Supplementary Information to Transcriptome Analysis of Male ...

Supplementary sex-specific transcriptome in human cortex1

Supplementary Material

Transcriptome Analysis of Male-Female Differences in Prefrontal Cortical Development

Cynthia Shannon Weickert, Ph.D.1,2, Michael Elashoff, Ph.D.3, Allen Brent Richards, Ph.D.1, Duncan Sinclair, BSc.2, Sabine Bahn, Ph.D.4, Svante Paabo, Ph.D.5, Philipp

Khaitovich, Ph.D.5,6, and Maree J. Webster, Ph.D.7

1MiNDS Unit, CBDB, NIMH, IRP, Bethesda, MD, 208942Schizophrenia Research Institute (SRI), University of New South Wales, Prince of Wales Medical Research Institute, Sydney, Australia3Cardiodx, Palo Alto, CA 943044Institute of Biotechnology, University of Cambridge, Cambridge, CB2 1QT, UK5Department of Evolutionary Genetics, Max Plank Institute for Evolutionary Anthropology, Leipzig, Germany6Institute for Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, 320 YueYang Rd., Shanghai, 200031 China7Stanley Medical Research Institute, 9800 Medical Center Drive, Rockville, MD 20850

Corresponding Author:Maree J. WebsterStanley Medical Research Institute9800 Medical Center Drive, Suite C-050Rockville, MD 20850Phone: 240 499 1171Fax: 301 251 8602Email: [email protected]

Acknowledgements: We acknowledge the assistance of Dr. H. Ronald Zielke and Robert Vigorito of the University of Maryland Brain and Tissue Bank for Developmental Disorders.

Funding: Supported by the Stanley Medical Research Institute, the intramural program of NIMH, the Schizophrenia Research Institute, the University of New South Wales and the Prince of Wales Medical Research Institute.

mailto:[email protected]


Supplementary Introduction

Males and female humans differ in their cognitive, psychological and emotional

development; however, it is not known if and when they differ in global gene expression

patterns during brain development. Gender dimorphisms exist in spatial ability and

aggressiveness (1) and in the propensity to develop psychiatric disorders such as autism,

attention deficit hyperactivity disorder (ADHD) and depression (2). Sex differences exist

in cortical complexity (3), in adult regional brain volumes (4) and in childhood gray

matter volumes (5). Divergent development of the male and female brain is believed to

result from the sex-specific development of gonads that indirectly influence the

developing brain via gonadal hormones (6). However, this endocrine mechanism may

work in concert with other more local acting sex specific developmental signals

originating in the brain itself.

Neuronal nuclei of males and females contain a different complement of sex

chromosomes that harbor distinct genes that may directly impact the developing brain.

We hypothesized that gender dimorphisms in human brain development may include

differential cortical expression of genes unique to the sex chromosomes (7, 8) as well as

differential cortical expression of genes on the autosomes. In this study, we first give a

broad overview of differential gene expression across human postnatal development, then

we identify and functionally group 130 transcripts that are differentially expressed in the

brain of developing males compared to females. While we identify thousands of

transcripts that change with age, only a small subset of these change in a sex-specific

manner during development. Therefore, sex does not appear to dictate ubiquitous

transcriptional changes across development, but rather may impact human cortical


development via selective control of specific genes. The sex-specific changes in gene

expression that we find in the developing human frontal cortex represent part of the

molecular biological substrate that distinguishes the male from the female brain.

Supplementary Material and Methods

Tissue Collection

Sixty cases ranging in age from 6 weeks to 49 years (Supplementary Table 1)

were obtained from the University of Maryland Brain and Tissue Bank for

Developmental Disorders (UMBB; NICHHD contract # NO1-HD8-3283; 37 males and

23 females, 33 African Americans and 27 Caucasians). Frozen tissue samples from 7-13

cases (defined as normal controls by the forensic pathologists at UMBB) were selected

from each of seven developmental periods. Samples were included in the cohort if the

pH was above 6.25 and if the RNA was of good quality [over 5.8, (9)] as determined by

the high resolution Bioanalyzer electrophoresis system (Agilent Technologies, Palo Alto,

CA, USA). The cases used in each experiment did not differ significantly within each age

group according to brain pH or RNA Integrity Number (RIN) value.

Total RNA Isolation

Total RNA was extracted from 300 mg gray matter of the middle frontal gyrus

(Brodman’s area 46) using Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to

the manufacturer’s instructions (10). To assess RNA quality, approximately 100-200 ng

RNA was applied to an RNA 6000 Nano LabChip, without heating prior to loading. The

RIN was calculated by an algorithm incorporating information from the entire

electrophoretic trace and used as an indicator of RNA quality, ranging from 1 (lowest

quality) to 10 (highest quality). cDNA was synthesised in three reactions per sample and


pooled, from 3 µg of total RNA per 26.25 µl reaction using the Superscript First-Strand

Synthesis Kit (Invitrogen) according to the manufacturer’s protocol.

Microarray Experimental Design

Total RNA from 45 cases was purified through a Qiagen RNA miniKit column

(Qiagen Inc, Valencia CA USA) according to the manufacturers protocol. RNA was

processed through the Affymetrix preparation protocol [www.affymetrix.com, (11)] and

hybridized to HG-U133 version 2.0+ (GeneChips, Affymetrix CA, USA). Hybridized

arrays were subjected to rigorous quality control including analysis of 5’ 3’ ratios

(included range 0.40-0.79), percent present (included range 37-47%), average pair-wise

correlation analysis and principle component analysis (PCA), resulting in the exclusion of

3 individuals.

Affymetrix Microarray Suite (MAS 5.0) was used for image processing and data

acquisition. The Bioconductor package was used to compute normalized expression

values from the Affymetrix.cel files. Statistical analysis was performed using R and

Bioconductor software. Probe sets that met the criteria of being 50% present in at least

one of the age/gender subgroups were retained in the analysis (33 210 probes sets

retained, 61% of total number). Differential gene expression across chronological age and

between males and females were analyzed in a linear regression model including age (log

scale), gender and their interaction as independent factors and gene expression (log scale)

as the dependent variable.

http://www.affymetrix.com/


Microarray Validation

We confirmed selected results from the microarray experiment using quantitative real-

time PCR (RT-PCR) from RNA extracted from 58 individuals as more cases were added

to the collection over time (9). We targeted expression levels of a selection of transcripts

that showed large expression changes between genders. Pre-designed TaqMan Gene

Expression Assays (Applied Biosystems, Foster City, CA, USA) with specific primer and

probe combinations were chosen for each of the genes analyzed: SMCY

(Hs01104401_g1), NLGN4Y (Hs01034378_s1), PCDH11X/Y (Hs00263173_m1),

PCDH11X (Hs01673213_m1), HSPA1A (Hs00271229_s1), HSPH1 (Hs00971475_m1),

DNAJB1 (Hs00428680_m1), HSPD1 (Hs01036746_g1), HSPB1 (Hs00356629_g1) and

HSP90AA1 (Hs00743767_sH; Table S5). The location along the transcript that was

targeted by the primer/probe set for each gene was chosen to match as closely as possible

the location along the transcript targeted by the Affymetrix gene chip assay. The Y

version of PCDH11 could not be targeted specifically by qPCR, so we measured both the

"pan" transcript (PCDH11X/Y) and the X version (PCDH11X). The ‘pan’ probe

(Hs00263173_m1) detected mRNA from both PCDH11Y (isoform c) and PCDH11X

(isoforms c and d). The other probe (Hs01673213_m1) was specific to the X

chromosome (PCDH11X isoforms c and d).

Each 10 µl qPCR reaction contained primers (final concentration 900 nmol/L),

FAM-labeled probe (250 nmol/L), and 1.14 ng cDNA in 1x Taqman Universal

Mastermix containing AmpliTaq Gold DNA polymerase, deoxynucleoside triphosphates

(dNTPs), uracil-N-glycosylase and passive reference. The PCR protocol used involved

incubation at 50ºC for 2 min and 95ºC for 10 min, followed by 40 consecutive cycles of


95ºC for 15 s and 60ºC for 1 min. Serial dilutions of pooled cDNA, synthesized from

seven samples, one from each developmental time point, were included on every qPCR

plate and used by Sequence Detection Software (SDS; Applied Biosystems) to quantify

sample expression by the relative standard curve method. Control wells containing no

cDNA template displayed no amplification in any assay. Efficiencies of the qPCR

reactions ranged from 77% to 104%, with R2 values of between 0.95 and 1.00. All

reactions were performed in triplicate. Samples were excluded if standard deviation of

the triplicates was greater than 30% of the mean. Expression levels were normalized to

the geometric mean of three ’housekeeper’ genes that did not change expression with

development: HMBS (Hs00609297_m1), GUSB (Hs99999908_m1), and PPIA

(Hs99999904_m1; Table S5). A fourth housekeeper gene, UBC (Hs00824723_m1), was

included in normalization of HSPA1A, HSPH1, DNAJB1, HSPD1, HSPB1 and

HSP90AA1 as these qPCRs were run from a different RNA isolation performed later.

Population outliers were excluded if the normalized expression value was greater than 2

standard deviations from the group mean. One-way and two-way ANOVAs were

performed, with gender or gender and age group as independent variables and normalized

expression levels for the gene of interest as the dependent variable. To maximize power

to detect early gender differences in HSP mRNAs by qPCR, we combined the neonate

and infant groups and contrasted this to the post-infancy time points.

Supplementary Results

At each range of alpha level we found many more transcripts to be significantly changed

by subject age than what would be expected by chance (Table S2). We identified a total

of 8061 probe sets (5,136 genes) that changed significantly in expression across postnatal


development at a fairly conservative threshold (p<0.001, two-sided) Overall, a large

number (24%) of the transcripts expressed in the human prefrontal cortex changed across

development and this level of significance (p<0.001) corresponds to a false discovery rate

of ~0.4% based on a permutation analysis. Therefore, the majority of the developmental

changes detected are expected to be genuine. Transcripts that change in an age-dependent

manner were distributed among many functional GO categories and are also evenly

distributed across the human chromosomes (Table S2).

Of the 8,061 probe sets that changed significantly with age (p<0.001) the

magnitude of the fold change (maximum value vs minimum) in gene expression spanned

a considerable range. Most of the changes in transcript expression were moderate (under

2-fold; 6548 probe sets), many were substantial (over 2-fold but under 5-fold; 1397 probe

sets), and some were quite large (over 5-fold; 116 probe sets). When grouping significant

age-dependent changes (p<0.001) by functional groups or gene ontology (GO) categories

we identified hundreds of functional groups with genes that changed significantly during

the maturation of the frontal cortex. The overall percent of genes that were

developmentally regulated in any given GO category that contained 10 or more members

varied considerably (<1% to 78%, chi-square p<10-6). Thus, significant developmental

changes in gene expression did not occur uniformly across all cellular pathways as

defined by the GO classification. The GO categories were sorted according to the

percentage of transcripts that changed significantly by regression analysis or by ANOVA.

Twenty-one GO categories had at least 50% of their member transcripts changing

significantly with age. They are listed in order of those with the highest percentage of

changed transcripts to the lowest: 1) NADH dehydrogenase activity, 2) NADH


dehydrogenase (ubiquinone) activity 3) calcium- and calmodulin-dependent protein

kinase activity, 4) synapse, 5) cytosolic large ribosomal subunit (sensu Eukaryota), 6)

neurotransmitter secretion, 7) MAP kinase activity, 8) androgen receptor signaling

pathway, 9) nuclear pore, 10) actin cytoskeleton, 11) protein tyrosine/serine/threonine

phosphatase activity, 12) synaptic vesicle, 13) glycolysis, 14) membrane fusion, 15) actin

filament, 16) thyroid hormone receptor binding, 17) protein kinase binding, 18) axon

guidance, 19) JNK cascade, 20) Ras protein signal transduction and 21) vesicle-mediated

transport.

Sex specific changes in gene expression were not equally distributed among the

chromosomes (Table S2). Sex differences were found among <1% of the transcripts

located on the autosomes. Male versus female differences in expression of genes on the

autosomes were found to remain significant by doing a permutation test excluding X and

Y chromosomal genes. While expression of the Y-linked genes can be 35 times higher in

males as compared to females (Table S3), the fold change is somewhat arbitrary because

the level of expression of the Y-linked genes in the female, as reported by the array

software, corresponds to the background level for those genes.

In order to rule out that gender effects observed in HSP gene expression as

measured by qPCR were associated with a differential cause of death in infant females

we determined that no significant differences in frequencies of specific causes of death

between infant males [SIDS (2/9), asphyxia (4/9), other (3/9)] and infant females [SIDS

(4/5), asphyxia (1/5), other (0/5)] existed by chi-squared analysis (df=2, chi-

squared=4.71, less than p<0.05). Thus, overall we were able to replicate the gender

difference in HSPs in 4/6 transcripts by qPCR and we determined the gender difference


was due to an increase in expression in infant females which was not secondary to a

gender difference in the cause of death.

Supplementary Discussion

Protocadherin (PCDH11Y) belongs to a family of cell adhesion molecules that

direct the formation of specific neuronal circuits and synapses (12) in the developing

brain through neuroanatomically restricted expression and gene regulation (13).

PCDH11X differs from PCDH11Y in amino acid structure and function (14, 15). We

found a threefold increase in levels of total PCDH11 in infant males as compared to

females and our data demonstrate that the X-linked form of PCDH11 is expressed at

similar levels in males and females, indicating that the X-linked homolog is not

compensating for gender differences in expression levels by being up-regulated in

females. The increased expression of PCDH11 in the frontal cortex of infant males

suggests that it may play a unique role in the organization of the brain circuits involved in

sexually dimorphic behaviors and perhaps in the propensity to manifest psychiatric

illness in developing males. Protocadherin family members have been proposed as

etiological factors in the development of schizophrenia; however conclusive genetic

evidence linking protocadherin to schizophrenia is lacking (16-18). However, further

investigation of the role of protocadherins in developmental brain disorders that

particularly impact males and involve the prefrontal cortex seems warranted.

The increased mRNA for neuroligin (NLGN4Y) that we detect in males could

also drive male-specific brain development in the infant. NLGN4Y is a member of the

neuroligin family of cell adhesion molecules that can influence neuronal contacts by

changing the balance of excitatory and inhibitory synapses during development (19).


Genomic mutations in neuroligins have been linked to the human developmental brain

disorder, autism-spectrum disorder (20). The absence of NLGN4Y found by microarray

in females was confirmed by qPCR demonstrating the lack of NLGNY-like transcript

expression in females. Our results suggest that some Y chromosome genes may be more

active in early human life but the reasons for this are unknown. It is possible that the

increased secretion of testosterone that occurs peri-natally (21) and then decreases to low

levels in toddlers and school-age males, impacts the early expression of the protocadherin

and neuroligin gene in the male brain. However, it is unlikely that testosterone alone

controls the expression of these Y chromosome genes up-regulated early in life as they

are not up-regulated again during or after puberty when blood testosterone increases

again. Instead, it is possible that distinct transcription factors, DNA methylation events or

mRNA transcript stability may change as males develop postnatally.

In contrast to the transcripts that increased in infant males, another set of

transcripts was found to have increased in expression specifically in infant females. The

microarray study revealed eight heat shock protein (HSP) genes that differed in

expression levels according to gender. The qPCR analysis confirmed that four HSP genes

were significantly increased in females. The gender difference in expression of these HSP

mRNAs are driven by specific increases in infant females (3-12 months old) relative to

older females and all males. HSPs are known to play a crucial role in the folding,

maturation, translocation to the nucleus and transcription regulatory activity of hormone

receptors in general (22-24), and thus may be acting in a coordinated fashion to mediate

some aspects of gender-specific response to hormones during brain development. More

specifically, since HSP40, HSP70 and HSP90 compose the majority of components that


are critical for maturation of the glucocorticoid receptor into a high steroid affinity state

(25, 26), the infant female brain may be better equipped to bind, process and modulate

stress hormones than the male brain or than older female brain.

Alternately, HSPs are versatile molecules responsive to more generalized cellular

stress and thus are not only capable of refolding denatured proteins and promoting

cellular survival, but are also active in immune surveillance (27). Hence, the infant

female brain may be more resilient to oxidative damage or immune challenge. Increased

HSP70 expression has been shown to decrease neonatal hypoxic/ischemic brain injury in

a mouse model by moderating the pro-apoptotic effects of apoptosis-inducing factor

(AIF) (28) and cytochrome c (29). These data suggest that gender differences in HSP

expression, acting to oppose apoptotic pathways, may contribute to the decreased risk of

respiratory infant death in females (30). Female levels of HSP expression are twice as

high in rat heart, muscle, and kidney and in human serum (31, 32) suggesting the female

bias in HSP expression is not restricted to the brain. HSP gene transcription is regulated

by estrogen in the brain (33), however infant females have much lower levels of estrogen

as compared to adult females when HSPs are down-regulated suggesting that estrogen

may not be critical for this early gender difference in HSP mRNA expression. In the adult

brain, gender differences in HSP expression are not always apparent, however gender

differences in cortical and hippocampal HSP70 and HSP90 expression in response to

antidepressant administration (34) and in midbrain HSP70 after alcohol-feeding (35) have

been found. Thus, gender differences in HSPs in brain may be restricted to infants unless

provoked by environmental triggers.


Recent evidence has suggested a role for HSPs in the aetiology of a number of

neurodevelopmental disorders, including schizophrenia and autism. Polymorphisms in

the HSP70 gene have been associated with schizophrenia in a Korean cohort (36), and

increased expression of HSPA1A, HSPA1B and HSPB1 has been observed in the

prefrontal cortex of patients with schizophrenia (37). Increased prevalence of antibodies

against HSP60 (38), HSP70 and HSP90 (39) have been reported in patients with

schizophrenia, with particular increases in HSP60 in female patients (40). HSPA6,

HSPB1, HSPA1A and DNAJB1 have been shown to be increased in expression in

individuals with autism (41). If HSPs contribute to the aetiology of such developmental

brain disorders, gender dimorphism in their expression during development could

contribute to sex differences observed in their onset, prevalence and vulnerability to

neurodevelopmental insults and merit further investigation. For example, the increased

expression of HSP in infant females may serve to protect them from neonatal damage due

to hypoxia or immune challenge, and in so doing reduce the risk of later psychiatric

illness, particularly since foetal hypoxia and 2nd trimester infection are considered risk

factors for schizophrenia (42, 43). Indeed the elevated levels of HSP mRNA found in the

prefrontal cortex of adult patients with schizophrenia have been suggested to be a long

lasting response to early infective immune challenge (37).

Gender difference in cognitive and behavioral development of humans exist and

for the first time we show that gender differences extend to the transcriptome in the

cortex of normal humans, suggesting that there may be a molecular basis for gender

differences in brain function. Psychological differences between males and females are

most prominent in the area of spatial reasoning and verbal function (1). Thus, we suggest


that part of the biological substrate for these gender differences may be related to

differential expression of genes on the sex chromosomes and on the autosomes. The

early expression of Y chromosome genes in the infant male brain may be a mechanism

that directs male specific development of brain cells. The sexually dimorphic expression

of autosomal genes encoding heat shock proteins may provide insight into the molecular

mechanisms protecting the female brain from early insults. While we have focused here

on gender differences in gene expression across human brain development, the majority

of genes in the frontal cortex are expressed in a similar developmental pattern in both

males and females. Thus, at the molecular level, we find that gene expression in males

and females is more similar than different, which may reflect the fact that there are many

similarities in the overall cognitive and behavioral development of the genders (1).

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Supplementary Table 1. Demographic details. Abbreviation: m, male; f, female; PMI, postmortem interval defined as interval between death and freezing of the brain; AA, African American; C, Caucasian; RIN, RNA integrity number.

GroupPMI (hours) Gender

Age (years) pH Race

average RIN cause of death

neonatea,b 28 m 0.21 6.6 AA 8.5 sids

neonatea,b 11 m 0.15 6.9 C 8.3 congenital heart defect

neonatea,b 17 m 0.15 6.6 AA 8.7 sids

neonateb 19 f 0.18 6.5 C 7.1 asphyxia

neonatea,b 25 m 0.19 6.5 AA 8.0 asphyxia

neonatea,b 27 f 0.16 6.5 AA 7.9 pneumonia

neonatea,b 27 m 0.11 6.5 AA 7.9 asphyxia

neonatea,b 24 f 0.24 6.7 AA 7.7 positional asphyxia

infanta,b 14 f 0.25 6.5 AA 8.8 sids

infanta,b 22 f 0.52 6.8 AA 8.4 sids

infantb 18 f 0.48 6.5 AA 6.5 sids

infanta,b 9 m 0.38 6.5 AA 7.2 sids

infanta,b 18 m 0.91 6.9 AA 8.0 sids

infanta,b 24 m 0.52 6.7 AA 8.2 accident/asphyxia

infanta,b 5 m 0.39 6.8 AA 8.6 asthma

infantb 21 f 0.67 6.6 AA 5.9 sids

infanta,b 22 m 0.33 6.5 C 8.0 bronchoneumonia

infantb 10 f 0.91 6.4 AA 6.6 bronchiolitis

infantb 19 m 0.32 6.4 C 6.7 asphyxia suffocation

infanta,b 27 m 0.35 6.7 C 8.1 myocarditis

infantb 18 m 0.82 6.65 AA 7.3 hypothermia

toddlera,b 24 f 1.58 6.9 C 7.8 myocarditis

toddlera,b 18 m 4.64 6.9 C 7.0 accident

toddlera,b 19 m 4.86 6.7 AA 8.4 drowning

toddlerb 11 f 2.21 6.9 AA 7.4 meningitis

toddlera,b 22 f 2.45 6.7 AA 7.6 no anatomical cause

toddlerb 13 m 2.00 6.89 AA 6.9 cardiac arhythmia

toddlera,b 44 f 2.71 6.5 C 7.0 drowning

toddlera,b 27 m 2.19 6.6 AA 7.6 asthma

school agea,b 17 m 5.39 6.7 C 8.2 drowning

school agea,b 16 m 12.42 6.8 C 8.2 drowning

school ageb 18 m 7.84 6.8 AA 7.0 accident

school agea,b 18 f 12.98 6.9 C 7.8 accident

school ageb 12 f 8.92 6.4 C 6.7 cardiac arhythmia

school agea,b 12 f 11.54 6.4 C 7.3 asthma

school agea,b 20 f 8.14 6.8 C 7.6 asphyxia

school agea,b 5 m 8.01 6.8 AA 8.2 cardiac arhythmia


GroupPMI (hours) Gender

Age (years) pH Race

average RIN cause of death

teenagea 13 m 15.00 6.8 AA 6.2 accident

teenageb 16 m 17.49 6.7 C 6.5 accident

teenagea 12 m 17.82 6.8 C 9.2 accident/asphyxia

teenagea,b 16 m 17.69 6.8 AA 8.2 accident

teenagea,b 25 m 17.05 6.7 C 7.5 drowning

teenagea,b 19 m 17.38 6.84 C 6.8 accident

teenagea,b 16 f 16.68 6.81 C 7.6 multiple injuries

teenageb 20 f 16.34 6.6 C 6.7 multiple injuries

young adultb 32 f 25.10 6.54 C 6.8 pulmonary embolism

young adulta,b 16 f 25.38 6.73 C 8.3 accident

young adulta,b 4 m 22.92 6.84 AA 8.2 ASCVD

young adulta,b 13 m 21.93 6.96 C 7.8 mva

young adulta,b 18 m 20.14 6.5 AA 7.2 accident

young adultb 7 m 21.97 6.25 AA 6.9 obesity

young adulta,b 7 m 24.93 6.92 C 8.4 mva

young adulta,b 14 f 23.62 6.57 AA 8.1 asthma

adulta,b 18 m 46.18 6.75 AA 7.8 accident

adulta,b 18 m 42.94 6.49 C 7.3 accident

adulta,b 13 m 35.99 6.73 C 8.0 coronary artery dis

adulta,b 8 m 38.63 6.37 AA 7.6 ASCVD

adultb 12 m 47.44 6.56 C 6.4 ASCVD

adulta,b 19 f 38.42 6.98 AA 7.6 HASCVD

adulta,b 7 f 49.22 6.78 AA 7.4 cirrhosis of liver

a sample included in microarray and sex chromosome quantitative PCR experimentsb sample included in heat shock protein quantitative PCR experiments


Supplementary Table 2. The number of detectable probe sets tabulated according to chromosome. For each chromosome, the number and percentage of probe sets showing differential regulation (p<.001) by regression as a function of age, sex and the interaction of age and sex are shown.

Chromosome Probe Sets

Age Significant (p<.001)

M vs F Significant (p<.001)

Interaction Significant (p<.001)

1 2951 725 24.6% 11 0.37% 8 0.27%2 2130 590 27.7% 6 0.28% 7 0.33%3 1802 427 23.7% 4 0.22% 1 0.06%4 1239 323 26.1% 0 0.00% 3 0.24%5 1485 425 28.6% 1 0.07% 1 0.07%6 1614 423 26.2% 6 0.37% 5 0.31%7 1511 369 24.4% 5 0.33% 3 0.20%8 1095 329 30.0% 1 0.09% 1 0.09%9 1193 304 25.5% 1 0.08% 1 0.08%

10 1207 292 24.2% 1 0.08% 2 0.17%11 1555 391 25.1% 3 0.19% 2 0.13%12 1538 425 27.6% 3 0.20% 4 0.26%13 658 181 27.5% 2 0.30% 2 0.30%14 990 230 23.2% 7 0.71% 3 0.30%15 975 223 22.9% 2 0.21% 1 0.10%16 1154 260 22.5% 0 0.00% 0 0.00%17 1577 380 24.1% 5 0.32% 3 0.19%18 540 159 29.4% 2 0.37% 0 0.00%19 1466 295 20.1% 9 0.61% 9 0.61%20 802 208 25.9% 1 0.12% 0 0.00%21 317 71 22.4% 0 0.00% 2 0.63%22 612 136 22.2% 1 0.16% 0 0.00%X 934 244 26.1% 16 1.71% 3 0.32%Y 35 10 28.6% 23 65.71% 2 5.71%


Symbol Name C FCMean

FemaleSEM

FemaleMean Male

SEM Male

p-value M vs F probe sets

CYorf15A chromosome Y open reading frame 15A Y 2.89 29.91 1.75 84.9 4.07 >0.00001 236694_at

CYorf15B chromosome Y open reading frame 15B Y 2.06 30.56 1.93 63.51 2.39 >0.00001 214131_at, 223645_s_at, 223646_s_at,

DDX3Y DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, Y-linked Y 13.3 29.58 2.42 372.1 12.14 >0.00001 205000_at, 1570360_s_at, 205001_s_at

EIF1AY eukaryotic translation initiation factor 1A, Y-linked Y 3.96 30.98 1.96 117.91 5.31 >0.00001 204409_s_at, 204410_at

HSFY1 heat shock transcription factor, Y-linked 1 Y 1.64 31.47 2.11 47.69 2.08 >0.00001 224007_at

NLGN4Y Neuroligin 4Y Y 5.85 30 2.21 161.93 6.13 >0.00001 207703_at

PCDH11Y protocadherin 11Y Y 1.66 40.06 1.92 62.9 3.53 >0.00001 211227_s_at, 217049_x_at

RPS4Y1 ribosomal protein S4, Y-linked 1 Y 38.64 29.06 2.1 1045.8 19.3 >0.00001 201909_at

SMCY Smcy homolog, Y-linked Y 8.64 34.03 2.44 264.37 9.28 >0.00001 206700_s_at

TTTY15 testis-specific transcript Y15 Y 3.08 35.04 2.43 91.9 4.7 >0.00001 214983_at

TMSB4Y thymosin, beta 4, Y-linked Y 1.61 30.39 2.57 49.68 2.72 >0.001 206769_at

USP9Y ubiquitin specific protease 9, Y-linked Y 8.71 30.12 1.88 278.62 7.48 >0.00001 228492_at, 206624_at

UTY ubiquitously transcribed tetratricopeptide repeat gene, Y-linked Y 2.17 27.3 1.85 58.15 1.93 >0.00001 211149_at

ZFY zinc finger protein, Y-linked Y 3 27.19 2.2 83.5 2.58 >0.00001 230760_at, 207246_at

ASMTL acetylserotonin O-methyltransferase-like X/Y 1.26 259.92 7.68 325.42 7.63 >0.0001 36553_at, 36554_at, 209394_at

CD99 CD99 antigen X/Y 1.6 222.08 22.36 336.08 17.23 >0.0001 201029_s_at, 201028_s_at

DDX3X DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, X-linked X -1.22 1493.7 39.83 1217.61 20.42 >0.0001 201210_at

EIF2S3 eukaryotic translation initiation factor 2, subunit 3 gamma, 52kDa X -1.18 488.9 15.29 406.5 8.92 >0.001 224936_at

HDHD1A haloacid dehalogenase-like hydrolase domain containing 1A X -1.31 137.79 5.38 103.19 3.44 >0.0001 203974_at

OFD1 oral-facial-digital syndrome 1 X -1.38 64.05 5.62 51.33 2.11 >0.001 241751_at

PCDH11X protocadherin 11 X-linked X 2.56 92.59 8.86 238.97 18.38 >0.00001 210292_s_at, 241772_at

SMCX Smcy homolog, X-linked X -1.35 107.81 4.31 83.15 2.54 >0.0001 239207_at

USP9X ubiquitin specific protease 9, X-linked X -1.16 1233.34 43.33 1119.46 15.47 >0.001 201100_s_at

WBP5 WW domain binding protein 5 X -1.31 551.46 61.74 471.48 12.76 >0.001 217975_at

XIST X (inactive)-specific transcript X -116.65 3065.43 108.58 27.05 1.37 >0.00001 224588_at, 224589_at, 214218_s_at, 221728_x_at, 227671_at, 224590_at, 231592_at

Supplementary Table 3. Genes on the sex chromosomes that show a main effect of sex on expression levelsAbbreviations: C, Chromosome: FC, Fold Change; SEM, standard error of the mean.


Supplementary Table 4. Genes on the autosomes that show a main effect of sex on expression levels but do not show an effect of age on expression levels (* indicates those genes that show an interaction between sex and age)

Probe Name Symbol Locus C ANOVA p

Transcription Factor/Zinc Finger

202672_s_at activating transcription factor 3 * ATF3 467 1 6.44E-06

235963_at endothelial PAS domain protein 1 EPAS1 2034 2 0.000868918

230056_at fetal Alzheimer antigen FALZ 2186 17 0.000109062

209189_at v-fos FBJ murine osteosarcoma viral oncogene homolog * FOS 2353 14 0.000846797

201464_x_at v-jun sarcoma virus 17 oncogene homolog (avian) JUN 3725 1 0.00033453

228846_at MAX dimerization protein 1 * MXD1 4084 2 0.000121767

228388_atnuclear factor of kappa light polypeptide gene enhancer in B-cells

inhibitor, beta NFKBIB 4793 19 0.000924997

1569661_at neuronal PAS domain protein 3 NPAS3 64067 14 0.000585462

219459_at polymerase (RNA) III (DNA directed) polypeptide B POLR3B 55703 12 0.00057423

227891_s_atTAF15 RNA polymerase II, TATA box binding protein (TBP)-associated

factor, 68kDa * TAF15 8148 17 0.000323075

1565913_at zinc finger CCCH-type, antiviral 1ZC3HAV

1 56829 7 0.000716049

223163_s_at zinc finger, C3HC-type containing 1 ZC3HC1 51530 7 0.000111222

226650_at zinc finger, AN1-type domain 2AZFAND2

A 90637 7 8.01E-07

218401_s_at zinc finger protein 281 ZNF281 23528 1 6.24E-05

228392_at zinc finger protein 302 ZNF302 55900 19 0.000266119

225021_at zinc finger protein 532 ZNF532 55205 18 0.000853887

Intracellular Signalling

236908_at acid phosphatase-like 2 ACPL2 92370 3 0.000434466

240953_at acid phosphatase, prostate ACPP 55 3 0.000187691

237118_at acidic (leucine-rich) nuclear phosphoprotein 32 family, member A * ANP32A 8125 15 0.000790369

204170_s_at CDC28 protein kinase regulatory subunit 2 * CKS2 1164 9 1.23E-05

234158_at EPH receptor B2 EPHB2 2048 1 0.000696997

1560094_at guanine nucleotide binding protein (G protein), beta 5 GNB5 10681 15 0.000394315

232717_at kalirin, RhoGEF kinase KALRN 8997 3 0.000301639

212722_s_at phosphatidylserine receptor PTDSR 23210 17 0.000364625

202388_at regulator of G-protein signalling 2, 24kDa RGS2 5997 1 3.79E-05

235964_x_at SAM domain and HD domain 1 SAMHD1 25939 20 0.000726604

241737_x_at vaccinia related kinase 1 VRK1 7443 14 0.000528063

Protein folding/response to stress

200666_s_at DnaJ (Hsp40) homolog, subfamily B, member 1 * DNAJB1 3337 19 2.89E-05

209304_x_at growth arrest and DNA-damage-inducible, beta *GADD45

B 4616 19 3.27E-05

200800_s_at heat shock 70kDa protein 1A HSPA1A 3303 6 0.000879932

202581_at heat shock 70kDa protein 1B * HSPA1B 3304 6 3.84E-06

117_at heat shock 70kDa protein 6 (HSP70B') * HSPA6 3310 1 1.44E-05

201841_s_at heat shock 27kDa protein 1 * HSPB1 3315 7 0.000134513

211969_at heat shock 90kDa protein 1, alpha *HSP90A

A1 3320 14 0.000145585

200807_s_at heat shock 60kDa protein 1 (chaperonin) * HSPD1 3329 2 5.43E-06

206976_s_at heat shock 105kDa/110kDa protein 1 * HSPH1 10808 13 7.98E-05

Nucleus/DNA Function


Probe Name Symbol Locus C ANOVA p

210691_s_at calcyclin binding protein CACYBP 27101 1 0.000961985

204258_at chromodomain helicase DNA binding protein 1 CHD1 1105 5 7.36E-05

210387_at histone 1, H2bgHIST1H2

BG 8339 6 0.000750647

216175_at polymerase (DNA directed), delta 2, regulatory subunit 50kDa POLD2 5425 7 0.000568346

201292_at topoisomerase (DNA) II alpha 170kDa TOP2A 7153 17 0.000942269

Cytoskeleton

218658_s_at ARP8 actin-related protein 8 homolog (yeast) * ACTR8 93973 3 0.0004138

208862_s_at catenin (cadherin-associated protein), delta 1 CTNND1 1500 11 0.000992209

37966_at parvin, beta PARVB 29780 22 6.45E-05

203690_at tubulin, gamma complex associated protein 3 *TUBGCP

3 10426 13 0.000720854

RNA processing

230142_s_at cold inducible RNA binding protein * CIRBP 1153 19 0.000688481

203694_s_at DEAH (Asp-Glu-Ala-His) box polypeptide 16 * DHX16 8449 6 0.000607668

221768_atsplicing factor proline/glutamine-rich (polypyrimidine tract binding

protein associated) SFPQ 6421 1 0.000601195

Apoptosis

206864_s_at harakiri, BCL2 interacting protein (contains only BH3 domain) HRK 8739 12 0.00013468

214057_at myeloid cell leukemia sequence 1 (BCL2-related) MCL1 4170 1 0.000380653

Other

209732_at C-type lectin domain family 2, member B CLEC2B 9976 12 0.000804517

214328_s_at eukaryotic translation initiation factor 3, subunit 3 gamma, 40kDa EIF3S3 8667 8 0.000249265

1566079_atELOVL family member 5, elongation of long chain fatty acids

(FEN1/Elo2, SUR4/Elo3-like, yeast) * ELOVL5 60481 6 0.000488677

217534_at family with sequence similarity 49, member B FAM49B 51571 8 0.000817913

218611_at immediate early response 5 IER5 51278 1 0.000356753

239910_at pregnancy specific beta-1-glycoprotein 6 PSG6 5675 19 0.00081279

1557477_at stromal interaction molecule 1 STIM1 6786 11 0.000415669

1552657_a_at thioredoxin domain containing 2 (spermatozoa) TXNDC2 84203 18 0.000262761

Unknown Function

226630_at chromosome 14 open reading frame 106C14orf10

6 55320 14 0.000169868

222441_x_at chromosome 20 open reading frame 45 C20orf45 51012 20 9.79E-05


Supplementary Table 5 Taqman gene expression assays used for RT-PCR

Gene ABI assay ID GenBank numbersSMCY Hs01104401_g1 D87072.1, U52191.1, AK127269.1, AK095923.1NLGN4Y Hs01034378_s1 AB023168.1, AF376804.1, BX537428.1PCDH11X/Y Hs00263173_m1 AF206516.1, AF217288.1, AF332218.1, AF332219.1,

AY861433.1, AB037747.1, AF332217.1 PCDH11X Hs01673213_m1 AF332219.1, AY861434.1HMBS Hs00609297_m1 NM_000190.3 (RefSeq)GUSB Hs99999908_m1 M15182.1, CR593823.1, BC014142.2, AK223406.1, DQ896190.1PPIA Hs99999904_m1 NM_021130.3 (RefSeq)UBC Hs00824723_m1 AB009010.1, AK026846.1, NM_021009.4 (RefSeq)HSPA1A Hs00271229_s1 BC009322.2, BC002453.2, NM_005345.4 (RefSeq)HSPH1 Hs00971475_m1 AB003333.1, AB003334.1, NM_006644.2 (RefSeq)DNAJB1 Hs00428680_m1 D49547.1, X62421.1, NM_006145.1 (RefSeq)HSPD1 Hs01036746_g1 M34664.1, NM_199440.1 (RefSeq), NM_002156.4 (RefSeq)HSPB1 Hs00356629_g1 X54079.1, AL050380.1, X16477.1, NM_001540.2 (RefSeq)HSP90AA1 Hs00743767_sH X15183.1, NM_005348.3 (RefSeq), NM_001017963.2 (RefSeq)

http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=NM_001017963.2


http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=X15183.1



http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=AL050380.1




http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=M34664.1



http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=D49547.1


http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=AB003334.1

http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=AB003333.1


http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=BC002453.2

http://www.ncbi.nlm.nih.gov/entrez/viewer.fcgi?val=BC009322.2


FIGURE LEGENDS:

Figure 1: A principal component analysis (PCA) was conducted using all genes. The plot

shows the first principal component versus age (log scale). Points are colored by the pre-

defined developmental categories. The predominant expression profile in the study was

genes changing in a linear manner over time (log scale). By the nature of PCA, this

profile includes genes having either positive or negative slopes.

Figure 2: Microarray data showing the levels of gene expression (log scale) for eight

genes on the Y chromosome (SMCY, PCDH11Y, TTTY15, CYorf15B, TMSB4Y, CYorf

15A, EIFIAY, NLGN4Y) that vary significantly according to postnatal age (log scale).

Graphs for PCDH11X and PCDH11Y are labeled the same as their associated probe sets

on the affymetric chip. However, these probes sets may detect both X and Y versions of

the gene. The circles represent males (blue lines) and the triangles represent females (red

line, PCDH11X only). The lines represent the line of best fit based on regression. Points

are colored by the predefined developmental categories. Abbreviation; Reg R2, regression

R-squared.

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