towards a bioinformatics analysis of anti-alzheimer’s...

Towards a bioinformatics analysis ofanti-Alzheimer’s herbalmedicines froma target network perspectiveYi Sun, Ruixin Zhu, HaoYe, KailinTang, Jing Zhao,Yujia Chen, Qi Liu and Zhiwei CaoSubmitted: 16th January 2012; Received (in revised form): 22nd April 2012

AbstractWith the growth of aging population all over the world, a rising incidence of Alzheimer’s disease (AD) has been re-cently observed. In contrast to FDA-approved western drugs, herbal medicines, featured as abundant ingredientsand multi-targeting, have been acknowledged with notable anti-AD effects although the mechanism of action(MOA) is unknown. Investigating the possible MOA for these herbs can not only refresh but also extend the currentknowledge of AD pathogenesis. In this study, clinically tested anti-AD herbs, their ingredients as well as their cor-responding target proteins were systematically reviewed together with applicable bioinformatics resources andmethodologies. Based on above information and resources, we present a systematically target network analysisframework to explore the mechanism of anti-AD herb ingredients. Our results indicated that, in addition to thebinding of those symptom-relieving targets as the FDA-approved drugs usually do, ingredients of anti-AD herbsalso interact closely with a variety of successful therapeutic targets related to other diseases, such as inflammation,cancer and diabetes, suggesting the possible cross-talks between these complicated diseases. Furthermore, path-ways of Ca2þ equilibrium maintaining upstream of cell proliferation and inflammation were densely targeted by theanti-AD herbal ingredients with rigorous statistic evaluation. In addition to the holistic understanding of the patho-genesis of AD, the integrated network analysis on the MOA of herbal ingredients may also suggest new clues forthe future disease modifying strategies.

Keywords: Alzheimer’s disease (AD); network pharmacology; herbal medicine; therapeutic target

INTRODUCTIONMost complex diseases, such as cancer, inflammation

and neurodegenerative disorders, are usually caused

by multiple genes or their products. The efficacy of

single targeting therapies has often been found lim-

ited due to insufficient understanding of the complex

Yi Sun is a PhD student at Department of School of Life Sciences and Technology, Tongji University. Her research interests include

network pharmacology and TCM pharmacology.

Ruixin Zhu is a lecturer at Department of School of Life Sciences and Technology, Tongji University. His research interests include

TCM pharmacology and drug design.

HaoYe is a PhD student at Shanghai Center for Bioinformation and Technology. His research interests include TCM pharmacology

and network pharmacology.

KailinTang is a researcher at Shanghai Center for Bioinformation and Technology. Her research interests include TCM pharmacol-

ogy and machine learning.

JingZhao is a researcher at Shanghai Center for Bioinformation and Technology. Her research interests include TCM pharmacology

and network biology.

Yujia Chen is a master student at Department of School of Life Sciences and Technology, Tongji University. Her research interests

include network pharmacology and TCM pharmacology.

Qi Liu is an associate professor at Department of School of Life Sciences and Technology, Tongji University. His research interests

include TCM pharmacology and small RNA.

ZhiweiCao is a professor at Department of School of Life Sciences and Technology, Tongji University. She is also a principal investigator

at Shanghai Center for Bioinformation and Technology as well as the Key Laboratory of Liver and Kidney Diseases, Shanghai University

of Traditional Chinese Medicine, Ministry of Education. Her research interests include TCM pharmacology and system biology.

Corresponding authors. Qi Liu and Zhiwei Cao, School of Life Sciences and Technology, Tongji University, 1239 Siping Road,

Shanghai 200092, China. Tel: 86-21-54065003; Fax: 86-21-54065057; E-mail: [email protected] and [email protected]

BRIEFINGS IN BIOINFORMATICS. page 1 of 17 doi:10.1093/bib/bbs025

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network essence of these diseases [1]. Alzheimer’s

disease (AD) is one of such which is clinically char-

acterized by an age-dependent memory dysfunction

together with cognitive deterioration [2]. Although

the etiology of AD is still waiting to be revealed,

multiple pathogenetic factors have been identified

to be involved in this disease. Animal model studies

have shown that brain accumulation of amyloid beta

(Ab) and neurofibrillary tangles (NFTs) are essential

for the development of AD [3]. In addition, prom-

inent activation of inflammatory processes, the innate

immune response, mitochondrial dysfunction, oxi-

dative stress and neuron apoptosis have been

observed in AD progression as well [4].

Currently, there are mainly two types of

FDA-approved drugs. One is cholinesterase inhibi-

tors (ChEIs) designed based on therapeutic target of

cholinesterase. There are four approved cholinester-

ase inhibitors [5]: Tacrine, Donepezil, Rivastigmine

and Galantamine. They are used for the treatment of

mild to moderate AD, and postulated to exert thera-

peutic effect by enhancing cholinergic function [6].

The other one is N-methyl-D-aspartate antagonists

[7] specifically targeted to NMDA receptors. The

only approved NMDA receptor antagonist is

Memantine [5], which is applied in the treatment

of moderate to severe AD. As it is reported that

these approved drugs only help to relieve the symp-

toms of AD patients rather than producing efficient

disease-modifying effects due to their monotonous

therapeutic function [4], new AD therapies are in

urgent need. In the past few years, many small mol-

ecules or bioproducts are under development for the

treatment of AD, including chemical drugs and anti-

body therapeutics.

Among these drugs, herbal medicines, derived

from thousands of years of application, are especially

promising in the development of AD therapy. As

being supported by cellular and animal model stu-

dies, herbal medicines were reported to be effective

on AD from multiple pathways. Several herbal ex-

tracts, such as the Ginkgo biloba extract EGb761 and

Salvia officinalis extracts have been found with a com-

bination of anti-oxidative, anti-apoptosis, neuropro-

tective and neuromodulatory effects [8–11]. The use

of herbal medicines in the treatment of AD is attract-

ing more and more attention [12, 13], and some of

which have even advanced to clinical trials.

However, their anti-AD mechanism is not yet well

understood. Several questions are still remained to be

solved: what are the ingredients of these herbal

medicines and their target proteins? Are they over-

lapping with therapeutic targets of FDA-approved

western drugs? What pathways are herbal medicines

involving in disease treatment? What is the mechan-

istic difference between herbal medicines and west-

ern drugs? Studying these questions may not only

reveal the MOA of anti-AD herbs but also extend

the domain knowledge of AD pathogenesis.

In this paper, we extensively reviewed current

available in silico methods for anti-AD drug research

and development. Based on the survey of these bio-

informatics methodologies, we described a frame-

work to construct an easily-interpretable map that

could facilitate elucidating (MoA) for anti-AD

herbal medicines. First, the clinically supported

anti-AD herbs, their ingredients, as well as the

target proteins were collected. Then, they were stu-

died as compared to FDA-approved anti-AD drugs

in the context of target network. Finally, a holistic

understanding of the molecular mechanism respon-

sible for the anti-AD effects of the herbal medicines

was presented, and the molecular mechanism

involved in the AD was also refreshed and extended

from the observed action of those herb medicines.

COMPUTATIONAL STRATEGIESFOR ANTI-ADDRUGRESEARCHDuring the past decades, computational methods

have facilitated the exploration of anti-AD drugs

with the abundant bioactivity information. They

could mainly be classified into two categories, i.e.:

‘single-target’-directed and ‘multi-target’-directed

strategy.

‘Single-target’ strategy of anti-AD drugsSeveral in silico methods have been frequently em-

ployed into ‘single-target’ anti-AD drug research

based on the conception of ‘therapeutic target’. For

example, docking approaches, molecular dynamics

studies, quantum mechanical studies, and other

methods that could exhibit quantitative structure–

activity relationships (QSAR) have been applied

to facilitate the understanding of cholinesterase,

and to design new cholinesterase inhibitors

[14–18]. Furthermore, ligand-based computational

approaches have also been used to identify inhibitors

for other attractive targets for AD, such as

b-Secretase [19–21] and g-Secretase [22].

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‘Multi-target’ strategy of anti-AD drugsIn the resent years, the system-oriented design of

‘multi-target’ anti-AD drugs is an attractive approach

with increasing interests, as AD is a complex disease

involving multiple pathogenetic factors. Besides

the computational methods used in ‘single-target’

anti-AD drug design, several new approaches could

facilitate the development of ‘multi-target’ anti-AD

drugs with integration of various sources of informa-

tion, such as microarrays, literature mining-data, pro-

tein–protein interactions (PPIs), and so on. These

methods were summarized in Table 1.

There are various AD-related microarray gene-ex-

pression profiles, and many of them have been

well-collected in the Connectivity Map (cMAP)

[23], as well as the newly published searchable platformindependent expression database (SPIED) [24]. Different

methods have been applied to explore the micro-

array gene-expression dataset of AD, including un-

supervised machine learning approaches [25], integer

linear programming model [26] and network analysis

[27, 28]. Potential pathways and therapeutic targets

for AD were also identified in these studies.

Nevertheless, the gene-expression experiments are

expensive and time-consuming, and often reported

in disagreement with low-throughput experiments.

Hence, alternative methods without requiring

high-throughput microarray data are attractive as

well. For example, two newly published methods

were proposed based on the interacting profiles of

AD drugs and could help to uncover their thera-

peutic mechanisms by incorporating the ‘off-targets’.

Among them, one is the AD-specific drug–protein

connectivity map [29] built with protein interaction

networks and literature mining. The other one is the

AD drug-oriented chemical–protein interactome

(CPI) [30], which was constructed on the basis of

the relations between 10 drug molecules and 401

human protein pockets derived from the application

of DOCK program. Furthermore, a network was

integrated with published PPI data to study the func-

tions of AD proteins and the relationships among

them [31, 32]. This network analysis provides a sys-

tematic view of AD pathology, as it could help to

find not only important AD-related proteins, but also

important pathways involving in AD.

FRAMEWORK FORTHE ANTI-ADMECHANISM ANALYSIS OFHERBALMEDICINESAccording to the review of current in silico methods

for anti-AD drug discovery, there seems no pub-

lished work for the holistic understanding of the mo-

lecular mechanism for anti-AD drugs. In this section,

we considered to provide a low-cost and effective

way to explore the anti-AD mechanism at a system

level. Specifically, we focused on anti-AD herbs and

the network pharmacology analysis technologies. It

should be noted that network analysis is an increas-

ingly important approach in uncovering the mode of

drug action, which has been well-illustrated in sev-

eral articles [33–35]. Especially, this system-based

network technology is useful in revealing the

Table 1: Current bioinformatics methodologies for AD pharmacology

Data Methods Findings Reference[PMID]

Microarray Unsupervised machine learning approaches Identifying potential pathways and thera-peutic targets of AD

21989140

Microarray and PPI Integer linear programming model Identifying protein signal pathways of AD 21442495Microarray K-shortest paths and k-cycles network

analysisIdentifying network features and importantpathways in AD

19543432

Microarray Weighted gene coexpression networkanalysis

Identifying 12 distinct modules related toAD

18256261

PPI and literature mining data Disease-specific drug^protein connectivitymap

Finding molecular signature differencesbetween different classes of drugs in AD

19649302

DOCK data Drug-oriented chemical^protein interac-tome analysis

Finding interacting profiles of AD drugs andtheir potential therapeutic mechanism

20221449

Published work characterizingboth APP and Ab

Gene ontology and network analysis Identify potentially novel functional rela-tionships among AD-related proteins

20391539

AD-related genes and humanprotein interactions

An algorithm to rank AD-related proteins Functionally relevant AD proteins wereconsistently ranked at the top

17094253

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complex modulation essence of herbal medicines as

concluded in the review of Jing Zhao et al. [36]. As a

result, we attempt to (i) systematically collect and

review anti-AD herbs, their ingredients, and the

target proteins from public databases and literatures,

and (ii) study the anti-AD mechanism with the ap-

plication of network analysis. This two-part frame-

work was shown in Figure 1.

Mining of anti-AD herbs, theiringredients and the correspondingtarget proteinsThe main purpose of this part is to answer the fol-

lowing questions: what are the ingredients of herbal

medicines and their target proteins? Are they over-

lapping with therapeutic targets of FDA-approved

western drugs?

Anti-AD herbsWe have performed a large-scale text mining of

PubMed and the clinical trial database (www

.Clinicaltrials.gov, currently contains 119 213 trials

from 178 countries), and for the first time manually

extracted the available promising anti-AD herbs from

the English literatures (from 1995 to 2011) with the

keywords ‘herbal medicine’ and ‘Alzheimer’s’.

Among them, Ginkgo biloba, Huperzia serrata, Melissaofficinalis and Salvia officinalis are found to be the top

well studied ones in the large volume of AD-related

articles as shown in Table 2. It can be seen from

literatures that different herbs have been studied to

different extent. Articles relating Ginkgo biloba to AD

are the most abundant (233), probably because

Ginkgo biloba has been studied for a long time and

extensive papers have been accumulated (2357 art-

icles). This herb could significantly improve atten-

tion and memory performance of patients in a phase

III clinical trial [37]. The efficacy of Ginkgobiloba was

even comparable to the results of patients treated

with donepezil, a FDA-approved drug for mild to

moderate AD [37].

As these herbs have been studied to different

extent, a parameter is introduced to balance this

bias and further assess the association between them

and AD. The parameter is calculated as the

(AD-herb-related papers)/(herb-related papers)

ratio. P-value was applied to measure how improb-

able by chance it is to observe a certain level of

co-occurrences of each herb and AD in at least karticles [38]:

P ¼ 1�Xk�1

i¼0

f ðiÞ ¼1�Xk�1

i¼0

Ki

� �N � Kn� i

� �

Nn

� � ,

where N is the total number of papers in PubMed

(19 432 858 articles, as given by GoPubMed), K is

the number of papers associate with AD (52 535 art-

icles, as given by GoPubMed), n is the volume about

one single herb, k is the number of papers about the

effects of corresponding herb on AD. GoPubMed

was used to get the value of N, K, n and k.P-value indicates the significance of relevance be-

tween each herb and AD [39] (significant when

P-value < 0.01).

As a result, Huperzia serrata obtained the highest

ratio (35.71%; with P<< 0.01), indicating that

anti-AD may be one of the major therapeutic effects

found so far for this herb. Ginkgo biloba (9.89%; with

P<< 0.01) is second to Huperzia serrata, and then fol-

lowed by Salvia officinalis (3.99%; with P<< 0.01)

and Melissa officinalis (2.29%; with P<< 0.01).

Furthermore, different stages of clinical trials for

these four herbs were summarized from either clin-

ical trial databases or PubMed, as shown in Table 3.

This suggested that these herbal medicines are pro-

mising in the field of anti-AD drug discovery, as

random and double-blinded clinical studies gave

positive results. Among the four herbs, Ginkgo biloba

Figure 1: Workflow for target network analysis ofanti-AD herbal mechanism.



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was the most frequently studied one as 12 related

clinical trials can be found. Only 4 of the 12 trials

showed no significant differences from the placebo

groups, and all the other 8 studies have provided

supportive evidence for anti-AD effects of Ginkgobiloba. For Huperzia serrata, most of its clinical trials

were focused on the component named Huperzine

A and significant improvement has been observed in

four trials. As to Melissa officinalis and Salvia officinalis,there was only one trial for each.

Anti-AD herb ingredientsIn this study, the compounds contained in each

herb were collected from Traditional Chinese

Medicine Information Database (TCM-ID) [45],

‘Pharmacopoeia of People’s Republic of China’

and PubMed. It is noted that not all compounds

were extensively studied, and only those with litera-

ture support of target proteins are remained. Finally,

10 compounds were collected from the four herbs.

The significance of their correlations with AD were

also measured with the P-value introduced in the

‘Anti-AD herbs’ part, and were summarized in

Table 2. It is not surprising to observe that P-valuesfor the majority of the ingredients are not significant,

as herbal medicines are usually used in the form of

multiple components. Among these ingredients,

Huperzine A from Huperzia serrata has the highest

correlation with AD (25.81% and 80 articles),

and this correlation is significant as measured by

P-value. Actually, results of the most recent Phase

II clinical trial have indicated the significant cognitive

enhancement of Huperzine A for mild to moderate

AD patients [12]. The activity of this ingredient was

reported to be stronger than another ChEI galata-

mine [46]. Phase III and Phase IV trials of Huperzine

A are also anticipated, as indicated by the clinical trial

database.

Target proteins of anti-AD herb ingredientsInformation of target proteins for herbal ingredi-

ents was identified from HIT [47], a well-known

herb ingredient target database (http://lifecenter.

sgst.cn/hit/). A total of 221 direct targets of

586 herbal compounds have been collected in this

database. Totally 34 target proteins were retrieved

for the 10 anti-AD herbal ingredients (Table 4).

The interactions between the compounds and targets

have been classified into three categories, i.e. activa-

tion, inhibition and neutral binding of the target

proteins.

Then, to assess the relationship between these

target proteins and AD:

First, the 34 target proteins were regarded as a

whole to evaluate the distance between them and

known ‘AD-related proteins’ in the background of

a genome-wide human PPI network. If the distances

between the herbal targets and ‘AD-related proteins’

are significantly shorter than those of the random

counterparts in the context of human PPI, a close

Table 2: Correlations between herbs or compounds with AD

Herb Compound

Herb name Volume of articles Compoundname

Volume of articles BBBpermeability

Total Relevant to AD(Ratio; P-value)

Total Relevant to AD(Ratio; P-value)

Ginkgo biloba 2357 233 [74 are reviews](9.89%; P<< 0.01)

(i) Quercetin 6720 14 (0.21%; P¼ 0.8031) Y(ii) Kaempferol 1904 4 (0.21%; P¼ 0.5852) Y(iii) Ginkgolide a 77 2 [1 is review] (2.60%;

P¼ 0.0012)NK

(iv) Ginkgolide b 726 3 [1 is review] (0.41%;P¼ 0.1360)

Y

Huperzia serrata 56 20 [10 are reviews](35.71%; P<< 0.01)

(i) Huperzine A 310 80 [29 are reviews](25.81%; P<< 0.01)

Y

(ii) Huperzine B 27 6 (22.22%; P<< 0.01) NKMelissa officinalis 612 14 [2 are reviews]

(2.29%; P<< 0.01)(i) Quercetin 6720 14 (0.21%; P¼ 0.8031) Y(ii) Gallic acid 4304 3 (0.07%; P¼ 0.9970) Y

Salvia officinalis 276 11 [2 are reviews](3.99%; P< < 0.01)

(i) Apigenin 1553 2 (0.13%; P¼ 0.7900) Y(ii) b-Sitosterol 2591 3 [1 is review] (0.12%;

P¼ 0.9188)Y

(iii) Ursolic acid 682 3 (0.44%; P¼ 0.1156) NK

Note: Ratio¼ (AD-herb-related papers)/(herb-relatedpapers);Y, yes; NK, not known.



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Table

3:Inform

ationforclinicaltrialsof

anti-AD

herbs

Herbal

name

Stud

ySam

ple

Exp

erim

ental

design

Interv

ention

Outco

memea

sure

Con

clusion

(positiveor

nega

tive

eviden

ceforthehe

rbal

med

icinein

AD

trea

tmen

t)

Inform

ation

sour

ce

1Ginkgobiloba

PhaseIIIclinicaltrial

Area:US

Tim

e:20

00.10�2010.07

Age:�

75Num

ber:

3069

Status:no

rmal

cognition

(n¼25

87)

orMCI

(n¼48

2)

preventio

n;rand

o-mized;d

ouble

blind;

placebocon-

trol;p

arallela

ssign-

ment;safety/

Efficacystud

y

120mg/2days

G.biloba;

placebo

ADAS-cog;

CDR

Negative

Clinicaltriald

atabase

(NCT00

0108

03)

PubM

ed(PMID:19

017911;

19321833)

2Ginkgobiloba

Area:

Germany

Tim

e:24

weeks

Num

ber:

216

Status:

prim

ary

degen-

erative

AD

ormild

tomod

eratedementia

doubleblind;

placebo

control;rand

o-mized;m

ulticenter

240mg/dayG.

biloba

;placebo

ADAS-cog;

SKT

Positiv

ePu

bMed

(PMID:14

663654

;8741021)

3Ginkgobiloba

Area:US

Tim

e:26

weeks

Num

ber:

309

Status:m

ildto

severe

AD

ordementia

rand

omized;d

ouble

blind;

placebocon-

trol;p

arallelgroup;

multicenter

120mg/dayG.

biloba;

placebo

ADAS-Cog

Positiv

ePu

bMed

(PMID:10

867450

)

4Ginkgobiloba

Area:

Germany

Tim

e:3mon

ths

Age

:50

^80

Num

ber:

20Status:mild

tomod

erate

AD.

rand

omized;d

ouble

blind;

placebo

control

240mg/dayG.

biloba

;placebo

SKT;

ADAS

Positiv

ePu

bMed

(PMID:944

7569)

5Ginkgobiloba

Area:The

Netherlands

Tim

e:24

weeks

Num

ber:

214

Status:

Dem

entia

orage-associated

mem

ory

impairment

doubleblind;

rand

o-mized;p

lacebo

controll;

parallel

grou

p;multicenter

240mg/dayG.

biloba

;160mg/dayG.

biloba

;placebo

NAI-W

L;NAI-Z

N-G

Negative

PubM

ed(PMID:11037003)

6Ginkgobiloba

Area:US

Tim

e:26

weeks

Num

ber:

513

Status:d

ementia

rand

omized;p

lacebo

control;do

uble

blind;

parallel

grou

p;multicenter

240mg/dayG.

biloba

;120mg/dayG.

biloba

;placebo

ADAS-cog;

ADCS-CGIC

Negative

PubM

ed(PMID:16375657

)

7Ginkgobiloba

Tim

e:22

weeks

Age:�

50Num

ber:

96Status:AD

rand

omized;d

ouble

blind

240mg/dayG.

biloba

;do

nepe

zil

SKT

Positiv

ePu

bMed

(PMID:19347685)

8Ginkgobiloba

Area:Ukraine

Tim

e:22

weeks

Age:�

50Num

ber:

400

Status:AD

orvascular

dementia

rand

omized;d

ouble

blind

240mg/dayG.

biloba;

placebo

SKT

Positiv

ePu

bMed

(PMID:17341003)

9Ginkgobiloba

Area:UK

Tim

e:6mon

ths

Num

ber:176

Status:

early

stage

dementia

rand

omized;p

rag-

matic;d

oubleblind;

commun

itybased;

parallelgroup

120mg/dayG.

biloba;

placebo

ADAS-Cog

Negative

PubM

ed(PMID:18

5372

21)

(con

tinue

d)



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Table3Continued

Herbal

name

Stud

ySam

ple

Exp

erim

ental

design

Interv

ention

Outco

memea

sure

Con

clusion

(positiveor

nega

tive

eviden

ceforthehe

rbal

med

icinein

AD

trea

tmen

t)

Inform

ation

sour

ce

10Ginkgobiloba

Area:Italy

Tim

e:24

weeks

Age:50

^80

Status:mild

tomod

erate

dementia

rand

omized;p

lacebo

control;do

uble

blind

160mg/dayG.

biloba;

5mg/daydo

nepe

zil;

placebo

MMSE

Positiv

ePu

bMed

(PMID:16930364

)

11Ginkgobiloba

Area:US

Tim

e:52

weeks

Num

ber:168

Status:p

rimarily

visual-

construc

tional

impair-

ment

orpred

ominant

verbaldeficitsor

impaired

inbo

thcognitivedo

mains.

rand

omized;d

ouble

blind;

placebo

control

120mg/dayG.

biloba;

placebo

ADAS-Cog

Positiv

ePu

bMed

(PMID:13130389

)

12Ginkgobiloba

Area:US

Tim

e:52

weeks

Num

ber:

309

Status:AD

ormulti-infarctdementia

multicenter;rand

o-mized;d

ouble

blind;

parallelpla-

cebo

control;

120mg/dayG.

biloba;

placebo

ADAS-cog;

ADCS-CGIC

Positiv

ePu

bMed

(PMID:93434

63)

13Hup

APh

aseIIclinicaltrial

Area:US

Tim

e:24

weeks

Num

ber:150

rand

omized;m

ulti-

center;d

ouble

blind;

placebo

control

200mg/2

days

Hup

A40

0mg/2

days

Hup

A;

placebo

ADAS-cog

Positiv

eClinicaltriald

atabase

(NCT00

083590

)

14Hup

AArea:

China

Tim

e:8weeks

Num

ber:

50rand

omized;d

ouble

blind;

prospe

ctive;

parallel;placebo

control;

multicenter

200mg/day

Hup

A;

placebo

MMSE

Positiv

ePu

bMed

(PMID:8701750

)

15Hup

AArea:

China

Tim

e:60

days

Num

ber:

60Status:AD

rand

omized;d

ouble

blind;

parallel;pro-

spectiv

e;do

uble

mim

ic;m

ulticen-

ter;po

sitiv

econtrol

200mg/dayHup

A;

placebo

psycho

logicalevaluations

Positiv

ePu

bMed

(PMID:10

678137)

16Hup

AArea:

China

Tim

e:12

weeks

Num

ber:

202

Status:AD

rand

omized;d

ouble

blind;

multicenter;

placebocontrol;

parallela

ssignm

ent

400mg/dayHup

A;

placebo

ADAS-Cog;M

MSE

Positiv

ePu

bMed

(PMID:12181083)

17Melissa

officinalis

Area:Tehran,Iran

Tim

e:4mon

ths

Age:65

^80

Status:mild

tomod

erate

AD

rand

omized;d

ouble

blind;

parallel

grou

p;placebo

control

Melissa

officinalis;p

lacebo

ADAS-cog;

CDR

Positiv

ePu

bMed

(PMID:12

810768

)

18Salviaofficinalis

Area:Tehran,Iran

Tim

e:4mon

ths

Age:65

^80

Status:mild

tomod

erate

AD

rand

omized;d

ouble

blind;

parallel

grou

p;placebo

control

Salviaofficinalis;p

lacebo

ADAS-cog;

CDR

Positiv

ePu

bMed

(PMID:12

605619)

Note:ADAS-cog,

Alzheim

er’sdiseaseassessmentscale,Cognitiv

esubscale

[1];CDR,cognitiv

edrug

research

[2];SK

T,shor

tcognitiveperformance

test

[3];ADCS-CGIC,A

DCS-clinicians’globalimpressio

nof

change

scales

[4];MMSE,m

ini-m

entalstate

exam

ination[5];NAI-W

L,verballearning;N

AI-Z

N-G

,digitmem

oryspan.



ownloaded from


Table

4:Target

proteins

ofanti-AD

herbs

Target

name

Target

name

(abb

reviation)

Unipr

ot_ID

Gen

e_ID

Com

pou

ndInteracting

type

Stateof

therap

eutic

target

Herb

Pub

Med

_ID

1.Acetylcho

linesterase

AChE

P22303

43Hup

erzine

A;

Hup

erzine

Bi;i

Successful

target

(AD)

Huperziaserrata

1636

4207;16364

207

2.Glutamate[NMDA]receptor

NMDAR

Q0558

629

02Hup

erzine

Ai

Successful

target

(AD)

Huperziaserrata

119209

203.Cho

linesterase

BCHE

P06276

590

Hup

erzine

A;

Hup

erzine

Bi;i

Successful

target

(AD)

Huperziaserrata

1636

4207;16364

207

4.Ep

idermalgrow

thfactor

receptor

EGFR

P00533

1956

Quercetin

bSuccessful

target

(Cancer

andinflammation)

Ginkgobiloba;M

elissa

officinalis

186184

85

5.Receptortyrosin

e-protein

kinase

erbB

-2ErbB

2P0

4626

2064

Quercetin

bSuccessful

target

(Cancer

andinflammation)

Ginkgobiloba;M

elissa

officinalis

186184

85

6.5-lipo

xygenase

ALO

X5

P09917

240

Quercetin

iSuccessful

target

(Cancer

andinflammation)

Ginkgobiloba;M

elissa

officinalis

1586

4785

7.Estrogen

receptor

aESR1

P03372

2099

Kaempferol

aSuccessful

target

(Cancer

andinflammation)

Ginkgobiloba

15182386

8.Estrogen

receptor

bESR2

Q92731

2100

Quercetin;

Kaempferol

a;i

Successful

target

(Cancer

andinflammation)

Ginkgobiloba;M

elissa

officinalis

15182386

;1518238

6

9.Tumor

necrosisfactor

TNFA

P01375

7124

Kaempferol

iSuccessful

target

(Cancer

andinflammation)

Ginkgobiloba

17278014

10.Poly[ADP-ribo

se]po

lyper-

ase1

PARP-1

P098

74142

Quercetin

iSuccessful

target

(Cancer

andinflammation)

Ginkgobiloba;M

elissa

officinalis

1788

4996

11.P

eroxisom

eproliferator

activ

ated

receptor

gamma

PPARG

P37231

5468

Quercetin;

Kaempferol

a;a

Successful

target

(Diabe

te,C

ancerand

inflammation)

Ginkgobiloba;M

elissa

officinalis

18262572

;18262572

12.A

ldosereductase

AR

P15121

231

Quercetin

iSuccessful

target

(Diabe

te)

Ginkgobiloba;M

elissa

officinalis

1680

6328

13.C

almod

ulin-dependent

cal-

cineurin

Asubu

nita

isoform

CNA

Q08

209

5530

Kaempferol

iSuccessful

target

(Parkinson’sdisease)

Ginkgobiloba

1850

6853

14.N

itricoxidesynthase,

inducible

iNOS

P35228

4843

Kaempferol

iSuccessful

target

(ische-

miareperfusion

injuries)

Ginkgobiloba

17278014

15.D

NAtopo

isom

erase2-a

TOP2

AP11388

7153

Quercetin

iClinicaltrialtarget

(cancerand

inflammation)

Ginkgobiloba;M

elissa

officinalis

169508

06

16.A

poptosisregulatorBcl-2

Bcl2

P10415

596

Kaempferol

aClinicaltrialtarget

(cancerand

inflammation)

Ginkgobiloba

15182386

17.A

poptosisregulatorBAX

Bax

Q07812

581

Kaempferol


(cancerand

inflammation)

Ginkgobiloba

15857606

(con

tinue

d)



ownloaded from


Table4Continued

Target

name

Target

name

(abb

reviation)

Unipr

ot_ID

Gen

e_ID

Com

pou

ndInteracting

type

Stateof

therap

eutic

target

Herb

Pub

Med

_ID

18.Transcriptio

nfactor

AP-1

AP1

P05412

3725

b-Sitosterol


(cancerand

inflammation)

Salviaofficinalis

14612938

19.C

ycloox

ygenase-2

COX2

P35354

5743

Ursolicacid

iResearchtarget

(cancer

andinflammation)

Salviaofficinalis

1290

7607

20.C

aspase-9

Casp9

P55211

842

Gallic

acid

iResearchtarget

(cancer

andinflammation)

Melissa

officinalis

1768

5632

21.Interleuk

in-4

IL4

P05112

3565

Apigenin

iResearchtarget

(cancer

andinflammation)

Salviaofficinalis

1738

4531

22.S

teroid

andxeno

biotic

receptor

SXR

O75469

8856

Ginkg

olidea;

Ginkg

olideb

a;a

Researchtarget

(cancer

andinflammation)

Ginkgobiloba

19034627;19034

627

23.C

aspase-3

Casp3

P42574

836

Gallic

acid

iResearchtarget

(neuro-

degenerativ

edisease)

Melissa

officinalis

1768

5632

24.M

itogen-activated

proteins

kinase

MAPK

P284

825594

Quercetin;G

allic

acid

b;b

Researchtarget

(neuro-

degenerativ

edisease)

Ginkgobiloba;M

elissa

officinalis

1699

6034

;16308

312

25.S

arcoplasmic/end

oplasm

icreticulum

calcium

ATPa

se1/2

ATP2A1/2

O14983;

P16615

487;48

8Quercetin

iResearchtarget

(Diabe

te)

Ginkgobiloba;M

elissa

officinalis

18785622

26.C

ytochrom

eP450

1A2

CYP1A2

P05177

1544

Quercetin;

Kaempferol

i;i

Non

eGinkgobiloba;M

elissa

officinalis

16762476;16226778

27.C

ytochrom

eP450

1A1

CYP1A1

P04798

1543

Quercetin;

Kaempferol

i;i

Non

eGinkgobiloba;M

elissa

officinalis

1622

6778;16226778

28.P

rotein

kinase

CPK

C-B

P05771

5579

Quercetin

bNon

eGinkgobiloba;M

elissa

officinalis

14593496

29.G

lutathione

S-transferaseP

GST

P1P0

9211

2950

Quercetin

iNon

eGinkgobiloba;M

elissa

officinalis

1268

6490

30.N

uclear

factor

NF-k-Bp6

5subu

nit

REL

AQ04

206

5970

Kaempferol

iNon

eGinkgobiloba

17278014

31.M

atrixmetalloproteinase-1

MMP1

P03956

4312

Kaempferol

iNon

eGinkgobiloba

1788

6198

32.C

ollagena-1(III)chain

COL3

A1

P02461

1281

Ginkg

olideb

b;b

Non

eGinkgobiloba

18221374;182

21374

33.P

lateletbasic

protein

PBP

P027

7554

73Ginkg

olideb

iNon

eGinkgobiloba

17934819

34.S

teroid17-a-hydroxy

lase/

17,20lyase

CYP17

P050

931586

Gallic

acid

iNon

eMelissa

officinalis

1184

8291

Note:Interactingtype

:a-Active;i-Inhibit;b-Bind



ownloaded from


association between the targets and AD might be

implicated [48].

To perform the association analysis, a group of

AD-related proteins were collected. These proteins

include two parts, i.e. the successful therapeutic tar-

gets for FDA approved drugs, and the disease proteins

of AD. By searching the TTD database, five successful

therapeutic targets were collected with the key word

‘Alzheimer’s disease’: acetylcholinesterase (AChE),

5-hydroxytryptamine receptor 2A (5-HT2A), cho-

linesterase (BChE), 5-hydroxytryptamine receptor

1A (5-HT1A) and Glutamate [NMDA] receptor

(NMDAR). In addition, four AD disease genes

were further retrieved from the Online Mendelian

Inheritance in Man (OMIM) database [49]: amyloid

precursor protein (APP), presenilin 1 (PSEN1), pre-

senilin 2 (PSEN2) and Apolipoprotein E (APOE).

Their corresponding proteins were obtained accord-

ing to Uniprot database. Totally, nine AD-related

proteins were curated.

Our association analysis was performed in the fol-

lowing procedures:

Firstly, PPI data from the HPRD [50], Mint [51],

Intact [52], BioGRID [53], DIP [54] and MIPS [55]

Database was downloaded to construct a compre-

hensive background network. Secondly, the nine

‘AD-related proteins’ were mapped onto the back-

ground network as well as the 34 target proteins.

Then, to explore whether the herbal targets might

relate to the ‘AD-related proteins’ at a higher level of

organization, the shortest path length between both

sets of proteins in the network was measured [48].

The final averaged shortest path length between the

two groups of proteins was 3.118. To test whether

the result is statistically significant, with ‘AD-related

proteins’ fixed to the background network, 34 pro-

teins were randomly taken from the background PPI

network (10 523 proteins) as a control rather than the

34 herbal targets and nine ‘AD-related proteins’.

After 10 000 times of randomization, the distance

of each was obtained similarly. The statistical signifi-

cance between the actual distance and those of

random counterparts was estimated with the com-

monly used Z-score [56]. If the absolute value of

Z-score is >3, the deviation between the actual

value and the random ones is considered to be of

significance. Despite of the difference of

less-than-one on average, the 34 herbal targets ex-

hibit highly significantly close network distance

(3.118) to the nine ‘AD-related proteins’ (Z-score

�5.803), comparing to the randomly-selected 34

nodes from the background PPI network (3.677).

We believe that with more knowledge accumulated,

closer functional linkage of the herbal targets and AD

might be inferred in future.

Second, the relationship between individual target

protein and AD was further investigated by database

and literature searching.

Overlapping between herbal targets and known

therapeutic targets were identified with the informa-

tion from TTD database and DrugBank database

[57]. Among the 34 herbal targets, 3 are successful

anti-AD targets, 11 are successful targets of five dis-

eases, and another 11 are clinical trial targets or re-

search targets of the same five diseases.

The three anti-AD targets covered 60% of the five

known successful anti-AD targets as mentioned

above. They are Acetylcholinesterase (AChE),Cholinesterase (BChE), and Glutamate [NMDA] re-

ceptor (NMDAR). Through inhibiting AChE and

BChE, FDA-approved drugs [58], such as donepezil,

galantamine and rivastigmine have been considered

to be the first line pharmacotherapy for mild to mod-

erate AD. Meanwhile, memantine has been the only

approved drug for the treatment of moderate to

severe AD by repressing NMDAR [59]. From this

point of view, the anti-AD effect of the collected

herbal ingredients, Huperzine A and Huperzine B,

is probably contributed by inhibition of these suc-

cessful therapeutic targets of AD.

In addition to the successful anti-AD targets,

11 targets have been recorded in TTD as successful

targets of other diseases, such as various neurodegen-

erative diseases (e.g. Parkinson), ischemia reperfusion

injuries, cancer, inflammation and diabetes.

Specifically, serine/threonine–protein phosphatase

2B catalytic subunit a isoform (CNA) has been a

successful target for Parkinson’s disease, which is

shown among the 11 targets. Selective inhibition

of inducible NO synthase (iNOS) was shown to be

able to reduce ischemia reperfusion injuries [60],

while iNOS was suggested to play an important

role in Ab-mediated damage [61]. It is reasonable

to detect the relation between the anti-AD herbal

targets and other neurodegenerative diseases as well

as ischemia reperfusion injuries, because these are

related to brain diseases and their therapeutic

approaches are generally aiming at neuroprotection.

Such kind of association has also been reported in

previous studies [60, 62].

Interestingly, targets of other diseases: inflamma-

tion, cancer, and diabetes were retrieved as well in



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our study. First, eight of the remaining nine targets

have been reported as successful therapeutic tar-

gets for cancer and inflammation. Among

them, Estrogen receptor 2 (ESR2) and Peroxisome

proliferator-activated receptor gamma (PPARG) can

both be hit by two herbal ingredients, while the

other six are all hit by one individual ingredient,

respectively, as indicated in Table 2. Literature

search indicated their potential association with the

process of AD. For example, ALOX5 was observed

to be upregulated with aging [63, 64], while aging is

one import factor for AD. Meanwhile, ESR1,

TNFA and PARP-1 were suspected to be associated

with AD in several studies [65–67]. Second, aldose

reductase (AR) and PPARG are successful targets for

Diabetes.

Although close correlation between the herbal

targets and AD was not inferred by the statistical

tests, there were many evidences suggesting their

inner relationship. In summary, the herbal ingredi-

ents of collected anti-AD herbs can interact with a

wide range of protein targets, where the majority of

them (74%) are either successful or research targets

for AD-related diseases, including the three success-

ful therapeutic targets against AD. Inflammation is

secondary to protein accumulation in ADs, while

all the 16 therapeutic targets (including research

and clinical) for inflammation in our list are also

overlapping cancer targets. Three targets for diabetes,

three targets for Parkinson and one for ischemia

reperfusion injuries were also found within our

anti-AD target list. These results imply that

anti-AD herbs are primarily interacting with tens of

important proteins critical to disease treatment.

Although the relationship between these diseases is

not fully understood, these targets are serving as the

material basis for the therapeutic effects of the

anti-AD herbs, as well as suggesting important

cross-talks between AD and the other complex

diseases.

Part-two: target network analysis ofmolecular mechanism for anti-AD herbsThe main purpose of this part is to answer the fol-

lowing questions: What pathways are herbal medi-

cines involving in disease treatment? What is the

mechanistic difference between herbal medicines

and western drugs?

To better elaborate the holistic modulation of the

anti-AD herbal medicines, an integrated ‘AD-related

pathway’ was compiled based on the ‘Alzheimer’s

disease pathway’ from KEGG Pathway database

[68, 69] and human PPI data from online databases

[50–55]. First, proteins involved in the ‘Alzheimer’s

disease pathway’ were used as the seeds to obtain

their partner proteins in the context of PPIs.

Second, the direct interacting partners were used as

a new query to fish out the secondary partners. The

pathway was expanded step by step as more herbal

targets could be included. Third, closely connected

proteins were grouped together, and they were orga-

nized according to current knowledge of AD path-

ology. Fourth, in order to clearly display the

underlying mechanism of the herbal medicines, the

intermediate interacting partners were removed.

Finally, the pathway was synthesized by ‘Pathway

Diagrammer’ [70]. Of 34 target proteins, 28 can be

mapped or connected to the pathway. As shown in

Figure 2, several AD-related processes were

involved, including proliferation, Ab degradation

and cell death. The targets can be organized into

the following pathways: AD symptoms-associated

pathways, inflammation-associated pathways,

cancer-associated pathways, diabetes mellitus-

associated pathways, Ca2þ-associated pathways and

cell proliferation pathways:

Inhibiting theAD symptoms-associated pathwaysAs being mentioned above, three successful thera-

peutic targets for AD can be regulated by anti-AD

herbal ingredients, including Two GPCR family

members (acetylcholinesterase and cholinesterase),

and Glutamate [NMDA] receptor (NMDAR).

These three receptors are all located at cell mem-

brane, as being shown in Figure 2. The acetyl-

cholinesterase and the cholinesterase are involved

in the cholinergic system which has been thought

to have direct influence on cognitive processes

[71]. NMDAR is a glutamate-gated ion channel

which is linked to the induction of excitotoxic

neuron death [72]. It is pleased to see that the

ingredients of Huperzia serrata, could inhibit acetyl-

cholinesterase and cholinesterase [73], as well as

block NMDA ion channels [74]. As the FDA

approved anti-AD drugs usually improve symptoms

of AD patients through inhibiting these targets,

similarly, the anti-AD effects of herbs could prob-

ably be contributed by cognition enhancement

or glutamatergic excitotoxicity reduction to pre-

vent patients from memory and recognition

deterioration.



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Suppressing the inflammation-associated pathwaysAs described in Figure 2, a series of inflammatory

factors could be inhibited by herbal ingredients.

Specifically, kaempferol contained in Ginkgo bilobawas shown with a reduction of TNF, NF-kB and

iNOS protein levels in the aged gingival tissues [75],

while another component of Ginkgo biloba,Quercetin could inhibit LOX5 [76]. Moreover,

two ingredients of Salvia officinalli, Ursolic acid and

Apigenin, are correlated with the suppression of

COX-2 expression and the inhibition of IL4 synthe-

sis [77, 78], respectively. Actually, evidence for the

involvement of inflammatory process in AD patho-

genesis has been documented for a long time [79],

and higher peripheral concentrations of several cyto-

kines, chemokines and nitrogen species have been

observed in AD [80]. The downregulation of the

inflammatory cytokines by herbal ingredients will

probably help slowing down of AD progress.

Interfering with the cancer-associated pathwaysCancer-associated pathways could be modulated by

the herbal ingredients, as displayed in Figure 2.

Kaempferol was observed to decrease Bax level

[81] and increase Bcl-2 activity [82] in human

tumor cells, as well as inhibit MMP1 induction

in 12-O-tetradecanoylphorbol 13-acetate-treated

human dermal fibroblasts [83]. Quercetin was

found to reduce PARP1 level in pulmonary epithe-

lial cells [84], and inhibit GSTP1 in malignancies

[85]. A component of Melissa officinalis named Gallic

acid, was shown with decreased activity on Casp3

and Casp9 in 3T3-L1 pre-adipocytes [86], while

b-Sitosterol contained in Salvia officinali inhibited

the expression of AP1 in HT116 human colon

cancer cells [87]. As cancer has been found to be

closely related to AD in a prospective longitudinal

study [88], influencing the cancer-related machinery

may constitute an important part of anti-AD effects

of these herbal ingredients.

Regulating the Diabetes mellitus-associated pathwaysAs shown in Figure 2, Quercetin and Kaempferol

served as agonists of peroxisome proliferator receptor

gamma (PPARg) as validated in the PPARg reporter

gene assay [89], while Quercetin could also inhibit

AR activity by 54% as being measured in an enzyme

assay [90]. In fact, PPARg has been found to induce

insulin signaling as was recently reported to contrib-

ute to modulating Ab accumulation which has been

recognized as a major causative factor in AD patho-

genesis [91]. AR catalyzes the rate limiting step of the

polyol pathway of glucose metabolism, and is

thought to be involved in the pathogenesis of

Figure 2: Distribution of target proteins of herbs on compressed ‘AD pathway’.



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secondary diabetic complications [92]. Besides the

well-known involvement in glucose metabolism,

AR has recently been suggested by various groups

to play a pivotal role in inflammatory pathologies

which is tightly related to AD as mentioned above

[93]. These results show that PPARg activation and

AR inhibition of the herbs might contribute to the

anti-AD efficacy other than preventing diabetes role.

Downregulating the intracellular Ca2þ homeostasispathwaysFour Ca2þ-related proteins were targeted by

anti-AD herbal ingredients, such as Kaempferol and

Quercetin, being represented as blue squares in

Figure 2. Changes of intracellular Ca2þ are believed

to accompany with almost the whole-brain path-

ology process that has been observed in AD. These

processes include Ab production, tau phosphoryl-

ation, mitrochondrial dysfunction and presenilins

mutation [94]. This indicates that affecting Ca2þ

modulation effects may play an important role in

the anti-AD effects of the herbal ingredients.

Participating in cell proliferation processSome proteins involved in cell proliferaction were

also modulated by the herbal ingredients, as

marked in Figure 2. Ginkgolide a and Ginkgolide

b have been reported to activate SXR in human

primary hepatocytes [95], while Quercetin and

Kaempferol could activate ESR2 and ESR1 respect-

ively in primary rat neuronal cells [96]. Although the

exact mechanism is not very clear, the regulation of

cell proliferation might also be important for the

herbal ingredients in the treatment of AD.

Overall, the above analysis on the ‘AD-related

pathway’ suggests several possible anti-AD mechan-

isms for the herbal ingredients. The herbal ingredi-

ents could produce their anti-AD effect not only by

improving the symptom of AD patients, but also by

targeting the fundamental of the disease pathophysi-

ology such as the inflammation-associated pathways,

cancer-associated pathways, diabetes-associated path-

ways and so on.

As a complex network disease [97], accumulating

evidences have keep indicating the association be-

tween AD and other pathological diseases in the

past few years [79, 98, 99], although the underlying

genetic and molecular connections still need further

investigation.

In summary, from the target network perspective

of these herbal ingredients, it is suggested that

Huperzia serrata might produce anti-AD effects

mainly through a symptom-improving way, while

targets of Salvia officinalis are mainly involved in in-

flammation and cancer-associated pathways. As for

Ginkgo biloba and Melissa officinalis, their targets are

more diversely distributed in these fundamental

pathways of AD indicating their more extensive

anti-AD effects.

CONCLUSIONANDPERSPECTIVETarget proteins of anti-AD herbal ingredients were

systematically collected and analyzed from a network

perspective. Although the anti-AD herbs might

relate to other therapeutic effects or diseases, their

target proteins were found to be closely linked to

AD. Some of the target proteins are successful thera-

peutic targets for AD or cross-talk diseases to AD

(41.18%), while some others are clinical or research

targets for these diseases (32.35%). Furthermore, the

herbal targets were mapped to the ‘AD-related path-

way’ to explore the underlying mechanism of the

anti-AD herbs from the perspective of network

pharmacology. As been observed, the herbs might

treat AD symptomatically as FDA-approved

anti-AD drugs do, or modify the disease through

inflammation-associated pathways, cancer-associated

pathways, diabetes-associated pathways, etc., which

are closely related to AD.

In summary, our work may be beneficial to the

anti-AD field in two aspects:

The first aspectThe underlying mechanism derived from

‘multiple-ingredients multiple-targets’ strategy of

the anti-AD herbs might facilitate the development

of new anti-AD therapy as a complementary to the

current FDA-approved drugs. Besides the ‘multiple-

ingredients multiple-targets’, other properties may

also contribute to the therapeutic effects of

anti-AD herbal medicines. Our comprehensive lit-

erature searching found that 7 of the 10 collected

compounds could pass through the blood–brain bar-

rier (BBB) to produce anti-AD effects. For example,

quercetin and kaempferol have been shown to trans-

port across the BBB in rats [100]. This information

was also shown in Table 2.

On the other hand, although being viewed as low

toxicity, the safety of the herbal medicines should

also be noticed as adverse effects may lead to their

failure in anti-AD drug discovery, development and

marketing. Since few clinical and toxicological



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studies have been performed to study the side effects

of herbal medicines, databases and computational

tools designed for studying drug side effects would

be extremely useful. The recent developed side effect

resource (SIDER) database [101] and Drug Adverse

Reaction Database (DART) [102] has documented

the relationship between drugs, ADR targets and

known side effects. Additionally, several computa-

tional methods may also be helpful in the field of

safety evaluation [103–106]. Of course, adverse ef-

fects would be closely related to dosage of the medi-

cine while no bioinformatics report has been seen to

incorporate the information. It is anticipated that

above bioinformatics resources and computational

techniques would be useful to future adverse effects

calculation.

The second aspectRecent studies on disease–disease association have

indicated the cross-talks between different diseases.

As shown by the human disease network (HDN)

[107] and the expanded human disease network

(eHDN) [108], the genetic origins for the majority

of diseases are shared with other diseases, and most of

them are linked to only a few diseases, while a small

part of them are related to many different diseases. In

our paper, the AD-related target network has pro-

vided a more systematic and clear decipher of AD

pathogenesis, including the potential cross-talks with

inflammation, cancer, and diabetes. There are some

reports that people with AD, Parkinson’s and

Huntington’s diseases have a significantly low risk

of most cancers [88] and the reverse correlations

also hold true. The observation in this paper that

the cancer-associated pathways are modulated by

the anti-AD herbal ingredients gives reasonable ex-

planation to that. Secondly, it is known that brain

studies on AD have indicated clear evidence for an

activation of inflammatory pathways and long-term

use of anti-inflammatory drugs is linked with

reduced risk to develop the disease [109]. Since the

cause and effect relationships between them are still

under investigation, the widely repression of inflam-

mation pathway by anti-AD herbal medicine may

provide hints to the future fine tuning of inflamma-

tion to delay, prevent, or treat AD.

Lots of molecules have been studied at different

stages for their potential anti-AD effects. Among

them, herbal medicines are one promising source

with typical ‘multiple-ingredients multiple-targets’

property. Our work has focused on the holistic

understanding of this ‘multiple-ingredients

multiple-targets’ mechanism for anti-AD herbal

medicines at a molecular level. Suggested from

MOA of herbs, future anti-AD therapy might be

considered from multi-intervening several pathways

by systematic evaluation the different roles of com-

binational components.

Key Points

� Since the mechanism of herbal medicines is featured asmultiple-ingredients and multiple-targets network properties,integrated bioinformatics application can provide valuable sup-port to decode theMOAof herbmedicines intervening complexdiseases such as AD.

� Holistic target network analysis of the anti-AD herbs indicatednot only part of the commonmechanism shared between herbsand known FDA-approved drugs, but also the interestingcross-talks between AD and other diseases.

� The proposed bioinformatics method could have a great poten-tial if applied systematically to the inference of new therapeuticuses for natural compounds.

FUNDINGThis work was supported in part by grants from

Ministry of Health (2012ZX10005001), Ministry of

Science and Technology China (2010CB833601),

National Natural Science Foundation of China

(31100956, 61173117, 30900832, and Shanghai

Pujiang Talents Funding (11PJ1407400).

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