− 95 −

DEVELOPMENT OF NOVEL EVALUATION SYSTEM FOR BITTER-TASTE USING

CULTURED NEURONAL CELLS

Takeshi Akiyoshi

Department of Clinical Pharmacy, Faculty of Pharmaceutical Sciences, Keio University

1-5-30, Shibakoen, Minato-ku, Tokyo 105-8512, Japan

Abstract

As the proverb "A good medicine tastes bitter" becomes nominal now, for drug development

in the 21st century, there is a growing need for development of medicine that can be taken easily and

hence improves the quality of life in patients. The bitterness of the medicine is uncomfortable to the

patient, which sometimes leads to non-compliance that results in reduced benefit of the drug treatment.

Even though various bitterness-masking techniques have been designed, there have been few attempts

to quantify the bitterness of the drug substance and to evaluate the bitterness-masking techniques that

are useful for a theoretical design of taste-masking formulations. The gustatory sensation test by the

expert who is called a panelist, has been the method used for evaluation of bitterness for the medicines

or formulations, and is still the most common method. However, it is difficult to obtain reproducible

and objective results due to panelist's individual difference and the physical condition, etc., and also

safety and ethical issues needs to be considered as the test medicines sometimes have strong toxicity.

Therefore, the establishment of a simple and reliable technique to evaluate the intensity of bitterness

quantitatively using an objective standard has been strongly expected.

Taste buds (left) are composed of 50–150 Taste Receptor Cells -TRCs (depending on the

species), distributed across different papillae. Circumvallate papillae are found at the very back of the

tongue and contain hundreds (mice) to thousands (human) of taste buds. Foliate papillae are present at

the posterior lateral edge of the tongue and contain a dozen to hundreds of taste buds. Fungiform

papillae contain one or a few taste buds and are found in the anterior two-thirds of the tongue. TRCs

project microvillae to the apical surface of the taste bud, where they form the ‘taste pore’; this is the

site of interaction with tastants. b, Recent molecular and functional data have revealed that, contrary to

popular belief, there is no tongue ‘map’: responsiveness to the five basic modalities — bitter, sour,

sweet, salty and umami — is present in all areas of the tongue.

Taste signals are first detected by the taste receptor cells (TRCs), which are located in taste

buds existing in the tongue, soft palate, larynx and epiglottis. Taste receptor cells contact with the

chemical compounds in oral cavity through the apical processes which protrude into the taste pore.

Interaction between chemical compounds and the taste receptor produces activation of taste receptor

cells directly or indirectly. Then the signals are transmitted to gustatory nerve fibers and higher order

neurons.

Bitter taste is important to detect harmful compounds such as plant alkaloids. To elucidate

these sensations, chemical compounds in foods and drinks are first monitored by taste receptor cells in

the oral cavity. Bitter taste is mediated by a family of ~30 highly divergent GPCRs (the T2Rs). T2R

− 96 −

genes are selectively expressed in subsets of TRCs distinct from those containing sweet and umami

receptors, and are clustered in regions of the genome genetically linked to bitter taste in humans and

mice. A large number of T2Rs have been shown to function as bitter taste receptors in heterologous

expression assays, and several have distinctive polymorphisms that are associated with significant

variations in sensitivity to selective bitter tastants in mice, chimpanzees and humans.

A taste sensor was developed as a taste-measuring device, which imitates the mechanism of

taste perception systems in human. The taste sensor consists of three parts; the electrode part

consisting of the reference electrode and the artificial lipid/polymer film sensor which imitates lipid

bilayer membranes, the robot arm, and the computer for data analysis. When the electrode part is

soaked in the sample solution, the lipid membrane potential is changed by the electrostatic interaction

of the lipid film with the medicine that is measured, and/or by the adsorption of the medicine to the

surface of the lipid film. The difference between the electric potential of the each working electrode

and the reference electrode becomes the output, and these signals are sent to the computer through the

robot arm as the “taste information”. The taste sensor can install maximum number of 8 sensor

electrodes and the electrodes are selected according to the sample that is measured. Human receives

various taste stimuli via various receptors in the taste cell located in the taste bud. The sensor imitating

the mechanism of human taste perception systems is also able to recognize and identify various taste

from the various sensor response pattern received using various types of sensor electrodes with

different lipid membrane composition.

In the present study, I aim to construct the highly accurate, new evaluation system for

bitterness that was able to reflect a basic process of such a taste response, and to take the place of a

human gustatory sensation test.

In Chapter 1, the purpose of this study was to quantify the degree of suppression of the

perceived bitterness of quinine by various substances, including, Sucrose, aspartam phosphatidic acid

(BMI-40) and to examine the mechanism of bitterness suppression. This mechanism was examined in

a gustatory sensation test in human volunteers, a binding assay, and using an artificial taste sensor. In

this examination, I adopted the taste sensor as an alternative of a human gustatory sensation test and

proposed the method of evaluating the bitterness depression effect of the quinine by the BMI-40,

bitterness-receptor antagonist, by using the decrease in the sensor output value.

Next, in chapters 2 and 3, we seek the new indicator which can show the momentary-excited changes

and the signal transduction mechanisms in TRCs. We considered the following alternative methods.

In Chapter 2, I focused intracellular calcium level on cultured neuronal cell line (Neuro-2a cells), as

predictive indicator for bitterness-response. In the present study, we examined the effect of quinine on

[Ca2+

]i levels in mouse neuroblastoma neuro-2a cells using fluorescence imaging. The usage of the

neuro-2a has been reported in several articles which examined calcium regulation or signaling

mechanism. We examined the possibility of [Ca2+

]i levels in neuro-2a as a quick and sensitive

conventional physiological method for the evaluation of bitterness.

In chapter 2, we found that there are some voltage-dependent calcium channels which

become depolarized with bitter taste stimuli. Therefore, the important thing might be an increase of the

intracellular calcium level induced by the IP3.-dependent cascade. It is necessary to clarify the

relationship between these responses.

In Chapter 3, I focused, furthermore, on the membrane potential in which the excitement of

the cell. Moreover, In the present study, we used the membrane potential-sensitive probe

bis-(1,3-dibutylbarbituric acid) trimethine oxonol [DiBAC4(3)] and Calcium Green 1/AM to examine

the effects of quinine on the membrane potential and the [Ca2+

]i levels in pheochromocytoma PC 12

cultures, which at the present time are now commonly being used in neuroscience research. We also

examined the possibility of applying the present findings for the development of a bitter taste

evaluation system.

The indicator in this study targeted the increasing intracellular calcium and the

depolarization playing the most important role of them. These are the reflections of the excitement

-release coupling of the bitterness response. And, as for the culture cell which we used for this study, it

was possible for quantitative evaluation in the response. Therefore, as the responses using the cultured

cell was suggested that a similar responses and transductions to the bitterness response is evaluated

handily.

− 97 −

1)

12)

BMI-40

2

3 2 (

[Ca2+

]i)

NaCl

Tannic acid(TA) Phosphatidic acid PA

( )

1

5 (0.01, 0.03, 0.10, 0.30, 1.0 mM) (

) (0, 1, 2, 3, 4)

( )

− 98 −

PA 0.1%(w/v)

2.03 0.37 ( :82% )

TA 0.05%(w/v) 60% 0.05%(w/v)

(Fig.1A, B )

2

PA TA

Sucrose Aspartame NaCl (Fig.1a

)

1.0%PA 36.1 0.05%TA 28% (Fig.1B )

1%PA 80%

36.1 45.6 81.7% (Fig.2)

0.05 TA 61% 28%

0.15

!"#$%$&'()&*)+,-"./0("1&2)-3))/&-()&455)5&6./7)/-8,-"./0&.9&:4;&<=78.0)>&401,8-,?)>&,/5&@,6+>&,/5&:A;&B(.01(,-"5"7&47"5&,/5&',//"7&

47"5>&,/5&-()&A"--)8/)00&<-8)/#-(&CD18)00)5&,0&CE="F,+)/-&G="/"/)&6./7)/-8,-"./0&:H)9-&4D"0>&6./-"/=.=0&H"/)0;>&.8&I/2.=/5&*,-".0&:J;&

.9&G="/"/)&:*"#(-&4D"0>&K.--)5&H"/)0;&'()&)E="F,+)/-&E="/"/)&7./7)/-8,-"./0&,8)&5)8"F)5&98.?&(=?,/&#=0-,-.8L&0)/0,-"./&5,-,>&3("+)&-()&

=/2.=/5&E="/"/)&8,-".0&,8)&5)8"F)5&98.?&-()&2"/5"/#&0-=5L$&

− 99 −

!"#$&M$&*)+,-"./0("1&2)-3))/&<)/0.8L&6B4&

B8.9"+)&:6./-"/=.=0&H"/)0;&,/5&A"--)8/)00&

<-8)/#-(&:K.--)5&H"/)0;&CD18)00)5&,0&-()&

CE="F,+)/-&G="/"/)&6./7)/-8,-"./

:*)+,-"F)&N,+=)&J;&

'()&#=0-,-.8L&0)/0,-"./&5,-,&3,0&

.2-,"/)5&"/&(=?,/&F.+=/-))80&:!O%%;$&'()&

0)/0.8&5,-,&3,0&-()&?),/&F,+=)&.2-,"/)5&

"/&-(8))&)D1)8"?)/-0$&

&

3 Fig.3

1

CPA (Chart.1)

Sucrose Aspartame (CPA )

NaCl CPA 80% NaCl

PA CPA

Fig.3

The taste-sensing system (SA 402)

− 100 −

!"#$& P& C99)7-& .9& %& ??.+Q+& G="/"/)& ./& R6,

STU"&

H)F)+0&"/&@)=8.VS,&6)++0&

& W/&#8,1(0>&-()&LV,D"0&0(.30&R6,STU"&+)F)+0&:"Q"X;$&

R6,STU"& +)F)+0& "/& /)=8.VS,& 7)++0& )D1.0)5& -.&

E="/"/)&:?),0=8)5&,-&PV0&"/-)8F,+0;$& &

:,;& G="/"/)&%?Y&

1

Sucrose Aspartame NaCl

Na

PA

36.1 45.6 81.7%

(Fig.2)

0.05 TA 61% 28%

0.15

PA

PA

( )

1)

( [Ca2+

]i)

Kumazawa (N18)

4)( )

[Ca2+

]i

[Ca2+

]i

( Neuro-2a)

calcium calcium green-1-AM

[Ca2+

]i5)

Neuro-2a

( )

2

Neuro-2a [Ca2+

]i

− 101 −

!"#$&Z$&6.88)+,-"./&2)-3))/& -()&G="/"/)& W/5=7)5& R6,STU"&

H)F)+0& :!#MV[;& "/& @)=8.VS,& 6)++0& 6,+7"=?& ,/5& \=?,/&

A"--)8&',0-)&*)01./0)0&:!O];& &

!.8& 0-,-"0-"7,+& ,/,+L0"0>& 3)& =0)5& I*H& ,558)00)5& ,0&

9.++.30^&

(--1^QQ,._"S$0"$#=/?,=$,7$`1QA+,7_A.DQA+,7_A.D$(-?+&

:2;&G="/"/)&7./7)/-8,-"./V8)01./0)&7=8F)$&K,-,&

,8)&-()&?),/&.9&[ %S&?),0=8)?)/-0 <$C$Y$&

1 [Ca2+

]i

Neuro-2a [Ca2+

]i

(Fig.5(a)(b)) [Ca2+

]i

2 [Ca2+

]i

Ca2+

6)( ) PLC/IP3

7)

Neuro-2a [Ca2+

]i

Neuro-2a [Ca2+

]i Ca2+

free

Ca2+

store thapsigargin (Fig.6)

L Ca2+

Channel nifedipine N

Ca2+

Channel !"conotoxin

nifedipine !"conotoxin

PLC U-73122

(Fig.6) Neuro-2a N Ca2+

Channel

Ca2+

PLC/IP3 Ca2+

stor

3 Neuro-2a [Ca2+

]i

Fig.7 Neuro-2a [Ca2+

]i

0.918

2

Neuro-2a [Ca2+

]i

Neuro-2a

[Ca2+

]i

Neuro-2a

!"#$%"&' ()*+,)-)#+' ./0"#"$"1)#' 2/34566'

!"#$& a,$& C99)7-0& .9& 6,+7"=?V!8))&Y)5"=?& ,/5& %µ?.+Q+& '(,10"#,8#"/& ./& -()& W/78),0)& "/& R6,STU"&

H)F)+0&W/5=7)5&2L&%&??.+Q+&G="/"/)&"/&@)=8.VS,&6)++0&

6)++0&3)8)& "/7=2,-)5&3"-(&6,STV98))&?)5"=?&,/5&-(,10"#,8#"/& 9.8&%P&?"/&18".8& -.&,55"-"./&.9&

E="/"/)$&K,-,&,8)&-()&?),/&.9&[ %S&?),0=8)?)/-0 <$C$Y$&bb&$ X$X%>b&$ X$XP&7.?1,8)5&

3"-(&7./-8.+&:(;$ &!"#$&a2$&C99)7-0&.9&%X??.+Q+&@"9)5"1"/)& >&%??.+Q+&3V6./.-.D"/&,/5&%X??.+Q+&IZc%SS&./&-()&

W/78),0)&"/&R6,STU"&H)F)+0&W/5=7)5&2L&%&??.+Q+&G="/"/)&"/&@)=8.VS,&6)++0&

K,-,& ,8)& -()& ?),/& .9& [ %S& ?),0=8)?)/-0 <$C$Y$& bb& 1 X$X%>& b& 1 X$XP& 7.?1,8)5& 3"-(&

7./-8.+&:(;$&

:2;&

− 102 −

Ca2+

Ca

2+

Fig.8

( )

1)2

1

( PC12)

DiBAC (3)(1µM) 2

2 [Ca2+

]i

2

3

[Ca2+

]i

Ca2+

Channel Ca2+

5)

(Fig.8)

[Ca2+

]i

1

(Fig.9 (a)Control)

Ca2+

Ca2+

free Ca2+

store thapsigargin (Fig.9(a))

Ca2+

[Ca2+

]i

2 [Ca2+

]i

[Ca2+

]i (Fig.9(b))

Ca2+

Channel

Ca2+

PLC/IP3

[Ca2+

]i7)

Ca2+

store Ca2+

Ca2+

Ca2+

Fig.8

− 103 −

[Ca2+

]i

6)

[Ca2+

]i

PC12

( )

[Ca2+

]i PC12

1 [Ca2+

]i

(1mM) PC12 (Fig.10(a)) [Ca2+

]i

(Fig.10(a)(c)) PC12

PC12

PC12

2 PC12

PC12 5 [Ca2+

]i 18

(Fig.10(a)(c)) 20

[Ca2+

]i 2 (Fig.10(b))

[Ca2+

]i

(Fig10(a),(b))

PC12 [Ca2+

]i

PC12

PC12(a) (b)

Flu

ore

scen

ce (

F/F

o)

(b)

Quinine

1mM

0.9

1.0

1.1

1.2

1.3

1.4

0

30

60

90

120

150

180

210

Time (sec)

[Ca

2+]i

Flu

ore

scen

ce (

F/F

o)

(a) PC12

Quinine

1mM

0.8

5

0.9

5

1.0

5

1.1

5

1.2

5

1.3

5

1.4

5

0 1

0

2

0

3

0

4

0

5

0

6

0

[Ca

2+]i

Time (sec)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0

(c) PC12 [Ca2+

]i

Log mM

[Ca

2+]i

lev

el

(F/F

o)

PC12

[Ca2+

]i

Fig.10 PC12

Fig.9

Data are mean S.E. p<0.05 vs Control

Before Quinine

After Quinine

0.

9

1.

0

1.

1

1.

2

Control Ca2+

free Thapsigargin

(10µM)

F

luo

resc

en

ce (

F/F

o)

(a)

1.0

2

1.0

4

1.0

6

1.0

8

1.1

0

1.1

2

1.1

4

-2.

5

-2.

0

-1.

5

-1.

0

-0.

5

0.

0

0.

5

1.

0

1.

5

[Ca

2+]i

lev

el

(F/F

o)

(b) [Ca2+

]i

(Log mM)

[Ca2+

]i

Fig.9 p<0.05 vs Control

Before Quinine

Control Ca2+ free Thapsigargin (1 0 µM)

(a )

(Log mM)

1.14

1.12

1.10

1.08

1.06

1.04

1.02-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

(b) [Ca 2 +]i

[Ca 2 +]i After Quinine

1.2

1.1

1.0

0.9

Fluo

resc

ence

(F/F

o)

[Ca2+

]i le

vel (

F/Fo

)

Data are mean S.E.

PC12(a) (b)

Fluo

resc

ence

(F/F

o)

(b)

Quinine 1mM

0.9

1.0

1.1

1.2

1.3

1.4

0

30

60

90

120

150

180

210

Time (sec)

[Ca2+]i

Fluo

resc

ence

(F/F

o)

(a) PC12

Quinine 1mM

0.85

0.95

1.05

1.15

1.25

1.35

1.45

0 10

20

30

40

50

60

[Ca2+]i

Time (sec)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0

(c) PC12 [Ca2+]i

Log mM

[Ca2+

]i le

vel (

F/Fo

)

PC12

[Ca2+]i

Fig.10 PC12

− 104 −

Fig.11 (BMI-40)

Data are mean S.E. p<0.05 vs Control p<0.01 vs Control

0.9

1.0

1.1

1.2

1.3

Control

PC12

Flu

ore

scen

ce (

F/F

o)

(b)

BMI-40

0.025%

Before Quinine

After Quinine

0.

9

1.

0

1.

1

1.

2

1.

3

1.

4

Control

PC12 [Ca2+

]i

(c) [C

a2+]i

lev

el

(F/F

o)

BMI-40

0.025%

Before Quinine

After Quinine

BMI-40

0.025%

0.

9

1.0

1.

1

1.

2

Control

Flu

ore

scen

ce (

F/F

o)

(a) Before Quinine

After Quinine

[Ca2+

]i

[Ca2+

]i

[Ca2+

]i PC12

1 PC12[Ca2+

]i

2 Fig.10(c) PC12 [Ca2+

]i

0.905 0.03mM

2

BMI-40 BMI-40

6) 8)BMI-40

8)

[Ca2+]i

PC12 (Fig.11(a),(b),(c)) PC12

3) PC12[Ca2+

]i

Ca2+ 6)

PC12 -2)

[Ca2+

]i Ca2+

Channel

Ca2+

[Ca2+

]i

PC12 [Ca2+

]i 2 [Ca2+

]i

L N Ca2+

Channel Ca2+

PLC/IP3

Ca2+

stor (Fig.12(a)(b))

Fig.11 (BMI-40) Data are mean S.E. p<0.05 vs Control p<0.01 vs Control

0.9

1.0

1.1

1.2

1.3

Control

PC12

Fluo

resc

ence

(F/F

o)

(b)

BMI-40 0.025%

Before Quinine

After Quinine

0.

9

1.

0

1.

1

1.

2

1.

3

1.

4

Control

PC12 [Ca2+]i

(c) [C

a2+]i

leve

l (F/

Fo)

BMI-40 0.025%

Before Quinine After Quinine

BMI-40 0.025%

0.9

1.0

1.1

1.2

Control

Fluo

resc

ence

(F/F

o)

(a) Before Quinine

After Quinine

− 105 −

3

PC12

PC12 BMI-40 PC12

PC12 [Ca2+

]i

1.

2. Neuro-2a [Ca2+

]i

3. PC12 [Ca2+

]i

( )

[Ca2+

]i

1) Nagahama S, Kobatake Y, Kurihara K: J Gen Physiol., 80,785(1982)

2) , ,18,133-146(2002) ; 3)

3) Nakamura T, Tanigake A, Miyanaga Y, Ogawa T, Akiyoshi T, Matsuyama K, Uchida T; Chem. Pharm.

Bull.50 (12) 1589-1593(2002)

4) Kumazawa T, Kashiwayanagi M, Kurihara K: Brain Res, 333: 27-33 (1985)

5) Shinozuka K, Tanaka N, Kawasaki K, Mizuno H, Kubota Y, Nakamura K, Hashimoto M, Kunitomo M;

Clin.Exp.Pharmacol.Physiol: 28, 799-803 (2001)

6) Kurihara K, Katsuragi Y, Matauoka I, Kashiwayanagi M, Kumazawa T, Shoji T; Physio.Bihav 56. (6).

1125-1132. (1994)

7) Ogura T, S C.Kinnamon: J Neurophysiol.: 82(5):2657-66. (1999)

8) Katsuragi Y, Yasumasu T, Kurihara K; Brain Res 713: 240-245 (1996)

U-73343

(10µM)

[Ca

2+]i

lev

el

(F/F

o)

U-73122

(10µM)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

Control

Nifedipine

(10µM)

!-conotoxin

(1µM)

Before Quinine

After Quinine

[Ca

2+]i

lev

el

(F/F

o)

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

Control

Ca2+

free

Thapsigargin

(3µM)

(a) (b)

Fig.12 PC12[Ca2+

]i

Data are mean S.E. p<0.05 vs Control p<0.01 vs Control

Before Quinine

After Quinine

− 106 −

Neuro-2a/PC12