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BMC Genomics

Open Access

Psychoactive pharmaceuticals at environmental concentrations induce in vitro gene expression associated with neurological disorders

BMC Genomics201617(Suppl 3):435

https://doi.org/10.1186/s12864-016-2784-1

Published: 29 June 2016

Abstract

Background

A number of researchers have speculated that neurological disorders are mostly due to the interaction of common susceptibility genes with environmental, epigenetic and stochastic factors. Genetic factors such as mutations, insertions, deletions and copy number variations (CNVs) are responsible for only a small subset of cases, suggesting unknown environmental contaminants play a role in triggering neurological disorders like idiopathic autism. Psychoactive pharmaceuticals have been considered as potential environmental contaminants as they are detected in the drinking water at very low concentrations. Preliminary studies in our laboratory identified gene sets associated with neuronal systems and human neurological disorders that were significantly enriched after treating fish brains with psychoactive pharmaceuticals at environmental concentrations. These gene expression inductions were associated with changes in fish behavior. Here, we tested the hypothesis that similar treatments would alter in vitro gene expression associated with neurological disorders (including autism) in human neuronal cells. We differentiated and treated human SK-N-SH neuroblastoma cells with a mixture (fluoxetine, carbamazepine and venlafaxine) and valproate (used as a positive control to induce autism-associated profiles), followed by transcriptome analysis with RNA-Seq approach.

Results

We found that psychoactive pharmaceuticals and valproate significantly altered neuronal gene sets associated with human neurological disorders (including autism-associated sets). Moreover, we observed that altered expression profiles in human cells were similar to gene expression profiles previously identified in fish brains.

Conclusions

Psychoactive pharmaceuticals at environmental concentrations altered in vitro gene expression profiles of neuronal growth, development and regulation. These expression patterns were associated with potential neurological disorders including autism, suggested psychoactive pharmaceuticals at environmental concentrations might mimic, aggravate, or induce neurological disorders.

Background

There are approximately 3000 synthetic chemicals known to interact with humans through air, water, and food [1, 2]. These chemicals might serve as environmental contaminants and trigger neurological disorders, such as autism spectrum disorders (ASD), in genetically susceptible individuals [35]. Among this diverse group of environmental contaminants, we focused on pharmaceuticals and personal care products (PPCPs) due to their tendency to contaminate environmental water systems [2]. PPCPs include psychoactive pharmaceuticals that are highly prescribed in the United States and other chemicals like bis-phenol A (BPA) in plastics, phthalates in cosmetics and teratogenic chemicals [1].

Psychoactive pharmaceuticals like fluoxetine, venlafaxine and carbamazepine, have been detected in the drinking water at very low concentrations [2, 6, 7]. These pharmaceuticals, some of which are metabolically active and have relatively long half-lives for over a month, reach waste-water treatment plants (WWTP) through excretion by clinical patients [7, 8]. Due to the chemical properties of these drugs and inefficient filtration of WWTP, these drugs end up mixing up with the ground water, and thus reach drinking water at low concentrations [7, 8].

We previously hypothesized that psychoactive pharmaceuticals as environmental contaminants alter neuronal gene expression associated with neurological disorders like ASD. To determine this, our lab treated juvenile fathead minnows (Pimephales promelas) with psychoactive pharmaceuticals (fluoxetine, venlafaxine and carbamazepine) individually and in mixtures at environmentally relevant concentrations [2, 6]. After treating them for 15 days, we extracted the brains and carried out microarray analysis. Using gene set enrichment analysis (GSEA) [9], we identified enrichment (up- or down-regulation) of gene sets associated with neuronal growth, regulation and development in the juvenile minnow brains in response to psychoactive drug exposure [2, 6]. We also identified altered neuronal gene sets associated with neurological disorders, including ASD [6]. Moreover, fish exposed to psychoactive pharmaceuticals exposed had an altered behavioral phenotype [2, 10].

In the present study, we hypothesized that psychoactive pharmaceuticals (fluoxetine, venlafaxine and carbamazepine) at environmental concentrations would alter in vitro human neuronal gene expression that is 1) similar to gene expression profile in fathead minnow, and 2) associated with neuro-developmental disorders. To determine if altered gene expression profile was associated with idiopathic autism, we also treated human neuronal cells with valproic acid, which is known to induce autism-like phenotypes in mice [11]. Identifying altered gene expression profiles in treated cells and finding a similar pattern with valproic acid induced gene expression would reveal the extent to which psychoactive pharmaceuticals at very low concentrations could induce gene expression associated with potential neurological disorders like ASD.

Methods

Cell culture and differentiation

Human SK-N-SH cell line was obtained from American Type Culture Collection (ATCC #HTB-11). Cells were cultured in Eagle’s Minimum Essential Medium (EMEM; ATCC). This media was supplemented with 1 % penicillin-streptomycin-neomycin (Sigma) and 10 % (v/v) fetal bovine serum (FBS). Retinoic acid (RA; Sigma) was used to induce SK-N-SH cells [12] to differentiate into more neuron-like cells [13] because this cell line is a mixture of different cell types. Approximately 15,000 cells were cultured in T-75 flask (Corning) supplemented with EMEM media for two days, and then Retinoic acid (10 μM) was added. Cells were treated with RA for two weeks and media was replaced every three-four days [12]. Cultures were monitored visually using light microscopy for morphological changes, and evaluated for neuronal cell markers (NeuN, PSD95 and NCAM) during the differentiation process [12, 14].

Pharmaceuticals treatments

Stock solutions (10 mM) of fluoxetine (FLX; Sigma F133; Active metabolite), venlafaxine (VNX; Sigma D2069; ​Active metabolite) and carbamazepine (CBZ; Sigma C4206; ​Active metabolite), and 1 mM stock solution of valproic acid (VPA; Santa Cruz Biotechnology sc211393; Active metabolite) were prepared in dimethyl sulfoxide (DMSO). After differentiation for two weeks, cells were treated with a mixture composed of (MIX: FLX 10 μg/l; VNX 50 μg/l; CBZ 100 μg/l) [2], and Valproate (VPA: 4.9 mg/l) [15]. Control cells were treated with DMSO (vehicle) only, and the final concentration of DMSO in the cultures was 0.05 %. To examine if psychoactive pharmaceuticals in drinking water at environmental concentrations could induce neuronal gene expression, we chose to treat neuronal cells with the mixture of three pharmaceuticals (fluoxetine, carbamazepine and venlafaxine). Cells were treated in three replicates with the pharmaceuticals for 48 h in EMEM media without FBS to avoid any binding of pharmaceuticals with the serum proteins. All of the treatments were shown not to affect overall cell viability with respect to control (no treatment), based on the adherent nature of the monolayers and the result from CyQuant Viability Assay [16]. After treating cells for 48 h, they were collected with Versene solution (Gibco).

RNA extraction, cDNA synthesis and sequencing

After 48 h of exposure, cells were collected with trypsin, centrifuged, and RNA was extracted using Qiagen RNeasy Plus Mini Kit (74134) according to the manufacturer’s protocol. Total RNA concentration was determined using NanoDrop (Thermo Scientific), and the RNA integrity value (RIN) was analyzed on Agilent 2100 Bioanalyzer (Agilent, Santa Clara, CA). RNA was quantified with Qubit spectrophotometer (Life technologies). Using Illumina Tru-seq stranded total RNA kit, cDNA library was prepared in following steps: RiboZero depletion and fragmentation, first and second strand cDNA synthesis, adenylation of 3′ ends, adapter ligation, PCR amplification, library validation using qPCR – Kapa Biosystems Library Quantification Kit, and normalization and pooling in preparation for cluster generation on MiSeq. Samples were then loaded onto flow cell (MiSeq Reagent kit v3 150 cycle) and sequenced on Illumina MiSeq 2 according to the manufacturer’s instructions.

Bioinformatics data analysis

Quality control, alignment, and read counting

In total, three treatments (mixture, valproate, and control) with three replicates each were sequenced with five flow cells using Illumina MiSeq, which generated more than 10 M paired-end reads for each replicate (refer Additional file 1). The raw sequences in FASTQ files underwent quality control analysis using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc). All the samples were sequenced at 75b read length and they all passed the quality check. We aligned the quality checked reads to human genome hg19 using TopHat version 2.0.11 [17]. Most of the reads (~90 %) had average base quality above Q30. The reference genome sequence, the bowtie index files for the reference genome sequence, and the gene annotation file were downloaded from Illumina iGenomes project (http://support.illumina.com/sequencing/sequencing_software/igenome.html). The number of reads that map to human known genes were counted by summerizeOverlaps function in GenomicAlignments R package [18]. Only the genes that have at least one read for all replicates were retained for downstream analysis.

Analysis of differential gene expression

Differentially expressed genes were identified using DESeq2 version 1.6.1 [19]. Differentially expressed genes from 2 groups (Mixture and Valproate) with respect to the control treatment were identified.

Gene set enrichment analysis

We used GAGE version 2.14.4 [20] for gene set enrichment analysis. We used GAGE to analyze gene sets that were analyzed previously in Fish brains [2, 6]. These gene sets are grouped as Neuronal development, regulation, growth; Neurological Disorders (ND); Autism spectrum disorders (ASD) [2, 6]. For extensive analysis, we also analyzed gene sets from MSigDB ‘C2’ and ‘C5’ (refer Additional file 2). Enrichment analyses were carried out using gage function using non-parametric Kolmogorov-Smirnov tests. We used GAGE package to identify significantly enriched (significantly up- and/or down-regulated; P-value < 0.01 and Q-value < 0.1) gene sets within mixture and valproate treatments.

Results

Patterns of psychoactive pharmaceuticals – induced in vitro gene expression in neuronal development, growth and regulation

We postulated that psychoactive pharmaceuticals at environmental concentrations would alter gene expression of neuronal systems. Support for this hypothesis would suggest that dysregulation of neuronal systems would result in altered neuronal circuits and may result in fewer neuronal connections. To address this question we used differentiated SK-N-SH neuroblastoma cells and treated them with the mixture (MIX: FLX-10 μg/l; VNX-50 μg/l; CBZ-100 μg/l), and valproate (VPA: 4.9 mg/l) in replicates of three samples for each treatment.

For the development collection, we observed six enriched gene sets (significantly down-regulated, P-value < 0.01, Q-value < 0.1) by MIX treatment. VPA treatment enriched the expression of seven gene sets (four gene sets down- and three gene sets up-regulated, Additional file 3). AXONOGENESIS, REGULATION OF NEUROGENESIS and SYNAPSE PART gene sets were enriched in both VPA and MIX treatments. And, SYNAPSE PART gene set was also enriched (up-regulated) in fish brains.

For the regulation collection, we observed 14 enriched gene sets (13 gene sets down- and one gene set up-regulated, P-value < 0.01, Q-value < 0.1) by MIX treatment. VPA treatment enriched the expression of eight gene sets (up-regulated, Additional file 3). All eight up-regulated gene sets in VPA treatment were down-regulated in MIX treatment. Among those sets, two gene sets (NEUROTRANSMITTER_BINDING and SYNAPSE) were also enriched in fish brains.

For the growth collection, we observed eight enriched gene sets (all down-regulated, P-value < 0.01, Q-value < 0.1) by MIX treatment. VPA treatment enriched the expression of five gene sets (up-regulated, Additional file 3). All five up-regulated gene sets in VPA treatment were down-regulated in MIX treatment. Two gene sets (AXON and NEURON_PROJECTION) were also enriched in fish brains.

Patterns of psychoactive pharmaceuticals – induced in vitro gene expression in neurological disorders (ND) and ASD groups

We then sought to determine if altered in vitro gene expression was associated with neurological disorders and ASD. To accomplish this, we analyzed and compared the gene expression of already published ND and ASD gene sets in MIX and VPA treatments.

For the ND collection, we observed six enriched gene sets (four down- and two up-regulated, P-value < 0.01, Q-value < 0.1) by MIX treatment. VPA treatment enriched the expression of three gene sets (two gene sets down- and one gene set up-regulated, Additional file 3). AUTISM_IDIOPATHIC and PARKINSONS gene sets were enriched in both VPA and MIX treatments as well as in fish brains.

For the ASD collection, we observed two enriched gene sets (both up-regulated, P-value < 0.01, Q-value < 0.1) by MIX treatment. VPA treatment enriched the expression of five gene sets (down-regulated, Additional file 3). ASD_MILD gene set was enriched in both VPA and MIX treatments as well as in fish brains.

Ranked gene lists from the mixture and valproate treatment

We sorted all genes from gene expression profiles in human neuronal cells treated with the MIX (FLX, VNX, CBZ) and valproate (VPA). In each treatment, we sequenced ~ 17,000 genes and analyzed their expression. We then ranked them based on their fold change expression within each treatment. Tables 1 and 2 show, the 50 most strongly up-and down-regulated genes. We also reported the fold-change score of genes which were presented on the fish microarray chip.
Table 1

Ranked gene list from the mixture treatment (50 most up- and down-regulated)

Up-regulated genes

Down-regulated genes

Rank

Genes

Fold change

P-value

Fold change in fish brains

Rank

Genes

Fold change

P-value

Fold change in fish brains

1

FAM129A

3.25

0.000

 

17350

ANGPTL4

−3.36

0.000

 

2

CACNA2D3

1.99

0.000

0.90

17349

THBS1

−2.19

0.000

0.34

3

NUPR1

1.97

0.002

−0.10

17348

STARD13

−2.17

0.000

−0.46

4

GRM8

1.90

0.000

 

17347

DRP2

−2.15

0.000

 

5

PTCHD1

1.89

0.000

 

17346

TNNT2

−2.11

0.000

−0.23

6

TMEM132C

1.76

0.000

 

17345

AQP10

−2.10

0.000

 

7

DDIT3

1.55

0.000

0.73

17344

STRA6

−1.97

0.000

−0.10

8

XKR4

1.53

0.010

 

17343

PAPPA2

−1.92

0.000

 

9

CHRNA10

1.33

0.002

 

17342

NEDD9

−1.88

0.000

−1.21

10

LPHN3

1.24

0.004

 

17341

GGTA1

−1.85

0.001

 

11

SERPINF2

1.23

0.008

0.15

17340

SH3TC1

−1.83

0.000

 

12

NKX3-1

1.22

0.008

 

17339

ADAMTSL2

−1.80

0.000

−0.25

13

HRK

1.22

0.051

 

17338

RGS13

−1.78

0.000

 

14

SCARNA20

1.22

0.009

 

17337

ANO1

−1.77

0.000

 

15

PABPC3

1.21

0.006

 

17336

LOC283731

−1.75

0.000

 

16

KIT

1.18

0.001

0.15

17335

CMKLR1

−1.73

0.000

0.86

17

KIAA1045

1.18

0.002

−0.12

17334

VIP

−1.71

0.000

 

18

DLL1

1.15

0.013

0.05

17333

HTR2B

−1.69

0.000

 

19

C9orf150

1.12

0.000

 

17332

RAG2

−1.67

0.004

0.20

20

SNORA14A

1.11

0.164

 

17331

ACVRL1

−1.67

0.000

 

21

B3GALT1

1.09

0.134

 

17330

VCAN

−1.66

0.000

 

22

RIMBP3C

1.09

0.096

 

17329

C6

−1.65

0.006

−1.11

23

NOS1AP

1.06

0.172

0.37

17328

ENO3

−1.64

0.000

−0.00

24

GLYATL2

1.06

0.021

 

17327

TM4SF1

−1.63

0.008

0.22

25

STAC2

1.04

0.259

 

17326

PCTK3

−1.62

0.004

 

26

CRYBB2

1.04

0.020

0.01

17325

FAM65C

−1.60

0.000

 

27

SLC16A10

1.01

0.167

−0.31

17324

FLJ36208

−1.60

0.000

 

28

CDH7

1.01

0.031

−0.00

17323

LOC400950

−1.58

0.000

 

29

MYB

1.01

0.030

−0.17

17322

RARRES3

−1.58

0.000

 

30

LCA5L

1.00

0.176

 

17321

SLCO4C1

−1.53

0.000

 

31

ID4

0.99

0.033

−0.17

17320

PRAP1

−1.52

0.022

 

32

MALAT1

0.99

0.004

 

17319

SLC18A1

−1.51

0.000

 

33

SNAR-A3

0.99

0.230

 

17318

TFPI2

−1.51

0.000

 

34

EDN1

0.98

0.033

 

17317

FBP1

−1.50

0.000

0.43

35

VSTM2A

0.97

0.000

 

17316

FGL1

−1.49

0.001

 

36

HERV-FRD

0.97

0.036

 

17315

COL9A3

−1.48

0.001

 

37

CNTD1

0.97

0.037

−1.03

17314

LRRTM1

−1.48

0.015

0.25

38

ELL2

0.96

0.000

−0.30

17313

BTK

−1.48

0.000

−0.30

39

SLC13A3

0.96

0.027

0.02

17312

SLC24A2

−1.46

0.000

0.11

40

DENND1B

0.96

0.114

0.06

17311

IRF6

−1.45

0.000

0.17

41

CYP2C18

0.95

0.041

 

17310

LOC392232

−1.45

0.012

 

42

AMN

0.95

0.041

−0.33

17309

RD3

−1.45

0.000

 

43

NCRNA00087

0.95

0.301

 

17308

DDC

−1.43

0.000

 

44

FUT9

0.94

0.042

−0.19

17307

PRLHR

−1.43

0.008

 

45

SCGB1D2

0.92

0.047

 

17306

NOTUM

−1.42

0.052

−0.31

46

TRIB3

0.92

0.000

0.67

17305

TMIGD2

−1.41

0.008

 

47

MYBL1

0.91

0.019

 

17304

RTL1

−1.40

0.048

−0.40

48

ZNF726

0.91

0.260

 

17303

P2RX6

−1.40

0.000

 

49

DLEU1

0.90

0.173

 

17302

DKK1

−1.39

0.000

 

50

SLC7A11

0.90

0.309

−0.41

17301

TRIM9

−1.38

0.000

 

Table representing the list of 50 most up- and down-regulated genes in human neuronal cells treated with the mixture (FLX, VNX, CBZ). Genes were ranked based on their expression in fold-change in human cells. P-value represents the significance level of the expression change in the mixture treatment than control. Corresponding fold change of genes in fish brains is also reported in this table, where blank cells represents genes that are not found in the fish microarray chip

Table 2

Ranked gene list from the valproate treatment (50 most up- and down-regulated)

Up-regulated genes

Down-regulated genes

Rank

Genes

Fold change

P-value

Fold change in fish brains

Rank

Genes

Fold change

P-value

Fold change in fish brains

1

OPRK1

4.03

0.000

−0.29

17673

CMKLR1

−2.81

0.000

0.86

2

NEUROG2

3.55

0.000

 

17672

ADAMTS2

−2.74

0.000

0.09

3

VSNL1

3.19

0.000

2.84

17671

HIST1H4L

−2.67

0.000

 

4

GALNAC4S-6ST

3.09

0.000

0.96

17670

ELFN1

−2.44

0.000

 

5

POSTN

2.93

0.000

1.22

17669

COL4A2

−2.39

0.000

−0.42

6

EVX2

2.86

0.000

 

17668

GREM2

−2.33

0.000

 

7

SERPINE1

2.69

0.000

−1.45

17667

FLJ45455

−2.32

0.000

0.86

8

ODZ1

2.64

0.000

 

17666

VCAN

−2.31

0.000

 

9

DRD5

2.34

0.000

−0.23

17665

SOCS3

−2.29

0.000

0.02

10

CSGALNACT1

2.33

0.001

 

17664

TGFBI

−2.27

0.000

−1.11

11

HOXD13

2.31

0.000

 

17663

C11orf53

−2.26

0.000

 

12

C3orf57

2.29

0.000

 

17662

SNAI1

−2.17

0.000

−0.82

13

B3GALT1

2.29

0.000

 

17661

LOC283480

−2.11

0.000

 

14

SECTM1

2.28

0.001

 

17660

ID1

−2.05

0.000

0.23

15

PLAU

2.20

0.001

 

17659

PPP1R9A

−2.03

0.000

−0.05

16

PDE1A

2.19

0.000

 

17658

LOC646498

−2.02

0.004

 

17

VIM

2.14

0.000

0.25

17657

RPS16P5

−2.02

0.000

 

18

MME

2.11

0.000

 

17656

COL4A1

−1.98

0.000

0.10

19

KCNJ2

2.09

0.000

 

17655

GLT8D2

−1.98

0.000

 

20

HS6ST2

2.09

0.000

−0.15

17654

CYP26A1

−1.96

0.000

0.26

21

SNORA42

2.00

0.003

 

17653

SLC10A1

−1.95

0.004

 

22

RASEF

1.99

0.000

0.31

17652

PART1

−1.94

0.000

 

23

APOL6

1.93

0.000

 

17651

OXTR

−1.89

0.000

1.26

24

NCRNA00164

1.91

0.007

 

17650

PTPLAD2

−1.84

0.007

 

25

COLQ

1.91

0.000

 

17649

C9orf131

−1.83

0.001

 

26

CACNA2D3

1.90

0.000

0.90

17648

FOS

−1.80

0.000

−0.63

27

DEGS2

1.90

0.000

 

17647

C9orf135

−1.79

0.009

 

28

NHS

1.89

0.000

 

17646

GLP1R

−1.79

0.000

 

29

PTER

1.89

0.001

0.11

17645

ETS1

−1.79

0.000

−0.79

30

EDIL3

1.87

0.000

−0.41

17644

NXPH3

−1.79

0.000

 

31

INSM2

1.85

0.000

 

17643

LOC100128505

−1.79

0.013

 

32

GFRA3

1.82

0.000

 

17642

KCTD12

−1.76

0.000

0.49

33

PROKR2

1.82

0.000

 

17641

ANGPTL4

−1.74

0.000

 

34

GRM8

1.81

0.001

 

17640

FOSB

−1.73

0.000

 

35

TLE4

1.79

0.000

 

17639

MPPED2

−1.72

0.000

−0.62

36

WNT5A

1.77

0.001

−0.38

17638

C11orf92

−1.71

0.003

 

37

SMOC2

1.77

0.000

−0.26

17637

WDR38

−1.69

0.001

 

38

VCAM1

1.76

0.014

0.53

17636

GALNT14

−1.68

0.000

0.03

39

FRMD4B

1.74

0.000

0.10

17635

LOC283143

−1.67

0.002

 

40

PTRF

1.74

0.000

 

17634

ACCN1

−1.66

0.002

 

41

DLK1

1.70

0.000

0.69

17633

C14orf53

−1.66

0.002

 

42

PTCHD1

1.67

0.001

 

17632

TAPBPL

−1.65

0.021

 

43

GPR126

1.66

0.022

 

17631

NRP1

−1.63

0.000

1.12

44

ANKDD1B

1.65

0.000

 

17630

BFSP2

−1.63

0.002

 

45

MOXD1

1.65

0.002

−1.00

17629

SMAD7

−1.63

0.000

0.08

46

FAM111B

1.65

0.000

 

17628

HOPX

−1.62

0.028

 

47

LOC728739

1.63

0.002

 

17627

ERBB4

−1.62

0.000

 

48

CHRNA10

1.62

0.002

 

17626

PAPPA2

−1.62

0.000

 

49

NELL1

1.62

0.000

 

17625

PCDHGB8P

−1.62

0.011

 

50

NELL2

1.62

0.000

−0.76

17624

LOC284454

−1.60

0.000

 

Table representing the list of 50 most up- and down-regulated genes in human neuronal cells treated with the valproate. Genes were ranked based on their expression in fold- change in human cells. P-value represents the significance level of the expression change in the mixture treatment than control. Corresponding fold change of genes in fish brains is also reported in this table, where blank cells represents genes that are not found in the fish microarray chip

Discussion

Comparison between human MIX treatment and fish gene expression patterns

The results partially supported our first hypothesis that the MIX treatment on human SK-N-SH cell line induced gene sets enrichment patterns similar to that of the fish experiment following mixture treatment, although the degree of similarity was not high.

Among the neural circuit development gene sets, all the six significantly enriched gene sets in human neuronal cells following MIX treatment were enriched in a down-regulated manner; whereas in the fish experiment, the two significantly enriched sets were both up-regulated. We do not know why the direction was opposite, but we do notice that, one gene set, SYNAPSE PART, was enriched in both treatments [2]. Previous studies have found that altered expression of NCAM, IRX3 and NKX6.1 genes in SYNAPSE PART change the fate and position of neurons generated in the chick neural tube [21, 22]. Also within those down-regulated gene sets contains gene PSD95 (DLG4) and GABA, which have recently been found to be associated with neurological disorders like autism by altering synaptic assembly [14, 23, 24].

In the growth group, we observed similar patterns where all enriched sets in MIX treatment were down-regulated and all those in the fish experiment were up-regulated. Among those sets, two were enriched in both treatments, AXON and NEURON PROJECTION. Other studies have found that genes within these two gene sets modulate the fate, lineage, and timing of neuronal development by playing a critical role in the formation and maturation of neural circuits [23, 25, 26].

In the regulation group, we observed that NEUROTRANSMITTER BINDING gene set was down-regulated in fish brains as well as in human cells [2]. This could be possible due to the therapeutic effect of fluoxetine (SSRI) in the mixture treatment [2]. Fluoxetine is known to reduce the re-uptake of serotonin by inhibiting monoamine transporters on the pre-synaptic neuronal membrane [27, 28]. Due to the longer availability of neurotransmitters in the synaptic cleft, the expression of serotonin receptors is down-regulated, thus a decrease in neurotransmitter binding [29]. Another gene set SYNAPSE was significantly up-regulated in fish brains [2], but down-regulated in treated human cells. This gene set was responsible for modulating wiring of neuronal circuits by controlling the number of synapse as well as organization of synaptic assembly and specificity [26, 30]. Altered synaptogenesis has been strongly considered as a potential mechanism in ASD pathogenesis [31, 32].

Comparison between human MIX and VPA gene expression patterns

We used valproate (an anticonvulsant) to treat human neuronal cells as a positive control because prenatal exposure of valproate has been found to be strongly associated with autism [3] and valproate is known to induce autism-like phenotypes in mice [11]. Similarly, carbamazepine (presented in the mixture treatment) is a mood stabilizer and anticonvulsant [11, 33] and it also inhibits the epileptic effects in the brain by blocking sodium channels [11, 33]. By and large, the results support our second hypothesis that the MIX and VPA treatments change the RNA expression profile in similar ways, albeit in an interesting fashion, since the two treatments often enrich the same gene sets but in different directions.

In the development group of VPA treatment, we found four gene sets significantly down-regulated and three gene sets significantly up-regulated in human neuronal cells. Three gene sets (AXONOGENESIS, REGULATION OF NEUROGENESIS and SYNAPSE PART) were enriched in both mixture and valproate treatments, but in the opposite direction. Similar to fish gene expression, VPA treatment up-regulated SYNAPSE PART gene set [2]. This states that VPA exposure might be associated with disturbed neuronal fate and position [21, 22] as well as synaptic assembly [1, 22, 34].

In the growth group, we observed that five gene sets were up-regulated in the VPA treatment. Interestingly, the same five sets were all down-regulated in the MIX treatment. This suggested that both treatments disturbed the human gene expression in similar pathways despite the different directions. One gene set NEURON PROJECTION was up-regulated similar to fish brains [2]. In the regulation group, we noticed similar results. Eight gene sets were up-regulated in valproate treatment, but down-regulated in the mixture treatment. From this repeating phenomenon of opposite direction enrichment of the same gene set, we postulate a general pattern that the three pharmaceuticals (fluoxetine, venlafaxine and carbamazepine) and valproate dysregulate the same pathways in different directions. However, more investigation is needed to confirm or disprove this postulation. We also plotted multi-dimensional scaling using edgeR [35] and observed that mixture treatment samples were somewhat closer to valproate samples than control samples (refer Additional file 1). However, MIX and VPA samples were still apart, confirming that these two treatments did not exhibit similar response.

Association of human MIX and VPA gene expression patterns with neurological disorders

To determine the extent to which altered gene expression in both MIX and VPA treatments were associated with neurological disorders (including ASD), we examined the expression of the already-published gene sets from neurological disorders (ND) and ASD groups in both treatments. We also compared these to their corresponding expression pattern in fish brains. For ND group, MIX and VPA treatments altered PARKINSONS and Autism_Idiopathic gene sets significantly but in different directions. Interestingly, mixture treatment of human cells and fish brains up-regulated Autism_Idiopathic gene set [6]. For another ASD group, MIX treatment up-regulated two gene sets (ASD_2Class and ASD_Mild) similar to fish brains [6]. On the other side, VPA treatment down-regulated three gene sets (ASD_2Class, ASD_Mild, ASD_Savant) in a different direction compared to the expression in fish brains [6]. These expression patterns stated that VPA and MIX treatments of human cells exhibited a similar response to neurological disorders (including ASD), suggesting a common induction effect.

Insights into important genes in both MIX and VPA treatments

We sought to identify important, or novel genes that were significantly up- and down-regulated in human neuronal cells treated with MIX and VPA. We generated ranked lists of genes based on their fold change, and tabulated the most 50 strongly up- and down-regulated genes (Table 1 and 2). We also compared genes from ranked lists with the ones from fish microarray data. For MIX treatment, we noticed four genes (NUPR1, RTL1, THBS1 and HTR2B) which could be considered important and novel. The thrombospondin (THBS1) gene plays an important role in synaptogenesis in the developing brain [36]. Recent association studies have found that both rare and common variants of this gene are associated with autism [36]. Although this gene was found to be down-regulated by two-fold, it was up-regulated in fish brains under similar mixture treatment [2]. Another important gene, HTR2B, which codes for serotonin receptor 2B, was down-regulated by ~ two-fold in human cells. Similar serotonin receptor genes were also found to be down-regulated in fish brains. Moreover, recent protein studies in our lab showed that HTR2B protein was down-regulated in the same mixture (FLX, VNX, CBZ) treatment. This mechanism is explained by the drug effect of fluoxetine (SSRI), which provides more neurotransmitter in the synaptic cleft, thus reducing serotonin receptors [29].

In the ranked list of genes by valproate (VPA) treatment, we found three genes (VSNL1, PTER and OXTR) of particular importance. VSNL1 gene, which encodes for visinin-like protein 1 in humans, modulates neuronal morphology by controlling the key signaling pathways in CNS. This gene was up-regulated by three-fold in the valproate treatment. We observed similar up-regulation of this gene in fish brains following mixture (FLX, VNX, CBZ) treatment. Other studies have recently found the association of single-nucleotide polymorphisms (SNPs) in the VSNL1 gene with neurological disorders like schizophrenia [37]. Another important gene, oxytocin receptor (OXTR) was found to be down-regulated by two-fold in human cells treated with valproate (Table 2). However, this gene was up-regulated in fish brains exposed to the mixture of psychoactive pharmaceuticals [2]. Moreover, recent protein studies in our lab showed an increased expression of OXTR in the same cells treated with carbamazepine and the mixture (FLX, VNX, CBZ). Other studies have shown that OXTR serves as an anxiolytic agent by modulating serotonin release in serotonergic neurons of the raphe nuclei [38].

Conclusions

To investigate the environmental trigger for idiopathic autism, we focused on psychoactive pharmaceuticals, potential environmental contaminants that have been detected in drinking water. We found that psychoactive pharmaceuticals altered the gene expression of neuronal systems in vitro at environmental concentrations. These altered gene expressions are associated with potential neurological disorders by playing a key role in the formation, growth and regulation of neurons. Our data suggests that psychoactive pharmaceuticals might disrupt neuronal connections by altering the gene expression associated with neuronal growth, development and regulation.

Declarations

Acknowledgements

The authors thank Late Dr. Chris Cretekos for providing the guidance for using neuronal cells for our study. We also thank Dr. Jean Pfau for providing her laboratory space and reagents for neuronal cell culture.

Declarations

The publication costs for this article were funded by an Institutional Development Award (IDeA) from the National Institute of General Medical Sciences of the National Institutes of Health under Grant #P20GM103408.

This article has been published as part of BMC Genomics Volume 17 Supplement 3, 2016: Selected articles from the 12th Annual Biotechnology and Bioinformatics Symposium: genomics. The full contents of the supplement are available online at https://bmcgenomics.biomedcentral.com/articles/supplements/volume-17-supplement-3.

Availability of data and materials

The RNA-Seq dataset for this research article is available in the Gene Expression Omnibus (GEO) repository, [GSE80635 and http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE80635].

Authors’ contributions

GK, YX and MAT designed the experiments, and GK performed them. MAT provided the direction and guidance for the research. YX and LY carried out analyses using R-programming and GK wrote the manuscript. All authors have read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Biological Sciences, Idaho State University
(2)
Department of Medical Pathology and Laboratory Medicine, University of California at Davis
(3)
Present Address: Institute for Pediatric Regenerative Medicine, Shriners Hospitals for Children, Northern California
(4)
Division of Biological Sciences, University of Montana

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Copyright

© The Author(s). 2016

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