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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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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 »

The multi-step transfer of phosphate bond and hydrogen exchange energy

Curator: Larry H. Bernstein, MD, FCAP, Leaders in Pharmaceutical Intelligence

In this subtext of the series we expand on a tie between respiration and glycolysis, and the functioning of the mitochondrion to discover the key role played by oxidative phosphorylation, “acetyl coenzyme A, and electron transport.  This was crucial to understanding cellular energetics, which explains the high energy of fatty acid catabolism from stored adipose tissue, and the criticality of the multi-step sequence of reactions in energy transfer.

This portion considerably provides a response to the TWO points made by Jose EDS Rosallis:

  1. 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. This poiny needs further clarification, but he makes important observations here.
  • 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 had 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 led 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 these 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)
(Nathan O. Kaplan discovery) indicating this as his idea. The reason was my “chief” (SP) more than once,  said to me: “Leave these great ideas for the Houssay, Leloir etc…We must do our career with small things. ” However, as she also had a faulty ability for recollection she also used to arrive some time later, with the very same idea but in that case, as her idea.

[This reminds me of when I was studying the emergence of lactic dehysrogenase isoenzyme patterns in the developing eye lens of cattle, I raised reservations about Elliott Vessells challenge to Nathan Kaplan, but that not being my primary problem, my brilliant mentor (H.M.), a very young full professor of anatomy said – leave that to NOK.}

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 the Schroedinger’s “What is life?” reading with him he asked me if from biochemist in exile, to biochemist I expressed all of my thoughts to him. Since I had considered that Schrödinger did not confront Darlington & Haldane for being in exile. This may explain why Leloir could have answered a bad telephone call from P. Boyer, Editor of The Enzymes in a way that suggests 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).

  1. 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.

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/
  1. Signaling transduction tutorial.
    http://pharmaceuticalintelligence.com/2014/08/12/signaling-transduction-tutorial/
  1. Carbohydrate metabolism
    http://pharmaceuticalintelligence.com/2014/08/13/carbohydrate-metabolism/

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

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

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

Oxidation-Reduction Reactions

Rachel Casiday, Carolyn Herman, and Regina Frey
Department of Chemistry, Washington University
St. Louis, MO 63130

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/cytochromes.html

 

OX-Phos steps

OX-Phos steps

http://s1.hubimg.com/u/6583902_f496.jpg

 

Key Concepts:

  • ATP as Free-Energy Currency in the Body
  • Coupled Reactions
    • Standard Free-Energy Change for Coupled Reactions
    • ATP Dephosphorylation Coupled to Nonspontaneous Reactions
    • Coupled Reactions to Generate ATP
  • Structure and Function of the Mitochondria
  • Oxidation-Reduction Reactions in the Electron-Transport Chain
    • Electron-Carrier Proteins (NOTE: This section includes a separate link and an animation.)
    • Relationship Between Reduction Potentials and Free Energy
  • Proton Gradient as Means of Coupling Oxidative and Phosphorylation Components of Oxidative Phosphorylation
  • ATP Synthetase Uses Energy From Proton Gradient to Generate ATP

Every day, we build bones, move muscles, eat food, think, and perform many other activities with our bodies. All of these activities are based upon chemical reactions. However, most of these reactions are not spontaneous (i.e., they are accompanied by a positive change in free energy, DG>0) and do not occur without some other source of free energy. Hence, the body needs some sort of “free-energy currency,” (Figure 1) a molecule that can store and release free energy when it is needed to power a given biochemical reaction.

The four questions:

  1. How does the body “spend” free-energy currency to make a nonspontaneous reaction spontaneous? The answer, which is based on thermodynamics, is to use coupled reactions.
  2. How is food used to produce the reducing agents (NADH and FADH2) that can regenerate the free-energy currency? The answer, from biology, is found in glycolysis and the citric-acid cycle.
  3. How are the reducing agents (NADH and FADH2) able to generate the free-energy currency molecule (ATP)? Once again, coupled reactions are key.
  4. What mechanism does the body use to couple the reducing agent reactions and the generation of ATP? ATP is synthesized primarily by a two-step process consisting of an electron-transport chain and a proton gradient.  This process is based on electrochemistry and equilibrium, as well as thermodynamics.

The free-energy change (DG) for the net reaction is given by the sum of the free-energy changes for the individual reactions.  The phospholipids that form cell membranes are formed from glycerol with a phosphate group and two fatty-acid chains attached.This step actually consists of two reactions:

(1) the phosphorylation of glycerol, and

(2) the dephosphorylation of ATP (the free-energy-currency molecule). The reactions may be added as shown in Equations 2-4, below:

      Glycerol + HPO42- –>  (Glycerol-3-Phosphate)2- + H2O DGo= +9.2 kJ
(nonspontaneous)
(2)
+      ATP4- + H2O –>       ADP3- + HPO42- + H+ DGo30.5 kJ
(spontaneous)
(3)
     Glycerol + ATP4- –> (Glycerol-3-Phosphate)2- +ADP3- + H+ DGo21.3 kJ
(spontaneous)
(4)
   

ATP is the most important “free-energy-currency” molecule in living organisms (see Figure 2, below). Adenosine triphosphate (ATP) is a useful free-energy currency because the dephosphorylation reaction is very spontaneous; i.e., it releases a large amount of free energy (30.5 kJ/mol). Thus, the dephosphorylation reaction of ATP to ADP and inorganic phosphate (Equation 3) is often coupled with nonspontaneous reactions (e.g., Equation 2) to drive them forward. The body’s use of ATP as a free-energy currency is a very effective strategy to cause vital nonspontaneous reactions to occur.

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/ATP.jpg

structure of ATP

structure of ATP

This is the two-dimensional (ChemDraw) structure of ATP, adenosine triphosphate. The removal of one phosphate group (green) from ATP requires the breaking of a bond (blue) and results in a large release of free energy. Removal of this phosphate group (green) results in ADP, adenosine diphosphate.

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/ATP.jpg

flowchart of food energy

flowchart of food energy

This flowchart shows that the energy used by the body for its many activities ultimately comes from the chemical energy in our food. The chemical energy in our food is converted to reducing agents (NADH and FADH2). These reducing agents are then used to make ATP. ATP stores chemical energy, so that it is available to the body in a readily accessible form.

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/flowchart1.jpg

Glycolysis   Glucose + 2 HPO42- + 2 ADP3- + 2 NAD+ –>
2 Pyruvate + 2 ATP4- + 2 NADH + 2 H+ + 2 H2O
(5)
Intermediate Step   2(Pyruvate + Coenzyme A + NAD+ –>
Acetyl CoA + CO2 + NADH)
(6)
Citric-Acid Cycle 2(Acetyl CoA + 3 NAD++ FAD + GDP3-
+ HPO42- + 2H2O –> 2 CO2 + 3 NADH + FADH2
+ GTP4- + 2H+ + Coenzyme A)
(7)

The structures of the important molecules in Equations 5-7 are shown in Table 1, below.

How is Food Used to Make the Reducing Agents Needed for the Production of ATP?

To make ATP, energy must be absorbed. This energy is supplied by the food we eat, and then used to synthsize two reducing agents, NADH and FADH2 that are needed to produce ATP. One of the principal energy-yielding nutrients in our diet is glucose (see structure in Table 1 in the blue box below), a simple six-carbon sugar that can be broken down by the body. When the chemical bonds in glucose are broken, free energy is released. The complete breakdown of glucose into CO2 occurs in two processes: glycolysis and the citric-acid cycle. The reactions for these two processes are shown in the blue box below.

pyruvate

pyruvate

  Pyruvate

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/pyruvate.jpg

acetylCoA

acetylCoA

Acetyl CoA

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/acetylCoA.jpg

NADH

NADH

NADH

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/acetylCoA.jpg

 

FADH2

FADH2

FADH2

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/FADH2.jpg

two-dimensional representations of several important molecules in Equations 5-7.

As seen in Equations 5-7 in the blue box, glycolysis and the citric-acid cycle produce a net total of only four ATP or GTP molecules (GTP is an energy-currency molecule similar to ATP) per glucose molecule. This yield isfar below the amount needed by the body for normal functioning, and in fact is far below the actual ATP yield for glucose in aerobic organisms (organisms that use molecular oxygen). For each glucose molecule the body processes, the body actually gains approximately 30 ATP molecules! (See Figure 4, below.)  So, how does the body generate ATP?

The process that accounts for the high ATP yield is known as oxidative phosphorylation. A quick examination of Equations 5-7 shows that glycolysis and the citric-acid cycle generate other products besides ATP and GTP, namely NADH and FADH2 (blue). These products are molecules that are oxidized (i.e., give up electrons) spontaneously. The body uses these reducing agents (NADH and FADH2) in an oxidation-reduction reaction .  As you will see later in this tutorial, it is the free energy from these redox reactions that is used to drive the production of ATP.

flowchart - making of ATP

flowchart – making of ATP

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/flowchart2.jpg

This flowchart shows the major steps involved in breaking down glucose from the diet and converting its chemical energy to the chemical energy in the phosphate bonds of ATP, in aerobic (oxygen-using) organisms. Note: In this flowchart, red denotes a source of carbon atoms (originally from glucose),green denotes energy-currency molecules, and blue denotes the reducing agents that can be oxidized spontaneously.

In the discussion above, we see that glucose by itself generates only a tiny amount of ATP. However, during the breakdown of glucose, a large amount of NADH and FADHis produced; it is these reducing agents that dramatically increase the amount of ATP produced. How does this work?

How are the reducing agents (NADH and FADH2) able to generate the free-energy currency molecule (ATP)?

As discussed in an earlier section about coupling reactions, ATP is used as free-energy currency by coupling its (spontaneous) dephosphorylation (Equation 3) with a (nonspontaneous) biochemical reaction to give a net release of free energy (i.e., a net spontaneous reaction). Coupled reactions are also used to generate ATP by phosphorylating ADP. The nonspontaneous reaction of joining ADP to inorganic phosphate to make ATP (Equation 8, below, and Figure 2, above) is coupled to the oxidation reaction of NADH or FADH(Equation 9, below). (Recall, NADH and FADH2 are produced in glycolysis and the citric-acid cycle as described in the blue box). For simplicity, we shall henceforth discuss only the oxidation of NADH; FADH2 follows a very similar oxidation pathway.

The oxidation reaction for NADH has a larger, but negative, DG than the positive DG required for the formation of ATP from ADP and phosphate. This set of coupled reactions is so important that it has been given a special name: oxidative phosphorylation. This name emphasizes the fact that an oxidation (of NADH) reaction (Equation 9 and Figure 5, below) is being coupled to a phosphorylation (of ADP) reaction (Equation 8, below, and Figure 2, above). In addition, we must consider the reduction reaction (gaining of electrons) that accompanies the oxidation of NADH. (Oxidation reactions are always accompanied by reduction reactions, because an electron given up by one group must be accepted by another group.) In this case, molecular oxygen (O2) is the electron acceptor, and the oxygen is reduced to water (Equation 10, below) .

The individual reactions of interest for oxidative phosphorylation are:

Phosphorylation

ADP3- + HPO42- + H+ –>
ATP4- + H2O

DGo= +30.5 kJ
(nonspontaneous)
(8)
oxidation

NADH –> NAD+ + H+ +  2e

DGo158.2 Kj
(spontaneous)
(9)
reduction

1/2 O2 + 2H+ + 2e –> H2O

DGo61.9 kJ
(spontaneous)

                                                                       (10)                                    

The net reaction is obtained by summing the coupled reactions, as shown in Equation 11, below.

ADP3- + HPO42- + NADH + 1/2 O2 + 2H+ –>
ATP4- + NAD+ + 2 H2O
DGo= -189.6 kJ
(spontaneous)
(11)

The molecular changes that occur upon oxidation of NADH are shown:

NAD+_NADH

NAD+_NADH

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/NAD+_NADH.jpg

This is a two-dimensional (ChemDraw) representation showing the change that occurs when NADH is oxidized to NAD+. “R” represents the part of the structure that is shown in black in the drawing of NADH in Table 1, and does not change during the oxidation half-reaction. The molecular changes that occur upon oxidation are shown in red.

In this tutorial, we have seen that nonspontaneous reactions in the body occur by coupling them with a very spontaneous reaction (usually the ATP reaction shown in Equation 3). We have just seen that ATP is produced by coupling the phosphorylation reaction with NADH oxidation (a very spontaneous reaction). But we have not yet answered the question: by what mechanism are these reactions coupled?

Coupling Reactions in Biological Systems

Every day your body carries out many nonspontaneous reactions. As discussed earlier, if a nonspontaneous reaction is coupled to a spontaneous reaction, as long as the sum of the free energies for the two reactions is negative, the coupled reactions will occur spontaneously. How is this coupling achieved in the body? Living systems couple reactions in several ways, but the most common method of coupling reactions is to carry out both reactions on the same enzyme. Consider again the phosphorylation of glycerol (Equations 2-4). Glycerol is phosphorylated by the enzyme glycerol kinase, which is found in your liver. The product of glycerol phosporylation, glycerol-3-phosphate (Equation 2), is used in the synthesis of phospholipids.

Glycerol kinase is a large protein comprised of about 500 amino acids. X-ray crystallography of the protein shows us that there is a deep groove or cleft in the protein where glycerol and ATP attach (see Figure 6, below). Because the enzyme holds the ATP and the glycerol in place, the phosphate can be transferred directly from the ATP to glycerol. Instead of two separate reactions where ATP loses a phosphate (Equation 3) and glycerol picks up a phosphate (Equation 2), the enzyme allows the phosphate to move directly from ATP to glycerol (Equation 4).

The coupling in oxidative phosphorylation uses a more complicated (and amazing!) mechanism, but the end result is the same: the reactions are linked together, the net free energy for the linked reactions is negative, and, therefore, the linked reactions are spontaneous.

glyckin

glyckin

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/glyckin.jpg

This is a schematic representation of ATP and glycerol bound (attached) to glycerol kinase. The enzyme glycerol kinase is a dimer (consists of two identical subuits). There is a deep cleft between the subunits where ATP and glycerol bind. Since the ATP and phosphate are physically so close together when they are bound to the enzyme, the phosphate can be transferred directly from ATP to glycerol. Hence, the processes of ATP losing a phosphate (spontaneous) and glycerol gaining a phosphate (nonspontaneous) are linked together as one spontaneous process

Questions on ATP: The Body’s Free-Energy Currency (How Free-Energy Currency Works)

  • Biological systems involve many molecules containing phosphate groups, such as ATP. Although ATP is the most commonly used free-energy currency, any of these phosphorylated molecules could, in theory, be used as free-energy currency. The standard free-energy change (DGo) for the dephosphorylation (removal of a phosphate group) of several biological compounds is given below:
Acetyl phosphate DGo = -47.3 kJ/mol
Adenosine triphosphate (ATP) DGo = -30.5 kJ/mol
Glucose-6-phosphate DGo = -13.8 kJ/mol
Phosphoenolpyruvate (PEP) DGo = -61.9 kJ/mol
Phosphocreatine DGo = -43.1 kJ/mol

Neglecting any differences in difficulty synthesizing or accessing these molecules by biological systems, rank the molecules in order of their efficiency as a free-energy currency (i.e., the amount of nonspontaneous reactions enabled per phosphate removed from a molecule of free-energy currency) from the most efficient to the least efficient.

  • What, if any, changes are there in the shape of the ring as NADH is oxidized to NAD+(see Figure 5)? (Hint: Consider which atoms lie in the same plane in each structure.)

Mechanism of Coupling the Oxidative-Phosphorylation Reactions

In order to couple the redox and phosphorylation reactions needed for ATP synthesis in the body, there must be some mechanism linking the reactions together. In cells, this is accomplished through an elegant proton-pumping system that occurs inside special double-membrane-bound organelles (specialized cellular components) known as mitochondria. A number of proteins are required to maintain this proton-pumping system and catalyze the oxidative and phosphorylation reactions.

Synthesis of ATP (Equation 8) is coupled with the oxidation of NADH (Equation 9) and the reduction of O2 (Equation 10). There are three key steps in this process:

  1. Electrons are transferred from NADH, through a series of electron carriers, to O2. The electron carriers are proteins embedded in the inner mitochondrial membrane. (More detail about the structure of the mitochondria is presented in the next section.) (See Figure 7a.)
  2. Transfer of electrons by these carriers generates a proton (H+) gradient across the inner mitochondrial membrane. (See Figure 7b.)
  3. When Hspontaneously diffuses back across the inner mitochondrial membrane, ATP is synthesized. The large positive free energy of ATP synthesis is overcome by the even larger negative free energy associated with proton flow down the concentration gradient. (See Figure 7c.)

These steps are outlined below.

  1. Electron Transport (Oxidation-Reduction Reactions) Through a Series of Proteins in the Inner Membrane of the Mitochondria
e_transfer

e_transfer

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/e_transfer.jpg

Generation of H+(Proton) Concentration Gradient Across the Inner Mitochondrial Membrane During the Electron-Transport Process (via a Proton Pump)

. Generation of H+ (Proton) Concentration Gradient Across the Inner Mitochondrial Membrane

. Generation of H+ (Proton) Concentration Gradient Across the Inner Mitochondrial Membrane

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/gradient.jpg

Synthesis of ATP Using Free Energy Released From Spontaneous Diffusion of H+Back to the Matrix Inside the Inner Mitochondrial Membrane

. Synthesis of ATP Using Free Energy Released From Spontaneous Diffusion of H+

. Synthesis of ATP Using Free Energy Released From Spontaneous Diffusion of H+

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/ATP_produced.jpg

The three major steps in oxidative phosphorylation are

(a) oxidation-reduction reactions involving electron transfers between specialized proteins embedded in the inner mitochondrial membrane; 

(b) the generation of a proton (H+) gradient across the inner mitochondrial membrane (which occurs simultaneously with step (a)); and 

(c) the synthesis of ATP using energy from the spontaneous diffusion of electrons down the proton gradient generated in step (b).

Note: Steps (a) and (b) show cytochrome oxidase, the final electron-carrier protein in the electron-transport chain described above. When this protein accepts an electron (green) from another protein in the electron-transport chain, an Fe(III) ion in the center of a heme group (purple) embedded in the protein is reduced to Fe(II). The coordinates for the protein were determined using x-ray crystallography, and the image was rendered using SwissPDB Viewer and POV-Ray (see References).

Cells use a proton-pumping system made up of proteins inside the mitochondria to generate ATP. Before we examine the details of ATP synthesis, we shall step back and look at the big picture by exploring the structure and function of the mitochondria, where oxidative phosphorylation occurs.

Structure and Function of the Mitochondria

mitochondria

mitochondria

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/mitochondria.jpg

This is a schematic diagram showing the membranes of the mitochondrion. The purple shapes on the inner membrane represent proteins, which are described in the section below. An enlargement of the boxed portion of the inner membrane in this figure is shown in Figure.

The mitochondrial membranes are crucial for this organelle’s role in oxidative phosphorylation. As shown in Figure 8, mitochondria have two membranes, an inner and an outer membrane. The outer membrane ispermeable to most small molecules and ions, because it contains large protein channels called porins. The inner membrane is impermeable to most ions and polar molecules. The inner membrane is the site of oxidative phosphorylation. Although the membrane is mostly impermeable, it contains special H+ (proton) channels and pumps that enable the coupling of the redox reaction involving NADH and O2 (Equations 9-10) to the phosphorylation reaction of ADP (Equation 8), as described below (“Oxidation-Reduction Reactions and Proton Pumping in Oxidative Phosphorylation”). (Recall the discussion of protein channels in the “Maintaining the Body’s Chemistry: Dialysis in the Kidneys” Tutorial .)

As shown in Figure 8, inside the inner membrane is a space known as the matrix; the space between the two membranes is known as the intermembrane space. The matrix side of the inner membrane has a negative electrical charge relative to the intermembrane space due to an H+ gradient set up by the redox reaction (Equations 9 and 10). This charge difference is used to provide free energy (G) for the phosphorylation reaction (Equation 8).

Oxidation-Reduction Reactions and Proton Pumping in Oxidative Phosphorylation

Phosphorylation of ADP (Equation 8) is coupled to the oxidation-reduction reaction of NADH and O2 (Equations 9 and 10). Electrons are not transferred directly from NADH to O2, but rather pass through a series of intermediate electron carriers in the inner membrane of the mitochondrion. Why? This allows something very important to occur: the pumping of protons across the inner membrane of the mitochondrion. As we shall see, it is this proton pumping that is ultimately responsible for coupling the oxidation-reduction reaction to ATP synthesis.

Two major types of mitochondrial proteins (see Figure 9, below) are required for oxidative phosphorylation to occur. Both classes of proteins are located in the inner mitochondrial membrane.

  1. The electron carriers (NADH-Q reductase, ubiquinone (Q), cytochrome reductase, cytochrome c, and cytochrome oxidase shown in shades of purple in Figure 9 below) transport electrons in a stepwise fashion from NADH to O2.  Three of these carriers (NADH-Q reductase, cytochrome reductase, and cytochrome oxidase) are also proton pumps, and simultaneously pump H+ ions (protons) from the matrix to the intermembrane space. (Proton movement from one side of the membrane to the other is shown as blue arrows in Figure 9, below.) The protons that are pumped across the membrane complete the redox reaction (Equations 9 and 10). The creation of a proton gradient across the membrane is one way of storing free energy.
  2. ATP synthetase (shown in red in Figure 9 below) allows H+ ions to diffuse back into the matrix and uses the free energy released to synthesize ATP from ADP and HPO42-. The ATP synthetase is essential for the phosphorylation to occur (Equation 8). (Proton movement from one side of the membrane to the other is shown as blue arrows in Figure 9, below.)

The electron carriers can be divided into three protein complexes (NADH-Q reductase (1), cytochrome reductase (3), and cytochrome oxidase (5)) that pump protons from the matrix to the intermembrane space, and two mobile carriers (ubiquinone (2) and cytochrome c (4)) that transfer electrons between the three proton-pumping complexes. (Gold numbers refer to the labels on each protein in Figure 9, below.) Because electrons move from one carrier to another until they are finally transferred to O2, the electron carriers (shown in Figure 9,below) are said to form an electron-transport chain.

Figure  below, is a schematic representation of the proteins involved in oxidative phosphorylation. To see an animation of oxidative phosphorylation, click on “View the Movie.”

Proteins of inner space

Proteins of inner space

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/Proteins.jpg

This is a schematic diagram illustrating the transfer of electrons from NADH, through the electron carriers in the electron transport chain, to molecular oxygen. Please click on the pink button below to view a QuickTime animation of the functions of the proteins embedded in the inner mitochondrial membrane that are necessary for oxidative phosphorylation. Click the blue button below to download QuickTime 4.0 to view the movie.

NADH-Q reductase (1), cytochrome reductase (3) , and cytochrome oxidase (5) are electron carriers as well as proton pumps, using the energy gained from each electron-transfer step to move protons (H+) against a concentration gradient, from the matrix to the intermembrane space.Ubiquinone (Q) (2) and cytochrome c (Cyt C) (4) are mobile electron carriers. (Ubiquinone is not actually a protein.) All of the electron carriers are shown in purple, with lighter shades representing increasingly higher reduction potentials. Together, these electron carriers form a “chain” to transport electrons from NADH to O2. The path of the electrons is shown with the green dotted line.

ATP synthetase (red) has two components: a proton channel (allowing diffusion of protons down a concentration gradient, from the intermembrane space to the matrix), and a catalytic component to catalyze the formation of ATP.

For a more complete description of each step in oxidative phosphorylation (indicated by the gold numbers), click here.

view movie

view movie

http://www.apple.com/quicktime/

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/movie.jpg

http://www.chemistry.wustl.edu/~edudev/LabTutorials/Cytochromes/images/QuickTime.jpg

Click here for a brief description of each of the electron carriers in the electron-transport chain. It is important to note that, although NADH donates two electrons and O2 ultimately accepts four electrons, each of the carriers can only transfer one electron at a time. Hence, there are several points along the chain where electrons can be collected and dispersed. For the sake of simplicity, these points are not described in this tutorial.

In the section above, we see that the oxidation-reduction process is a series of electron transfers that occurs spontaneously and produces a proton gradient. Why are the electron tranfers from one electron carrier to the next spontaneous?

What causes electrons to be transferred down the electron-transport chain?

As seen in Table 2, below, and Figure 7a, in these carriers, the species being oxidized or reduced is Fe, which is found either in a iron-sulfur (Fe-S) group or in a heme group. (Recall the heme group from the Chem 151 tutorial “Hemoglobin and the Heme Group: Metal Complexes in the Blood“.) The iron in these groups is alternately oxidized and reduced between Fe(II) (reduced) or Fe(III) (oxidized) states.

Table 2 shows that the electrons are transferred through the electron-transport chain because of the difference in the reduction potential of the electron carriers. As explained in the green box below, the higher the electrical potential (e) of a reduction half reaction is, the greater the tendency is for the species to accept an electron. Hence, in the electron-transport chain, electrons are transferred spontaneously from carriers whose reduction results in a small electrical potential change to carriers whose reduction results in an increasingly larger electrical potential change.

Reduction Potentials and Relationship to Free Energy

An oxidation-reduction reaction consists of an oxidation half reaction and a reduction half reaction. Every half reaction has an electrical potential (e). By convention, all half reactions are written as reductions, and the electrical potential for an oxidation half-reaction is equal in magnitude, but opposite in sign, to the electrical potential for the corresponding reduction (i.e., the opposite reaction). The electrical potential for an oxidation-reduction reaction is calculated by

erxn = eoxidation + ereduction (12)

For example, for the overall reaction of the oxidation of NADH paired with the reduction of O2, the potential can be calculated as shown below.

Reduction Potentials ereduction
NAD+ + 2H+ + 2e –> NADH + H+ -0.32 V
(1/2) O2 + 2H+ + 2e –> H2O +0.82 V

The overall reaction is

NADH + H–> NAD+ + 2H+ + 2e eoxidation = 0.32 V
(1/2) O2 + 2H+ + 2e –> H2O ereduction = 0.82 V
net: NADH + (1/2)O2 + H+ –>
H2O + NAD+
erxn = 1.14 V

The electrical potential (erxn) is related to the free energy (DG) by the following equation:

DG= -nFerxn (13)

where n is the number of electrons transferred (in moles, from the balanced equation), and F is the Faraday constant (96,485 Coulombs/mole). (Using this equation, DG is given in Joules; one Joule = 1 Volt x 1 Coulomb.)

Hence the overall reaction for the oxidation of NADH paired with the reduction of O2 has a negative change in free energy (DG =-220 kJ); i.e., it is spontaneous. Thus, the higher the electrical potential of a reduction half reaction, the greater the tendency for the species to accept an electron.

Just as in the box above, the electrical potential for the overall reaction (electron transfer) between two electron carriers is the sum of the potentials for the two half reactions. As long as the potential for the overall reaction is positive the reaction is spontaneous. Hence, from Table 2 below, we see that cytochrome c1 (part of the cytochrome reductase complex, #3 in Figure 9) can spontaneously transfer an electron to cytochrome c (#4 in Figure 9). The net reaction is given by Equation 16, below.

reduced cytochrome c–> oxidized cytochrome c+ e eoxidation = – .220 V (14)
oxidized cytochrome c + e –> reduced cytochrome c ereduction = .250 V (15)
NET: reduced cyt c1 + oxidized cyt c –>
oxidized cyt c+ reduced cyt c
erxn = 0.030 V (16) Spontaneous

We can also see from Table 2 that cytochrome c1 cannot spontaneously transfer an electron to cytochrome b (Equation 19):

reduced cyt c–> oxidized cyt c+ e eoxidation = – .220 V (17)
oxidized cyt b + e –> reduced cyt b ereduction = – 0.34 V (18)
NET: reduced cyt c1 + oxidized cyt c –>
oxidized cyt c+ reduced cyt c
erxn = – 0.56 V (19) NOT Spontaneous

Table 2 lists the reduction potentials for each of the cytochrome proteins (i.e., the last three steps in the electron-transport chain before the electrons are accepted by O2) involved in the electron-transport chain. Note that each electron transfer is to a cytochrome with a higher reduction potential than the previous cytochrome. As described in the box above and seen in Equations 14-19, an increase in potential leads to a decrease in DG (Equation 13), and thus the transfer of electrons through the chain is spontaneous.

Complex Name Half Reaction Reduction Potential
Cytochrome reductase

(also known as cytochrome b-c1 complex)

(3 in Figure 9)

Cytochrome b (Fe(III) center)
+ e –>
Cytochrome b (Fe(II) center)
-0.34 V
(at pH 7, T=30oC)
Cytochrome c1 (Fe(III) center)
+ e– –>
Cytochrome c1 (Fe(II) center)
+0.220 V
(at pH 7, T=30oC)
Cytochrome c

(4 in Figure 9)

Cytochrome c (Fe(III) center)
+ e– –>
Cytochrome c (Fe(II) center)
+0.250 V
(at pH 7, T=30oC)
Cytochrome oxidase

(5 in Figure 9)

Cytochrome oxidase
( Fe(III) center) + e– –>
Cytochrome oxidase
(Fe(II) center)
+0.285 V
(at pH 7.4, T=25oC)
Table 2

To view the cytochrome molecules interactively using RASMOL, please click on the name of the complex to download the pdb file.

Hence, the electron-transport chain (which works because of the difference in reduction potentials) leads to a large concentration gradient for H+. As we shall see below, this huge concentration gradient leads to the production of ATP.

Questions on Electron Carriers: Steps in the Electron-Transport Chain; Reduction Potentials and Relationship to Free Energy

  • Briefly, explain why electrons travel from NADH-Q reductase, to ubiquinone (Q), to cytochrome reductase, rather than in the opposite direction.
  • One result of the transfer of electrons from NADH-Q reductase down the electron transport chain is that the concentration of protons (H+ ions) in the intermembrane space is increased.  Could cells move protons (H+ ions) from the matrix to the intermembrane space without transporting electrons?  Why or why not?

 ATP Synthetase: Production of ATP

We have seen that the electron-transport chain generates a large proton gradient across the inner mitochondrial membrane. But recall that the ultimate goal of oxidative phosphorylation is to generate ATP to supply readily-available free energy for the body. How does this occur? In addition to the electron-carrier proteins embedded in the inner mitochondrial membrane, a special protein called ATP synthetase (Figure 9, the red-colored protein) is also embedded in this membrane. ATP synthetase uses the proton gradient created by the electron-transport chain to drive the phosphorylation reaction that generates ATP (Figure 7c).

ATP synthetase is a protein consisting of two important segments: a transmembrane proton channel, and a catalytic component located inside the matrix. The proton-channel segment allows H+ ions to diffuse from the intermembrane space, where the concentration is high, to the matrix, where the concentration is low. Recall from the Kidney Dialysis tutorial that particles spontaneously diffuse from areas of high concentration to areas of low concentration. Thus, since the diffusion of protons through the channel component of ATP synthetase is spontaneous, this process is accompanied by a negative change in free energy (i.e., free energy is released). The catalytic component of ATP synthetase has a site where ADP can enter. Then, using the free energy released by the spontaneous diffusion of protons through the channel segment, a bond is formed between the ADP and a free phosphate group, creating an ATP molecule. The ATP is then released from the reaction site, and a new ADP molecule can enter in order to be phosphorylated.

Questions on ATP Synthetase: Production of ATP

  • A scientist has created a phospholipid-bilayer membrane containing ATP-synthetase proteins. Instead of a proton gradient, this scientist has created a large Cs+ gradient (many Cs+ ions on the side of the membrane without the catalytic unit, and few Cs+ ions on the side of the membrane with the catalytic unit). Would you expect the ATP-synthetase proteins in this membrane to be able to generate ATP, given an abundant supply of ADP and phosphate? Briefly, explain your answer. (HINT: Draw on your knowledge of the structure of protein channels to predict what effect replacing H+ ions with Cs+ ions would have.)
  • Certain toxins allow H+ ions to move freely across the inner mitochondrial membrane (i.e., without needing to pass through the channel in ATP synthetase). What effect do you expect these toxins to have on the production of ATP? Briefly, explain your answer.

Summary

In this tutorial, we have learned that the ability of the body to perform daily activities is dependent on thermodynamic, equilibrium, and electrochemical concepts.   These activities, which are typically based on nonspontaneous chemical reactions, are performed by using free-energy currency. The common free-energy currency is ATP, which is a molecule that easily dephosphorylates (loses a phosphate group) and releases a large amount of free energy. In the body, the nonspontaneous reactions are coupled to this very spontaneous dephosphorylation reaction, thereby making the overall reaction spontaneous (DG < 0). As the coupled reactions occur (i.e., as the body performs daily activities), ATP is consumed and the body regenerates ATP by using energy from the food we eat (Figure 3). As seen in Figure 4, the breakdown of glucose (glycolysis) obtained from the food we eat cannot by itself generate the large amount of ATP that is needed for metabolic energy by the body. However, glycolysis and the subsequent step, the citric-acid cycle, produce two easily oxidized molecules: NADH and FADH2. These redox molecules are used in an oxidative-phosphorylation process to produce the majority of the ATP that the body uses. This oxidative-phosphorylation process consists of two steps: the oxidation of NADH (or FADH2) and the phosphorylation reaction which regenerates ATP. Oxidative phosphorylation occurs in the mitochondria, and the two reactions (oxidation of NADH or FADHand phosphorylation to generate ATP) are coupled by a proton gradient across the inner membrane of the mitochondria (Figure 9). As seen in Figures 7 and 9, the oxidation of NADH occurs by electron transport through a series of protein complexes located in the inner membrane of the mitochondria. This electron transport is very spontaneous and creates the proton gradient that is necessary to then drive the phosphorylation reaction that generates the ATP. Hence, oxidative-phosphorylation demonstrates that free energy can be easily transferred by proton gradients. Oxidative-phosphorylation is the primary means of generating free-energy currency for aerobic organisms, and as such is one of the most important subjects in the study of bioenergetics (the study of energy and its chemical changes in the biological world).

Additional Link:

  • This fun description of oxidative phosphorylation by Dr. E.J.Oakeley contains step-by-step animated illustrations of the redox reactions involved, as well as a quiz to test your understanding of the material.

References:

Alberts, B. et al. In Molecular Biology of the Cell, 3rd ed., Garland Publishing, Inc.: New York, 1994, pp. 653-684.

Becker, W.M. and Deamer, D.W. In The World of the Cell, 2nd ed., The Benjamin/Cummings Publishing Co., Inc.: Redwood City, CA, 1991, pp. 291-307.

Fasman, G.D. In Handbook of Biochemistry and Molecular Biology, 3rd ed., CRC Press, Inc.: Cleveland, OH, 1976, Vol. I (Physical and Chemical Data), pp. 132-137.

Guex, N. and Peitsch, M.C. Electrophoresis, 1997, 18, 2714-2723. (SwissPDB Viewer) URL: http://www.expasy.ch/spdbv/mainpage.htm.

Moa, C., Ozer, Z., Zhou, M. and Uckun, F. X-Ray Structure of Glycerol Kinase Complexed with an ATP Analog Implies a Novel Mechanism for the ATP-Dependent Gylcerol Phosphorylation by Glycerol Kinase.Biochemical and Biophysical Reaearch Communications. 1999, 259, 640-644.

Persistence of Vision Ray Tracer (POV-Ray). URL: http://www.povray.org.

Stryer, L. In Biochemistry, 4th. ed., W.H. Freeman and Co.: New York, 1995, pp. 490, 509, 513, 529-557.

Zubay, G. Biochemistry, 3rd. ed., Wm. C. Brown Publishers: Dubuque, IA, 1983, p. 42.

Acknowledgements:

The authors thank Dewey Holten (Washington University in St. Louis) for many helpful suggestions in the writing of this tutorial.

The development of this tutorial was supported by a grant from the Howard Hughes Medical Institute, through the Undergraduate Biological Sciences Education program, Grant HHMI# 71199-502008 to Washington University.

Copyright 1999, Washington University, All Rights Reserved.

 

 

 

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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
————————————————————————————————————————————–
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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The role and importance of transcription factors

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

http://pharmaceuticalintelligence.com/2014/8/05/The-role-and-importance-of-transcripton-factors

The following is a second in the 2nd series that is focused on the topic of the impact of genomics and transcriptomics in the evolution of 21st century of medicine, which shall have to be more efficient and more effective by the end of this decade, if the prediction for the funding of Medicare is expected to run out. Even so, Social Security was devised by none other than the Otto von Bismarck, who unified Germany, and United Kingdom has had a charity hospital care system begun to protect the widows of the ravages of war, and nursing was developed by Florence Nightengale as a result of the experience of war. It can only be concluded that the care for the elderly, the infirm, and those who have little resources to live on has a long history in western civilization, and it will not cease to exist as a public social obligation anytime soon. The 20th century saw an explosive development of physics; organic, inorganic, biochemistry, and medicinal chemistry, and the elucidation of the genetic code and its mechanism of translation in plants, microorganisms, and eukaryotes.  All of which occurred irrespective of the most horrendous wars that have reshaped the world map.

The following are the second portions of a puzzle in construction that is intended to move into deeper complexities introduced by proteomics, cell metabolism, metabolomics, and signaling.  This is the only manner by which I can begin to appreciate what a wonder it is to view and live in this world with all its imperfections.

We have already visited the transcription process, by which an RNA sequence is read.  This is essential for protein synthesis through the ordering of the amino acids in the primary structure. However, there are microRNAs and noncoding RNAs, and there are transcription factors.  The transcription factors bind to chromatin, and the RNAs also have some role in regulating the transcription process. We shall examine this further.

  1. 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/

  1. The role and importance of transcription factors?
    Larry H. Bernstein, MD, FCAP, Writer and Curator
    http://pharmaceuticalintelligence.com/2014/8/05/What-is-the-meaning-of-so-many-RNAs
  2. What is the meaning of so many RNAs?

Larry H. Bernstein, MD, FCAP, Writer and Curator
http://pharmaceuticalintelligence.com/2014/8/05/What-is-the-meaning-of-so-many-RNAs

  1. Pathology Emergence in the 21st Century
    Larry Bernstein, MD, FCAP, Author and Curator
    http://pharmaceuticalintelligence.com/2014/08/03/pathology-emergence-in-the-21st-century/
  2. The Arnold Relman Challenge: US HealthCare Costs vs US HealthCare Outcomes

Larry H. Bernstein, MD, FCAP, Reviewer and Curator; and
Aviva Lev-Ari, PhD, RN, Curator
http://pharmaceuticalintelligence.com/2014/08/05/the-relman-challenge/

 

 

 

Quantifying transcription factor kinetics: At work or at play?

Posted online on September 11, 2013. (doi:10.3109/10409238.2013.833891)

Florian Mueller1,2, Timothy J. Stasevich3, Davide Mazza4, and James G. McNally5
1Institut Pasteur, Computational Imaging and Modeling Unit, CNRS, Paris, Fr
2Functional Imaging of Transcription, Institut de Biologie de l’Ecole Normale Supérieure, Paris, Fr
3Graduate School of Frontier Biosciences, Osaka University, Osaka, Jp
4Istituto Scientifico Ospedale San Raffaele, Centro di Imaging Sperimentale e Università Vita-Salute
San Raffaele, Milano, It, and
5Fluorescence Imaging Group, National Cancer Institute, NIH, Bethesda, MD, USA

Read More: http://informahealthcare.com/doi/abs/10.3109/10409238.2013.833891?goback=%2Egde_3795224_member_273907669#%2EUjYZ8jMt8mo%2Elinkedin

Abstract

Transcription factors (TFs) interact dynamically in vivo with chromatin binding sites. Here we summarize and compare the four different techniques that are currently used to measure these kinetics in live cells, namely fluorescence recovery after photobleaching (FRAP), fluorescence correlation spectroscopy (FCS), single molecule tracking (SMT) and competition ChIP (CC). We highlight the principles underlying each of these approaches as well as their advantages and disadvantages. A comparison of data from each of these techniques raises an important question: do measured transcription kinetics reflect biologically functional interactions at specific sites (i.e. working TFs) or do they reflect non-specific interactions (i.e. playing TFs)? To help resolve this dilemma we discuss five key unresolved biological questions related to the functionality of transient and prolonged binding events at both specific promoter response elements as well as non-specific sites. In support of functionality, we review data suggesting that TF residence times are tightly regulated, and that this regulation modulates transcriptional output at single genes. We argue that in addition to this site-specific regulatory role, TF residence times also determine the fraction of promoter targets occupied within a cell thereby impacting the functional status of cellular gene networks. Thus, TF residence times are key parameters that could influence transcription in multiple ways.

Keywords: Competition-ChIP, kinetic modeling, live-cell imaging, non-specific binding, specific binding, transcription, transcription factor dynamics http://informahealthcare.com/doi/abs/10.3109/10409238.2013.833891?goback=%2Egde_3795224_member_273907669#%2EUjYZ8jMt8mo%2Elinkedin

The Transcription Factor Titration Effect Dictates Level of Gene ExpressionCalifornia Institute of Technology

Robert C. Brewster, Franz M. Weinert, Hernan G. Garcia, Dan Song, Mattias Rydenfelt, and Rob Phillips  CalTech
 Cell Mar 13, 2014; 156:1312–1323,.

Models of transcription are often built around a picture of RNA polymerase and transcription factors (TFs) acting on a single copy of a promoter. However, most TFs are shared between multiple genes with varying binding affinities. Beyond that, genes often exist at high copy number—in multiple identical copies on the chromosome or on plasmids or viral vectors with copy numbers in the hundreds. Using a thermodynamic model, we characterize the interplay between TF copy number and the demand for that TF. We demonstrate the parameter-free predictive power of this model as a function of the copy number of the TF and the number and affinities of the available specific binding sites; such predictive control is important for the understanding of transcription and the desire to quantitatively design the output of genetic circuits. Finally, we use these experiments to dynamically measure plasmid copy number through the cell cycle.

 

 

Optimal reference genes for normalization of qRT-PCR data from archival formalin-fixed, paraffin-embedded breast tumors controlling for tumor cell content and decay of mRNA.

Tramm TSørensen BSOvergaard JAlsner J.

Diagn Mol Pathol. 2013 Sep;22(3):181-7. http://dx.doi.org:/10.1097/PDM.0b013e318285651e

Gene-expression analysis is increasingly performed on degraded mRNA from formalin-fixed, paraffin-embedded tissue (FFPE), giving the option of examining retrospective cohorts. The aim of this study was to select robust reference genes showing stable expression over time in FFPE, controlling for various content of tumor tissue and decay of mRNA because of variable length of storage of the tissue.

Sixteen reference genes were quantified by qRT-PCR in 40 FFPE breast tumor samples, stored for 1 to 29 years. Samples included 2 benign lesions and 38 carcinomas with varying tumor content. Stability of the reference genes were determined by the geNorm algorithm. mRNA was successfully extracted from all samples, and the 16 genes quantified in the majority of samples.

Results showed 14% loss of amplifiable mRNA per year, corresponding to a half-life of 4.6 years. The 4 most stable expressed genes were CALM2, RPL37A, ACTB, and RPLP0. Several of the other examined genes showed considerably instability over time (GAPDH, PSMC4, OAZ1, IPO8).

In conclusion, we identified 4 genes robustly expressed over time and independent of neoplastic tissue content in the FFPE block.   PMID:23846446

 

Structures of Cas9 Endonucleases Reveal RNA-Mediated Conformational Activation

Martin Jinek1,*,Fuguo Jiang2,*David W. Taylor3,4,*Samuel H. Sternberg5,*Emine Kaya2, et al.

 

1Department of Biochemistry, University of Zurich, CH-8057 Zurich, Switzerland. 2Department of Molecular and Cell Biology,3Howard Hughes Medical Institute, 4California Institute for Quantitative Biosciences, 5Department of Chemistry, 6Physical Biosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720,. 7The Laboratory for Molecular Infection Medicine Sweden, Umeå University, Umeå S-90187, Sweden. 8Helmholtz Centre for Infection Research, Department of Regulation in Infection Biology, D-38124 Braunschweig, Germany. 9Hannover Medical School, D-30625 Hannover, Germany. 10Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720.

‡ Present address: Friedrich Miescher Institute for Biomedical Research, Maulbeerstrasse 66 CH-4058 Basel, Switzerland.

§ Present address: Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA.

 

Science  http://dx.doi.org:/10.1126/science.1247997

 

Type II CRISPR-Cas systems use an RNA-guided DNA endonuclease, Cas9,

  • to generate double-strand breaks in invasive DNA during an adaptive bacterial immune response.

Cas9 has been harnessed as a powerful tool for genome editing and gene regulation in many eukaryotic organisms.

Here, we report 2.6 and 2.2 Å resolution crystal structures of two major Cas9 enzymes subtypes,

  • revealing the structural core shared by all Cas9 family members.

The architectures of Cas9 enzymes define nucleic acid binding clefts, and

single-particle electron microscopy reconstructions show that the two structural lobes harboring these clefts undergo guide

  • RNA-induced reorientation to form a central channel where DNA substrates are bound.

The observation that extensive structural rearrangements occur before target DNA duplex binding

  • implicates guide RNA loading as a key step in Cas9 activation.

MicroRNA function in endothelial cells
Dr. Virginie Mattot
Angiogenesis, endothelium activation
Solving the mystery of an unknown target gene using microRNA Target Site Blockers

Dr. Virgine Mattot works in the team “Angiogenesis, endothelium activation and Cancer” directed by Dr. Fabrice Soncin at the Institut de Biologie de Lille in France where she studies the roles played by microRNAs in endothelial cells during physiological and pathological processes such as angiogenesis or endothelium activation. She has been using Target Site Blockers to investigate the role of microRNAs on putative targets which functions are yet unknown.

What is the main focus of the research conducted in your lab?

We are studying endothelial cell functions with a particular interest in angiogenesis and endothelium activation during physiological and tumoral vascular development.

How did your research lead to the study of microRNAs?

A few years ago, we identified

  • an endothelial cell-specific gene which
  • harbors a microRNA in its intronic sequence.

We have since been working on understanding the functions of

  • both this new gene and its intronic microRNA in endothelial cells.

What is the aim of your current project?

While we were searching for the functions of the intronic microRNA,

  • we identified an unknown gene as a putative target.

The aim of my project was to investigate if this unknown gene was actually a genuine target and if regulation of this gene by the microRNA was involved in endothelial cell function. We had already characterized the endothelial cell phenotype associated with the inhibition of our intronic microRNA. We then used miRCURY LNA™ Target Site Blockers to demonstrate

  • the expression of this unknown gene is actually controlled by this microRNA.
  • the microRNA regulates specific endothelial cell properties through regulation of this unknown gene.

How did you perform the experiments and analyze the results?

LNA™ enhanced target site blockers (TSB) for our microRNA were designed by Exiqon. We

  • transfected the TSBs into endothelial cells using our standard procedure and
  • analysed the induced phenotype.

As a control for these experiments, a mutated version of the TSB was designed by Exiqon and transfected into endothelial cells. We first verified that this TSB was functional by analyzing

  • the expression of the miRNA target against which the TSB was directed
  • we then showed the TSB induced similar phenotypes as those when we inhibited the microRNA in the same cells.

What do you find to be the main benefits/advantage of the LNA™ microRNA target site blockers from Exiqon?

Target Site Blockers are efficient tools to demonstrate the specific involvement of

  • putative microRNA targets in the function played by this microRNA.

What would be your advice to colleagues about getting started with microRNA functional analysis?

  • it is essential to perform both gain and loss of functions experiments.

 Changing the core of transcription

Different members of the TAF family of proteins work in differentiated cells, such as motor neurons or brown fat cells, to control the expression of genes that are specific to each cell type.

Katherine A Jones
Jones. eLife 2014;3:e03575. http://dx.doi.org:/10.7554/eLife.03575

 

Related research articles: Herrera FJ, Yamaguchi T, Roelink H, Tjian R. 2014. Core promoter factor TAF9B regulates neuronal gene expression. eLife 3:e02559. http://dx.doi.org:/10.7554eLife.02559

Zhou H, Wan B, Grubisic I, Kaplan T, Tjian R. 2014. TAF7L modulates brown adipose tissue formation. eLife 3:e02811. Http://dx.doi.org:/10.7554/eLife.02811

 

Motor neurons (green) being grown in vitro

Motor neurons (green) being grown in vitro

Image Motor neurons (green) being grown in vitro

 

In a developing organism, different genes are expressed at different times

 

  • the pattern of gene expression can often change abruptly.

 

Expressing a gene involves multiple steps:

 

  • the DNA must be transcribed into a molecule of messenger RNA,
  • which is then trans­lated into a protein.

 

The mechanisms that start the transcription of protein-coding genes in rap­idly growing cells are reasonably well understood: two types of proteins—

 

  • DNA-binding activators and general transcription factors—

 

cooperate to recruit an enzyme called RNA polymerase, which then transcribes the gene (Kadonaga, 2012).

 

These proteins bind to a region of the gene called the promoter, which is

 

  • upstream from the protein-coding region of the gene.

 

TATA-binding protein is a general transcrip­tion factor that

  • binds to certain sequences of DNA bases found within promoters

14 TATA-binding protein associated factors (TAFs) are included into two different protein complexes called TFIID and SAGA (Müller et al., 2010). which, in budding yeast, can recruit TATA-binding protein to gene promoters (Basehoar et al., 2004), but not all genes require all of the general transcription factors, and some genes require both TFIID and SAGA complexes.

Although the steps that are required to switch on genes when cells are rapidly dividing are fairly well known,

  • the same is not true for cells that are differentiating into specialised cell types.

In these cells, many transcription factors are downregulated and

  • the entire pattern of gene expression changes dramatically.

Moreover, certain TAFs are strongly up-regulated during differentiation. The core transcriptional machinery is essentially rebuilt at the genes that are expressed in differentiated cells.

Over the years Robert Tjian of the University of California Berkeley and co-workers have illu­minated how individual TAFs can affect how a cell differentiates in different contexts (Figure 1). Now, in eLife, Francisco Herrera of UC Berkeley and co-workers—including Teppei Yamaguchi, Henk Roelink and Tjian—have identified a critical role for a TAF called TAF9B in the expression of genes in motor neurons (Herrera et al., 2014).

Herrera et al. found that TAF9B predominantly associates with the SAGA complex, rather than the TFIID complex, in the motor neuron cells. Mice in which the gene for TAF9B had been deleted had less neuronal tissue in the developing spinal cord. Moreover, the genes that are involved in forming the branches of neurons were not properly regu¬lated in these mice.

Recently, in another eLife paper, Tjian and co-workers at Berkeley, Fudan University and the Hebrew University of Jerusalem—including Haiying Zhou as first author, Bo Wan, Ivan Grubisic and Tommy Kaplan—reported that another TAF protein, called TAF7L, works as part of the TFIID complex to up-regulate genes that direct cells to become brown adipose tissue (Zhou et al., 2014).

 

TATA-binding protein associated factors

TATA-binding protein associated factors

Figure 1. TATA-binding protein associated factors (TAFs) regulate transcription in specific cell types. TAF3, for example, works with another transcription factor to regulate the expression of genes that are critical for the differentiation of the endoderm in the early embryo (Liu et al., 2011). TAF3 also forms a complex with the TATA-related factor, TRF3, to regulate Myogenin and other muscle-specific genes to form myotubes (Deato et al., 2008). TAF7L interacts with another transcription factor to activate genes involved in the formation of adipocytes (‘fat cells’) and adipose tissue (Zhou et al., 2013; Zhou et al., 2014). Finally, TAF9B is a key regulator of transcription in motor neurons (Herrera et al., 2014). The names of some of the genes regulated by the TAFs are shown in brackets.

TAF9B

Deleting the gene for TAF9B in mouse embryonic stem cells revealed that this TAF

  • is not needed for the growth of stem cells, or
  • required for the expression of genes that prevent differentiation:

both of these processes are known to be highly-dependent upon the TFIID complex
(Pijnappel et al., 2013). However,

  • genes that would normally be expressed specifically in neurons were not
  • up-regulated when cells without the TAF9B gene started to specialise.

Herrera et al. identified numerous genes that can only be switched on when the TAF9B protein is present, which means that it joins a growing list of TAF proteins that are dedicated to controllingthe expression of genes in specialised cell types.

TAF9B activates neuron-specific genes by binding to sites that

  • reside outside of these genes’ core promoters.

Further, many of these sites were also bound by a master regulator of motor neuron-specific genes.

TAF7L

 

Whilst most of the fat tissue in humans is white adipose tissue, which contains cells that store fatty molecules, some is brown adipose tissue, or ‘brown fat’, that instead generates heat. When TAF7L promotes the differentiation of brown fat, it up-regulates genes that are targeted by a tran­scription

factor called PPAR-γ; last year it was shown that this transcription factor also promotes the differentiation of white adipose tissue (Zhou et al., 2013).
Mice without the TAF7L gene had 40% less brown fat than wild-type mice, and also grew too much skeletal muscle tissue. TAF7L was specifi­cally required to activate genes that control how brown fat develops and functions. Thus TAF7L expression appears to shift the fate of a stem cell towards brown adipose tissue, potentially at the expense of skeletal muscle, as both cell types develop from the same group of stem cells.

When stem cells with less TAF7L than normal are differentiated in vitro, they yield more muscle than fat cells. Conversely, cells with an excess of TAF7L express brown fat-specific genes and switch off muscle-specific genes.

The work of Herrera et al. and Zhou et al. reinforces the idea that different TAFs

  • provide the flexibility needed to control gene expression in a tissue-specific manner, and
  • enable differenti­ating cells to change which genes they express rapidly.

However many interesting questions remain:

Which signals lead to the destruction of core transcription factors?
Are core promoter ele­ments at tissue-specific genes designed to rec­ognise variant TAFs?
What determines whether variant TAFs are incorporated within TFIID, SAGA, or other complexes?

Shortly after RNA polymerase II starts to tran­scribe a gene, it briefly pauses. Interestingly, a DNA sequence associated with this pausing, called the pause button, closely matches the sequences that bind to two subunits of TFIID (TAF6 and TAF9; Kadonaga, 2012). Consequently, TAF6 and TAF9 might be involved in pausing transcription, and if so, the variant TAF9B could play a similar role at motor neuron genes.

Molecular basis of transcription pausing

Jeffrey W. Roberts
Science 344, 1226 (2014);  http://dx.doi.org:/10.1126/science.1255712
http://www.sciencemag.org/content/344/6189/1226.full.html

During RNA synthesis, RNA polymerase moves erratically along DNA, frequently
resting as it produces an RNA copy of the DNA sequence. Such pausing helps coordinate the appearance of a transcript with its utilization by cellular processes; to this end,

  • the movement of RNA polymerase is modulated by mechanisms that determine its rate. For example,
  • pausing is critical to regulatory activities of the enzyme such as the termination of transcription. It is also
  • essential during early modifications of eukaryotic RNA polymerase II that activate the enzyme for elongation.

 

Two reports analyzing transcription pausing on a global scale in Escherichia coli, by Larson et al. ( 1) and by Vvedenskaya et al. ( 2) on page 1285 of this issue, suggest

 

  • new functions of pausing and important aspects of its molecular basis.

 

The studies of Larson et al. and Vvedenskaya et al. follow decades of analysis of

bacterial transcription that has illuminated the molecular basis of polymerase pausing

events that serve critical regulatory functions.

 

A transcription pause specified by the DNA sequence synchronizes the translation of RNA into protein

 

  • with the transcription of leader regions of operons (groups of genes transcribed together) for amino acid biosynthesis;

 

  • this coordination controls amino acid synthesis in response to amino acid availability ( 3).

A protein induced pause occurs when the E. coli initiation factor σ70 restrains RNA polymerase by binding a second occurrence of the “–10” promoter element.

 

This paused polymerase provides a structure for engaging a transcription antiterminator (the bacteriophage λ Q protein) ( 4) that, in turn, inhibits transcription

pauses, including those essential for transcription termination.

 

Biochemical and structural analyses have identified an endpoint of the pausing process called the “elemental pause” in which the catalytic structure in the active site is distorted,

 

  • preventing further nucleotide addition ( 7).

 

The elemental paused state also involves distinct

 

  • conformational changes in the polymerase that may favor transcription termination
  • and allow the his and related pauses to be stabilized by RNA hairpins ( 8).

A consensus sequence for ubiquitous pauses was identified, with two important elements:

 

  • a preference for pyrimidine [mostly cytosine (C)] at the newly formed RNA end
  • followed by G to be incorporated next—just as found for the his pause; and a preference for G at position –10 of the RNA (10 nucleotides before the 3’ end)

 

 

Polymerase, paused

Polymerase, paused

Polymerase, paused. During transcription, RNA exists in two states as RNA polymerase progresses: pretranslocated, just after the addition of the last nucleotide [here, cytosine (C)];

and posttranslocated, after all nucleic acids have shifted in register by one nucleotide relative

to the enzyme, exposing the active site for binding of the next substrate molecule [here, guanine (G)]. The pretranslocated state is dominant in the pause. The critical G-C base (RNA-DNA) pair at position –10 in the pretranslocated state and the nontemplate DNA strand G bound in the

polymerase in the posttranslocated state are marked with an asterisk.
Binding of G at position 􀀀1 to CRE only occurs in the posttranslocated state, which would thus

be favored over the pretranslocated state. Hence, if G binding inhibits pausing, then the rate-limiting paused structure must be in the pretranslocated state (a conclusion also made by Larson et al. from biochemical experiments).
This is an important insight into the sequence of protein–nucleic acid interactions that occur in pausing. Vvedenskaya et al. suggest that the actual role of the G binding site is to promote translocation and thus

inhibit pausing, to smooth out adventitious pauses in genomic DNA.
The studies by Larson et al. and Vvedenskaya et al. provide a refined and detailed analysis of DNA sequence–induced transcription pausing.
Processive Antitermination

Robert A. Weisberg1* and Max E. Gottesman2

Section on Microbial Genetics, Laboratory of Molecular Genetics, National Institute of Child Health and

Human Development, National Institutes of Health, Bethesda, Maryland 20892-2785,1 and

Institute of Cancer Research, Columbia University, New York, New York 100322

Journal Of Bacteriology, Jan. 1999; 181(2): 359–367.
After initiating synthesis of RNA at a promoter, RNA polymerase (RNAP) normally continues to elongate the transcript until it reaches a termination site. Important elements of termination sites are transcribed before polymerase translocation stops, and the resulting RNA is an active element of the termination pathway. Nascent transcripts of intrinsic sites can halt transcription without the assistance of additional factors, and

those of Rho-dependent sites recruit the Rho termination protein to the elongation complex. In both cases, RNAP, the transcript, and the template dissociate (reviewed in references 76 and 80).

 

Termination is rarely, if ever, completely efficient, and the expression of downstream genes can be controlled by altering the efficiency of terminator readthrough. Two distinct mechanisms of elongation control have been reported for bacterial RNA polymerases. In one, exemplified by attenuation of the his and trp operons of Salmonella typhimurium and Escherichia coli, respectively,

  • a single terminator is inactivated by interaction with an upstream sequence in the transcript, with a terminator-specific protein, or with a translating ribosome that follows closely behind RNAP (reviewed in references 35 and 104).

In a second, whose prototype is antitermination of phage l early transcription,

  • polymerase is stably modified to a terminator-resistant form after it leaves the promoter.

In this case, the modified enzyme not only transcribes through sequential downstream terminators,

  • but also it is less sensitive to the pause sites that normally delay transcript elongation.

Both pathways are widespread in nature, but in this minireview we consider only the second,

  • known as processive antitermination
    (for previous reviews, see references 22, 23, 27, and 32).

The recent explosive growth in our understanding of transcription elongation (reviewed in references 57, 96, and 99) make this an especially appropriate time to survey regulatory elements that target the transcription elongation complex.

Antitermination in l is induced by two quite distinct mechanisms.

  • the result of interaction between l N protein and its targets in the early phage transcripts,
  • an interaction between the l Q protein and its target in the late phage promoter.

We describe the N mechanism first. Lambda N, a small basic protein of the arginine- rich motif (ARM) (Fig. 1) family of RNA binding proteins, binds to a 15-nucleotide (nt) stem-loop called BOXB (17) (Fig. 2).

 

FIG. 1. [not shown] (A) Alignment of phage N proteins and the HK022 Nun protein. The color groupings reflect the frequency of amino acid substitutions in evolutionarily related protein domains: an amino acid is more likely to be replaced by one in the same color group than by one in a different color group in related proteins (34).

The amino-proximal ARM regions were aligned by eye and according to the structures of the P22 and l ARMs complexed to their cognate nut sites (see text and Fig. 2), and the remainder of the proteins was aligned by ClustalW (38). The dots indicate gaps introduced to improve the alignment. Aside from the ARM regions, the

proteins fall into three very distantly related (or unrelated) families: (i) l and phage 21; (ii) P22, phage L, and HK97; and (iii) HK022 Nun.

 

FIG. 2. [not shown] BOXA and BOXB RNAs and their interaction with the ARM of their cognate N proteins. The amino acid-nucleotide interactions are shown to the left except for BOXB of phage 21, for which the structure of the complex is unknown. The sequences of BOXA and BOXA-BOXB spacer are shown to the right. The dots

to the left and right of the spacer sequences are for alignment. (A) l N-ARM-BOXB complex (adapted from reference 48 with permission of the publisher). Open circles, pentagons, and rectangles represent phosphates, riboses, and bases, respectively. Watson-Crick base pairs (????) are indicated. The zigzag line denotes a sheared

G z A base pair. Open circles, open rectangles, and arrowheads depict ionic, hydrophobic, and hydrogen-bonding interactions, respectively. Guanine-11, indicated by a bold rectangle, is extruded from the BOXB loop (see text). (B) P22 N-ARM-BOXB complex (adapted from reference 15 with permission of the publisher). Open

circles, pentagons, rectangles, and ovals represent phosphates, riboses, bases, and amino acids, respectively. The solid pentagons indicate riboses with a C29-endo pucker.

Base stacking ( ), intermolecular hydrogen bonding or electrostatic interactions (,—–), intermolecular hydrophobic or van der Waals interactions (4), intramolecular hydrogen bonds (– – – –) and Watson-Crick base pairs (?????) are indicated. Cytosine-11 is extruded from the loop (see text). Note that the amino-terminal amino acid

residue in the complex corresponds to Asn-14 in the complete protein (Fig. 1), and the displayed amino acids are numbered accordingly. (C) NUTL site of phage 21. The arrows indicate the inverted sequence repeats of BOXB.

 

FIG. 3. [not skown] HK022 put sites and folded PUT RNAs. (A) Alignment of putL and putR (43). The numbers give distances from the start sites of the PL and PR promoters, respectively, and the pairs of arrows indicate inverted sequence repeats. (B) Folded PUTL and PUTR RNAs. The structures, which were generated by energy

minimization as described (43), have been partially confirmed by genetic and biochemical studies (7, 43).
The active bacterial elongation complex consists of

  • core RNAP,
  • template, and
  • RNA product.

The 39 end of the RNA

  • is engaged in the active site of the enzyme,
  • The following ;8 nt are hybridized to the template strand of the DNA, and
  • the next ;9 nt remain closely associated with RNAP (64).
  • About 17 nt of the nontemplate DNA strand are separated from the template strand in the transcription bubble.

Elongation complexes can also contain NusA and/or NusG. These proteins, which

  • increase the stability of the N-mediated antitermination complex (see above),
  • have different effects on elongation.
  • NusA decreases and NusG increases the elongation rate, and
  • both proteins alter termination efficiency in a terminator-specific manner (13, 14, 86; see reference 76).

An elongation complex, unless located at a terminator, is extraordinarily stable,

  • even when translocation is prevented by removal of substrates.

Recent observations suggest that this stability depends mainly on

  • interactions between RNAP and the RNA-DNA hybrid as well as
  • between polymerase and the downstream duplex DNA template (63, 87).

Nascent RNA emerging from the hybrid region and upstream duplex DNA

  • do not appear to be required.

The strength of the RNA-DNA hybrid is believed to

  • assure the lateral stability of the complex.

 

Reducing the strength of the RNA-DNA bonds, for example

  • by incorporation of nucleotide analogs,
  • favors backsliding of RNAP on the template, with consequent
  • disengagement of the 39 RNA end from the active site, and
  • concerted retreat of the RNA-DNA hybrid region from the 39 end (65).

Such a disengaged complex retains its resistance to dissociation and

  • is capable of resuming elongation if the original or a newly created 39 end reengages with the active site (10, 44, 45, 65, 71, 95).

Intrinsic terminators consist of a guanine- and cytosine-rich RNA hairpin stem

  • immediately followed by a short uracil-rich segment
  • within which termination can occur.

 

If termination does not occur at this point,

  • polymerase continues to elongate the transcript with normal processivity
  • until it reaches the next terminator.

Neither the stem nor the uracil-rich segment

  • is sufficient for termination, although
  • either can transiently slow elongation.

The weakness of base pairing between rU and dA

  • destabilizes the RNA-DNA hybrid in the uracil-rich segment, and
  • this probably contributes to termination.

Formation of the hairpin stem as nascent terminator RNA emerges from polymerase

  • destabilizes the RNA-DNA hybrid and
  • interrupts contacts between the emerging nascent RNA and RNAP (62a).

It might also interfere with the stabilizing interactions between

  • RNAP and the hybrid or those between RNAP and
  • the downstream region of the template.

Cross-linking of nucleic acid to RNAP suggests that

  • both the downstream DNA and the nascent RNA
  • that emerges from the hybrid region, and
  • within which the terminator hairpin might form,
  • are located close to the same regions of the enzyme (64).

Conversely, modifications that render RNAP termination resistant

  • could prevent the terminator stem from destabilizing one or more of these targets,
  • at least while the 39 end of the RNA is within the uracil rich segment of the terminator.

The l N and Q proteins and HK022 PUT RNA

  • also suppress Rho-dependent terminators (43a, 79, 103) which,
  • in contrast to intrinsic terminators, lack a precisely determined termination point.

Rho is an RNA-dependent ATPase that binds to cytosine-rich, unstructured regions in nascent RNA and acts preferentially

  • to terminate elongation complexes that are paused at nearby downstream sites
    (19, 29, 46, 47, 59, 60).

Rho possesses RNA-DNA helicase activity, and this activity is directional,

  • unwinding DNA paired to the 39 end of the RNA molecule (11, 90).
  • This corresponds to the location of the hybrid and of RNAP
    in an active ternary elongation complex.

The ability of antiterminators to suppress Rho-dependent and -independent terminators

  • suggests that they prevent a step that is common to both classes.

Given the helicase activity of Rho, a likely candidate for this step is disruption of the RNA-DNA

hybrid. However, other candidates, such as destabilization of RNAP-template or RNAP-hybrid interactions, are also plausible.

Alternatively, the ability of N, Q, and PUT to suppress RNAP pausing (31, 43, 54, 74)

  • suggests that they prevent Rho-dependent termination
  • by accelerating polymerase away from Rho bound at upstream RNA sites.

This explanation raises the problem of why NusG,

  • which also accelerates polymerase,
  • enhances rather than suppresses Rho-dependent termination (see above).

Clearly, the molecular details of processive antitermination remain poorly understood despite the 30 years that have elapsed since its discovery.

 

 

System wide analyses have underestimated protein abundances and the importance of transcription in mammals

OPEN ACCESS

Jingyi Jessica Li1, 2, Peter J Bickel1 and Mark D Biggin3

1Department of Statistics, University of California, Berkeley, CA, USA

2Departments of Statistics and Human Genetics, University of California, Los Angeles, CA, USA

3Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Academic editor – Barbara Engelhardt   http://dx.doi.org:/10.7717/peerj.270

Distributed under Creative-Commons CC-0

ABSTRACT

Large scale surveys in mammalian tissue culture cells suggest that the protein ex-

pressed at the median abundance is present at 8,000_16,000 molecules per cell and

that differences in mRNA expression between genes explain only 10_40% of the dif-

ferences in protein levels. We find, however, that these surveys have significantly un-

derestimated protein abundances and the relative importance of transcription.

Using individual measurements for 61 housekeeping proteins to rescale whole proteome

data from Schwanhausser et al. (2011), we find that the median protein detected is

expressed at 170,000 molecules per cell and that our corrected protein abundance

estimates show a higher correlation with mRNA abundances than do the uncorrected

protein data. In addition, we estimated the impact of further errors in mRNA and

protein abundances using direct experimental measurements of these errors.

The resulting analysis suggests that mRNA levels explain at least

  • 56% of the differences in protein abundance for the 4,212 genes

detected by Schwanhausser et al. (2011), though because one major source of error

could not be estimated the true percent contribution should be higher.
We also employed a second, independent strategy to

  • determine the contribution of mRNA levels to protein expression.

The variance in translation rates directly measured by ribosome profiling is only 12%

of that inferred by Schwanhausser et al. (2011), and

  • the measured and inferred translation rates correlate poorly (R2 D 13).

Based on this, our second strategy suggests that

  • mRNA levels explain _81% of the variance in protein levels.

We also determined the percent contributions of

  • transcription,
  • RNA degradation,
  • translation
  • and protein degradation

to the variance in protein abundances using both of our strategies.

While the magnitudes of the two estimates vary, they both suggest that

  • transcription plays a more important role than the earlier studies implied and
  • translation a much smaller role.

Finally, the above estimates only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately

  • 40% of genes in a given cell within a population express no mRNA.

Since there can be no translation in the absence of mRNA, we argue that

  • differences in translation rates can play no role in determining the expression levels for the _40% of genes that are non-expressed.

Subjects Bioinformatics, Computational Biology

Keywords Transcription, Translation, Mass spectrometry, Gene expression, Protein abundance

How to cite this article Li et al. (2014), System wide analyses have underestimated protein abundances and the importance of transcription in mammals. PeerJ 2:e270; 

http://dx.doi.org:/10.7717/peerj.270

 

 

Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

Evgeny Shmelkov1,2, Zuojian Tang2, Iannis Aifantis3, Alexander Statnikov2,4

Shmelkov et al. Biology Direct 2011, 6:15  http://www.biology-direct.com/content/6/1/15

 

Background: Pathway databases are becoming increasingly important and almost omnipresent in most types of biological and translational research. However, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors: MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA.

The employed benchmarking methodology first

  • involves integrating genome-wide binding with functional gene expression data to derive direct targets of transcription factors.
  • Then the lists of experimentally obtained direct targets are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.

Results: The results of this study show that for the majority of pathway databases,

  • the overlap between experimentally obtained target genes and targets reported in transcriptional regulatory pathway databases is surprisingly small and often is not statistically significant.

The only exception is MetaCore pathway database which yields statistically significant intersection with experimental results in 84% cases. Additionally, we suggest that

  • the lists of experimentally derived direct targets obtained in this study can be used to reveal new biological insight in transcriptional regulation and
  • suggest novel putative therapeutic targets in cancer.

Conclusions: Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by solid scientific evidence and rigorous empirical evaluation.

 

Illustration of statistical methodology

Illustration of statistical methodology

 

Figure 2 Illustration of statistical methodology for comparison

between a gold-standard and a pathway database

 

Additional material

Additional file 1: Supplementary Information. Table S1: Functional gene expression data. Table 2: Transcription factor-DNA binding data. Table S3: Most confident direct transcriptional targets of each of the four transcription factors. These targets were obtained by overlapping several gold-standards obtained with different datasets for the same transcription factor. Table S4: Genes directly regulated by two or more of the three transcription factors: MYC, NOTCH1, and RELA. Figure S1: Comparison of gene sets of transcriptional targets derived from ten different pathway databases by Jaccard index. In case, where Jaccard index of an overlap could not be determined due to comparison of two empty gene lists, we assigned value 0. Cells are colored according to the Jaccard index, from white (Jaccard index equal to 0) to dark-orange (Jaccard index equal to 1). Each sub-figure gives results for a different transcription factor: (a) AR, (b) BCL6, (c) MYC, (d) NOTCH1, (e) RELA, (f) STAT1, (g) TP53

 

http://dx.doi.org:/10.1186/1745-6150-6-15

 

Cite this article as: Shmelkov et al.: Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale. Biology Direct 2011 6:15

 

 

The Functional Consequences of Variation in Transcription Factor Binding
Darren A. Cusanovich1, Bryan Pavlovic1,2, Jonathan K. Pritchard1,2,3*, Yoav Gilad1*

1 Department of Human Genetics, University of Chicago, 2 Howard Hughes Medical Institute, University of Chicago, Chicago,

Illinois, 3 Departments of Genetics and Biology and Howard Hughes Medical Institute, Stanford University, Stanford, California,

 

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly play an important role in determining gene expression outputs, yet the regulatory logic underlying functional transcription factor binding is poorly understood. Many studies have focused on characterizing the genomic locations of TF binding, yet it is unclear to what extent TF binding at any specific locus has functional consequences with respect to gene expression output.

To evaluate the context of functional TF binding we knocked down

  • 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line.
  • We identified genes whose expression was affected by the knockdowns.
  • We intersected the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq) within 10 kb of the transcription start sites

This combination of data allowed us to infer functional TF binding.

  • we found that only a small subset of genes bound by a factor were differentially expressed following the knockdown of that factor, suggesting that
  • most interactions between TF and chromatin do not result in measurable changes in gene expression levels of putative target genes.
  • functional TF binding is enriched in regulatory elements that harbor
    • a large number of TF binding sites,
    • at sites with predicted higher binding affinity, and
    • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

Author Summary

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to

study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and it is generally accepted that much of the binding does not strongly influence gene expression. To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then examined which genes were impacted by the reduced transcription factor level. Our results implicate some attributes that might

influence what binding is functional, but they also suggest that a simple model of functional vs. non-functional binding may not suffice.

Citation: Cusanovich DA, Pavlovic B, Pritchard JK, Gilad Y (2014) The Functional Consequences of Variation in Transcription Factor Binding. PLoS Genet 10(3):e1004226. http://dx.doi.org:/10.1371/journal.pgen.1004226

Editor: Yitzhak Pilpel, Weizmann Institute of Science, Israel

 

 

Effect sizes for differentially expressed genes

Effect sizes for differentially expressed genes

Figure 2. Effect sizes for differentially expressed genes.

Boxplots of absolute Log2(fold-change) between knockdown arrays

and control arrays for all genes identified as differentially expressed in

each experiment. Outliers are not plotted. The gray bar indicates the

interquartile range across all genes differentially expressed in all

knockdowns. Boxplots are ordered by the number of genes differentially

expressed in each experiment. Outliers were not plotted.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g002

 

 

Intersecting binding data and expression data for each knockdown

Intersecting binding data and expression data for each knockdown

 

 

 

 

 

Figure 3. Intersecting binding data and expression data for each knockdown. (a) Example Venn diagrams showing the overlap of binding and differential expression for the knockdowns of HCST and IRF4 (the same genes as in Figure 1). (b) Boxplot summarizing the distribution of the fraction of all expressed genes that are bound by the targeted gene or downstream factors. (c) Boxplot summarizing the distribution of the fraction of

bound genes that are classified as differentially expressed, using an FDR of either 5% or 20%.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g003

 

Degree of binding correlated with function

Degree of binding correlated with function

 

Figure 4. Degree of binding correlated with function. Boxplots comparing (a) the number of sites bound, and (b) the number of differentially expressed transcription factors binding events near functionally or non-functionally bound genes. We considered binding for siRNA-targeted factor and any factor differentially expressed in the knockdown. (c) Focusing only on genes differentially expressed in common between each pairwise set of knockdowns we tested for enrichments of functional binding (y-axis). Pairwise comparisons between knock-down experiments were binned by the fraction of differentially expressed transcription factors in common between the two experiments. For these boxplots, outliers were not plotted.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g004

 

Distribution of functional binding about the TSS

Distribution of functional binding about the TSS

 

Figure 5. Distribution of functional binding about the TSS. (a) A density plot of the distribution of bound sites within 10 kb of the TSS for both functional and non-functional genes. Inset is a zoom-in of the region +/21 kb from the TSS (b) Boxplots comparing the distances from the TSS to the binding sites for functionally bound genes and non-functionally bound genes. For the boxplots, 0.001 was added before log10 transforming

the distances and outliers were not plotted.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g005

 

Magnitude and direction of differential expression after knockdown

Magnitude and direction of differential expression after knockdown

 

 

Figure 6. Magnitude and direction of differential expression after knockdown. (a) Density plot of all Log2(fold-changes) between the knockdown arrays and controls for genes that are differentially expressed at 5% FDR in one of the knockdown experiments as well as bound by the targeted transcription factor. (b) Plot of the fraction of differentially expressed putative direct targets that were up-regulated in each of the knockdown experiments.

http://dx.doi.org:/10.1371/journal.pgen.1004226.g006

 

To test whether the number of paralogs or the degree of similarity with the closest paralog for each transcription factor knocked down might influence the number of genes differentially expressed in our experiments, we obtained definitions of paralogy and the calculations of percent identity for 29 different factors from Ensembl’s BioMart (http://useast.ensembl.org/biomart/martview/) [31]. We used genome build GRCh37.p13.

For each gene, we counted the number of paralogs classified as a ‘‘within_species_paralog’’. After selecting only genes considered a ‘‘within_species_paralog’’, we also assigned the maximum percent identity as the closest paralog.

To evaluate the effect that an independent assignment of target genes to regulatory regions might have on our analyses, we used the definition of target genes defined by Thurman et al. (ftp://ftp.ebi.ac.uk/pub/databases/…)

which use correlations in DNase hypersensitivity between distal and proximal regulatory regions across different cell types to link distal elements to putative target genes [38].

We intersected the midpoints of our called binding events (defined above) with these regulatory elements in order to assign our binding events to specific target genes and then re-analyzed the overlap between

binding and differential expression in our experiments.

PLOS Genetics 6 Mar 2014; 10 (3), e1004226

 

 

 

The essential biology of the endoplasmic reticulum stress response

for structural and computational biologists

Sadao Wakabayashia, Hiderou Yoshidaa,*

aDepartment of Molecular Biochemistry, Graduate School of Life Science,

University of Hyogo, Hyogo 678-1297, Japan

CSBJ Mar 2013; 6(7), e201303010, http://dx.doi.org/10.5936/csbj.201303010

 

Abstract: The endoplasmic reticulum (ER) stress response is a cytoprotective mechanism that maintains homeostasis of the ER by

  • upregulating the capacity of the ER in accordance with cellular demands.

If the ER stress response cannot function correctly, because of reasons such as aging, genetic mutation or environmental stress,

  • unfolded proteins accumulate in the ER and cause ER stress-induced apoptosis,
  • resulting in the onset of folding diseases,
    • including Alzheimer’s disease and diabetes mellitus.

Although the mechanism of the ER stress response has been analyzed extensively by biochemists, cell biologists and molecular biologists, many aspects remain to be elucidated. For example,

  • it is unclear how sensor molecules detect ER stress, or
  • how cells choose the two opposite cell fates
    (survival or apoptosis) during the ER stress response.

To resolve these critical issues, structural and computational approaches will be indispensable, although the mechanism of the ER stress response is complicated and difficult to understand holistically at a glance. Here, we provide a concise introduction to the mammalian ER stress response for structural and computational biologists.

The basic mechanism of the mammalian ER stress response

The mammalian ER stress response consists of three pathways: the ATF6, IRE1 and PERK pathways, of which the main functions are

  • augmentation of folding and ERAD capacity, and
  • translational attenuation, respectively.

Although these response pathways cross-talk with each other and have several branched subpathways, we focus on the main pathways in this section.

  • The ATF6 pathway regulates the transcriptional induction of ER chaperone genes
  • pATF6(P) is a sensor molecule comprising a type II transmembrane protein residing on the ER membrane (Figure 2).

When pATF6(P) detects ER stress,

  • the protein is transported to the Golgi apparatus through vesicular transport in a COP-II vesicle
  • and is sequentially cleaved by two proteases residing in the Golgi,
    • namely site 1 protease (S1P) and site 2 protease (S2P)

The cytoplasmic portion of pATF6(P) (pATF6(N)) is

  1. released from the Golgi membrane,
  2. translocates into the nucleus,
  3. binds to an enhancer element called the ER stress response element (ERSE),
  4. and activates the transcription of ER chaperone genes,
  • including BiP, GRP94, calreticulin and protein disulfide isomerase (PDI)

The consensus nucleotide sequence of ERSE is CCAAT(N9)CCACG, and pATF6(N) recognizes both the CCACG portion and another transcription factor NF-Y,

  • which binds to the CCAAT portion

NF-Y is a general transcription factor required for

  • the transcription of various human genes

 

Figure 2. The ATF6 pathway. The sensor molecule pATF6(P) located on the ER membrane is transported to the Golgi apparatus by transport vesicles in response to ER stress. In the Golgi apparatus, pATF6(P) is sequentially cleaved by two proteases, S1P and S2P, resulting in release of the cytoplasmic portion pATF6(N) from the ER membrane. pATF6(N) translocates into the nucleus and activates transcription of ER chaperone genes through binding to the cis-acting enhancer ERSE.

 

Figure 3. The IRE1 pathway. In normal growth conditions, the sensor molecule IRE1 is an inactive monomer, whereas IRE1 forms an active oligomer in response to ER stress. Activated IRE1 converts unspliced XBP1 mRNA to mature mRNA by the cytoplasmic mRNA splicing. From mature XBP1 mRNA, an active transcription factor pXBP1(S) is translated and activates the transcription of ERAD genes through binding to the enhancer UPRE.

 

Figure 4. The PERK pathway. When PERK detects unfolded proteins in the ER, PERK phosphorylates eIF2α, resulting in translational attenuation and translational induction of ATF4. ATF4 activates the transcription of target genes encoding translation factors, anti-oxidation factors and a transcription factor CHOP. Other kinases such as PKR, GCN2 and HRI also phosphorylate eIF2α, and phosphorylated eIF2α is dephosphorylated by CReP, PP1C-GADD34 and p58IPK

 

Figure 7. Three functions of pXBP1(U). pXBP1(U) translated from XBP1(U) mRNA binds to pXBP1(S) and enhances its degradation. The CTR region of pXBP1(U) interacts with the ribosome tunnel and slows translation, while the HR2 region anchors XBP1(U) mRNA to the ER membrane, in order to enhance splicing of XBP1(U) mRNA by IRE1.

 

Figure 8. Major pathways of ER stress-induced apoptosis. ER stress induces apoptosis through various pathways, including transcriptional induction of CHOP by the PERK and ATF6 pathways, the IRE1-TRAF2 pathway and the caspase-12 pathway.

If cells are damaged by strong and sustained ER stress that they cannot deal with and ER stress still persists and hampers the survival of the organism, the ER stress response activates the apoptotic pathways and disposes of damaged cells from the body.

Computational simulation of response pathways to analyze the decision mechanism that determines cell fate (survival or apoptosis) provides a valuable analysis tool, although there have been few such studies to date.

Read Full Post »

Scientific Curation Fostering Expert Networks and Open Innovation: Lessons from Clive Thompson

Life-cycle of Science 2

 

 

 

 

 

 

 

 

 

 

 

Curators and Writer: Stephen J. Williams, Ph.D. with input from Curators Larry H. Bernstein, MD, FCAP, Dr. Justin D. Pearlman, MD, PhD, FACC and Dr. Aviva Lev-Ari, PhD, RN

(this discussion is in a three part series including:

Using Scientific Content Curation as a Method for Validation and Biocuration

Using Scientific Content Curation as a Method for Open Innovation)

 

Every month I get my Wired Magazine (yes in hard print, I still like to turn pages manually plus I don’t mind if I get grease or wing sauce on my magazine rather than on my e-reader) but I always love reading articles written by Clive Thompson. He has a certain flair for understanding the techno world we live in and the human/technology interaction, writing about interesting ways in which we almost inadvertently integrate new technologies into our day-to-day living, generating new entrepreneurship, new value.   He also writes extensively about tech and entrepreneurship.

October 2013 Wired article by Clive Thompson, entitled “How Successful Networks Nurture Good Ideas: Thinking Out Loud”, describes how the voluminous writings, postings, tweets, and sharing on social media is fostering connections between people and ideas which, previously, had not existed. The article was generated from Clive Thompson’s book Smarter Than you Think: How Technology is Changing Our Minds for the Better.Tom Peters also commented about the article in his blog (see here).

Clive gives a wonderful example of Ory Okolloh, a young Kenyan-born law student who, after becoming frustrated with the lack of coverage of problems back home, started a blog about Kenyan politics. Her blog not only got interest from movie producers who were documenting female bloggers but also gained the interest of fellow Kenyans who, during the upheaval after the 2007 Kenyan elections, helped Ory to develop a Google map for reporting of violence (http://www.ushahidi.com/, which eventually became a global organization using open-source technology to affect crises-management. There are a multitude of examples how networks and the conversations within these circles are fostering new ideas. As Clive states in the article:

 

Our ideas are PRODUCTS OF OUR ENVIRONMENT.

They are influenced by the conversations around us.

However the article got me thinking of how Science 2.0 and the internet is changing how scientists contribute, share, and make connections to produce new and transformative ideas.

But HOW MUCH Knowledge is OUT THERE?

 

Clive’s article listed some amazing facts about the mountains of posts, tweets, words etc. out on the internet EVERY DAY, all of which exemplifies the problem:

  • 154.6 billion EMAILS per DAY
  • 400 million TWEETS per DAY
  • 1 million BLOG POSTS (including this one) per DAY
  • 2 million COMMENTS on WordPress per DAY
  • 16 million WORDS on Facebook per DAY
  • TOTAL 52 TRILLION WORDS per DAY

As he estimates this would be 520 million books per DAY (book with average 100,000 words).

A LOT of INFO. But as he suggests it is not the volume but how we create and share this information which is critical as the science fiction writer Theodore Sturgeon noted “Ninety percent of everything is crap” AKA Sturgeon’s Law.

 

Internet live stats show how congested the internet is each day (http://www.internetlivestats.com/). Needless to say Clive’s numbers are a bit off. As of the writing of this article:

 

  • 2.9 billion internet users
  • 981 million websites (only 25,000 hacked today)
  • 128 billion emails
  • 385 million Tweets
  • > 2.7 million BLOG posts today (including this one)

 

The Good, The Bad, and the Ugly of the Scientific Internet (The Wild West?)

 

So how many science blogs are out there? Well back in 2008 “grrlscientistasked this question and turned up a total of 19,881 blogs however most were “pseudoscience” blogs, not written by Ph.D or MD level scientists. A deeper search on Technorati using the search term “scientist PhD” turned up about 2,000 written by trained scientists.

So granted, there is a lot of

goodbadugly

 

              ….. when it comes to scientific information on the internet!

 

 

 

 

 

I had recently re-posted, on this site, a great example of how bad science and medicine can get propagated throughout the internet:

http://pharmaceuticalintelligence.com/2014/06/17/the-gonzalez-protocol-worse-than-useless-for-pancreatic-cancer/

 

and in a Nature Report:Stem cells: Taking a stand against pseudoscience

http://www.nature.com/news/stem-cells-taking-a-stand-against-pseudoscience-1.15408

Drs.Elena Cattaneo and Gilberto Corbellini document their long, hard fight against false and invalidated medical claims made by some “clinicians” about the utility and medical benefits of certain stem-cell therapies, sacrificing their time to debunk medical pseudoscience.

 

Using Curation and Science 2.0 to build Trusted, Expert Networks of Scientists and Clinicians

 

Establishing networks of trusted colleagues has been a cornerstone of the scientific discourse for centuries. For example, in the mid-1640s, the Royal Society began as:

 

“a meeting of natural philosophers to discuss promoting knowledge of the

natural world through observation and experiment”, i.e. science.

The Society met weekly to witness experiments and discuss what we

would now call scientific topics. The first Curator of Experiments

was Robert Hooke.”

 

from The History of the Royal Society

 

Royal Society CoatofArms

 

 

 

 

 

 

The Royal Society of London for Improving Natural Knowledge.

(photo credit: Royal Society)

(Although one wonders why they met “in-cognito”)

Indeed as discussed in “Science 2.0/Brainstorming” by the originators of OpenWetWare, an open-source science-notebook software designed to foster open-innovation, the new search and aggregation tools are making it easier to find, contribute, and share information to interested individuals. This paradigm is the basis for the shift from Science 1.0 to Science 2.0. Science 2.0 is attempting to remedy current drawbacks which are hindering rapid and open scientific collaboration and discourse including:

  • Slow time frame of current publishing methods: reviews can take years to fashion leading to outdated material
  • Level of information dissemination is currently one dimensional: peer-review, highly polished work, conferences
  • Current publishing does not encourage open feedback and review
  • Published articles edited for print do not take advantage of new web-based features including tagging, search-engine features, interactive multimedia, no hyperlinks
  • Published data and methodology incomplete
  • Published data not available in formats which can be readably accessible across platforms: gene lists are now mandated to be supplied as files however other data does not have to be supplied in file format

(put in here a brief blurb of summary of problems and why curation could help)

 

Curation in the Sciences: View from Scientific Content Curators Larry H. Bernstein, MD, FCAP, Dr. Justin D. Pearlman, MD, PhD, FACC and Dr. Aviva Lev-Ari, PhD, RN

Curation is an active filtering of the web’s  and peer reviewed literature found by such means – immense amount of relevant and irrelevant content. As a result content may be disruptive. However, in doing good curation, one does more than simply assign value by presentation of creative work in any category. Great curators comment and share experience across content, authors and themes. Great curators may see patterns others don’t, or may challenge or debate complex and apparently conflicting points of view.  Answers to specifically focused questions comes from the hard work of many in laboratory settings creatively establishing answers to definitive questions, each a part of the larger knowledge-base of reference. There are those rare “Einstein’s” who imagine a whole universe, unlike the three blind men of the Sufi tale.  One held the tail, the other the trunk, the other the ear, and they all said this is an elephant!
In my reading, I learn that the optimal ratio of curation to creation may be as high as 90% curation to 10% creation. Creating content is expensive. Curation, by comparison, is much less expensive.

– Larry H. Bernstein, MD, FCAP

Curation is Uniquely Distinguished by the Historical Exploratory Ties that Bind –Larry H. Bernstein, MD, FCAP

The explosion of information by numerous media, hardcopy and electronic, written and video, has created difficulties tracking topics and tying together relevant but separated discoveries, ideas, and potential applications. Some methods to help assimilate diverse sources of knowledge include a content expert preparing a textbook summary, a panel of experts leading a discussion or think tank, and conventions moderating presentations by researchers. Each of those methods has value and an audience, but they also have limitations, particularly with respect to timeliness and pushing the edge. In the electronic data age, there is a need for further innovation, to make synthesis, stimulating associations, synergy and contrasts available to audiences in a more timely and less formal manner. Hence the birth of curation. Key components of curation include expert identification of data, ideas and innovations of interest, expert interpretation of the original research results, integration with context, digesting, highlighting, correlating and presenting in novel light.

Justin D Pearlman, MD, PhD, FACC from The Voice of Content Consultant on The  Methodology of Curation in Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation The Art of Scientific & Medical Curation

 

In Power of Analogy: Curation in Music, Music Critique as a Curation and Curation of Medical Research Findings – A Comparison, Drs. Larry Bernstein and Aviva Lev-Ari likens the medical and scientific curation process to curation of musical works into a thematic program:

 

Work of Original Music Curation and Performance:

 

Music Review and Critique as a Curation

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

… leading to new, curated, and collaborative works by networks of experts to generate (in this case) ebooks on most significant trends and interpretations of scientific knowledge as relates to medical practice.

 

In Summary: How Scientific Content Curation Can Help

 

Given the aforementioned problems of:

        I.            the complex and rapid deluge of scientific information

      II.            the need for a collaborative, open environment to produce transformative innovation

    III.            need for alternative ways to disseminate scientific findings

CURATION MAY OFFER SOLUTIONS

        I.            Curation exists beyond the review: curation decreases time for assessment of current trends adding multiple insights, analyses WITH an underlying METHODOLOGY (discussed below) while NOT acting as mere reiteration, regurgitation

 

      II.            Curation providing insights from WHOLE scientific community on multiple WEB 2.0 platforms

 

    III.            Curation makes use of new computational and Web-based tools to provide interoperability of data, reporting of findings (shown in Examples below)

 

Therefore a discussion is given on methodologies, definitions of best practices, and tools developed to assist the content curation community in this endeavor.

Methodology in Scientific Content Curation as Envisioned by Aviva lev-Ari, PhD, RN

 

At Leaders in Pharmaceutical Business Intelligence, site owner and chief editor Aviva lev-Ari, PhD, RN has been developing a strategy “for the facilitation of Global access to Biomedical knowledge rather than the access to sheer search results on Scientific subject matters in the Life Sciences and Medicine”. According to Aviva, “for the methodology to attain this complex goal it is to be dealing with popularization of ORIGINAL Scientific Research via Content Curation of Scientific Research Results by Experts, Authors, Writers using the critical thinking process of expert interpretation of the original research results.” The following post:

Cardiovascular Original Research: Cases in Methodology Design for Content Curation and Co-Curation

 

http://pharmaceuticalintelligence.com/2013/07/29/cardiovascular-original-research-cases-in-methodology-design-for-content-curation-and-co-curation/

demonstrate two examples how content co-curation attempts to achieve this aim and develop networks of scientist and clinician curators to aid in the active discussion of scientific and medical findings, and use scientific content curation as a means for critique offering a “new architecture for knowledge”. Indeed, popular search engines such as Google, Yahoo, or even scientific search engines such as NCBI’s PubMed and the OVID search engine rely on keywords and Boolean algorithms …

which has created a need for more context-driven scientific search and discourse.

In Science and Curation: the New Practice of Web 2.0, Célya Gruson-Daniel (@HackYourPhd) states:

To address this need, human intermediaries, empowered by the participatory wave of web 2.0, naturally started narrowing down the information and providing an angle of analysis and some context. They are bloggers, regular Internet users or community managers – a new type of profession dedicated to the web 2.0. A new use of the web has emerged, through which the information, once produced, is collectively spread and filtered by Internet users who create hierarchies of information.

.. where Célya considers curation an essential practice to manage open science and this new style of research.

As mentioned above in her article, Dr. Lev-Ari represents two examples of how content curation expanded thought, discussion, and eventually new ideas.

  1. Curator edifies content through analytic process = NEW form of writing and organizations leading to new interconnections of ideas = NEW INSIGHTS

i)        Evidence: curation methodology leading to new insights for biomarkers

 

  1. Same as #1 but multiple players (experts) each bringing unique insights, perspectives, skills yielding new research = NEW LINE of CRITICAL THINKING

ii)      Evidence: co-curation methodology among cardiovascular experts leading to cardiovascular series ebooks

Life-cycle of Science 2

The Life Cycle of Science 2.0. Due to Web 2.0, new paradigms of scientific collaboration are rapidly emerging.  Originally, scientific discovery were performed by individual laboratories or “scientific silos” where the main method of communication was peer-reviewed publication, meeting presentation, and ultimately news outlets and multimedia. In this digital era, data was organized for literature search and biocurated databases. In an era of social media, Web 2.0, a group of scientifically and medically trained “curators” organize the piles of data of digitally generated data and fit data into an organizational structure which can be shared, communicated, and analyzed in a holistic approach, launching new ideas due to changes in organization structure of data and data analytics.

 

The result, in this case, is a collaborative written work above the scope of the review. Currently review articles are written by experts in the field and summarize the state of a research are. However, using collaborative, trusted networks of experts, the result is a real-time synopsis and analysis of the field with the goal in mind to

INCREASE THE SCIENTIFIC CURRENCY.

For detailed description of methodology please see Cardiovascular Original Research: Cases in Methodology Design for Content Co-Curation The Art of Scientific & Medical Curation

 

In her paper, Curating e-Science Data, Maureen Pennock, from The British Library, emphasized the importance of using a diligent, validated, and reproducible, and cost-effective methodology for curation by e-science communities over the ‘Grid:

“The digital data deluge will have profound repercussions for the infrastructure of research and beyond. Data from a wide variety of new and existing sources will need to be annotated with metadata, then archived and curated so that both the data and the programmes used to transform the data can be reproduced for use in the future. The data represent a new foundation for new research, science, knowledge and discovery”

— JISC Senior Management Briefing Paper, The Data Deluge (2004)

 

As she states proper data and content curation is important for:

  • Post-analysis
  • Data and research result reuse for new research
  • Validation
  • Preservation of data in newer formats to prolong life-cycle of research results

However she laments the lack of

  • Funding for such efforts
  • Training
  • Organizational support
  • Monitoring
  • Established procedures

 

Tatiana Aders wrote a nice article based on an interview with Microsoft’s Robert Scoble, where he emphasized the need for curation in a world where “Twitter is the replacement of the Associated Press Wire Machine” and new technologic platforms are knocking out old platforms at a rapid pace. In addition he notes that curation is also a social art form where primary concerns are to understand an audience and a niche.

Indeed, part of the reason the need for curation is unmet, as writes Mark Carrigan, is the lack of appreciation by academics of the utility of tools such as Pinterest, Storify, and Pearl Trees to effectively communicate and build collaborative networks.

And teacher Nancy White, in her article Understanding Content Curation on her blog Innovations in Education, shows examples of how curation in an educational tool for students and teachers by demonstrating students need to CONTEXTUALIZE what the collect to add enhanced value, using higher mental processes such as:

  • Knowledge
  • Comprehension
  • Application
  • Analysis
  • Synthesis
  • Evaluation

curating-tableA GREAT table about the differences between Collecting and Curating by Nancy White at http://d20innovation.d20blogs.org/2012/07/07/understanding-content-curation/

 

 

 

 

 

 

 

 

 

 

 

University of Massachusetts Medical School has aggregated some useful curation tools at http://esciencelibrary.umassmed.edu/data_curation

Although many tools are related to biocuration and building databases but the common idea is curating data with indexing, analyses, and contextual value to provide for an audience to generate NETWORKS OF NEW IDEAS.

See here for a curation of how networks fosters knowledge, by Erika Harrison on ScoopIt

(http://www.scoop.it/t/mobilizing-knowledge-through-complex-networks)

 

“Nowadays, any organization should employ network scientists/analysts who are able to map and analyze complex systems that are of importance to the organization (e.g. the organization itself, its activities, a country’s economic activities, transportation networks, research networks).”

Andrea Carafa insight from World Economic Forum New Champions 2012 “Power of Networks

 

Creating Content Curation Communities: Breaking Down the Silos!

 

An article by Dr. Dana Rotman “Facilitating Scientific Collaborations Through Content Curation Communities” highlights how scientific information resources, traditionally created and maintained by paid professionals, are being crowdsourced to professionals and nonprofessionals in which she termed “content curation communities”, consisting of professionals and nonprofessional volunteers who create, curate, and maintain the various scientific database tools we use such as Encyclopedia of Life, ChemSpider (for Slideshare see here), biowikipedia etc. Although very useful and openly available, these projects create their own challenges such as

  • information integration (various types of data and formats)
  • social integration (marginalized by scientific communities, no funding, no recognition)

The authors set forth some ways to overcome these challenges of the content curation community including:

  1. standardization in practices
  2. visualization to document contributions
  3. emphasizing role of information professionals in content curation communities
  4. maintaining quality control to increase respectability
  5. recognizing participation to professional communities
  6. proposing funding/national meeting – Data Intensive Collaboration in Science and Engineering Workshop

A few great presentations and papers from the 2012 DICOSE meeting are found below

Judith M. Brown, Robert Biddle, Stevenson Gossage, Jeff Wilson & Steven Greenspan. Collaboratively Analyzing Large Data Sets using Multitouch Surfaces. (PDF) NotesForBrown

 

Bill Howe, Cecilia Aragon, David Beck, Jeffrey P. Gardner, Ed Lazowska, Tanya McEwen. Supporting Data-Intensive Collaboration via Campus eScience Centers. (PDF) NotesForHowe

 

Kerk F. Kee & Larry D. Browning. Challenges of Scientist-Developers and Adopters of Existing Cyberinfrastructure Tools for Data-Intensive Collaboration, Computational Simulation, and Interdisciplinary Projects in Early e-Science in the U.S.. (PDF) NotesForKee

 

Ben Li. The mirages of big data. (PDF) NotesForLiReflectionsByBen

 

Betsy Rolland & Charlotte P. Lee. Post-Doctoral Researchers’ Use of Preexisting Data in Cancer Epidemiology Research. (PDF) NoteForRolland

 

Dana Rotman, Jennifer Preece, Derek Hansen & Kezia Procita. Facilitating scientific collaboration through content curation communities. (PDF) NotesForRotman

 

Nicholas M. Weber & Karen S. Baker. System Slack in Cyberinfrastructure Development: Mind the Gaps. (PDF) NotesForWeber

Indeed, the movement of Science 2.0 from Science 1.0 had originated because these “silos” had frustrated many scientists, resulting in changes in the area of publishing (Open Access) but also communication of protocols (online protocol sites and notebooks like OpenWetWare and BioProtocols Online) and data and material registries (CGAP and tumor banks). Some examples are given below.

Open Science Case Studies in Curation

1. Open Science Project from Digital Curation Center

This project looked at what motivates researchers to work in an open manner with regard to their data, results and protocols, and whether advantages are delivered by working in this way.

The case studies consider the benefits and barriers to using ‘open science’ methods, and were carried out between November 2009 and April 2010 and published in the report Open to All? Case studies of openness in research. The Appendices to the main report (pdf) include a literature review, a framework for characterizing openness, a list of examples, and the interview schedule and topics. Some of the case study participants kindly agreed to us publishing the transcripts. This zip archive contains transcripts of interviews with researchers in astronomy, bioinformatics, chemistry, and language technology.

 

see: Pennock, M. (2006). “Curating e-Science Data”. DCC Briefing Papers: Introduction to Curation. Edinburgh: Digital Curation Centre. Handle: 1842/3330. Available online: http://www.dcc.ac.uk/resources/briefing-papers/introduction-curation– See more at: http://www.dcc.ac.uk/resources/briefing-papers/introduction-curation/curating-e-science-data#sthash.RdkPNi9F.dpuf

 

2.      cBIO -cBio’s biological data curation group developed and operates using a methodology called CIMS, the Curation Information Management System. CIMS is a comprehensive curation and quality control process that efficiently extracts information from publications.

 

3. NIH Topic Maps – This website provides a database and web-based interface for searching and discovering the types of research awarded by the NIH. The database uses automated, computer generated categories from a statistical analysis known as topic modeling.

 

4. SciKnowMine (USC)- We propose to create a framework to support biocuration called SciKnowMine (after ‘Scientific Knowledge Mine’), cyberinfrastructure that supports biocuration through the automated mining of text, images, and other amenable media at the scale of the entire literature.

 

  1. OpenWetWareOpenWetWare is an effort to promote the sharing of information, know-how, and wisdom among researchers and groups who are working in biology & biological engineering. Learn more about us.   If you would like edit access, would be interested in helping out, or want your lab website hosted on OpenWetWare, pleasejoin us. OpenWetWare is managed by the BioBricks Foundation. They also have a wiki about Science 2.0.

6. LabTrove: a lightweight, web based, laboratory “blog” as a route towards a marked up record of work in a bioscience research laboratory. Authors in PLOS One article, from University of Southampton, report the development of an open, scientific lab notebook using a blogging strategy to share information.

7. OpenScience ProjectThe OpenScience project is dedicated to writing and releasing free and Open Source scientific software. We are a group of scientists, mathematicians and engineers who want to encourage a collaborative environment in which science can be pursued by anyone who is inspired to discover something new about the natural world.

8. Open Science Grid is a multi-disciplinary partnership to federate local, regional, community and national cyberinfrastructures to meet the needs of research and academic communities at all scales.

 

9. Some ongoing biomedical knowledge (curation) projects at ISI

IICurate
This project is concerned with developing a curation and documentation system for information integration in collaboration with the II Group at ISI as part of the BIRN.

BioScholar
It’s primary purpose is to provide software for experimental biomedical scientists that would permit a single scientific worker (at the level of a graduate student or postdoctoral worker) to design, construct and manage a shared knowledge repository for a research group derived on a local store of PDF files. This project is funded by NIGMS from 2008-2012 ( RO1-GM083871).

10. Tools useful for scientific content curation

 

Research Analytic and Curation Tools from University of Queensland

 

Thomson Reuters information curation services for pharma industry

 

Microblogs as a way to communicate information about HPV infection among clinicians and patients; use of Chinese microblog SinaWeibo as a communication tool

 

VIVO for scientific communities– In order to connect this information about research activities across institutions and make it available to others, taking into account smaller players in the research landscape and addressing their need for specific information (for example, by proving non-conventional research objects), the open source software VIVO that provides research information as linked open data (LOD) is used in many countries.  So-called VIVO harvesters collect research information that is freely available on the web, and convert the data collected in conformity with LOD standards. The VIVO ontology builds on prevalent LOD namespaces and, depending on the needs of the specialist community concerned, can be expanded.

 

 

11. Examples of scientific curation in different areas of Science/Pharma/Biotech/Education

 

From Science 2.0 to Pharma 3.0 Q&A with Hervé Basset

http://digimind.com/blog/experts/pharma-3-0/

Hervé Basset, specialist librarian in the pharmaceutical industry and owner of the blog “Science Intelligence“, to talk about the inspiration behind his recent book  entitled “From Science 2.0 to Pharma 3.0″, published by Chandos Publishing and available on Amazon and how health care companies need a social media strategy to communicate and convince the health-care consumer, not just the practicioner.

 

Thomson Reuters and NuMedii Launch Ground-Breaking Initiative to Identify Drugs for Repurposing. Companies leverage content, Big Data analytics and expertise to improve success of drug discovery

 

Content Curation as a Context for Teaching and Learning in Science

 

#OZeLIVE Feb2014

http://www.youtube.com/watch?v=Ty-ugUA4az0

Creative Commons license

 

DigCCur: A graduate level program initiated by University of North Carolina to instruct the future digital curators in science and other subjects

 

Syracuse University offering a program in eScience and digital curation

 

Curation Tips from TED talks and tech experts

Steven Rosenbaum from Curation Nation

http://www.youtube.com/watch?v=HpncJd1v1k4

 

Pawan Deshpande form Curata on how content curation communities evolve and what makes a good content curation:

http://www.youtube.com/watch?v=QENhIU9YZyA

 

How the Internet of Things is Promoting the Curation Effort

Update by Stephen J. Williams, PhD 3/01/19

Up till now, curation efforts like wikis (Wikipedia, Wikimedicine, Wormbase, GenBank, etc.) have been supported by a largely voluntary army of citizens, scientists, and data enthusiasts.  I am sure all have seen the requests for donations to help keep Wikipedia and its other related projects up and running.  One of the obscure sister projects of Wikipedia, Wikidata, wants to curate and represent all information in such a way in which both machines, computers, and humans can converse in.  About an army of 4 million have Wiki entries and maintain these databases.

Enter the Age of the Personal Digital Assistants (Hellooo Alexa!)

In a March 2019 WIRED article “Encyclopedia Automata: Where Alexa Gets Its Information”  senior WIRED writer Tom Simonite reports on the need for new types of data structure as well as how curated databases are so important for the new fields of AI as well as enabling personal digital assistants like Alexa or Google Assistant decipher meaning of the user.

As Mr. Simonite noted, many of our libraries of knowledge are encoded in an “ancient technology largely opaque to machines-prose.”   Search engines like Google do not have a problem with a question asked in prose as they just have to find relevant links to pages. Yet this is a problem for Google Assistant, for instance, as machines can’t quickly extract meaning from the internet’s mess of “predicates, complements, sentences, and paragraphs. It requires a guide.”

Enter Wikidata.  According to founder Denny Vrandecic,

Language depends on knowing a lot of common sense, which computers don’t have access to

A wikidata entry (of which there are about 60 million) codes every concept and item with a numeric code, the QID code number. These codes are integrated with tags (like tags you use on Twitter as handles or tags in WordPress used for Search Engine Optimization) so computers can identify patterns of recognition between these codes.

Now human entry into these databases are critical as we add new facts and in particular meaning to each of these items.  Else, machines have problems deciphering our meaning like Apple’s Siri, where they had complained of dumb algorithms to interpret requests.

The knowledge of future machines could be shaped by you and me, not just tech companies and PhDs.

But this effort needs money

Wikimedia’s executive director, Katherine Maher, had prodded and cajoled these megacorporations for tapping the free resources of Wiki’s.  In response, Amazon and Facebook had donated millions for the Wikimedia projects.  Google recently gave 3.1 million USD$ in donations.

 

Future postings on the relevance and application of scientific curation will include:

Using Scientific Content Curation as a Method for Validation and Biocuration

 

Using Scientific Content Curation as a Method for Open Innovation

 

Other posts on this site related to Content Curation and Methodology include:

The growing importance of content curation

Data Curation is for Big Data what Data Integration is for Small Data

6 Steps to More Effective Content Curation

Stem Cells and Cardiac Repair: Content Curation & Scientific Reporting

Cancer Research: Curations and Reporting

Cardiovascular Diseases and Pharmacological Therapy: Curations

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

Exploring the Impact of Content Curation on Business Goals in 2013

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

conceived: NEW Definition for Co-Curation in Medical Research

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

Reconstructed Science Communication for Open Access Online Scientific Curation

 

 

Read Full Post »

Larry H Bernstein, MD, FCAP, Reporter and Curator

http://pharmaceyticalinnovation.com/7/10/2014/A new relationship identified in preterm stress and development of autism or schizophrenia/

 

This is a fascinating study.  It is of considerable interest because it deals with several items that need to be addressed with respect to neurodevelopmental disruptive disorders.  It leaves open some aspects that are known, but not subject to investigation in the experiments.  Then there is also no reporting of some associations that are known at the time of deveopment of these disorders – autism spectrum, and schizophrenia.  Of course, I don’t know how it would be possible to also look at prediction of a possible relationship to later development of mood disorders.

  1. The placenta functions as an endocrine organ in the conversion of androsteinedione to testosterone during pregnancy, which is delivered to the fetus.
  2. The conversion is by a known enzymatic pathway – and there is a sex difference in the depression of testosterone in males, females not affected.
  3. There is a greater susceptibility of males to autism and schizophrenia than of females, which I as reader, had not known, but if this is true, it would lend some credence to a biological advantage to protect the females of animal species, and might raise some interest into what relationship it has to protecting multitasking for females.
  4. It is well known that the twin studies that have been carried out determined that in identical twins, there is discordance as a rule.  Those studies are old, and they did not examine whether the other identical twin might be anywhere on the autism spectrum disorder (not then termed “spectrum”.
  5. However, there is a clear effect of stress on “gene expression”, and in this case we are looking at enzymation suppression at the placental level affecting trascriptional activity in the male fetus.  The same genetic signature exists in the male genetic profile, so we are not looking at a clear somatic mutation in this study.
  6. There is also much less specific an association with the MTHFR gene mutation at either one or two loci. This would have to be looked at as a possible separate post translational somatic mutation.
  7. Whether there is another component expressed later in the function of the zinc metalloproteinase under stress in the affected subject is worth considering, but can’t be commented on with respect to the study.

Penn Team Links Placental Marker of Prenatal Stress to Neurodevelopmental Problems 

By Ilene Schneider          July 8, 2014

When a woman experiences a stressful event early in pregnancy, the risk that her child will develop autism spectrum disorders or schizophrenia increases. The way in which maternal stress is transmitted to the brain of the developing fetus, leading to these problems in neurodevelopment, is poorly understood.

New findings by University of Pennsylvania School of Veterinary Medicine scientists suggest that an enzyme found in the placenta is likely playing an important role. This enzyme, O-linked-N-acetylglucosamine transferase, or OGT, translates maternal stress into a reprogramming signal for the brain before birth. The study was supported by the National Institute of Mental Health.

“By manipulating this one gene, we were able to recapitulate many aspects of early prenatal stress,” said Tracy L. Bale, senior author on the paper and a professor in the Department of Animal Biology at Penn Vet. “OGT seems to be serving a role as the ‘canary in the coal mine,’ offering a readout of mom’s stress to change the baby’s developing brain. Bale, who also holds an appointment in the Department of Psychiatry, co-authored tha paper with postdoctoral researcher Christopher L. Howerton, for PNAS.

OGT is known to play a role in gene expression through chromatin remodeling, a process that makes some genes more or less available to be converted into proteins. In a study published last year in PNAS, Bale’s lab found that placentas from male mice pups had lower levels of OGT than those from female pups, and placentas from mothers that had been exposed to stress early in gestation had lower overall levels of OGT than placentas from the mothers’ unstressed counterparts.

“People think that the placenta only serves to promote blood flow between a mom and her baby, but that’s really not all it’s doing,” Bale said. “It’s a very dynamic endocrine tissue and it’s sex-specific, and we’ve shown that tampering with it can dramatically affect a baby’s developing brain.”

To elucidate how reduced levels of OGT might be transmitting signals through the placenta to a fetus, Bale and Howerton bred mice that partially or fully lacked OGT in the placenta. They then compared these transgenic mice to animals that had been subjected to mild stressors during early gestation, such as predator odor, unfamiliar objects or unusual noises, during the first week of their pregnancies.

The researchers performed a genome-wide search for genes that were affected by the altered levels of OGT and were also affected by exposure to early prenatal stress using a specific activational histone mark and found a broad swath of common gene expression patterns.

They chose to focus on one particular differentially regulated gene called Hsd17b3, which encodes an enzyme that converts androstenedione, a steroid hormone, to testosterone. The researchers found this gene to be particularly interesting in part because neurodevelopmental disorders such as autism and schizophrenia have strong gender biases, where they either predominantly affect males or present earlier in males.

Placentas associated with male mice pups born to stressed mothers had reduced levels of the enzyme Hsd17b3, and, as a result, had higher levels of androstenedione and lower levels of testosterone than normal mice.

“This could mean that, with early prenatal stress, males have less masculinization,” Bale said. “This is important because autism tends to be thought of as the brain in a hypermasculinized state, and schizophrenia is thought of as a hypomasculinized state. It makes sense that there is something about this process of testosterone synthesis that is being disrupted.”

Furthermore, the mice born to mothers with disrupted OGT looked like the offspring of stressed mothers in other ways. Although they were born at a normal weight, their growth slowed at weaning. Their body weight as adults was 10 to 20 percent lower than control mice.

Because of the key role that that the hypothalamus plays in controlling growth and many other critical survival functions, the Penn Vet researchers then screened the mouse genome for genes with differential expression in the hypothalamus, comparing normal mice, mice with reduced OGT and mice born to stressed mothers.

They identified several gene sets related to the structure and function of mitochrondria, the powerhouses of cells that are responsible for producing energy. And indeed, when compared by an enzymatic assay that examines mitochondria biogenesis, both the mice born to stressed mothers and mice born to mothers with reduced OGT had dramatically reduced mitochondrial function in their hypothalamus compared to normal mice. These studies were done in collaboration with Narayan Avadhani’s lab at Penn Vet. Such reduced function could explain why the growth patterns of mice appeared similar until weaning, at which point energy demands go up.

“If you have a really bad furnace you might be okay if temperatures are mild,” Bale said. “But, if it’s very cold, it can’t meet demand. It could be the same for these mice. If you’re in a litter close to your siblings and mom, you don’t need to produce a lot of heat, but once you wean you have an extra demand for producing heat. They’re just not keeping up.”

Bale points out that mitochondrial dysfunction in the brain has been reported in both schizophrenia and autism patients. In future work, Bale hopes to identify a suite of maternal plasma stress biomarkers that could signal an increased risk of neurodevelopmental disease for the baby.

“With that kind of a signature, we’d have a way to detect at-risk pregnancies and think about ways to intervene much earlier than waiting to look at the term placenta,” she said.

 

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