1

Supplementary Information

Methods

Primer design and validation

Primers for P450s in families CYP1-CYP3 were previously validated (Girault et al., 2005, Bieche et al., 2007). Locations and lengths of amplicons are

shown in supplementary table 2. Primers for CYP46A1, ABCB1 and ABCG2 and the four transcription factors (AhR, CAR, PXR and PPAR) were

also described previously (supplementary table 3; (Dutheil et al., 2009, Dauchy et al., 2008)). ABCA1, CYP4X1 and the human microsomal

cytochrome P450 reductase (CPR) were detected using primers validated previously (supplementary table 3). Primers for β-actin (ACTB) have been

published and validated previously (Chen et al., 2011) and primers for human asparagine-linked glycosylation 9-alpha-1,2-mannosyltransferase

homolog (S. cerevisiae) (ALG9) were as described in PrimerBank (http://pga.mgh.harvard.edu/cgi-bin/primerbank/new_search2.cgi; primer bank ID:

118026936b1). Details of ACTB, ALG9 and the other RG primers used in this study (glyceraldehyde 3-phosphate dehydrogenase, GAPDH;

phosphoglycerate kinase 1, PGK1; and ribosomal protein L13A, RPL13A) are shown in supplementary table 3.

The first primer pair used for AdR (“AdR”; supplementary table 3) has been published and validated previously (Tsutsumi et al., 2008), whereas the

second pair (“AdR1”) and the primers for Adx (“Adx”) were designed specifically for this study. To avoid amplification of contaminating genomic

DNA, one of the two primers spanned an exon-exon junction. For each primer pair, a BLASTn search (http://www.ncbi.nlm.nih.gov/) was conducted,

to ensure lack of significant similarity to genes other than the target.

To optimise PCR conditions and to check PCR efficiency for each primer pair, the amplification efficiency was assessed as previously described

(Dauchy et al., 2008), using triplicates of five cDNA dilutions from sample UN1, corresponding to 0.224 to 140 ng of reverse-transcribed RNA. For

2

each primer pair, triplicate, no-template controls (NTC) were also run on each PCR plate and a no reverse-transcription (RT-free) control was

performed once to confirm no amplification of genomic DNA.

Comparison of RNA quality and demographic variables between brain areas and case groups

For comparison of RQI values between brain areas, only AMG and PFC samples from the same individuals were used (n = 13 for each brain area) and

matched samples were compared with a paired, two-tailed, Student’s t-test. For comparison between case groups, RQI values were pooled for all

smoker and alcoholic cases, and compared to NANS with an unpaired, two-tailed, Student’s t-test, separately for each brain area. Data on demographic

variables (i.e. post-mortem interval (PMI), age, brain pH and brain weight) were pooled for all cases and compared to NANS with an unpaired, two-

tailed, Student’s t-test.

Analysis of qRT-PCR data

Differences in expression of genes of interest (GOI) between case groups were obtained by successively calculating:

Cq = Cq GOI

– CqRG

and Cq = Cqcase

– Cqcontrols

(where Cqcontrols

= average Cq of all NANS samples from homotropic brain area) for each sample. Similarly, differences in expression between brain

areas from the same individual were calculated as follows:

Cq = Cq GOI

– CqRG

for each sample, and Cq = Cq

AMG –Cq

PFC for each individual

Results were then converted to fold changes, i.e. 2-Cq

values (Livak and Schmittgen, 2001) and a mean and standard deviation (SD) of the fold

change was calculated from the collated data from n = 13 individuals. The effect of alcoholism and/or smoking was evaluated with one-way ANOVA

3

on Cq values, followed by Tukey’s post hoc analysis, where appropriate. The statistical significance of fold changes in expression of GOIs between

the AMG and PFC of the same individual was evaluated using a paired, two-tailed, Student’s t-test on GOI expression normalized to that of the RG in

the same sample (i.e. Cq). Correlation between the expression of GOIs and their transcription factors was examined with a two-tailed Pearson’s

correlation for -Cq values, with all samples considered together, as well as separately for each brain area. Correlation with demographic variables was

examined separately for each brain area. GraphPad Prism 5 (La Jolla, CA, USA) was used for all statistical analyses.

Identification of outliers in gene expression data

For every gene, technical triplicates were tested for outliers with the Grubbs’ test (Grubbs, 1969), which identifies the replicate with highest absolute

deviation from the average, in terms of standard deviations. The test was performed both on raw Cq values (logarithmic scale), as well as on the

linearised 2-Cq

values, and the same outliers were identified. Outliers were rejected, and a new average was taken from the remaining replicates for

downstream data analysis.

Investigation and validation of RGs

The expression of five RGs was analyzed in this study to find the optimal candidate for normalization: ACTB, ALG9, GAPDH, PGK1 and RPL13A.

When examined in previous studies on gene expression in alcoholics and smokers or in AMG and PFC, they showed no apparent regulation by the test

conditions under examination or between brain areas (Flatscher-Bader and Wilce, 2006, MacKay et al., 2011, Ridge et al., 2008, Noriega et al., 2010,

Boda et al., 2009). Moreover, with the exception of GAPDH and PGK1, which are both involved in glycolysis, all other genes code for proteins with

very different functions, minimizing the chances of having co-regulated RGs in the panel which would bias the ranking.

Raw Cq values were imported into the software packages GeNorm (used on the qbasePLUS

platform, Biogazelle, Zwijnaarde, Belgium) and NormFinder

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(used on the GenEx platform, MultiD Analyses AB, Göteborg, Sweden). Since the stability of reference transcripts as determined by geNorm and

NormFinder can sometimes be in disagreement with the apparent inter-sample and inter-group variation as judged by relative quantification ((Gebhardt

et al., 2010); M. Kubista, http://www.qpcrforum.com/, last accessed on 15th

December 2011), expression levels were also inspected manually. The

geometric mean of GAPDH and ALG9 was also included in this assessment as a sixth candidate “gene” for normalization.

Firstly, the fold changes for all the alcoholic and smoker cases combined were estimated over the NANS controls separately for each brain area (n = 12

and 4 for AMG for cases and NANS respectively; n = 14 and 5 for PFC) (Livak and Schmittgen, 2001). Essentially, the average NANS Cq value was

subtracted from the value for each sample, and the resultant Cq data were converted to fold changes, i.e. 2-Cq

. Cq values were compared between

cases and NANS with an unpaired, two-tailed, Student’s t-test. Similarly, the fold changes in expression between brain areas were evaluated by

subtracting each PFC Cq value from those of each AMG sample from the same individual (n = 13 for each brain area), and transforming this Cq value

to 2-Cq

. The 2-Cq

values from the two brain areas from the same individual were then compared with a paired, two-tailed, Student’s t-test. Next, the

inter-individual variability of each RG was estimated by calculating the fold changes over its average expression across all samples. This was

calculated as 2-Cq

, where Cq is the difference between the Cq value of each sample and the average for each transcript (Boda et al., 2009). Finally, -Cq

values were used to examine any influence of RQI on RG expression with a two-tailed Pearson’s correlation across all samples. Correlation of

demographic variables (i.e. PMI, age, brain pH and brain weight) with RG expression was assessed in the same way, separately for each brain area.

Results

Characterization of primers and determination of PCR conditions

BLASTn search of the primers specifically designed for this study (i.e. those for Adx, AdR and the RGs) showed 100% similarity to the target but no

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other genes. The specificity of each reaction and primer-dimer formation were also assessed by melting-curve analysis after each run. This revealed a

single amplification product in all cases (data not shown), except for AdR, for which both primer sets showed little amplification across all brain

samples. Primer-dimers were observed in some no-template controls, which otherwise showed no detectable signal. However, even when minor primer

dimers were present in complete reactions, the technical replicate was excluded only if its levels were affected by the dimer formation, upon screening

with Grubb’s test. For each primer pair, RT-free controls were also included, and produced either no signal or insignificant signals (Cq values usually

~5-10 higher than the target signal; data not shown), suggesting that genomic DNA contamination was absent or negligible.

Efficiency values obtained from standard curves of cDNA serial dilutions were all between 94 % and 101 % for the RG primers (Supplementary table

4). The efficiency of the Adx primers was 83 %, i.e. below the optimal range of 90-110 %; however this primer pair was still used, as it showed good

melting curve profiles (data not shown).

PCR efficiency and specificity of primer pairs for all P450s, CPR, ABC transporters and transcription factors were previously assessed (Dauchy et al.,

2008, Girault et al., 2005). All primer pairs showed efficiency values between 90 and 110 % combined with high specificity, with the exception of

ABCA1 primers, which showed an efficiency of 81 %. In the current study, melting-curve analysis was performed after each run, and showed a single

amplification product in all cases, except for CYP1A2, CYP2A6, CYP2C19, CYP2W1 and CAR, which showed little or no amplification in the

majority of samples (data not shown).

Validation of RGs for normalization

Details of the characterization of primers and validation of PCR conditions for qRT-PCR are presented in section 1.1 of this Supplementary

Information and in section 2.5 of the main text, respectively.

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Expression levels differed for the five RGs genes, with GAPDH and ACTB having the lowest average Cq values (19.5 0.9 and 20.9 0.9

respectively), i.e. being the most abundant transcripts, followed by RPL13A (23.0 0.5), PGK1 (23.0 1.0) and ALG9 (27.5 0.9) (Supplementary

figure 4. The stability of candidate reference transcripts was assessed using three methods. Firstly, the geNorm algorithm ranked each transcript

according to the average variation of its expression from that of each other candidate, with the lowest “M value” indicating the most stable gene. All

genes showed excellent stability (all M values 0.5; supplementary table 5), with ALG9 ranked the best (M value = 0.27), followed closely by

GAPDH (M value = 0.28) and ACTB (M value = 0.29). The optimal number of RGs, to be used as their geometric mean, was computed to be two,

namely ALG9 and GAPDH. Good agreement was found with the second algorithm, NormFinder when groups were compared with the two brain areas

combined; again the top three genes were ALG9, ACTB and GAPDH (Supplementary table 5). When groups were combined to examine variations

between brain areas, ALG9 was again ranked the best, followed by GAPDH and ACTB (Supplementary table 5). Group X Area comparison was not

performed due to the small sample size. The comparable ranking of candidate RGs obtained with the two algorithms used here indicates that no co-

regulation of two transcripts occurred, since geNorm and NormFinder compare transcripts in a different manner (pair-wise vs globally).

The algorithms used by geNorm and NormFinder can sometimes return stability rankings different from those obtained by relative quantification of

reference transcripts across samples and case groups (Gebhardt et al., 2010); Kubista, 2011). Expression variability was thus also evaluated by manual

comparison of relative quantities between brain areas and case groups. All reference transcripts showed slightly higher expression in alcoholic and

smoker cases compared to the controls, but this difference failed to reach statistical significance (Supplementary figure 5). After ranking each gene

according to their fold changes over NANS (Supplementary table 6), an almost identical ranking was obtained in both brain regions, with RPL13A

being the transcript showing the lowest variation (fold changes were 1.2 0.3 and 1.4 0.5 in AMG and PFC, respectively). When differences in

7

expression between PFC and AMG were considered, the candidate gene showing the lowest fold changes was again RPL13A (1.0 0.3;

supplementary figure 6). No statistically significant differences were seen in RPL13A expression levels, represented by 2-Cq

values, when the two

brain areas from the same individual were compared (p = 0.48, paired, two-tailed, Student’s t-test). All other transcripts showed a significantly lower

expression in AMG than in PFC, with average fold changes ranging from 0.3 0.2 for PGK1 to 0.6 0.3 for ACTB (all p values < 0.001, paired, two-

tailed, Student’s t-test; Supplementary figure 6). Finally, all genes showed slightly higher inter-individual variability in AMG than in PFC, with the CV

ranging from 0.4 for RPL13A to 0.7 for PGK1 in AMG, and from 0.3 for RPL13A to 0.5 for PGK1 in PFC. However, overall variation from the mean

was very low for all candidates, with fold changes ranging from 1.1 0.4 for RPL13A to 1.2 0.8 for PGK1 (Supplementary figure 7A).

Correlation with RNA quality and demographic variables was also examined. A p value of less than 0.01 and r coefficient higher than 0.5 were chosen

to define statistical significance (two-tailed Pearson’s correlation). RQI values showed a strong positive correlation with RG expression, indicating that

expression increased with increasing RNA quality (Supplementary figure 7B). When all samples were considered together, a two-tailed Pearson’s

correlation returned p values lower than 0.0001 and r coefficients between 0.89 and 0.92 for all transcripts, except for RPL13A, for which the r

coefficient was slightly lower (0.77) (Supplementary figure 7B). No significant correlation with demographic variables was observed for the

expression of any reference transcript, except for that of RPL13A, which positively correlated with brain pH in AMG (p = 0.0092, r = 0.63).

Thus, when variation between groups, brain areas and individuals was analyzed by calculating relative quantities with the Cq method and the

coefficient of variation across all samples, RPL13A consistently showed the lowest variation, and PGK1 the highest. However, when RNA quality was

considered, expression of RPL13A showed the lowest correlation to RQI values among all candidate RGs, with PGK1 showing the highest. This

suggested that RPL13A underestimated and PGK1 over compensated for differences in RNA quality. ALG9, which has the additional benefit of having

8

expression levels within the same linear range as GOIs, was thus chosen for normalization, and all results shown in the main text reflect normalization

with this RG.

References

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levels from cytochrome (cyp)1, cyp2 and cyp3 families in 22 different human tissues. Pharmacogenetics and Genomics 17:731-742.

Boda E, Pini A, Hoxha E, Parolisi R, Tempia F (2009) Selection of reference genes for quantitative real-time rt-pcr studies in mouse brain. J Mol

Neurosci 37:238-253.

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Chen D, Pan X, Xiao P, Farwell MA, Zhang B (2011) Evaluation and identification of reliable reference genes for pharmacogenomics,

toxicogenomics, and small rna expression analysis. J Cell Physiol 226:2469-2477.

Dauchy S, Dutheil F, Weaver RJ, Chassoux F, Daumas-Duport C, Couraud PO, Scherrmann JM, De Waziers I, Decleves X (2008) Abc transporters,

cytochromes p450 and their main transcription factors: Expression at the human blood-brain barrier. J. Neurochem. 107:1518-1528.

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Duyckaerts C, Loriot MA (2009) Xenobiotic-metabolizing enzymes and transporters in the normal human brain: Regional and cellular mapping

as a basis for putative roles in cerebral function. Drug Metab. Dispos. 37:1528-1538.

Flatscher-Bader T, Wilce PA (2006) Chronic smoking and alcoholism change expression of selective genes in the human prefrontal cortex. Alcohol

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Grubbs FE (1969) Procedure for detecting outlying observations in samples. Technometrics 11:1-21.

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25:402-408.

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rhesus macaque brain. BMC Mol Biol 11:47.

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Current status. Arch Toxicol 82:667-715.

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1139:10-19.

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Supplementary Table 1.

Details of human cases. Gender: F, female; M, male. Age: y, years. Post-mortem interval (PMI): h, hours. Y, brain area available; N, brain area

unavailable.

Case

Brain areas

available

Gender

Age

(y)

PMI

(h)

Brain

pH

Brain

Weight (g)

Alcohol

daily intake

(g)

Nicotine daily

intake

(cigarettes) Cause of death AMG PFC

Non-alcoholic non-smoker

NANS1 Y Y M 53 16 6.8 1590 10 0 Dilated cardiomyopathy

NANS2 Y Y M 82 36 6.2 1300 10 0 Myocardial

infarction/cardiac failure

NANS3 N Y M 54 29 6.8 1510 < 20 0 Coronary artery

atheroma

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NANS4 Y Y M 74 10 6.2 1528 0 0 Respiratory arrest

NANS5 Y Y M 68 22 6.6 1630 < 5 0 Suicide (hanging)

Non-alcoholic smoker

NAS1 Y Y M 38 13.5 6.3 1620 10-30 20 Atherosclerotic

cardiovascular disease

NAS2 Y Y M 56 37 6.8 1510 10 15 Left ventricular scarring,

hypertension, and

cardiomegaly

NAS3 Y Y M 69 16 6.6 1500 < 30 Yesa Atherosclerotic

cardiovascular disease

NAS4 Y Y M 58 12 6.5 1350 0 Yesa Ischaemic heart disease

NAS5 Y Y M 56 24 6.5 1635 0 Yesa Coronary artery

atheroma

12

Alcoholic non-smoker

ANS1 Y N F 62 22 6.7 1200 100 0 Mixed drug toxicity

(paracetamol,

doxylamine);

encephalopathy, hepatic

steatosisb

ANS2 Y N M 56 45 6.5 1284 100 Unknown Bleeding oesophageal

varices

ANS3 N Y M 59 24 6.6 1520 80 0 Cardiomyopathy

ANS4 N Y M 37 17 6.3 1500 80 0 Acute alcohol

poisoningb

ANS5 N Y M 73 43.5 6.6 1400 80-150 0 Coronary artery

atheroma

ANS6 N Y M 73 24 6.3 1300 225 0 Consequences of

13

cirrhosis

ANS7 N Y M 42 41 6.5 1400 120 0 Combined bromoxynil

and alcohol toxicity

Alcoholic smoker

AS1 Y Y M 39 24 6.6 1360 80 20 Aortic stenosis

AS2 Y N M 53 59.5 6.8 1340 400 Yesa Chronic airflow limitation

AS3 Y Y M 34 8.5 6.6 1140 160 20 Suicide (hanging)

AS4 Y Y M 61 21 6.9 1340 200 5 Combined effect of

hypertensive heart disease

and chronic alcoholism

AS5 Y Y M 56 22 6.5 1230 80 Yesa Coronary artery disease

and upper gastro-intestinal

hemorrhage

14

Undetermined

UN1 Y Y M 48 24 6.7 1330 10-30 Occasional smoker Ischemic heart disease

a Diagnosed as long-term heavy smoker.

b These subjects had detectable blood levels of drugs known to induce P450 expression and/or activity in humans or rodents (ANS1, doxylamine;

ANS4, carbamazepine; (Pelkonen et al., 2008, Bookstaff et al., 1996)), but neither of these subjects showed increased transcript expression for any

P450. The possibility that some subjects had been treated with other, undetected, P450 inducers prior to death cannot be excluded.

15

Supplementary table 2. Selected CYP1-2 genes with amplicon sizes and locations (from (Girault et al., 2005)).

Gene GenBank Accession Number Chromosomal Location Amplicon size (bp) Amplicon Location

CYP1A1 NM_000499 15q24.1 96 Exons 2–3

CYP1A2 NM_000761 15q24.1 132 Exons 2–3

CYP1B1 NM_000104 2p22 102 Exons 2–3

CYP2A6 NM_000762 19q13.2 78 Exons 3–4

CYP2B6 NM_000767 19q13.2 107 Exons 8–9

CYP2C8 NM_000770 10q24.1 112 Exons 4–5

CYP2C9 NM_000771 10q24.1 63 Exons 2–3

CYP2C18 NM_000772 10q24.1 83 Exons 5–6

CYP2C19 NM_000769 10q24.1 102 Exons 5–6

16

CYP2D6 NM_000106 22q13.1 70 Exons 7–8

CYP2E1 NM_000773 10q24.3qter 115 Exons 6–7

CYP2J2 NM_000775 1p31.3-p31.2 115 Exons 4–5

CYP2S1 NM_030622 19q13.2 70 Exons 6–7

CYP2U1 NM_183075 4q25 109 Exons 4–5

CYP2W1 NM_017781 7p22.3 79 Exons 2–3

CYP3A4 NM_017460 7q21.1 88 Exons 11–12

CYP3A5 NM_000777 7q21.1 120 Exons 3–5

17

Supplementary table 3. Sequences of primers used for CYP46, reductases, ABC transporters, transcription factors and RGs. F: forward; R: reverse.

Gene Amplicon size

(bp)

GenBank Accession Number Primer Sequence (5'-3')

CYP46A1 80 NM_006668 F CGAGTCCTGAGTCGGTTAAGAAGTT

R AGTCTGGAGCGCACGGTACAT

CPR 96 NM_000941 F AGACCCCACCGACAATG

R TTCCCAAGACCAAACACC

Adx 202 NM_004109 F CTTGTTCAACCTGTCACCTC

R GCTGATGCCAGACAATCCAT

AdR 195 NM_024417/

NM_001258012

F CCTGAGCACCTGGAGAGAAC

R TGGATTTCTTGGGTCTCCAG

18

AdR1 121 NM_024417/

NM_004110/

NM_001258012

F TTCTCCACACAGGAGAAGAC

R GGACATCTACGAGAAACAGC

ABCA1 75 NM_005502.3 F CTTCTCCGGAAGGCTTGTCA

R GGCTGGTCATTAACTGTTTTCCA

ABCB1 81 NM_000927 F CACCCGACTTACAGATGATG

R TTCTTTCAGTCAATGGCAAC

ABCG2 122 NM_004827 F TGACGGTGAGAGAAAACTTAC

R AGGTCTGGATAAAGTGGCA

AhR 132 NM_001621 F AGAGGCTCAGGTTATCAGTTT

R CCCTTGGAAATTCATTGCCAGA

19

CAR 122 NM_005122 F GGGGTTCCAGGTAGAGTTT

R GTTTTGTGTTTGGAGACAGAAAGTGGT

PXR 115 NM_003889 F CGCTTCCTGAGTCTTTTCA

R GCTGGGTGTGCTGAGCATTGAT

PPAR 70 NM_005036 F GGCTCGGTCATCACGGACA

R CGGGGACCACAGGATAAGTCA

ACTB 126 NM_001101 F CTCACCGAGCGCGGCTACAG

R

GGAGCTGGAAGCAGCCGTGG

ALG9 84 NM_001077690 F GTTCTCAGCACTGGCATGTTT

R AGTCATGGCTATCAACGTAGTGT

20

GAPDH 226 NM_002046 F GAAGGTGAAGGTCGGAGT

R GAAATCCCATCACCATCTTC

PGK1 62 NM_000291 F CTGTGGCTTCTGGCATACC

R TGGTCCTGAAAGCAGCAAG

RPL13A 133 NM_012423 F CATGAGGCTACGGAAACAGGC

R TCATGCGTTGCCTGCCCTT

21

Supplementary table 4. Amplification efficiency for all primer sets designed for this study. For ACTB and ALG9 primers the efficiency values

represent the average of measurements in AMG and PFC.

Transcript Slope R2 Efficiency (%)

ACTB -3.30 0.9995 101

ALG9 -3.34 0.9996 99

GAPDH -3.47 0.9987 94

PGK1 -3.46 0.9924 95

RPL13A -3.43 0.9993 96

Adx -3.82 0.9860

83

22

Supplementary table 5. Ranking of candidate RGs by geNorm and NormFinder.

geNorm NormFinder

Ranking M values

Between

groups Variability

Between

areas Variability

1 ALG9 0.27 ALG9 0.0435 ALG9 0.1038

2 GAPDH 0.28 GAPDH 0.0563 ACTB 0.1074

3 ACTB 0.29 ACTB 0.0566 GAPDH 0.1372

4 PGK1 0.35 PGK1 0.1313 PGK1 0.2976

5 RPL13A 0.53 RPL13A 0.2198 RPL13A 0.4105

Best

combination

ALG9 +

GAPDH

23

Supplementary table 6. Ranking of candidate RGs by the Cq method. GeoMean, geometric mean

of ALG9 and GAPDH Cq values; CV, coefficient of variation.

Ranking

Cases vs NANS

Between areas

CV

AMG PFC AMG PFC

1 RPL13A RPL13A RPL13A RPL13A RPL13A

2 ACTB ACTB ACTB ACTB ACTB

3 GAPDH GAPDH GAPDH GAPDH ALG9

4 GeoMean PGK1 GeoMean GeoMean GeoMean

5 ALG9 GeoMean ALG9 ALG9 GAPDH

6 PGK1 ALG9 PGK1 PGK1 PGK1

24

Supplementary figures

Supplementary figure 1. Average RNA quality in brain areas and case groups. RNA integrity

was assayed on an Experion automated electrophoresis system, and RNA quality indicator (RQI)

values were determined by the Experion 3.20 software. No samples had a RQI value below 3 and

all but two had an RQI above 5 (one sample being in each of the 3-4 and 4-5 bands). A. RQI values

were compared between amygdala (AMG) and prefrontal cortex (PFC) samples from the same

individuals (n = 13) with a paired, two-tailed, Student’s t-test. Significantly lower values were

found in AMG as compared to PFC (p = 0.0019). B. RQI values were pooled for all smokers and

alcoholic cases and compared to non-alcoholic non-smokers (NANS) with an unpaired, two-tailed,

Student’s t-test, separately in each brain area. No statistically significant differences were observed.

Each bar represents the average RQI value and the error bars represent standard deviations.

25

Supplementary figure 2. Correlation of target transcript expression with age. Correlation of

transcript -Cq values with age was examined with a two-tailed Pearson’s correlation, separately for

each brain area. Correlation was statistically significant only in AMG and only for a few transcripts:

A. CYP2U1; B. PPAR C. CPR; and D. ABCA1. However a trend towards a positive correlation

with age was found for CYP2D6 in PFC (E).

26

Supplementary figure 3. CYP1A1 genotyping. cDNA samples from one brain area of each subject

(n = 22) were analyzed for the presence of the CYP1A1 variant A1384G, which is a coding

mutation leading to I462V. A. An initial PCR screening with primers specific to all CYP1A1

genetic variants detected a band of the expected size in each sample. Amplification of a 500 bp

sequence from lambda phage DNA was used as positive control. Dark spots correspond to the

migration of the loading dyes, while fast-migrating paler spots represent primer dimers. The size of

the bands in the marker and the main products is indicated. B and C. A second, allele-specific PCR,

with a forward primer terminating on the polymorphic site, revealed a band in two samples from the

non-alcoholic non-smoker (NANS) group, namely NANS3 and NANS5. D and E. Products from

the first PCR screening of NANS3, NANS5, AS2 and AS4 were purified and subjected to

automated sequencing, using the Big Dye Terminator method at the Australian Genome Research

Facility (Brisbane node, University of Queensland). Sequences were analyzed with FinchTV, 1.4.0

(Geospiza Inc.). The two AS samples showed a sequence compatible with homozygote wild-type

CYP1A1 (D) whereas the two NANS were heterozygous for the polymorphic variant (E). The allele

frequency for the variant A1384G (I462V) was calculated to be 4.5 % (n = 22 individuals).

27

28

29

Supplementary figure 4. Expression levels of candidate reference transcripts across all

samples. Values are shown in Cq values, which are inversely related to expression levels. The mean

Cq value for each transcript is indicated by a horizontal line. GAPDH and ACTB showed the lowest

average Cq values, i.e. were the most abundant transcripts, followed by RPL13A, PGK1 and ALG9.

Each circle represents the average of the technical replicates for each sample after exclusion of

outliers. Geomean denotes the geometric mean of ALG9 and GAPDH.

30

Supplementary figure 5. Relative expression values of reference transcripts between case and

control groups. Fold changes (2-Cq

values) were calculated for all combined alcoholic and smoker

cases over the non-alcoholic non-smokers (NANS), separately for each brain area. The average

NANS Cq value was subtracted from the Cq value for each sample, and the resultant Cq data was

converted to 2-Cq

(n = 12 and n = 4 in amygdala (AMG) for cases and NANS respectively; n = 14

and n = 5 in prefrontal cortex (PFC)).Cq values were compared between cases and NANS with an

unpaired, two-tailed, Student’s t-test. Each bar represents the average fold changes and the error

bars represent standard deviations. RPL13A showed the lowest fold changes, but no statistically

significant differences were observed for any transcript. A. ACTB. B. ALG9. C. GAPDH. D.

PGK1. E. RPL13A. F. Geometric mean of ALG9 and GAPDH (GeoMean).

31

32

Supplementary figure 6. Relative expression values of reference transcripts between brain

areas. Fold changes in expression between brain areas were calculated by subtracting each

prefrontal cortex (PFC) Cq value from those of each amygdala (AMG) sample from the same

individual (n = 13 for each brain area), and transforming this Cq value to 2-Cq

. The 2-Cq

data from

the two brain areas of the same individual were compared with a paired, two-tailed, Student’s t-test.

Each bar represents the average fold changes and the error bars represent standard deviations.

RPL13A showed the lowest fold changes, and no statistically significant differences. All other

transcripts had a significantly lower expression in AMG than in PFC (*** P < 0.001).

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Supplementary Figure 7. Inter-individual variability of reference transcripts and correlation

to RNA quality. A. Inter-individual variability of reference transcripts. Fold changes over mean

expression across all samples were calculated for each reference transcript. For each transcript, the

average Cq value was subtracted from the Cq value of each sample, and the resulting Cq values

were converted to 2-Cq

. Each bar represents the average fold changes and the error bars represent

standard deviations. RPL13A showed the lowest inter-individual variability. B. Correlation of

reference transcript expression to RNA quality. Correlation between reference transcript expression

(-Cq values) and RNA quality (RQI values) across all samples was examined with a two-tailed

Pearson’s correlation. RQI values showed a strong correlation with reference transcript expression.

R coefficients were: ACTB, 0.896; ALG9, 0.912; GAPDH, 0.889; PGK1, 0.916; RPL13A, 0.772;

Geometric mean of ALG9 and GAPDH (GeoMean): 0.904. All p values were lower than 0.0001.

The slope of the correlation plot was consistent for all reference transcripts, and was between 0.9

and 1.1, except RPL13A which showed a slope of 0.5.