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A systematic review and integrative approach to decode the common molecular link between levodopa response and Parkinson’s disease

  • Debleena Guin1,
  • Manish Kumar Mishra1, 3,
  • Puneet Talwar1,
  • Chitra Rawat1, 4,
  • Suman S. Kushwaha2,
  • Shrikant Kukreti3 and
  • Ritushree Kukreti1, 4Email author
BMC Medical GenomicsBMC series – open, inclusive and trusted201710:56

https://doi.org/10.1186/s12920-017-0291-0

Received: 29 April 2017

Accepted: 24 August 2017

Published: 19 September 2017

Abstract

Background

PD is a progressive neurodegenerative disorder commonly treated by levodopa. The findings from genetic studies on adverse effects (ADRs) and levodopa efficacy are mostly inconclusive. Here, we aim to identify predictive genetic biomarkers for levodopa response (LR) and determine common molecular link with disease susceptibility. A systematic review for LR was conducted for ADR, and drug efficacy, independently. All included articles were assessed for methodological quality on 14 parameters. GWAS of PD were also reviewed. Protein-protein interaction (PPI) analysis using STRING and functional enrichment using WebGestalt was performed to explore the common link between LR and PD.

Results

From 37 candidate studies on levodopa toxicity, 18 genes were found associated, of which, CAn STR 13, 14 (DRD2) was most significantly associated with dyskinesia, followed by rs1801133 (MTHFR) with hyper-homocysteinemia, and rs474559 (HOMER1) with hallucination. Similarly, 8 studies on efficacy resulted in 4 genes in which rs28363170, rs3836790 (SLC6A3) and rs4680 (COMT), were significant. To establish the molecular connection between LR with PD, we identified 35 genes significantly associated with PD. With 19 proteins associated with LR and 35 with PD, two independent PPI networks were constructed. Among the 67 nodes (263 edges) in LR, and 62 nodes (190 edges) in PD pathophysiology, UBC, SNCA, FYN, SRC, CAMK2A, and SLC6A3 were identified as common potential candidates.

Conclusion

Our study revealed the genetically significant polymorphism concerning the ADRs and levodopa efficacy. The six common genes may be used as predictive markers for therapy optimization and as putative drug target candidates.

Keywords

Parkinson’s disease Levodopa Dyskinesia Adverse effects Levodopa response

Background

Parkinson’s disease (PD) is a second most common progressive neurodegenerative disorder followed by Alzheimer’s disease [1]. It affects 1.5% of the global population over the age of 65 years [2]. Characterised by motor symptoms, like gait dysfunctioning, bradykinesia, rigidity, and resting tremors, PD has been believed to be caused due to loss of dopamine at the dopaminergic neurons in the substantia nigra pars compacta [3]. Along with the dopaminergic disruption, other non-motor dysfunctioning like depression, sleep disorder, dementia are also observed in PD patients which can be a plausible consequence of both dopaminergic and non-dopaminergic systems. Pathological confirmation is obtained by the presence of Lewy bodies- fibrillar aggregates, mostly consisting of protein alpha synuclein, in the affected neurons of the brain [4].

Levodopa (or L-Dopa), ever since its discovery, has been used as a potent anti-Parkinson’s medication and functions as symptoms alleviating therapy, by maintaining the dopamine concentration at the synapse and reduce the motor fluctuations observed in PD patients [5]. Almost 15–20% of the patients do not respond to the therapy or show adverse profiles primarily, levodopa-induced dyskinesia [6] after 5 years of therapy. Managing ADR is thus one of the most challenging aspects of PD. Carriers of specific genetic polymorphisms of drug metabolising enzymes, drug transporters, drug receptors and proteins involved in drug pathway of anti-Parkinson’s drugs may predispose to adverse reactions or altered efficacy.

Several susceptibility loci have been studied already with the familial cases of PD, like SNCA (PARK1), LRRK2, PRKN (PARK2), PINK1 (PARK6), DJ-1 (PARK7) [7]. However very less has been elucidated about the genetic background of the sporadic cases of PD. Neurodegenerative diseases including PD are multifactorial in nature. Mechanisms like mitochondrial dysfunction, Lewy body formation, oxidative stress, altered protein handling, and inflammatory change are considered to lead to cell dysfunction and death by apoptosis or autophagy. Ageing is one of the most studied risk factor for PD, and the biochemical changes that are a consequence of aging amplify these abnormalities in PD patients’ brain [8]. Candidate studies have pin- pointed genes like NAT2, MAOB, GST, mitochondrial tRNA, S18Y variant of UCHL1, SNCA, MAPT H1 haplotype and LRRK2 [9]. GWA studies have identified more risk loci: BST1, GAK, HLA-DR, ACMSD, STK39, MCCC1/LAMP3, SYT11, PARK16, FGF20, and GPNMB, but with lower significance to establish a valid association for clinical management [10]. Also, since the mechanism of development and progression of PD have not been elucidated fully, current treatment options are only targeted at providing symptomatic respite. Understanding of these multiple aspects of PD may potentially reward this field of study for clinical intervention.

The aim of the present article is to summarize all the studies carried out on polymorphism-association of administration of levodopa on sporadic PD patients and its treatment outcome as ADR and the altered efficacy of the drug. We, also describe the interplay of the molecular pathways involved in the mechanism of levodopa induced ADRs, LR and the disease pathology. This is an attempt to identify the molecular targets as genes and if the polymorphisms in such genes predispose certain patient population susceptible to causing ADRs and altered efficacy. For this purpose, we perform a systematic review through several online databases, select the relevant articles on the basis of pre-defined inclusion and exclusion criteria based on the focus of our study, separately, for LR, and PD disease susceptibility. These articles are further assessed for their methodological quality and finally the data was extracted for the list of genes (and its variants) associated with the drug response and disease risk. This effort has been further elaborated using computational approaches like network modelling to rule out the systematic biases from high-throughput multiple datasets and identify if there is any molecular mechanism involved in LR and PD susceptibility that intersect each other. Such proteins can be plausible targets to minimize toxicity, elaborate the therapeutic efficacy and capture disease risk.

Methods

All the methodologies performed in the study were drawn following the Human Genome Epidemiology Network for the systematic review of genetic association studies [1114] and the PRISMA guidelines [15].

Data source and search strategy

A systematic search in Medline [16] and Web of science [17] was performed using standard MeSH terms “Parkinsons’s disease”, “variant”, “Polymorphism”, “SNP”, “single nucleotide polymorphism”, “pharmacogenomics”, “response” with AND/OR Boolean operators to identify all the human studies on genetics of Parkinson’s disease and/or on drug response by anti-Parkinson drugs. Also, a check for the studies that were not identified by the previous search, of pharmacogenetic relevance from the PharmGKB database [18], were added using search term “Parkinson’s disease”. The searches were limited to human studies.

Study selection criteria

The study selection was carried out independently in two stages by two different authors (DG and MKM) from relevant articles published up to March 9, 2016. All the articles that are reviews, commentary, erratums, editorials, technical reports, news, evaluation studies were initially screened (n = 173). Articles that were in duplicate (n = 260) and published in languages other than English (n = 36) were also excluded. At first, the articles were screened by titles based on relevance, as obtained by the search. Secondly, the abstracts of all primarily screened articles were retrieved and assessed according to the inclusion and exclusion criteria provided in Additional file 1: s4. Further, the articles were distinctly segregated into ADR of levodopa and efficacy of the drug. Only full text articles were included in the final study corpus. In case of disagreement regarding the screening of the articles, an independent reviewer (PT) was consulted to resolve the discrepancies. The cross-references of the finally selected articles were also searched for additional relevant articles. Further, the MeSH term for the adverse effect with ‘levodopa’ or ‘L-Dopa’ were again retrieved to double-check for any missing articles of purpose. All the baseline univariate significant allelic/genotypic associations with ADR/s and with L-dopa efficacy are reported in the Additional file 2: Table S1(8a) and Additional file 3: Table S2(8b), respectively. Similar search was performed for the drug response related articles as well.

Data extraction and quality assessment

Data were extracted by DG and MKM and checked by PT and RK. In case of sequential or multiple publications from the same group of authors, only the recent article has been included or studies which report exclusive findings. Data extracted from each eligible publication is provided in Table 1 (complete in Additional file 2: Table S1(8a)) for ADR articles and Table 2 (complete in Additional file 3: Table S2(8b) for drug efficacy studies. Ethnicity was classified as African, Asian or Caucasian [1923]. If the ethnicity was not reported, the source population based on the country in which the study was conducted was considered e.g. Chicago. The genetic associations were stratified by ethnicity/population to explore the inter-ethnic variations. All the populations of the subjects have been categorised into its respective super-populations based on the 1000genome project (Phase II).
Table 1

Characteristics of included studies for assessment of association between genetic variants and ADRs in PD

Study

Population/ ethnicity

Response criteria

Age (years)a

Gender

Number of samples

Type of ADR

Genes

Studied variants

p- value

OR

(95% CI)

Dosea (drug)

FPa (years)

Score

M

F

Total

ADR

Non

ADR

Schuh A F S et al. [64]; t

Brazilian

UPDRS, HY, MMSE

67.38 ± 10.34

105

100

205

86

119

Dyskinesia

HOMER1

rs4704559

(GG/GA) 0.04

0.53 (0.29-0.98) *

200 (L)

1

13

Rieck M et al. [65]; t

Brazilian

UPDRS-IV

66.88  ± 10.80

110

98

208

90

118

Dyskinesia

ADORA2A

rs2298383

CT-0.04

1.89 (1.03–3.45)

805.14 ± 310.17 (L)

8.34 ± 4.86

13

TT-0.02

2.06 (1.10–3.82)

rs3761422

CC-0.02

3.12 (1.22-7.96)

CT-0.01

3.28 (1.30-8.27)

Strong J.A et al [23]. $

Caucasian

NR

65.3 ± 1.56 early; 69.4 ± 1.25 late

57

35

92

92

NA

Dyskinesia

DRD2

14 allele

0.04

3.4 (1.1-10.4)

NA (L, C)

5

9

14/15

0.003

27.2 (1.4-51.0)

Rieck M et.al [66]

Brazilian

UPDRS-IV

65.52 ± 9.99

102

97

199

83

116

Dyskinesia

DRD2

rs2283265

0.05

-

780.12 ± 308.08 (L)

8.44 ± 4.92

13

ANKK1

rs1800497

0.02

Oliveri R.L et al. [57]

Italian

UPDRS-ME, AIMS, MMSE, Hamilton

64.6 ± 9.4

53

45

98

49

49

Dyskinesia

DRD2

13

0.02

0.37 (0.15- 0.89) *

25mg (C); 250mg (L)

5

13

14

0.02

0.25 (0.07- 0.92) *

15

0.02

1.94 (1.08- 3.49) *

13/16

0.05

-

15/16

0.01

3.88 (1.28- 11.74)

Gorgone G et al. [32]

Italian

HY

64.5 ± 7.7

(cases)

64

78

142

60 cases

82 control

Hyper-homocysteinemia

MTHFR

rs1801133

<0.0001

2.59 (1.20-5.57) *

452.0 ± 170.0 (L)

1

12

Acuña G et al. [61] $

European

NR

NA

261

148

409

135

274

Elevated liver

transaminase levels

UGT1A

C908G

0.0018

-

NA (T, L)

NA

7

T232G

0.01060

A528G

0.0008

A754G

0.0023

A765C

0.0023

A197C

0.024

G551T

0.049

A555C

0.0494

A556G

0.0494

T786C

0.0252

Foltynie T et al. [45];#; t

UK Caucasian

UPDRS

62.2

194

121

315

47

268

Dyskinesia

BDNF

rs6265

0.001

2.12 (1.36-3.38)

NA (L)

1-2

11

Kiferle L et al. [19]

Caucasian

UPDRS, MMSE, HY

62.69±11.52

59

63

312

60

62

Visual hallucinations/ Psychosis (psy.)

SLC6A4

rs25531

>0.01

0.86 (0.52-1.44) *

(L)-259 ± 117.30 (psy.), 278.2 ± 181.98 (no psy.); (DA) 2.98 ± 1.73 (psy.), 2.78 ± 1.66 (no psy.)

≥ 4

13

HTR2A

rs6313

>0.05

0.94 (0.57- 1.55) *

Stefanovic M et al. [29] $

Croatian

HY (2.5)

62

81

105

41 case, 145 control

NA

NA

Wearing on- off, Dyskinesia

CYP2D6

*3, *4, *6, *7, and *8

0.03 (*4)

2.1 (1.11-3.99)

NA (L)

NA

5

De Bonisa ML et al. [36]

Italian

UPDRS, HY (1.5-3)

71.96±4.69 (A1), 65.75±9.60 (A2),

38

18

44 (treated)

NA

NA

Hyperhomocysteinemia

MTHFR

rs1801133

< 0.0001

-

NA (L)

NA

10

Schuh AFS et al. [28]

Brazilian

MMSE, HY

68.0±10.3

100

96

196

50

146

Visual hallucinations

DAT1

rs28363170

0.02

2.5 (1.13–5.5)

793.2 ± 409.1 (L)

> 1

12

Fujii C et al. [30]; $; a

Japanese

NR

68.2±9.2 cases, 64.0±9.0 controls

130

81

116 case, 95 control

23

93

Hallucination

CCK

rs1799923

0.02

0.28 (0.10-0.77) *

350.4 ± 140.7 (L)

3.9 ± 4.5

10

Yuan RY et al.[31]

Taiwanese

HY (1-3)

71.37 ± 9.86

85

101

76 cases, 110 control

48

28

Hyper-homocysteinemia

MTHFR

rs1801133 (C677T),

CT-0.004 TT-0.02

-

360.21 ± 137.62 (L, A, S/R)

6.23 ± 4.33

11

rs1801131 (A1298C)

AA <0.001 AC-0.01

-

Paus S et al.[38]

German

HY

64.7 ± 10.1

364

227

591

117

474

Chorea

DRD3

rs6280

0.0005

-

NA (L)

NA

13

92

499

Dystonia

Ivanova SA et al.[20] $

Caucasian

AIMS

NA

NA

NA

143

143

NA

Dyskinesia

GRIN2A

   

NA (L, DA)

≥ 3

7

rs7192557

0.0062

3.21 (1.37-7.51)

rs8057394

0.0033

3.59 (1.48-8.71)

De Luca V et al. [60]; t

Southern Italian

UPDRS, HY, MMSE

70.87 ± 7.59

65

66

131

47

84

Hallucination

HOMER1

rs4704559

0.004

5.89 (1.33-26.14) *

676.42 ± 244.38 (L)

6 months

12

rs4704560

0.04

1.79 (1.03-3.10) *

Wu H et al. [33]

Chinese

NA

NA

144

115

516

259 cases

257 control

Wearing off

COMT

rs4680

GA vs AA-0.01

6.54 (1.49-28.57)

407.45 (Multiple)

NA

10

GG vs AA-<0.001

8.84 (4.74-16.39)

de Lau L M et al. [37]; t

Dutch

HY, UPDRS, MMSE

49.9

143

76

219

98

121

Dyskinesia

COMT

rs4680

A allele-0.004

-

(A-P M)

NA

10

Zappia M et al. [59]

Italian

UPDRS, HY

65.2  ± 8.4

123

92

215

105

110

Dyskinesia

DRD2

13, 14 + CA n STR repeat

0.005

0.45 (0.26-0.79)

654.5 ± 289.6 (L)

0.5

12

Kaplan N et al. [68]

Israeli

NR

55.2 ±13.5

213

139

352

192

160

Dyskinesia

SLC6A3

rs393795

0.000041

4.96 (2.3-10.9)

NA (L)

5 ± 4.5

11

Greenbaum L et al. [46]

Jewish Israeli, Italian

UPDRS

NR

230

160

390

128

75

Tardive dyskinesia

ABCC8

rs886292

0.05, 0.88

1.63 (1–2.67), 1.03 (0.75–1.41)

NA (L)

≥ 3

12

RYR1

rs11880894

0.26, 0.03

0.7 (0.39–1.29), 1.26 (0.81–1.97)

DRD2

rs1800497

0.53, 0.04

1.25 (0.63–2.48), 0.64 (0.42–0.98)

M male, F female, ADR Adverse drug reaction, FP Follow-up Period, AJ Ashkenazi Jews; UKPDS-BBC,UK Parkinson’s disease society Brain Bank Criteria, UPDRS Unified Parkinson’s disease rating scale; HY Hoehn and Yahr Staging of Parkinson's Disease, MMSE Mini mental state examination, AIMS Abnormal Involuntary Movement Scale, PPRS Parkinson’s Psychosis Rating Scale; PDSK,DDSK14; ADL Activities of Daily living, WHO-UMC World health organization-Uppsala Monitoring Centre, PCR-RFLP Polymerase chain reaction- Restriction fragment length polymorphism; OR Odds Ratio, CI Confidence Interval; Drugs are L-levodopa, C-carbidopa, A-amantadine, T-, DA-Dopamine Agonist, MBI-MAO-B inhibitor, S-Selegiline, R-Ropinirole, E-Entacapone, P-Pramipexole, p- pergolide; LEDD, Levodopa equivalent drug dose; NA No association, -; Insufficient data. Score- Cumulative score for Methodological Quality Assessment (Ref Additional file 4: Table S1a for detailed scoring) Odds Ratio, Prevalence Ratio and Hazard Ratio are synonymously used in the table. *OR calculated using reported frequencies from the respective article. Dose of drug are in mg/day. aUnit of Age, Dose and Follow up period are represented with Mean  ± standard deviation; Greenbaum L et al. two p-values are of Israeli and Italian, respectively. All the studies recruited PD patients diagnosed by United Kingdom Parkinson’s Disease society brain bank criteria expect $- Not reported, #- by Neurologist/PD Specialist, €- CAPIT, β- Gelb Criteria. Most of the studies followed PCR-RFLP for genotyping except t- TaqMan, s- Sequenom iPLEXTM, r- RT-PCR, a- ABI PRISM 310. Bold are significant polymorphisms (p≤ 0.05) and their corresponding genes

Table 2

Characteristics of included studies for assessment of association between genetic variants and LR in PD

Study

Population/Ethnicity

Response criteria

Agea (years)

Gender

Number of samples

Genes

Studied variants

p-value

OR (95% CI)

Dosea (Drug)

Treatment length (year)

Score

M

F

Total

R

NR

Tan EK et al. [48]

Singapore

UPDRS

69.9±7.6

24

15

39

NA

NA

COMT

rs4680

0.004

-

7.37 mg/ week (P); 421.5  ± 226.2mg (L)

at least 3 months

12

Liu YZ et al. [51]

Chinese

UPDRS-I, HY <2.5

61.90  ± 8.20

14

16

30

11

19

DRD3

rs6280 (Ser/Ser)

0.024

9.75( 1.60- 59.70) *

0.125mg/thrice a day (L, B,P)

> 3 months

12

Devos D et al. [52]

Caucasian

UPDRS-III, HY

>30

23

10

33

14

19

DDC

rs921451

0.048

-

NA (L,B)

NA

13

8

25

rs3837091

Moreau C et al. [54]

French

UPDRS II, III

60-63

NA

NA

61

NA

NA

SLC6A3

rs28363170

0.005

-

710  ± 90.8mg/day (L)

16-17

12

rs3836790

<0.001

-

M male, F female, R responder, NR non-responder, bp base pair; Dose of drug are in mg/day. Unit of Age, Dose and Follow up period are represented with Mean  ± standard deviation; a unit of age and dose are represented with Mean ± standard deviation; OR, Odds Ratio; *OR calculated using reported frequencies from the respective article; UPDRS Unified Parkinson’s disease rating scale, HY Hoehn and Yahr Staging of Parkinson's Disease; Drugs are L-levodopa, C-carbidopa, A-amantadine, T-, DA-Dopamine Agonist, MBI-MAO-B inhibitor, S-Selegiline, R-Ropinirole, E-Entacapone, P-Pramipexole; PCR-RFLP, Polymerase chain reaction- Restriction fragment length polymorphism. NA No Association; -,Insufficient data. Bold are significant polymorphisms (p≤ 0.05) and their corresponding genes

On a systematic analysis of the GWAS, conducted so far on PD susceptibility risk, included twenty GWAS studies (consisting of 45,465 cases and 173,222 controls). Sixty-one loci have been identified as significantly associated with the disease risk (p ≤ 0.01 × 10−8) (Table 3). A detailed summary of these significant genetic variants obtained from GWAS in the field of PD has been represented in Additional file 4: Table S1. The genes BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, ITGA8, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25 are disease risk loci following the collaborative meta-analyses. SNCA (p ≤ 4.16 × 10−73) and MAPT (p ≤ 2.37 × 10−48) have been studied in 9 and 7 GWA studies establishing the functional relevance in the disease physiology, making them the most prominent loci. Followed by LRRK2 and GAK in 4 studies, GBA/ SYT11 and MCCC1/ LAMP3 in 3 studies respectively.
Table 3

Significant markers from GWAS studies on Parkinson's disease susceptibility risk

Study

Gender

Age

Discovery

Follow-up

# Genes

Genes

Variants

p-value

OR

M

F

 

# SNPs

Population

# Cases

# Ctrls

Population

# Cases

# Ctrls

Edwards TL et al. [85]

381

224

64.56 ± 12.18

4,22,322

Caucasian-MIHG

605

621

   

16

PLEKHM1

rs11012

5.65×10−8

0.70

SNCA

rs2736990

6.74×10−8

1.29

Satake W et al. [86]

   

4,35,470

Japanese

1,078

2,628

Japanese

612

14,139

4

PARK16

rs823128

4.88×10−9

1.41

rs823122

5.22×10−8

1.37

rs947211

1.52×10−12

1.30

rs823156

3.60×10−9

1.37

rs708730

2.43×10−8

1.33

4p15

rs11931532

5.13×10−9

1.24

BST1

rs12645693

8.65×10−9

1.24

rs4698412

1.78×10−8

1.24

rs4538475

3.94×10−9

1.24

4q22

rs11931074

7.35×10−17

1.37

Japanese

321

1,614

SNCA

rs3857059

5.68×10−16

1.36

rs6532194

4.15×10−13

1.32

12q12

rs1994090

2.72×10−8

1.39

LRRK2

rs7304279

5.06×10−8

1.38

rs2708453

9.67×10−8

1.38

rs2046932

4.34×10−8

1.39

Sanchez JS et al. [87]

515.27

472.73

55.9±15.1

4,63,185

stage 1 USA

988

3071

   

3

MAPT

rs393152

1.95×10−16

0.77

rs199533

1.09×10−14

0.78

rs17563986

1.67×10−14

0.78

452.98

304.02

56±11.64

Germany

757

976

SNCA

rs2736990

2.24×10−14

1.23

rs3857059

3.74×10−15

1.48

rs11931074

1.62×10−14

1.46

1083.8

444.19

62.5±8.55

stage 2 USA

1528

2044

OTHERS

rs823128

7.29×10−8

0.66

Do CB et al. [88]

2065.9

1360.122

64.3±10.6

5,22,782

primarily European (23andMe)

3426

29624

European descent (IPDGC)

6584

15470

11

LRRK2

rs34637584

1.82x10-28

9.615

GBA

i4000416

5.17x10-21

4.048

SNCA

rs356220

2.29x10-19

1.285

MAPT

rs12185268

2.72x10-14

0.769

MCCC1/ LAMP3

rs10513789

2.67x10-10

0.803

            

SCARB2

rs6812193

7.55x10-10

0.839

GAK

rs6599389

3.87x10-8

1.311

SREBF1/RAI1

rs11868035

5.61x10-8

0.851

Burns EMH et al. [89]

1063

502

67.59±10.68

7.2 million

USA (NGRC cohort)

1565

1986

White, non-Hispanics (NINDS cohort)

621,102

797

4

SNCA

rs356220

1.00x10-9

1.37

HLA

rs3129882

5.00×10-10

1.38

IPDGC [90]

577.75

393.255

55.9±15.1

76,89,524

USA-NIA

971

3034

US

2,807

2,215

11

SYT11

chr1:154105678

3.50×10-12

1.47

966.74

738.265

65.8±10.8

UK

1705

5200

UK

1,271

1,864

AMCSD

rs6710823

6.75×10-9

1.1

446.68

295.316

56±11.6

Germany

742

944

Dutch

304

402

STK39

rs2102808

4.23×10-10

1.18

610.93

428.068

48.9±12.8

France

1039

1984

German

1153

712

MCCC1/ LAMP3

rs11711441

8.04×10-12

0.84

522.1

353.904

61.5±9.2

USA- dbGAP

876

857

French

267

363

GAK

chr4:911311

3.67×10-12

1.16

BST1

rs11724635

1.21×10-16

0.87

SNCA

rs356219

1.82×10 -47

1.29

HLA-DR

chr6:32588205

2.24×10-14

0.78

LRRK2

rs1491942

6.01×10-14

1.27

CCDC62/ HIP1R

rs12817488

3.20×10-13

1.14

MAPT

rs2942168

1.47×10-28

0.79

Nalls MA et al. [69]

315

289

62.2±12.3

78,93,274

IPDGC-DC

604

4,916

IPDGC-FR

553

474

32

GBA/SYT1

rs35749011

1.37×10-29

1.824

579.18

405.82

48.9±12.8

IPDGC-FR

985

1984

IPDGC-GE

1044

871

RAB7L1/ NUCKS1

rs823118

1.66×10-16

1.122

401.53

265.466

55.7±11.5

IPDGC-GE

667

937

IPDGC-GK

944

877

SIPA1L2

rs10797576

4.87×10-10

1.131

476.16

267.84

55.6 ± 11.8

IPDGC-NE

744

2019

IPDGC-NIA

2407

2782

ACMSD/ TMEM163

rs6430538

9.13×10-20

0.875

557.52

379.485

57.8±13.2

IPDGC-NIA

937

1896

   

STK39

rs1474055

1.15×10-20

1.214

 

1972.9

1288.095

64.2±11.2

 

23andMe.v2

3261

29499

IPDGC-UK

405

547

 

MCCC1

rs12637471

2.14×10-21

0.842

528.26

337.74

63.9±10.9

23andMe.v3

866

32538

TMEM175/ GAK/ DGKQ

rs34311866

1.02×10 -43

0.786

179.02

88.976

59.9±12.1

Ash Jewish

268

178

BST1

rs11724635

9.44×10-18

1.126

362.19

211.806

57.2±12.03

HIHG

574

619

FAM47E/SCARB2

rs6812193

2.95×10-11

0.907

1306.6

649.392

58.6±11.7

NGRC

1956

1982

SNCA

rs356182

4.16×10 -73

0.76

495.97

332.028

62.1±10.7

PGPD

828

852

HLA-DQB1

rs9275326

1.19×10-12

0.826

58.957

48.043

73.0 ± 5.1

CHARGE-CHS

107

3164

GPNMB

rs199347

1.18×10-12

1.11

34.98

25.02

76.2 ± 10.8

CHARGE-FHS

60

3889

INPP5F

rs117896735

4.34×10-13

1.624

MIR4697

rs329648

9.83×10-12

1.105

LRRK2

rs76904798

5.24×10-14

1.155

CCDC62

rs11060180

6.02×10-12

1.105

GCH1

rs11158026

5.85×10-11

0.904

TMEM229B

rs1555399

6.63×10-14

0.897

VPS13C

rs2414739

1.23×10-11

1.113

BCKDK/ STX1B

rs14235

2.43×10-12

1.103

MAPT

rs17649553

2.37×10 -48

0.769

RIT2

rs12456492

7.74×10-12

0.904

DDRGK1

rs8118008

3.04×10-11

1.111

FGF20

rs591323

6.68×10-8

0.916

Pankratz N et al. [91]

NA

NA

NA

2633913

 

4,238

4,239

 

3,738

2,111

6

GBA

E326K

5.00×10−8

1.71

GAK

rs11248060

3.00×10−9

1.26

SNCA

rs356220

8.00× 10 −35

1.38

rs356198

5.00×10−9

0.82

HLA reg

rs2395163

3.00×10−11

0.81

MAPT

rs199515

3.00×10−17

0.76

RIT2

rs12456492

2.00×10−10

1.19

Hamza TH et al. [92]

1346

654

58.34±11.93

8,11,597

Caucasian

2000

1986

NA

NA

NA

6

HLA-DRA

rs3129882

1.90×10−10

1.26

GAK9

rs11248051

3.20×10−9

1.46

SNCA

rs356220

3.40×10−11

1.38

Spencer CCA et al. [93]

1244.2

460.81

65.8

532 588

UK

1705

5175

French

1039

1984

6

SNCA

rs2736990

1.36×10-27

1.23

MAPT

rs393152

4.75×10-28

1.31

Vacic V et al. [94]

   

haplotypes

Ashkenazi jews

1,130

1,807

Ashkenazi Jews

306

2583

7

MAPT

rs17577094

4.51×10-10

0.64

#, Number of; (D),Discovery; Ctrls, control; MIHG, Miami Institute for Human Genomics; IPDGC, International Parkinson's Disease Genomics Consortium; NGRC, NeuroGenetics Research consortium; NIA, National Institute on Aging; dbGAP, database of Genotypes and Phenotypes; HIHG, Hussman Institute of Human Genomics; PGPD, Physician Group Practice Demonstration; CHARGE-CHS, Cohorts for Health and Aging Research in Genetic Epidemiology; NA, Not applicable;

Two reviewers (DG and MKM) independently assessed the methodological quality of all the selected articles using a predefined set of criteria. All included studies were assessed for the quality of data presented by using modified criteria suggested by Wells K. et al. [24]. The quality assessment was scored on 14 parameters (Additional file 1: s5 and s6), with a positive score awarded for each detail present in study, the lack of detail was described as either NA (not applicable) or NR (not reported). NA was assessed with an equal positive score and was given only when the study was deemed independent of the parameter; NR was equated to no scoring and was independently awarded by the authors if the methodology was found insufficient or unreported. The detailed list of the 14 parameters used for the quality assessment has been discussed in Additional file 1: s7. Conflicting scores were reached to a consensus upon discussing (RK and PT). If the score was obtained as 11 or higher, the study was ranked as high quality.

Protein-protein interaction network

To decipher the connecting molecular link between the roles of the genes studied for LR and disease risk would help us rule out the bias if any to identify the interacting proteins of drug response thus elucidate the genetic landscape of the disease. Two independent protein-protein interaction (PPI) networks were constructed using STRING application with minimum required interaction score of 0.7 (high confidence), active interaction sources were set for only known interactions (databases and experiments) and maximum number of interactors to show in the 1st shell- no more than 50, and no 2nd shell interaction [25]. Pathway enrichment analysis was conducted by WebGestalt [26]. Using pathway commons enrichment analysis, default GO slim classification, 0.001 significance level, and minimum no. of genes in a category was set at 5. The enrichment analysis was run adjusting the false discovery rate (FDR) using the Benjamini-Hochberg (BH) procedure to obtain the results independently for the two set of genes.

Results

Search and study selection

The workflow of the search and study selection has been represented in Fig. 1. A total of 1041 articles were obtained, of which 469 studies were excluded that includes duplicates, reviews, and articles in other languages (n = 469). The remaining 572 articles were screened by title and abstract following the inclusion and exclusion criteria resulting in further exclusion of 498 articles: 189 studies discussing other diseases or comorbid conditions, 16 studies on familial PD were removed as current study focuses on sporadic form of the disease, 76 studies were based on animal or in vitro models for PD, 40 studies were not on genetic association and 166 others did not discuss any drug response, finally 11 papers on drugs other than levodopa were also excluded to narrow down the scope of current study to the most widely prescribed medication. A total of 74 eligible publications were further divided into studies that are on adverse effect of levodopa or the efficacy of the drug in the patient cohort. In case of unavailable full text articles, the authors were contacted (n = 22). Responses received (n = 16) were included in the study, rest excluded (n = 6). From the cross-references of the included studies, six additional articles fulfilled the inclusion criteria [19, 2731]. Thus, finally 38 eligible publications on levodopa induced ADR and 8 on drug response had sufficient data available for extraction to carry forward the systematic review.
Fig. 1

Flow diagram representing the selection of studies for systematic review of levodopa response studies

Study characteristics

The methodological and demographic characteristics of the ADR studies and the drug efficacy related studies of levodopa are summarised in Table 1 and Table 2 respectively [complete in Additional file 2: Table S1(8a) and Additional file 3: Table S2(8b)]. A total of 4127 subjects with ADRs were enrolled in the 37 studies. Most of the studies included were cohort studies except four which were case-control studies [29, 3234]. Apart from original research articles, four letters [19, 21, 35, 36], two brief reports [37, 38], and two short communications [33, 39] were included as they had sufficient data pertaining to our criteria of inclusion and exclusion, one randomized control trial [40] was included. All the patients recruited in the independent studies were primarily administered with levodopa alone (n = 21), dopamine agonist (n = 4), DDC inhibitor/ carbidopa (n = 4), COMT inhibitor/ entacapone/tolcapone (n = 5) or MAO-B inhibitor (n = 1). The dose of levodopa administered ranged between 200.00 to 805.14 mg/day. The range of follow up period for the recruited subjects in the studies was between 6 to 10.3 months. The PD subjects recruited were diagnosed by UK Brain Bank Criteria (UK BBC) [41] in 26 articles, by Gelb’s criteria [42] in 1 article, by CAPIT [43] (Core assessment programme for intra-cerebral transplantations) in 1 article or by an experienced neurologist in 2 articles. The different motor functioning assessment scales used are provided in Additional file 2: Table S1(8a). The variability in HY assessment scale of 3.5 or more have been used.

Out of thirty-seven studies, thirteen focussed on levodopa induced dyskinesia exclusively, three on other motor fluctuation exclusively, one on wearing on/off, three on hyper-homocysteinemia, five studies were on hallucination, one study each on COMT inhibitor induced toxicity and elevated liver transaminase levels, and twelve studies discussed multiple ADR in the same cohort of recruited patients. Motor fluctuations were observed to be the most common adverse effect of anti-Parkinson’s medications. Dyskinesia (subjects of tardive dyskinesia, peak dose dyskinesia, diaphasic dyskinesia were grouped together) being the most prevalent among the subjects with ADRs, was present in 45.72% of patients. This group had an early age at onset of motor symptoms, longer disease duration and 560.96 ± 321.97 mg/day levodopa daily mean doses. In addition to levodopa, around one-third of the total patients were administered with COMT or MAO-B inhibitor or DDC inhibitor like carbidopa, entacapone or tolcapone. In addition to dyskinesia, adverse effects like other motor complications (motor impulsivity, wearing on-off, chorea, dystonia) were observed in 35.74% of total ADR subjects, and hyper-homocysteinemia 2.62%. Hallucinations occur as a consequence of psychosis, hence the two have been synonymously used and their subjects have been summed up together constituting 12.38%. One paper each discussed about COMT inhibitor induced toxicity (0.26%) and elevated transaminase level (3.27%).

Europeans (EUR) constituted as the major population of the studied subjects comprising 47.47%, followed by East Asian (EAS) at 26.52%, South Asian (SAS) at 13.32% closely followed by American (AMR) 12.66%. Most of the studies recruited subjects of the same population except Cheshire P. et al. (2013) [44] included patients from UK and Australia, Foltynie T. et al. (2014) [45] included all UK Caucasians, one Afro-Carribean, two Asian-Indian and one half Caucasian and half Asian-Indian, Greenbaum L. et al. (2013) [46] included Jewish and Italian subjects and Ziegler DA. et al. (2014) [47] included 122 American and 1 Asian.

A total of 645 subjects have been include in the 8 studies of levodopa efficacy. Of these, three studies are brief communications [4850]; three were cohort studies [5153] and one each of RCT [54], and letter [55]. Three studies recruited patients administered with levodopa alone [49, 53, 54], four studies prescribed levodopa with DDC inhibitors like Benserazide [5052], Pyridoxine [48], and one on Dopamine agonist (Pramipexole) [51] and COMT inhibitor (carbidopa) [55]. The mean levodopa dose administered is 356.8 mg/day. All the patients were diagnosed by UK BBC [41], expect Moreau C. et al. [54] by Gibb’s criteria [56], Tan EK et al. [48] and Xie T et al. [49] by neurologists and Devos D. et al. [52] do not mention the diagnosing criteria. Two hundred twenty seven subjects responded to the therapy assessed by the scoring criteria. The study populations included Asians (54.57%) and Europeans (45.42%).

Methodological quality

The cumulative quality assessment score obtained by individual ADR studies are represented in Additional file 2: Table S1(8a) and that of drug efficacy in Additional file 3: Table S2(8b). In ADR studies, the mean methodological assessment score was calculated to be 10.56 (SD 2.15), range 7 to 13. On the modified scale, twenty of thirty seven articles were deemed as good quality with a cut off score of ≥11, thirteen articles scoring ≥9–10 were categorised under moderate quality and finally any scores below 9 were judged as poor quality which included four articles. In L-dopa efficacy studies, the mean methodological score was 11.13 (SD 1.86), ranging 7 to 13. Six articles qualified to be good, and one each as moderate and poor quality.

Genetic factors in levodopa induced adverse effects

From total publications, 40 variants within 18 genes (HOMER1, ADORA2A, ANKK1, MTHFR, DRD2, SLC6A3, COMT, UGT1A, ACE2, BDNF, ABCC8, RYR1, DRD3, GRIN2A, SLC6A4, HTR2A, CYP2D6, CCK) were found to have significant association (p ≤ 0.05) with any type of levodopa induced ADR in PD (Additional file 2: Table S1(8a)).

Europeans (EUR) studies included 4258 subjects and reported 30 variants associated with ADRs. Among EUR, 1347 subjects were Italian from 8 studies [32, 35, 36, 46, 5760]. With 13, 14 CAn STR repeats in DRD2 gene being the most significant variant associated with dyskinesia, followed by rs1801133 (MTHFR) associated with hyper-homocysteinemia, rs474559 (HOMER1) with hallucinations. Other polymorphisms including rs886292 (ABCC8), rs11880894 (RYR1), rs1800497 (DRD2), rs6265 (BDNF), rs11646587, rs7192557 and rs8057394 (GRIN2A) were found to be associated with dyskinesia in patients administered with levodopa medication. 5-HTTLPR and rs6313 (5-HTR2A) were reported in higher frequency in patients with psychosis and Acuña G. et al. (2002) [61] suggested the association of 10 UGT1A SNPs with elevated liver transaminase level. Among East Asians (EAS), two Chinese studies [62, 63] constituting 746 subjects concluded, rs4680 (COMT) and I/D polymorphism (ACE2) associated in patients with wearing on-off and psychosis respectively. A Japanese study [30] showed significant association with -45C/T (CCK) in patients with hallucination and rs1801131, rs1801133 (MTHFR) with elevated plasma homocysteine levels in a Taiwanese study [31]. In American (AMR) population, Brazilians constituted four studies [28, 6466] determining rs4704559 (HOMER1), rs2298383 (ADORA2A), rs1800497 (ANKK1) associated with dyskinesia, rs3761422 (ADORA2A) with motor fluctuation and rs28363170 (DAT1) with hallucination. Among South Asian (SAS) population, Israelis were most abundantly studied representing association of rs393795 (DAT1), rs886292 (ABCC8), rs11880894 (RYR1) and rs1800497 (DRD2) with dyskinesia [39, 46, 67, 68].

Genetic factors in other LR

On elaborate systematic extraction of published literature on LR, in terms of efficacy, of the drug, only eight studies deemed our defined inclusion and exclusion criteria. The enzymes directly involved in the metabolism and activity of levodopa is evidently been mostly studied with the altered LR. rs4680 (COMT) [48], rs6280 (DRD3) [51], rs921451, rs3837091 (DDC) [52], rs28363170, rs3836790 (SLC6A3) [54] were the significant variants with reduced LR (Additional file 3: Table S2(8b)). However, no conclusive results could be drawn from this systematic analysis due to large variability and low significance.

Genetics of PD susceptibility

Employing GWAS dataset to stratify disease susceptibility loci, we follow an unbiased approach to identify such loci in sporadic PD cases. Nalls et al. (2014) [69] recently conducted a large scale meta-analysis to identify the associated loci with disease risk. Keeping this study as the base of the systematic review of all the GWAS on PD risk, and adding the recent studies to it. Twenty studies were included for the systematic review with 45,465 cases and 173,222 controls, mostly from including Caucasian population followed by Jewish, Chinese and Japanese. Sixty one loci in genes like BST1, CCDC62/HIP1R, TMEM175/DGKQ/GAK, GBA, ITGA8, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25 were associated with the disease susceptibility as shown in Table 3. Additional file 4: Table S1 tabulates all the loci found to be associated with disease susceptibility and the significant single nucleotide polymorphism (SNPs) in bold (p value ≤1.0 × 10−8).

Protein-protein interaction network

We performed PPI analysis using genes obtained from the systematic review of LR and disease risk in order to understand the functional association among the genes in the respective gene modules. With 19 proteins associated with LR and 35 with PD, two independent PPI networks were constructed respectively using STRING database to identify critical candidate genes/proteins (Fig. 2). In LR, the 67 nodes, represent the genes, and 263 edges weight the likelihood of nodes in common biological functions.) In PD susceptibility, 62 nodes linked with 190 edges are depicted. Functional enrichment analysis using WebGestalt identified three common pathways (Alpha synuclein signalling, ADP-ribosylation factor 6 (Arf6) downstream pathway, and Insulin-like growth factor 1 (IGF1) pathway) in the top 20 pathways for LR and PD (Fig. 3). The common six proteins, UBC, SNCA, FYN, SRC, SLC6A3, CAMK2A, between LR and PD were thus obtained. The functional significance of the identified nodes in PPIs are clearly substantiated by the biological processes obtained from functional enrichment analysis.
Fig. 2

Protein-protein interaction network between PD response related genes and disease related genes, respectively. Networks representing protein-protein interaction of gene modules identified from (a) Parkinson’s disease susceptibility genes from GWAS studies and (b) levodopa response genes identified from systematic review. The lines with different colours represent data annotated based on different association evidence. The colour coding are Query proteins- red, other coloured nodes are 1st shell interactors. Interacting lines- blue- from curated databases, pink- experimentally determined

Fig. 3

Enrichment analysis of disease associated and L-Dopa response related proteins. (a) Represents the overlap between the two protein sets. Plot 2(b) & (c) represents the top 20 pathways with PD and L-Dopa response, respectively

Discussion

PD is a progressive brain disease which causes significant movement disability [70]. The treatment is aimed at symptomatic management rather than complete cure. However, the challenge is large clinical variability in drug response and adverse effects on prolonged therapy. Discerning the genetic factors responsible for this variability to the drug toxicity and efficacy can provide better clinical management. This study identifies such genetic variants in genes involved in L-dopa metabolism and in the disease etiology by a systematic review approach. Further from the limited number of genes obtained from the systematic review, we extended our effort to integrated computational approaches like network modelling and functional enrichment to identify the other interacting proteins and thereby distinguish the common proteins and molecular pathways that participate in LR and the disease. We additionally show the limitations of the published literature and give insights that may be useful to future studies.

We have implemented a modified scale of Wells K et al. (2009) [24] criteria with five additional parameters, to assess the quality of articles included in the systematic review. To the best of our knowledge our study has incorporated the most comprehensive methodological quality assessment scoring for screening articles of systematic review. Candidate gene studies have been screened for the systematic review of LR.

A total of 18 genes from the 37 ADR studies and 4 genes from the 8 efficacy studies were retrieved after the systematic review. Most of the genes are related to dopaminergic pathway and their role have been depicted in Fig. 4. Most of the studies included the genes related to dopaminergic pathway. For instance, among ADR studies, CAn STR 13, 14 (DRD2) was found to be most significantly associated with dyskinesia, rs1801133 (MTHFR) with hyper-homocysteinemia, and rs474559 (HOMER1) with hallucination. Carriers of 13, 14 alleles are found to have lower risk of developing dyskinesia but role of this repeat is still unknown. Patients with the TT677 (rs1801133, 677C > T) genotype exhibit 50% reduced activity of MTHFR enzyme, consequently elevating the plasma homocysteine levels [32]. rs474559 G allele (HOMER1) have lower prevalence of dyskinesia as it might disrupt the glutamatergic transmission [60]. In efficacy related studies, rs28363170, rs3836790 (SLC6A3) and rs4680 (COMT), were important. Individuals with rs3836790 6/6 or rs28363170 10/10 (SLC6A3) genotypes have higher transporter expression leading to lower dopamine levels at the synapse [71]. The haplotype structure formed by four SNPs (rs6269: A > G, rs4633: C > T, rs4818: C > G, rs4680:A > G) characterises the COMT enzyme activity to low (ACCG), medium (ATCA) and high (GCGG) [72, 73]. Accordingly, the levodopa metabolism is affected, altering the synaptic dopamine concentration. Also we observed that SLC6A3, COMT, and DRD3 genes were common between the ADR and efficacy studies resulting in 19 exclusive genes from LR studies.
Fig. 4

Crosstalk between the genes (in blue) obtained from the systematic review of LR and their biological functions in the dopaminergic neurons. Levodopa is metabolized by COMT and DDC. Dopamine in the pre-synaptic neuron is produced from and released into the synapse by exocytosis. The receptors in the post-synaptic neurons [DRD1, DRD2, DRD3, ADORA2, glutamate receptors (mGLUR1/S), NMDA, HTR2A] uptake dopamine and further downstream cellular signalling leads to altered gene expression. Signalling pathways like the Ras-GRF1 mediated signalling, Ca2+-calmodulin dependent pathway and adenylyl cyclase participate in dopaminergic response. The function of 6 common genes (in bold) found with overlapping roles in LR and PD risk are discussed in text

In addition, to identify the disease genes involved in drug response and vice versa, genes found implicated in PD susceptibility from GWAS were also retrieved. This led to obtaining 61 significantly associated SNPs (p value ≤1.0 × 10−8) pertaining to 35 genes (Additional file 4: Table S1). Then an integrated network analysis resulted in six common molecular targets (SNCA, FYN, SRC, UBC, CAMK2A, and SLC6A3) from the overlap between 67 nodes (and 263 edges) in LR and 62 nodes (and 190 edges) in PD pathophysiology, respectively. Among the six common molecular targets, SNCA has been widely established to be a major player in PD susceptibility as it a major component of Lewy bodies and mutant SNCA has a greater tendency to acquire misfolding [70, 71]. Aggregation of SNCA has been shown to be neurotoxic for the cell through the formation of intermediate aggregates called protofibrils [74]. In a recent report the distinct role of alpha-synuclein forming fibrils as the major toxic, resulting in progressive motor impairment and cell death leading to neurotoxic phenotypes in PD is demonstrated [74]. FYN, a tyrosine kinase family protein, found inside nerve cells and helps in communicating signals or chemical instructions between different cellular components. This protein has been observed to get modified on levodopa administration, causing dyskinesia [75]. Wang et al. (2016) validated neuro-inflammation inhibition by SRC (SRC proto-oncogene, non-receptor tyrosine kinase) signalling pathway to be a potential drug and disease candidate which supports our finding SRC as the common molecular bridge to both drug response and disease pathology [76]. UBC belongs to the Ubiquitin family C which carries out the ubiquitin mediated proteolysis and aggregate ubiquitin monomers in the diseased brain. The ubiquitin proteins cause aberrations in the ubiquitin proteasome system (UPS) leading to PD pathogenesis [77]. The neuronal protein, CAMK2A alters with intracellular calcium ion concentration change that is abnormally activated following dopamine depletion thus modulating the neuronal function in striatum [78]. Zhang et al. (2014) also established an interaction between CaMK2A and Dopamine D2 receptors in striatal neurons, sensitive to long-term levodopa administration to PD rats [79]. Finally, the SLC6A3/DAT1 variants have a significant effect on striatal activation and performance in PD as suggested by Habak et al. These results have furnished evidences on the role of these candidates in both levodopa metabolism impairment and disease risk. Further, these plausible biomarkers might bridge the path between levodopa metabolism and disease pathology resulting in reduced ADRs, optimum efficacy and, accurate diagnosis.

Functional enrichment analysis revealed prominently [74], alpha-synuclein pathway to be the most significant candidate with the set of disease and response related genes respectively followed by other growth factor signalling pathways like Afr6 downstream pathway, IGF1 pathway, and so on. Arf6, ADP-ribosylation factor, signalling plays a role in the Ras- mediated cell signalling [80]. It is also responsible in the intracellular trafficking of DRD2 by GRK and PKC proteins [81]. The potential role of IGF1, Insulin-like growth factor 1, signalling has been studied with neurodegeneration in human, participating in functions like brain neuron survival, synaptic transmission as well as plasticity [82]. Bernhard FP. et al. (2016) [83] also established that IGF1 might serve as a PD prediction marker, observing elevated levels of IGF1 in PD patients. Additional file 5: s2 and Additional file 6: s3 tabulate the enriched functions obtained by levodopa response genes and PD related genes. We highlighted the potential usefulness of these biological functions in PD treatment which can be affirmed by in vitro and/or in vivo model systems.

Although significant findings have been observed in our study, several limitations exist. The papers included in the systematic review presented high heterogeneity in terms of diagnosis, response criteria, drugs administered with different doses and genotyping techniques. As suggested by Schumacher-Schuh et al. (2014) [84] the phenotypic heterogeneity in terms of adverse effects lacks clinical instrument to adequately measure the ADR, whereas in terms of efficacy, several response rating scales have been incorporated. In GWAS studies, the assayed SNPs are usually to mark a genome region that influences the studied phenotype. However, we have picked up the annotated genes corresponding to the significantly associated SNPs from the respective studies, to identify the proteins that play a role in the biological processes which ultimately influences the phenotype. Motor fluctuation, a common ADR of levodopa, lacks clear clinical classification and hence assessment. A regular record of patient motor state could be preferred. Genetic heterogeneity is another source of variability between studies because different markers in the same genes were employed for these associations; moreover, patients with different genetic backgrounds may not be strictly comparable. One major limitation of network biology is the quality and the coverage of the interactions. The rate of discovery of false positives and false negatives are high which shows the need to rank the reported interactions for further validation.

Conclusion

In summary, the present study provides a framework for better understanding of the molecular interplay between L-Dopa metabolism with PD pathophysiology and also a means to evaluate putative biomarkers to bridge the gap in treatment outcome and disease risk. We propose the above six genes could be useful in predicting both the LR and disease risk, simultaneously. This however warrants further experimental validations to develop into a targeted therapy. Translating these evidences into future validation would present pre-diagnostic marker development which can be applicable in clinical manifestation. A definitive role of these molecular targets in the disease progression can also lead to substantive advancement in PD treatment.

Abbreviations

ABCC8

ATP binding cassette subfamily C member 8

ACE2

Angiotensin I converting enzyme 2

ACMSD

Aminocarboxymuconate semialdehyde decarboxylase

ADORA2A

Adenosine A2a receptor

ADRs: 

Adverse drug reactions

AMR: 

Americans

ANKK1

Ankyrin repeat and kinase domain containing 1

Arf6

ADP-ribosylation factor 6

BDNF

Brain derived neurotrophic factor

BH: 

Benjamini-Hochberg

BST1

Bone marrow stromal cell antigen 1

CAMK2A

Calcium/calmodulin-dependent protein kinase II alpha

CAPIT: 

Core assessment programme for intra-cerebral transplantations

CCDC62/HIP1R

Coiled-coil domain containing 62

CCK

Cholecystokinin

COMT

Catechol-O-methyltransferase

CYP2D6

Cytochrome P450 family 2 subfamily D member 6

DDC

Dopa decarboxylase

DJ-1

dj-1 protein

DRD2

Dopamine Receptor D2

DRD3

Dopamine receptor D3

EAS: 

East Asians

EUR: 

European

FDR: 

False discovery rate

FGF20

Fibroblast growth factor 20

FYN

FYN proto-oncogene, Src family tyrosine kinase

GAK

Cyclin G associated kinase

GBA

Glucosylceramidase beta

GO: 

Gene ontology

GPNMB

Glycoprotein nmb

GRIN2A

Glutamate ionotropic receptor NMDA type subunit 2A

GRK: 

G protein-coupled receptor kinase

GST

Glutathione S-transferase

GWAS: 

Genome-wide association study

HLA-DR

Major histocompatibility complex, class II, DR

HOMER1

Homer scaffolding protein 1

HTR2A

5-hydroxytryptamine receptor 2A

HY: 

Hoehn and Yahr scale

IGF1

Insulin-like Growth Factor 1

ITGA8

Integrin subunit alpha 8

LR: 

Levodopa Response

LRRK2

Leucine rich repeat kinase 2

MAOB

Monoamine oxidase B

MAPT

Microtubule associated protein tau

MCCC1/LAMP3

Methylcrotonoyl-CoA carboxylase 1

MeSH: 

Medical subject headings

MTHFR

Methylenetetrahydrofolate reductase

NA: 

Not applicable

NAT2

N-acetyltransferase 2

NR: 

Not reported

PARK16

Parkinson disease 16 (susceptibility)

PARK2

Parkinson disease (autosomal recessive, juvenile) 2, parkin

PARK6

PTEN induced putative kinase 1

PARK7

Parkinsonism associated deglycase

PD: 

Parkinson’s disease

PINK1

PTEN induced putative kinase 1

PKC: 

Protein kinase C

PPI: 

Protein-protein interaction

PRKN

Parkin RBR E3 ubiquitin protein ligase

RYR1

Ryanodine receptor 1

SAS: 

South Asians

SD: 

Standard Deviation

SLC6A3

Solute carrier family 6 member 3

SLC6A4

Solute carrier family 6 member 4

SNCA

Synuclein alpha

SNPs: 

Single nucleotide polymorphisms

SRC

SRC proto-oncogene, non-receptor tyrosine kinase

STK39

Serine/threonine kinase 39

STR: 

Short tandem repeat

SYT11

Synaptotagmin 11

TMEM175/DGKQ/GAK

Transmembrane protein 175

tRNA: 

Transfer RNA

UBC

Ubiquitin C

UCHL1

Ubiquitin C-terminal hydrolase L1

UGT1A

UDP glucuronosyltransferase family 1 member A complex locus

UK BBC: 

United Kingdom’s brain bank criteria

UK: 

United Kingdom

Declarations

Acknowledgements

We would like to acknowledge the Director, CSIR-IGIB, Dr. Sanjay Kumar, for his vision and support. The authors would like to thank, Nikhil Sahai, B.Tech student, Jaypee University of Information Technology, Himachal Pradesh for partially compiling the GWAS gene set and tabulate the same as a part of his dissertation.

Funding

Financial support from Council of Scientific and Industrial Research (CSIR) and Indian Council of Medical Research (ICMR) is duly acknowledged. Financial support from CSIR- funded projects - GENCODE-A (BSC0123) and GOMED (MLP1601) are duly acknowledged to carry out this study. DG acknowledges CSIR projects (BSC0123 and MLP1601), MKM acknowledges MLP0901, PT acknowledges CSIR (Research Associateship) and CR acknowledges UGC, Govt. of India for providing fellowship.

Availability of data and materials

The data supporting the results of this research paper are included within this article and its additional supplementary files.

Authors’ contributions

DG conceived and wrote the manuscript. DG and MKM contributed in study designing and data retrieval from literature. In case of disagreement in systematic review or cumulative quality assessment, PT helped in resolving. DG, MKM, PT and CR contributed in writing manuscript. RK, SK and SSK contributed for critical evaluation of the study and improving the manuscript. RK conceived, interpreted and supervised the study design. All the authors read and approved the final manuscript.

Ethics approval and consent to participate

No patient samples were collected and analysed during this study. All GWAS data were provided as summary statistics by the consortia acknowledged in this study having been collected in accordance with ethical regulations in the partner countries and as defined in original research publications by such consortia.

Consent for publication

Not applicable.

Competing interests

The authors declare no potential conflict of interest.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Genomics and Molecular Medicine Unit, Institute of Genomics and Integrative Biology (IGIB), Council of Scientific and Industrial Research (CSIR)
(2)
Institute of Human Behaviour and Allied Sciences
(3)
Department of Chemistry, Nucleic Acids Research Lab, University of Delhi (North Campus)
(4)
Academy of Scientific & Innovative Research (AcSIR), CSIR- Institute of Genomics and Integrative Biology (CSIR-IGIB) Campus

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