Download Supplemental Information for Free D

Supplemental Information for
Free D-aspartate regulates neuronal dendritic morphology, synaptic plasticity, grey matter volume
and brain activity in mammals
F Errico1,2, R Nisticò3,4, A Di Giorgio5, M Squillace1, D Vitucci1,6, A Galbusera7, S Piccinin8, D
Mango3, L Fazio9, S Middei3, S Trizio9, N B Mercuri3,10, M Ammassari Teule3, D Centonze3,10, A
Gozzi7, G Blasi9, A Bertolino9, 11, A Usiello1,12
1
Ceinge Biotecnologie Avanzate, Naples, Italy;
Department of Molecular Medicine and Medical Biotechnology, University of Naples “Federico
II”, Naples, Italy;
3
Centro Europeo per la Ricerca sul Cervello (CERC)/Fondazione Santa Lucia, Rome, Italy;
4
Department of Physiology and Pharmacology, Sapienza University of Rome, Rome, Italy;
5
Istituto di Ricovero e Cura a Carattere Scientifico “Casa Sollievo della Sofferenza”, San Giovanni
Rotondo, Italy;
6
Faculty of Motor Sciences, University of Naples “Parthenope”, Naples, Italy;
7
Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, Rovereto, Italy;
8
Pharmacology of Synaptic Plasticity Unit, European Brain Research Institute (EBRI), Rome, Italy;
9
Group of Psychiatric Neuroscience, Department of Neuroscience, Basic Sciences and Sense
Organs, University of Bari “Aldo Moro”, Bari, Italy;
10
Department of Neuroscience, Tor Vergata University Hospital Foundation, Rome, Italy
11
pRED, Neuroscience DTA, Hoffman-La Roche, Ltd;
12
Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Second
University of Naples (SUN), Caserta, Italy.
2
Content:
Supplemental Methods
Supplemental Results
4 Supplemental Figures
2 Supplemental Tables
Supplemental References
Supplemental Methods
Electrophysiology
For patch clamp recordings, current signals were filtered at 1 kHz and digitized at 10 kHz using a
Multiclamp 700B differential amplifier linked to a Digidata 1440A, operated by the pClamp10
software (Molecular Devices, Sunnyvale, CA, USA). No series resistance compensation was
implemented, in order to keep a low signal-to-noise ratio, however, recordings were discarded if
series resistance changed by more than 15 % from control conditions. The spontaneous events were
detected off-line from 3 min trace records using Clampfit (Molecular Devices, USA), through an
algorithm based on the minimization of the sum of squared errors between data and a template
function. Changes in amplitude or inter-event interval of the spontaneous events were compared
according to their cumulative distributions, using the Kolmogorov-Smirnov (K-S) test.
To estimate the EC50 and maximal response, concentration-response curve were fitted using the
logistic equation Y= a/{1+exp[-(x-x0)/b]} where a is a maximal effect, b is minimum effect, x is a
drug concentration, x0 is the concentration attaining half maximal effect. mEPSC were recorded in
the continuous presence of MPEP in order to block metabotropic glutamate receptor mGlu5. For all
statistical comparisons, the n used was the number of animals rather than number of slices or
neurons.
Rapamycin injection, Golgi-Cox staining and measurement of dendritic morphology
Rapamycin powder (LC Laboratories, Woburn, MA) was dissolved at the concentration of 5mg/kg
in a solution of 5% dimethyl sulfoxide (DMSO), 15% PEG-400 (polyethylene glycol, molecular
weight 400) and 5% Tween-20, and was prepared freshly before use1. Chronic treatment was
performed as previously described2. Mice were injected intraperitoneally with either rapamycin (5
mg/kg body weight) or vehicle twice per day for 3 weeks (see Suppl. Fig. 4a) in a volume of 2 ml
per kilogram body weight.
Golgi staining, dendritic length measure and Sholl analysis were performed as described in
the main text. Spine density was assessed by counting the number of spines in four/five dendrite
segments per neuron chosen in branch orders 2–4 of apical and basal dendrites. All protrusions,
with or without bulbous expansion, but no longer than 1.2 μm, were counted as spines if they were
continuous with the dendritic shaft. The average spine density (number of spines per 10-μm-long
segment) was averaged for a neuron mean. To determine dendritic spine morphology, we acquired
the spines on four/five apical and basal dendrite segments per neuron under higher magnification
(100×/1.25 NA) using a digital camera (Axiocam, Zeiss). Spines were classified into one of the four
morphological categories: 1) Mushroom: big spines with a large head and mushroom appearance; 2)
Thin: spines with a long neck and small head; 3) Stubby: very short spines without a notable neck;
and 4) Chubby: big spines without a notable neck. Morphology was estimated by measuring spine
head diameter using ImageJ (http://rsbweb.nih.gov/ij/). Values were averaged for neuron and compared among groups.
Association of DDO SNPs with mRNA expression in post-mortem prefrontal cortex of healthy
subjects.
The publicly available collection used in the present study included post-mortem human brains
taken from autopsy primarily from the Offices of the Chief Medical Examiner of the District of
Columbia, and of the Commonwealth of Virginia, Northern District, all with informed consent from
the legal next of kin (protocol 90-M-0142 approved by the NIMH/NIH Institutional Review Board).
Additional post-mortem fetal, infant, child and adolescent brain tissue samples were provided by
the National Institute of Child Health and Human Development Brain and Tissue Bank for
Developmental Disorders (http://www.BTBank.org) under contracts NO1-HD-4-3368 andNO1HD-4-3383. The Institutional Review Board of the University of Maryland at Baltimore and the
State of Maryland approved the protocol, and the tissue was donated to the NIMH under the terms
of a Material Transfer Agreement. Clinical characterization, diagnoses, and macro- and microscopic
neuropathological examinations were performed on all CBDB cases using a standardized paradigm.
Details of tissue acquisition, handling, processing, dissection, clinical characterization, diagnoses,
neuropathological examinations, RNA extraction and quality control measures were described
previously3, 4. The Brain and Tissue Bank cases were handled in a similar fashion
(http://medschool.umaryland.edu/BTBank/ProtocolMethods.html).
Toxicological
analysis
was
performed on every case. Subjects with evidence of macro- or microscopic neuropathology, drug
use, alcohol abuse, or psychiatric illness were excluded.
From each subject in the brain collection, RNA from prefrontal grey matter was analyzed
using spotted oligonucleotide microarrays yielding data from 30,176 gene expression probes and
allowing us to focus on DDO mRNA expression. In particular, total RNA was extracted, amplified
and fluorescently labeled. Reference RNA was pooled from all samples and treated identically to
sample RNAs. Labeled RNAs were hybridized to two-color custom-spotted arrays from the NHGRI
microarray core facility. After normalization5, log2 intensity ratios were further adjusted to reduce
the impact of known and unknown sources of systematic noise on gene expression measures using
surrogate variable analysis6. DNA from cerebellar tissue was studied with Illumina BeadChips
producing 625,439 SNP genotypes called using the BeadExpress software for each subject as
previously described4.
Association of DDO rs3757351 with prefrontal grey matter and prefrontal activity in
Caucasian healthy humans.
Subjects
Healthy Caucasian subjects from the region of Puglia were recruited for the study and were
evaluated with the Structured Clinical Interview for DSM-IV (SCID)7 to exclude any psychiatric
disorder. Further exclusion criteria were history of drug or alcohol abuse, active drug use in the past
year, head trauma with loss of consciousness, and any significant medical condition. Handedness
(Edinburgh Inventory)8, and total IQ (WAIS-R) were also measured. The study was approved by
the local Institutional Review Board (Comitato Etico Locale Indipendente Azienda Ospedaliera
“Ospedale Policlinico Consorziale” Bari). After complete description of the protocol and
procedures, written informed consent was obtained by all participants, in accordance with the
Helsinki Declaration. All subjects were genotyped for DDO rs3757351 and rs2057149. DNA was
extracted from whole blood samples using standard procedures. All alleles displayed HardyWeinberg equilibrium.
Statistical Analysis for demographics and behavioral performance
ANOVAs and χ2 were used to compare demographic data across genotype groups. Repeated
measures ANOVA, with genotype as predictor and working memory loads as the repeated measures
factor, putative association of genotypes with behavioral data. Fisher's Least Significant Difference
Test was used for all post-hoc analyses.
Association of DDO rs3757351 with prefrontal grey matter
Three-dimensional images were acquired with a 3.0 Tesla General Electric MR scanner using a T1weighted SPGR sequence (TE=min full; flip angle, 6°; field of view, 250 mm; bandwidth, 31.25;
matrix, 256 x 256) with 124 1.3-mm-axial slices. Voxel Brain Morphometry Analysis (VBM) of the
sMRI data was performed using SPM8. The T1-weighted scans were partitioned into different
tissue classes- grey matter (GM), white matter and non-brain voxels (cerebrospinal fluid, skull) based on separate tissue probability maps for each tissue class using the “new segmentation”
approach in SPM89. In order to compare brains of different subjects, the resulting segments were
normalized to a population template generated from the complete dataset using a diffeomorphic
registration algorithm10. This high-dimensional non-linear warping algorithm selects conserved
features, which are informative for registration, thus minimizing structural variation among subjects
and providing optimal inter-subject registration. Subsequently, all images were “modulated” by the
Jacobian determinants from the normalisation steps to preserve initial volumes. Thus, images were
smoothed by convolution with an isotropic Gaussian kernel of 8 mm full-width at half maximum.
We examined the main effect of genotype by creating voxel-based, whole-brain, statistical
parametric maps using Gaussian random fields theory and the general linear model. More
specifically, we used a full factorial Analisys of Covariance (ANCOVA) including as factor the
DDO genetic variants and as “nuisance” variables orthogonalized first- and second-order
polynomials of age, gender and total GM volume. We included such variables in order to control for
any independent effects on our findings and to ensure that the analysis identified regionally specific
“non-global” effects11. Because of our strong a priori hypothesis based on the effects of DDO
variants on mRNA levels in prefrontal cortex, we focalized our analysis on this brain region and we
corrected our results for the WFU_PickAtlas Brodmann’s area within prefrontal cortex in which
significant clusters were located (BA46). With this purpose, statistical non-stationary inference12
was performed at the cluster level at P<0.05 corrected within the BA46 by using the ns toolbox
(http://fmri.wfubmc.edu/cms/NS-General) implemented in SPM8, to avoid increased false-positive
rate due to the non-stationary structural images. VBM results are reported with reference to the
MNI standard space within SPM8. To further examine differences between genotype groups, posthoc analysis outside of SPM8 was also performed on grey matter volumes extracted from the
cluster showing a main effect of DDO genetic variants using MarsBar.
Association of DDO rs3757351 with prefrontal activity
fMRI task, N-back: “N-back” refers to how far back in the sequence of stimuli that the subject had
to recall. The stimuli consisted of numbers (1–4) shown in random sequence and displayed at the
points of a diamond-shaped box13. There was a non-memory-guided control condition (0-back) that
presented the same stimuli but simply required subjects to identify the stimulus currently seen. As
memory load increased, the task required the recollection of a stimulus seen one (1-back) or two (2back) stimuli beforehand while continuing to encode additionally incoming stimuli. We used a two
runs simple block design in which each block consisted of eight alternating 0- and 1-, or 0- and 2back (each lasting 30 seconds). Each task combination was obtained in 4 minutes and 8 seconds.
Responses were recorded through a fiber optic response box allowing measurement of accuracy (%)
and reaction time (ms).
fMRI data acquisition and preprocessing: fMRI data were acquired with a 3.0 Tesla General
Electric MR scanner with a gradient-echo echo planar imaging sequence (repetition time, 2000 ms;
echo time, 28 ms; 20 interleaved axial slices; thickness, 4 mm; gap, 1 mm; voxel size, 3.75 x 3.75 x
3.75; flip angle, 90°; field of view, 24 cm; matrix, 64 x 64). The first four scans at the beginning of
each time series were acquired to allow the signal to reach a steady state and were not included in
the final analysis. Data processing and analysis were performed with freely available Statistical
Parametric Mapping software (SPM8; Wellcome Trust Centre for Neuroimaging, London, UK
http://www.fil.ion.ucl.ac.uk/spm). Images for each subject were realigned to the first volume in the
time series and movement parameters were extracted to exclude subjects with excessive head
motion (> 2 mm of translation, > 2° rotation). Images were then re-sampled to a 2 mm isotropic
voxel size, spatially normalized into a standard stereotactic space (Montreal Institute on Neurology,
MNI template) and smoothed using a 10 mm full-width half-maximum isotropic Gaussian kernel to
minimize noise and to account for residual inter-subject differences. For each experimental
condition of the N-back task11, a box car model convolved with the haemodynamic response
function at each voxel was modeled. To account for potential differences in head movement
between groups, residual movement was also modeled as a regressor of no interest. Pre-determined
condition effects at each voxel were calculated using a t-statistic, producing a statistical image for
the contrast of 2-back versus 0-back, 1-back versus 0-back.
All the individual contrast images were entered in a second level random effects analysis.
Repeated Measure Analysis of Variance was then performed, with DDO genotype as predictor and
WM load as the repeated measures factor. Because of our strong a priori hypothesis based on the
effects of DDO variants on mRNA levels in prefrontal cortex, we focused our analysis on this brain
region and we corrected our results with Family Wise Error procedure at P<0.05 within the
WFU_PickAtlas (www.fmri.wfubmc.edu/cms/software#PickAtlas) prefrontal Brodmann’s area in
which significant clusters were located (BA10). Moreover, to further explore differences between
genotype groups, post-hoc analysis outside of SPM8 was also performed on BOLD responses
extracted from the cluster showing the main effect of DDO variants using MarsBar
(http://marsbar.sourceforge.net/).
Supplemental Results
D-aspartate modulates mEPSC in a concentration-dependent manner.
Exogenous application of D-Asp for 10 min on medial PFC layer II/III pyramidal neurons induced a
significant concentration-dependent increase in mEPSC frequency. The maximal effect of D-Asp
was achieved at a concentration above 300 µM. The plot of the data, shown in Suppl. Fig. 1, has an
EC50 of ≈ 12 µM .
Changes in the dendritic morphology of D-Asp-treated mice.
In the pyramidal neurons of the prefrontal cortex, statistical analysis revealed a significant increase
in the length of basal dendrites in D-Asp-treated mice, compared to untreated animals (P<0.01,
Student’s t test, Suppl. Fig. 2a). On the other side, no difference emerged in the apical dendritic
length between D-Asp-treated and control mice (P>0.05, Suppl. Fig. 2b). We also evaluated the
density of spines protruding from different branch orders of basal and apical dendrites (Suppl. Fig.
2c). We found that D-Asp treatment produced an overall increase of spine density along both basal
(two-way ANOVA with repeated measures, treatment: F(1,32)=5.606, P=0.0454) and apical branch
orders (treatment: F(1,16)=5.412, P=0.0484). In detail, spine density was significantly increased in
the fourth-order segment of basal dendrites in D-Asp-treated mice, compared to controls (P<0.05,
Fisher’s post-hoc comparison). We did not observe significant changes between treatments in the
density of mushroom, thin, chubby or stubby spines (Suppl. Fig. 2d-g).
In the CA1 area of the hippocampus, basal dendritic length of pyramidal neurons was
comparable between treatments (P>0.05, Suppl. Fig. 2h) while apical dendrites were significantly
longer in mice treated with D-Asp, compared to untreated animals (P<0.05, Suppl. Fig. 2i). Spine
density was significantly increased in the basal dendritic order (treatment: F(1,40)=9.869, P=0.0138)
while no difference emerged for apical fibers (treatment: F(1,24)=2.531, P=0.1503) (Suppl. Fig. 2j).
Density of each of the mushroom, thin, chubby and stubby spines did not change significantly
between treatments (Suppl. Fig. 2k-n).
Changes in the dendritic morphology of Ddo-/- mice.
In the prefrontal cortex of Ddo-/- mice, pyramidal neurons show unchanged basal dendritic length
(P>0.05, Student’s t test, Suppl. Fig. 3a) while apical dendritic length was significantly increased
(P<0.05, Suppl. Fig. 3b), compared to Ddo+/+ littermates. Analysis of spine density measured along
both basal and apical branch order revealed no difference between genotypes (basal: F(1,32)=2.973,
P=0.1230; apical: F(1,24)=0.846, P=0.3846; Suppl. Fig. 3c). Density of mushroom-shaped spines
was significantly increased in knockout mice, compared to wild type animals (P<0.01, Suppl. Fig.
3d). On the other hand, the density of thin, chubby and stubby spines was unchanged between
genotypes (P>0.05, Student’s t test, Suppl. Fig. 3e-g).
In the hippocampal CA1 subfield, basal dendrites were longer in Ddo-/- mice, compared to
Ddo+/+ littermates (P<0.05, Suppl. Fig. 3h). No significant difference between genotypes emerged
in the length of apical dendrites (P>0.05, Suppl. Fig. 3i). Knockout mice displayed increased spine
density along basal dendritic orders, compared to controls (genotype: F(1,48)=11.882, P=0.0087),
while no significant change between genotypes appeared in the apical branch orders (genotype:
F(1,16)=2.250, P=0.1720) (Suppl. Fig. 3j). The density of mushroom, thin and stubby spines was
unaltered between genotypes (P>0.05, Suppl. Fig. 3k, l and n) while chubby-shaped spines
increased significantly in Ddo-/- mice, compared to Ddo+/+ animals (P<0.01, Suppl. Fig. 3m).
Effect of rapamycin on the dendritic morphology of Ddo-/- mice.
To evaluate whether rapamycin could differentially affect the dendritic morphology of Ddo+/+ and
Ddo-/- mice, we treated animals with 5 mg/kg of this drug twice/day for three weeks, according to a
protocol previously described2 (Suppl. Fig. 4a). Total dendritic length of hippocampal pyramidal
neurons of the CA1 subfield was significantly reduced by rapamycin treatment, regardless of
genotypes (two-way ANOVA: treatment, F(1,16)=11.125, P=0.0042; Suppl. Fig. 4b). Indeed, the
following one-way ANOVA evidenced a similar effect of rapamycin in both genotypes (P<0.05,
per each genotype). We then evaluated the complexity of dendritic tree by Sholl analysis (Suppl.
Fig. 4c). In wild type animals, two-way ANOVA revealed that rapamycin treatment did not affect
the arborization of basal (F(1,56)=4.293, P=0.0720) and apical dendrites (F(1,80)=1.975, P=0.1975).
However, we found a significant interaction between treatment and distance from soma
(F(10,80)=2.162, P=0.0287) in the apical dendrites. In particular, the complexity of apical dendritic
tree was significantly reduced at 50 and 100 µm from soma in consequence to the rapamycin
treatment (P<0.05, Fisher’s post-hoc analysis). In Ddo-/- mice, chronic administration of rapamycin
reduced the complexity of basal dendritic tree (F(1,56)=10.022, P=0.0133). The arborization was
significantly decreased in consequence to rapamycin treatment between 50 and 100 µm and at 150
µm from soma (P<0.05 at 50, 100 and 150 µm; P<0.01 at 125 µm; Fisher’s post-hoc analysis). No
difference between treatments was found in the organization of apical dendrites (F(1,80)=1.519,
P=0.2527). Analysis of spine density did not reveal significant effect of chronic rapamycin
treatment (two-way ANOVA: treatment, F(1,16)=1.523, P=0.2350; treatment x genotype,
F(1,16)=0.056, P=0.8166; Suppl. Fig. 4d). Finally, we found that the administration of rapamycin
did not even affect the shape of spines in both genotypes (P>0.05, compared to the respective
vehicle-treated group, Student’s t test; Suppl. Fig. 4e). Overall, these results show a similar
rapamycin-dependent decrease in dendritic length and arborization between genotypes in the CA1
area of the hippocampus. On the other hand, rapamycin does not induce any significant change in
spine density, compared to vehicle treatment. This result, as well as the absence of genotypedependent effect on the morphological parameters herein analyzed, could be due to the predominant
effect of prolonged stress produced by injection, known to substantially reorganize dendritic
architecture14.
Supplemental Figure 1
Perfusion of D-Asp (0.3-300 µM, 10 min) enhances mEPSC frequency in a concentrationdependent manner. Each plot represents the recordings of at least six separate cells.
Supplemental Figure 2
Analysis of dendritic morphology in the pyramidal neurons of the (a-g) prefrontal cortex (n=5
mice/treatment, 5 neurons/mouse) and (h-n) CA1 subfield of the hippocampus (n=5/treatment, 4
neurons/mouse) of C57BL/6J mice treated with D-Asp or H2O, after Golgi-Cox staining. (a,h)
Basal and (b,i) apical dendritic length (in μm) measured in the (a,b) prefrontal cortex and (h,i) CA1
subfield of the hippocampus. (c,j) Spine density (number of spines/10 μm) evaluated per dendritic
order in the (c) prefrontal cortex and (j) CA1 area. (d-g, k-n) Spine density (number of spines/10
μm) evaluated per type of spines in the (d-g) prefrontal cortex and (k-n) CA1 area. *P<0.05,
**P<0.01, compared to untreated mice (Student’s t test). #P<0.05, compared to untreated mice
(Fisher’s post-hoc).
Supplemental Figure 3
Analysis of dendritic morphology in the pyramidal neurons of the (a-g) prefrontal cortex (n=5
mice/treatment, 5 neurons/mouse) and (h-n) CA1 subfield of the hippocampus (n=5/treatment, 4
neurons/mouse) of Ddo+/+ and Ddo-/- mice after Golgi-Cox staining. (a,h) Basal and (b,i) apical
dendritic length (in μm) measured in the (a,b) prefrontal cortex and (h,i) CA1 subfield of the
hippocampus. (c,j) Spine density (number of spines/10 μm) evaluated per dendritic order in the (c)
prefrontal cortex and (j) CA1 area. (d-g, k-n) Spine density (number of spines/10 μm) evaluated per
type of spines in the (d-g) prefrontal cortex and (k-n) CA1 area. *P<0.05, **P<0.01, compared to
Ddo+/+ mice (Student’s t test). #P<0.05, compared to Ddo+/+ mice (Fisher’s post-hoc).
Supplemental Figure 4
Analysis of dendritic morphology in pyramidal neurons of the hippocampal CA1 area of Ddo+/+ and
Ddo-/- mice after chronic treatment with rapamycin (n=5 mice/genotype/treatment, 4
neurons/mouse). (a) Schedule for rapamycin injection and processing of brains for Golgi-Cox
staining. (b) Total dendritic length (in μm). (c) Number of intersections between basal or apical
dendrites and Sholl concentric circle lines at different distances from soma center. Concentric
circles increase in diameter by 25 μm increments. (d) Density of total spines (number of spines/10
μm). (e) Percentage of individual types of spines. *P<0.05, compared to vehicle-treated mice
(Student’s t test). #P<0.05, ##P<0.01, compared to vehicle-treated mice (Fisher’s post-hoc).
Supplemental Table 1: Demographic and genetic data of the sample used to investigate the
association of DDO genetic variants with mRNA expression in human post-mortem prefrontal
cortex.
N
Race (AA/ A/ H/ C)
Gender (M/F)
Age (Mean ±SD,ys)
Expr (Mean ±SD)
PH (Mean ±SD)
PMI (Mean ±SD)
RIN (Mean ±SD)
rs3757351
268
146/4/6/112
176/92
27.89±22.15
0.20±0.65
6.53±0.29
26.37±17.11
8.37±0.94
CC
120
98/3/1/20
80/40
26.10±23.03
0.06±0.69
6.54±0.27
27.76±19.52
8.44±0.92
CT
107
48/1/2/56
72/35
30.13±22.07
0.21±0.59
6.55±0.30
25.57±15.62
8.40±0.89
TT
41
2/0/3/36
24/17
27.30±19.59
0.60±0.50
6.45±0.30
24.40±12.74
8.07±1.06
N
Race (AA, A, H, C)
Gender (M/F)
Age (Mean ±SD,ys)
Expr (Mean ±SD)
PH (Mean ±SD)
PMI (Mean ±SD)
RIN (Mean ±SD)
rs2057149
268
146/4/6/111
176/92
27.89±22.15
0.20±0.65
6.53±0.29
26.37±17.11
8.37±0.94
AA
166
114/3/4/45
102/64
28.05±21.94
0.07±0.62
6.57±0.27
25.82±16.54
8.44±0.87
AG
89
30/1/1/57
66/23
28.70±22.67
0.41±0.62
6.48±0.30
28.47±18.40
8.24±0.98
GG
13
2/0/1/10
8/5
20.40±21.66
0.47±0.80
6.40±0.35
19.07±13.10
8.33±1.38
Abbreviations: AA – African American, H – Hispanic, A – Asian, C – Caucasian, PMI – postmortem interval, RIN – RNA quality.
Supplemental Table 2: Demographic and genetic data of Caucasian healthy subjects included in
the VBM analysis
N
Gender (M/F)
Age (Mean ±SD,ys)
IQ (Mean ±SD)
rs3757351
152
86/66
28±8.20
106±11.32
TT
57
29/28
27±6.84
106±11.72
CT
76
44/32
29±9.15
106±11.41
CC
19
13/6
28±7.41
107±10.26
Supplemental Table 3: Demographic and genetic data of Caucasian healthy subjects included in
the fMRI analysis
N
Gender (M/F)
Age (Mean ±SD,ys)
IQ (Mean ±SD)
rs3757351
143
83/60
28.8±7.80
108.4±12.1
TT
53
28/25
27.7±7.1
108.01±11.7
CT
71
42/29
29.6±8.5
107.01±12.8
CC
19
13/6
28.7±7.4
107.10±11.20
Supplemental Note: Lack of association of DDO rs2057149 with prefrontal volume and activity
may relate to the lower effect size on DDO mRNA expression when comparing genotypes of this
SNP (rs2057149 Cohen’s d: AA vs AG and vs GG=0.5). This aspect may decrease power of
detection of association of subtle genetic variations with complex phenotypes at greater biological
distance from gene effects. On the other hand, these effects may be better captured when looking at
rs3757351, which is associated with greater effect size on DDO mRNA expression (Cohen’s d: CC
vs CT=0.7; CC vs TT=0.9).
Supplemental References
1.
Santini E, Heiman M, Greengard P, Valjent E, Fisone G. Inhibition of mTOR signaling in
Parkinson's disease prevents L-DOPA-induced dyskinesia. Sci Signal 2009; 2(80): ra36.
2.
Zhou J, Blundell J, Ogawa S, Kwon CH, Zhang W, Sinton C, et al. Pharmacological
inhibition of mTORC1 suppresses anatomical, cellular, and behavioral abnormalities in
neural-specific Pten knock-out mice. J Neurosci 2009; 29(6): 1773-1783.
3.
Lipska BK, Deep-Soboslay A, Weickert CS, Hyde TM, Martin CE, Herman MM, et al.
Critical factors in gene expression in postmortem human brain: Focus on studies in
schizophrenia. Biol Psychiatry 2006; 60(6): 650-658.
4.
Colantuoni C, Lipska BK, Ye T, Hyde TM, Tao R, Leek JT, et al. Temporal dynamics and
genetic control of transcription in the human prefrontal cortex. Nature 2011; 478(7370):
519-523.
5.
Colantuoni C, Henry G, Zeger S, Pevsner J. SNOMAD (Standardization and NOrmalization
of MicroArray Data): web-accessible gene expression data analysis. Bioinformatics 2002;
18(11): 1540-1541.
6.
Leek JT, Storey JD. Capturing heterogeneity in gene expression studies by surrogate
variable analysis. PLoS Genet 2007; 3(9): 1724-1735.
7.
First MB, Gibbon M, Spitzer RL, Williams JBW. Guide for the structured clinical interview
for DSM-IV axis I disorders-Research version. Biometrics Research: New York, 1996.
8.
Oldfield RC. The assessment and analysis of handedness: the Edinburgh inventory.
Neuropsychologia 1971; 9(1): 97-113.
9.
Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007; 38(1):
95-113.
10.
Ashburner J, Friston KJ. Unified segmentation. Neuroimage 2005; 26(3): 839-851.
11.
Ashburner J, Friston KJ. Voxel-based morphometry--the methods. Neuroimage 2000; 11(6
Pt 1): 805-821.
12.
Hayasaka S, Phan KL, Liberzon I, Worsley KJ, Nichols TE. Nonstationary cluster-size
inference with random field and permutation methods. Neuroimage 2004; 22(2): 676-687.
13.
Bertolino A, Taurisano P, Pisciotta NM, Blasi G, Fazio L, Romano R, et al. Genetically
determined measures of striatal D2 signaling predict prefrontal activity during working
memory performance. PLoS One 2010; 5(2): e9348.
14.
Seib LM, Wellman CL. Daily injections alter spine density in rat medial prefrontal cortex.
Neurosci Lett 2003; 337(1): 29-32.