Báo cáo khoa học: Inhibition kinetics of catabolic dehydrogenases by elevated moieties of ATP and ADP – implication for a new regulation mechanism in Lactococcus lactis potx

Lactate dehydrogenase LDH regenerates NAD by Keywords ADP; ATP; dehydrogenase; Lactococcus lactis; multiple inhibition kinetics Correspondence E.. Here we demonstrate mixed inhibition fo

elevated moieties of ATP and ADP – implication for a new regulation mechanism in Lactococcus lactis

Rong Cao, Ahmad A Zeidan, Peter Ra˚dstro¨m and Ed W J van Niel

Department of Applied Microbiology, Lund University, Sweden

Introduction

The lactic acid bacterium, Lactococcus lactis, plays

an essential role in the manufacture of a wide range

of dairy products In recent years, L lactis has also

been used in industrial lactic acid production, as it

has a rather simple and well-characterized

metabo-lism and converts sugars mainly into lactate via

gly-colysis [1] However, under certain conditions, this

homolactic fermentation is shifted to mixed-acid pro-duction, i.e formate, acetate and ethanol, in addition

to lactate [2]

In glycolysis, glyceraldehyde-3-phosphate dehydroge-nase (GAPDH) converts NAD+ to NADH, which must be regenerated for continued carbon catabolism Lactate dehydrogenase (LDH) regenerates NAD by

Keywords

ADP; ATP; dehydrogenase; Lactococcus

lactis; multiple inhibition kinetics

Correspondence

E W J van Niel, Department of Applied

Microbiology, Lund University, PO Box 124,

SE-221 00 Lund, Sweden

Fax: +46 46 2224203

Tel: +46 46 2220619

E-mail: ed.van_niel@tmb.lth.se

(Received 22 September 2009, revised

18 December 2009, accepted 1 February

2010)

doi:10.1111/j.1742-4658.2010.07601.x

ATP and ADP inhibit, in varying degrees, several dehydrogenases of the central carbon metabolism of Lactococcus lactis ATCC 19435 in vitro, i.e glyceraldehyde-3-phosphate dehydrogenase (GAPDH), lactate dehydroge-nase (LDH) and alcohol dehydrogedehydroge-nase (ADH) Here we demonstrate mixed inhibition for GAPDH and competitive inhibition for LDH and ADH by adenine nucleotides in single inhibition studies The nonlinear negative co-operativity was best modelled with Hill-type kinetics, showing greater flexibility than the usual parabolic inhibition equation Because these natural inhibitors are present simultaneously in the cytoplasm, multi-ple inhibition kinetics was determined for each dehydrogenase For ADH and LDH, the inhibitor combinations ATP plus NAD and ADP plus NAD are indifferent to each other Model discrimination suggested that the weak allosteric inhibition of GAPDH had no relevance when multiple inhibitors are present Interestingly, with ADH and GAPDH the combina-tion of ATP and ADP exhibits lower dissociacombina-tion constants than with either inhibitor alone Moreover, the concerted inhibition of ADH and GAPDH, but not of LDH, shows synergy between the two nucleotides Similar kinetics, but without synergies, were found for horse liver and yeast ADHs, indicating that dehydrogenases can be modulated by these nucleo-tides in a nonlinear manner in many organisms The action of an elevated pool of ATP and ADP may effectively inactivate lactococcal ADH, but not GAPDH and LDH, providing leverage for the observed metabolic shift

to homolactic acid formation in lactococcal resting cells on maltose There-fore, we interpret these results as a regulation mechanism contributing to readjusting the flux of ATP production in L lactis

Abbreviations

ADH, alcohol dehydrogenase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; LDH, lactate dehydrogenase; PFL, pyruvate

formate-lyase; rmse, root-mean-square error; TEA, triethanolamine.

converting the end product of glycolysis, pyruvate, to

lactate An alternative way for lactococci to regenerate

NAD in anaerobic conditions is through alcohol

dehy-drogenase (ADH), which is part of the pyruvate

formate-lyase (PFL) pathway The first enzyme in the

PFL pathway converts pyruvate to formate and acetyl

coenzyme A, which is further metabolized to either

ethanol or acetate PFL is inactive in the presence of

oxygen and at a low pH [3,4] With an active PFL

pathway, three molecules of ATP are produced per

hexose molecule catabolized, compared with the two

ATP molecules conserved per hexose molecule when

LDH is used The extra ATP is derived from the

pro-duction of acetate catalysed by acetate kinase NAD+

is then regenerated via reduction of acetyl coenzyme

A, with ethanol as an end product

Homolactic behaviour is seen only during rapid

growth in the presence of excess glucose and in resting

cells [1], whereas mixed-acid fermentation is observed

in glucose-limited conditions [2], and with growth on

maltose, galactose or trehalose [5–7] Under various

growth conditions, the shift from mixed-acid to

homolactic formation in L lactis has been ascribed to

allosteric regulation of: (a) PFL by

glyceraldehyde-3-phosphate and dihydroxyacetone phosphate [4]; (b)

LDH by the ratio of fructose 1,6-diphosphate and

orthophosphate [4,8,9]; and (c) GAPDH and LDH by

the redox charge (or NADH⁄ NAD ratio) [10,11] The

latter hypothesis has been disproved in other studies in

which the enzymatic level of GAPDH has been altered

[11,12] However, all of these regulations probably

work in concert

Myriads of previous studies have identified adenine

nucleotides, i.e ATP, ADP and AMP, as inhibitors

for dehydrogenases [13–18] Nakamura et al [17]

sug-gested that GAPDH, which plays a regulatory role in

glycolysis in round spermatids, is strongly inhibited by

AMP and ADP at physiological concentrations In

addition, the inhibition mechanism by ATP and the

relationship of this inhibition to regulate glycolysis in

resting and contracting muscle cells was hypothesized

[18] Palmfeldt et al [1] indicated that the ATP plus

ADP moiety might have a regulating function in

non-growing cells of L lactis ATCC 19435 fermenting

maltose The conclusion was partly based on changes

in this moiety and the in vitro-determined inhibition of

GAPDH, LDH and ADH by ATP and ADP

indepen-dently

Herein we characterize the inhibition kinetics of

these three dehydrogenases with their most important

natural inhibitors, i.e ATP, ADP and the product of

their coenzyme, i.e NAD or NADH Our approach

was to estimate the kinetic parameters of the enzymes

in cell extracts, rather than of the purified enzymes, for mimicking the complete system [1] Studies with purified enzymes may not reflect what is happen-ing in the whole cell [19] It is known that L lactis possesses isozymes of each of these dehydrogenases, e.g L lactis IL1403 contains three genes for LDH, two for ADH and two for GAPDH [20], which all could have been affected one way or another by both ATP and ADP However, the few studies related to the expression of the dominant isozymes [21–23], including our own unpublished results, are discussed From the kinetics study, it was concluded that the inhibition action of the ADP plus ATP moiety is of a co-operative nature and mainly affects ADH such that it will contribute to inhibiting mixed-acid forma-tion A similar nonlinear inhibition was also observed with purified enzymes, i.e commercial horse liver and yeast ADH, justifying the determinations carried out with cell extracts Moreover, it also demonstrates that this type of inhibition can occur in eukaryotic ADHs, indicating that this type of inhibition might be wide-spread in nature

Results

Inhibition kinetics by a single inhibitor The inhibition kinetics of lactococcal GAPDH, LDH and ADH were determined in vitro for each inhibitor, ATP, ADP, AMP and their corresponding coenzyme product, i.e NAD or NADH Cornish–Bowden plots revealed that the nature of inhibition for most of these cases was that of the parabolic competitive (LDH, Fig 1B) or parabolic mixed (GAPDH) inhibition type, except for the inhibition of LDH by NAD (Fig 1A)

As an example, the parabolic competitive inhibition of LDH by ADP is shown in Fig 1B (for all other cases, see Fig S1) The Cornish–Bowden plot demonstrated inhibition of GAPDH by AMP as mixed inhibition (Fig S1), but model fitting resulted in high confi-dence intervals for most of the parameters (Table 1) The competitive inhibition model (Eqn 2) also fitted well [adjusted R2= 0.987, root-mean-square error (rmse) = 0.0016], but had parameter values with lower confidence intervals (e.g Ki=0.47 ± 0.19,

n= 1.15 ± 0.26) The inhibitory effect of NAD on ADH and that of NADH on GAPDH was more com-plex than according to Eqns (2) or (3) and was not investigated further

Mathematically, this parabolic inhibition could be described by introducing the Hill-type kinetics to the inhibition terms as described in Eqns (2, 3) and through statistical evaluation (Table 1) it was found to

be superior and more flexible than the equation nor-mally used for parabolic inhibition [24]:

KM ½1 þ 2  I

K ICþ1

c ð I

K ICÞ2 þ S ð1Þ containing c as a factor by which the first inhibitor molecule changes the intrinsic dissociation constant of the vacant site

Interestingly, only the complete and partial inhibi-tion displayed the Hill-type of inhibiinhibi-tion Eqns (2, 3)

No good fits were obtained when a Hill-type inhibition was introduced to the uncompetitive part In conclu-sion, the inhibitors bind to the active site of all three dehydrogenases, and in the case of GAPDH will also bind to an allosteric site

The various parameter values were estimated using Eqn (2) for LDH and ADH and Eqn (3) for GAPDH (Table 1, Figs S2, S3) For LDH, ATP and NAD have nearly the same inhibitory strength with Hill coeffi-cients close to 1, whereas ADP is a slightly stronger inhibitor, having a Hill coefficient higher than 1 For ADH, the dissociation constants and Hill coefficients are low for ADP, but high for ATP Thus, separately each inhibitor affects ADH activity only moderately ATP and ADP are strong competitive inhibitors for GAPDH due to their small dissociation constants and relatively high Hill coefficients The uncompetitive inhibition of GAPDH by ATP and ADP, on the other hand, is weak, as illustrated by their high dissociation constants However, it is still significantly present, as concluded from the data fitting: with Eqn (2) larger confidence intervals and rmse and lower adjusted R2

(0.950 and 0.968 for ATP and ADP, respectively) were obtained than with Eqn (3) (Table 1)

0

0.005

0.01

0.015

0.02

0

0.01

0.02

0.03

A

B

Fig 1 Cornish–Bowden plots of single inhibition of LDH by NAD +

and ADP (A) LDH competitive inhibition by NAD at different

NADH concentrations (m M ): 0.2 (h), 0.18 ( ), 0.14 (D), 0.1 ( ),

0.06 (o) (B) Parabolic competitive inhibition of LDH by ADP at

dif-ferent NADH concentrations (m M ): 0.2 (h), 0.18 ( ), 0.12 (D),

0.09 ( ).

Table 1 The estimated VMAXand KM(m M ) of the cofactor substrate (NADH or NAD) and estimated parameter values (KICand KIU; m M ) and Hill coefficient (n) with 95% confidence intervals for the competitive inhibition kinetics (Eqn 2) of LDH and ADH and the mixed inhibition kinetics (Eqn 3) of GAPDH with ATP, ADP, AMP and cofactor product (NAD for LDH and ADH, and NADH for GAPDH) rmse, root-mean-square error.

Inhibitor

GAPDH ATP 2.03 ± 0.36 4.16 ± 0.92 3.07 ± 0.69 0.14 ± 0.02 0.06 ± 0.00 0.9930 0.9920 0.0015

ADP 0.96 ± 0.26 5.38 ± 2.76 1.70 ± 0.39 0.14a 0.21 ± 0.01 0.9825 0.9808 0.0091 AMP 0.27 ± 0.26 5.21 ± 5.69 0.79 ± 0.38 0.14 a 0.06 ± 0.01 0.9902 0.9888 0.0015

a Set as a fixed value as determined in one of the other assays.

Multiple inhibition kinetics

The reduced and oxidized forms of the coenzyme [i.e

NAD(H)], ATP and ADP are all present in significant

concentrations in the cytoplasm Hence, they will

inhi-bit the considered dehydrogenases simultaneously The

Yonitani–Theorell plots were used to determine the

multiple inhibition kinetics and to evaluate any

inter-actions between the inhibitors [25] As an example, the

plots for the inhibition by ATP and ADP of LDH,

ADH and GAPDH are given (Fig 2) Usually these

plots show linear relationships between the inhibitor

concentrations and V0 ⁄ Vi(with V0 and Vias the

reac-tion velocities in the absence and presence of the

inhib-itor, respectively) (Fig 2A) However, a parabolic plot

emerged in the case of GAPDH, but with ADH the

parabolic profile started only at higher ATP

concentra-tions (Fig 2B, C) Similar nonlinear plots were also

obtained with other inhibitor combinations for LDH

and ADH (Fig S4) This nonlinearity was reflected in

the multiple inhibition models through the values of

the Hill coefficients and the interaction factor (a; Eqn

4) Thus, the multiple inhibition kinetics of all

combi-nations could be adequately described by Eqn (4) for

all three enzymes Indeed, the multiple mixed

inhibi-tion model (Eqn 5) for GAPDH resulted in equal or

slightly better fittings, but it also resulted in large

con-fidence intervals for most of the parameters Keeping a

fixed value for the affinity constants of the substrate

(KM) as determined in the single inhibitions, all

remaining parameter values were estimated by

nonlin-ear regression of Eqn (4) (Table 2, Figs S5, S6) For

LDH, the relatively high values for a make it clear

that the inhibitors are indifferent to each other at the

active site In addition, the dissociation constants for

the inhibitors did not change dramatically (Table 2)

Hence, the LDH activity was hardly influenced by any

of the combinations of inhibitors A similar conclusion

can be drawn for the combination of ADP + NAD

for ADH, whereas there was a slight increase in

inhibi-tion of ADH by the combinainhibi-tion of ATP + NAD

The combinations ATP + NADH and ADP +

NADH had a severe inhibitory effect on GAPDH, but

mainly because the dissociation constants of ATP and

ADP were decreased by 50%, which more than

com-pensates the concomitantly lower values of the Hill

coefficients for the strength of inhibition (Tables 1, 2)

In stark contrast, there is a synergy between ATP and

ADP (a < 1) at the active site of ADH and GAPDH,

and, in addition, the dissociation constants were lower

in the presence of the other inhibitor (Table 2) In

con-clusion, when interpreted as the pool of ATP and ADP

[using Eqn (6) and parameter values in Table 2], the

two nucleotides affected ADH most strongly (> 95% inhibition), whereas most of the LDH activity was maintained (30–40% inhibition) (Fig 3A)

Multiple inhibition kinetics of eukaryotic ADH

To investigate whether the nonlinear nature of the multiple inhibition by ATP and ADP is unique for

0 1 2 3 4

ATP (m M )

0 1 2 3 4 5 6 7

ATP (m M )

A

C

0 1 2 3 4 5 6 7 8

ATP (m M )

Vo

/Vi

Vo

Vo

/Vi

B

Fig 2 Multiple inhibition of LDH, ADH and GAPDH by ATP and ADP using Yonetani–Theorell plots (A) Multiple inhibition of LDH

by ATP and ADP at different ADP concentrations (m M ): 8 (h),

6 ( ), 4 (D), 2 ( ), 0 (s) (B) Multiple inhibition of ADH by ATP and ADP at different ADP concentrations (m M ): 4.5 (h), 4 ( ), 3 (D), 1.5 ( ), 0 (s) (C) Multiple inhibition of GAPDH by ATP and ADP at different ADP concentrations (m M ): 2.5 (h), 2 ( ), 1.5 (D), 1 ( ), 0.5 (s), 0 (•).

L lactis, two commercial eukaryotic ADHs, of horse

liver and yeast, were tested Indeed, when plotted as

the rate versus the pool of ATP and ADP, a similar

strong nonlinear profile was found (Fig 3B)

How-ever, in this case there was no synergy between ATP

and ADP (a >> 1, Eqn 6), but the combination of

relatively low dissociation constants and high Hill

coefficients accounted for the high nonlinearity

(Table 2)

Discussion

The inhibition kinetics of LDH, ADH and GAPDH

of L lactis ATCC 19435 investigated might be a

result of various isozymes of each of these

dehydro-genases because of the use of cell extracts However,

unpublished transcriptomics results with this strain

have revealed that ldh, positioned in the las-operon

(EC 1.1.1.27), gapB, coding for one of the

NAD-dependent GAPDHs (EC 1.2.1.12), and adhE,

coding for the alcohol-acetaldehyde dehydrogenase

(EC 1.2.1.10), are those that are predominantly

expressed (data not shown) ldh is expressed 37- and

14-fold higher than ldhX and ldhB, respectively; gapB

is expressed seven-fold higher than gapA; and adhE is

expressed 10-fold higher than adhA These results are

consistent with those obtained with other lactococcal

strains [21–23] For instance, the KM value of LDH

of strain ATCC 19435 for NADH (Table 1) was

iden-tical to the LDH coded by ldh (KM= 0.06 mm), but

not to the one coded by ldhB (KM= 0.2 mm), as

found in L lactis strain NZ9000 and strain NZ9015

[21], respectively Therefore, we conclude that the

kinetics determined herein for all three dehydrogenases pertain to only one of their isozymes, i.e the ones mentioned above

The analysis of the single inhibition with the Cornish–Bowden plots and model discrimination revealed that LDH and ADH of L lactis ATCC

19435 are inhibited by all inhibitors studied in a differ-ent manner than GAPDH Inhibition of LDH and ADH is competitive for ATP, ADP and AMP, whereas inhibition of GAPDH by ATP, ADP and AMP appeared to be mixed However, the high disso-ciation constants for the uncompetitive part suggest the presence of only a weak allosteric binding site for ATP, ADP and AMP Having such high confidence intervals, it is arguable whether in situ AMP inhibits GAPDH mainly in a competitive manner The more complex inhibition of ADH and GAPDH by NADH and NAD, respectively, remains unclear and was not investigated further

Interestingly, a parabolic inhibition of each of the dehydrogenases was observed, which especially came

to the fore at elevated concentrations of ATP and ADP (Fig 2) Mathematically, this could be described through introducing a Hill coefficient for each inhibi-tor to the usual inhibition equations (Eqns 2, 3) In those forms, Eqns (2, 3) fitted the data more satisfacto-rily than the conventional parabolic model (Eqn 1), even though the same number of parameters had to be estimated From the data analysis, it was understood that with Hill coefficients a higher flexibility was intro-duced and may be related to the multimeric nature of the enzymes involved The outcome supports the view

of a recently published theory that Hill-type kinetics

Table 2 Estimated parameter values with 95% confidence intervals for the multiple inhibition kinetics of LDH, ADH and GAPDH with ATP, ADP and the cofactor product (NAD for ADH and LDH, and NADH for GAPDH) as inhibitors Similarly, for purified horse liver and yeast ADHs, with ATP and ADP as inhibitors.

I 1 + I 2 Enzyme

ATP +

ADP

LDH 2.53 ± 0.43 1.26 ± 0.16 1.42 ± 0.15 1.40 ± 0.10 4.53 ± 1.77 0.50 ± 0.01 0.9960 0.9951 0.00669 ADH 2.87 ± 0.30 0.72 ± 0.24 4.86 ± 1.25 1.28 ± 0.23 0.90 ± 0.48 0.04 ± 0.02 0.9869 0.9831 0.00119 GAPDH 0.76 ± 0.10 1.25 ± 0.14 1.31 ± 0.14 1.65 ± 0.19 0.83 ± 0.27 0.65 ± 0.02 0.9927 0.9911 0.0107 ATP +

NAD(H)

LDH 4.31 ± 0.40 0.65 ± 0.15 2.08 ± 0.23 0.95 ± 0.16 14.6 ± 14.5 0.32 ± 0.01 0.9893 0.9875 0.00420 ADH 3.20 ± 0.25 0.18 ± 0.05 4.79 ± 0.79 1.04 ± 0.14 3.16 ± 1.40 0.13 ± 0.00 0.9876 0.9850 0.00333 GAPDH 1.19 ± 0.21 0.07 ± 0.02 1.30 ± 0.21 0.96 ± 0.19 1.80 ± 0.98 0.70 ± 0.03 0.9834 0.9797 0.0128 ADP +

NAD(H)

LDH 2.23 ± 0.36 0.41 ± 0.12 1.66 ± 0.21 1.03 ± 0.17 36.7 ± 61.2 0.22 ± 0.01 0.9855 0.9830 0.0038 ADH 0.74 ± 0.37 0.02 ± 0.07 1.49 ± 0.41 0.57 ± 0.42 120 ± 656 0.10 ± 0.01 0.9686 0.9566 0.0036 GAPDH 1.03 ± 0.16 0.04 ± 0.01 1.34 ± 0.22 1.17 ± 0.13 2.58 ± 1.13 0.63 ± 0.02 0.9922 0.9901 0.00898 ATP +

ADP

Horse a 1.16 ± 0.23 0.04 ± 0.11 5.66 ± 1.07 0.86 ± 0.74 – 0.50 ± 0.02 0.9987 0.9979 0.00798 Yeast b 1.91 ± 0.23 1.48 ± 0.63 5.93 ± 1.32 1.52 ± 1.27 – 0.77 ± 0.03 0.9990 0.9984 0.00837

a KM value for NADH (3.6 l M ) taken from [26] b KMvalue for NADH (122 l M ) taken from Brenda (http://www.brenda-enzymes.org/php/ result_flat.php4?ecno=1.1.1.1).

can be used to describe allosteric inhibitor behaviour

[27] Model discrimination demonstrated that the

Hill-type only worked for the complete and partial

competitive inhibition, indicating a nonlinear negative

co-operativity at the active site [24] as the means to

‘deactivate’ the dehydrogenases

Inhibition of dehydrogenases by ATP or ADP is not

novel, but their role as regulators of enzymes other than

kinases remains underestimated To compare the

strength of inhibition of ATP and ADP, the single

‘inhi-bition term’ [defined as {1 + (I⁄ KI)n}] was plotted

against the inhibitor concentration (Fig 4A) using the

parameter values in Table 1 It revealed that GAPDH is

most severely inhibited by each of these inhibitors,

whereas LDH and ADH are only moderately inhibited

In the case of multiple inhibition, the inhibitors act

indifferently at the active site of LDH Hence, the

presence of all three inhibitors does not amplify the

inhibition of LDH, leaving this enzyme only mildly

inhibited This can be illustrated by plotting the multi-ple inhibition term {defined as [1 + (I1⁄ KI1)n + (I2⁄

KI2)n + (I1I2⁄ aKI1KI2)n]} against the concentration of the pool of ATP + ADP (Fig 4B), displaying the same profile as for the single inhibition (Fig 4A) For GAPDH in general, both the Hill coefficients and the dissociation constants were slightly lower than in the case of separate single inhibitions, resulting in the inhi-bition not being significantly different from the single inhibition (compare Fig 4A, 4B) Again, multiple inhi-bition by ATP and ADP did not possess a stronger regulation of GAPDH activity In contrast, for ADH, multiple inhibition revealed a drastic change to the sin-gle inhibition by ATP and ADP (Fig 4) Especially through decreased values of the dissociation constants and a low value of a (Table 2), ADH became more strongly inhibited than GAPDH only at high levels of ATP + ADP, although this was not apparent at nor-mal levels of the ATP + ADP moiety (Fig 4B)

0

0.25

0.5

0.75

1

Vi

/Vo

Vi

/Vo

ATP + ADP pool (m M )

0

0.25

0.5

0.75

1

ATP + ADP pool (m M )

A

B

Fig 3 Multiple inhibition of the lactococcal dehydrogenases and

ADH of yeast and horse liver as a function of the ATP and ADP

pool Criterion for all data points chosen: [ATP] > [ADP] (A)

Dehy-drogenases from Lactococcus lactis ATCC19435 LDH (D), GAPDH

(h), ADH ( ) (B) Comparison of eukaryotic ADHs Baker’s yeast

(•), horse liver (s) The lines represent the fitted model (Eqn 6).

0 50 100 150

Inhibitor (m M )

0 50 100 150 200 250

ATP+ADP pool (m M )

A

B

Fig 4 Comparison between the effect of the single inhibitor and multiple inhibitors on the lactococcal dehydrogenases as expressed

by the ‘inhibition term’ (A) Effect of the single inhibitors ATP (closed symbols) and ADP (open symbols) on LDH ( , h), ADH ( ,D), GAPDH (•, s) (B) Effect of the combined action of ATP and ADP on LDH ( ), ADH ( ), GAPDH (•).

Hence, only at elevated levels the regulating

mecha-nism by this moiety becomes visible In this way,

strong inhibition of ADH, but low inhibition of LDH

by the moiety guarantees a redirection of the catabolic

metabolism from mixed-acid to homolactic acid

forma-tion, as observed by Palmfeldt et al [1] To our

knowl-edge, this is the first time this phenomenon has been

described A strong regulation system by ATP has

been described for GAPDH in rabbit muscle cells, but

has not been studied in depth [18] From simulations

of in vivo conditions, the authors concluded that

physi-ological concentrations of ATP and ADP regulate the

glycolytic flux by inhibiting GAPDH by 90%

ATP and ADP function as energy carriers,

metabo-lites in RNA synthesis and as allosteric regulators of

key enzymes in various pathways, and are thus

ubiqui-tous within the metabolic network [28] Usually, ATP

and ADP are antagonistic in regulation, i.e one

func-tions as a positive, whereas the other funcfunc-tions as a

negative regulator In general, intracellular

concentra-tions of ATP and ADP in proliferating prokaryotes

and yeast are in the order of 2–5 and 1–2 mm,

respec-tively [29,30] Most studies with respect to ATP and

ADP are carried out in exponential growing cells, e.g

in steady-state situations of continuous cultures Few

studies have looked into changing levels of ATP and

ADP under stress conditions, such as growth in the

presence of high sugar concentrations [31,32] Fewer

studies have been dedicated to nongrowing cells, i.e

stationary phase and resting cells Those studies have

focused on ATP concentrations alone [10] or on

both ATP and ADP concentrations in, for example,

L lactis [1,32], Escherichia coli (E M

Lohmeier-Vogel, personal communication) and yeast [30] These

studies have revealed elevated levels of both

com-pounds, giving moieties up to 12–21 mm [1,32] The

reason for this could be that the nucleotide metabolic

network in active nongrowing cells is less wide than in

growing cells, for instance because of a lack of high

RNA turnover In such a case, completely different

mechanisms of enzyme regulation may emerge, not

normally operating in (rapidly) growing cells The

inhi-bition kinetics of the dehydrogenases as described in

this study could be an example

We would therefore like to propose the negative

co-operative regulation of ADH by the ATP–ADP

moiety as a new regulation mechanism in L lactis, and

it remains to be seen whether it is more widespread in

nature In L lactis, this system is most probably

adapted to regulate the flux of ATP production

through strong nonlinear inhibition of ADH: by

obtaining two instead of three ATPs per sugar unit in

excess concentrations of ATP and ADP [1]

Materials and methods

Organism and cultivation conditions

American Type Culture Collection (Manassas, VA, USA)

maltose 10 The pH was maintained at 6.5 by controlled addition of 5 m sodium hydroxide The cultures were stir-red with a magnetic stirrer at a speed of 100 r.p.m and

head-space The biomass was monitored by measuring the optical density at 620 nm At the end of the log phase, the cells

washed twice in triethanolamine (TEA) buffer (50 mm

Enzyme assays

with 30 s intervals of cooling on ice) Cell debris was

supernatant was collected and liberated from interfering metabolites below 10 kDa using a PD10-desalting column (Sigma Aldrich, St Louis, MO, USA), which was equili-brated with 25 mL TEA buffer before use Cell extract (2.5 mL) was added to the column and eluted with 2 mL TEA buffer, and subsequently kept on ice during analysis All assays were carried out with an Ultrospec 2100 pro spectrophotometer (Amersham Biosciences, Little Chalfont, UK) The buffer pH was set at 7.2 to mimic the intracellu-lar pH conditions of L lactis cells [34]

ADH activity was measured spectrophotometrically by

standard assay mixture contained (in total volume of

glutathi-one (0.5 mm); NADH (0.06–0.25 mm), cell extract and glutathi-one

of the four inhibitors: NAD (0–4 mm), ATP (0–10 mm), ADP (0–8 mm), AMP (0–16 mm) The reaction was started

by adding acetaldehyde (10 mm) LDH activity was mea-sured spectrophotometrically at 340 nm by monitoring the

mixture contained (total volume of 1 mL): TEA (50 mm,

extract and one of the four inhibitors: NAD (0–10 mm), ATP (0–6 mm), ADP (0–5 mm), AMP (0–10 mm) The reaction was started by adding sodium pyruvate (10 mm) GAPDH activity was measured at 340 nm by monitoring

One millilitre of the reaction mixture contained: TEA

fol-lowing inhibitors: NADH (0–0.3 mm), ATP (0–4 mm),

ADP (0–4 mm), AMP (0–2 mm) The reaction was started

by adding the glyceraldehyde-3-phosphate (10 mm) [35]

The assays for yeast and horse liver ADH (EC 1.1.1.1) and

multiple inhibition analysis were performed similarly as

above Three concentrations of cell extract were used for

each assay to test the linearity of the initial enzyme

activi-ties with the protein concentration All assays were based

on determining the initial conversion rates The baseline

was corrected for any background activity, measured for

several minutes before adding the substrate to start the

assay The linearity of the assay was monitored over time

by applying the standard assay (= complete assay without

inhibitors) every 0.5 h Any activity loss of the cell extract

was corrected for The majority of the inhibition datasets

were carried out in duplicate, resulting in the same

inhibi-tion trends The most elaborate datasets were chosen for

fitting the models, the remaining duplicate datasets were

used to validate the model (data not shown) Datasets for

each case of single and multiple inhibition therefore

con-sisted of measured inhibition trends instead of duplicates

All chemicals and enzymes were obtained from Sigma

Aldrich

Data analysis

To visualize the effect of the competitive inhibitor

concen-tration on the conversion rate, the data were plotted as rate

(v) versus substrate concentration (S) for each inhibitor

concentration (I) to which, for this study, a Hill-type

inhibi-tion has been introduced:

K n IC

constant for NADH and n is the Hill coefficient Similarly,

the mixed inhibition kinetics can be expressed as:

KM 1 þ I n

K n IC

þ S  ð1 þ I

K IUÞ

ð3Þ

the allosteric site (uncompetitive inhibition)

Multiple competitive inhibition could best be expressed by:

n1 1

n2 2

n1

1  In2 2

IC1 Kn2 IC2

Þ þ S

ð4Þ

respective Hill coefficients n1 and n2, and a as an inter-action constant In this way, the model describes the con-comitant inhibition kinetics of each inhibitor plus the synergy (0 < a < 1), or indifference (a > 1) (Fig S7) between the inhibition actions of both inhibitors at the active site

When dealing with mixed inhibition, Eqn (4) becomes:

allo-steric site (uncompetitive inhibition) and b as the mutual influence of the two inhibitors on the binding of each other

at the allosteric site

Plotting the multiple competitive inhibition kinetics as

is the inverse of the Yonetani–Theorell equation [25]:

2

IC1 Kn2

Þ ð6Þ

apparent dissociation constants for the competitive

Data fitting and statistical analysis Parameter estimation and statistical analysis were carried out using the Surface Fitting Tool (sftool) in matlab (R2009a) The parametric data fitting was based on non-linear regression and the method of least squares Model discrimination and choice was based on the goodness of

fit The goodness of fit was evaluated by visual examina-tion of the fitted curves, 95% confidence bounds for the fitted coefficients and statistical analysis for determining

The combination of smaller confidence bounds, values

KM ð1 þ I

n1 1

KIC1n1 þ

In22

KIC2n2 þ

I1n1 In22

a Kn1 IC1 Kn2 IC2

Þ þ S  1 þ I1

KIU1þ I2

KIU2þ I1 I2

b KIU1 KIU2

closer to 0 was used as the criterion for indicating a

better fit

Acknowledgement

This study was financially supported by the Swedish

Research Council for Environment, Agricultural

Sciences and Spatial Planning

References

1 Palmfeldt J, Paese M, Hahn-Ha¨gerdal B & Van Niel

EWJ (2004) The pool of ADP and ATP regulates

anaerobic product formation in resting cells of

Lacto-coccus lactis Appl Environ Microbiol 70, 5477–5484

2 Thomas TD, Ellwood DC & Longyear VM (1979)

Change from homo- to heterolactic fermentation by

in anaerobic chemostat cultures J Bacteriol 138,

109–117

3 Takahashi N, Abbe K, Takahashi-Abbe S & Yamada T

(1987) Oxygen sensitivity of sugar metabolism and

interconversion of pyruvate formate-lyase in intact cells

of Streptococcus mutans and Streptococcus sanguis

Infect Immun 55, 652–656

4 Crow VL & Pritchard GG (1977) Fructose

1,6-diphos-phate-activated l-lactate dehydrogenase from

Strepto-coccus lactis: kinetic properties and factors affecting

activation J Bacteriol 131, 82–91

5 Thomas TD, Turner KW & Crow VL (1980) Galactose

fermentation by Streptococcus lactis and

Streptococ-cus cremoris: pathways, products, and regulation

J Bacteriol 144, 672–682

6 Lohmeier-Vogel EM, Hahn-Ha¨gerdal B & Vogel HJ

(1995) Phosphorus-31 and carbon-13 nuclear magnetic

resonance studies of glucose and xylose metabolism in

Microbiol 61, 1414–1419

7 Levander F, Andersson U & Ra˚dstro¨m P (2001)

Physiological role of b-phosphoglucomutase in

Lactococcus lactis Appl Environ Microbiol 67,

4546–4553

8 Martinez-Irujo JJ, Villahermosa ML, Mercapide J,

Cabodevilla JF & Santiago E (1998) Analysis of the

combined effect of two linear inhibitors on a single

enzyme Biochem J 329, 689–698

9 Cocaign-Bousquet M, Garrigues C, Loubiere P &

Lindley ND (1996) Physiology of pyruvate metabolism

in Lactococcus lactis Antonie Van Leeuwenhoek 70,

253–267

10 Neves AR, Ventura R, Mansour N, Shearman C,

Gasson MJ, Maycock C, Ramos A & Santos H (2002)

Is the glycolytic flux in Lactococcus lactis primarily

controlled by the redox charge? Kinetics of NAD(+)

and NADH pools determined in vivo by 13C NMR

J Biol Chem 277, 28088–28098

11 Garrigues C, Loubiere P, Lindley ND & Cocaign-Bousquet M (1997) Control of the shift from homo-lactic acid to mixed-acid fermentation in Lactococcus

ratio

J Bacteriol 179, 5282–5287

12 Wouters JA, Kamphuis HH, Hugenholtz J, Kuipers

OP, de Vos WM & Abee T (2000) Changes in glycolytic activity of Lactococcus lactis induced by low tempera-ture Appl Environ Microbiol 66, 3686–3691

13 Wang CS & Alaupovic P (1980) Glyceraldehyde-3-phos-phate dehydrogenase from human erythrocyte mem-branes Kinetic mechanism and competitive substrate inhibition by glyceraldehyde 3-phosphate Arch Biochem Biophys 205, 136–145

14 Wittenberger CL (1968) Kinetic studies on the inhibi-tion of a D(-)-specific lactate dehydrogenase by adeno-sine triphosphate J Biol Chem 243, 3067–3075

15 Brunner NA, Brinkmann H, Siebers B & Hensel R

dehydrogenase from Thermoproteus tenax The first identified archaeal member of the aldehyde dehydroge-nase superfamily is a glycolytic enzyme with unusual regulatory properties J Biol Chem 273, 6149–6156

16 Avigad G (1966) Inhibition of glucose 6-phosphate dehydrogenase by adenosine 5¢-triphosphate Proc Natl Acad Sci USA 56, 1543–1547

17 Nakamura M, Fujiwara A, Yasumasu I, Okinaga S & Arai K (1982) Regulation of glucose metabolism by adenine nucleotides in round spermatids from rat testes

J Biol Chem 257, 13945–13950

18 Oguchi M, Meriwether BP & Park JH (1973) Interac-tion between adenosine triphosphate and glyceraldehyde 3-phosphate dehydrogenase 3 Mechanism of action and metabolic control of the enzyme under simulated in vivo conditions J Biol Chem 248, 5562–5570

19 Teusink B, Passarge J, Reijenga CA, Esgalhado E, van der Weijden CC, Schepper M, Walsh MC, Bakker BM, van Dam K, Westerhoff HV et al (2000) Can yeast glycolysis be understood in terms of in vitro kinetics of the constituent enzymes? Testing biochemistry

Eur J Biochem 267, 5313–5329

20 Bolotin A, Wincker P, Mauger S, Jaillon O, Malarme

K, Weissenbach J, Ehrlich SD & Sorokin A (2001) The complete genome sequence of the lactic acid bacterium

731–753

21 Bongers RS, Hoefnagel MHN, Starrenburg MJC, Siemerink MAJ, Arends JGA, Hugenholtz J &

Kleerebezem M (2003) IS981-mediated adaptive evolution recovers lactate production by ldhB transcrip-tion activatranscrip-tion in a lactate dehydrogenase-deficient strain of Lactococcus lactis J Bacteriol 185, 4499–4507

22 Willemoe¨s M, Kilstrup M, Roepstorff P & Hammer K

(2002) Proteome analysis of a Lactococcus lactis strain

overexpressing gapA suggests that the gene product is

an auxiliary glyceraldehyde 3-phosphate dehydrogenase

Proteomics 2, 1040–1046

23 Arnau J, Jørgensen F, Madsen SM, Vrang A &

Israelsen H (1998) Cloning of the Lactococcus lactis

dehydrogenase, by complementation of a fermentative

mutant of Escherichia coli J Bacteriol 180, 3049–3055

24 Leskovac V (2004) Comprehensive Enzyme Kinetics

Kluwer, New York, p 106

25 Yonetani T & Theorell H (1964) Studies on liver

alco-hol hydrogenase complexes 3 Multiple inhibition

kinet-ics in the presence of two competitive inhibitors Arch

Biochem Biophys 106, 243–251

26 Ryzewski CN & Pietruszko R (1977) Horse liver

alco-hol dehydrogenase SS: purification and characterization

of the homogenous isozyme Arch Biochem Biophys 183,

73–82

27 Hanekom AJ, Hofmeyr JH, Snoep JL & Rohwer JM

(2006) Experimental evidence for allosteric modifier

sat-uration as predicted by the bi-substrate Hill equation

Syst Biol (Stevenage) 153, 342–345

28 Fell DA & Wagner A (2000) The small world of

metab-olism Nat Biotechnol 18, 1121–1122

29 Albe KR, Butler MH & Wright BE (1990) Cellular

con-centrations of enzymes and their substrates J Theor

Biol 143, 163–195

30 Pahlman IL, Gustafsson L, Rigoulet M & Larsson C

(2001) Cytosolic redox metabolism in aerobic chemostat

cultures of Saccharomyces cerevisiae Yeast 18, 611–620

31 Meyer CL & Papoutsakis ET (1989) Increased levels of

ATP and NADH are associated with increased solvent

production in continuous cultures of Clostridium

acet-obutylicum Appl Microbiol Biotechnol 30, 450–459

32 Papagianni M, Avramidis N & Filiousis G (2007)

Glycolysis and the regulation of glucose transport in

culture Microb Cell Fact 6, 16

33 Schleifer KH, Kraus J, Dvorak C, Kilpper-Ba¨lz R,

Collins MD & Fischer W (1985) Transfer of

genus Lactococcus gen nov Syst Appl Microbiol 6, 183–195

34 Poolman B, Bosman B, Kiers J & Konings WN (1987) Control of glycolysis by glyceraldehyde-3-phos-phate dehydrogenase in Streptococcus cremoris and Streptococcus lactis J Bacteriol 169, 5887– 5890

35 Even S, Garrigues C, Loubiere P, Lindley ND & Cocaign-Bousquet M (1999) Pyruvate metabolism in

glyceraldehyde-3-phosphate dehydrogenase activity Metab Eng 1, 198–205

Supporting information

The following supplementary material is available: Fig S1 Cornish–Bowden plots of the single inhibition kinetics

Fig S2 A three-dimensional plot of fitting Eqn (2) through the complete dataset

Fig S3 Two-dimensional plots of fitting Eqn (2) or (3) through the complete datasets

Fig S4 Yonetani–Theorell plots of the multiple inhibi-tion kinetics

Fig S5 A three-dimensional plot of fitting Eqn (4) through the complete dataset

Fig S6 Two-dimensional plots of fitting Eqn (4) through the complete datasets

Fig S7 Evaluation of the effect of the interaction fac-tor (a) on the strength of inhibition

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