Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression Abstract Chronic arsenic exposure can result in adverse development effects including decreased intellectual function, reduced birth weight, and altered locomotor activity. Previous in vitro studies have shown that arsenic inhibits stem cell differentiation. MicroRNAs (miRNAs) are small noncoding RNAs that regulate multiple cellular processes including embryonic development and cell differentiation. The purpose of this study was to examine whether altered miRNA expression was a mechanism by which arsenic inhibited cellular differentiation. The pluripotent P19 mouse embryonal carcinoma cells were exposed to 0 or 0.5 μM sodium arsenite for 9 days during cell differentiation, and changes in miRNA expression was analyzed using microarrays. We found that the expression of several miRNAs important in cellular differentiation, such as miR-9 and miR-199 were decreased by 1.9- and 1.6-fold, respectively, following arsenic exposure, while miR-92a, miR-291a, and miR-709 were increased by 3-, 3.7-, and 1.6-fold, respectively. The members of the miR-466-669 cluster and its host gene, Scm-like with 4 Mbt domains 2 (Sfmbt2), were significantly induced by arsenic from 1.5- to 4-fold in a time-dependent manner. Multiple miRNA target prediction programs revealed that several neurogenic transcription factors appear to be targets of the cluster. When consensus anti-miRNAs targeting the miR-466-669 cluster were transfected into P19 cells, arsenic-exposed cells were able to more effectively differentiate. The consensus anti-miRNAs appeared to rescue the inhibitory effects of arsenic on cell differentiation due to an increased expression of NeuroD1. Taken together, we conclude that arsenic induces the miR-466-669 cluster, and that this induction acts to inhibit cellular differentiation in part due to a repression of NeuroD1. arsenic, microRNA, Sfmbt2, miR-466-669 cluster, P19 cells, differentiation, NeuroD1 Arsenic is a ubiquitous element that can leach into ground water and surface water (Amini et al., 2008). Studies have shown that millions of people in countries such as China, Argentina, Taiwan, and Bangladesh are exposed to high levels of arsenic in their drinking water, above the 10 ppb drinking water standard (Blanes et al., 2011; Chen et al., 1994; Mandal and Suzuki, 2002; Nickson et al., 1998; Ning et al., 2007; Smedley and Kinniburgh, 2002). Other than drinking water, humans can also be exposed to arsenic by consuming crops, such as rice, that are grown in soil with former arsenic pesticide use (Zavala and Duxbury, 2008). Arsenic can cross the placental barrier such that arsenic concentrations in cord blood are almost as high as in maternal blood (Concha et al., 1998). Therefore, children can be affected by arsenic during pregnancy. Epidemiological studies have shown that perinatal arsenic exposure can reduce birth weight (Hopenhayn et al., 2003; Huyck et al., 2007; Rahman et al., 2009) and weight gain in children (Saha et al., 2012). Moreover, data show that children exposed to arsenic levels > 10 ppb have reduced verbal intelligence quotient, spatial memory, and long-term memory, indicating that arsenic can impair intellectual function (reviewed in Tolins et al., 2014). In addition, early childhood exposure to arsenic is correlated with changes in sensory ability and reaction time, and decreased sense of taste and smell (Rodríguez-Barranco et al., 2016; Rosado et al., 2007), while in adults, arsenic concentrations of 10–50 ppb are associated with altered taste and smell, reduced touch perception, an increase in a tingling sensation in the extremities, and visual processing (Edwards et al., 2014; Hafeman et al., 2005; Kawasaki et al., 2002; Rahman et al., 2003; Mukherjee et al., 2014; O’Bryant et al., 2011). Similarly, mice exposed to arsenic cannot appropriately repair a muscle injury because of reduced myogenin expression (Yen et al., 2010), altered muscle oxygen consumption, and increased expression of matrix remodeling genes (Zhang et al., 2016). Arsenic exposure also alters spontaneous motor activity, increases body spasms, and decreases forelimb grip strength (Bardullas et al., 2009; Markowski et al., 2012). In addition, arsenic exposure during the perinatal period decreases learning and spatial memory (Chandravanshi et al., 2014; Luo et al., 2009; Martinez et al., 2008; Martinez-Finley et al., 2009; Nagaraja and Desiraju, 1994; Rodrı´guez et al., 2002). It also reduces the number of neuronal progenitor cells and the formation of new neurons in rodents (Cronican et al., 2013; Liu et al., 2012; Luo et al., 2013; Tyler et al., 2013). From in vitro studies, it appears that arsenic impairs cellular differentiation. For example, exposing C2C12 myoblasts to 0.1 μM arsenic trioxide or 20 nM sodium arsenite inhibits myotube formation by downregulating the expression of myogenic transcription factors, such as MyoD and myogenin (Steffens et al., 2011; Yen et al., 2010). Arsenic also disrupts neuronal differentiation in both PC12 and Neuro-2a neuroblastoma cells (Frankel et al., 2009; Wang et al., 2010). Our previous work demonstrates that arsenic inhibits the differentiation of mouse P19 embryonal carcinoma cells into sensory neurons by reducing the expression of the neurogenic transcription factors NeuroD and neurogenin 1 and 2 (Hong and Bain, 2012; McCoy et al., 2015). One potential mechanism for altering transcription factor expression is via microRNAs (miRNAs). MicroRNAs are small, single stranded, noncoding RNAs that regulate gene expression by typically binding imperfectly to the 3′-untranslated region (UTR) of mRNA, leading to targeted mRNA translational repression (Yang et al., 2005). MicroRNAs can be located in introns or outside of genes (Rodriguez et al., 2004). If several miRNAs are clustered together, they can be transcribed as a polycistronic transcript. For instance, the miR-290 cluster is a 2.2-kb region on chromosome 7, and the primary transcript generates 14 mature miRNAs which play an important role in embryonic stem cells (Judson et al., 2009). The miR-466 cluster is located in intron 10 of the mouse Sfmbt2 gene and contains 65 miRNA genes (Lehnert et al., 2011). There is little information reported on this cluster, although it is known to be involved in cell proliferation and apoptosis, and Sfmbt2 is expressed in embryonic stem cells (Druz et al., 2012; Zheng et al., 2011). Because of their ability to regulate gene expression, miRNAs play an important role in development. In mice, deletion of the Dicer-1 gene, which cleaves pre-miRNA to produce mature miRNAs, can result in embryonic lethality (Bernstein et al., 2003). Moreover, mouse embryonic stem cells that lack Dicer-1 have significant defects in cell differentiation (Kanellopoulou et al., 2005). MicroRNAs can also impact stem cell differentiation into skeletal muscle and neurons. For instance, miR-1 and miR-133 are regulated by the transcription factors MyoD and MEF2 (Liu et al., 2007; Zhao et al., 2005), and they are potent repressors of nonmuscle genes during cell lineage commitment (Ivey et al., 2008). miR-9 and miR-124 are both are highly expressed in the brain (Cheng et al., 2009; Shibata et al., 2011), and they regulate several neurogenic transcription factors, including Foxg1, Sox9, and Pax6 (Akerblom et al., 2012; Clark et al., 2010; Otaegi et al., 2011). Compared with other epigenetic modifications, the effects of arsenic on miRNA regulation are relatively underexplored. For example, in bone marrow mesenchymal cells, arsenic trioxide reduces the expression of miR-204, which increases the differentiation of osteoblasts and reduces the differentiation of adipocytes (Zhao et al., 2014), while arsenite can induce angiogenesis by decreasing miR-9 and miR-181b expression in a human umbilical vein cell line (Cui et al., 2012). During the process of malignant transformation, long-term arsenite exposure can alter the expression of miRNAs that target ras in prostate epithelial cells (Ngalame et al., 2014), reduce miR-199a-50 in lung BEAS-2B cells (He et al., 2014), and decrease several let-7 family members in HaCaT keratinocyte cells (Jiang et al., 2014). Because increasing evidence indicates that miRNAs are important in cellular differentiation, and that arsenic exposure can alter miRNAs levels, we hypothesized that a mechanism by which arsenic could inhibit stem cell differentiation was by changing miRNA expression. In this study, we exposed mouse P19 embryonal carcinoma cells to arsenite during their differentiation into skeletal myotubes and sensory neurons, and determined that several miRNAs known to be involved in cellular differentiation were altered. Interestingly, the miRNA-466-669 cluster, along with its host gene Sfmbt2, were significantly increased following arsenic exposure. When the miR-466-669 cluster was inhibited by anti-miRs, the inhibitory effects on differentiation mediated by arsenic were rescued, in part, due to increases in NeuroD1, a transcription factor which is a target gene of the cluster. These results indicate that miR-466-669 cluster is highly expressed when cells are exposed to arsenic, and that this cluster plays a role in impairing stem cell differentiation. MATERIALS AND METHODS Cell culture and differentiation Mouse embryonal carcinoma P19 cells are commonly used to study the differentiation process because they can differentiate into multiple cell types, including skeletal myocytes, cardiac myocytes, and neurons. P19 cells (ATCC, Manassas, Virginia) were cultured in α-minimal essential medium (MEM) medium (Hyclone, Logan, Utah) with 7.5% bovine calf serum (Hyclone), 2.5% fetal bovine serum (Sigma-Aldrich, St Louis, Missouri), 1% L-glutamine, and 1% penicillin/streptomycin (designated as growth medium). Cells were maintained in a humidified incubator containing 5% CO2 at 37°C. Cells were exposed to 0 or 0.5 μM arsenic (as sodium arsenite; Sigma) and 1% DMSO during embryoid body formation and differentiation (Liu and Bain, 2014). Briefly, cells were aggregated for 2 days into embryoid bodies using the hanging drop method (500 cells/20 μl drop). Each embryoid body was transferred to a 96-well ultralow attachment plate for an additional 3 days (day 5). The embryoid bodies were then transferred to a 48-well plate coated with 0.1% gelatin and allowed to differentiate for 4 additional days (day 9). Culture medium was renewed every 48 h. MicroRNA microarrays P19 cells were exposed to 0 or 0.5 μM arsenic as sodium arsenite and induced to differentiate for 9 days (n = 2 per group). Cells were harvested, and total RNA was extracted with TRIzol (Life Technologies, Grand Island, New York) with extra ethanol cleaning step to improve the quality of RNA used. The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform GPL21312). The array included probes for all available mature mouse miRNAs (miRBase database, Release 21, July 2014). Fluorescence intensity was corrected by background subtraction and normalized with the statistical mean of all detectable transcripts. Intensity values for the control and 0.5 μM arsenic-exposed cells were averaged, and statistical differences assessed using Student’s t test. Data analysis was conducted by LC Science. MicroRNA reverse transcription and miRNA-specific qPCR Total RNA (1 μg) was used to reverse transcribe miRNA (QuantiMir RT kit, System Biosciences, Mountain View, California). Quantitative real-time PCR was performed with RT2 SYBR green master mix (Qiagen, Valencia, California) using adapter-specific reverse primers. Gene-specific miRNA forward primers are listed in Table 1. A standard curve was generated by serial dilution to determine PCR efficiency. Samples were run in triplicate and expression data were analyzed and normalized with U6 as a housekeeper using the comparative threshold (Ct) method (Livak and Schmittgen, 2001). Table 1. Primer Sequences Gene Name  Forward (5′–3′)  Reverse (5′–3′)  miR-9  ATAAAGCTAGATAACCGAAA  Universal reverse primer  miR-92a  TATTGCACTTGTCCCGGCCTG  miR-199a  ACAGTAGTCTGCACATTGGTTA  miR-291a  CATCAAAGTGGAGGCCCTCT  miR-466b  CCAAGCCAACTTTATGTCAGGG  miR-466f  ATCAGCCAACCAAGAGACAGCAGA  miR-466g  GCCTCTGAGCACAGAAAGTCATCA  miR-466h  AAAGAACTGCGGCAAGTTTTTG  miR-466i  GCCAAGCAACCTGCTGAAAGTCTA  miR-467a  CATATACATACACACACCTACA  miR-467d  ATATACATACACACACCTACAC  miR-669c  TACACACACACACACAAGTAAA  miR-669e  TGAATATACACACACTTACAC  miR-669f  CATATACATACACACACACGTAT  miR-669p  CATAACATACACACACACACGTAT  miR-709  GGAGGCAGAGGCAGGAGGA  U6  TGGCCCCTGCGCAAGGATG  Consensus-1  TATACATACACACACACACATA  Consensus-2  TGTGTGCATGTGCATGTGTGTA  Consensus-3  TAACTCCGTGCATGTATATG  Consensus-4  AGTTGTGTGTGCATGTGTAT  Sfmbt2  AAGATAACCGGCTCGCAAATG  TCTCTTCCAAATAGTCTCCCCAG  Lats2  CCAGAAAGGGAACCACATGA  CTCTGCTCCAGGGTCTTTAAC  NeuroD1  GTCATGAGTGCCCAGCTTAAT  AAGGGCTGCCTTCTGTAAAC  Gapdh  TGCGACTTCAACAGCAACTC  ATGTAGGCCATGAGGTCCAC  Gene Name  Forward (5′–3′)  Reverse (5′–3′)  miR-9  ATAAAGCTAGATAACCGAAA  Universal reverse primer  miR-92a  TATTGCACTTGTCCCGGCCTG  miR-199a  ACAGTAGTCTGCACATTGGTTA  miR-291a  CATCAAAGTGGAGGCCCTCT  miR-466b  CCAAGCCAACTTTATGTCAGGG  miR-466f  ATCAGCCAACCAAGAGACAGCAGA  miR-466g  GCCTCTGAGCACAGAAAGTCATCA  miR-466h  AAAGAACTGCGGCAAGTTTTTG  miR-466i  GCCAAGCAACCTGCTGAAAGTCTA  miR-467a  CATATACATACACACACCTACA  miR-467d  ATATACATACACACACCTACAC  miR-669c  TACACACACACACACAAGTAAA  miR-669e  TGAATATACACACACTTACAC  miR-669f  CATATACATACACACACACGTAT  miR-669p  CATAACATACACACACACACGTAT  miR-709  GGAGGCAGAGGCAGGAGGA  U6  TGGCCCCTGCGCAAGGATG  Consensus-1  TATACATACACACACACACATA  Consensus-2  TGTGTGCATGTGCATGTGTGTA  Consensus-3  TAACTCCGTGCATGTATATG  Consensus-4  AGTTGTGTGTGCATGTGTAT  Sfmbt2  AAGATAACCGGCTCGCAAATG  TCTCTTCCAAATAGTCTCCCCAG  Lats2  CCAGAAAGGGAACCACATGA  CTCTGCTCCAGGGTCTTTAAC  NeuroD1  GTCATGAGTGCCCAGCTTAAT  AAGGGCTGCCTTCTGTAAAC  Gapdh  TGCGACTTCAACAGCAACTC  ATGTAGGCCATGAGGTCCAC  Quantitative PCR P19 cells were exposed to 0 or 0.5 μM arsenic, induced to differentiate, and collected at day 0, 2, 5, and 9 (n = 3 per treatment group per day). Total RNA were extracted using TRIzol, and 2 μg RNA were reverse transcribed using M-MLV reverse transcriptase (Promega, Madison, Wisconsin). Real-time qPCR was conducted using RT2 SYBR green (Qiagen) and gene-specific primers (Table 1). A standard curve was generated with 5 concentration points (10−3–10−7 ng cDNA) to quantify expression levels and assess reaction efficiency. Samples were run in triplicate and gene expression data were analyzed and normalized with Gapdh as a housekeeper using the comparative threshold (Ct) method. Gapdh is an often-used housekeeping gene in differentiating stem cells (Willems and Leyns, 2008), and our present and published studies show that its expression is not changed in P19 cells with arsenic exposures up to 1 μM. miR-466-669 cluster target prediction Because the miR-466 cluster appeared to be important in regulating cellular differentiation, potential targets for individual miR-466-467-669 cluster members were initially examined using miRanda (microRNA.org). Because cluster members are presumably transcribed all together, once target genes with good mirSVR scores were initially predicted, we then assessed whether multiple members of the cluster targeted those genes using miRanda (Betel et al., 2010; Enright et al., 2003). The genes in which at least 5 miRNAs from the cluster were predicted to bind were then validated with TargetScan (targetscan.org) and PITA (genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html). Only those genes to which miRNAs were predicted by at least 2 of the programs were considered to be potential targets (Table 2). Table 2. Neurogenic Transcription Factors that Are Potentially Targeted by the miR-466/669 Cluster Target Gene Name  miRNA Name  NeuroD1  miR-466fh  miR-466de-3p  miR-466bcd-5p  miR-467g  miR-669abdkl  miR-669h-5p  NeuroD2  miR-466fkl  miR-466bc-3p  miR-466d-5p  miR-669ab  NeuroD4  miR-466gl  miR-466abcde-3p  miR-466abce-5p  miR-467bcg  miR-669gij  miR-669h-3p  NeuroD6  miR-466gh  miR-467egh  miR-669ao  Neurogenin 1  miR-467ae  miR-669b  Neurogenin 2  miR-466-fkl  miR-466abcdel-3p  miR-466adef-5p  miR-467dg  miR-669bfk  Target Gene Name  miRNA Name  NeuroD1  miR-466fh  miR-466de-3p  miR-466bcd-5p  miR-467g  miR-669abdkl  miR-669h-5p  NeuroD2  miR-466fkl  miR-466bc-3p  miR-466d-5p  miR-669ab  NeuroD4  miR-466gl  miR-466abcde-3p  miR-466abce-5p  miR-467bcg  miR-669gij  miR-669h-3p  NeuroD6  miR-466gh  miR-467egh  miR-669ao  Neurogenin 1  miR-467ae  miR-669b  Neurogenin 2  miR-466-fkl  miR-466abcdel-3p  miR-466adef-5p  miR-467dg  miR-669bfk  miRNAs with good mirSVR scores were initially predicted using miRanda (microRNA.org), and then further validated with PITA (genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html) and TargetScan (targetscan.org). Anti-miRNA transfection Four consensus sequences which target the miR-466-669 cluster were selected for anti-miRNA generation (Figure 4). The oligonucleotides (Integrated DNA Technologies, Coralville, Iowa) were modified by employing 2′-O-methyl RNA nucleotides linkages positioned at both ends to block exonuclease attack (Lennox et al., 2013). P19 cells were transfected with 100 nM miRNA inhibitor using Lipofectamine 2000 (Invitrogen, Waltham, Massachusetts) and allowed to differentiate as described earlier for 5 days with or without 0.5 μM sodium arsenite (n = 3 replicates per exposure group). At day 5, a portion of the embryoid bodies were collected and stored in either TRIzol at −80°C for transcript expression or were fixed overnight in 10% neutral buffered formalin at 4°C for immunohistochemistry. A second portion of the embryoid bodies were transferred to gelatin coated plates and cultured for 4 more days without additional anti-miRNA to observe morphological changes. Six individual differentiated embryoid bodies per treatment group were imaged on day 9. The length of differentiating cells moving away from the embryoid body was quantified using ImageJ. Afterwards, cells were collected and stored in TRIzol at −80°C for transcript expression. Immunohistochemistry Following overnight fixation of the day 5 embryoid bodies, they were dehydrated in ethanol, cleared in xylene, embedded in paraffin, and cut in 5 μm sections. Citric acid buffer (pH = 6) was used for antigen retrieval. Slides were blocked with 5% bovine serum albumin (BSA) in PBST. The NeuroD1 primary antibody (Abcam, Cambridge, Massachusetts, AB60704; 1:250 dilution) was incubated overnight at 4°C. Slides were incubated in secondary antibodies conjugated to Alexa Fluor 594 (1:2000), and counterstained with DAPI (Invitrogen). Fluorescence intensity was assessed with Nikon Ti Eclipse Inverted microscope, and the expression of NeuroD1 was quantified using ImageJ. In brief, we selected 10 representative cells per field of view for each section, making sure to avoid cells that were undergoing mitosis and those in the center of the embryoid body that would not be undergoing differentiation. The total cell area was traced and average fluorescence intensity quantified. Afterwards, the average nuclear intensity for the same cell was quantified. Cytoplasmic fluorescence was determined by subtraction of the nuclear from the total cell fluorescence. Data from the 10 representative cells were averaged, giving an overall mean per section. Statistical analysis Results are expressed as mean ± SD. Data were analyzed by ANOVA followed by Tukey’s or by Student’s t test, as appropriate. Statistical significance was achieved at P < .05. RESULTS Arsenic Alters miRNA Expression Profiles during P19 Cell Differentiation P19 cells were exposed to 0 or 0.5 μM arsenite during a 9-day differentiation period. Changes in miRNA expression after 9 days were determined (doi: 10.5061/dryad.sc281) using murine miRNA microarrays that contain 1900 mature miRNAs (miRBase Release 21.0). A total of 59 miRNAs were significantly changed, with 27 miRNAs decreased and 32 miRNAs increased, in the arsenic-exposed cells compared with control cells (Figure 1). Several of these miRNAs are known to impact development and stem cell differentiation. miR-92a is highly expressed in stem cells and its expression decreases when cells undergo differentiation (Wilson et al., 2009). miR-291a belongs to the miR-290-295 cluster, which promotes pluripotency and prevents cells from differentiating (Lichner et al., 2011). miR-709 inhibits adipocyte differentiation by targeting the GSK3β/Wnt signaling pathway (Chen et al., 2014). Arsenic exposure to P19 cells increased these miRNAs, which is consistent with the repression of differentiation (Figure 1). These changes were validated by miRNA qPCR, in which miR-92a, miR-291a, and miR-709 were all significantly induced in arsenic treated cells by 3-, 3.5-, and 1.6-fold, respectively (Figs. 2A–C). In contrast, miR-9 induces neuronal cell differentiation and miR-199a increases chondrogenesis in the skeletal system (Lee et al., 2009; Lin et al., 2009; Zhao et al., 2009). Arsenite exposure reduced the expression of miR-9 and miR-199a by 2- and 2.5-fold, respectively (Figs. 2D and 2E). These data suggest that during cell differentiation, arsenic exposure increases miRNAs associated with pluripotency and decreases those associated with cell lineage commitment. Figure 1. View largeDownload slide Expression profiles of microRNAs (miRNAs) between control and arsenic exposure in differentiating P19 cells. P19 cells were induced to differentiate with or without 0.5 µM of arsenic for 9 days. MicroRNA expression was detected via a miRNA microarray and plotted as a heat map using CIM Miner (NIH). Darker shading indicates increased expression. Only statistically different miRNAs are listed in the map (Student’s t test; P value < .05). Figure 1. View largeDownload slide Expression profiles of microRNAs (miRNAs) between control and arsenic exposure in differentiating P19 cells. P19 cells were induced to differentiate with or without 0.5 µM of arsenic for 9 days. MicroRNA expression was detected via a miRNA microarray and plotted as a heat map using CIM Miner (NIH). Darker shading indicates increased expression. Only statistically different miRNAs are listed in the map (Student’s t test; P value < .05). Figure 2. View largeDownload slide Validation of miRNA transcript levels by qPCR. Day 9 differentiated P19 cells were used to confirm miRNA expression by qPCR (n = 3 per treatment). Significantly changed miRNAs with known roles in development were examined, including miR-92a (A), miR-291a (B), miR-709 (C), miR-199a (D), and miR-9 (E). Expression values were normalized with U6 snRNA and fold differences calculated from control cells using the delta Ct method. Values are expressed as mean ± SD and statistical differences were determined by Student’s t test (*P < .05). Figure 2. View largeDownload slide Validation of miRNA transcript levels by qPCR. Day 9 differentiated P19 cells were used to confirm miRNA expression by qPCR (n = 3 per treatment). Significantly changed miRNAs with known roles in development were examined, including miR-92a (A), miR-291a (B), miR-709 (C), miR-199a (D), and miR-9 (E). Expression values were normalized with U6 snRNA and fold differences calculated from control cells using the delta Ct method. Values are expressed as mean ± SD and statistical differences were determined by Student’s t test (*P < .05). Arsenic Induces the miR-466-669 Cluster and Its Host Gene, Sfmbt2 Clustered miRNAs are often coexpressed, and several studies have shown that they have a small but significant tendency to cotarget the same genes (Hausser and Zavolan, 2014). As shown in Figure 1, 10 of the top 31 miRNAs whose expression were increased in response to arsenic are in the miR-466-467-669 cluster. To verify the expression of these 10 miRNAs, qPCR was used. With the exception of miR-466i, all miRNA-466-467-669 family members were significantly induced in the arsenic-exposed cells by 1.7- to 3.7-fold (Figure 3A). Among those transcripts, miR-466g, -467d, -467a, and -669p have more than 3-fold increase in day 9 cells exposed to arsenic. Figure 3. View largeDownload slide Arsenic exposure induces members of the miR-466-669 cluster along with its host gene, Sfmbt2. P19 cells were differentiated and RNA was extracted from cells exposed to 0 or 0.5 μM arsenite on days 0, 2, 5, and 9 (n = 3 per treatment per day). MicroRNA or mRNA expression was determined by qPCR. Day 9 samples were used to determine the expression of miRNA-466-669 cluster genes (A) and the host gene Sfmbt2 (B). Samples from days 0, 2, 5, to 9 were used to determine the expression of miR-467d (C), miR-669p (D), and Sfmbt2 (E). Expression values were normalized with U6 snRNA for the miRNAs, and Gapdh for Sfmbt2. Fold changes were compared with unexposed cells, and time-dependent qPCR expression fold changes were compared with day 0 unexposed cells. Data are shown as mean ± SD. Statistical differences were determined by ANOVA followed by Tukey’s test or by Student’s t test (*P < .05). Figure 3. View largeDownload slide Arsenic exposure induces members of the miR-466-669 cluster along with its host gene, Sfmbt2. P19 cells were differentiated and RNA was extracted from cells exposed to 0 or 0.5 μM arsenite on days 0, 2, 5, and 9 (n = 3 per treatment per day). MicroRNA or mRNA expression was determined by qPCR. Day 9 samples were used to determine the expression of miRNA-466-669 cluster genes (A) and the host gene Sfmbt2 (B). Samples from days 0, 2, 5, to 9 were used to determine the expression of miR-467d (C), miR-669p (D), and Sfmbt2 (E). Expression values were normalized with U6 snRNA for the miRNAs, and Gapdh for Sfmbt2. Fold changes were compared with unexposed cells, and time-dependent qPCR expression fold changes were compared with day 0 unexposed cells. Data are shown as mean ± SD. Statistical differences were determined by ANOVA followed by Tukey’s test or by Student’s t test (*P < .05). The miR-466-467-669 cluster is located on chromosome 2 within the 10th intron of the Scm-like with 4 Mbt domains 2 (Sfmbt2) gene. Thus, the expression of the host gene was also assessed by qPCR to see if coregulation of Sfmbt2 and the miR-466-669 cluster existed. Indeed, on day 9, Sfmbt2 expression is induced by 2-fold in the arsenic-exposed cells (Figure 3B). We further wanted to determine if the miRNA cluster and host gene expression patterns were similar during embryoid body formation and differentiation, so transcript levels for the 2 most highly expressed cluster member following arsenic exposure, miR-467d and -669p, were selected. Their expression patterns were examined by real-time PCR at days 0, 2, 5, and 9 of P19 cell differentiation. Starting at day 2 of embryoid body formation, the levels of miR-467d, miR-669p, and Sfmbt2 were dramatically induced by 11-, 40-, and 55-fold, respectively, as compared with the day 0 cells (Figs. 3C–E). This suggests that these miRNAs play a role in early embryonic stem cell differentiation. As time goes on, expression of all 3 transcripts decreases in the control cells, while the levels remain higher and decline more slowly in the arsenic-exposed cells. For example, at day 5, the highest point of transcript expression, miR-467d, miR-669p, and Sfmbt2 were induced by arsenic by 3-, 3-, and 2-fold, respectively (Figs. 3C–E). Sfmbt2 has a similar pattern of expression as the miR-466-669 cluster suggesting that increased transcription of the host gene drives expression of this miRNA cluster. Potential Targets of miR-466-669 Cluster Members Include Neurogenic Transcription Factors With multiple members of the miR-466-669 cluster induced by arsenic, we wanted to examine potential target genes involved in cellular differentiation processes. Initially, we used miRanda (microRNA.org) to examine target mRNAs for each of the 65 cluster members. The list was narrowed down by requiring at least 5 members of the miR-466-669 cluster be predicted to bind to the gene’s 3′-UTR. Next, we used 2 other miRNA prediction tools, PITA analysis (genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html) (Kertesz et al., 2007) and TargetScan (targetscan.org) (Agarwal et al., 2015) to examine their prediction consistency across the 3 different platforms. From these criteria, we developed a list of 6 potential genes involved in neurogenic differentiation in which at least 2 different programs predicted a particular miRNA to bind to the transcript (Table 2). NeuroD1 had the highest number of miRNAs predicted to bind to it, with 25 cluster members predicted using miRanda and 14 miRNAs predicted using at least 2 programs, while NeuroD4 had 20 cluster members predicted using miRanda and 18 miRNAs predicted using at least 2 programs (Table 2). miR-466-669 Cluster Members Share Multiple Consensus Sequences Because we could not transfect anti-miRNAs of all cluster members simultaneously, consensus anti-miRNAs were designed. Clustal Omega was used to determine sequence similarity between all mature miRNA sequences within this cluster (miRBase version 21). The cluster sequences used included 26 members of the miR-466 family, 13 sequences in the miR-467 family, and 26 sequences in the miR-669 family. A total of 24 miRNA-3p members have high degree of similarity, as shown in consensus sequence 1 (Figure 4A). This first consensus sequence is modified from another study which aligned all miRNA-3p members together (Luo et al., 2014). Interestingly, 15 of the -3p members share the seed sequence, UAUACAU, with 2 individual cluster members being in the miR-466 family, 6 members belonging to the miR-467 family, and 7 members being in the miR-669 family (Figure 4A). The miRNA-5p members clustered together but did not have the same degree of similarity to each other. So, these were subdivided into 3 additional clusters with miR-466 (h-5p, j, m-5p) grouping together (Figure 4B), miR-467 (a, b, c, d, e, h-5p) grouping together (Figure 4C), and miR-669 (a, b, c, d, e, f, l, o, n-5p) grouping together (Figure 4D). These sequence similarities suggest that these miRNA families may work together and target similar transcripts. Figure 4. View largeDownload slide The miR-466-669 cluster share sequence similarities. Sequences of mature miRNA from the miR-466-669 cluster were aligned using Clustal Omega. All mature sequences were initially aligned together and then were subdivided into 4 major groups which have high similarity. The highlighted nucleotides were used to derive the 4 consensus sequences (as light blue). An asterisk indicates the sequence identity among all miRNAs within the group. Figure 4. View largeDownload slide The miR-466-669 cluster share sequence similarities. Sequences of mature miRNA from the miR-466-669 cluster were aligned using Clustal Omega. All mature sequences were initially aligned together and then were subdivided into 4 major groups which have high similarity. The highlighted nucleotides were used to derive the 4 consensus sequences (as light blue). An asterisk indicates the sequence identity among all miRNAs within the group. Inhibition of the miR-466-669 Cluster Rescues the Inhibitory Effects of Arsenic on Stem Cell Differentiation To evaluate the effects of the miR-466-669 families on stem cell differentiation and its inhibition by arsenic, each of the 4 consensus anti-miRNAs (Figure 4) were added during cell differentiation from days 0 to 5, when cells were cultured with or without 0.5 μM arsenic (Figure 5). A portion of day 5 embryoid bodies were harvested for qPCR and immunohistochemistry. The remaining embryoid bodies were grown out to day 9 for qPCR and morphological analyses. When cells were transfected with all 4 consensus inhibitors (mixed anti-miRNAs), the cells exposed to arsenic were now able to form neuronal cells, indicating a rescue effect (Figure 5A). To quantify the relative amount of differentiating cells, their migration distance away from the embryoid body was measured. On average, the unexposed embryoid bodies had differentiating cells migrating out to 0.87 mm (Figure 5B). The cells exposed to arsenic had 5.1-fold less differentiating cells relative to control. When cells were transfected with consensus anti-miRNAs, the relative amount of differentiating cells was restored to control levels (Figure 5B). Lastly, to confirm the morphological observation that these differentiating cells were neurons, mRNA levels of NeuroD1 were assessed by qPCR arsenic exposure during the 9 days of differentiation reduced the number of NeuroD1 transcripts by 2-fold, while transfection of the consensus anti-miRNAs restored the level of NeuroD1 mRNA back to control levels (Figure 5C). Figure 5. View largeDownload slide Inhibiting the miR-466-669 cluster during differentiation rescues the morphological loss of neurons following arsenic exposure. P19 cells were transfected with 100 nM anti-miRNA oligonucleotides which target 4 consensus sequences of the miRNA-466-467-669 cluster. Cells were coexposed to 0 or 0.5 μM arsenic for the 5 days of embryoid body formation. Only the arsenic exposure was maintained for the entire 9 days of differentiation, after which cell morphology was observed. Transfections include oligonucleotides sequences that do not target any miRNAs, designated as negative control, (N.C.), and a mixed transfection that combined all consensus anti-miRNAs. Arrows indicate neuronal cells (A). The distance of cells differentiating away from the embryoid body was quantitated using ImageJ and is expressed in mm (n = 6 replicate embryoid bodies per group) (B). mRNA levels of the neuronal cell marker NeuroD1 on day 9 was assessed by qPCR (C). mRNA expression levels were normalized with Gapdh using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#). Figure 5. View largeDownload slide Inhibiting the miR-466-669 cluster during differentiation rescues the morphological loss of neurons following arsenic exposure. P19 cells were transfected with 100 nM anti-miRNA oligonucleotides which target 4 consensus sequences of the miRNA-466-467-669 cluster. Cells were coexposed to 0 or 0.5 μM arsenic for the 5 days of embryoid body formation. Only the arsenic exposure was maintained for the entire 9 days of differentiation, after which cell morphology was observed. Transfections include oligonucleotides sequences that do not target any miRNAs, designated as negative control, (N.C.), and a mixed transfection that combined all consensus anti-miRNAs. Arrows indicate neuronal cells (A). The distance of cells differentiating away from the embryoid body was quantitated using ImageJ and is expressed in mm (n = 6 replicate embryoid bodies per group) (B). mRNA levels of the neuronal cell marker NeuroD1 on day 9 was assessed by qPCR (C). mRNA expression levels were normalized with Gapdh using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#). To verify if the miRNAs themselves were knocked down by mixed anti-miRNAs, primers for the 4 individual consensus sequences were designed. Expression of each consensus sequence was examined in day 5 embryoid bodies treated with a scrambled miRNA or a mixture of the 4 consensus anti-miRNAs. Arsenic exposure significantly induced the expression of consensus sequences 1 and 3 by 1.5- to 2-fold over control cells (Figs. 6A and 6C). Consensus sequence 1 contains mostly the miR-466-3p’s, miR-467-3p’s, and miR-669-3p’s while consensus sequence 3 contains the miRs-467-5p’s. Importantly, the induced expression by arsenic in the consensuses 1 and 3 groups was knocked down to control levels when treated with the mixed anti-miRNAs (Figs. 6A and 6C), while expression of consensuses 2 and 4 was not altered by either arsenic or anti-miRNA transfection (Figs. 6B and 6D). The expression of a representative consensus sequence 1 miRNA, miR-669a-3p, was examined by qPCR. Arsenic induced its expression by 1.8-fold, and the mixed anti-miRNA transfections knocked down its expression in both control and arsenic-exposed cells (Figure 6E). To determine if the inhibitory effects are due to the miRNAs themselves or their host gene, Sfmbt2 expression was also examined by qPCR. Arsenic significantly induced Smbt2 expression whether or not the anti-miRNAs were added (Figure 6F), and there was a significant inhibition in Sfmbt2 expression when the mixed anti-miRNAs were added to the arsenic-exposed group. Finally, we assessed whether the anti-miR transfection could increase the level of Lats2, a known target of miR-466f-3p (Zheng et al., 2011). Indeed, the mixed consensus anti-miRNAs transfection significantly increases Lats2 levels by approximately 1.6-fold (Figure 6G). These results imply that the mixed anti-miRNAs are functional and active. Figure 6. View largeDownload slide Consensus anti-miRNAs are active and functional and can rescue the expression of miR-466-467-669 target genes. P19 cells were transfected with all 4 miRNA inhibitors, with or without 0.5 μM arsenic (n = 3 replicates), allowed to form embryoid bodies for 5 days, and examined for mRNA expression of each of the 4 consensus sequences (A–D), 1 individual miRNA, miR-669a-3p (E), the host gene Sfmbt2 (F), and a known target gene for the consensus 1 cluster, Lats2 (G). mRNA expression levels were normalized with Gapdh, and miRNA expression levels were normalized with shRNA U6, using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#) in (A–F). A 1-way ANOVA followed by Tukey’s test (P < .05) was run to determine significance (#) in (G). Figure 6. View largeDownload slide Consensus anti-miRNAs are active and functional and can rescue the expression of miR-466-467-669 target genes. P19 cells were transfected with all 4 miRNA inhibitors, with or without 0.5 μM arsenic (n = 3 replicates), allowed to form embryoid bodies for 5 days, and examined for mRNA expression of each of the 4 consensus sequences (A–D), 1 individual miRNA, miR-669a-3p (E), the host gene Sfmbt2 (F), and a known target gene for the consensus 1 cluster, Lats2 (G). mRNA expression levels were normalized with Gapdh, and miRNA expression levels were normalized with shRNA U6, using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#) in (A–F). A 1-way ANOVA followed by Tukey’s test (P < .05) was run to determine significance (#) in (G). Members of the miR-466-669 Cluster Repress NeuroD1 Expression Transfection of consensus anti-miRNA mixture morphologically restored cellular differentiation, particularly into neurons (Figure 5A) and miRNA target prediction tools suggested that several transcription factors were likely targets of the miR-466-467-669 cluster (Table 2). So, we determined whether transfection of the mixed consensus anti-miRNAs could restore the expression of one of these predicted transcription factors, NeuroD1. When NeuroD1 protein expression was examined by immunohistochemistry (Figure 7A), exposure to 0.5 μM arsenic for 5 days reduced both total and nuclear expression of NeuroD1 by 1.5- and 1.6-fold, respectively (Figure 7B). Coexposure with the mixed consensus anti-miRNAs significantly rescued NeuroD1 expression in the arsenic-exposed cells (Figure 7B). Similarly, NeuroD1 transcript levels were reduced by 1.4-fold in the arsenic-exposed cells, and the mixed consensus anti-miRNAs were able to rescue NeuroD1 mRNA expression back to control levels (Figure 7B). NeuroD2 and NeuroD6 are not expressed in differentiating P19 cells (data not shown), so levels of these 2 transcription factors were not examined. NeuroD4 is expressed but its mRNA levels were not changed by anti-miRNA transfection (data not shown). These results suggest that the miR-466-467-669 cluster is induced by arsenic, and these miRNAs act to reduce cellular differentiation by inhibiting transcription factors important in cell fate determination, such NeuroD1. Figure 7. View largeDownload slide Mixed consensus miRNA inhibitors rescue arsenic’s inhibitory effects on NeuroD1 expression. P19 cells were transfected with a combined mixture of the 4 anti-miRNAs, with or without 0.5 μM arsenic (n = 3 replicates per anti-miRNA and per exposure group), and allowed to form embryoid bodies for 5 days. Immunohistochemistry was used to examine expression of NeuroD1 protein (red) in the embryoid bodies. Cells were counterstained with DAPI (blue) to indicate the nuclei (A). High magnification images of cells are shown in the (A) inserts. For 10 representative cells (examples are shown in the blue boxes), expression of NeuroD1 in the whole cell, cytoplasm, and nuclei were quantified using ImageJ (B). NeuroD1 transcript levels were quantified by qPCR, and normalized with Gapdh using the comparative delta Ct method with fold changes compared with N.C. (B). Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#). Figure 7. View largeDownload slide Mixed consensus miRNA inhibitors rescue arsenic’s inhibitory effects on NeuroD1 expression. P19 cells were transfected with a combined mixture of the 4 anti-miRNAs, with or without 0.5 μM arsenic (n = 3 replicates per anti-miRNA and per exposure group), and allowed to form embryoid bodies for 5 days. Immunohistochemistry was used to examine expression of NeuroD1 protein (red) in the embryoid bodies. Cells were counterstained with DAPI (blue) to indicate the nuclei (A). High magnification images of cells are shown in the (A) inserts. For 10 representative cells (examples are shown in the blue boxes), expression of NeuroD1 in the whole cell, cytoplasm, and nuclei were quantified using ImageJ (B). NeuroD1 transcript levels were quantified by qPCR, and normalized with Gapdh using the comparative delta Ct method with fold changes compared with N.C. (B). Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#). DISCUSSION This study demonstrates that arsenic can impair stem cell differentiation by altering miRNA expression, including miR-9, miR-92a, miR-199a, miR-291a, miR-709, and members of the miR-466-467-669 cluster. In addition, these data ascribe a novel function for the murine miR-466-467-669 cluster in embryonic stem cells. This cluster appears to be inhibiting the expression of NeuroD1, thereby reducing the cell’s ability to differentiate into neurons. Previous studies have shown that arsenic at concentrations between 0.1 and 0.5 μM inhibit P19 embryonic stem cell differentiation into skeletal muscle and sensory neurons (Bain et al., 2016; Hong and Bain, 2012; Liu and Bain, 2014; McCoy et al., 2015). One potential mechanism for this inhibition is through changes in miRNA expression in arsenic-exposed cells, as miRNAs have been shown to play a role in arsenic toxicity and carcinogenesis (reviewed in Bustaffa et al., 2014; Paul and Giri, 2015; Ren et al., 2011). Comparing over 1900 miRNAs from the control and arsenic-exposed P19 cells undergoing differentiation indicated that 10 of the top 31 miRNAs whose expression was increased in response to arsenic are in the miR-466-467-669 cluster. The miRNA-466-467-669 cluster is one of the largest miRNA clusters in mice and rats and is located in the 10th intron of the Sfmbt2 gene, a polycomb group protein (Lehnert et al., 2011). Studies have implicated changes in the expression of this cluster following alterations in glucose levels. For example, miR-466h is induced in chinese hamster ovary (CHO) cells treated with nutrient-depleted or low glucose media, which induces apoptosis (Druz et al., 2011,, 2012). In contrast, high levels of glucose in neural stem cells can reduce the expression of miR-466a-3p and miR-466d-3p (Shyamasundar et al., 2013). Our studies show that arsenic exposure during P19 stem cell differentiation increases the expression of multiple members of the miR-466 cluster, with these 3 specific transcripts: miR-466a, -466d, and -466h being increased, albeit not significantly based upon the arrays. We did not validate the expression these specific transcripts using qPCR. However, these specific miRNAs all do fall within the consensus 1 sequences, as shown in Figure 4. Several additional studies have shown that the miR-466-467-669 family members appear to be involved in cellular differentiation in a variety of cell types and models. For example, miR-466 family members are increased in bone marrow-derived progenitor cells derived from diabetic animals. These animals have reduced angiogenesis, which is a result of the miR-466 family members targeting and decreasing the expression of angiopoetin (Wang et al., 2017). Similarly, miR-466 reduces lymphangiogenesis in primary lympathic endothelial cells by targeting a pro-lymphatic transcription factor called Prospero homeobox 1 (Prox1). This reduction in lymphangiogenesis can be rescued by a miR-466 inhibitor (Seo et al., 2015). In primary murine neural stem cells, decreased expression of miR-466a-3p and miR-466d-3p results in increased doublecortin levels, along with an increase in neurogenic differentiation (Shyamasundar et al., 2013). Similar to the studies described earlier, our data indicate that both miR-466a-3p and -466d-3p are increased by approximately 2-fold in the stem cells with impaired differentiation following arsenic exposure. Taken together, these data imply that one function of the miR-466-467-669 cluster is to regulate cellular differentiation. Because multiple members of the miR-466-467-669 family were increased following arsenic treatment, an approach that involved developing consensus sequences for the 65 cluster members was undertaken so that anti-miRNAs could be produced. Using Clustal analyses, 4 separate consensus sequences were identified, with the -3p sequences falling into consensus 1 and the -5p sequences being divided into the consensuses 2–4 groups. Because arsenic inhibits differentiation into neurons, target genes discovered by in silico analyses included NeuroD1, NeuroD2, NeuroD4, NeuroD6, Neurogenin 1, and Neurogenin 2. Several of these genes have been previously shown to be inhibited after arsenic exposure (Hong and Bain, 2012; Liu and Bain, 2014; McCoy et al., 2015). Transfection of consensus anti-miRNAs designed to inhibit most members of the miR-466-467-669 cluster showed that neurogenesis was increased when the cells were exposed to arsenic, likely due to the increased expression of NeuroD1. These results imply that the miR-466-467-669 cluster reduces neurogenesis by targeting transcription factors required for neuronal differentiation. Although NeuroD2 and 6 are potential targets genes for the miR-466-669 cluster, they are not expressed in differentiating P19 cells. They both appear to play a role in corticogenesis and the maintenance of pyramidal neurons (Bormuth et al., 2013), and thus, they are unlikely to be expressed using our procedure, in which the cells differentiate into sensory neurons. In addition to the miR-466-467-669 cluster miRNAs such as the miR-290 cluster are also important in maintaining stem cell pluripotency and inhibiting embryonic stem cell differentiation into multiple lineages (Sinkkonen et al., 2008). Interestingly, several members of miRNA-290 cluster were induced by arsenic in our microarray, including a 4-fold induction of miR-291a and miR-291b, a 2-fold induction of miR-290a, a 2.8-fold induction of miR-292a, and a 4.7-fold fold induction of miR-294. We further determined the time-dependent expression of the most significantly induced family member, miR-291a by qPCR. As the cells were undergoing differentiation, miR-291a levels decreased by 3.2-fold on day 5 and 4-fold on day 9. However, arsenic exposure kept miR-291a at nearly the same levels as in day 0 stem cells (Supplementary Figure 1). This indicates that cellular pluripotency is maintained when cells are exposed to arsenic. However, mechanisms by which arsenic induces the miR-290 cluster expression are not known. Several studies report that arsenic alters miRNA expression. Lung epithelial cells chronically exposed to 1 μM sodium arsenite for 26 weeks were transformed into cancer-like cells that had significantly reduced miR-199a expression (He et al., 2014). Interestingly, the reduction of miR-199a is consistent with our expression data. This reduction may indicate that altered miR-199a expression occurs as a result of arsenic exposure, or occurs when cells are not differentiating appropriately. As measured by the arrays and by qPCR, miR-9 is significantly reduced by 2- to 3-fold in arsenic exposed embryoid bodies. miR-9 is known to be induced during neural lineage differentiation, and its inhibition blocks neurogenesis in embryonic stem cells (Krichevsky et al., 2006). This indicates that miR-9 plays an important role during embryonic development and reduction by arsenic might also be involved in the inhibitory effects seen in our study. Collectively, our results indicate that low-level arsenic exposure to P19 embryonic stem cells during their differentiation alters the expression profile of miRNAs, including multiple members of miRNA-466-467-669 cluster, along with their host gene Sfmbt2. These miRNAs appear to inhibit neurogenesis by targeting NeuroD1. These data ascribe a novel mechanism for arsenic’s ability to inhibit cellular lineage development. ACKNOWLEDGMENTS The authors thank Dana Symkowicz for her assistance with immunohistochemistry. FUNDING Funding for this study was provided by the National Institute of Environmental Health Sciences (grant number ES023065). SUPPLEMENTARY DATA Supplementary data are available at Toxicological Sciences online. REFERENCES Agarwal V., Bell G. W., Nam J.-W., Bartel D. P. ( 2015). Predicting effective microRNA target sites in mammalian mRNAs. Elife  4, eLife.05005. Akerblom M., Sachdeva R., Barde I., Verp S., Gentner B., Trono D., Jakobsson J. ( 2012). MicroRNA-124 is a subventricular zone neuronal fate determinant. J. Neurosci.  32, 8879– 8889. Google Scholar CrossRef Search ADS PubMed  Amini M., Abbaspour K. C., Berg M., Winkel L., Hug S. J., Hoehn E., Yang H., Johnson C. A. ( 2008). Statistical modeling of global geogenic arsenic contamination in groundwater. Environ. Sci. Technol.  42, 3669– 3675. Google Scholar CrossRef Search ADS PubMed  Bain L. J., Liu J.-T., League R. E. ( 2016). Arsenic inhibits stem cell differentiation by altering the interplay between the Wnt3a and Notch signaling pathways. Toxicol. Rep.  3, 405– 413. http://dx.doi.org/10.1016/j.toxrep.2016.03.011 Google Scholar CrossRef Search ADS PubMed  Bardullas U., Limón-Pacheco J. H., Giordano M., Carrizales L., Mendoza-Trejo M. S., Rodríguez V. M. ( 2009). Chronic low-level arsenic exposure causes gender-specific alterations in locomotor activity, dopaminergic systems, and thioredoxin expression in mice. Toxicol. Appl. Pharmacol.  239, 169– 177. Google Scholar CrossRef Search ADS PubMed  Bernstein E., Kim S. Y., Carmell M. A., Murchison E. P., Alcorn H., Li M. Z., Mills A. A., Elledge S. J., Anderson K. V., Hannon G. J. ( 2003). Dicer is essential for mouse development. Nat. Genet.  35, 215– 217. Google Scholar CrossRef Search ADS PubMed  Betel D., Koppal A., Agius P., Sander C., Leslie C. ( 2010). Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol.  11, R90. Google Scholar CrossRef Search ADS PubMed  Blanes P. S., Buchhamer E. E., Gimenez M. C. ( 2011). Natural contamination with arsenic and other trace elements in groundwater of the Central-West region of Chaco, Argentina. J. Environ. Sci. Health A Tox. Hazard. Subst. Environ. Eng . 46, 1197– 1206. http://dx.doi.org/10.1080/10934529.2011.598774 Google Scholar CrossRef Search ADS PubMed  Bormuth I., Yan K., Yonemasu T., Gummert M., Zhang M., Wichert S., Grishina O., Pieper A., Zhang W., Goebbels S., et al.   ( 2013). Neuronal basic helix-loop-helix proteins Neurod2/6 regulate cortical commissure formation before midline interactions. J. Neurosci.  33, 641– 651. Google Scholar CrossRef Search ADS PubMed  Bustaffa E., Stoccoro A., Bianchi F., Migliore L. ( 2014). Genotoxic and epigenetic mechanisms in arsenic carcinogenicity. Arch. Toxicol.  88, 1043– 1067. Google Scholar CrossRef Search ADS PubMed  Chandravanshi L. P., Yadav R. S., Shukla R. K., Singh A., Sultana S., Pant A. B., Parmar D., Khanna V. K. ( 2014). Reversibility of changes in brain cholinergic receptors and acetylcholinesterase activity in rats following early life arsenic exposure. Int. J. Dev. Neurosci.  34, 60– 75. Google Scholar CrossRef Search ADS PubMed  Chen H., Mo D., Li M., Zhang Y., Chen L., Zhang X., Li M., Zhou X., Chen Y. ( 2014). miR-709 inhibits 3T3-L1 cell differentiation by targeting GSK3β of Wnt/β-catenin signaling. Cell. Signal.  26, 2583– 2589. Google Scholar CrossRef Search ADS PubMed  Chen S. L., Dzeng S. R., Yang M. H., Chiu K. H., Shieh G. M., Wai C. M. ( 1994). Arsenic species in groundwaters of the blackfoot disease area, Taiwan. Environ. Sci. Technol.  28, 877– 881. Google Scholar CrossRef Search ADS PubMed  Cheng L. C., Pastrana E., Tavazoie M., Doetsch F. ( 2009). miR-124 regulates adult neurogenesis in the subventricular zone stem cell niche. Nat. Neurosci.  12, 399– 408. http://dx.doi.org/10.1038/nn.2294 Google Scholar CrossRef Search ADS PubMed  Clark A. M., Goldstein L. D., Tevlin M., Tavare S., Shaham S., Miska E. A. ( 2010). The microRNA miR-124 controls gene expression in the sensory nervous system of Caenorhabditis elegans. Nucleic Acids Res.  38, 3780– 3793. Google Scholar CrossRef Search ADS PubMed  Concha G., Vogler G., Lezcano D., Nermell B., Vahter M. ( 1998). Exposure to inorganic arsenic metabolites during early human development. Toxicol. Sci.  44, 185– 190. Google Scholar CrossRef Search ADS PubMed  Cronican A. A., Fitz N. F., Carter A., Saleem M., Shiva S., Barchowsky A., Koldamova R., Schug J., Lefterov I., Johnson R. ( 2013). Genome-wide alteration of histone H3K9 acetylation pattern in mouse offspring prenatally exposed to arsenic. PLoS One  8, e53478. Google Scholar CrossRef Search ADS PubMed  Cui Y., Han Z., Hu Y., Song G., Hao C., Xia H., Ma X. ( 2012). MicroRNA-181b and microRNA-9 mediate arsenic-induced angiogenesis via NRP1. J. Cell. Physiol.  227, 772– 783. http://dx.doi.org/10.1002/jcp.22789 Google Scholar CrossRef Search ADS PubMed  Druz A., Betenbaugh M., Shiloach J. ( 2012). Glucose depletion activates mmu-miR-466h-5p expression through oxidative stress and inhibition of histone deacetylation. Nucleic Acids Res.  40, 7291– 7302. http://dx.doi.org/10.1093/nar/gks452 Google Scholar CrossRef Search ADS PubMed  Druz A., Chu C., Majors B., Santuary R., Betenbaugh M., Shiloach J. ( 2011). A novel microRNA mmu-miR-466h affects apoptosis regulation in mammalian cells. Biotechnol. Bioeng.  108, 1651– 1661. Google Scholar CrossRef Search ADS PubMed  Edwards M., Johnson L., Mauer C., Barber R., Hall J., O’Bryant S. ( 2014). Regional specific groundwater arsenic levels and neuropsychological functioning: A cross-sectional study. Int. J. Environ. Health Res.  24, 546– 557. Google Scholar CrossRef Search ADS PubMed  Enright A. J., John B., Gaul U., Tuschl T., Sander C., Marks D. S. ( 2003). MicroRNA targets in Drosophila. Genome Biol.  5, R1. Google Scholar CrossRef Search ADS PubMed  Frankel S., Concannon J., Brusky K., Pietrowicz E., Giorgianni S., Thompson W. D., Currie D. A. ( 2009). Arsenic exposure disrupts neurite growth and complexity in vitro. Neurotoxicology  30, 529– 537. Google Scholar CrossRef Search ADS PubMed  Hafeman D. M., Ahsan H., Louis E. D., Siddique A. B., Slavkovich V., Cheng Z., van Geen A., Graziano J. H. ( 2005). Association between arsenic exposure and a measure of subclinical sensory neuropathy in Bangladesh. J. Occup. Environ. Med.  47, 778– 784. Google Scholar CrossRef Search ADS PubMed  Hausser J., Zavolan M. ( 2014). Identification and consequences of miRNA-target interactions—Beyond repression of gene expression. Nat. Rev. Genet.  15, 599– 612. Google Scholar CrossRef Search ADS PubMed  He J., Wang M., Jiang Y., Chen Q., Xu S., Xu Q., Jiang B. H., Liu L. Z. ( 2014). Chronic arsenic exposure and angiogenesis in human bronchial epithelial cells via the ROS/miR-199a-5p/HIF-1α/COX-2 pathway. Environ. Health Perspect.  122, 255– 261. Google Scholar PubMed  Hong G.-M., Bain L. J. ( 2012). Arsenic exposure inhibits myogenesis and neurogenesis in P19 stem cells through repression of the b-catenin signaling pathway. Toxicol. Sci.  129, 146– 156. http://dx.doi.org/10.1093/toxsci/kfs186 Google Scholar CrossRef Search ADS PubMed  Hopenhayn C., Ferreccio C., Browning S., Huang B., Peralta C., Gibb H., Hertz-Picciotto I. ( 2003). Arsenic exposure from drinking water and birth weight. Epidemiology  14, 593– 602. Google Scholar CrossRef Search ADS PubMed  Huyck K. L., Kile M. L., Mahiuddin G., Quamruzzaman Q., Rahman M., Breton C. V., Dobson C. B., Frelich J., Hoffman E., Yousuf J., et al.   ( 2007). Maternal arsenic exposure associated with low birth weight in Bangladesh. J. Occup. Environ. Med.  49, 1097– 1104. Google Scholar CrossRef Search ADS PubMed  Ivey K. N., Muth A., Arnold J., King F. W., Yeh R. F., Fish J. E., Hsiao E. C., Schwartz R. J., Conklin B. R., Bernstein H. S., et al.   ( 2008). MicroRNA regulation of cell lineages in mouse and human embryonic stem cells. Cell Stem Cell  2, 219– 229. Google Scholar CrossRef Search ADS PubMed  Jiang R., Li Y., Zhang A., Wang B., Xu Y., Xu W., Zhao Y., Luo F., Liu Q. ( 2014). The acquisition of cancer stem cell-like properties and neoplastic transformation of human keratinocytes induced by arsenite involves epigenetic silencing of let-7c via Ras/NF-κB. Toxicol. Lett.  227, 91– 98. Google Scholar CrossRef Search ADS PubMed  Judson R. L., Babiarz J. E., Venere M., Blelloch R. ( 2009). Embryonic stem cell-specific microRNAs promote induced pluripotency. Nat. Biotechnol.  27, 459– 461. http://dx.doi.org/10.1038/nbt.1535 Google Scholar CrossRef Search ADS PubMed  Kanellopoulou C., Muljo S. A., Kung A. L., Ganesan S., Drapkin R., Jenuwein T., Livingston D. M., Rajewsky K. ( 2005). Dicer-deficient mouse embryonic stem cells are defective in differentiation and centromeric silencing. Genes Dev.  19, 489– 501. Google Scholar CrossRef Search ADS PubMed  Kawasaki S., Yazawa S., Ohnishi A., Ohi T. ( 2002). [Chronic and predominantly sensory polyneuropathy in Toroku Valley where a mining company produced arsenic]. Rinsho Shinkeigaku  42, 504– 511. Google Scholar PubMed  Kertesz M., Iovino N., Unnerstall U., Gaul U., Segal E. ( 2007). The role of site accessibility in microRNA target recognition. Nat. Genet.  39, 1278– 1284. Google Scholar CrossRef Search ADS PubMed  Krichevsky A. M., Sonntag K. C., Isacson O., Kosik K. S. ( 2006). Specific microRNAs modulate embryonic stem cell-derived neurogenesis. Stem Cells  24, 857– 864. Google Scholar CrossRef Search ADS PubMed  Lee Y.-B., Bantounas I., Lee D.-Y., Phylactou L., Caldwell M. A., Uney J. B. ( 2009). Twist-1 regulates the miR-199a/214 cluster during development. Nucleic Acids Res.  37, 123– 128. Google Scholar CrossRef Search ADS PubMed  Lehnert S., Kapitonov V., Thilakarathne P. J., Schuit F. C. ( 2011). Modeling the asymmetric evolution of a mouse and rat-specific microRNA gene cluster intron 10 of the Sfmbt2 gene. BMC Genomics  12, 257. Google Scholar CrossRef Search ADS PubMed  Lennox K. A., Owczarzy R., Thomas D. M., Walder J. A., Behlke M. A. ( 2013). Improved performance of anti-miRNA oligonucleotides using a novel non-nucleotide modifier. Mol. Ther. Nucleic Acids  2, 117. Google Scholar CrossRef Search ADS   Lichner Z., Páll E., Kerekes A., Pállinger É., Maraghechi P., Bősze Z., Gócza E. ( 2011). The miR-290-295 cluster promotes pluripotency maintenance by regulating cell cycle phase distribution in mouse embryonic stem cells. Differentiation  81, 11– 24. Google Scholar CrossRef Search ADS PubMed  Lin E. A., Kong L., Bai X.-H., Luan Y., Liu C.-J. ( 2009). miR-199a, a bone morphogenic protein 2-responsive microRNA, regulates chondrogenesis via direct targeting to Smad1. J. Biol. Chem.  284, 11326– 11335. http://dx.doi.org/10.1074/jbc.M807709200 Google Scholar CrossRef Search ADS PubMed  Liu J.-T., Bain L. J. ( 2014). Arsenic inhibits hedgehog signaling during P19 cell differentiation. Toxicol. Appl. Pharm.  281, 243– 253. http://dx.doi.org/10.1016/j.taap.2014.10.007 Google Scholar CrossRef Search ADS   Liu N., Williams A. H., Kim Y., McAnally J., Bezprozvannaya S., Sutherland L. B., Richardson J. A., Bassel-Duby R., Olson E. N. ( 2007). An intragenic MEF2-dependent enhancer directs muscle-specific expression of microRNAs 1 and 133. Proc. Natl. Acad. Sci. U.S.A.  104, 20844– 20849. Google Scholar CrossRef Search ADS PubMed  Liu S., Piao F., Sun X., Bai L., Peng Y., Zhong Y., Ma N., Sun W. ( 2012). Arsenic-induced inhibition of hippocampal neurogenesis and its reversibility. Neurotoxicology  33, 1033– 1039. Google Scholar CrossRef Search ADS PubMed  Livak K. J., Schmittgen T. D. ( 2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods  25, 402– 408. http://dx.doi.org/10.1006/meth.2001.1262 Google Scholar CrossRef Search ADS PubMed  Luo J., Qiu Z., Chen J., Zhang L., Liu W., Tan Y., Shu W. ( 2013). Maternal and early life arsenite exposure impairs neurodevelopment and increases the expression of PSA-NCAM in hippocampus of rat offspring. Toxicology  311, 99– 106. Google Scholar CrossRef Search ADS PubMed  Luo J. H., Qiu Z. Q., Shu W. Q., Zhang Y. Y., Zhang L., Chen J. A. ( 2009). Effects of arsenic exposure from drinking water on spatial memory, ultra-structures and NMDAR gene expression of hippocampus in rats. Toxicol. Lett.  184, 121– 125. Google Scholar CrossRef Search ADS PubMed  Luo Y., Liu Y., Liu M., Wei J., Zhang Y., Hou J., Huang W., Wang T., Li X., He Y., et al.   ( 2014). Sfmbt2 10th intron-hosted miR-466(a/e)-3p are important epigenetic regulators of Nfat5 signaling, osmoregulation and urine concentration in mice. Biochim. Biophys. Acta  1839, 97– 106. Google Scholar CrossRef Search ADS PubMed  Mandal B., Suzuki T. ( 2002). Arsenic around the world: A review. Talanta  58, 201– 235. http://dx.doi.org/10.1016/S0039-9140(02)00268-0 Google Scholar CrossRef Search ADS PubMed  Markowski V. P., Reeve E. A., Onos K., Assadollahzadeh M., McKay N. ( 2012). Effects of prenatal exposure to sodium arsenite on motor and food-motivated behaviors from birth to adulthood in C57BL6/J mice. Neurotoxicol. Teratol.  34, 221– 231. Google Scholar CrossRef Search ADS PubMed  Martinez E. J., Kolb B. L., Bell A., Savage D. D., Allan A. M. ( 2008). Moderate perinatal arsenic exposure alters neuroendocrine markers associated with depression and increases depressive-like behaviors in adult mouse offspring. Neurotoxicology  29, 647– 655. Google Scholar CrossRef Search ADS PubMed  Martinez-Finley E. J., Ali A. M., Allan A. M. ( 2009). Learning deficits in C57BL/6J mice following perinatal arsenic exposure: Consequence of lower corticosterone receptor levels? Pharmacol. Biochem. Behav.  94, 271– 277. http://dx.doi.org/10.1016/j.pbb.2009.09.006 Google Scholar CrossRef Search ADS PubMed  Mukherjee B., Bindhani B., Saha H., Sinha D., Ray M. R. ( 2014). Platelet hyperactivity, neurobehavioral symptoms and depression among Indian women chronically exposed to low level of arsenic. Neurotoxicology  45, 159– 167. Google Scholar CrossRef Search ADS PubMed  Nagaraja T. N., Desiraju T. ( 1994). Effects on operant learning and brain acetylcholine esterase activity in rats following chronic inorganic arsenic intake. Hum. Exp. Toxicol.  13, 353– 356. http://dx.doi.org/10.1177/096032719401300511 Google Scholar CrossRef Search ADS PubMed  Ngalame N. N., Tokar E. J., Person R. J., Xu Y., Waalkes M. P. ( 2014). Aberrant microRNA expression likely controls RAS oncogene activation during malignant transformation of human prostate epithelial and stem cells by arsenic. Toxicol. Sci.  138, 268– 277. Google Scholar CrossRef Search ADS PubMed  Nickson R., McArthur J., Burgess W., Ahmed K. M., Ravenscroft P., Rahman M. ( 1998). Arsenic poisoning of Bangladesh groundwater. Nature  395, 338. Google Scholar CrossRef Search ADS PubMed  Ning Z., Lobdell D. T., Kwok R. K., Liu Z., Zhang S., Ma C., Riediker M., Mumford J. L. ( 2007). Residential exposure to drinking water arsenic in Inner Mongolia, China. Toxicol. Appl. Pharm.  222, 351– 356. Google Scholar CrossRef Search ADS   O’Bryant S. E., Edwards M., Menon C. V., Gong G., Barber R. ( 2011). Long-term low-level arsenic exposure is associated with poorer neuropsychological functioning: A project Frontier study. Int. J. Environ. Res. Public Health  8, 861– 874. Google Scholar CrossRef Search ADS PubMed  Otaegi G., Pollock A., Hong J., Sun T. ( 2011). MicroRNA miR-9 modifies motor neuron columns by a tuning regulation of FoxP1 levels in developing spinal cords. J. Neurosci.  31, 809– 818. http://dx.doi.org/10.1523/JNEUROSCI.4330-10.2011 Google Scholar CrossRef Search ADS PubMed  Paul S., Giri A. K. ( 2015). Epimutagenesis: A prospective mechanism to remediate arsenic-induced toxicity. Environ. Int.  81, 8– 17. http://dx.doi.org/10.1016/j.envint.2015.04.002 Google Scholar CrossRef Search ADS PubMed  Rahman A., Vahter M., Smith A. H., Nermell B., Yunus M., El Arifeen S., Persson L. A., Ekström E. C. ( 2009). Arsenic exposure during pregnancy and size at birth: A prospective cohort study in Bangladesh. Am. J. Epidemiol.  169, 304– 312. Google Scholar CrossRef Search ADS PubMed  Rahman M. M., Mandal B. K., Chowdhury T. R., Sengupta M. K., Chowdhury U. K., Lodh D., Chanda C. R., Basu G. K., Mukherjee S. C., Saha K. C., Chakraborti D. ( 2003). Neuropathy in arsenic toxicity from groundwater arsenic contamination in West Bengal, India. J. Environ. Sci. Health A Tox. Hazard. Subst. Environ. Eng . 38, 165– 183. Google Scholar CrossRef Search ADS PubMed  Ren X., McHale C. M., Skibola C. F., Smith A. H., Smith M. T., Zhang L. ( 2011). An emerging role for epigenetic dysregulation in arsenic toxicity and carcinogenesis. Environ. Health Perspect.  119, 11– 19. Google Scholar CrossRef Search ADS PubMed  Rodriguez A., Griffiths-Jones S., Ashurst J. L., Bradley A. ( 2004). Identification of mammalian microRNA host genes and transcription units. Genome Res.  14, 1902– 1910. Google Scholar CrossRef Search ADS PubMed  Rodrı´guez V. M., Carrizales L., Mendoza M. S., Fajardo O. R., Giordano M. ( 2002). Effects of sodium arsenite exposure on development and behavior in the rat. Neurotoxicol. Teratol.  24, 743– 750. Google Scholar CrossRef Search ADS PubMed  Rodríguez-Barranco M., Gil F., Hernández A. F., Alguacil J., Lorca A., Mendoza R., Gómez I., Molina-Villalba I., González-Alzaga B., Aguilar-Garduño C., et al.   ( 2016). Postnatal arsenic exposure and attention impairment in school children. Cortex  74, 370– 382. Google Scholar CrossRef Search ADS PubMed  Rosado J. L., Ronquillo D., Kordas K., Rojas O., Alatorre J., Lopez P., Garcia-Vargas G., Del Carmen Caamaño M., Cebrián M. E., Stoltzfus R. J. ( 2007). Arsenic exposure and cognitive performance in Mexican schoolchildren. Environ. Health Perspect.  115, 1371– 1375. Google Scholar CrossRef Search ADS PubMed  Saha K. K., Engstrom A., Hamadani J. D., Tofail F., Rasmussen K. M., Vahter M. ( 2012). Pre- and postnatal arsenic exposure and body size to two years of age: A cohort study in rural Bangladesh. Environ. Health Perspect.  120, 1208– 1214. Google Scholar CrossRef Search ADS PubMed  Seo M., Choi J. S., Rho C. R., Joo C. K., Lee S. K. ( 2015). MicroRNA miR-466 inhibits lymphangiogenesis by targeting prospero-related homeobox 1 in the alkali burn corneal injury model. J. Biomed. Sci.  22, 3. http://dx.doi.org/10.1186/s12929-014-0104-0 Google Scholar CrossRef Search ADS PubMed  Shibata M., Nakao H., Kiyonari H., Abe T., Aizawa S. ( 2011). MicroRNA-9 regulates neurogenesis in mouse telencephalon by targeting multiple transcription factors. J. Neurosci.  31, 3407– 3422. Google Scholar CrossRef Search ADS PubMed  Shyamasundar S., Jadhav S. P., Bay B. H., Tay S. S. W., Kumar S. D., Rangasamy D., Dheen S. T., Pant A. B. ( 2013). Analysis of epigenetic factors in mouse embryonic neural stem cells exposed to hyperglycemia. PLoS One  8, e65945. Google Scholar CrossRef Search ADS PubMed  Sinkkonen L., Hugenschmidt T., Berninger P., Gaidatzis D., Mohn F., Artus-Revel C. G., Zavolan M., Svoboda P., Filipowicz W. ( 2008). MicroRNAs control de novo DNA methylation through regulation of transcriptional repressors in mouse embryonic stem cells. Nat. Struct. Mol. Biol.  15, 259– 267. Google Scholar CrossRef Search ADS PubMed  Smedley P., Kinniburgh D. ( 2002). A review of the source, behaviour and distribution of arsenic in natural waters. Appl. Geochem.  17, 517– 568. http://dx.doi.org/10.1016/S0883-2927(02)00018-5 Google Scholar CrossRef Search ADS   Steffens A. A., Hong G.-M., Bain L. J. ( 2011). Sodium arsenite delays the differentiation of C2C12 mouse myoblast cells and alters methylation patterns on the transcription factor myogenin. Toxicol. Appl. Pharm.  250, 154– 161. http://dx.doi.org/10.1016/j.taap.2010.10.006 Google Scholar CrossRef Search ADS   Tolins M., Ruchirawat M., Landrigan P. ( 2014). The developmental neurotoxicity of arsenic: Cognitive and behavioral consequences of early life exposure. Ann. Glob. Health  80, 303– 314. http://dx.doi.org/10.1016/j.aogh.2014.09.005 Google Scholar CrossRef Search ADS PubMed  Tyler C. R., Allan A. M., Homberg J. ( 2013). Adult hippocampal neurogenesis and mRNA expression are altered by perinatal arsenic exposure in mice and restored by brief exposure to enrichment. PLoS One  8, e73720. Google Scholar CrossRef Search ADS PubMed  Wang J. M., Qiu Y., Yang Z. Q., Li L., Zhang K. ( 2017). Inositol requiring enzyme 1 facilitates diabetic wound healing through modulating microRNAs. Diabetes  66, 177– 192. http://dx.doi.org/10.2337/db16-0052 Google Scholar CrossRef Search ADS PubMed  Wang X., Meng D., Chang Q., Pan J., Zhang Z., Chen G., Ke Z., Luo J., Shi X. ( 2010). Arsenic inhibits neurite outgrowth by inhibiting the LKB1-AMPK signaling pathway. Environ. Health Perspect.  118, 627– 634. Google Scholar CrossRef Search ADS PubMed  Willems E., Leyns L. ( 2008). Patterning of mouse embryonic stem cell-derived pan-mesoderm by activin A/nodal and Bmp4 signalling requires fibroblast growth factor activity. Differentiation  76, 745– 759. http://dx.doi.org/10.1111/j.1432-0436.2007.00257.x Google Scholar CrossRef Search ADS PubMed  Wilson K. D., Venkatasubrahmanyam S., Jia F., Sun N., Butte A. J., Wu J. C. ( 2009). MicroRNA profiling of human-induced pluripotent stem cells. Stem Cells Dev.  18, 749– 757. Google Scholar CrossRef Search ADS PubMed  Yang M., Li Y., Padgett R. W. ( 2005). MicroRNAs: Small regulators with a big impact. Cytokine Growth Factor Rev.  16, 387– 393. http://dx.doi.org/10.1016/j.cytogfr.2005.02.008 Google Scholar CrossRef Search ADS PubMed  Yen Y. P., Tsai K. S., Chen Y. W., Huang C. F., Yang R. S., Liu S. H. ( 2010). Arsenic inhibits myogenic differentiation and muscle regeneration. Environ. Health Perspect . 118, 949– 956. Google Scholar CrossRef Search ADS PubMed  Zavala Y. J., Duxbury J. M. ( 2008). Arsenic in rice: I. Estimating normal levels of total arsenic in rice grain. Environ. Sci. Technol.  42, 3856– 3860. http://dx.doi.org/10.1021/es702747y Google Scholar CrossRef Search ADS PubMed  Zhang C., Ferrari R., Beezhold K., Stearns-Reider K., D’Amore A., Haschak M., Stolz D., Robbins P. D., Barchowsky A., Ambrosio F. ( 2016). Arsenic promotes NF-κB-mediated fibroblast dysfunction and matrix remodeling to impair muscle stem cell function. Stem Cells . 34, 732– 742. Google Scholar CrossRef Search ADS PubMed  Zhao C., Sun G., Li S., Shi Y. ( 2009). A feedback regulatory loop involving microRNA-9 and nuclear receptor TLX in neural stem cell fate determination. Nat. Struct. Mol. Biol.  16, 365– 371. http://dx.doi.org/10.1038/nsmb.1576 Google Scholar CrossRef Search ADS PubMed  Zhao J., Wang C., Song Y., Fang B. ( 2014). Arsenic trioxide and microRNA-204 display contrary effects on regulating adipogenic and osteogenic differentiation of mesenchymal stem cells in aplastic anemia. Acta Biochim. Biophys. Sin . 46, 885– 893. http://dx.doi.org/10.1093/abbs/gmu082 Google Scholar CrossRef Search ADS   Zhao Y., Samal E., Srivastava D. ( 2005). Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature  436, 214– 220. http://dx.doi.org/10.1038/nature03817 Google Scholar CrossRef Search ADS PubMed  Zheng G. X., Ravi A., Gould G. M., Burge C. B., Sharp P. A. ( 2011). Genome-wide impact of a recently expanded microRNA cluster in mouse. Proc. Natl. Acad. Sci. U.S.A.  108, 15804– 15809. Google Scholar CrossRef Search ADS PubMed  © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Toxicological Sciences Oxford University Press

Arsenic Induces Members of the mmu-miR-466-669 Cluster Which Reduces NeuroD1 Expression

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Abstract

Abstract Chronic arsenic exposure can result in adverse development effects including decreased intellectual function, reduced birth weight, and altered locomotor activity. Previous in vitro studies have shown that arsenic inhibits stem cell differentiation. MicroRNAs (miRNAs) are small noncoding RNAs that regulate multiple cellular processes including embryonic development and cell differentiation. The purpose of this study was to examine whether altered miRNA expression was a mechanism by which arsenic inhibited cellular differentiation. The pluripotent P19 mouse embryonal carcinoma cells were exposed to 0 or 0.5 μM sodium arsenite for 9 days during cell differentiation, and changes in miRNA expression was analyzed using microarrays. We found that the expression of several miRNAs important in cellular differentiation, such as miR-9 and miR-199 were decreased by 1.9- and 1.6-fold, respectively, following arsenic exposure, while miR-92a, miR-291a, and miR-709 were increased by 3-, 3.7-, and 1.6-fold, respectively. The members of the miR-466-669 cluster and its host gene, Scm-like with 4 Mbt domains 2 (Sfmbt2), were significantly induced by arsenic from 1.5- to 4-fold in a time-dependent manner. Multiple miRNA target prediction programs revealed that several neurogenic transcription factors appear to be targets of the cluster. When consensus anti-miRNAs targeting the miR-466-669 cluster were transfected into P19 cells, arsenic-exposed cells were able to more effectively differentiate. The consensus anti-miRNAs appeared to rescue the inhibitory effects of arsenic on cell differentiation due to an increased expression of NeuroD1. Taken together, we conclude that arsenic induces the miR-466-669 cluster, and that this induction acts to inhibit cellular differentiation in part due to a repression of NeuroD1. arsenic, microRNA, Sfmbt2, miR-466-669 cluster, P19 cells, differentiation, NeuroD1 Arsenic is a ubiquitous element that can leach into ground water and surface water (Amini et al., 2008). Studies have shown that millions of people in countries such as China, Argentina, Taiwan, and Bangladesh are exposed to high levels of arsenic in their drinking water, above the 10 ppb drinking water standard (Blanes et al., 2011; Chen et al., 1994; Mandal and Suzuki, 2002; Nickson et al., 1998; Ning et al., 2007; Smedley and Kinniburgh, 2002). Other than drinking water, humans can also be exposed to arsenic by consuming crops, such as rice, that are grown in soil with former arsenic pesticide use (Zavala and Duxbury, 2008). Arsenic can cross the placental barrier such that arsenic concentrations in cord blood are almost as high as in maternal blood (Concha et al., 1998). Therefore, children can be affected by arsenic during pregnancy. Epidemiological studies have shown that perinatal arsenic exposure can reduce birth weight (Hopenhayn et al., 2003; Huyck et al., 2007; Rahman et al., 2009) and weight gain in children (Saha et al., 2012). Moreover, data show that children exposed to arsenic levels > 10 ppb have reduced verbal intelligence quotient, spatial memory, and long-term memory, indicating that arsenic can impair intellectual function (reviewed in Tolins et al., 2014). In addition, early childhood exposure to arsenic is correlated with changes in sensory ability and reaction time, and decreased sense of taste and smell (Rodríguez-Barranco et al., 2016; Rosado et al., 2007), while in adults, arsenic concentrations of 10–50 ppb are associated with altered taste and smell, reduced touch perception, an increase in a tingling sensation in the extremities, and visual processing (Edwards et al., 2014; Hafeman et al., 2005; Kawasaki et al., 2002; Rahman et al., 2003; Mukherjee et al., 2014; O’Bryant et al., 2011). Similarly, mice exposed to arsenic cannot appropriately repair a muscle injury because of reduced myogenin expression (Yen et al., 2010), altered muscle oxygen consumption, and increased expression of matrix remodeling genes (Zhang et al., 2016). Arsenic exposure also alters spontaneous motor activity, increases body spasms, and decreases forelimb grip strength (Bardullas et al., 2009; Markowski et al., 2012). In addition, arsenic exposure during the perinatal period decreases learning and spatial memory (Chandravanshi et al., 2014; Luo et al., 2009; Martinez et al., 2008; Martinez-Finley et al., 2009; Nagaraja and Desiraju, 1994; Rodrı´guez et al., 2002). It also reduces the number of neuronal progenitor cells and the formation of new neurons in rodents (Cronican et al., 2013; Liu et al., 2012; Luo et al., 2013; Tyler et al., 2013). From in vitro studies, it appears that arsenic impairs cellular differentiation. For example, exposing C2C12 myoblasts to 0.1 μM arsenic trioxide or 20 nM sodium arsenite inhibits myotube formation by downregulating the expression of myogenic transcription factors, such as MyoD and myogenin (Steffens et al., 2011; Yen et al., 2010). Arsenic also disrupts neuronal differentiation in both PC12 and Neuro-2a neuroblastoma cells (Frankel et al., 2009; Wang et al., 2010). Our previous work demonstrates that arsenic inhibits the differentiation of mouse P19 embryonal carcinoma cells into sensory neurons by reducing the expression of the neurogenic transcription factors NeuroD and neurogenin 1 and 2 (Hong and Bain, 2012; McCoy et al., 2015). One potential mechanism for altering transcription factor expression is via microRNAs (miRNAs). MicroRNAs are small, single stranded, noncoding RNAs that regulate gene expression by typically binding imperfectly to the 3′-untranslated region (UTR) of mRNA, leading to targeted mRNA translational repression (Yang et al., 2005). MicroRNAs can be located in introns or outside of genes (Rodriguez et al., 2004). If several miRNAs are clustered together, they can be transcribed as a polycistronic transcript. For instance, the miR-290 cluster is a 2.2-kb region on chromosome 7, and the primary transcript generates 14 mature miRNAs which play an important role in embryonic stem cells (Judson et al., 2009). The miR-466 cluster is located in intron 10 of the mouse Sfmbt2 gene and contains 65 miRNA genes (Lehnert et al., 2011). There is little information reported on this cluster, although it is known to be involved in cell proliferation and apoptosis, and Sfmbt2 is expressed in embryonic stem cells (Druz et al., 2012; Zheng et al., 2011). Because of their ability to regulate gene expression, miRNAs play an important role in development. In mice, deletion of the Dicer-1 gene, which cleaves pre-miRNA to produce mature miRNAs, can result in embryonic lethality (Bernstein et al., 2003). Moreover, mouse embryonic stem cells that lack Dicer-1 have significant defects in cell differentiation (Kanellopoulou et al., 2005). MicroRNAs can also impact stem cell differentiation into skeletal muscle and neurons. For instance, miR-1 and miR-133 are regulated by the transcription factors MyoD and MEF2 (Liu et al., 2007; Zhao et al., 2005), and they are potent repressors of nonmuscle genes during cell lineage commitment (Ivey et al., 2008). miR-9 and miR-124 are both are highly expressed in the brain (Cheng et al., 2009; Shibata et al., 2011), and they regulate several neurogenic transcription factors, including Foxg1, Sox9, and Pax6 (Akerblom et al., 2012; Clark et al., 2010; Otaegi et al., 2011). Compared with other epigenetic modifications, the effects of arsenic on miRNA regulation are relatively underexplored. For example, in bone marrow mesenchymal cells, arsenic trioxide reduces the expression of miR-204, which increases the differentiation of osteoblasts and reduces the differentiation of adipocytes (Zhao et al., 2014), while arsenite can induce angiogenesis by decreasing miR-9 and miR-181b expression in a human umbilical vein cell line (Cui et al., 2012). During the process of malignant transformation, long-term arsenite exposure can alter the expression of miRNAs that target ras in prostate epithelial cells (Ngalame et al., 2014), reduce miR-199a-50 in lung BEAS-2B cells (He et al., 2014), and decrease several let-7 family members in HaCaT keratinocyte cells (Jiang et al., 2014). Because increasing evidence indicates that miRNAs are important in cellular differentiation, and that arsenic exposure can alter miRNAs levels, we hypothesized that a mechanism by which arsenic could inhibit stem cell differentiation was by changing miRNA expression. In this study, we exposed mouse P19 embryonal carcinoma cells to arsenite during their differentiation into skeletal myotubes and sensory neurons, and determined that several miRNAs known to be involved in cellular differentiation were altered. Interestingly, the miRNA-466-669 cluster, along with its host gene Sfmbt2, were significantly increased following arsenic exposure. When the miR-466-669 cluster was inhibited by anti-miRs, the inhibitory effects on differentiation mediated by arsenic were rescued, in part, due to increases in NeuroD1, a transcription factor which is a target gene of the cluster. These results indicate that miR-466-669 cluster is highly expressed when cells are exposed to arsenic, and that this cluster plays a role in impairing stem cell differentiation. MATERIALS AND METHODS Cell culture and differentiation Mouse embryonal carcinoma P19 cells are commonly used to study the differentiation process because they can differentiate into multiple cell types, including skeletal myocytes, cardiac myocytes, and neurons. P19 cells (ATCC, Manassas, Virginia) were cultured in α-minimal essential medium (MEM) medium (Hyclone, Logan, Utah) with 7.5% bovine calf serum (Hyclone), 2.5% fetal bovine serum (Sigma-Aldrich, St Louis, Missouri), 1% L-glutamine, and 1% penicillin/streptomycin (designated as growth medium). Cells were maintained in a humidified incubator containing 5% CO2 at 37°C. Cells were exposed to 0 or 0.5 μM arsenic (as sodium arsenite; Sigma) and 1% DMSO during embryoid body formation and differentiation (Liu and Bain, 2014). Briefly, cells were aggregated for 2 days into embryoid bodies using the hanging drop method (500 cells/20 μl drop). Each embryoid body was transferred to a 96-well ultralow attachment plate for an additional 3 days (day 5). The embryoid bodies were then transferred to a 48-well plate coated with 0.1% gelatin and allowed to differentiate for 4 additional days (day 9). Culture medium was renewed every 48 h. MicroRNA microarrays P19 cells were exposed to 0 or 0.5 μM arsenic as sodium arsenite and induced to differentiate for 9 days (n = 2 per group). Cells were harvested, and total RNA was extracted with TRIzol (Life Technologies, Grand Island, New York) with extra ethanol cleaning step to improve the quality of RNA used. The samples were hybridized onto LC Sciences murine miRNA microarray chips (Houston, Texas; Geo Platform GPL21312). The array included probes for all available mature mouse miRNAs (miRBase database, Release 21, July 2014). Fluorescence intensity was corrected by background subtraction and normalized with the statistical mean of all detectable transcripts. Intensity values for the control and 0.5 μM arsenic-exposed cells were averaged, and statistical differences assessed using Student’s t test. Data analysis was conducted by LC Science. MicroRNA reverse transcription and miRNA-specific qPCR Total RNA (1 μg) was used to reverse transcribe miRNA (QuantiMir RT kit, System Biosciences, Mountain View, California). Quantitative real-time PCR was performed with RT2 SYBR green master mix (Qiagen, Valencia, California) using adapter-specific reverse primers. Gene-specific miRNA forward primers are listed in Table 1. A standard curve was generated by serial dilution to determine PCR efficiency. Samples were run in triplicate and expression data were analyzed and normalized with U6 as a housekeeper using the comparative threshold (Ct) method (Livak and Schmittgen, 2001). Table 1. Primer Sequences Gene Name  Forward (5′–3′)  Reverse (5′–3′)  miR-9  ATAAAGCTAGATAACCGAAA  Universal reverse primer  miR-92a  TATTGCACTTGTCCCGGCCTG  miR-199a  ACAGTAGTCTGCACATTGGTTA  miR-291a  CATCAAAGTGGAGGCCCTCT  miR-466b  CCAAGCCAACTTTATGTCAGGG  miR-466f  ATCAGCCAACCAAGAGACAGCAGA  miR-466g  GCCTCTGAGCACAGAAAGTCATCA  miR-466h  AAAGAACTGCGGCAAGTTTTTG  miR-466i  GCCAAGCAACCTGCTGAAAGTCTA  miR-467a  CATATACATACACACACCTACA  miR-467d  ATATACATACACACACCTACAC  miR-669c  TACACACACACACACAAGTAAA  miR-669e  TGAATATACACACACTTACAC  miR-669f  CATATACATACACACACACGTAT  miR-669p  CATAACATACACACACACACGTAT  miR-709  GGAGGCAGAGGCAGGAGGA  U6  TGGCCCCTGCGCAAGGATG  Consensus-1  TATACATACACACACACACATA  Consensus-2  TGTGTGCATGTGCATGTGTGTA  Consensus-3  TAACTCCGTGCATGTATATG  Consensus-4  AGTTGTGTGTGCATGTGTAT  Sfmbt2  AAGATAACCGGCTCGCAAATG  TCTCTTCCAAATAGTCTCCCCAG  Lats2  CCAGAAAGGGAACCACATGA  CTCTGCTCCAGGGTCTTTAAC  NeuroD1  GTCATGAGTGCCCAGCTTAAT  AAGGGCTGCCTTCTGTAAAC  Gapdh  TGCGACTTCAACAGCAACTC  ATGTAGGCCATGAGGTCCAC  Gene Name  Forward (5′–3′)  Reverse (5′–3′)  miR-9  ATAAAGCTAGATAACCGAAA  Universal reverse primer  miR-92a  TATTGCACTTGTCCCGGCCTG  miR-199a  ACAGTAGTCTGCACATTGGTTA  miR-291a  CATCAAAGTGGAGGCCCTCT  miR-466b  CCAAGCCAACTTTATGTCAGGG  miR-466f  ATCAGCCAACCAAGAGACAGCAGA  miR-466g  GCCTCTGAGCACAGAAAGTCATCA  miR-466h  AAAGAACTGCGGCAAGTTTTTG  miR-466i  GCCAAGCAACCTGCTGAAAGTCTA  miR-467a  CATATACATACACACACCTACA  miR-467d  ATATACATACACACACCTACAC  miR-669c  TACACACACACACACAAGTAAA  miR-669e  TGAATATACACACACTTACAC  miR-669f  CATATACATACACACACACGTAT  miR-669p  CATAACATACACACACACACGTAT  miR-709  GGAGGCAGAGGCAGGAGGA  U6  TGGCCCCTGCGCAAGGATG  Consensus-1  TATACATACACACACACACATA  Consensus-2  TGTGTGCATGTGCATGTGTGTA  Consensus-3  TAACTCCGTGCATGTATATG  Consensus-4  AGTTGTGTGTGCATGTGTAT  Sfmbt2  AAGATAACCGGCTCGCAAATG  TCTCTTCCAAATAGTCTCCCCAG  Lats2  CCAGAAAGGGAACCACATGA  CTCTGCTCCAGGGTCTTTAAC  NeuroD1  GTCATGAGTGCCCAGCTTAAT  AAGGGCTGCCTTCTGTAAAC  Gapdh  TGCGACTTCAACAGCAACTC  ATGTAGGCCATGAGGTCCAC  Quantitative PCR P19 cells were exposed to 0 or 0.5 μM arsenic, induced to differentiate, and collected at day 0, 2, 5, and 9 (n = 3 per treatment group per day). Total RNA were extracted using TRIzol, and 2 μg RNA were reverse transcribed using M-MLV reverse transcriptase (Promega, Madison, Wisconsin). Real-time qPCR was conducted using RT2 SYBR green (Qiagen) and gene-specific primers (Table 1). A standard curve was generated with 5 concentration points (10−3–10−7 ng cDNA) to quantify expression levels and assess reaction efficiency. Samples were run in triplicate and gene expression data were analyzed and normalized with Gapdh as a housekeeper using the comparative threshold (Ct) method. Gapdh is an often-used housekeeping gene in differentiating stem cells (Willems and Leyns, 2008), and our present and published studies show that its expression is not changed in P19 cells with arsenic exposures up to 1 μM. miR-466-669 cluster target prediction Because the miR-466 cluster appeared to be important in regulating cellular differentiation, potential targets for individual miR-466-467-669 cluster members were initially examined using miRanda (microRNA.org). Because cluster members are presumably transcribed all together, once target genes with good mirSVR scores were initially predicted, we then assessed whether multiple members of the cluster targeted those genes using miRanda (Betel et al., 2010; Enright et al., 2003). The genes in which at least 5 miRNAs from the cluster were predicted to bind were then validated with TargetScan (targetscan.org) and PITA (genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html). Only those genes to which miRNAs were predicted by at least 2 of the programs were considered to be potential targets (Table 2). Table 2. Neurogenic Transcription Factors that Are Potentially Targeted by the miR-466/669 Cluster Target Gene Name  miRNA Name  NeuroD1  miR-466fh  miR-466de-3p  miR-466bcd-5p  miR-467g  miR-669abdkl  miR-669h-5p  NeuroD2  miR-466fkl  miR-466bc-3p  miR-466d-5p  miR-669ab  NeuroD4  miR-466gl  miR-466abcde-3p  miR-466abce-5p  miR-467bcg  miR-669gij  miR-669h-3p  NeuroD6  miR-466gh  miR-467egh  miR-669ao  Neurogenin 1  miR-467ae  miR-669b  Neurogenin 2  miR-466-fkl  miR-466abcdel-3p  miR-466adef-5p  miR-467dg  miR-669bfk  Target Gene Name  miRNA Name  NeuroD1  miR-466fh  miR-466de-3p  miR-466bcd-5p  miR-467g  miR-669abdkl  miR-669h-5p  NeuroD2  miR-466fkl  miR-466bc-3p  miR-466d-5p  miR-669ab  NeuroD4  miR-466gl  miR-466abcde-3p  miR-466abce-5p  miR-467bcg  miR-669gij  miR-669h-3p  NeuroD6  miR-466gh  miR-467egh  miR-669ao  Neurogenin 1  miR-467ae  miR-669b  Neurogenin 2  miR-466-fkl  miR-466abcdel-3p  miR-466adef-5p  miR-467dg  miR-669bfk  miRNAs with good mirSVR scores were initially predicted using miRanda (microRNA.org), and then further validated with PITA (genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html) and TargetScan (targetscan.org). Anti-miRNA transfection Four consensus sequences which target the miR-466-669 cluster were selected for anti-miRNA generation (Figure 4). The oligonucleotides (Integrated DNA Technologies, Coralville, Iowa) were modified by employing 2′-O-methyl RNA nucleotides linkages positioned at both ends to block exonuclease attack (Lennox et al., 2013). P19 cells were transfected with 100 nM miRNA inhibitor using Lipofectamine 2000 (Invitrogen, Waltham, Massachusetts) and allowed to differentiate as described earlier for 5 days with or without 0.5 μM sodium arsenite (n = 3 replicates per exposure group). At day 5, a portion of the embryoid bodies were collected and stored in either TRIzol at −80°C for transcript expression or were fixed overnight in 10% neutral buffered formalin at 4°C for immunohistochemistry. A second portion of the embryoid bodies were transferred to gelatin coated plates and cultured for 4 more days without additional anti-miRNA to observe morphological changes. Six individual differentiated embryoid bodies per treatment group were imaged on day 9. The length of differentiating cells moving away from the embryoid body was quantified using ImageJ. Afterwards, cells were collected and stored in TRIzol at −80°C for transcript expression. Immunohistochemistry Following overnight fixation of the day 5 embryoid bodies, they were dehydrated in ethanol, cleared in xylene, embedded in paraffin, and cut in 5 μm sections. Citric acid buffer (pH = 6) was used for antigen retrieval. Slides were blocked with 5% bovine serum albumin (BSA) in PBST. The NeuroD1 primary antibody (Abcam, Cambridge, Massachusetts, AB60704; 1:250 dilution) was incubated overnight at 4°C. Slides were incubated in secondary antibodies conjugated to Alexa Fluor 594 (1:2000), and counterstained with DAPI (Invitrogen). Fluorescence intensity was assessed with Nikon Ti Eclipse Inverted microscope, and the expression of NeuroD1 was quantified using ImageJ. In brief, we selected 10 representative cells per field of view for each section, making sure to avoid cells that were undergoing mitosis and those in the center of the embryoid body that would not be undergoing differentiation. The total cell area was traced and average fluorescence intensity quantified. Afterwards, the average nuclear intensity for the same cell was quantified. Cytoplasmic fluorescence was determined by subtraction of the nuclear from the total cell fluorescence. Data from the 10 representative cells were averaged, giving an overall mean per section. Statistical analysis Results are expressed as mean ± SD. Data were analyzed by ANOVA followed by Tukey’s or by Student’s t test, as appropriate. Statistical significance was achieved at P < .05. RESULTS Arsenic Alters miRNA Expression Profiles during P19 Cell Differentiation P19 cells were exposed to 0 or 0.5 μM arsenite during a 9-day differentiation period. Changes in miRNA expression after 9 days were determined (doi: 10.5061/dryad.sc281) using murine miRNA microarrays that contain 1900 mature miRNAs (miRBase Release 21.0). A total of 59 miRNAs were significantly changed, with 27 miRNAs decreased and 32 miRNAs increased, in the arsenic-exposed cells compared with control cells (Figure 1). Several of these miRNAs are known to impact development and stem cell differentiation. miR-92a is highly expressed in stem cells and its expression decreases when cells undergo differentiation (Wilson et al., 2009). miR-291a belongs to the miR-290-295 cluster, which promotes pluripotency and prevents cells from differentiating (Lichner et al., 2011). miR-709 inhibits adipocyte differentiation by targeting the GSK3β/Wnt signaling pathway (Chen et al., 2014). Arsenic exposure to P19 cells increased these miRNAs, which is consistent with the repression of differentiation (Figure 1). These changes were validated by miRNA qPCR, in which miR-92a, miR-291a, and miR-709 were all significantly induced in arsenic treated cells by 3-, 3.5-, and 1.6-fold, respectively (Figs. 2A–C). In contrast, miR-9 induces neuronal cell differentiation and miR-199a increases chondrogenesis in the skeletal system (Lee et al., 2009; Lin et al., 2009; Zhao et al., 2009). Arsenite exposure reduced the expression of miR-9 and miR-199a by 2- and 2.5-fold, respectively (Figs. 2D and 2E). These data suggest that during cell differentiation, arsenic exposure increases miRNAs associated with pluripotency and decreases those associated with cell lineage commitment. Figure 1. View largeDownload slide Expression profiles of microRNAs (miRNAs) between control and arsenic exposure in differentiating P19 cells. P19 cells were induced to differentiate with or without 0.5 µM of arsenic for 9 days. MicroRNA expression was detected via a miRNA microarray and plotted as a heat map using CIM Miner (NIH). Darker shading indicates increased expression. Only statistically different miRNAs are listed in the map (Student’s t test; P value < .05). Figure 1. View largeDownload slide Expression profiles of microRNAs (miRNAs) between control and arsenic exposure in differentiating P19 cells. P19 cells were induced to differentiate with or without 0.5 µM of arsenic for 9 days. MicroRNA expression was detected via a miRNA microarray and plotted as a heat map using CIM Miner (NIH). Darker shading indicates increased expression. Only statistically different miRNAs are listed in the map (Student’s t test; P value < .05). Figure 2. View largeDownload slide Validation of miRNA transcript levels by qPCR. Day 9 differentiated P19 cells were used to confirm miRNA expression by qPCR (n = 3 per treatment). Significantly changed miRNAs with known roles in development were examined, including miR-92a (A), miR-291a (B), miR-709 (C), miR-199a (D), and miR-9 (E). Expression values were normalized with U6 snRNA and fold differences calculated from control cells using the delta Ct method. Values are expressed as mean ± SD and statistical differences were determined by Student’s t test (*P < .05). Figure 2. View largeDownload slide Validation of miRNA transcript levels by qPCR. Day 9 differentiated P19 cells were used to confirm miRNA expression by qPCR (n = 3 per treatment). Significantly changed miRNAs with known roles in development were examined, including miR-92a (A), miR-291a (B), miR-709 (C), miR-199a (D), and miR-9 (E). Expression values were normalized with U6 snRNA and fold differences calculated from control cells using the delta Ct method. Values are expressed as mean ± SD and statistical differences were determined by Student’s t test (*P < .05). Arsenic Induces the miR-466-669 Cluster and Its Host Gene, Sfmbt2 Clustered miRNAs are often coexpressed, and several studies have shown that they have a small but significant tendency to cotarget the same genes (Hausser and Zavolan, 2014). As shown in Figure 1, 10 of the top 31 miRNAs whose expression were increased in response to arsenic are in the miR-466-467-669 cluster. To verify the expression of these 10 miRNAs, qPCR was used. With the exception of miR-466i, all miRNA-466-467-669 family members were significantly induced in the arsenic-exposed cells by 1.7- to 3.7-fold (Figure 3A). Among those transcripts, miR-466g, -467d, -467a, and -669p have more than 3-fold increase in day 9 cells exposed to arsenic. Figure 3. View largeDownload slide Arsenic exposure induces members of the miR-466-669 cluster along with its host gene, Sfmbt2. P19 cells were differentiated and RNA was extracted from cells exposed to 0 or 0.5 μM arsenite on days 0, 2, 5, and 9 (n = 3 per treatment per day). MicroRNA or mRNA expression was determined by qPCR. Day 9 samples were used to determine the expression of miRNA-466-669 cluster genes (A) and the host gene Sfmbt2 (B). Samples from days 0, 2, 5, to 9 were used to determine the expression of miR-467d (C), miR-669p (D), and Sfmbt2 (E). Expression values were normalized with U6 snRNA for the miRNAs, and Gapdh for Sfmbt2. Fold changes were compared with unexposed cells, and time-dependent qPCR expression fold changes were compared with day 0 unexposed cells. Data are shown as mean ± SD. Statistical differences were determined by ANOVA followed by Tukey’s test or by Student’s t test (*P < .05). Figure 3. View largeDownload slide Arsenic exposure induces members of the miR-466-669 cluster along with its host gene, Sfmbt2. P19 cells were differentiated and RNA was extracted from cells exposed to 0 or 0.5 μM arsenite on days 0, 2, 5, and 9 (n = 3 per treatment per day). MicroRNA or mRNA expression was determined by qPCR. Day 9 samples were used to determine the expression of miRNA-466-669 cluster genes (A) and the host gene Sfmbt2 (B). Samples from days 0, 2, 5, to 9 were used to determine the expression of miR-467d (C), miR-669p (D), and Sfmbt2 (E). Expression values were normalized with U6 snRNA for the miRNAs, and Gapdh for Sfmbt2. Fold changes were compared with unexposed cells, and time-dependent qPCR expression fold changes were compared with day 0 unexposed cells. Data are shown as mean ± SD. Statistical differences were determined by ANOVA followed by Tukey’s test or by Student’s t test (*P < .05). The miR-466-467-669 cluster is located on chromosome 2 within the 10th intron of the Scm-like with 4 Mbt domains 2 (Sfmbt2) gene. Thus, the expression of the host gene was also assessed by qPCR to see if coregulation of Sfmbt2 and the miR-466-669 cluster existed. Indeed, on day 9, Sfmbt2 expression is induced by 2-fold in the arsenic-exposed cells (Figure 3B). We further wanted to determine if the miRNA cluster and host gene expression patterns were similar during embryoid body formation and differentiation, so transcript levels for the 2 most highly expressed cluster member following arsenic exposure, miR-467d and -669p, were selected. Their expression patterns were examined by real-time PCR at days 0, 2, 5, and 9 of P19 cell differentiation. Starting at day 2 of embryoid body formation, the levels of miR-467d, miR-669p, and Sfmbt2 were dramatically induced by 11-, 40-, and 55-fold, respectively, as compared with the day 0 cells (Figs. 3C–E). This suggests that these miRNAs play a role in early embryonic stem cell differentiation. As time goes on, expression of all 3 transcripts decreases in the control cells, while the levels remain higher and decline more slowly in the arsenic-exposed cells. For example, at day 5, the highest point of transcript expression, miR-467d, miR-669p, and Sfmbt2 were induced by arsenic by 3-, 3-, and 2-fold, respectively (Figs. 3C–E). Sfmbt2 has a similar pattern of expression as the miR-466-669 cluster suggesting that increased transcription of the host gene drives expression of this miRNA cluster. Potential Targets of miR-466-669 Cluster Members Include Neurogenic Transcription Factors With multiple members of the miR-466-669 cluster induced by arsenic, we wanted to examine potential target genes involved in cellular differentiation processes. Initially, we used miRanda (microRNA.org) to examine target mRNAs for each of the 65 cluster members. The list was narrowed down by requiring at least 5 members of the miR-466-669 cluster be predicted to bind to the gene’s 3′-UTR. Next, we used 2 other miRNA prediction tools, PITA analysis (genie.weizmann.ac.il/pubs/mir07/mir07_prediction.html) (Kertesz et al., 2007) and TargetScan (targetscan.org) (Agarwal et al., 2015) to examine their prediction consistency across the 3 different platforms. From these criteria, we developed a list of 6 potential genes involved in neurogenic differentiation in which at least 2 different programs predicted a particular miRNA to bind to the transcript (Table 2). NeuroD1 had the highest number of miRNAs predicted to bind to it, with 25 cluster members predicted using miRanda and 14 miRNAs predicted using at least 2 programs, while NeuroD4 had 20 cluster members predicted using miRanda and 18 miRNAs predicted using at least 2 programs (Table 2). miR-466-669 Cluster Members Share Multiple Consensus Sequences Because we could not transfect anti-miRNAs of all cluster members simultaneously, consensus anti-miRNAs were designed. Clustal Omega was used to determine sequence similarity between all mature miRNA sequences within this cluster (miRBase version 21). The cluster sequences used included 26 members of the miR-466 family, 13 sequences in the miR-467 family, and 26 sequences in the miR-669 family. A total of 24 miRNA-3p members have high degree of similarity, as shown in consensus sequence 1 (Figure 4A). This first consensus sequence is modified from another study which aligned all miRNA-3p members together (Luo et al., 2014). Interestingly, 15 of the -3p members share the seed sequence, UAUACAU, with 2 individual cluster members being in the miR-466 family, 6 members belonging to the miR-467 family, and 7 members being in the miR-669 family (Figure 4A). The miRNA-5p members clustered together but did not have the same degree of similarity to each other. So, these were subdivided into 3 additional clusters with miR-466 (h-5p, j, m-5p) grouping together (Figure 4B), miR-467 (a, b, c, d, e, h-5p) grouping together (Figure 4C), and miR-669 (a, b, c, d, e, f, l, o, n-5p) grouping together (Figure 4D). These sequence similarities suggest that these miRNA families may work together and target similar transcripts. Figure 4. View largeDownload slide The miR-466-669 cluster share sequence similarities. Sequences of mature miRNA from the miR-466-669 cluster were aligned using Clustal Omega. All mature sequences were initially aligned together and then were subdivided into 4 major groups which have high similarity. The highlighted nucleotides were used to derive the 4 consensus sequences (as light blue). An asterisk indicates the sequence identity among all miRNAs within the group. Figure 4. View largeDownload slide The miR-466-669 cluster share sequence similarities. Sequences of mature miRNA from the miR-466-669 cluster were aligned using Clustal Omega. All mature sequences were initially aligned together and then were subdivided into 4 major groups which have high similarity. The highlighted nucleotides were used to derive the 4 consensus sequences (as light blue). An asterisk indicates the sequence identity among all miRNAs within the group. Inhibition of the miR-466-669 Cluster Rescues the Inhibitory Effects of Arsenic on Stem Cell Differentiation To evaluate the effects of the miR-466-669 families on stem cell differentiation and its inhibition by arsenic, each of the 4 consensus anti-miRNAs (Figure 4) were added during cell differentiation from days 0 to 5, when cells were cultured with or without 0.5 μM arsenic (Figure 5). A portion of day 5 embryoid bodies were harvested for qPCR and immunohistochemistry. The remaining embryoid bodies were grown out to day 9 for qPCR and morphological analyses. When cells were transfected with all 4 consensus inhibitors (mixed anti-miRNAs), the cells exposed to arsenic were now able to form neuronal cells, indicating a rescue effect (Figure 5A). To quantify the relative amount of differentiating cells, their migration distance away from the embryoid body was measured. On average, the unexposed embryoid bodies had differentiating cells migrating out to 0.87 mm (Figure 5B). The cells exposed to arsenic had 5.1-fold less differentiating cells relative to control. When cells were transfected with consensus anti-miRNAs, the relative amount of differentiating cells was restored to control levels (Figure 5B). Lastly, to confirm the morphological observation that these differentiating cells were neurons, mRNA levels of NeuroD1 were assessed by qPCR arsenic exposure during the 9 days of differentiation reduced the number of NeuroD1 transcripts by 2-fold, while transfection of the consensus anti-miRNAs restored the level of NeuroD1 mRNA back to control levels (Figure 5C). Figure 5. View largeDownload slide Inhibiting the miR-466-669 cluster during differentiation rescues the morphological loss of neurons following arsenic exposure. P19 cells were transfected with 100 nM anti-miRNA oligonucleotides which target 4 consensus sequences of the miRNA-466-467-669 cluster. Cells were coexposed to 0 or 0.5 μM arsenic for the 5 days of embryoid body formation. Only the arsenic exposure was maintained for the entire 9 days of differentiation, after which cell morphology was observed. Transfections include oligonucleotides sequences that do not target any miRNAs, designated as negative control, (N.C.), and a mixed transfection that combined all consensus anti-miRNAs. Arrows indicate neuronal cells (A). The distance of cells differentiating away from the embryoid body was quantitated using ImageJ and is expressed in mm (n = 6 replicate embryoid bodies per group) (B). mRNA levels of the neuronal cell marker NeuroD1 on day 9 was assessed by qPCR (C). mRNA expression levels were normalized with Gapdh using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#). Figure 5. View largeDownload slide Inhibiting the miR-466-669 cluster during differentiation rescues the morphological loss of neurons following arsenic exposure. P19 cells were transfected with 100 nM anti-miRNA oligonucleotides which target 4 consensus sequences of the miRNA-466-467-669 cluster. Cells were coexposed to 0 or 0.5 μM arsenic for the 5 days of embryoid body formation. Only the arsenic exposure was maintained for the entire 9 days of differentiation, after which cell morphology was observed. Transfections include oligonucleotides sequences that do not target any miRNAs, designated as negative control, (N.C.), and a mixed transfection that combined all consensus anti-miRNAs. Arrows indicate neuronal cells (A). The distance of cells differentiating away from the embryoid body was quantitated using ImageJ and is expressed in mm (n = 6 replicate embryoid bodies per group) (B). mRNA levels of the neuronal cell marker NeuroD1 on day 9 was assessed by qPCR (C). mRNA expression levels were normalized with Gapdh using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#). To verify if the miRNAs themselves were knocked down by mixed anti-miRNAs, primers for the 4 individual consensus sequences were designed. Expression of each consensus sequence was examined in day 5 embryoid bodies treated with a scrambled miRNA or a mixture of the 4 consensus anti-miRNAs. Arsenic exposure significantly induced the expression of consensus sequences 1 and 3 by 1.5- to 2-fold over control cells (Figs. 6A and 6C). Consensus sequence 1 contains mostly the miR-466-3p’s, miR-467-3p’s, and miR-669-3p’s while consensus sequence 3 contains the miRs-467-5p’s. Importantly, the induced expression by arsenic in the consensuses 1 and 3 groups was knocked down to control levels when treated with the mixed anti-miRNAs (Figs. 6A and 6C), while expression of consensuses 2 and 4 was not altered by either arsenic or anti-miRNA transfection (Figs. 6B and 6D). The expression of a representative consensus sequence 1 miRNA, miR-669a-3p, was examined by qPCR. Arsenic induced its expression by 1.8-fold, and the mixed anti-miRNA transfections knocked down its expression in both control and arsenic-exposed cells (Figure 6E). To determine if the inhibitory effects are due to the miRNAs themselves or their host gene, Sfmbt2 expression was also examined by qPCR. Arsenic significantly induced Smbt2 expression whether or not the anti-miRNAs were added (Figure 6F), and there was a significant inhibition in Sfmbt2 expression when the mixed anti-miRNAs were added to the arsenic-exposed group. Finally, we assessed whether the anti-miR transfection could increase the level of Lats2, a known target of miR-466f-3p (Zheng et al., 2011). Indeed, the mixed consensus anti-miRNAs transfection significantly increases Lats2 levels by approximately 1.6-fold (Figure 6G). These results imply that the mixed anti-miRNAs are functional and active. Figure 6. View largeDownload slide Consensus anti-miRNAs are active and functional and can rescue the expression of miR-466-467-669 target genes. P19 cells were transfected with all 4 miRNA inhibitors, with or without 0.5 μM arsenic (n = 3 replicates), allowed to form embryoid bodies for 5 days, and examined for mRNA expression of each of the 4 consensus sequences (A–D), 1 individual miRNA, miR-669a-3p (E), the host gene Sfmbt2 (F), and a known target gene for the consensus 1 cluster, Lats2 (G). mRNA expression levels were normalized with Gapdh, and miRNA expression levels were normalized with shRNA U6, using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#) in (A–F). A 1-way ANOVA followed by Tukey’s test (P < .05) was run to determine significance (#) in (G). Figure 6. View largeDownload slide Consensus anti-miRNAs are active and functional and can rescue the expression of miR-466-467-669 target genes. P19 cells were transfected with all 4 miRNA inhibitors, with or without 0.5 μM arsenic (n = 3 replicates), allowed to form embryoid bodies for 5 days, and examined for mRNA expression of each of the 4 consensus sequences (A–D), 1 individual miRNA, miR-669a-3p (E), the host gene Sfmbt2 (F), and a known target gene for the consensus 1 cluster, Lats2 (G). mRNA expression levels were normalized with Gapdh, and miRNA expression levels were normalized with shRNA U6, using the comparative delta Ct method. Fold changes were compared with N.C. anti-miRNA. Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#) in (A–F). A 1-way ANOVA followed by Tukey’s test (P < .05) was run to determine significance (#) in (G). Members of the miR-466-669 Cluster Repress NeuroD1 Expression Transfection of consensus anti-miRNA mixture morphologically restored cellular differentiation, particularly into neurons (Figure 5A) and miRNA target prediction tools suggested that several transcription factors were likely targets of the miR-466-467-669 cluster (Table 2). So, we determined whether transfection of the mixed consensus anti-miRNAs could restore the expression of one of these predicted transcription factors, NeuroD1. When NeuroD1 protein expression was examined by immunohistochemistry (Figure 7A), exposure to 0.5 μM arsenic for 5 days reduced both total and nuclear expression of NeuroD1 by 1.5- and 1.6-fold, respectively (Figure 7B). Coexposure with the mixed consensus anti-miRNAs significantly rescued NeuroD1 expression in the arsenic-exposed cells (Figure 7B). Similarly, NeuroD1 transcript levels were reduced by 1.4-fold in the arsenic-exposed cells, and the mixed consensus anti-miRNAs were able to rescue NeuroD1 mRNA expression back to control levels (Figure 7B). NeuroD2 and NeuroD6 are not expressed in differentiating P19 cells (data not shown), so levels of these 2 transcription factors were not examined. NeuroD4 is expressed but its mRNA levels were not changed by anti-miRNA transfection (data not shown). These results suggest that the miR-466-467-669 cluster is induced by arsenic, and these miRNAs act to reduce cellular differentiation by inhibiting transcription factors important in cell fate determination, such NeuroD1. Figure 7. View largeDownload slide Mixed consensus miRNA inhibitors rescue arsenic’s inhibitory effects on NeuroD1 expression. P19 cells were transfected with a combined mixture of the 4 anti-miRNAs, with or without 0.5 μM arsenic (n = 3 replicates per anti-miRNA and per exposure group), and allowed to form embryoid bodies for 5 days. Immunohistochemistry was used to examine expression of NeuroD1 protein (red) in the embryoid bodies. Cells were counterstained with DAPI (blue) to indicate the nuclei (A). High magnification images of cells are shown in the (A) inserts. For 10 representative cells (examples are shown in the blue boxes), expression of NeuroD1 in the whole cell, cytoplasm, and nuclei were quantified using ImageJ (B). NeuroD1 transcript levels were quantified by qPCR, and normalized with Gapdh using the comparative delta Ct method with fold changes compared with N.C. (B). Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#). Figure 7. View largeDownload slide Mixed consensus miRNA inhibitors rescue arsenic’s inhibitory effects on NeuroD1 expression. P19 cells were transfected with a combined mixture of the 4 anti-miRNAs, with or without 0.5 μM arsenic (n = 3 replicates per anti-miRNA and per exposure group), and allowed to form embryoid bodies for 5 days. Immunohistochemistry was used to examine expression of NeuroD1 protein (red) in the embryoid bodies. Cells were counterstained with DAPI (blue) to indicate the nuclei (A). High magnification images of cells are shown in the (A) inserts. For 10 representative cells (examples are shown in the blue boxes), expression of NeuroD1 in the whole cell, cytoplasm, and nuclei were quantified using ImageJ (B). NeuroD1 transcript levels were quantified by qPCR, and normalized with Gapdh using the comparative delta Ct method with fold changes compared with N.C. (B). Data are shown as mean ± SD. Two-way ANOVA followed by Bonferroni (P < .05) was run to determine interactions and statistical differences between arsenic concentrations (*) and between the N.C. anti-miRNA and consensus anti-miRNA transfections (#). DISCUSSION This study demonstrates that arsenic can impair stem cell differentiation by altering miRNA expression, including miR-9, miR-92a, miR-199a, miR-291a, miR-709, and members of the miR-466-467-669 cluster. In addition, these data ascribe a novel function for the murine miR-466-467-669 cluster in embryonic stem cells. This cluster appears to be inhibiting the expression of NeuroD1, thereby reducing the cell’s ability to differentiate into neurons. Previous studies have shown that arsenic at concentrations between 0.1 and 0.5 μM inhibit P19 embryonic stem cell differentiation into skeletal muscle and sensory neurons (Bain et al., 2016; Hong and Bain, 2012; Liu and Bain, 2014; McCoy et al., 2015). One potential mechanism for this inhibition is through changes in miRNA expression in arsenic-exposed cells, as miRNAs have been shown to play a role in arsenic toxicity and carcinogenesis (reviewed in Bustaffa et al., 2014; Paul and Giri, 2015; Ren et al., 2011). Comparing over 1900 miRNAs from the control and arsenic-exposed P19 cells undergoing differentiation indicated that 10 of the top 31 miRNAs whose expression was increased in response to arsenic are in the miR-466-467-669 cluster. The miRNA-466-467-669 cluster is one of the largest miRNA clusters in mice and rats and is located in the 10th intron of the Sfmbt2 gene, a polycomb group protein (Lehnert et al., 2011). Studies have implicated changes in the expression of this cluster following alterations in glucose levels. For example, miR-466h is induced in chinese hamster ovary (CHO) cells treated with nutrient-depleted or low glucose media, which induces apoptosis (Druz et al., 2011,, 2012). In contrast, high levels of glucose in neural stem cells can reduce the expression of miR-466a-3p and miR-466d-3p (Shyamasundar et al., 2013). Our studies show that arsenic exposure during P19 stem cell differentiation increases the expression of multiple members of the miR-466 cluster, with these 3 specific transcripts: miR-466a, -466d, and -466h being increased, albeit not significantly based upon the arrays. We did not validate the expression these specific transcripts using qPCR. However, these specific miRNAs all do fall within the consensus 1 sequences, as shown in Figure 4. Several additional studies have shown that the miR-466-467-669 family members appear to be involved in cellular differentiation in a variety of cell types and models. For example, miR-466 family members are increased in bone marrow-derived progenitor cells derived from diabetic animals. These animals have reduced angiogenesis, which is a result of the miR-466 family members targeting and decreasing the expression of angiopoetin (Wang et al., 2017). Similarly, miR-466 reduces lymphangiogenesis in primary lympathic endothelial cells by targeting a pro-lymphatic transcription factor called Prospero homeobox 1 (Prox1). This reduction in lymphangiogenesis can be rescued by a miR-466 inhibitor (Seo et al., 2015). In primary murine neural stem cells, decreased expression of miR-466a-3p and miR-466d-3p results in increased doublecortin levels, along with an increase in neurogenic differentiation (Shyamasundar et al., 2013). Similar to the studies described earlier, our data indicate that both miR-466a-3p and -466d-3p are increased by approximately 2-fold in the stem cells with impaired differentiation following arsenic exposure. Taken together, these data imply that one function of the miR-466-467-669 cluster is to regulate cellular differentiation. Because multiple members of the miR-466-467-669 family were increased following arsenic treatment, an approach that involved developing consensus sequences for the 65 cluster members was undertaken so that anti-miRNAs could be produced. Using Clustal analyses, 4 separate consensus sequences were identified, with the -3p sequences falling into consensus 1 and the -5p sequences being divided into the consensuses 2–4 groups. Because arsenic inhibits differentiation into neurons, target genes discovered by in silico analyses included NeuroD1, NeuroD2, NeuroD4, NeuroD6, Neurogenin 1, and Neurogenin 2. Several of these genes have been previously shown to be inhibited after arsenic exposure (Hong and Bain, 2012; Liu and Bain, 2014; McCoy et al., 2015). Transfection of consensus anti-miRNAs designed to inhibit most members of the miR-466-467-669 cluster showed that neurogenesis was increased when the cells were exposed to arsenic, likely due to the increased expression of NeuroD1. These results imply that the miR-466-467-669 cluster reduces neurogenesis by targeting transcription factors required for neuronal differentiation. Although NeuroD2 and 6 are potential targets genes for the miR-466-669 cluster, they are not expressed in differentiating P19 cells. They both appear to play a role in corticogenesis and the maintenance of pyramidal neurons (Bormuth et al., 2013), and thus, they are unlikely to be expressed using our procedure, in which the cells differentiate into sensory neurons. In addition to the miR-466-467-669 cluster miRNAs such as the miR-290 cluster are also important in maintaining stem cell pluripotency and inhibiting embryonic stem cell differentiation into multiple lineages (Sinkkonen et al., 2008). Interestingly, several members of miRNA-290 cluster were induced by arsenic in our microarray, including a 4-fold induction of miR-291a and miR-291b, a 2-fold induction of miR-290a, a 2.8-fold induction of miR-292a, and a 4.7-fold fold induction of miR-294. We further determined the time-dependent expression of the most significantly induced family member, miR-291a by qPCR. As the cells were undergoing differentiation, miR-291a levels decreased by 3.2-fold on day 5 and 4-fold on day 9. However, arsenic exposure kept miR-291a at nearly the same levels as in day 0 stem cells (Supplementary Figure 1). This indicates that cellular pluripotency is maintained when cells are exposed to arsenic. However, mechanisms by which arsenic induces the miR-290 cluster expression are not known. Several studies report that arsenic alters miRNA expression. Lung epithelial cells chronically exposed to 1 μM sodium arsenite for 26 weeks were transformed into cancer-like cells that had significantly reduced miR-199a expression (He et al., 2014). Interestingly, the reduction of miR-199a is consistent with our expression data. This reduction may indicate that altered miR-199a expression occurs as a result of arsenic exposure, or occurs when cells are not differentiating appropriately. As measured by the arrays and by qPCR, miR-9 is significantly reduced by 2- to 3-fold in arsenic exposed embryoid bodies. miR-9 is known to be induced during neural lineage differentiation, and its inhibition blocks neurogenesis in embryonic stem cells (Krichevsky et al., 2006). This indicates that miR-9 plays an important role during embryonic development and reduction by arsenic might also be involved in the inhibitory effects seen in our study. Collectively, our results indicate that low-level arsenic exposure to P19 embryonic stem cells during their differentiation alters the expression profile of miRNAs, including multiple members of miRNA-466-467-669 cluster, along with their host gene Sfmbt2. These miRNAs appear to inhibit neurogenesis by targeting NeuroD1. These data ascribe a novel mechanism for arsenic’s ability to inhibit cellular lineage development. ACKNOWLEDGMENTS The authors thank Dana Symkowicz for her assistance with immunohistochemistry. FUNDING Funding for this study was provided by the National Institute of Environmental Health Sciences (grant number ES023065). SUPPLEMENTARY DATA Supplementary data are available at Toxicological Sciences online. REFERENCES Agarwal V., Bell G. W., Nam J.-W., Bartel D. P. ( 2015). Predicting effective microRNA target sites in mammalian mRNAs. Elife  4, eLife.05005. Akerblom M., Sachdeva R., Barde I., Verp S., Gentner B., Trono D., Jakobsson J. ( 2012). MicroRNA-124 is a subventricular zone neuronal fate determinant. J. Neurosci.  32, 8879– 8889. Google Scholar CrossRef Search ADS PubMed  Amini M., Abbaspour K. C., Berg M., Winkel L., Hug S. J., Hoehn E., Yang H., Johnson C. A. ( 2008). Statistical modeling of global geogenic arsenic contamination in groundwater. Environ. Sci. Technol.  42, 3669– 3675. Google Scholar CrossRef Search ADS PubMed  Bain L. J., Liu J.-T., League R. E. ( 2016). Arsenic inhibits stem cell differentiation by altering the interplay between the Wnt3a and Notch signaling pathways. Toxicol. Rep.  3, 405– 413. http://dx.doi.org/10.1016/j.toxrep.2016.03.011 Google Scholar CrossRef Search ADS PubMed  Bardullas U., Limón-Pacheco J. H., Giordano M., Carrizales L., Mendoza-Trejo M. S., Rodríguez V. M. ( 2009). Chronic low-level arsenic exposure causes gender-specific alterations in locomotor activity, dopaminergic systems, and thioredoxin expression in mice. Toxicol. Appl. Pharmacol.  239, 169– 177. Google Scholar CrossRef Search ADS PubMed  Bernstein E., Kim S. Y., Carmell M. A., Murchison E. P., Alcorn H., Li M. Z., Mills A. A., Elledge S. J., Anderson K. V., Hannon G. J. ( 2003). Dicer is essential for mouse development. Nat. Genet.  35, 215– 217. Google Scholar CrossRef Search ADS PubMed  Betel D., Koppal A., Agius P., Sander C., Leslie C. ( 2010). Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome Biol.  11, R90. Google Scholar CrossRef Search ADS PubMed  Blanes P. S., Buchhamer E. E., Gimenez M. C. ( 2011). Natural contamination with arsenic and other trace elements in groundwater of the Central-West region of Chaco, Argentina. J. Environ. Sci. Health A Tox. Hazard. Subst. Environ. Eng . 46, 1197– 1206. http://dx.doi.org/10.1080/10934529.2011.598774 Google Scholar CrossRef Search ADS PubMed  Bormuth I., Yan K., Yonemasu T., Gummert M., Zhang M., Wichert S., Grishina O., Pieper A., Zhang W., Goebbels S., et al.   ( 2013). Neuronal basic helix-loop-helix proteins Neurod2/6 regulate cortical commissure formation before midline interactions. J. Neurosci.  33, 641– 651. Google Scholar CrossRef Search ADS PubMed  Bustaffa E., Stoccoro A., Bianchi F., Migliore L. ( 2014). Genotoxic and epigenetic mechanisms in arsenic carcinogenicity. Arch. Toxicol.  88, 1043– 1067. Google Scholar CrossRef Search ADS PubMed  Chandravanshi L. P., Yadav R. S., Shukla R. K., Singh A., Sultana S., Pant A. B., Parmar D., Khanna V. K. ( 2014). Reversibility of changes in brain cholinergic receptors and acetylcholinesterase activity in rats following early life arsenic exposure. Int. J. Dev. Neurosci.  34, 60– 75. Google Scholar CrossRef Search ADS PubMed  Chen H., Mo D., Li M., Zhang Y., Chen L., Zhang X., Li M., Zhou X., Chen Y. ( 2014). miR-709 inhibits 3T3-L1 cell differentiation by targeting GSK3β of Wnt/β-catenin signaling. Cell. Signal.  26, 2583– 2589. Google Scholar CrossRef Search ADS PubMed  Chen S. L., Dzeng S. R., Yang M. H., Chiu K. H., Shieh G. M., Wai C. M. ( 1994). Arsenic species in groundwaters of the blackfoot disease area, Taiwan. Environ. Sci. Technol.  28, 877– 881. Google Scholar CrossRef Search ADS PubMed  Cheng L. C., Pastrana E., Tavazoie M., Doetsch F. ( 2009). miR-124 regulates adult neurogenesis in the subventricular zone stem cell niche. Nat. Neurosci.  12, 399– 408. http://dx.doi.org/10.1038/nn.2294 Google Scholar CrossRef Search ADS PubMed  Clark A. M., Goldstein L. D., Tevlin M., Tavare S., Shaham S., Miska E. A. ( 2010). The microRNA miR-124 controls gene expression in the sensory nervous system of Caenorhabditis elegans. Nucleic Acids Res.  38, 3780– 3793. Google Scholar CrossRef Search ADS PubMed  Concha G., Vogler G., Lezcano D., Nermell B., Vahter M. ( 1998). Exposure to inorganic arsenic metabolites during early human development. Toxicol. Sci.  44, 185– 190. Google Scholar CrossRef Search ADS PubMed  Cronican A. A., Fitz N. F., Carter A., Saleem M., Shiva S., Barchowsky A., Koldamova R., Schug J., Lefterov I., Johnson R. ( 2013). Genome-wide alteration of histone H3K9 acetylation pattern in mouse offspring prenatally exposed to arsenic. PLoS One  8, e53478. Google Scholar CrossRef Search ADS PubMed  Cui Y., Han Z., Hu Y., Song G., Hao C., Xia H., Ma X. ( 2012). MicroRNA-181b and microRNA-9 mediate arsenic-induced angiogenesis via NRP1. J. Cell. Physiol.  227, 772– 783. http://dx.doi.org/10.1002/jcp.22789 Google Scholar CrossRef Search ADS PubMed  Druz A., Betenbaugh M., Shiloach J. ( 2012). Glucose depletion activates mmu-miR-466h-5p expression through oxidative stress and inhibition of histone deacetylation. Nucleic Acids Res.  40, 7291– 7302. http://dx.doi.org/10.1093/nar/gks452 Google Scholar CrossRef Search ADS PubMed  Druz A., Chu C., Majors B., Santuary R., Betenbaugh M., Shiloach J. ( 2011). A novel microRNA mmu-miR-466h affects apoptosis regulation in mammalian cells. Biotechnol. Bioeng.  108, 1651– 1661. Google Scholar CrossRef Search ADS PubMed  Edwards M., Johnson L., Mauer C., Barber R., Hall J., O’Bryant S. ( 2014). Regional specific groundwater arsenic levels and neuropsychological functioning: A cross-sectional study. Int. J. Environ. Health Res.  24, 546– 557. Google Scholar CrossRef Search ADS PubMed  Enright A. J., John B., Gaul U., Tuschl T., Sander C., Marks D. S. ( 2003). MicroRNA targets in Drosophila. Genome Biol.  5, R1. Google Scholar CrossRef Search ADS PubMed  Frankel S., Concannon J., Brusky K., Pietrowicz E., Giorgianni S., Thompson W. D., Currie D. A. ( 2009). Arsenic exposure disrupts neurite growth and complexity in vitro. Neurotoxicology  30, 529– 537. Google Scholar CrossRef Search ADS PubMed  Hafeman D. M., Ahsan H., Louis E. D., Siddique A. B., Slavkovich V., Cheng Z., van Geen A., Graziano J. H. ( 2005). Association between arsenic exposure and a measure of subclinical sensory neuropathy in Bangladesh. J. Occup. Environ. Med.  47, 778– 784. Google Scholar CrossRef Search ADS PubMed  Hausser J., Zavolan M. ( 2014). Identification and consequences of miRNA-target interactions—Beyond repression of gene expression. Nat. Rev. Genet.  15, 599– 612. Google Scholar CrossRef Search ADS PubMed  He J., Wang M., Jiang Y., Chen Q., Xu S., Xu Q., Jiang B. H., Liu L. Z. ( 2014). Chronic arsenic exposure and angiogenesis in human bronchial epithelial cells via the ROS/miR-199a-5p/HIF-1α/COX-2 pathway. Environ. Health Perspect.  122, 255– 261. Google Scholar PubMed  Hong G.-M., Bain L. J. ( 2012). Arsenic exposure inhibits myogenesis and neurogenesis in P19 stem cells through repression of the b-catenin signaling pathway. Toxicol. Sci.  129, 146– 156. http://dx.doi.org/10.1093/toxsci/kfs186 Google Scholar CrossRef Search ADS PubMed  Hopenhayn C., Ferreccio C., Browning S., Huang B., Peralta C., Gibb H., Hertz-Picciotto I. ( 2003). Arsenic exposure from drinking water and birth weight. Epidemiology  14, 593– 602. Google Scholar CrossRef Search ADS PubMed  Huyck K. L., Kile M. L., Mahiuddin G., Quamruzzaman Q., Rahman M., Breton C. V., Dobson C. B., Frelich J., Hoffman E., Yousuf J., et al.   ( 2007). Maternal arsenic exposure associated with low birth weight in Bangladesh. J. Occup. Environ. Med.  49, 1097– 1104. Google Scholar CrossRef Search ADS PubMed  Ivey K. N., Muth A., Arnold J., King F. W., Yeh R. F., Fish J. E., Hsiao E. C., Schwartz R. J., Conklin B. R., Bernstein H. S., et al.   ( 2008). MicroRNA regulation of cell lineages in mouse and human embryonic stem cells. Cell Stem Cell  2, 219– 229. Google Scholar CrossRef Search ADS PubMed  Jiang R., Li Y., Zhang A., Wang B., Xu Y., Xu W., Zhao Y., Luo F., Liu Q. ( 2014). The acquisition of cancer stem cell-like properties and neoplastic transformation of human keratinocytes induced by arsenite involves epigenetic silencing of let-7c via Ras/NF-κB. Toxicol. Lett.  227, 91– 98. Google Scholar CrossRef Search ADS PubMed  Judson R. L., Babiarz J. E., Venere M., Blelloch R. ( 2009). Embryonic stem cell-specific microRNAs promote induced pluripotency. Nat. Biotechnol.  27, 459– 461. http://dx.doi.org/10.1038/nbt.1535 Google Scholar CrossRef Search ADS PubMed  Kanellopoulou C., Muljo S. A., Kung A. L., Ganesan S., Drapkin R., Jenuwein T., Livingston D. M., Rajewsky K. ( 2005). Dicer-deficient mouse embryonic stem cells are defective in differentiation and centromeric silencing. Genes Dev.  19, 489– 501. Google Scholar CrossRef Search ADS PubMed  Kawasaki S., Yazawa S., Ohnishi A., Ohi T. ( 2002). [Chronic and predominantly sensory polyneuropathy in Toroku Valley where a mining company produced arsenic]. Rinsho Shinkeigaku  42, 504– 511. Google Scholar PubMed  Kertesz M., Iovino N., Unnerstall U., Gaul U., Segal E. ( 2007). The role of site accessibility in microRNA target recognition. Nat. Genet.  39, 1278– 1284. Google Scholar CrossRef Search ADS PubMed  Krichevsky A. M., Sonntag K. C., Isacson O., Kosik K. S. ( 2006). Specific microRNAs modulate embryonic stem cell-derived neurogenesis. Stem Cells  24, 857– 864. Google Scholar CrossRef Search ADS PubMed  Lee Y.-B., Bantounas I., Lee D.-Y., Phylactou L., Caldwell M. A., Uney J. B. ( 2009). Twist-1 regulates the miR-199a/214 cluster during development. Nucleic Acids Res.  37, 123– 128. Google Scholar CrossRef Search ADS PubMed  Lehnert S., Kapitonov V., Thilakarathne P. J., Schuit F. C. ( 2011). Modeling the asymmetric evolution of a mouse and rat-specific microRNA gene cluster intron 10 of the Sfmbt2 gene. BMC Genomics  12, 257. Google Scholar CrossRef Search ADS PubMed  Lennox K. A., Owczarzy R., Thomas D. M., Walder J. A., Behlke M. A. ( 2013). Improved performance of anti-miRNA oligonucleotides using a novel non-nucleotide modifier. Mol. Ther. Nucleic Acids  2, 117. Google Scholar CrossRef Search ADS   Lichner Z., Páll E., Kerekes A., Pállinger É., Maraghechi P., Bősze Z., Gócza E. ( 2011). The miR-290-295 cluster promotes pluripotency maintenance by regulating cell cycle phase distribution in mouse embryonic stem cells. Differentiation  81, 11– 24. Google Scholar CrossRef Search ADS PubMed  Lin E. A., Kong L., Bai X.-H., Luan Y., Liu C.-J. ( 2009). miR-199a, a bone morphogenic protein 2-responsive microRNA, regulates chondrogenesis via direct targeting to Smad1. J. Biol. Chem.  284, 11326– 11335. http://dx.doi.org/10.1074/jbc.M807709200 Google Scholar CrossRef Search ADS PubMed  Liu J.-T., Bain L. J. ( 2014). Arsenic inhibits hedgehog signaling during P19 cell differentiation. Toxicol. Appl. Pharm.  281, 243– 253. http://dx.doi.org/10.1016/j.taap.2014.10.007 Google Scholar CrossRef Search ADS   Liu N., Williams A. H., Kim Y., McAnally J., Bezprozvannaya S., Sutherland L. B., Richardson J. A., Bassel-Duby R., Olson E. N. ( 2007). An intragenic MEF2-dependent enhancer directs muscle-specific expression of microRNAs 1 and 133. Proc. Natl. Acad. Sci. U.S.A.  104, 20844– 20849. Google Scholar CrossRef Search ADS PubMed  Liu S., Piao F., Sun X., Bai L., Peng Y., Zhong Y., Ma N., Sun W. ( 2012). Arsenic-induced inhibition of hippocampal neurogenesis and its reversibility. Neurotoxicology  33, 1033– 1039. Google Scholar CrossRef Search ADS PubMed  Livak K. J., Schmittgen T. D. ( 2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods  25, 402– 408. http://dx.doi.org/10.1006/meth.2001.1262 Google Scholar CrossRef Search ADS PubMed  Luo J., Qiu Z., Chen J., Zhang L., Liu W., Tan Y., Shu W. ( 2013). Maternal and early life arsenite exposure impairs neurodevelopment and increases the expression of PSA-NCAM in hippocampus of rat offspring. Toxicology  311, 99– 106. Google Scholar CrossRef Search ADS PubMed  Luo J. H., Qiu Z. Q., Shu W. Q., Zhang Y. Y., Zhang L., Chen J. A. ( 2009). Effects of arsenic exposure from drinking water on spatial memory, ultra-structures and NMDAR gene expression of hippocampus in rats. Toxicol. Lett.  184, 121– 125. Google Scholar CrossRef Search ADS PubMed  Luo Y., Liu Y., Liu M., Wei J., Zhang Y., Hou J., Huang W., Wang T., Li X., He Y., et al.   ( 2014). Sfmbt2 10th intron-hosted miR-466(a/e)-3p are important epigenetic regulators of Nfat5 signaling, osmoregulation and urine concentration in mice. Biochim. Biophys. Acta  1839, 97– 106. Google Scholar CrossRef Search ADS PubMed  Mandal B., Suzuki T. ( 2002). Arsenic around the world: A review. Talanta  58, 201– 235. http://dx.doi.org/10.1016/S0039-9140(02)00268-0 Google Scholar CrossRef Search ADS PubMed  Markowski V. P., Reeve E. A., Onos K., Assadollahzadeh M., McKay N. ( 2012). Effects of prenatal exposure to sodium arsenite on motor and food-motivated behaviors from birth to adulthood in C57BL6/J mice. Neurotoxicol. Teratol.  34, 221– 231. Google Scholar CrossRef Search ADS PubMed  Martinez E. J., Kolb B. L., Bell A., Savage D. D., Allan A. M. ( 2008). Moderate perinatal arsenic exposure alters neuroendocrine markers associated with depression and increases depressive-like behaviors in adult mouse offspring. Neurotoxicology  29, 647– 655. Google Scholar CrossRef Search ADS PubMed  Martinez-Finley E. J., Ali A. M., Allan A. M. ( 2009). Learning deficits in C57BL/6J mice following perinatal arsenic exposure: Consequence of lower corticosterone receptor levels? Pharmacol. Biochem. Behav.  94, 271– 277. http://dx.doi.org/10.1016/j.pbb.2009.09.006 Google Scholar CrossRef Search ADS PubMed  Mukherjee B., Bindhani B., Saha H., Sinha D., Ray M. R. ( 2014). Platelet hyperactivity, neurobehavioral symptoms and depression among Indian women chronically exposed to low level of arsenic. Neurotoxicology  45, 159– 167. Google Scholar CrossRef Search ADS PubMed  Nagaraja T. N., Desiraju T. ( 1994). Effects on operant learning and brain acetylcholine esterase activity in rats following chronic inorganic arsenic intake. Hum. Exp. Toxicol.  13, 353– 356. http://dx.doi.org/10.1177/096032719401300511 Google Scholar CrossRef Search ADS PubMed  Ngalame N. N., Tokar E. J., Person R. J., Xu Y., Waalkes M. P. ( 2014). Aberrant microRNA expression likely controls RAS oncogene activation during malignant transformation of human prostate epithelial and stem cells by arsenic. Toxicol. Sci.  138, 268– 277. Google Scholar CrossRef Search ADS PubMed  Nickson R., McArthur J., Burgess W., Ahmed K. M., Ravenscroft P., Rahman M. ( 1998). Arsenic poisoning of Bangladesh groundwater. Nature  395, 338. Google Scholar CrossRef Search ADS PubMed  Ning Z., Lobdell D. T., Kwok R. K., Liu Z., Zhang S., Ma C., Riediker M., Mumford J. L. ( 2007). Residential exposure to drinking water arsenic in Inner Mongolia, China. Toxicol. Appl. Pharm.  222, 351– 356. Google Scholar CrossRef Search ADS   O’Bryant S. E., Edwards M., Menon C. V., Gong G., Barber R. ( 2011). Long-term low-level arsenic exposure is associated with poorer neuropsychological functioning: A project Frontier study. Int. J. Environ. Res. Public Health  8, 861– 874. Google Scholar CrossRef Search ADS PubMed  Otaegi G., Pollock A., Hong J., Sun T. ( 2011). MicroRNA miR-9 modifies motor neuron columns by a tuning regulation of FoxP1 levels in developing spinal cords. J. Neurosci.  31, 809– 818. http://dx.doi.org/10.1523/JNEUROSCI.4330-10.2011 Google Scholar CrossRef Search ADS PubMed  Paul S., Giri A. K. ( 2015). Epimutagenesis: A prospective mechanism to remediate arsenic-induced toxicity. Environ. Int.  81, 8– 17. http://dx.doi.org/10.1016/j.envint.2015.04.002 Google Scholar CrossRef Search ADS PubMed  Rahman A., Vahter M., Smith A. H., Nermell B., Yunus M., El Arifeen S., Persson L. A., Ekström E. C. ( 2009). Arsenic exposure during pregnancy and size at birth: A prospective cohort study in Bangladesh. Am. J. Epidemiol.  169, 304– 312. Google Scholar CrossRef Search ADS PubMed  Rahman M. M., Mandal B. K., Chowdhury T. R., Sengupta M. K., Chowdhury U. K., Lodh D., Chanda C. R., Basu G. K., Mukherjee S. C., Saha K. C., Chakraborti D. ( 2003). Neuropathy in arsenic toxicity from groundwater arsenic contamination in West Bengal, India. J. Environ. Sci. Health A Tox. Hazard. Subst. Environ. Eng . 38, 165– 183. Google Scholar CrossRef Search ADS PubMed  Ren X., McHale C. M., Skibola C. F., Smith A. H., Smith M. T., Zhang L. ( 2011). An emerging role for epigenetic dysregulation in arsenic toxicity and carcinogenesis. Environ. Health Perspect.  119, 11– 19. Google Scholar CrossRef Search ADS PubMed  Rodriguez A., Griffiths-Jones S., Ashurst J. L., Bradley A. ( 2004). Identification of mammalian microRNA host genes and transcription units. Genome Res.  14, 1902– 1910. Google Scholar CrossRef Search ADS PubMed  Rodrı´guez V. M., Carrizales L., Mendoza M. S., Fajardo O. R., Giordano M. ( 2002). Effects of sodium arsenite exposure on development and behavior in the rat. Neurotoxicol. Teratol.  24, 743– 750. Google Scholar CrossRef Search ADS PubMed  Rodríguez-Barranco M., Gil F., Hernández A. F., Alguacil J., Lorca A., Mendoza R., Gómez I., Molina-Villalba I., González-Alzaga B., Aguilar-Garduño C., et al.   ( 2016). Postnatal arsenic exposure and attention impairment in school children. Cortex  74, 370– 382. Google Scholar CrossRef Search ADS PubMed  Rosado J. L., Ronquillo D., Kordas K., Rojas O., Alatorre J., Lopez P., Garcia-Vargas G., Del Carmen Caamaño M., Cebrián M. E., Stoltzfus R. J. ( 2007). Arsenic exposure and cognitive performance in Mexican schoolchildren. Environ. Health Perspect.  115, 1371– 1375. Google Scholar CrossRef Search ADS PubMed  Saha K. K., Engstrom A., Hamadani J. D., Tofail F., Rasmussen K. M., Vahter M. ( 2012). Pre- and postnatal arsenic exposure and body size to two years of age: A cohort study in rural Bangladesh. Environ. Health Perspect.  120, 1208– 1214. Google Scholar CrossRef Search ADS PubMed  Seo M., Choi J. S., Rho C. R., Joo C. K., Lee S. K. ( 2015). MicroRNA miR-466 inhibits lymphangiogenesis by targeting prospero-related homeobox 1 in the alkali burn corneal injury model. J. Biomed. Sci.  22, 3. http://dx.doi.org/10.1186/s12929-014-0104-0 Google Scholar CrossRef Search ADS PubMed  Shibata M., Nakao H., Kiyonari H., Abe T., Aizawa S. ( 2011). MicroRNA-9 regulates neurogenesis in mouse telencephalon by targeting multiple transcription factors. J. Neurosci.  31, 3407– 3422. Google Scholar CrossRef Search ADS PubMed  Shyamasundar S., Jadhav S. P., Bay B. H., Tay S. S. W., Kumar S. D., Rangasamy D., Dheen S. T., Pant A. B. ( 2013). Analysis of epigenetic factors in mouse embryonic neural stem cells exposed to hyperglycemia. PLoS One  8, e65945. Google Scholar CrossRef Search ADS PubMed  Sinkkonen L., Hugenschmidt T., Berninger P., Gaidatzis D., Mohn F., Artus-Revel C. G., Zavolan M., Svoboda P., Filipowicz W. ( 2008). MicroRNAs control de novo DNA methylation through regulation of transcriptional repressors in mouse embryonic stem cells. Nat. Struct. Mol. Biol.  15, 259– 267. Google Scholar CrossRef Search ADS PubMed  Smedley P., Kinniburgh D. ( 2002). A review of the source, behaviour and distribution of arsenic in natural waters. Appl. Geochem.  17, 517– 568. http://dx.doi.org/10.1016/S0883-2927(02)00018-5 Google Scholar CrossRef Search ADS   Steffens A. A., Hong G.-M., Bain L. J. ( 2011). Sodium arsenite delays the differentiation of C2C12 mouse myoblast cells and alters methylation patterns on the transcription factor myogenin. Toxicol. Appl. Pharm.  250, 154– 161. http://dx.doi.org/10.1016/j.taap.2010.10.006 Google Scholar CrossRef Search ADS   Tolins M., Ruchirawat M., Landrigan P. ( 2014). The developmental neurotoxicity of arsenic: Cognitive and behavioral consequences of early life exposure. Ann. Glob. Health  80, 303– 314. http://dx.doi.org/10.1016/j.aogh.2014.09.005 Google Scholar CrossRef Search ADS PubMed  Tyler C. R., Allan A. M., Homberg J. ( 2013). Adult hippocampal neurogenesis and mRNA expression are altered by perinatal arsenic exposure in mice and restored by brief exposure to enrichment. PLoS One  8, e73720. Google Scholar CrossRef Search ADS PubMed  Wang J. M., Qiu Y., Yang Z. Q., Li L., Zhang K. ( 2017). Inositol requiring enzyme 1 facilitates diabetic wound healing through modulating microRNAs. Diabetes  66, 177– 192. http://dx.doi.org/10.2337/db16-0052 Google Scholar CrossRef Search ADS PubMed  Wang X., Meng D., Chang Q., Pan J., Zhang Z., Chen G., Ke Z., Luo J., Shi X. ( 2010). Arsenic inhibits neurite outgrowth by inhibiting the LKB1-AMPK signaling pathway. Environ. Health Perspect.  118, 627– 634. Google Scholar CrossRef Search ADS PubMed  Willems E., Leyns L. ( 2008). Patterning of mouse embryonic stem cell-derived pan-mesoderm by activin A/nodal and Bmp4 signalling requires fibroblast growth factor activity. Differentiation  76, 745– 759. http://dx.doi.org/10.1111/j.1432-0436.2007.00257.x Google Scholar CrossRef Search ADS PubMed  Wilson K. D., Venkatasubrahmanyam S., Jia F., Sun N., Butte A. J., Wu J. C. ( 2009). MicroRNA profiling of human-induced pluripotent stem cells. Stem Cells Dev.  18, 749– 757. Google Scholar CrossRef Search ADS PubMed  Yang M., Li Y., Padgett R. W. ( 2005). MicroRNAs: Small regulators with a big impact. Cytokine Growth Factor Rev.  16, 387– 393. http://dx.doi.org/10.1016/j.cytogfr.2005.02.008 Google Scholar CrossRef Search ADS PubMed  Yen Y. P., Tsai K. S., Chen Y. W., Huang C. F., Yang R. S., Liu S. H. ( 2010). Arsenic inhibits myogenic differentiation and muscle regeneration. Environ. Health Perspect . 118, 949– 956. Google Scholar CrossRef Search ADS PubMed  Zavala Y. J., Duxbury J. M. ( 2008). Arsenic in rice: I. Estimating normal levels of total arsenic in rice grain. Environ. Sci. Technol.  42, 3856– 3860. http://dx.doi.org/10.1021/es702747y Google Scholar CrossRef Search ADS PubMed  Zhang C., Ferrari R., Beezhold K., Stearns-Reider K., D’Amore A., Haschak M., Stolz D., Robbins P. D., Barchowsky A., Ambrosio F. ( 2016). Arsenic promotes NF-κB-mediated fibroblast dysfunction and matrix remodeling to impair muscle stem cell function. Stem Cells . 34, 732– 742. Google Scholar CrossRef Search ADS PubMed  Zhao C., Sun G., Li S., Shi Y. ( 2009). A feedback regulatory loop involving microRNA-9 and nuclear receptor TLX in neural stem cell fate determination. Nat. Struct. Mol. Biol.  16, 365– 371. http://dx.doi.org/10.1038/nsmb.1576 Google Scholar CrossRef Search ADS PubMed  Zhao J., Wang C., Song Y., Fang B. ( 2014). Arsenic trioxide and microRNA-204 display contrary effects on regulating adipogenic and osteogenic differentiation of mesenchymal stem cells in aplastic anemia. Acta Biochim. Biophys. Sin . 46, 885– 893. http://dx.doi.org/10.1093/abbs/gmu082 Google Scholar CrossRef Search ADS   Zhao Y., Samal E., Srivastava D. ( 2005). Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature  436, 214– 220. http://dx.doi.org/10.1038/nature03817 Google Scholar CrossRef Search ADS PubMed  Zheng G. X., Ravi A., Gould G. M., Burge C. B., Sharp P. A. ( 2011). Genome-wide impact of a recently expanded microRNA cluster in mouse. Proc. Natl. Acad. Sci. U.S.A.  108, 15804– 15809. Google Scholar CrossRef Search ADS PubMed  © The Author 2017. Published by Oxford University Press on behalf of the Society of Toxicology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

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Toxicological SciencesOxford University Press

Published: Mar 1, 2018

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