bdnf val66met is associated with performance in a computerized visual-motor tracking test in healthy...

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BDNF Val66Met is associated with performance in a

computerized visual-motor tracking test in healthy adults

Yeimy González-Giraldo, BSc 1,7; Johana Rojas, BSc 2,7; Shane T. Mueller, PhD 3;

Brian J. Piper, PhD 4; Ana Adan, PhD 5,6; Diego A. Forero, MD, PhD 1,*

1 Laboratory of NeuroPsychiatric Genetics, Biomedical Sciences Research Group, School of Medicine, Universidad

Antonio Nariño. Bogotá, Colombia.

2 School of Health Sciences, Universidad Colegio Mayor de Cundinamarca. Bogotá, Colombia.

3 Department of Cognitive and Learning Sciences, Michigan Technological University, Houghton, Michigan, USA.

4 School of Pharmacy, Husson University, Bangor, Maine, USA.

5 Department of Psychiatry and Clinical Psychobiology, University of Barcelona, Barcelona, Spain.

6 Institute for Brain, Cognition and Behavior (IR3C), Barcelona, Spain

7 These authors contributed equally to this work

* Correspondence: Prof. Dr. Diego Forero, MD, PhD. Laboratory of NeuroPsychiatric Genetics, Biomedical Sciences

Research Group, School of Medicine, Universidad Antonio Nariño. Bogotá, Colombia. Phone: + 57 313 2610427.

Email: [email protected]

Word Count in Abstract: 120

Word Count in Body of Manuscript: 2514

Tables: 2, Figures: 1, Supplementary Files: 1

Keywords: Neurogenetics, Endophenotypes, Latin America, Motor Learning, Candidate

Genes, Brain Derived Neurotrophic Factor.

Running Title: BDNF gene and visual-motor tracking performance

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Abstract

Brain derived neurotrophic factor (BDNF) is known to play an important role in

neuroplasticity and cognitive processes. We explored the association of BDNF Val66Met

polymorphism with performance in a visual-motor tracking test. One hundred and sixty

seven young healthy Colombian adults completed a computerized version of the Pursuit

Rotor Task, using the Psychology Experiment Building Language (PEBL) platform. DNA

genotyping was performed by allele-specific PCR. We found that BDNF Val/Met and

Met/Met subjects had a better performance in the pursuit rotor task (p value: 0.03). Our

findings suggest that BDNF gene is key to understand differences in motor performance

in healthy participants in different populations. This approach could be useful for future

fine mapping of genetic modifiers for neuropsychiatric diseases.

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Introduction

Adequate functioning of the mechanisms underlying motor learning and performance

are important for daily activities in normal subjects and for pathophysiological

mechanisms of neuropsychiatric diseases and related processes for rehabilitation

(Chang, 2014; Dayan & Cohen, 2011). Neural plasticity in a number of brain regions and

neurophysiological networks (such as the primary motor cortex, premotor cortex and the

supplementary motor area) is known to be correlated with these processes (Dayan &

Cohen, 2011). Several experimental paradigms, such as finger tapping, have been used

for an initial exploration of the basis of motor learning and performance in humans

(Dayan & Cohen, 2011).

In recent years, explorations of genetic factors have started to provide evidence of

specific molecular mechanisms associated with motor learning and performance in

healthy subjects (Kleim et al., 2006; Smolders, Rijpkema, Franke, & Fernandez, 2012).

Given the important role of proteins involved in synaptic plasticity, the brain derived

neurotrophic factor (BDNF) gene has been a key candidate (Kleim et al., 2006).

BDNF is a commonly studied neurotrophin that plays an important role in several basic

neural processes such as synaptic plasticity and long-term potentiation and cognitive

phenomena, such as learning and memory processes (Forero, Benitez, et al., 2006).

This protein of 267 amino acids is encoded by the BDNF gene, which is located on

chromosome 11 and has 8 exons (gene size: 65 kb) (Liu et al., 2005). A change of

Valine to Methionine in codon 66 (Val66Met) constitutes a functional Single Nucleotide

Polymorphism (SNP) in BDNF (dbSNP ID: rs6265); this variation affects intracellular

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traffic and activity-dependent release of BDNF in an allele-specific manner (Egan et al.,

2003).

BDNF Val66Met polymorphism has been studied as a genetic candidate for differences

in performance in cognitive tests in healthy subjects, showing that Val/Val carriers had

better declarative memory and hippocampal activation (Kambeitz et al., 2012). Previous

studies have used relatively simple behavioral paradigms, such as finger tapping, to

study the possible association of BDNF gene and motor learning and performance

(Kleim et al., 2006; Lee et al., 2013; McHughen et al., 2010) (Table 1, for further

details). In general, the tasks requiring placing and moving manual skills (Piper, 2011)

show no associations with BNDF genotype; with a few cases using visual-motor tracking

tasks in which Val/Met subject exhibited poorer performance after relatively long-term

learning.

The aim of this study was to test the hypothesis that healthy carriers of the BDNF Met

allele could show differences in performance in a computerized visual motor tracking

task, using the Pursuit Rotor Test (PRT) of the Psychology Experiment Building

Language battery. The PRT has long been used as a measure of both perceptual-motor

skill, and of motor adaptation and learning (R. Ammons, 1955; R. B. Ammons, 1947).

More recent research has suggested that such motor learning is governed by two

complementary processes (Smith, Ghazizadeh, & Shadmehr, 2006; Trewartha, Garcia,

Wolpert, & Flanagan, 2014): a fast process linked to declarative memory that adapts

and decays quickly (Keisler & Shadmehr, 2010) and a slower process that adapts and

decays more gradually. We hypothesized that our results could differ from these

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previous results for two reasons. First, the majority of null results in Table 1 involved fine

motor movement and placing, whose motor control differs from smooth pursuit (Wolpert,

Diedrichsen, & Flanagan, 2011). Second, we tested participants from a South American

population (whereas previous studies were carried out in samples of European and

Asian descent) and our sample size is several times larger than previous studies

(median sample for previous studies was 38 participants). Environmental or epigenetic

factors (Forero, Velez-van-Meerbeke, Deshpande, Nicolini, & Perry, 2014) could lead to

this population having different performance advantages. We thus expected that a test

as such as the PEBL PRT, that hypothetically requires fast learning and adaptation

linked to declarative memory systems, could identify allele-specific effects of BDNF on

motor learning.

Materials and methods

Subjects

One hundred and sixty eight unrelated subjects were recruited at a University in Bogotá,

Colombia. The population living in Bogotá is composed of a European genetic

background with some historical admixture with Amerindians (Forero, Arboleda, Yunis,

Pardo, & Arboleda, 2006; Ojeda et al., 2013). Participants were only included if they

were young university students whose four grandparents were born in Colombia.

Participants were excluded if they had personal history of neuropsychiatric diseases,

were taking medications acting on nervous system, or if they have incomplete

phenotypic or genotypic data. All participants provided written informed consent

(Beskow et al., 2001) and the study was approved by the institutional ethics committee.

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Phenotypic Evaluations

All participants completed a self-administered questionnaire. The questionnaire was

used to collect socio-demographic variables (age, sex, personal and familial history of

neuropsychiatric disorders). We applied to all participants a computerized task, the

PEBL Pursuit Rotor Test, for analyzing quantitative and objective measures of motor

performance, using the Psychology Experiment Building Language (PEBL) platform,

which is freely available at http://pebl.sourceforge.net/ and has been described

previously (Mueller & Piper, 2014). Pursuit rotor test consists on following, with the

mouse pointer, a small red circle that rotates clockwise at a constant speed around a

larger circle (Piper, 2011) (Figure 1). Subjects received verbal and visual instructions

before the start of the testing phase, where they had 30 seconds to complete two rounds

of the test (each one with a duration of 15 seconds) Source code was modified to allow

its application in the Spanish language, using a laptop (Hewlett-Packard Company, Palo

Alto, CA, USA) running a Windows 7 operating system. Performance scores were given

by the total time on target for each individual. The PEBL Pursuit Rotor task has been

shown to be sensitive to a number of neurologically and genetic factors, including

development, gender, and handedness (Piper, 2011), tremor (Walecki, Lasoń, Kunc, &

Gorzelańczyk, 2013), synaptic plasticity deficits related to Tourrette's Syndrome (Brandt

et al., 2014), and relatively unimpacted by time of day (which nevertheless did impact an

attention task) or extensive practice (which did improve performance a dynamic

compensatory tracking task), see (Ahonen, Dunham, Getty, & Kosmowski, 2012).

Neuroimaging studies have identified a possible role of circuits in the prefrontal cortex

and the presupplementary motor area related to the pursuit rotor task (Hatakenaka,

Miyai, Mihara, Sakoda, & Kubota, 2007).

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Genotyping

A sample of peripheral blood from each participant was taken for genomic DNA

extraction, using a salting out method (Miller, Dykes, & Polesky, 1988; Morales et al.,

2009). Genotyping of BDNF Val66Met was performed by tetra-primer allele specific

Polymerase Chain Reaction (PCR) in a Labnet MultiGene 96-well thermal cycler (Labnet

International Inc, Edison, NJ, USA). PCR products were separated in a 2% agarose gel

at 140V and visualized with ultraviolet light after SYBR®-safe staining (Invitrogen,

Carlsbad, CA, USA). We used 4 primers: (P1: CCT ACA GTT CCA CCA GGT GAG

AAG AGT G, P2: TCA TGG ACA TGT TTG CAG CAT CTA GGT A, P3: CTG GTC CTC

ATC CAA CAG CTC TTC TAT AAC and P4: ATC ATT GGC TGA CAC TTT

CGA ACC CA) (Sheikh, Hayden, Kryski, Smith, & Singh, 2010). The PCR reactions

included 2 µl of genomic DNA (~50 ng), 1.5 mM MgCl 2, 10X reaction buffer, 1 µM of

each primers, 1 mM of dNTPs, Betaine 1 M and 0.75 U of Taq polymerase (Bioline,

London, United Kingdom) in a final volume of 20 µl. PCR program consisted of

a 3 -min denaturation step at 94°C (1 cycle), 94°C for 20s, 63°C for 25s, 72°C for 40s

(30 cycles), and 72°C for 5 minutes (1 cycle). Alleles and genotypes were scored as

follows: An allele as 401 and 204 bp bands and G allele as 401 and 253 bp bands. A

random subsample (10% of subjects) was reanalyzed for both polymorphisms to assure

consistency in the genotyping results. All genotypes were checked by two different

investigators in order to confirm and validate the results (Benitez et al., 2010; Ojeda,

Nino, et al., 2014). Additionally, genotyping data were validated by a method based in

qPCR, which combines high resolution melting and allele-specific PCR (Ojeda, Lopez-

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Leon, & Forero, 2014), in a CFX96 Touch Real-Time PCR Detection System (BioRad,

Hercules, California).

Statistical analyses

X2 test was used for calculation of Hardy–Weinberg equilibrium and estimation of

genotype and allele frequencies was carried out with the SNPStats program (Perea et

al., 2014; Sole, Guino, Valls, Iniesta, & Moreno, 2006). Performance scores were given

by the total time on target (values from 0 to 15.000 milliseconds) for the second round of

the test: Higher values for time on target corresponded to better performances. A linear

regression model was used to analyze the association of BDNF genotypes with

quantitative measures of motor learning, correcting for sex and age (Piper, 2011). A p

value < 0.05 was considered as statistically significant.

Results

In total 167 subjects participated in this study, 118 were women and 49 men, with ages

from 18 to 34 years (mean: 21.2, SD: 2.9). Scores for pursuit rotor task ranged from

1344 to 14058 time-on-target (in milliseconds) with a theoretical maximum of 15000

milliseconds (average time-on-target of 8599 milliseconds; SE: 220.9). There were no

significant differences in mean scores between female and male subjects (p value:

0.37).

Minor allele frequency for the Val66Met polymorphism in BDNF was 0.13 (A/Met allele),

the distribution of genotypes was: Val/Val (G/G) n=128 (77%), Met/Val (A/G) n=34 (20

%) and Met/Met (A/A) n= 5 (3%). Observed genotypes were in Hardy-Weinberg

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equilibrium (p value: 0.17). Mean performance scores (time-on-target in milliseconds) for

the G/G, A/G and A/A subjects were 8342.7, 9657.0 and 7956.6, respectively (Figure

S1).

A linear regression model was used to assess the association between BDNF Val66Met

genotypes and pursuit rotor task scores, correcting by sex and age. We found a

significant association, under a dominant model, where Met carrier subjects (Val/Met

and Met/Met) had a better performance in comparison with Val/Val homozygous

subjects (p value 0.03) (Table 2).

Discussion

Previous studies on BDNF and motor learning in healthy subjects have been carried out

with a variety of tests, such as finger tapping, mainly in populations of European descent

(Table 1) (Cirillo, Hughes, Ridding, Thomas, & Semmler, 2012; Freundlieb et al., 2012;

Joundi et al., 2012; Kleim et al., 2006; Lee et al., 2013; Li Voti et al., 2011; McHughen &

Cramer, 2013; McHughen et al., 2010; Smolders et al., 2012; Witte et al., 2012). It is

important to highlight that, in comparison with previous studies, our work is the largest

(in terms of sample size) in this field and that our study is the first work that uses the

computerized PEBL PRT as a quantitative endophenotype of fine motor function, an

automated brief visuomotor tracking test, for exploration of genetic correlates. As shown

in Table 1, most previous studies of the impact of BDNF Val66Met on performance of

manual tasks have involved tasks requiring tapping and manipulating and longer-term

visuomotor learning, whereas the Pursuit Rotor test is a very brief assessment of

visuomotor tracking. Research has indicated that pursuit motor control behaves as a

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series of ballistic (Craik, 1948) Fitts-law movements (Zhang & Hornof, 2012), with

corrective movements involving a tight perceptual-motor cycle. This type of motor control

is fundamentally different from simple keypress motor responses whose motor control

and decision properties obey the Hick-Hyman law (Seow, 2005). Furthermore,

adaptation on the PRT may primarily involve a fast motor learning process linked to

declarative memory, which (Kambeitz et al., 2012) showed was associated with BNDF

genotype). In addition, other works have used tests that are possibly more demanding

cognitively or involve a slower motor learning process (Cirillo et al., 2012; Joundi et al.,

2012; McHughen & Cramer, 2013; Smolders et al., 2012); and although one used a

visuomotor tracking task (Cirillo et al., 2012) it is different from the task we used in this

work (lines that moved down the screen while making left and right movements that

were unpredictable). It is possible that its small sample size (only 29 subjects in total, 17

Met carriers vs 12 non Met carriers) and differences in genetic background could also

explain the differences in findings. Joundi et al (Joundi et al., 2012) used a task that

was oriented to test visuomotor adaptation (a 60 degree deviation) and Smolders et al

(Smolders et al., 2012) used a task oriented to assess bimanual motor control.

In this study, we found that the subjects that are carriers of the Met allele (Val/Met and

Met/Met genotypes) showed a better performance on the pursuit rotor task. Our current

findings are in agreement with previous neurophysiological and imaging studies that

found differences in brain activation and functioning in key areas between BDNF

genotypes (evidence of motor cortex plasticity), following experimental paradigms that

used motor training or external stimuli (Kleim et al., 2006; Lee et al., 2013; McHughen et

al., 2010). Our findings of a positive association of BDNF and the PEBL pursuit rotor test

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is compatible with current theories of motor learning that suggest that the initial stage of

motor learning (fast stage) is related to the function of brain circuits involved in superior

cognitive functions, such as the dorsolateral prefrontal cortex (Dayan & Cohen, 2011;

Wolpert et al., 2011), considering the role of BDNF dependent plasticity on those neural

circuits (Hong, Liou, & Tsai, 2011). A recent animal model of chronic stroke showed that

Met/Met genotype carriers showed a better motor and neurophysiological functioning

(Qin et al., 2014). There is evidence from previous studies that the alleles associated

with better cognitive performance in other ethnicities are different from those found in

samples of European descent (Gonzalez-Giraldo et al., 2014; Solis-Ortiz, Perez-Luque,

Morado-Crespo, & Gutierrez-Munoz, 2010; Wang et al., 2013).

Analysis of different populations is a key factor in genetic analysis of human brain

endophenotypes (Forero et al., 2014). Frequencies of risk alleles, patterns of linkage

disequilibrium and interactions with environmental factors are known to vary between

populations (Gatt, Burton, Williams, & Schofield, 2015). Limitations of the current study

includes: Use of a single task for the analysis of motor function and study of a single

functional SNP in BDNF gene. Analysis of genetic and epigenetic variants in other

candidate molecules involved in synaptic plasticity mechanisms will be fundamental for

a better understanding of motor learning and performance in healthy subjects (Forero,

Arboleda, Vasquez, & Arboleda, 2009; Hernandez, Tse, Pang, Arboleda, & Forero,

2013; Strazisar et al., 2014).

In the context of molecular genetics of cognitive endophenotypes, the use of an

automated neuropsychological battery (such as PEBL), has several advantages,

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allowing a quantitative assessment (in real time) of multiple cognitive domains for a

large number of participants (Gonzalez-Giraldo et al., 2014; Ness et al., 2011; Tumkaya

et al., 2013). In addition, as PEBL is freely available, it facilitates testing in different

economic backgrounds, leading to the possibility of sampling a greater genetic diversity

in different countries and ethnicities (Mueller & Piper, 2014). Our analysis of genetic

factors associated with motor learning endophenotypes could be also useful in the

context of the fine mapping of genetic modifiers for neurological and psychiatric

diseases in patients (Alaerts et al., 2009; Castro et al., 2013; Galvez et al., 2014; He,

Zhang, Yung, Zhu, & Wang, 2013).

Acknowledgements

This study was supported by grants from Universidad Antonio Nariño (VCTI-UAN,

Project # 20131079). YGG was supported by a fellowship from VCTI-UAN (Young

Scientists Program).

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Table 1. Overview of published studies on allele-specific effects of BDNF Val66Met

and performance in fine-motor tasks

Author, Year Country Sample size Age Tests Association

Kleim, 2006 USA 26 (14 males) 18-29 Finger tapping,

Pegboard, Pinch grip No significant differences

McHughen, 2010a

USA 24 (14 males) 18-30 Finger tapping,

Pegboard, Pinch grip No significant differences

McHughen, 2010b

USA 29 (18 males) 18-30 Driving-Based Motor

Learning Task

Val/Met subjects showed more driving errors and poorer retention

over 4 days

Li Voti, 2011 Italy 38 (22 males) 28 +/- 5.6 Index Finger movements No significant differences

Smolders, 2012

Netherlands 69 (35 males) 18-35 Digital versión of Preilowski´s Task

Women that were Met carriers showed a poorer performance on

difficult angles

Joundi, 2012 Spain 42 (32 males) 20.6 Joystick-controlled

visuomotor adaptation task

Val/Met subjects showed slower rates of adaptation to a visuomotor deviation during initial learning and

for 24-h retention

Cirillo, 2012 Australia 29 (17 males) 18-39 Index finger abduction

acceleration, Visuomotor tracking task

No significant differences in motor performance or motor learning

Freundlieb, 2012

Germany 38 (12 males) 22-27 Serial Reaction Time

Task No significant differences

Witte, 2012 Germany 32 (0 males) 20-49 Serial Reaction Time

Task No significant differences

McHughen, 2013

USA 38 (14 males) 73.3

Peg board, Finger tapping, Pinch grip,

Speed of reaction time, Driving-based motor

learning task

No significant differences in elderly subjects

Lee, 2013 Korea 82 (42 male) 20-40 Finger tapping

No significant basal differences. Met/Met subjects did not show increase in performance after

intermittent theta burst stimulation

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Table 2. Association between BDNF Val66Met polymorphism and performance on

PEBL pursuit rotor task.

Genotype Groups n Time on Target a p value b

Val/Val (G/G)

Val/Met and Met/Met (A/G and A/A)

128 39

8342.7 (260.4) 9439.0 (380.1)

0.03

a. Time on target (in milliseconds), presented as means (SE).

b. Linear regression test, correcting by sex and age, performed in SNPstats.

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Figure 1. Overview of the PEBL Pursuit Rotor Test

Pursuit rotor test consists on following, with the mouse pointer of the computer, a small

red circle that rotates clockwise at a constant speed around a larger circle presented on

the computer screen. Performance scores for each individual were given by the total

time on target (in milliseconds).

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81x81mm (150 x 150 DPI)

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Figure S1. Scatter plot showing the distribution scores for the PEBL pursuit rotor task

(Time-on-target, in milliseconds) according to the BDNF Val66Met genotypes.

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bdnf val66met is associated with performance in a computerized visual-motor tracking test in healthy...

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