causing vasodilatation, in nervous system NO acts as a signaling molecule and in immune system it is used against pathogens by the phagocytosis cells. These pioneering studies opened the path of investigation of role of NO in biology.

NO modulates vascular tone, fibrinolysis, blood pressure and proliferation of vascular smooth muscles. In cardiovascular system disruption of NO pathways or alterations in NO production can result in preponderance to hypertension, hypercholesterolemia, diabetes mellitus, atherosclerosis and thrombosis. The three enzyme isoforms of NO synthase family are responsible for generating NO in different tissues under various circumstances.

Reduction in NO production is implicated as one of the initial factors in initiating endothelial dysfunction. This reduction could be due to

  • reduction in eNOS production
  • reduction in eNOS enzymatic activity
  • reduced bioavailability of NO

Nitric oxide is one of the smallest molecules involved in physiological functions in the body. It is seeks formation of chemical bonds with its targets.  Nitric oxide can exert its effects principally by two ways:

  • Direct
  • Indirect

Direct actions, as the name suggests, result from direct chemical interaction of NO with its targets e.g. with metal complexes, radical species. These actions occur at relatively low NO concentrations (<200 nM)

Indirect actions result from the effects of reactive nitrogen species (RNS) such as NO2 and N2O3. These reactive species are formed by the interaction of NO with superoxide or molecular oxygen. RNS are generally formed at relatively high NO concentrations (>400 nM)

Although it can be tempting for scientists to believe that RNS will always have deleterious effects and NO will have anabolic effects, this is not entirely true as certain RNS mediated actions mediate important signalling steps e.g. thiol oxidation and nitrosation of proteins mediate cell proliferation and survival, and apoptosis respectively.

  • Cells subjected to NO concentration between 10-30 nM were associated with cGMP dependent phosphorylation of ERK
  • Cells subjected to NO concentration between 30-60 nM were associated with Akt phosphorylation
  • Concentration nearing 100 nM resulted in stabilisation of hypoxia inducible factor-1
  • At nearly 400 nM NO, p53 can be modulated
  • >1μM NO, it nhibits mitochondrial respiration

 

Nitric oxide signaling, oxidative stress,  mitochondria, cell damage

Recent data suggests that other NO containing compounds such as S- or N-nitrosoproteins and iron-nitrosyl complexes can be reduced back to produce NO. These NO containing compounds can serve as storage and can reach distant tissues via blood circulation, remote from their place of origin. Hence NO can have both paracrine and ‘endocrine’ effects.

Intracellularly the oxidants present in the cytosol determine the amount of bioacitivity that NO performs. NO can travel roughly 100 microns from NOS enzymes where it is produced.

NO itself in low concentrations have protective action on mitochondrial signaling of cell death.

The aerobic cell was an advance in evolutionary development, but despite the energetic advantage of using oxygen, the associated toxicity of oxygen abundance required adaptive changes.

Oxidation-reduction reactions that are necessary for catabolic and synthetic reactions, can cumulatively damage the organism associated with cancer, cardiovascular disease, neurodegerative disease, and inflammatory overload.  The normal balance between production of pro-oxidant species and destruction by the antioxidant defenses is upset in favor of overproduction of the toxic species, which leads to oxidative stress and disease.

We reviewed the complex interactions and underlying regulatory balances/imbalances between the mechanism of vasorelaxation and vasoconstriction of vascular endothelium by way of nitric oxide (NO), prostacyclin, in response to oxidative stress and intimal injury.

Nitric oxide has a ubiquitous role in the regulation of glycolysis with a concomitant influence on mitochondrial function. The influence on mitochondrial function that is active in endothelium, platelets, vascular smooth muscle and neural cells and the resulting balance has a role in chronic inflammation, asthma, hypertension, sepsis and cancer.

Potential cytotoxic mediators of endothelial cell (EC) apoptosis include increased formation of reactive oxygen and nitrogen species (ROSRNS) during the atherosclerotic process. Nitric oxide (NO) has a biphasic action on oxidative cell killing with low concentrations protecting against cell death, whereas higher concentrations are cytotoxic.

ROS induces mitochondrial DNA damage in ECs, and this damage is accompanied by a decrease in mitochondrial RNA (mtRNA) transcripts, mitochondrial protein synthesis, and cellular ATP levels.

NO and circulatory diseases

Blood vessels arise from endothelial precursors that are thin, flat cells lining the inside of blood vessels forming a monolayer throughout the circulatory system. ECs are defined by specific cell surface markers that characterize their phenotype.

Scientists at the University of Helsinki, Finland, wanted to find out if there exists a rare vascular endothelial stem cell (VESC) population that is capable of producing very high numbers of endothelial daughter cells, and can lead to neovascular growth in adults.

VESCs discovered that reside at the blood vessel wall endothelium are a small population of CD117+ ECs capable of self-renewal.  These cells are capable of undergoing clonal expansion unlike the surrounding ECs that bear limited proliferating potential. A single VESC cell isolated from the endothelial population was able to generate functional blood vessels.

Among many important roles of Nitric oxide (NO), one of the key actions is to act as a vasodilator and maintain cardiovascular health. Induction of NO is regulated by signals in tissue as well as endothelium.

Chen et. al. (Med. Biol. Eng. Comp., 2011) developed a 3-D model consisting of two branched arterioles and nine capillaries surrounding the vessels. Their model not only takes into account of the 3-D volume, but also branching effects on blood flow.

The model indicates that wall shear stress changes depending upon the distribution of RBC in the microcirculations of blood vessels, lead to differential production of NO along the vascular network.

Endothelial dysfunction, the hallmark of which is reduced activity of endothelial cell derived nitric oxide (NO), is a key factor in developing atherosclerosis and cardiovascular disease. Vascular endothelial cells play a pivotal role in modulation of leukocyte and platelet adherence, thrombogenicity, anticoagulation, and vessel wall contraction and relaxation, so that endothelial dysfunction has become almost a synonym for vascular disease. A single layer of endothelial cells is the only constituent of capillaries, which differ from other vessels, which contain smooth muscle cells and adventitia. Capillaries directly mediate nutritional supply as well as gas exchange within all organs. The failure of the microcirculation leads to tissue apoptosis/necrosis.

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Life-work in Engineering of Improved Heart Valve

Curator and Reporter: Larry H Bernstein, MD, FCAP

 

An authority and author of the book on cardiovascular valve devices is challenged by patient’s mother to go beyond what is available.  The results are splendid after re-engineering the design to the problem.

 

Reverse Engineering A Human Heart Valve

By Jim Pomager

aortic valve - a remarkable piece of biomechanical engineering

aortic valve – a remarkable piece of biomechanical engineering

 

 

 

The aortic valve is a remarkable piece of biomechanical engineering. On any given day, the leaflets (or cusps) of a healthy aortic valve will open and close 100,000+ times, allowing the proper amount of blood to flow from the heart to the rest of the body. Over a lifetime, a healthy valve endures more than 3.4 billion heartbeats.

Unfortunately, the aortic valve doesn’t always remain healthy. (What organ does?) According to the American Heart Association, up to 1.5 million people in the United States suffer from aortic stenosis (AS), a calcification of the aortic valve that narrows its opening and restricts blood flow. In the early stages, the disease is often asymptomatic, but as it progresses, it can cause chest pain, weakness, and difficulty breathing. And in approximately 300,000 people worldwide, the condition develops into severe AS, which has a one-year survival rate of approximately 50 percent, if left untreated.

Fortunately, there are treatment options.  The most common and successful is aortic valve replacement (AVR), wherein a mechanical or tissue-based valve is substituted for the diseased valve. For decades, replacement valves were implanted via open heart surgery, which involves an extended hospital stay and months of recovery. But in recent years, a promising new approach has emerged: transcatheter aortic valve implementation (TAVI), also known as transcatheter aortic valve replacement (TAVR). In TAVI, a tissue-based artificial valve is delivered into the diseased heart valve via a blood vessel, rather than through a large incision in the chest.

TAVI has many benefits, the most obvious (and compelling) of which is its noninvasiveness, which means shorter recovery times and faster attainment of quality-of-life outcomes for the patient. Replacement of a transcatheter aortic valve (TAV) can also be a minimally invasive exercise — a second TAV can simply be implanted within the first.

On the other hand, the use of TAVI procedures in U.S. hospitals is not yet widespread (though it is growing rapidly). The longevity of current-generation TAVs also remains unknown because it is an emerging technology, compared to evidence of 15+ years for surgically implanted heart valves. Plus, TAVI is only approved in the U.S. for use in AS patients who are either ineligible for surgical valve replacement or at high risk. (TAVI has been available in Europe since 2007, and clinical trials are underway in the U.S. for its use in intermediate-risk patients.)

What’s really needed is an improved TAV — one that outperforms current transcatheter valves, is as durable as a surgical valve, and operates more like … well, a healthy human aortic valve. Such a valve would open the door to TAVI’s use in the hundreds of thousands of lower-risk (and generally younger) AS patients whose only current option is a surgically implanted valve, and who would rather not have their chest opened.

Now, a man who has dedicated his professional career to studying the aortic valve has invented a new artificial valve design that he says will revolutionize TAVI. And if everything goes according to plan, his TAV will reach European patients in 2015 and U.S. patients soon after. How did he and his startup company design such technology? By reverse engineering the aortic valve.

The Man Behind The Valve

Mano Thubrikar

Mano Thubrikar

 

 

 

Mano Thubrikar, quite literally wrote the book on heart valves and heart disease — two of them, in fact. His The Aortic Valve (1989) and Vascular Mechanics and Pathology (2007) are leading textbooks in cardiovascular studies, and the former is widely used as a guide in the design of bioprosthetic heart valves.

After earning an undergraduate degree in metallurgy, a master’s in materials science, and a Ph.D. in biomedical engineering, Dr. Thubrikar spent the first 30 years of his career exclusively in academic research. He studied the aortic valve and bioprostheses from almost every conceivable angle while working at the University of Virginia (UVA) and at the Carolinas Medical Center and the University of North Carolina (UNC) at Charlotte.

But in 2003, Dr. Thubrikar received a phone call that would change the trajectory of his career and set him on the path to develop a novel TAV technology. A woman contacted him to discuss her son, a 35-year-old athlete with a calcified aortic valve. The condition was the result of a bicuspid valve, a congenital condition where the aortic valve has two cusps, rather than the customary three. The man needed a valve replacement, and his only choice was to have a mechanical heart valve surgically implanted. However, the surgical valve meant he would have to stay on anticoagulants for the rest of his life, effectively ending his athletic pursuits. Dr. Thubrikar informed the mother that there just weren’t any treatments available that would allow her son to continue his active lifestyle.

“Didn’t you write the book on the aortic valve?” she asked. “Why didn’t you make a valve that my son could use?”

The conversation and question deeply affected the researcher. “I went home and was so disturbed,” he told me during a recent visit to his office. “I talked to my wife and said, “You know what? Years of research, writing papers, and giving presentations — that’s done. I now need to make a heart valve.”

Soon after, Dr. Thubrikar left Carolinas Medical Center to embark on his new mission. He joined artificial heart valve pioneer Edwards Lifesciences as a Distinguished Scientist, but left after it became clear that the company’s plans for him didn’t align with his own.

So in 2007 — coincidentally, the same year Edwards launched the first commercially available TAV device — Dr. Thubrikar returned to academia, joining the staff at the South Dakota School of Mines & Technology. There he spent the next three years working on a new artificial valve design — one based on decades of research on the physics behind the human aortic valve.

Looking To The Human Body For Design Output
According to Dr. Thubrikar’s research, the natural aortic valve follows four strong design principles for maximum longevity and optimal hemodynamic performance. Those criteria are:

1. A specific coaptation height — When the valve’s three leaflets come together to close the valve, there is some surface-to-surface contact between the leaflets, rather than an edge-to-edge seal. This safety margin helps prevent against blood leakage back into the left ventricle.

2. No folds in the leaflets — Natural aortic valve cusps flex without folding. Folds would crease the tissue and cause unwanted stress on the leaflets, negatively impacting durability.

3. Minimum overall height — Extra height would produce dead space, which can lead to a variety of issues.

4. Minimum leaflet flexion — The human aortic valve manages to open completely with the leaflets moving only 70 degrees, not the 90 degrees you might expect. Again, this improves the valve’s longevity.

“You almost need to be a solid geometry design engineer to understand the math and the equations behind these principles,” he explained. “With these criteria, however, you have design parameters for the aortic valve. The mathematical equations give you the output of how an artificial valve should be designed.”

Dimensions of the natural aortic valve

Dimensions of the natural aortic valve

Dimensions of the natural aortic valve

 

 

Based on these four principles, Dr. Thubrikar reverse engineered the aortic heart valve, developing a new artificial valve design that mimics the aortic valve’s precise geometry. In October 2010, he launched a startup company called Thubrikar Aortic Valve, Inc. to commercialize his new creation, which he calls Optimum TAV and touts as “nature’s valve by design.”

“When someone asks me, ‘How does your valve compare with Edwards’?’ or ‘How does your valve compare with Medtronic’s?’, I say ‘We don’t compare our valve to them,'” Dr. Thubrikar told me. “We compare our valve with the natural aortic valve.”

On the surface, Optimum TAV looks similar to other artificial heart valves on the market, with three leaflets of bovine pericardium tissue mounted on a metal stent-frame. (In fact, the design is often mistaken for another widely used surgical valve.) But according to Dr. Thubrikar, it has a unique combination of features that will help it overcome the major design limitations of current-generation TAVs (if we’re going to compare). Those design limitations include:

  • Suture holes in the leaflet body — While all TAVs (including Optimum TAV) are constructed by sewing animal tissue to a metal frame, piercing the flexion zone of the leaflets leads to potential wear. Optimum TAV does not have a single suture hole in the working portion of the leaflet body.
  • Blood flow through frame — Some TAV frames are as tall as 5 cm in height, extending up into the aorta once implanted. As a result, blood must pass through the frame to enter the coronary arteries. Proteins in the blood will accumulate on the frame, and can eventually break loose and cause thromboembolisms (blood clots).  Optimum TAV is only 2 cm in height. (Related, the low height of the Thubrikar valve also makes it less likely to require a pacemaker.)
  • Thick outer frame — The thicker the frame, the smaller the valve opening will be, allowing less blood to pass through. This opening is referred to as the valve’s EOA, or effective orifice area. The average EOA of a surgical valve is around 1.9 cm2, and some TAVs have EOAs as small as 1.5 cm2(technically, a mild form of stenosis). In bench tests, Optimum TAV’s EOA was 2.3 to 2.4 cm2. (A healthy aortic valve has an EOA of approximately 2.7 cm2.)
  • Clipped calcified leaflets — Some current TAVs are anchored to the patient’s original valve using a paper-clip like mechanism. In this design, there is the potential that the TAVs leaflets will come into contact with the old, calcified leaflets during the operation, causing wear. Optimum TAV’s design eliminates the possibility of contact between the leaflets and native valve.
  • Paravalvular leakage — In some cases, a space forms between the outside of a TAV and the surrounding heart tissue, and blood can leak through. Optimum TAV has a high skirt to prevent this type of gap from developing. In addition, Optimum TAV’s novel frame architecture allows it to conform to and seal off either a round or elliptical annulus (the ring-shaped base of the original valve). This is particularly helpful in minimizing or eliminating leakage in bicuspid patients, who often have an irregularly shaped annulus.
  • Balloon expansion — TAV frames made of stainless steel must be forced open by a balloon. The TAV’s tissue can get caught between the balloon and the frame and potentially tear. Optimum TAV’s frame is made of nitinol, which automatically expands once deployed from the catheter.

 

optimum TAV

optimum TAV

 

 

Optimum TAV

“Other technologies have built-in issues,” Dr. Thubrikar said. “To be able to avoid those problems in a comprehensive fashion is no small feat.”

Trial By Fire
During the two and a half years following the establishment of Thubrikar Aortic Valve, Optimum TAV seemed to be moving steadily toward market. The company raised enough funding to get started, primarily from friends, family, physicians, entrepreneurs, and technology industry executives. Patent applications were filed, suppliers were selected, valves were painstakingly produced (by hand, over one-and-a-half to two days each), and preclinical testing began.

Members of the Thubrikar Aortic Valve team

Members of the Thubrikar Aortic Valve team

 

 

Members of the Thubrikar Aortic Valve team (left to right): Deodatt Wadke, member of the board of directors and cofounder; Samir Wadke, executive director of business development and cofounder; Dr. Mano Thubrikar, president and founder; Samuel Evans, research engineer II; and Nikhil Heble, counsel, secretary, and cofounder

But the fledgling company was dealt a major setback in April 2013, when a fire destroyed the Horsham, Pa. office building to which the Thubrikar Aortic Valve laboratory had recently relocated (from South Dakota). All of its equipment was destroyed and needed to be replaced. The company had to relocate to nearby Norristown, Pa. Not an ideal scenario for a startup trying to make the most of extremely limited resources.

The company was undeterred by the fire, and the last year has been a successful one for Thubrikar. The company completed most of its preclinical testing (including implants in 12 animals and two diseased human cadaver hearts), reached design freeze on Optimum TAV, filed a provisional patent application for its proprietary delivery catheter, and achieved almost $2 million in total funding. Perhaps the biggest milestone came in August 2013, when Optimum TAV met the International Organization for Standardization’s (ISO’s) durability requirements by surpassing 200 million cycles in a third-party ISO certified laboratory.

The durability testing has continued, and Optimum TAV continues to function beyond 390 million cycles, which approximates 11 years in vivo. Surgical valves typically last anywhere from 12 to 18 years, and Thubrikar expects his valve to last at least that long.

“I would not be surprised if it surpasses the longevity of even the surgical valve,” he said.

The company also received its first institutional investment, from Delaware Crossing Investor Group (DCIG), in 2014. The primary DCIG investor, Marv Woodall, led the commercialization of the world’s first stents as president of Johnson & Johnson Interventional Systems (now Cordis) and was on the board of director of the first TAV company, Percutaneous Valve Technologies (PVT, now part of Edwards Lifesciences). Thubrikar has recruited him as its business advisor.

What Lies Ahead
Like many other developers of novel medical devices, Thubrikar Aortic Valve has decided to take its product to market through Europe initially, given European regulators’ comfort level with TAV and the FDA’s steep requirement for clinical trials. “We have spoken to the FDA and will continue to do so on a regular basis,” according to Dr. Thubrikar. “But they asked for a lot more preclinical testing than the European Notified Bodies to start a clinical trial.”

The company is now working to raise an additional $2 million to $10 million, and expects the granting of its patent for Optimum TAV in 2014. The finances will enable Thubrikar to not only conduct a first-in-human (FIH) feasibility study in up to 15 patients this year, but also to expand to a full European clinical trial of about 65 additional patients in 2015. If all goes well, a 2015 CE Mark for Optimum TAV isn’t out of the question.

However, trial success is vital, since today’s investors — and large companies in search of technology acquisitions — wait for significant clinical data to accumulate before backing a medical device. “We realize that until we actually implant the valve in a patient, other companies will think, ‘You don’t know what can go wrong,'” Dr. Thubrikar explained. “We had one big company say, ‘We will pay you four times as much once the product is in a patient.’ They want you to de-risk everything, to work out all the bugs yourself on your own dime.”

Yet Dr. Thubrikar thinks its only a matter of time until his life’s work finally arrives in the hands of interventional cardiologists, who he said have been “knocking at his door” since he first presented a paper on the technology in 2012. Since then, he has spoken at several of the largest interventional cardiology conferences, and word continues to spread about Optimum TAV. Like many other researchers-turned-entreprenuers, he steadfastly believes that his invention will eventually reach the market, where it can begin helping patients — like the one whose mother contacted him a decade ago.

“If hell freezes over, if we don’t get any money, I don’t care,” he said. “I don’t care how it happens. We are going to make a heart valve. That’s the only mission in my life.”

For more information on Thubrikar Aortic Valve and Optimum TAV, visit http://tavi.us/.

 

 

 

 

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