extfieldsofstudy: Biology Medicine

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The Heterochromatin Landscape in Migrating Cells and the Importance of H3K27me3 for Associated Transcriptome Alterations

H3K9me3, H3K27me3, and H4K20me1 are epigenetic markers associated with chromatin condensation and transcriptional repression. Previously, we found that migration of melanoma cells is associated with and dependent on global chromatin condensation that includes a global increase in these markers. Taken together with more recent reports by others suggests it is a general signature of migrating cells. Here, to learn about the function of these markers in migrating cells, we mapped them by ChIP-seq analysis. This analysis revealed that induction of migration leads to expansion of these markers along the genome and to an increased overlapping between them. Significantly, induction of migration led to a higher increase in H3K9me3 and H4K20me1 signals at repetitive elements than at protein-coding genes, while an opposite pattern was found for H3K27me3. Transcriptome analysis revealed 182 altered genes following induction of migration, of which 33% are dependent on H3K27me3 for these changes. H3K27me3 was also required to prevent changes in the expression of 501 other genes upon induction of migration. Taken together, our results suggest that heterochromatinization in migrating cells is global and not restricted to specific genomic loci and that H3K27me3 is a key component in executing a migration-specific transcriptional plan.

Introduction
Cell migration is one of cancer hallmarks that is fundamental for metastasis formation [1]. Tumor cell migration is highly dependent on cytoplasmic changes in the cytoskeleton and in the activity of motor proteins [2][3][4], but more recently alterations in chromatin structures were also found to be required [5]. Chromatin basic repetitive packaging unit is the nucleosome, which is composed of 147bp of DNA that are wrapped around an octamer of histones [6]. Nucleosomes are organized in higher order structures, of which relatively decondensed and transcribed regions are termed euchromatin, while more condensed and nontranscribed regions are termed heterochromatin. Euchromatin and heterochromatin are decorated by different levels and/or types of epigenetic marks, such as histone modifications. Heterochromatin-associated histone modifications include H3K9me3, H3K27me3, and H4K20me1 [7]. H3K9me3 is enriched in constitutive heterochromatin regions, such as repetitive elements and pericentromeric regions, where it is important for their repression [8]. Still, H3K9me3 was also found at promoters of repressed genes, as well as at some active genes [9]. H3K27me3 accumulates over cell-type specific repressed genes (facultative heterochromatin) and the inactivate X chromosome [9][10][11]. H4K20me1 is associated with repression of the inactive X chromosome and specific genes, but it was also found to accumulate downstream from the transcription start site (TSS) of highly active genes [12,13]. The inconsistent observations regarding H4K20me1 may arise from various cross-talks of H4K20me1 with other epigenetic marks and the fact that H4K20me1 is an initial step in the process of generating the highly repressive mark H4K20me3.
Previously, we found that migration of mouse melanoma cells is associated with and dependent on global chromatin condensation that includes more than a two-fold increase in the levels of H3K9me3, H3K27me3, and H4K20me1 [14][15][16]. More recently, migration-associated global chromatin condensation was reported in primary and transformed T-cells [17], primary tenocytes [18], and mesenchymal stem cells [19]. Reliance of cell migration on chromatin condensation has been reported in various types of cells, including lung cancer cells [20], embryonic fibroblasts [21], breast adenocarcinoma cells [22][23][24], colorectal cancer cells [24], prostate cancer cells [25], glioma cells [26], chondrosarcoma cells [27], epidermal cancer stem cells [28], primary tenocytes [18], and primary and transformed T-cells [17]. Thus, the dependence of tumor cell migration on chromatin condensation is a ubiquitous mechanism. Still, the exact roles of heterochromatin formation in migrating cells are not fully understood.
Here, to gain a mechanistic insight on the roles of heterochromatin in migrating cells, we mapped the changes in heterochromatin spread and in the transcriptome in melanoma cells in response to migration signals by ChIP-seq and RNA-seq, respectively. Induction of migration was associated with a more diffuse distribution of H3K9me3, H3K27me3, and H4K20me1 that generated lower number of peaks, but had a higher overlapping between the different histone modifications. Following induction of migration, H3K9me3 and H4K20me1 accumulated to a higher degree in repetitive regions, while H3K27me3 redistributed towards genes. In parallel, we identified 182 genes with altered RNA levels, of which one third were dependent on migration-induced methylation of H3K27.

RNA Purification and RNA-seq
Total RNA was purified by the NucleoZOL kit (MACHEREY-NAGEL, 740404.200, Duren, Germany) according to the manufacturer's instructions. Purified RNA was sent for poly A containing mRNA selection, library preparation, and sequencing at the Technion Genome Center. Replicates number was five for untreated control and migrating cells and three for cells treated with GSK343 or DRB.

Peak Calling and Peak Analysis
Quality control checks of the raw sequence data were carried out using the FastQC tool (version 0.10.1) [30]. Then, the Trim_galore (version 0.4.1) [31] tool that is based on cutadapt [32] was used for adapters trimming and for removing of low quality bases from the ends of reads. The first four nucleotides, which were of bad quality, were trimmed from reads by a fastX trimmer (FATSX-Toolkit version 0.0.13) [33]. Cleaned, high-quality reads were aligned to the mouse genome (build mm10) using Bowtie2 [34] with the default parameters. PCR bias of duplicated reads were removed using the "MarkDuplicates" command implemented by the Picard tool (version 1.77) [35]. Broad peaks enriched in immunoprecipitation over input were identified by SICER [36] with "fragment size" of 350, " effective genome fraction" of 0.8, and " FDR" of 0.05. SICER-df was applied to determine differential peaks enriched in migrating cells compared to control cells and vice versa. Operations on genomic intervals were performed using BEDTools [37].

Calculation of ChIP-seq Coverage and Signal Distribution Across Specific Genomic Location
The genomic locations of Refseq genes and repetitive elements were downloaded from the UCSC table browser [38]. The enhancers' locations were downloaded from HOMER [39] and the genomic coordinates were converted to mm10 assembly using the UCSC liftOver tool. Promoters were defined as 1000 bp upstream of transcription start sites (TSS). Within each list, overlapping intervals were merged into a single feature that spans all of the combined features. In order to avoid intervals overlapping more than a single defined annotated element, we removed from each list nucleotides that belong to another annotation list by assigning the following priority: Coding genes, noncoding genes, promoters, enhancers and repetitive elements. The coverage of the ChIP signal across these regions was presented as a percentage of reads mapping within genomic regions out of the total mapped reads without redundancy, or as a percentage of bp mapping within genomic regions out of the total number of bp included within differential peaks, and was calculated using coverageBed [37] and intersectBed [37], respectively. The distribution of ChIP signal across functional genomic regions was done using ngs.plot [40].

Correlation Analysis and Combinatorial Pattern of Heterochromatin Markers
The entire genome was divided into non-overlapping equally sized bins (10 kb), and the average ChIP score was calculated for each bin using the multiBamSummary command from deepTools [41]. Spearman correlation coefficients were computed using deepTool's multiBamSummary and plotCorrelation commands. Hidden Markov model-based chromatin state definition was performed using the ChromHMM v1.14 [42]. Five states were used to segment the genome, and the enrichment of these states across different genomic features was calculated.

RNA-seq Analysis
Reads were aligned to the mouse genome (mm10) using TopHat [43] after removal of adapter sequences and critical examination of quality controls using Trim_galore [31] with the default parameters. The number of reads mapping to each mouse gene (as annotated in Ensembl release GRCm38.86) was counted using the 'intersection-nonempty' mode of HTseq-count script [44]. Differential expression analysis was performed using the edgeR [45] and Limma [46] packages from the Bioconductor framework [47]. Briefly, features with less than 1 read per million in 3 samples were removed. The remaining gene counts were normalized using the TMM method, followed by voom transformation [48]. Linear models were used to remove the batch effect and to find differentially expressed genes. Up-and down-regulated genes having FDR <0.05 and fold change ≥1.3 were analyzed for pathway enrichment using the Ingenuity Pathway Analysis [49] (IPA). H3K27me3 dependency was detected by comparing genes found to be differentially expressed between migrating and control cells to genes found to be differentially expressed between migrating cells treated with GSK343 to control cells. H3K27me3-dependent genes are genes whose expression changes abolished upon treatment with GSK343, while H3K27me3-independent genes are those whose change in expression was preserved even after treatment with GSK343.

Distribution of Heterochromatin Signals Across High and Low Abundant Genes
Average gene expression was calculated across five replicates of RNA-seq samples for control cells and migrating cells and genes with an average expression which was lower than 1 cpm (counts per million) reads were excluded. Genes were ranked based on their expression level, and the top and bottom 500 genes were chosen. The distribution of histone modifications signal across these genes was plotted using the ngs.plot tool [40].

Migration-Induced Changes in the Genomic Distribution of H3K9me3, H3K27me3, and H4K20me1
Induction of migration leads to a global increase of 2-4-fold in the levels of the heterochromatin markers H3K9me3, H3K27me3, and H4K20me1, as we previously detected by immunostaining [14][15][16]. To learn about the mechanistic role of these histone modifications in the migration process, we mapped their relative distribution along the genome by ChIP-seq. Migration of the B16-F1 mouse melanoma cells was induced by the wound healing assay [50], in which multiple scratches were performed in each plate to generate large numbers of migrating cells. This way of induction of migration enabled us to receive an enriched population of migrating cells as measured by a 2-5-fold increase in the levels of H3K9me3 and H3K27me3 at the promoters of E-cadherin, Gapdh, and Line ( Figure S1).
H3K9me3, H3K27me3, and H4K20me1 ChIP-seq-mapped reads were at the range of 27-42 million and the coverage values at the range of 0.68-1.52 ( Figure S2a). As expected from the nature of heterochromatin modifications, more than 50% of the signals of these modifications did not accumulate at defined and short loci to form sharp peaks ( Figure 1a). Moreover, following indication of migration, this phenomenon further increased, resulting in the accumulation of only 7.45%, 9.62%, and 29.64% of the reads of H3K9me3, H3K27me3, and H4K20me1, respectively, inside peaks ( Figure 1a). In agreement, upon induction of migration, the intensities of the peaks were reduced by 14-17% (Figure 1a-d), and the number of identified peaks was reduced by 30-40%, while the number of differential peaks was reduced by more than 90% ( Figure S2b). Significantly, upon induction of migration, the average peak length of H3K9me3 and H4K20me1 was increased by 34% and 20%, respectively, while the average peak length of H3K27me3 was reduced by 20% (Figure 1a-d). Taken together, the above analyses indicate more diffused signals of H3K9me3, H3K27me3, and H4K20me1 upon induction of migration. This pattern suggests a possible increase in the degree of overlap between the three modifications following induction of migration. Indeed, in migrating cells, the correlation between these modifications increased significantly over any evaluated genomic element (promoters, repetitive elements, enhancers, and gene bodies) (Figure 1e,f and Figure S3).
To assess which genomic regions are more prone to being affected in migrating cells by each of the above modifications, we counted the number of mapped reads overlapping specific genomic regions and calculated them as the percentage of the total mapped reads ( Figure 2a). We also calculated the relative distribution of differential peaks that fall inside different genomic elements (Figure 2b).  This analysis revealed a migration-induced increase in the relative distribution of nucleotides in differential peaks of H3K9me3 and H4K20me1 at repetitive elements by 83% and 446%, respectively, and a migration-induced decrease of these modifications at protein-coding genes by 23% and 37%, respectively. On contrary, upon induction of migration, the relative distribution of nucleotides in differential peaks of H3K27me3 increased by 92% at protein-coding genes, while it decreased by 54% at repetitive elements (Figure 2b). A similar trend was seen in the relative distribution of the total reads of these modifications, as well (Figure 2a,c-e).
To verify these results, we analyzed the average signal distribution of these modifications across different types of repetitive elements and across protein-coding genes. In agreement with the previous analysis, the signals of H3K9me3 and H4K20me1 were higher across LINE, SINE, LTR, and DNA transposons in migrating cells than in control cells, while the signal of H3K27me3 was lower across the same repetitive elements in migrating cells than in control cells. An opposite pattern emerged over protein-coding genes: Reduced levels of H4K20me1 together with increasing levels of H3K27me3 in migrating cells than in control cells (Figure 3a).
Classifying the combinatorial pattern of the above modifications using a hidden-Markov-model (HMM) based approach revealed that upon migration, there is a reduction in the genomic coverage by regions with similar levels of the three markers (state no. 4, Figure 3b), while there is an increase in the percentage of genomic regions that are decorated by all three markers, but at differential levels (states no. 1, 3, and 5, Figure 3b). Interestingly, regions free of all three markers occur in repetitive elements (LINE and LTR), but their percentage decreases upon induction of migration (state no. 2, Figure 3b).
Importantly, upon induction of migration, the changes in the combinations of the modifications across promoters and gene bodies were much smaller than the changes over the whole genome, thus raising the question if there are any transcriptional changes in migrating cells.

Migration-Induced Transcriptome Changes
To determine the changes in the transcriptome upon induction of migration, we carried out an RNA-seq analysis ( Figure S4). Differential expression analyses identified 801 genes with altered expression levels (FDR < 0.05) in cells that were induced to migrate for 3 h; 465 genes were up-regulated and 336 genes were down-regulated. Out of them, 182 genes were altered with a fold change of >1.3; 129 up-regulated genes and 53 down-regulated genes (Table S1 and Figure S5). Surprisingly, although induction of migration induces a global increase in chromatin condensation, a higher number of genes were up-regulated than down-regulated. To search for common functions of the altered genes, genes found to be differentially expressed with FDR >0.05 and fold change >1.3 were subjected to Ingenuity Pathway Analysis (IPA). As shown in Figure 4, the most significantly enriched pathways included ones that are known to be associated with tumor cell proliferation and migration, such as the TGF-β, IGF-1 and ERK5 signaling pathways [51][52][53][54][55], and pathways involved in energy generation: Glycolysis and pyridoxal 5 -phosphate (PLP) salvage pathways. PLP is an active form of vitamin B6, which is important for the metabolism of carbohydrates, amino acids, and fats, the generation of the methyl donor S-adenosylmethionine (SAM), and in neutralizing oxidative stress [56,57]. Activation of oxidative stress response was also indicated by the up-regulation of NRF2 targets (Figure 4). Analysis of upstream regulators and downstream-affected functions identified a strong link to cell migration ( Figure S6). Cells 2018, 7, x FOR PEER REVIEW 8 of 17   To evaluate the correlations between H3K9me3, H3K27me3, and H4K20me1 and transcription in our system, we assessed the spread of each modification over the genes that are found in peak areas of the same modification. H3K9me3 enrichment was not found at unexpressed genes, which may be repressed by DNA methylation. In expressed genes, H3K9me3 enrichment along gene bodies correlated with higher gene expression levels, while H3K9me3 enrichment at promoters correlated with lower gene expression levels ( Figure S7: Cluster no. 1 versus cluster no. 3, respectively). A similar pattern was found for H4K20me1: High enrichment levels of H4K20me1 along gene bodies correlated with high gene expression levels, whereas high enrichment levels of H4K20me1 at promoters or its moderate enrichment over gene bodies associated with low gene expression levels ( Figure S8). Significantly, the correlation of H3K27me3 enrichment with low gene expression was the most pronounced out of the three evaluated modifications ( Figure S9).

H3K27me-Dependent Transcriptome Changes upon Induction of Migration
Out of the three analyzed histone modifications, only H3K27me3 displayed increased levels over genes upon induction of migration ( Figure 3). Therefore, we analyzed which migration-associated transcriptome changes are dependent on H3K27me3 by RNA-seq of migrating cells that were treated with the Ezh2-specific inhibitor, GSK343, at a concentration that we previously found to inhibit migration [16]. This analysis revealed that 33% of the migration-altered genes were not changed once GSK343 was added, indicating they are H3K27me3-dependent genes. The 67% of the migrationaltered genes that were changed also in migrating cells treated with GSK343 were termed H3K27meindependent genes (Figure 5a, Table S2, Figures S4 and S10). Significantly, most of the signaling pathways altered in migrating cells, such as TGF- and ERK5, were H3K27me-dependent, while the most significant H3K27me independent-pathways were glycolysis and NRF2-mediated response (Figure 5b). In addition, 501 genes that normally do not change upon induction of migration did change once H3K27 methylation was inhibited ( Figure 5, Table S2, Figure S10). These genes (termed H3K27me-buffered genes) are mainly involved in cholesterol metabolism. Thus, H3K27 methylation in migrating cells is important not only for establishing a new transcriptional plan, but also to limit the transcriptional changes to specific genes. Analyzing the levels of H3K27me3 at the promoters of H3K27me-buffered genes revealed a clear increase in H3K27me3 levels upon induction of migration that was not found in the H3K27me-indepednent genes (Figure 5c). A similar analysis of the H3K27me-dependent genes did not find significant changes between control and migrating cells, probably due to the low number of genes in this group. To evaluate the correlations between H3K9me3, H3K27me3, and H4K20me1 and transcription in our system, we assessed the spread of each modification over the genes that are found in peak areas of the same modification. H3K9me3 enrichment was not found at unexpressed genes, which may be repressed by DNA methylation. In expressed genes, H3K9me3 enrichment along gene bodies correlated with higher gene expression levels, while H3K9me3 enrichment at promoters correlated with lower gene expression levels ( Figure S7: Cluster no. 1 versus cluster no. 3, respectively). A similar pattern was found for H4K20me1: High enrichment levels of H4K20me1 along gene bodies correlated with high gene expression levels, whereas high enrichment levels of H4K20me1 at promoters or its moderate enrichment over gene bodies associated with low gene expression levels ( Figure S8). Significantly, the correlation of H3K27me3 enrichment with low gene expression was the most pronounced out of the three evaluated modifications ( Figure S9).

H3K27me-Dependent Transcriptome Changes upon Induction of Migration
Out of the three analyzed histone modifications, only H3K27me3 displayed increased levels over genes upon induction of migration ( Figure 3). Therefore, we analyzed which migration-associated transcriptome changes are dependent on H3K27me3 by RNA-seq of migrating cells that were treated with the Ezh2-specific inhibitor, GSK343, at a concentration that we previously found to inhibit migration [16]. This analysis revealed that 33% of the migration-altered genes were not changed once GSK343 was added, indicating they are H3K27me3-dependent genes. The 67% of the migration-altered genes that were changed also in migrating cells treated with GSK343 were termed H3K27me-independent genes (Figure 5a, Table S2, Figures S4 and S10). Significantly, most of the signaling pathways altered in migrating cells, such as TGF-β and ERK5, were H3K27me-dependent, while the most significant H3K27me independent-pathways were glycolysis and NRF2-mediated response (Figure 5b). In addition, 501 genes that normally do not change upon induction of migration did change once H3K27 methylation was inhibited ( Figure 5, Table S2, Figure S10). These genes (termed H3K27me-buffered genes) are mainly involved in cholesterol metabolism. Thus, H3K27 methylation in migrating cells is important not only for establishing a new transcriptional plan, but also to limit the transcriptional changes to specific genes. Analyzing the levels of H3K27me3 at the promoters of H3K27me-buffered genes revealed a clear increase in H3K27me3 levels upon induction of migration that was not found in the H3K27me-indepednent genes (Figure 5c). A similar analysis of the H3K27me-dependent genes did not find significant changes between control and migrating cells, probably due to the low number of genes in this group. Cells 2018, 7, x FOR PEER REVIEW 10 of 17 Overlapping genes are termed "H3K27me-independent", genes altered only when migrating cells were compared to control cells are termed "H3K27me-dependent", and differentially expressed genes only in GSK343-treated migrating cells are termed "H3K27me-buffered". (b) Top 10 pathways altered according to the "H3K27me-independent" gene list, the "H3K27me-dependent" gene list, and the "H3K27me-buffered" gene list as identified by IPA. (c) Distribution of H3K27me3 signal across "H3K27me-dependent", "H3K27me-independent" and "H3K27me-buffered" genes in control cells (green) and in migrating cells (orange). The region between 5'End and the 3'End represents genes bodies.  Overlapping genes are termed "H3K27me-independent", genes altered only when migrating cells were compared to control cells are termed "H3K27me-dependent", and differentially expressed genes only in GSK343-treated migrating cells are termed "H3K27me-buffered". (b) Top 10 pathways altered according to the "H3K27me-independent" gene list, the "H3K27me-dependent" gene list, and the "H3K27me-buffered" gene list as identified by IPA. (c) Distribution of H3K27me3 signal across "H3K27me-dependent", "H3K27me-independent" and "H3K27me-buffered" genes in control cells Still, examination of single H3K27me-dependent down-regulated genes identified increased H3K27me3 levels around the TSS in most of them, including Rassf7, Zfp382, and Tcaim (Figure 5d-f). Significantly, these genes can affect the expression of additional genes since they are involved in signaling (Rassf7 and Tcaim) or in transcriptional control (Zfp382).

H3K27me-Dependent Genes Are Less Prone to RNA Stability Control
Finding that 67% of the migration-altered transcripts are independent of H3K27 methylation means that additional mechanisms are involved in their regulation. To learn if alterations in their RNA stability levels are involved, we determined the transcriptome of control and migrating cells following 3 h of RNA polymerase II inhibition by DRB. Calculation of the ratio between the mRNA levels in DRB-treated migrating cells and DRB-treated control cells identified 653 down-regulated genes and 365 up-regulated genes having decreased and increased RNA stability upon induction of migration, respectively (Figure 6a, Table S3, Figures S4 and S11). Notably, whereas only 10% of the H3K27me-dependent genes had migration-dependent changes in their RNA stability levels, 23% of the H3K27me-independent genes had migration-dependent changes in their RNA stability levels. IPA analysis revealed that genes with increased RNA stability in migrating cells are mainly involved in translational control and energy production, while genes with decreased RNA stability in migrating cells are mainly involved in cell cycle control and DNA damage response (Figure 6b), suggesting that regulation at the levels of RNA stability and translation rate may be highly important for cell migration. Still, examination of single H3K27me-dependent down-regulated genes identified increased H3K27me3 levels around the TSS in most of them, including Rassf7, Zfp382, and Tcaim (Figure 5d-f). Significantly, these genes can affect the expression of additional genes since they are involved in signaling (Rassf7 and Tcaim) or in transcriptional control (Zfp382).

H3K27me-Dependent Genes Are Less Prone to RNA Stability Control
Finding that 67% of the migration-altered transcripts are independent of H3K27 methylation means that additional mechanisms are involved in their regulation. To learn if alterations in their RNA stability levels are involved, we determined the transcriptome of control and migrating cells following 3 h of RNA polymerase II inhibition by DRB. Calculation of the ratio between the mRNA levels in DRB-treated migrating cells and DRB-treated control cells identified 653 down-regulated genes and 365 up-regulated genes having decreased and increased RNA stability upon induction of migration, respectively ( Figure 6a, Table S3, Figures S4 and S11). Notably, whereas only 10% of the H3K27me-dependent genes had migration-dependent changes in their RNA stability levels, 23% of the H3K27me-independent genes had migration-dependent changes in their RNA stability levels. IPA analysis revealed that genes with increased RNA stability in migrating cells are mainly involved in translational control and energy production, while genes with decreased RNA stability in migrating cells are mainly involved in cell cycle control and DNA damage response (Figure 6b), suggesting that regulation at the levels of RNA stability and translation rate may be highly important for cell migration.

Discussion
In this study, we found that upon induction of migration, the increase in heterochromatin markers does not occur in narrow regions to generate peaks, but rather spreads over larger genomic regions. Migration signals alter the RNA levels of 182 genes that are involved in signaling pathways, such as TGF-β and ERK5, but also in glucose metabolism and oxidative stress response. One third of the transcriptome alterations is dependent on methylation of H3K27, while altered genes in an H3K27me-indepednent manner are more prone to regulation at their RNA stability level.
ChIP-seq protocols depend on the assumption that the overall yields of DNA are identical per cell under different conditions. Accordingly, the same amounts of total DNA are taken for analysis and the resulting data are normalized to each other, so that the total amounts of signals are identical. Since induction of migration is associated with a dramatic increase in the amount of heterochromatin, ChIP doesn't yield the same amount of DNA in control and migrating cells. Hence, the DNA normalization done during library preparation reduces the quantitative difference between the two conditions, which explains the lack of global increase in heterochromatin markers upon induction of migration in our ChIP-seq experiments. Therefore, our analysis has been mainly focused on the changes in signal distribution across genomic features upon induction of migration.
Induction of migration led to a more diffuse distribution of H3K9me3, H3K27me3, and H4K20me1 along the genome as measured by the reduced number of ChIP-seq peaks and ChIP-seq signals within peaks (Figure 1 and Figure S2). In agreement with this pattern, the overlapping between the different markers increased dramatically (Figure 1). These changes were accompanied by a shift in the distribution of these markers between the various genomic elements: H3K9me3 and H4K20me1 signals shifted from protein-coding regions to repetitive elements, while the H3K27me3 signal shifted in the opposite direction ( Figure 2). These patterns suggest that although the overall signals of each of these markers is increased by 2-4-fold, as previously measured by immunostaining [14][15][16], the effects on transcription may be moderate, since the signals do not accumulate at narrow regions. Indeed, transcriptome analysis revealed that upon induction of migration, only 182 genes were altered with a fold change of >1.3, while only 30% of them were down-regulated.
Reports on the transcriptome of migrating cells are quite scarce [58][59][60]. In previous reports, the transcriptome was evaluated by microarrays that had a limited genomic coverage. Analyzing our RNA-seq data by IPA identified the activation of TGF-β IL-8 and ERK5 signaling pathways that were found to be activated in previous transcriptome analyses of migrating cells [58][59][60] (Figure 4). These pathways are well-established regulators of cancer cell migration and invasion [51][52][53][54][55]. In addition, we found an increase in three glycolytic enzymes (Glucose-6-Phosphate Isomerase, Phosphofructokinase, and Aldolase C) that can increase energy production in migrating cells. The increase in oxidative stress response genes may be required due to the increase in ATP production.
Analyzing the association of H3K9me3, H3K27me3, and H4K20me1 with gene expression levels revealed a positive correlation between high H4K20me1 levels downstream the TSS and gene expression levels, and negative correlation between H4K20me1 accumulation upstream the TSS and gene expression levels ( Figure S8). Thus, H4K20me1 may support transcription when localized downstream the TSS, whereas it may be involved in transcriptional repression when localized upstream of the TSS or at low levels over the gene body. We also found a positive association between H3K9me3 enrichment along gene bodies and high expression levels ( Figure S7), as found before [61,62].
Using an Ezh2 specific inhibitor, we were able to find that methylation of H3K27 is required for the transcriptional regulation of 33% of the migration-altered genes, as well as to prevent changes in 501 additional genes upon induction of migration ( Figure 5). Migration signals lead to the activation of several transcriptional regulators, such as SMAD4 and SP1 ( Figure S6) that have the potential to affect the transcription of several hundreds of genes. We hypothesize that the changes in the transcription of many of these genes are not required or may even interfere with the migration process, and H3K27me3 serves to prevent the binding or the activity of transcription factors at these genes. H3K27 methylation is mainly known for its negative effect on transcription; however, the group of H3K27me-depedent genes includes 41 up-regulated genes and 19 down-regulated genes. We hypothesize that only a fraction of these genes is affected directly by H3K27 methylation and this fraction of genes regulates the expression of many additional genes. In support of this hypothesis, we identified increased H3K27me3 levels at the promoter of many of the H3K27me-depedent migration-repressed genes, such as Rassf7, Zfp382, and Tciam (Figure 5d-f). Interestingly, Rassf7 was shown to inhibit the MKK7/JNK signaling pathway [63], Zfp382 was shown to inhibit the transcription of various tumor and migration promoting genes [64,65], and Tciam was found to inhibit LPS signaling and NF-κB activity [66].
In analyzing the importance of transcription for cell migration, we previously found that active transcription is dispensable for migration during a time period of 3 h, whereas it is important for migration during a longer period of time (11 h) [14]. Thus, the changes we identified here are the initial changes that will have an accumulative effect as the migration time increases. The spread of the heterochromatin markers upon induction of migration may indicate additional roles for these markers during cell migration such as increasing nuclear rigidity [5,18,67,68]. Overall, our results support a model in which migration-induced chromatin condensation serves to alter both the transcription plan as well as the physical properties of the nucleus.


Effect of Geranylgeranyl Pyrophosphate Synthase on Hypoxia/Reoxygenation-Induced Injury in Heart-Derived H9c2 Cells

Recent studies have revealed that geranylgeranyl pyrophosphate synthase (GGPPS), a key enzyme involved in protein prenylation, plays a critical role in postnatal heart growth by regulating cardiomyocyte size. However, the role of GGPPS in myocardial ischemia/reperfusion (MIR) injury is still not clear. The objective of this work was to investigate the effect of GGPPS on MIR injury in H9c2 cells subjected to hypoxia/reoxygenation (HR) to mimic MIR. Prior to HR, the cells were transfected with GGPPS, shGGPPS, or shGFP. The results showed that cell viability was reduced, and cell injury and cell apoptosis were increased as a result of overexpression of GGPPS. Knockdown of GGPPS improved cell viability, and decreased cell injury and cell apoptosis. Furthermore, overexpression of GGPPS increased Rac1 activity and ROS generation, while GGPPS silencing decreased Rac1 activity and ROS generation. Based on these findings, we propose that the alteration of GGPPS expression changed the Rac1 activity and ROS production, and finally led to the different severity of HR-induced injury in H9c2 cells. These findings indicate that GGPPS might be a potential target in preventing H9c2 cells from HR-induced injury. (Int Heart J 2018; 59: 821-828)

I schemic heart disease is the most important cause of cardiac mortality and morbidity. Timely recovery of coronary flow is necessary to resuscitate the ischemic or hypoxic myocardium. However, previous studies have suggested that upon reperfusion, exposure of ischemic tissues to molecular oxygen greatly augments organ damage, a phenomenon termed "ischemia/reperfusion (IR) injury". [1][2][3] IR results in the generation of reactive oxygen species (ROS). [3][4][5][6] ROS are produced in two stages, ischemia and reperfusion, at low and high levels, respectively. 7) The production of ROS and tissue function are related to each other. Excess ROS production in the reperfusion phase is injurious for cardiac tissue, since ROS can attack cellular components and provoke apoptosis and necrosis. 8) NADPH oxidases are known to be the only enzyme family producing ROS as their sole and primary function. 9,10) Furthermore, activation of the small GTP-binding protein Rac1 leads to NADPH oxidase activation. 11) Moreover, studies of nonphagocytic cells have demonstrated that the production of ROS appears to require participation of Rac 1. [12][13][14][15] Geranylgeranyl pyrophosphate synthase (GGPPS) is a key enzyme in the synthesis of geranylgeranyl pyrophosphate (GGPP). It catalyzes the formation of GGPP 16) from one molecule of farnesyl diphosphate (FPP) 17) and one molecule of isopentenyl diphosphate. 18) GGPP mainly participates in post-translational modification for various proteins, including the Rho/Rac, Rap, and Rab families. 19) Studies have indicated that Rac1 plays an important role in IR-induced injury. 14,15) In addition, recent studies have revealed that GGPPS participates in hepatocyte apoptosis, 20) as well as cardiomyocyte hypertrophic growth. 21) However, the effect of GGPPS on IR injury is still unknown.
In this study, heart-derived H9c2 cells were transfected with GGPPS or shGGPPS. We investigated the effect of GGPPS on HR-induced injury and the relevant mechanisms in heart-derived H9c2 cells.
H9c2 cells were seeded in 6-well plates, 24-well plates, or 96-well plates for 24 hours. The cells were transfected with plasmid DNA mixed with Opti-MEM (GIBCO, MO, USA) and lipofectamine 3000 transfection reagent (Invitrogen, CA, USA). Cells were transfected with pAV-GFP plasmid to represent the transfection efficiency of PAV-rGGPPS plasmid. After transfection, the cells were incubated in complete medium (DMEM with 10%FBS) for 48 hours. Cell hypoxia/reoxygenation: Transfection cells in plates were supplied with hypoxic solution 22) and the plates were placed in a sealed chamber with an anaerobic pouch at 37°C for 8 hours. Hypoxia was followed by 4 hours of reoxygenation using complete medium at 37°C and 5% CO2/95%O2. 23) Real-time polymerase chain reaction: Total RNA was extracted using RNAisoPlus (TaKaRa, Dalian, China) and transcribed into cDNA using a PrimeScript RT Reagent Kit (TaKaRa) according to the manufacturer's protocol. GGPPS mRNA expression was relatively quantified via real-time PCR (RT-PCR) using a 7500 Real-Time PCR system (Thermo Scientific, MA, USA). RT-PCR was performed using SYBR Premix Ex Taq (TaKaRa). The GGPPS primers were 5'-ATGGTGTTGCAGAAGACAG G-3' and 5'-TCTGGAAGAA GAGCCCAAGT-3'. GGPPS mRNA levels were normalized to GAPDH with the following primers: 5'-GGAAAGCTGTGGCGTGAT-3' and 5'-AAGGTGGAAG AATGGGAGTT-3' Western blot analysis: Western blot analysis was performed to detect GGPPS protein. Whole cell lysates were prepared in 1× RIPA lysis buffer (Millipore, CA, USA) containing a 1× protease and phosphatase inhibitor cocktail (Thermo Scientific, MA, USA). Protein concentrations were determined using a BCA protein assay kit (Thermo Scientific, IL, USA) according to the manufacturer's protocol. Equal volumes of lysate (30 μg) were separated in sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto a polyvinylidene difluoride (PVDF) membrane. After 1 hour of blocking with 5% BSA in TBST at room temperature, the PVDF membranes were incubated overnight with anti-GGPPS antibody (Abcam, London, UK) diluted in 1× TBST with 5% BSA at 4°C. Membranes were then washed 3 times with 1× TBST and incubated with horseradish peroxidase (HRP)conjugated secondary antibodies (EarthOx, CA, USA) for 1 hour at room temperature. Hybridizing bands were visualized using an ECL-HRP chemiluminescence kit (Biological Industries, BI, Israel). Tubulin was used as an endogenous control. Cell viability assessment: A CCK assay was used to assess cell viability. Cells were seeded into 96-well plates at a concentration of 5 × 10 3 cells/mL. After transfection and HR, 10 μL of Cell Counting Kit (CCK) (7sea biotech, Shanghai, China) mixed with 100 μL of complete medium was added into each well followed by incubation for 2 hours. Results were quantified using a microplate reader (Molecular Devices) at an absorbance of 450 nm. Measurement of LDH or SOD: Lactate dehydrogenase (LDH) or superoxide dismutase (SOD) was examined to evaluate cell injury. Cells were seeded into 6-well plates (1 × 10 4 cells/mL). Following transfection and HR, LDH release in the medium was measured using a LDH Assay Kit (Jiancheng Co, Nanjing, China). SOD generation in cell lysates was measured using a SOD Assay Kit (Jiancheng Co) according to the manufacturer's protocol. Results were quantified using a microplate reader (Molecular Devices) at an absorbance of 450 nm. Protein concentrations were determined using a BCA protein assay kit (Thermo Scientific, IL, USA) according to the manufacturer's protocol. Cell apoptosis assessment via flow cytometry: Apoptotic cell death was measured using a PE AnnexinV Apoptosis Detection Kit (BD Biosciences, NJ, USA) according to the manufacturer's protocol. Briefly, cells were seeded into 6-well plates. After transfection and HR, the cells were washed twice with cold PBS and then resuspended in 1 × binding buffer at a concentration of 1 × 10 6 cells/mL. A 100 μL aliquot of the solution was transferred to a 5 mL culture tube. Cells in each tube were stained using 5 μL of PE Annexin V mixed with 5 μL 7-AAD for 15 minutes at room temperature in the dark. To each tube 400 μL of 1 × binding buffer was added and the cells were analyzed using flow cytometry. Activation of RhoA, Rac1, Cdc42 and Ras: RhoA, Rac1, Cdc42 or Ras activity was determined by an absorbance-based G-LISA Activation Assay Biochemistry Kit (Cytoskeleton, Denver, CO, USA) according to manufacturer's protocol. Results were quantified using a microplate reader (Molecular Devices) at an absorbance of 490 nm. Protein concentrations were determined using a BCA protein assay kit (Thermo Scientific, IL, USA) according to the manufacturer's protocol. Measurement of ROS: A Cellular ROS Red Fluorescence Assay Kit (Genmed Scientific Inc., Alington, TX, USA) was used to detect ROS generation according to the manufacturer's protocol. Briefly, cells were seeded into 6well plates. After transfection and HR, the cells were collected into a 15 mL centrifuge tube and counted using a cell counter. Cells (1 × 10 6 ) were transferred into a new centrifuge tube and incubated with the Cellular ROS Red Fluorescence Assay Kit working reagent for 20 minutes at  37°C in the dark. Cells were measured using fluorescence microscopy with an excitation wavelength of 540 nm and emitter band pass of 590 nm. Measurement of NADPH oxidase activity: A proportion of the supernatant from the homogenized solutions extracted from the incubation cells were used to determine NADPH oxidase activity. Protein concentration was determined using a BCA protein assay kit (Thermo Scientific, IL, USA) according to the manufacturer's protocol. NADPH oxidase activity was evaluated spectrophotometrically using an NADPH Oxidase Activity Assay kit (GMS 50095.2 v.A; Genmed Scientific Inc.,) according to the manufacturer's protocol. Statistical analyses: Significant differences were determined using SPSS16.0 software. All data are represented as the mean ± SEM. For direct comparison of differences between two groups, Student's t-test was used. Analysis of variance (ANOVA) in the case of serial measurements, followed by the multiple-comparison Bonferroni t-test, was used to assess differences among groups. A P < 0.05 was considered significant. All experiments were repeated 3 times.

PAV-rGGPPS and shGGPPS altered GGPPS expression of H9c2 cells:
Approximately 50~55% of cultured H9c2 cells were transfected by pAV-rGGPPS, shGGPPS, or shGFP ( Figure 1A and 1B). Compared with the shGFP group, GGPPS protein was highly expressed in the pAV-rGGPPS group and was down-regulated in the shGGPPS group ( Figure 1C and 1D). RT-PCR results showed that GGPPS mRNA was highly increased in the pAV-rGGPPS group and markedly reduced in the shRNA group ( Figure  1E).

Overexpression of GGPPS attenuated cell viability and GGPPS silencing improved cell viability:
To investigate the effect of GGPPS on cell viability after HR in H9c2 cells, pAV-rGGPPS, shGGPPS, or shGFP was used to transfect H9c2 cells. Cell viability was quantified using Dai, ET AL  CCK assay after HR. We found that overexpression of GGPPS reduced the viability and GGPPS silencing increased the viability in H9c2 cells after HR (Figure 2). Overexpression of GGPPS increased cell injury and GGPPS silencing decreased cell injury: LDH and SOD levels were measured to assess cell injury after HR. The results indicated that LDH release in the medium significantly increased in the pAV-rGGPPS group and decreased in the shGGPPS group ( Figure 3A). SOD generation was significantly reduced in the pAV-rGGPPS group and elevated in the shGGPPS group ( Figure 3B). Figure 4, after HR, the level of apoptosis (both early and late-stage) of H9c2 cells in the shGFP group was 10.26 ± 0.66%. The transfection of GGPPS elevated the apoptotic cell ratio to 13.75 ± 0.64%. In the shGGPPS group, the apoptotic cell ratio was decreased to 7.8 ± 0.47%. GGPPS altered the Rac1 activation: Protein geranylgeranylation is necessary for the activation of small GTPases. In this experiment, it was shown that Rac1 activity was significantly increased in the pAV-rGGPPS group and decreased in the shGGPPS group ( Figure 5A) compared with the shGFP group. However, our findings did not show significant changes in Ras activity ( Figure 5B). Also, the RhoA and Cdc42 activity were not significantly changed ( Figure 5C and 5D). Thus, the results suggested a direct relationship between the GGPPS gene and Rac1. This implied that GGPPS was involved in HR-induced injury through Rac1.

Overexpression of GGPPS increased ROS generation and GGPPS silencing decreased ROS generation:
The production of ROS and tissue function are related to each other. Studies of nonphagocytic cells have demonstrated that the production of ROS appears to require the participation of the small GTP-binding protein Rac1. [12][13][14][15] Therefore, the effects of GGPPS on ROS generation in HR were studied. Figure 6 shows the measurement of intracellular ROS induced HR treatment in cultured H9c2 cells. As assessed by dihydroethidium bromide (DHE) fluorescence, the ROS positive cells as well as the fluorescence levels were increased as a result of overexpression of GGPPS, and decreased in the GGPPS silencing group. Overexpression of GGPPS increased NADPH oxidase activity and GGPPS silencing decreased NADPH oxidase activity: NADPH oxidase activity was measured to evaluate the generation of ROS. NADPH oxidase activity was significantly increased in the pAV-rGGPPS group and decreased in the shGGPPS group (Figure 7) compared Dai, ET AL Figure 6. ROS levels in H9c2 cells. A: After 48 hours transfection, following 8 hours of hypoxia and 4 hours of reoxygenation, the ROS levels were detected by DHE fluorescence (red) and nuclei were counterstained with DAPI (blue). B: Quantification of ROS levels. ImageJ software was employed to analyze the optical density in each cell represented in A. Data are expressed as the mean ± SEM (n = 3). ### P < 0.001 versus CON. **P < 0.01 versus shGFP. ***P < 0.001 versus shGFP. CON indicates control; and HR, hypoxia/reoxygenation. Figure 7. Levels of NADPH oxidase in H9c2 cells. After 48 hours of transfection, following 8 hours of hypoxia and 4 hours of reoxygenation. NADPH oxidase activity was detected. Data are expressed as the mean ± SME (n = 3). ### P < 0.001 versus CON. **P < 0.01 versus shGFP + HR. ***P < 0.001 versus shGFP + HR. CON indicates control; and HR, hypoxia/reoxygenation. with the shGFP group.

Discussion
Recovery of oxygen supply to hypoxic cardiomyo-cytes can cause necrotic and apoptotic cell death. 24) IR results in the generation of reactive oxygen species (ROS), [3][4][5][6] and oxidative stress arising from excessive production of reactive oxygen species has long been associated with IR injury, 25,26) and this pathological process can be mimicked when cultured cells are subjected to HR challenge. 24,27) A recent study indicated that the IL-1 signaling pathway participates in the process of ROS production through the recruitment and activation of neutrophils. 28) The MAPK signaling pathway plays an important role in the pathogenesis of IR injury. 29) Other studies have demonstrated that inhibition of ROS production or scavenging of ROS offers protection against tissue injury incurred during ischemia/reperfusion. Moreover, other studies have aimed at inhibiting the source(s) responsible for the production of ROS. 30,31) Studies have demonstrated that inhibition of Rac1 protects IR-induced injury by decreasing the production of ROS. 14,15) GGPP is necessary for the activation of small GTPases including Rac1. We designed this study to investigate the effect of GGPPS on HRinduced injury in vitro, and to explore its possible mechanism.
GGPPS is a key enzyme in the mevalonate pathway. GGPPS participates in the synthesis of GGPP which is necessary for protein geranylgeranylation and activation of small GTPases such as RhoA and Rac1. Recent studies have revealed that GGPPS participates in hepatocyte apoptosis, 20) as well as cardiomyocyte hypertrophic growth. 21) We speculate that GGPPS has an influence on MIR-induced injury in cardiomyocytes. In order to verify this hypothesis, we used heart-derived H9c2 cells which were subjected to 8 hours of hypoxia following 4 hours of reoxygenation to mimic MIR. PAV-rGGPPS or shGGPPS was transfected into H9c2 cells to achieve the objective of overexpression of GGPPS or GGPPS silencing. Cell viability, cell injury, and cell apoptosis were detected. Both cell viability and cell apoptosis can reflect the effect of the intervention on cells directly. Our results showed that GGPPS silencing improved cell viability and decreased cell injury and apoptotic cell death, while overexpression of GGPPS had an opposite effect. According to the results, we can conclude that knockdown of GGPPS has a protective effect on cell viability and increases cell apoptosis.
The Rho family of small GTP-binding proteins, including the Rho, Rac, and Cdc42 subfamilies, regulates cytoskeletal function in many aspects. 32) Studies have indicated that Rac1 plays an important role in IR-induced injury, 14,15) and conditional cardiac-specific overexpression of RhoA in mice protected the heart against IR-induced injury. 33) Another study has suggested that inhibition of Rhokinase protected the mouse heart against IR-induced injury. 34) To further characterize the effect of GGPPS on HR injury, small GTP-binding proteins related to HR injury such as Ras, RhoA, Rac1, and Cdc42 were examined. The results showed that Rac1 activity was significantly improved in the pAV-rGGPPS group and reduced in the shGGPPS group. There was no significant difference in basal Rac1 activity between the groups. The reason for this phenomenon is that HR is an inducing factor which stimulates the joining of the 20-carbon geranylgeranyl group (GGPP) to protein-cysteines (Rac1) at or near their carboxy-termini that is catalyzed by protein geranylgeranyltransferase-I (GGTase-I). 35) There was no significant difference compared with the shGFP group for other small GTP-binding proteins. This may be explained in terms of the alteration of GGPPS expression changed the Rac1 activation.
ROS produced during reperfusion can initiate a series of cellular events that eventually lead to inflammation, necrosis, and/or apoptosis. [36][37][38] Activation of the small GTPbinding protein rac1 leads to the activation of NADPH oxidases, which is known to be the only enzyme family producing reactive oxygen species (ROS) as their sole and primary function. 10) Also, it has been reported that the production of ROS appears to require the participation of the small GTP-binding protein rac1. [12][13][14][15] Thus, we detected ROS generation to explore whether it participates in HRinduced injury in H9c2 cells. Our study showed that the generation of ROS was significantly increased in the pAV-rGGPPS group during HR, and was significantly reduced in the shGGPPS group. Thus, we speculated that the effect of GGPPS on HR injury was associated with ROS generation which was influenced by Rac1 ( Figure 8).
In conclusion, we provide here important experimental evidence that overexpression of GGPPS aggravates HR-induced injury in heart-derived H9c2 cells, and GGPPS silencing protects heart-derived H9c2 cells against HR-induced injury. These are accompanied by the alteration of Rac1 activity and ROS generation. These findings indicate that GGPPS might be a potential target in preventing H9c2 cells from HR-induced injury. Further studies are required to investigate the effect of GGPPS in primary cardiomyocytes and in vivo.

Conflicts of interest:
Authors have no conflict of interest to declare.


Time to (finally) acknowledge that fish have emotionality and pain

The increasing work using fish as a model organism calls for a better understanding of their sentience. While growing evidence suggests that pain and emotionality exist in zebrafish, many deniers continue to ignore the evidence. Here we revisit the main conceptual breakthroughs in the field that argue clearly for pain and emotionality. We call for an end to denial and a focus on studying the mechanisms of fish pain and emotionality, and their translational relevance to human conditions. Konstantin A. Demin, doctoral candidate, Saint-Petersburg State University and Research Assistant, Almazov National Medical Research Centre, works in biological psychiatry and drug discovery. His main focus is experimental models of stress, anxiety, and depression as well as CNS drug-evoked conditions using zebrafish as a model organism. Researchgate Anton M. Lakstygal studied rodent CNS models at Saint Petersburg State University and is now involved in preclinical bioscreening studies in zebrafish at the Russian Research Center for Radiology and Surgical Technologies (St. Petersburg). Researchgate Allan V. Kalueff, neuroscientist and pharmacologist, is Director of the ZENEREI Research Center (New Orleans, USA), Professor of Neuroscience and Pharmacology at Southwest University (Chongqing, China), Professor of Neuroscience at Saint Petersburg State University, Leading Researcher at Almazov National Medical Research Centre, Ural Federal University and Institute of Physiology and Fundamental Medicine (Russia), and President of the International Stress and Behavior Society (ISBS). Researchgate Animal Sentience 2018.146: Demin et al. on Sneddon et al. on Sentience Denial 2 As the use of fish as model objects in biomedicine is rapidly growing, some concerns merit further consideration and critical discussion. Recent studies suggest that fish, like mammals, appear to have sentience (Brown, 2015; Rey et al., 2015), including mounting evidence that fish feel pain, respond to it behaviourally, and have all the requisite pain mechanisms and pathways (Malafoglia, Bryant, Raffaeli, Giordano, & Bellipanni, 2013). Fish behavior is complex and emotionality-driven (Kalueff et al., 2012). Some skepticism remains, however, as to whether zebrafish are sentient (Diggles et al., 2017; Key, 2015, 2016; Rose, 2002, 2007, 2016; Rose et al., 2014), and whether, from a bioethical perspective, they should be protected (similarly to the way mammals are). A recent study (Sneddon et al., 2018) rigorously challenges such views through a thorough discussion of nociception and analgesia studies in zebrafish (Danio rerio). Today, zebrafish are widely used in biomedical research as a powerful tool for translational research in various fields, including neuroscience. Despite their simpler central nervous system (CNS) organization relative to mammals, zebrafish have complex, welldescribed behavior which is emotionally driven and extends far beyond simple instinctive/reflexive reactions to different stimuli (Broom, 2007; Kalueff, Stewart, & Gerlai, 2014). The high level of genetic homology between zebrafish and humans is another argument in support of their sentience. Zebrafish have 26,206 protein-coding genes, and a large share of these genes (71,4%) are human gene orthologs (Howe et al., 2013). Pharmacological effects and molecular targets of various drugs are also highly conserved in zebrafish and humans (Milan, Peterson, Ruskin, Peterson, & MacRae, 2003), especially for receptors and enzymes (Schaaf et al., 2008). Neurochemistry in zebrafish and humans is evolutionarily conserved, with all major neurotransmitter systems (glutamate, GABA, catecholamines, acetylcholine) present in both fish and human CNS (Panula et al., 2010). Fish also possess a well-established nociceptive system strikingly paralleling that in mammals (Sneddon, 2002, 2003, 2009). For example, fish display aversive behavioral and physiological reactions to noxious stimuli that cause pain in other animals and humans (Sneddon, 2009). Fish also respond to a wide range of analgesics, including both opioid (e.g., morphine) and non-opioid drugs (e.g., aspirin and ibuprofen) (Malafoglia et al., 2013), further bridging the ostensible physiological and neurochemical gap between fish and mammals. Various experimental models of pain have been developed in zebrafish (Currie, 2014), fostering anti-pain medication screening and the investigation of genetic mutations involved in nociceptive pathways. Among the common pain response, the pain-inducing drugs cause altered motor activity in zebrafish that can be assessed using automated track records (Kalueff et al., 2013) and corrected by anti-pain medication. Zebrafish, being relatively homologous to humans in terms of amino acid sequences in key genes in the opioid system (Gonzalez-Nunez, Barrallo, Traynor, & Rodriguez, 2006; PinalSeoane et al., 2006), represent a valuable tool for the study of the central opioid system and its role in pain regulation (Demin et al., 2018). Not only do all mammalian opioid receptor genes have counterparts in the zebrafish genome (Klee et al., 2012), but the zebrafish opioid system can be described as more complex than in mammals in terms of the number of opioid genes and their more diverse physiological functions (see Demin et al. (2018) for a recent comprehensive discussion). Thus, zebrafish models enable the use of this organism not only as a simple “copy” of mammalian models of pain, but as a complementary tool to further assess the nature of the opioid system in vivo and its role in pain physiology. In general Darwinian terms, the existence of a fast-acting physiological system that informs individuals about potentially harmful stimuli is crucial for animal survival. It is hence Animal Sentience 2018.146: Demin et al. on Sneddon et al. on Sentience Denial 3 unsurprising that fish (like mammals) have such a system and express specific behaviors in response to pain. It is naïve, however, to focus on the description of the nociceptive system and pain-associated behavior in zebrafish and ignore conceptual “hard problems”. For example, the discussion of animal sentience is over-focused on the potential existence of subjective perceptual experience. No branch of science has reliable knowledge about the true nature of consciousness; and no methods exist to prove or disprove pain awareness for any organism except humans. In disputing fish sentience, another concern of Diggles et al. (2017) is the methods used to activate nociceptors. The study of nociception in zebrafish does need further investigation at the level of receptors, neurons and nerves involved in pain sensation, which is crucial for confirming the validity of the currently available fish-based main models. However, it is unfair to negate zebrafish nociception models based on this argument since we need more such research, not less (see also Woodruff, 2018). At the same time, some questions raised by a relatively more developed critique of fish sentience by Key (2016) do remain open. Despite being widely criticized (Balcombe, 2016; Braithwaite & Droege, 2016; Broom, 2016; Brown, 2016; Derbyshire, 2016; Dinets, 2016; Elwood, 2016; Manzotti, 2016; Seth, 2016), Key notes the lack of precise brain correlates of feeling pain that can be used to translate nociception from human to fish. We agree that identifying such easy-to-translate correlates is an important step for today’s translational research in the field of nociception. Moreover, sensory systems for pain in fishes can potentially vary physiologically (by analogy with vision in different taxa); hence such cross-taxa correlates with humans may not be as straightforward as might be expected (Elwood, 2016). Finally, we argue that fish not only have the ability to experience pain per se, but also drive their behaviors accordingly, as part of complex emotionality-driven behavioral phenotypes that in many domains resemble mammalian emotionality (Kalueff et al., 2012). Zebrafish nociception, relatively well-studied recently, is close to becoming universally accepted despite resistance from some authors whose views are dated and unsupported by modern objective scientific evidence (Angoa-Perez et al., 2014; Rose, 2002, 2007, 2016; Rose et al., 2014). The field of bioethics has a powerful enough basis for granting the protection deserved by species proved to have pronounced avoidance reactions to harmful stimuli and cognitive skills. Using fish models can be regulated by applying the bioethical laws already applied to other sentient animals involved in biomedical research, such as mammals. Our knowledge of animal sentience is fundamental to many disciplines and imperative to animal welfare (Proctor, Carder, & Cornish, 2013). Therefore, in recognizing fish sentience, we should also promote fish welfare (Martins et al., 2012; Webster, 2014). For example, mounting evidence suggests that zebrafish need environmental enrichment (Collymore, Tolwani, & Rasmussen, 2015; Giacomini et al., 2016; Marcon et al., 2018) as part of their husbandry standards and experimental procedures, as in the widely recognized use of environmental enrichment in mammals. Despite all arguments about the large phylogenetic distance between fish and mammals, it is necessary to provide an equal level of protection in research in order to avoid methodological and conceptual obstacles that may significantly impede scientific progress and discovery. In summary, whereas the denial of fish nociception and emotionality (Diggles et al., 2017; Key, 2015, 2016; Rose, 2016; Rose et al., 2014) continues to ignore the rapidly growing body of positive empirical evidence (Kalueff et al., 2012; Lopez-Luna, Al-Jubouri, Al-Nuaimy, & Sneddon, 2017; Sneddon, 2002, 2003, 2009; Sneddon et al., 2018; Stewart et al., 2015), the field should also adapt and reshape in response. Specifically, it can turn away from discussing dated and unsupported views and move full speed ahead to focus

As the use of fish as model objects in biomedicine is rapidly growing, some concerns merit further consideration and critical discussion. Recent studies suggest that fish, like mammals, appear to have sentience (Brown, 2015;Rey et al., 2015), including mounting evidence that fish feel pain, respond to it behaviourally, and have all the requisite pain mechanisms and pathways (Malafoglia, Bryant, Raffaeli, Giordano, & Bellipanni, 2013). Fish behavior is complex and emotionality-driven (Kalueff et al., 2012). Some skepticism remains, however, as to whether zebrafish are sentient (Diggles et al., 2017;Key, 2015Key, , 2016Rose, 2002Rose, , 2007Rose, , 2016Rose et al., 2014), and whether, from a bioethical perspective, they should be protected (similarly to the way mammals are). A recent study (Sneddon et al., 2018) rigorously challenges such views through a thorough discussion of nociception and analgesia studies in zebrafish (Danio rerio).
Today, zebrafish are widely used in biomedical research as a powerful tool for translational research in various fields, including neuroscience. Despite their simpler central nervous system (CNS) organization relative to mammals, zebrafish have complex, welldescribed behavior which is emotionally driven and extends far beyond simple instinctive/reflexive reactions to different stimuli (Broom, 2007;Kalueff, Stewart, & Gerlai, 2014). The high level of genetic homology between zebrafish and humans is another argument in support of their sentience. Zebrafish have 26,206 protein-coding genes, and a large share of these genes (71,4%) are human gene orthologs (Howe et al., 2013). Pharmacological effects and molecular targets of various drugs are also highly conserved in zebrafish and humans (Milan, Peterson, Ruskin, Peterson, & MacRae, 2003), especially for receptors and enzymes (Schaaf et al., 2008). Neurochemistry in zebrafish and humans is evolutionarily conserved, with all major neurotransmitter systems (glutamate, GABA, catecholamines, acetylcholine) present in both fish and human CNS (Panula et al., 2010). Fish also possess a well-established nociceptive system strikingly paralleling that in mammals (Sneddon, 2002(Sneddon, , 2003(Sneddon, , 2009. For example, fish display aversive behavioral and physiological reactions to noxious stimuli that cause pain in other animals and humans (Sneddon, 2009). Fish also respond to a wide range of analgesics, including both opioid (e.g., morphine) and non-opioid drugs (e.g., aspirin and ibuprofen) (Malafoglia et al., 2013), further bridging the ostensible physiological and neurochemical gap between fish and mammals. Various experimental models of pain have been developed in zebrafish (Currie, 2014), fostering anti-pain medication screening and the investigation of genetic mutations involved in nociceptive pathways. Among the common pain response, the pain-inducing drugs cause altered motor activity in zebrafish that can be assessed using automated track records (Kalueff et al., 2013) and corrected by anti-pain medication.
Zebrafish, being relatively homologous to humans in terms of amino acid sequences in key genes in the opioid system (Gonzalez-Nunez, Barrallo, Traynor, & Rodriguez, 2006;Pinal-Seoane et al., 2006), represent a valuable tool for the study of the central opioid system and its role in pain regulation (Demin et al., 2018). Not only do all mammalian opioid receptor genes have counterparts in the zebrafish genome (Klee et al., 2012), but the zebrafish opioid system can be described as more complex than in mammals in terms of the number of opioid genes and their more diverse physiological functions (see Demin et al. (2018) for a recent comprehensive discussion). Thus, zebrafish models enable the use of this organism not only as a simple "copy" of mammalian models of pain, but as a complementary tool to further assess the nature of the opioid system in vivo and its role in pain physiology.
In general Darwinian terms, the existence of a fast-acting physiological system that informs individuals about potentially harmful stimuli is crucial for animal survival. It is hence unsurprising that fish (like mammals) have such a system and express specific behaviors in response to pain. It is naïve, however, to focus on the description of the nociceptive system and pain-associated behavior in zebrafish and ignore conceptual "hard problems". For example, the discussion of animal sentience is over-focused on the potential existence of subjective perceptual experience. No branch of science has reliable knowledge about the true nature of consciousness; and no methods exist to prove or disprove pain awareness for any organism except humans. In disputing fish sentience, another concern of Diggles et al. (2017) is the methods used to activate nociceptors. The study of nociception in zebrafish does need further investigation at the level of receptors, neurons and nerves involved in pain sensation, which is crucial for confirming the validity of the currently available fish-based main models. However, it is unfair to negate zebrafish nociception models based on this argument since we need more such research, not less (see also Woodruff, 2018).
At the same time, some questions raised by a relatively more developed critique of fish sentience by Key (2016) do remain open. Despite being widely criticized (Balcombe, 2016;Braithwaite & Droege, 2016;Broom, 2016;Brown, 2016;Derbyshire, 2016;Dinets, 2016;Elwood, 2016;Manzotti, 2016;Seth, 2016), Key notes the lack of precise brain correlates of feeling pain that can be used to translate nociception from human to fish. We agree that identifying such easy-to-translate correlates is an important step for today's translational research in the field of nociception. Moreover, sensory systems for pain in fishes can potentially vary physiologically (by analogy with vision in different taxa); hence such cross-taxa correlates with humans may not be as straightforward as might be expected (Elwood, 2016).
Finally, we argue that fish not only have the ability to experience pain per se, but also drive their behaviors accordingly, as part of complex emotionality-driven behavioral phenotypes that in many domains resemble mammalian emotionality (Kalueff et al., 2012).
Zebrafish nociception, relatively well-studied recently, is close to becoming universally accepted despite resistance from some authors whose views are dated and unsupported by modern objective scientific evidence (Angoa-Perez et al., 2014;Rose, 2002Rose, , 2007Rose, , 2016Rose et al., 2014). The field of bioethics has a powerful enough basis for granting the protection deserved by species proved to have pronounced avoidance reactions to harmful stimuli and cognitive skills. Using fish models can be regulated by applying the bioethical laws already applied to other sentient animals involved in biomedical research, such as mammals. Our knowledge of animal sentience is fundamental to many disciplines and imperative to animal welfare (Proctor, Carder, & Cornish, 2013). Therefore, in recognizing fish sentience, we should also promote fish welfare (Martins et al., 2012;Webster, 2014). For example, mounting evidence suggests that zebrafish need environmental enrichment (Collymore, Tolwani, & Rasmussen, 2015;Giacomini et al., 2016;Marcon et al., 2018) as part of their husbandry standards and experimental procedures, as in the widely recognized use of environmental enrichment in mammals. Despite all arguments about the large phylogenetic distance between fish and mammals, it is necessary to provide an equal level of protection in research in order to avoid methodological and conceptual obstacles that may significantly impede scientific progress and discovery.
In summary, whereas the denial of fish nociception and emotionality (Diggles et al., 2017;Key, 2015Key, , 2016Rose, 2016;Rose et al., 2014) continues to ignore the rapidly growing body of positive empirical evidence (Kalueff et al., 2012;Lopez-Luna, Al-Jubouri, Al-Nuaimy, & Sneddon, 2017;Sneddon, 2002Sneddon, , 2003Sneddon, , 2009Sneddon et al., 2018;Stewart et al., 2015), the field should also adapt and reshape in response. Specifically, it can turn away from discussing dated and unsupported views and move full speed ahead to focusing on what really matters: studying the mechanisms of fish pain and emotionality and their translational relevance to human conditions in order to build a reliable, reproducible and ethically sound line of research using these sentient aquatic species.