The effect of interaction between EtOH dosage and exposure time on gene expression in DPSC

Accepted Manuscript The effect of interaction between EtOH dosage and exposure time on gene expression in DPSC

Jianqiang Li, Zhirui Wang, Weiliang Qiu, Ji-Jiang Yang, Qing Wang, Shi Chen, Hui Pan PII: DOI: Reference:

S0888-7543(18)30153-8 doi:10.1016/j.ygeno.2018.03.009 YGENO 8999

To appear in:

Genomics

Received date: Revised date: Accepted date:

22 October 2017 11 January 2018 7 March 2018

Please cite this article as: Jianqiang Li, Zhirui Wang, Weiliang Qiu, Ji-Jiang Yang, Qing Wang, Shi Chen, Hui Pan , The effect of interaction between EtOH dosage and exposure time on gene expression in DPSC. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Ygeno(2017), doi:10.1016/ j.ygeno.2018.03.009

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ACCEPTED MANUSCRIPT

The Effect of Interaction between EtOH Dosage and Exposure Time on Gene Expression in DPSC

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Jianqiang Lia, Zhirui Wanga* , Weiliang Qiub, Ji-Jiang Yangc** , Qing Wangc, Shi Chend, Hui Pand

School of Software Engineering, Beijing University of Technology, Beijing, China

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Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical

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a

c

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School, Boston, USA

Tsinghua National Laboratory for Information Science and Technology, Tsinghua University,

Department of Endocrinology, Peking Union Medical College Hospital, Beijing, China

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d

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Beijing, China

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Co-first author Corresponding author E-mail: [email protected] (J.-J. Yang) **

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Abstract Alcohol (EtOH) dosage and exposure time can affect gene expression. However, whether there exists synergistic effect is unknown. Here, we analyzed the hDPSC gene microarray dataset

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GSE57255 downloaded from Gene Expression Omnibus and found that the interaction between EtOH dosage and exposure time on gene expression are statistically signific ant for two probes:

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201917_s_at near gene SLC25A36 and 217649_at near gene ZFAND5. GeneMania showed that

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SLC25A36 and ZFAND5 were related to 20 genes, three of which had alcohol-related functions. WebGestalt revealed that the 22 genes were enriched in 10 KEGG pathways, four of which are

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related to alcoholic diseases. We explored the possible nonlinear interaction effect and got 172 gene probes with significant p-values. However, no significantly enriched pathways based on the 172

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probes were detected. Our analyses indicated a possible molecular mechanism that could help explain why alcohol consumption has both deleterious and beneficial effects on human health.

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Keywords: DPSC; Alcohol consumption; Two-way factorial design; Synergistic effect; KEGG; FDR

ACCEPTED MANUSCRIPT 1. Introduction The effect of alcohol on our health depends on how much and how often we drink. The National Health Interview Survey (NHIS) of the United States showed that moderate drinkers were more likely than nondrinkers or heavy drinkers to be at a healthy weight [1]. Compared with nondrinkers, the incidences of bile stones and type 2 diabetes are lower in moderate drinkers.

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Moreover, short-term moderate alcohol consumption can lower cholesterol level, improve intellectual

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performance, and protect healthy adults from developing coronary heart disease [2; 3]. In contrast,

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drinking too much can damage human health. For instance, drinking too much on a single occasion or over a long time can damage the heart and cause problems including cardiomyopathy, arrhythmias,

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and high blood pressure. Heavy drinking can also damage the liver, leading to a variety of liver problems, such as steatosis, alcoholic hepatitis, and cirrhosis [4]. Alcohol could also interfere with the

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brain’s communication pathways, hence, with the way how the brain works [5; 6]. Ethanol (EtOH) is one important ingredient in alcoholic drinks. EtOH has a dose-dependent

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relationship to many disease conditions happened on human organs, including alcoholic liver cirrhosis

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as well as alcohol disrupts neurogenesis in the ad ult brain. Maurel et al.(2012) showed that bone health was related to both the dosage and exposure duration of EtOH[7]. It is well known that EtOH has deleterious effects on numerous different cellular functions,

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such as cell proliferation and differentiation. For example, alcoholic cardiomyopathy is due to the toxicity of ethanol and alcoholic metabolites, such as acetaldehyde, in the myocardial cell [8]. To study the molecular effects of EtOH, researchers have used human stem cells as research models [9; 10; 11]. Human stem cells can undergo self-renewal and multi-directional differentiation as well as have a strong cloning ability[12]. The fibroblasts isolated from the pulp tissue are called dental pulp stem cells (DPSCs). DPSCs can be easily separated from adult teeth and can be kept for an extended period, which is superior to other types of stem cells[13; 14]. Khalid et al.(2014) showed that the pluripotency (differentiation potential) of human embryonic stem cells (hESCs) was significantly reduced after

ACCEPTED MANUSCRIPT 24-hour low dose (20 mM) exposure to EtOH treatment[9]. Although both the dosage and exposure time of EtOH affect gene expression, it is largely unknown yet whether there exists synergistic effect between EtOH dosage and exposure time on gene expression. Khalid et al.(2014)[10] mentioned that they evaluated the effect of interaction between EtOH dosage and exposure time on gene expression using data from a DSPC experiment and found

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significant results. However, no details (e.g., the number, names, and functions of gene probes that

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were significant in testing for the interaction effect) were given. In this research, we hypothesize that

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there exist synergistic effect between alcohol dosage and exposure duration on gene expression. To test this hypothesis, we used the gene microarray data from Khalid et al.(2014)[10]. However, we used

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an approach different from that of Khalid et al.(2014) [10] to preprocess data. In addition to testing for the linear interaction effect between EtOH dosage and exposure time on gene expression as Khalid et

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al.(2014) [10] did, we also tested for the non-linear interaction effect between EtOH dosage and

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exposure time.

2. Methods

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2.1 Khalid et al.’s Experimental Design Khalid et al.(2014) [10] conducted a DPSC experiment by using a two-way factorial design[10; The two factors in the design are dosage with six levels (0, 1, 5, 10, 20, and 50 mM) and EtOH

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exposure time with two levels (24 hours and 48 hours). Each combination of dosage and exposure time has two replicates (Table 1). According to the descriptions in Khalid et al.(2014)[10], DPSCs at early passage (P1–P2) that were isolated from adult molar teeth were cultured in α-MEM supplekmented with 10% fetal bovine serum (v/v), 2 mML-glutamine, 100 μML-ascorbate-2-phosphate, 50 units/ml penicillin and 50μg/ml streptomycin. Exponentially growing DPSCs were treated with 6 different concentrations (0, 1, 5, 10, 20, and 50 mM) of EtOH diluted from absolute EtOH (FW = 21.7 M) for 24 hours or 48 hours. Details about the experiment can be found in Khalid et al.(2014) [10] and Hoang et

ACCEPTED MANUSCRIPT al.(2016)[11]. Table 1. The numbe r of DPSC samples used in each combination of dosage and exposure time in Khalid et al.(2014)[10]’s experiment.

the number of samples exposure time

0ml

1ml

5ml

10ml

20ml

50ml

total

24h 48h total

2 2 4

2 2 4

2 2 4

2 2 4

2 2 4

2 2 4

12 12 24

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dosage

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This table lists the number of samples used in each combination of EtOH dosage and exposure time to

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illustrate the 2-way factorial design conducted by Khalid et al.(2014)[10] : rows are exposure time, columns are dosage, and matrix cells are numbers of samples.

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2.2 Gene Expression Data

The gene expression data from Khalid et al.(2014)[10]’s experiment can be downloaded from

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Gene Expression Omnibus[15; 16] using the GEO Accession ID: GSE57255. It provides gene expression data for 24 DPSC samples treated with ethanol (EtOH). Affymetrix HG-U133A 2.0

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platform was used. 54,675 gene probes were measured for each sample. The GSE57255 dataset includes three matrices: (1) gene expression data matrix (rows are gene probes, and columns are

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arrays), (2) feature data matrix (rows are gene probes, and columns are feature variables describing probes), and (3) phenotype data matrix (rows are arrays, and columns are phenotype variables describing arrays). The feature variable ‘Sequence Type’ indicates that there are three types of probes: 'Consensus sequence' probes, 'Exemplar sequence' probes, and 'Control sequence' probes. A Consensus sequence is a nucleotide sequence that is assembled by Affymetrix and is based on one or more

sequences

from

a

public

database

(http://www.ebi.ac.uk/arrayexpress/arrays/A-GEOD-13158/?ref=E-MTAB-2024). An Exemplar is a single nucleotide sequence taken directly from a public database.

ACCEPTED MANUSCRIPT 2.3 Data Quality Check We first excluded 10,541 probes with empty annotations for gene symbols or Entrez ids. We then excluded 14,736 ‘Exemplar sequence’ probes and 10 ‘Control sequence’ probes. And 29,388 ‘Consensus sequence’ probes were kept, which were more likely from the genome. The plot of

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quantiles of expression levels across arrays (S1 Fig) shows that the empirical distributions of gene expression levels for arrays are highly skewed. To make the empirical distributions closer to normal

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distributions that are required in the downstream statistical analyses, we performed the log2

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transformation to gene expression levels. The plot of quantiles of log2 transformed expression levels across arrays (S2 Fig) shows that the empirical distributions are roughly symmetric to the medians,

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indicating that the normality assumption would be reasonable. The horizontal trajectories of quantiles

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across arrays (S2 Fig) show that there are no apparent outlying arrays and probes, or technical batch effects. We then drew scatter plots of the first two principal components (PCA plots) (S3 and S4 Figs), which show two obvious clouds of samples corresponding to the two exposure times. To reduce the

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effect of technical noises, we next performed quantile normalization, after which PCA plot s (S5 and

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S6 Figs) again show no obvious patterns for dosage and the apparent effect of EtOH exposure time. This observation is consistent with Khalid et al. (2014)[10] ’s observations. By using two-way ANOVA, we also formally tested the effects of dosage and exposure time on the first principal component,

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which accounted for around 6% total variation of gene expression. The p-values for the main effects and interaction effects are 0.0748 (dosage), 6.73e-09 (exposure time) and 0.0716 (interaction effects) (S1 Table), which is consistent with what we observed from the PCA plot. The R Bioconductor packages iCheck(version:1.4.0) and lumi(version:1.1.0) were used to check data quality and do the data transformation and normalization[17][18].

2.4 Statistical Analysis

ACCEPTED MANUSCRIPT We regarded dosage as a continuous variable taking six values (0, 1, 5, 10, 20, or 50 mM) and used a dummy variable time to indicate the two EtOH exposure times (1 indicates 48 hours, and 0 indicates 24 hours). For each gene probe, we assumed a general linear regression model

yi   0   1dosagei   2timei   3dosagei * timei   i ,

(1)

where yi is the preprocessed expression level of the probe for the i-th sample, dosagei and timei are the

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dosage and exposure time for the i-th sample, respectively, and εi is the random error term, i=1,.., 24.

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We are interested in testing if the effect β3 of the interaction between dosage and time is significantly

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different from zero. To control for multiple testing, we adjusted p-values so that false discovery rate (FDR) < 0.05. In other words, if a probe has FDR-adjusted p-value < 0.05 for testing if β3 =0, we then

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claim that it has a significant interaction effect. The R Bioconductor package iCheck (version:1.4.0) was used.

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We also explored the possible nonlinear effect of dosage by regarding dosage as a categorical

dosage and exposure time.

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variable and applied two-way ANOVA for each gene probe to test for the effect of interaction between

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2.5 Biological Pathway Analysis

We used GeneMania and WebGestalt to infer the biological pathways that are related to the

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genes corresponding to the probes with a significant interaction effect. Specifically, to predict the functions of the genes that correspond to the probes having significant interaction effects on gene expression, we used the web tool GeneMANIA[19; 20; 21], which searches an extensive set of functional association data to find related genes. To detect pathways which these genes were enriched in,

we

used

the web

tool: WEB-based

GEne SeT

AnaLysis

Toolkit

(WebGestalt)

(http://www.webgestalt.org/option.php#), which is designed for functional genomic, proteomic and large-scale genetic studies.

3. Results

ACCEPTED MANUSCRIPT Among the 29,388 probes, we identified 2 probes with FDR-adjusted p-value < 0.05 for testing the effect of interaction between EtOH dosage and exposure time by using the general linear regression model (1). The test information about the 2 probes is shown in Table 2. The most significant probe was 201917_s_at, which was near gene SLC25A36 and had FDR-adjusted p-value = 0.023. The other significant probe is 217649_at, which is near gene ZFAND5 and had FDR-adjusted

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p-value = 0.026.

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Table 2. Two probes with a significant effect of interaction between dosage and exposure time. gene symbol

test statistic

p-value

201917_s_at

SLC25A36

-6.90

7.71e-07

0.023

217649_at

ZFAND5

-6.53

1.75e-06

0.026

FDR-adjusted p-value

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probeIDs

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Fig 1 and Fig 2 showed the scatter plot of preprocessed expression level versus EtOH dosa ge for the two significant probes (201917_s_at near SLC25A36 and 217649_at near ZFAND5),

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respectively, in which the black circles represent cells exposed to alcohol for 24 hours and the red triangles represent cells exposed to alcohol for 48 hours. For eac h probe, we used the loess[22] (locally

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weighted smoothing) algorithm to fit the trend that the log2 expression level changes as alcohol dose increases for the two exposure times (24h or 48h), respectively. The scatter plots show that for both

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probes, the log2 expression level in the DPSC cells exposed to EtOH for 48 hours apparently decreases with the increase of alcohol dosage, while the log2 expression level in the DPSC cells exposed to EtOH for 24 hours almost does not change as alcohol dosage increases.

Fig 1. Scatter plot of the log2 expression level of probe 201917_s_at (SLC25A36) versus EtOH dosage.

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Black circles and red triangles are samples exposed to EtOH for 24-hour and 48-hour, respectively. Black solid line and red dashed line are fitted curves for 24-hour and 48-hour obtained by using loess, respectively.

Fig 2. Scatter plot of the log2 expression level of probe 217649_s_at (ZFAND5) versus EtOH dosage.

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Black circles and red triangles are samples exposed to EtOH for 24-hour and 48-hour, respectively. Black solid line and red dashed line are fitted curves for 24-hour and 48-hour obtained by using loess, respectively.

To evaluate possible non-linear interaction effect, we performed the two-way ANOVA by regarding dosage as a categorical variable and got 172 significant probes corresponding to 158 genes (S2 Table). The top two probes were the probe 214803_at near gene CDH6 and the probe 227404_s_at near gene EGR1. CDH6, which involves in inflammation and immunity, is associated

ACCEPTED MANUSCRIPT with concentrations of liver enzymes in plasma[23]. It has been shown that high concentrations of liver enzymes in plasma are observed in liver injury caused by multiple insults including alcohol misuse[23]. Cubero et al.(2012)[24] showed that, ethanol can promote the nuclear translocation of early growth response-1 (Egr1) induced by Arachidonic acid. The nuclear translocation of early growth response-1 (Egr1) can promote Kupffer cell activation, promote TNFα production and lead to

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alcoholic liver disease[24]. Cubero et al.(2012)[24] also showed that there was an Egr1 inhibitor that

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could inhibit alcoholic liver disease. The trajectories of the expression levels across doses for the top

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2 probes show obvious nonlinear trends (Fig. S7). The two-way ANOVA can detect not only non-linear interaction effect but also linear interaction effect. The probe 201917_s_at near gene

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SLC25A36, which was detected by the general linear model, was among the 172 significant probes (FDR-adjusted p-value=0.0389). However, the probe 217649_s_at near gene ZFAND5 was not

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among the 172 probes (FDR-adjusted p-value=0.0547), although its raw p-value, 3.37e-4, was still pretty small.

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Based on NCBI, the gene SLC25A36 is one of the mitochondrial transporters for pyrimidine (deoxy) nucleotides, which is essential for mitochondrial DNA and RNA metabolism. The protein

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expressed by SLC25A36 is a mitochondrial transporter that imports/exports pyrimidine nucleotides into and from mitochondria. This protein participates in mitochondrial genome maintenance,

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regulation of mitochondrial membrane potential and mitochondrial respiration. Mitochondria are essential organelles in cells, and their functions are related to the production and defense of reactive oxygen species (ROS).

We did not find any existing literature relating SLC25A36 to alcohol consumption by using PubMed search and Google search. However, ethanol promotes oxidative stress by increasing ROS formation,which is significant in the liver. This effect is consistent with the fact that the liver is the main part of the body's ethanol metabolism. Ethanol- induced mitochondrial dysfunction is associated with the occurrence or development of alcohol- induced liver disease [25]. Increased ROS in chronic

ACCEPTED MANUSCRIPT alcoholic myopathy causes the damage of mitochondria. The damage of membrane proteins makes the flow of mitochondrial membrane of skeletal muscle decreased in chronic alcoholism myopathy (CAM) [26]. In addition, mitochondria of hepatocyte are the preferred targets for alcohol challenge. Harmful effects involve mechanisms of mitochondrial morphological changes, oxidative stress, osmotic transport, protein changes, protein acetylation, DNA damage, and lipid peroxidation. When

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the liver cells are exposed to ethanol for a long time, the mitochondrial respiratory efficiency is

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reduced, the synthesis of ATP is reduced, the hypoxia of the cells is increased, and the apoptotic

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pathway is initiated, leading to the occurrence of alcoholic liver disease (ALD)[27]. Based on NCBI, the gene ZFAND5 encodes the protein Zfand5, which is a 23-kDa cytosolic

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protein with one A20 zinc finger domain and one AN1-type zinc finger domain. Based on GeneCards (http://www.genecards.org/), the gene ZFAND5 (1) involves in protein degradation via the

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ubiquitin-proteasome system, (2) may act by anchoring ubiquitinated proteins to the proteasome, (3) plays a role in ubiquitin- mediated protein degradation during muscle atrophy, and (4) is a potent

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inhibitory factor for osteoclast differentiation.

We did not find any existing literature relating ZFAND5 to alcohol consumption by using

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PubMed search and Google search. However, huma n Zfand5 wasidentified from the Morton fetal cochlea library as a novel cochlear-expressed protein called ZNF216. ZNF216 is expressed mostly in

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skeletal muscles and is highly expressed in the brain[28]. One research suggested that ZNF216 functioned in protein degradation via the UPS and played a crucial role in muscle atrophy[29]. There is a keen interest in the role of ZNF216 in the pathogenesis of neurodegenerative diseases, although whether ZNF216 is involved in aggresome formation is unclear. Also, the level of anti-ZNF216 antibody in serum of patients with hepatocellular carcinoma (an alcohol- related disease) is higher than that in normal individuals. The expression of ZNF216 mRNA in hepatocellular carcinoma is significantly higher than that in adjacent tissues. Overexpression of ZNF216 in serum and cancer

ACCEPTED MANUSCRIPT tissues of hepatocellular carcinoma may be involved in the occurrence of hepatocellular carcinoma, but further research is needed[30]. GeneMANIA (http://genemania.org/) analysis showed that there are 20 genes relating to the two genes (SLC25A36 and ZFAND5) based on existing literature (Fig 3 and S3 Table). We found that 3 (FOXO4, OPR1, and TRAF6) of the 22 genes in Figure 3 have alcohol- related functions. For gene

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FOXO4, NCBI showed that tobacco-specific nitrosamine (NNK) modulated expression of neuroglial

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genes. Moreover, ethanol, NNK, and ethanol + NNK increased the expression of FOXO4. This effect

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has a compensatory response to brain insulin resistance and may be a co- factor in alcohol-related brain disease (ARBD)[31].

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Fig 3. GeneMania networks of gene SLC25A36 and gene ZFAND5.

ACCEPTED MANUSCRIPT NCBI showed that alcohol affected the expression of gene OPR1 and thus affected the function of mitochondria[32]. The resulting mitochondrial dysfunction may lead to the occurrence or development of alcohol-induced liver disease such as ALD[27]. For gene TRAF6, NCBI showed that alcoholism induced an inflammatory process. This process may produce proinflammatory cytokines by the stimulation of TLR4-dependent signaling

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pathway[33]. NCBI also indicated that dioscin attenuated inflammation [34], hence decreased TLR4

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overexpression and led to the markedly reduced levels of TRAF6. Also, xuebijing (XBJ), an

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alcohol-related traditional Chinese medicine, blocked the activity of TRAF6. XBJ can significantly reduce the permeability of lung induced by cecal ligation and puncture (CLP). So the expression of

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TRAF6 gene affected by alcohol may be related to lung permeability [35]. Sixteen of the 22 genes obtained by GeneMania are among the 29,388 gene probes in the

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QC-cleaned GSE57255 dataset. Five of the 16 genes correspond to 8 probes had raw p-values < 0.05 for testing the interaction effect in the analysis of the general linear regression (S4 Table). We used

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the general linear regression model to obtain the p-values of main effects (dosage and exposure time) for the 16 genes and found that fourteen of the 16 genes are nominally associated with dosage (8

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genes), time (14 genes), or interaction (8 genes). WebGestalt analysis (http://www.webgestalt.org/option.php) showed that the 22 genes were

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enriched in 10 KEGG pathways (Table 3), four (NF-kappa B signaling pathway, Toll- like receptor signaling pathway, hepatitis C, and small cell lung cancer) of which relate to alcoholic diseases. For NF-kappa B signaling pathway, NCBI showed that Platycodin D (PD) effectively protected against alcohol- induced fatty liver (AFL). And this anti- inflammatory and anti-endotoxic process mainly via TLR4-MyD88-NF-κB signal path based on Wu et al.(2016)[36]’s experiment. NCBI also showed that alcohol- induced suppressed inflammatory response was linked to nuclear factor-kappaB (NF-ĸB). The decrease of nuclear levels of canonical NF-ĸB signaling pathway mediate alcohol’s anti-inflammatory effects[37].

ACCEPTED MANUSCRIPT Toll- like receptors are the sensors of microbial and endogenous danger signals in the innate immune system. Toll- like Receptors are expressed and activated in innate immune cells and lead to inflammatory cascade activation and thereby contribute to alcoholic liver disease (ALD) [38]. Specifically, ethanol can activate Toll- like receptors (TLRs) such as TLR3 and TLR4. The activation of Toll- like receptors leads to kinase activation, transcription factor activation, and the increase of

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transcription of proinflammatory cytokines. The activation of Toll- like receptors increases

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proinflammatory signaling and finally induces inflammatory processes [39; 40; 41]. Ethanol increases

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the up-regulation of the gene expression of TLR2 and TLR4 and triggers TLR4/ TLR2 association. This effect induces the production of inflammatory mediators, which induces the production of

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inflammatory mediators, triggers reactive oxygen species generation and induces neuronal apoptosis. The above process is related to the neuroinflammation and neurodegeneration associated with alcohol

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abuse [38]. Also, Dioscin markedly decreases the level of TRAF6 and significantly attenuates inflammation by decreasing TLR4 overexpression[38]. What's more, Toll- like receptor (TLR)

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signaling is the mechanism of liver stem cell/progenitor transformation to hepatocellular carcinoma (HCC). The upregulation of the TLR signaling pathways leads to liver tumor formation[42]. Finally,

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Toll- like receptors (TLR) play a role in chronic liver diseases such as alcoholic liver disease, hepatitis C, and hepatocellular carcinoma. Synergism between alcohol and hepatitis C virus (HCV) may lead to

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liver tumorigenesis through TLR signaling [43]. The above effect indicates the association between Hepatitis C and alcohol[44]. Hepatoprotection against alcohol- induced inflammation may be mediated by decreased TLR-4 signaling[45].

ACCEPTED MANUSCRIPT Table 3. Enriched KEGG pathways based on the WebGestalt analysis for the list of the 22 genes from the GeneMania analysis. Name Epstein-Barr virus infection - Homo sapiens (human)

hsa04064

NF-kappa B signaling pathway - Homo sapiens 4 (human)

1.5e-03

hsa04621

9.91e-03

hsa04668

NOD-like receptor signaling pathway - Homo sapiens 4 (human) RIG-I-like receptor signaling pathway - Homo sapiens 3 (human) Toll-like receptor signaling pathway - Homo sapiens 3 (human) TNF signaling pathway - Homo sapiens (human) 3

hsa05160

Hepatitis C - Homo sapiens (human)

3

4.32e-02

hsa04623

Cytosolic DNA-sensing pathway - Homo sapiens 2 (human) Small cell lung cancer - Homo sapiens (human) 2

1.64e-01

hsa05222

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hsa04620

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hsa04622

#Gene 5

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ID hsa05169

Chagas disease (American trypanosomiasis) - Homo sapiens (human) Pathways related to alcoholic diseases are highlighted.

2

1.14e-02 2.9e-02 2.9e-02

2.59e-01 3.36e-01

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hsa05142

FDR 1.5e-03

For hepatitis C pathway, it has been shown that alcohol use disorder (AUD) and hepatitis C

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virus (HCV) infection usually occur at the same time and their association increases morbidity and mortality[46]. The rehabilitation and abstinence of alcohol can reduce the risk of liver-related

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complications such as reducing the burden of chronic HCV infection[47]. About small cell lung cancer pathway, it has been shown that alcohol consumption is associated with p53 mutations in non-small cell lung cancer [48]. And alcohol may enhance the mutagenic effects of cigarette smoke in the lung. We also performed pathway enrichment analysis based on the genes corresponding to the 172 probes showing significant interaction effect in two-way ANOVA. No significantly enriched pathways were found.

ACCEPTED MANUSCRIPT 4. Discussion Whether there exists synergistic effect of dosage and duration of alcohol consumption on gene expression is largely unknown. In this study, we identified two gene probes (201917_s_at near gene SLC25A36 and 217649_at near gene ZFAND5) that show different linear expression trends across alcohol dosages between two alcohol exposure times based on the data from a DPSC cell experiment

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conducted by Khalid et al.(2014)[1]. To the best of our knowledge, no existing literature has reported

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yet that the genes SLC25A36 and ZFAND5 are directly related to alcohol consumption. GeneMANIA

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analysis and WebGestalt analysis showed that SLC25A36 and ZFAND5 were related to three alcohol-related genes (FoxO4, OPA1, and TRAF6) and four alcohol- related KEGG pathways

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(NF-kappa B signaling pathway, Toll- like receptor signaling pathway, hepatitis C, small cell lung cancer ).

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The log2 expression levels of both probes (201917_s_at and 217649_at) decreased with the increase of EtOH dosage when DPSCs were exposed to EtOH for 48 hours, while the log2 expression

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levels were almost not changed when the exposure time is 24 hours no matter what dosage was. This

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observation indicates that high EtOH dosage might inhibit the expression of the two genes only when exposed to EtOH for a long time. For short-time EtOH exposure, these two genes could be resistant to the damages caused by EtOH. This observation might help understand the molecular mechanisms of

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the phenomenon that only high-dosage and long-exposure-time of alcohol consumption cause damage to human health. This interaction effect also indicates that we might develop a gene therapy in future to fix the alcohol-caused damage by preventing the decreasing of the expression levels of these two genes. We examined the boxplots and the scatter plot of the log2 expression levels of the two probes 201917_s_at and 217649_at, irrespective of dosage and exposure time (S8 Fig. and S9 Fig.). The box plots show that the distributions of the two gene probes are both close to normal distributions and have similar variations. Also, the mean level of the probe 201917_s_at is much higher than that of

ACCEPTED MANUSCRIPT 217649_at. The scatter plot shows that the expression levels of the two probes are highly correlated, indicating that the two corresponding genes (SLC25A36 and ZFAND5) might be co-expressed. Note that SLC25A36 and ZFAND5 were not in the co-expression network in Fig 3 produced by GeneMania. This indicates that the co-expression of SLC25A36 and ZFAND5 would be novel. Further investigation of the co-expression of SLC25A36 and ZFAND5 would be warranted.

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The expression of gene SLC25A36 has a significant effect on the operation of mitochondria

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that are essential organelles in cells. Mitochondrial dysfunction can lead to various diseases, some of

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which are associated with alcohol, such as alcoholic liver disease, chronic alcoholism myopathy, and dominant optic atrophy[25, 26]. That is, there is an indirect relationship between SLC25A36 and

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alcohol-related diseases. The gene ZFAND5 encodes one type of protein named ZNF216 that is expressed highly in skeletal muscles and brain and plays a crucial role in muscle atrophy[28, 29]. In

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addition, the level of anti-ZNF216 antibody in serum of patients with hepatocellular carcinoma was higher than that in normal individuals[30]. This observation indicates that overexpression of ZNF216

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in serum and cancer tissues of hepatocellular carcinoma may be involved in the occurrence of

disease.

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hepatocellular carcinoma. It is consistent with what we have known that alcohol can lead to liver

We also detected 172 gene probes corresponding to 158 genes by using two-way ANOVA to

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detect potential nonlinear expression trends across alcohol doses between the two alcohol exposure times. Most of the 172 gene probes showed nonlinear expression trends. No significantly enriched pathways were found for these 158 genes. This might be due to small sample size, or due to that the 158 genes might scatter around in many different biological pathways. In addition to small sample size of the study, it is a challenge to interpret nonlinear expression trends. We will investigate these nonlinear expression trends in future research. Researchers have investigated the effect of alcohol consumption on gene expression. For instance, fetal alcohol spectrum disorders (FASDs) are a variety of prenatal and postnatal collectively

ACCEPTED MANUSCRIPT disorders caused by alcohol (ethanol, EtOH) consumption during pregnancy. Khalid et al. (2014) [9] carried out a genome-wide analysis of the maintenance and differentiation of human embryonic stem cells (hESCs) cultured by alcohol in order to better understand the molecular causes of FASDs. They found significant alterations in gene profiles associated with molecular pathways for metabolic processes and oxidative stress. They also found that ethanol induction results in significant

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hypermethylation in many chromosomal regions. They produced a set of differentiation-related genes

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that were dysregulated by EtOH- induced DNA methylation changes, which might play a role in

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EtOH- induced decreases in hESC pluripotency and identified the major sequence of potential binding sites that represent transcription factors.

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Hoang et al.(2016) [11] aimed to discover the molecular mechanisms that control stem cell potency of dental pulp stem cells (DPSCs). They performed an integrative analysis of genome-wide

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gene expression data, DNA methylome data, and data from a pathway-focused RT-PCR array analysis based on DPSCs exposed to EtOH. Their study demonstrated that alcohol- induced inhibition of

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KDM6B played a role in dysregulation of DPSC differentiation. Khalid et al.(2014)[10] described the data set GSE57255 that we used in the present study. They

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reported that they found gene probes that were significant in testing for the effect of interaction between dosage and time on gene expression. However, no details (e.g., the number, names, and

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functions of gene probes that were significant in testing for interaction effect) were given. In add ition, they used different data preprocessing procedure and statistical analysis as we did. They removed probes with expression lower than the overall sample median and kept 27,327 probes for statistical analysis. We used only ‘Consensus sequence’ probes with non-empty annotations of gene symbols and Entrez ids, which are more likely from the genome. There are 29,388 probes used in our statistical analysis. Khalid et al.(2014) [10] also did background correction before log2 transformation and quantile normalization. We did not perform background correction because GSE57255 does not provide information about background expression levels and the data showed good quality based on

ACCEPTED MANUSCRIPT our quality check. While we used the same general linear regression model (including both the main effects of dose and time and the effect of dose by time interaction) to test for the significance of dose by time interaction for each probe as Khalid et al. (2014) [10] did, we used different criteria to determine if a test is significant or not. To control for multiple testing, Khalid et al.(2014) [10] claimed that a test is significant if its Q-value < 0.05, by using the R package “Qvalue”. That is, for a given probe with

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Q-value < 0.05, less than 5% probes are false positive among all probes with smaller p-value than this

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probe[49]. We used FDR-adjusted p-value (using the function ‘p.adjust’ in the R package “stats”) to

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control for multiple testing. That is, among these probes with FDR-adjusted p-values < 0.05, less than 5% probes are false positive [50]. In addition, we applied two-way ANOVA to test potential differential

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nonlinear trends of expression levels across doses between the two exposure times. There are some limitations in the present study. First, the sample size of this study was small

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(only 24 samples from 12 DPSCs), which limits the power for assessing interaction effects (only two arrays at each time point). Second, some potential confounding factors were uncontrolled. For

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example, ethanol is a volatile substance that decreases in cultures. Khalid et al.(2014)[10] did not measure ethanol concentration daily. Hence, we are not sure if the ethanol concentrations are stabilized

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in Khalid et al.’s(2014)[10] experiment. Therefore, we could not be certain that the temporal effects of alcohol dosages were not confounded by alcohol breakdown. Third, DPSCs were likely to be a

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developmental model and perhaps restricted to mesoderm fate. Hence, gene expression patterns might be a little different with the patterns in liver and lung. Fourth, there had no independent data available yet to replicate our results. However, we detected two gene probes with a significant effect of interaction after controlling for multiple testing (FDR-adjusted p-value < 0.05). In addition, functional analysis based on GeneMANIA and WebGestalt showed indirectly relationship between the two genes (SLC25A36 and ZFAND5) and alcohol- related diseases. Although these limitations, Khalid et al.’s (2014) data set was from a rigorous 2 by 2 factorial design, which is statistically efficie nt to evaluate

ACCEPTED MANUSCRIPT the joint effect of two factors. We will examine our results when more data sets are available in future. The two probes (genes) we identified and the information of the alcohol-related functions of the genes that we queried may contribute to revealing the molecular effects and physiological regulation of the regenerative capabilities of stem cells. And these results may contribute to the

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potential mechanisms and treatments of disease caused by heavy alcohol consumption in clinical

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practice. The alcohol- induced effect on the gene expression of stem cells can also serve as a guide for

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stem cell differentiation in organ transplantation.

It would be a future research topic to investigate the mechanisms about how the

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interaction between alcohol dosage and exposure time affects the gene expression of the two probes, and further affects human health (such as, chronic liver diseases and neurodevelopmental processes).

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One possible mechanism connecting environmental exposure to gene expression is epigenetics, which could respond to the environmental stimulation and regulate gene expression without

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changing genetic code (DNA sequence) of human genome [51; 52; 53; 54]. Epigenetics includes DNA methylation, chromatin modification, nucleosome position, and posttranscriptional gene regulation

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by noncoding RNAs [52]. When Shukla et al. (2008) reviewed the literature on the epigenetic effects of ethanol, they found that a large body of data have come from studies in liver and in neuronal

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systems and involve post-translational modifications in histones and methylations in DNA[51]. This is not surprising because (1) alcohol dosage and exposure duration are the critical parameters of exposure that affect both liver[55] and fetal development (e.g., the development of the central nervous system)[56]; and (2) chronic exposure to beverage alcohol (i.e., ethanol) can induce epigenetic changes [57]. Alcohol is the second most common cause of chronic liver disease, which includes chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma (HCC) [55]. Shukla et al. (2008) showed that alcohol has deleterious effects on the liver that are associated with epigenetic changes[51]. Wilson et al. (2017) reviewed the recent findings about the epigenetic control of chronic

ACCEPTED MANUSCRIPT liver disease and concluded that (1) the hepatic epigenome is remarkable in its capacity to adapt to dietary, metabolic, xenobiotic, and mircrobial challenges in order to maintain cellular and functional homeostasis; and (2) when this capacity is breached, epigenetic adaptions can contribute to disease pathogenesis[58]. Marjonen et al. (2015) and Portales-Casamar et al. (2016) observed hyper- and hypo- methylation in fetuses exposed to ethanol and are related to neurodevelopmental process and

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diseases [59; 60]. Mandal et al. (2017) reviewed the association between the gestational a lcohol

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exposure and DNA methylation in the developing fetus. They found that DNA methylation has a

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potential role in the etiology of the neurobiological problems found in children with fetal alco hol spectrum disorder (FASD) [56]. Fetal alcohol syndrome (FAS) is the most severe form of FASD and

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manifests central nervous system (CNS) deficiencies [61; 62].

Finally, we would like to emphasize that experimental validation of our findings is needed in

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future research and that more evidence is needed to generalize the results based on tooth pulp to other

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Acknowledgments

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tissues.

This work is supported by China National Key Technology Research and Development

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Program projects with no. 2015BAH13F01. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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ACCEPTED MANUSCRIPT Supporting information S1 Fig. The plot of quantiles of expression levels across arrays. It showed that the empirical distribution of gene expression levels for arrays are highly skewed. To make the empirical distributions closer to normal distributions that are required in the downstream statistical analysis, we

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need to perform the log2 transformation to gene expression levels. S2 Fig. The plot of quantiles of log2 transforme d expression levels across arrays. We hope the

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trajectories of quantiles are horizontal. If not, it indicates the existence of some outlying probes, arrays,

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or batch effects. This quantile plot shows that no obvious outlying probes, arrays, or batch effects.

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S3 Fig. The scatter plot of the first two principal components based on EtOH dosages. The

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samples are colored based on EtOH doses.

S4 Fig. The scatter plot of the first two principal components based on EtOH exposure times.

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The samples are colored based on EtOH exposure times. S5 Fig. The scatter plot of the first two principal components based on dosages after quantile

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normalization. No obvious patterns for dosage in the PCA plot. S6 Fig. The scatter plot of the first two principal components based on exposure times after

PCA plot.

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quantile normalization. Obvious two clouds of samples correspond to the 2 exposure times in the

S7 Fig. The plots of the trajectories of gene expression across EtOH dosage for the 2 EtOH exposure times for the 172 probes obtained by two-way ANOVA (c.f. the file ‘S7 Fig.pdf’). S8 Fig. The box plots of the log2 expression levels of the two probes (201917_s_at and 217649_at). It shows that the two gene probes have quite different mea n log2 expression levels, but similar variations. Also, both distributions are close to normal distributions.

ACCEPTED MANUSCRIPT S9 Fig. The scatter plot of the two probes (201917_s_at and 217649_at). It shows that the log2 expression levels of two probes are highly correlated. S1 Table. The ANOVA table for testing the effects of dosage and exposure time on the first principal component of gene expression.

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S2 Table. Information about the 172 significant probes obtained by two-way ANOVA. (c.f. the file ‘table s2.xlsx’). ‘stat’, ‘pval’, and ‘p.adj’ are the test statistic, p- value, and FDR-adjusted p-value

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for testing the effect of interaction between EtOH dosage and exposure time on gene expression.

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S3 Table. Information about the 22 genes exported by GeneMania (c.f. the file ‘table s3.xlsx’).

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S4 Table. Information about the five genes among the 22 genes obtained by GeneMania analysis, which contain probes having raw p-values < 0.05 for testing the interaction effect using the general linear regression.

ACCEPTED MANUSCRIPT Highlights 

We investigated the synergistic effect of EtOH dosage and exposure time on gene expression.

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We identified two gene probes (201917_s_at near gene SLC25A36 and 217649_at near gene ZFAND5), the expression levels of which are affected by the interaction between EtOH dosage and exposure time.

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No studies have linked the two genes (SLC25A36 and ZFAND5) with alcohol consumption yet.

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Literature search via GeneMania showed that the two genes are related to 20 genes, three of which had alcohol-related functions. The 22 genes (SLC25A36, ZFAND5 and the 20 related genes) were enriched in 10 KEGG pathways, four of

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which are related to alcoholic diseases.

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