G C A T T A C G G C A T

Article Fluorescent Light Incites a Conserved Immune and Inflammatory Genetic Response within Vertebrate Organs (Danio rerio, Oryzias latipes and Mus musculus)

Mikki Boswell 1, Yuan Lu 1, William Boswell 1, Markita Savage 1, Kim Hildreth 2, Raquel Salinas 1, Christi A. Walter 2 and Ronald B. Walter 1,*

1 The Xiphophorus Genetic Stock Center, Department of Chemistry and Biochemistry, Texas State University, San Marcos, TX 78666, USA; [email protected] (M.B.); [email protected] (Y.L.); [email protected] (W.B.); [email protected] (M.S.); [email protected] (R.S.) 2 Department of Cellular Systems and Anatomy, The University of Texas Health San Antonio, San Antonio, TX 78229, USA; [email protected] (K.H.); [email protected] (C.A.W.) * Correspondence: [email protected]; Tel.: +1-512-245-0357

 Received: 8 March 2019; Accepted: 29 March 2019; Published: 3 April 2019 

Abstract: Fluorescent light (FL) has been utilized for 60 years and has become a common artificial ≈ light source under which animals, including , spend increasing amounts of time. Although the solar spectrum is quite dissimilar in both wavelengths and intensities, the genetic consequences of FL exposure have not been investigated. Herein, we present comparative RNA-Seq results that establish expression patterns within skin, brain, and liver for Danio rerio, Oryzias latipes, and the hairless mouse (Mus musculus) after exposure to FL. These animals represent diurnal and nocturnal lifestyles, and 450 million years of evolutionary divergence. In all three organisms, FL induced ≈ transcriptional changes of the acute phase response signaling pathway and modulated inflammation and innate immune responses. Our pathway and clustering analyses suggest cellular perception of oxidative stress is promoting induction of primary up-stream regulators IL1B and TNF. The skin and brain of the three animals as well as the liver of both fish models all exhibit increased inflammation and immune responses; however, the mouse liver suppressed the same pathways. Overall, the conserved nature of the genetic responses observed after FL exposure, among fishes and a mammal, suggest the presence of light responsive genetic circuitry deeply embedded in the vertebrate genome.

Keywords: ; RNA-Seq; fluorescent light; acute phase; oxidative stress

1. Introduction Over three billion years of evolution occurred exclusively under sunlight. Thus, all spectral wavelengths were represented, and each organism had the opportunity to adaptively pair genetic responses with discrete regions and/or intensities of solar spectrum wavelengths inherent to their environmental niche. In contrast to the solar spectrum, fluorescent light (FL) has only been used for 60 years. The FL light spectrum is comprised of a much narrower range of wavelengths ≈ and exhibits sharp peaks and valleys of intensity for select wavelengths, when compared to the broad and consistent solar spectrum. We recently characterized changes in gene expression after FL exposure to be both robust and conserved in the skin of three divergent small fish species; Xiphophorus maculatus (platyfish), Danio rerio (zebrafish), and Oryzias latipes (medaka), that are utilized as experimental models in biomedical research [1,2]. In addition, we have reported that exposure to select wavelengths of light may serve to induce, or suppress, specific genetic pathways in Xiphophorus

Genes 2019, 10, 271; doi:10.3390/genes10040271 www.mdpi.com/journal/genes Genes 2019, 10, 271 2 of 25 skin [3]. These studies established that exposure to one wavelength may activate genes in specific biochemical pathways, while exposure to a different wavelength of light can be used to suppress the same pathway. These studies support the concept that, over evolutionary time, each wavelength in the solar spectrum may have been conscripted for regulation of specific genetic pathways. If so, exposure to the full complement of solar wavelengths may be necessary to produce the agonistic/antagonistic genetic signaling needed for proper interaction of an organism with its environment. Use of broad-spectrum artificial lighting has been shown to have the potential to produce adverse effects on health, including depression, disturbed sleep–wake cycles, obesity, altered body temperature, and altered circadian rhythms [4–11]. However, little research has been conducted to determine genetic consequences, if any, from use of common artificial light sources. We have reported that exposure to FL in the skin of both zebrafish and medaka modulates gene expression patterns consistent with activation of inflammation and cellular immune responses [1]. However, in addition to the conserved primary response, each fish also shows other light-induced gene expression alterations that are unique to each species. Such species-specific genetic responses to the same FL source may reflect genetic fine-tuning of the organismal light response, according to evolution within unique environmental niches. This may suggest ocular photoreception and the genetic perception response to light are overlapping but separate processes. Additional evidence for differences in the genetic perception of light have also been observed in male and female X. maculatus exposed to ultraviolet light (UVB, 311 nm, [12]). When highly inbred siblings (i.e., over 100 generations) are exposed to the same UVB light source, most of the induced gene expression responses are shared by both sexes. However, males and females also show opposite UVB induced modulation of gene sets in important pathways, such as synaptic development, cell differentiation, wound healing, glucose metabolism, and free radical scavenging. Thus, gender differences within a species may affect the genetic response to light exposure. While these studies document some differences in the specific gene sets modulated after light exposure, it is important to note that for skin, the primary consequence of FL exposure for all fish species examined appears to a be well-conserved induction of the inflammation and immune response. Previous studies of the genetic response to FL, UVB, or specific wavelengths of light have involved only the skin of several fish models. Potential transcriptional effects within internal organs after light exposure of intact animals has not been reported. In addition, fish skin is a genetically dynamic organ compared to the skin of mammals. Thus, it is of interest to determine whether the conserved fish gene expression responses to FL exposure would extend to the skin of mammals. Herein we report results of comparative studies using RNA-Seq to assess modulation of transcriptional profiles following exposure to 4100 K “cool-white” FL for two commonly utilized fish models (zebrafish and medaka) and a mammalian model (the hairless mouse). FL induced responses in gene expression were measured in skin, a direct light receiving organ, and two internal organs, the brain and liver. In spite of considerable evolutionary divergence (i.e., 450 My) and drastically ≈ different lifestyles (i.e., diurnal fish and nocturnal mice), a highly conserved primary genetic response to FL exposure was observed. In skin, brain, and liver of all three animals, FL exposure led to modulation of the acute phase response pathway, with concurrent promotion of inflammation and immune responses. The only major difference in the primary response to FL was observed in the mouse liver, that modulated the same pathways as the fish livers, but in the opposite direction compared to the fishes (i.e., suppression in mouse liver, induction in fish livers). These findings support the concept that light-induced genetic circuitry is highly conserved and deeply embedded in the vertebrate genome.

2. Materials and Methods

2.1. Fish Utilized and Fluorescent Light Exposure Mature adult male Danio rerio (TU, zebrafish, 3) from the Zebrafish International Resource Center in Eugene, OR, and three mature adult male Oryzias latipes (Cab, medaka) from the Xiphophorus Genes 2019, 10, 271 3 of 25

Genetic Stock Center were used for FL exposure (40 min, 35 kJ/m2). All protocols were carried out as previously described [1,13]. Prior to light exposure, fish were placed individually into 100 mL of filtered home aquaria water and kept in the dark for 14 h. FL exposure occurred in UV-transparent cuvettes (9 7.5 1.5 cm) in 90 mL of water. The exposure cuvettes were suspended 10 cm between × × two banks of two (total of 4 lights) unfiltered 4,100 K fluorescent lights (Philips F 20T12/CW 20 watts, Alto, 40 min, 35 kJ/m2), mounted horizontally on each side of a wooden box exposure chamber. After FL exposure, all fish were returned to the dark in 100 mL filtered aquaria water for 6 h and then euthanized and dissected for RNA isolation. All organs were dissected into 300 µL RNAlater (Life Technologies, Grand Island, NY, USA) and stored in the 80 C freezer except for the skin samples, which were − ◦ immediately placed in 300 µL TRI Reagent (Sigma Inc., St Louis, MO, USA) and flash frozen in an ethanol dry ice bath. In addition, 3 fish of each species were placed into the cuvettes following the procedure outlined above and placed into the exposure chamber but with the lights turned off (sham treated fish). All other protocols described above were followed for the sham-treated samples.

2.2. Mice Utilized and Fluorescent Light Exposure Three young (7 weeks old) adult male hairless mice (M. musculus, SKH1-ELITE, Charles River Laboratories, Wilmington, MA, USA) were used for FL exposure (60 min, 52.5 kJ/m2). The mice were housed and cared for at the University of Texas Health San Antonio; all experimental treatments and protocols were performed to mimic the fish exposures but adjusted for the larger body mass. Prior to FL exposure, all mice were housed in cages, each containing 2 mice per cage, and placed into a dark cabinet in the exposure room. Mice were placed, individually, into round quartz glass chambers, 8 cm in diameter with 6-cm high sidewalls. These chambers allowed the mice to turn around and/or move freely. During light exposure, each chamber, with a single mouse, was covered with mesh screening and placed into the same light box utilized for fish exposures (see above), so the center of the chamber wall was the same distance from the bottom of the exposure box bottom as the fish exposure cuvettes. Following FL treatment, the mice were placed back in their cages and returned to the dark for 4 h. Three sham-treated mice were subjected to the same protocol outlined above, but the FL lights were off. All mice were sacrificed following the 4 h time period using cervical dislocation and dissected into organs. All organs were placed into pre-labeled 15 or 50 mL conical vials containing RNAlater, corresponding to the size of the organ. Vials were placed on dry ice until after dissections were complete and stored in the 80 C freezer until RNA was subsequently isolated. − ◦ 2.3. Statement of Animal Use All medaka and zebrafish utilized in these studies were maintained in the Xiphophorus Genetic Stock Center (http://www.xiphophorus.txstate.edu/) in accordance with protocols approved by the Institutional Animal Care and Use Committee (IACUC, IACUC#2016103230 and IACUC#20173294956). These fish were maintained in accordance with the applicable OLAW guidelines governing animal experimentation in the USA. Mice were maintained and treated in an AAALAC-accredited animal facility at The University of Texas Health San Antonio, according to IACUC approved protocols.

2.4. RNA Isolation and Sequencing Total RNA was isolated from skin, brain, and liver of zebrafish, medaka, and mouse samples using a TRI Reagent (Sigma Inc., St Louis, MO, USA) chloroform extraction followed by the Qiagen RNeasy (Qiagen, Valencia, CA, USA) isolation protocol. Skin was homogenized in 600 µL TRI Reagent using a handheld tissue disruptor, followed by addition of 120 µL of chloroform. Samples were vigorously shaken and then phases partitioned by centrifugation (12,000 g for 15 min at 4 C). × ◦ After extraction, the RNA was precipitated with 500 µL 70% EtOH and further purified using a Qiagen RNeasy mini RNA following the manufacturer’s protocol. Residual DNA was eliminated with an on-column DNase treatment at 25 ◦C for 15 min. RNA quality was assessed with an Agilent 2100 Genes 2019, 10, 271 4 of 25 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), and quantified with a Qubit 2.0 fluorometer (Life Technologies, Grand Island, NY, USA). All samples sent for sequencing had RIN scores 8.0. ≥ 2.5. Differentially Expressed Gene (DEG) Analysis RNA sequencing was performed on libraries constructed using the Illumina TrueSeq library preparation system that employs a polyA selection. RNA libraries were sequenced as 100 bp paired-end fragments using an Illumina Hi-Seq 2000 system (Illumina, Inc., San Diego, CA, USA). Forty-seven to 90 million raw reads were generated for each RNA sample. All raw reads were subsequently truncated by similarity to remove library adaptor sequences using a custom Perl script, and short reads were filtered based on quality scores using a custom filtration algorithm that removed low-scoring sections of each read and preserved the longest remaining fragment (Table S2) [14]. Filtered reads were mapped using Tophat2 [15] to the corresponding Danio rerio (zv9), Oryzias latipes (medaka1.81.2), or Mus musculus (mm10) genome. The percentage of reads mapped were assessed by Tophat2, and sequencing depth assessed by SAMtools depth, respectively [15,16]. Gene expression was assessed by featureCounts using genome annotation from Ensembl database v79 [17], and differentially modulated genes were determined using the R-Bioconductor (www.bioconductor.org) package edgeR [18] with a |log2(fold change)| 1.0 (FDR < 0.05), (Table1). ≥ Table 1. Differentially expressed genes for fluorescent light (FL) exposed samples. Total modulated genes are the output file from EdgeR (column 2) that had a log (fold change) |2.0| and a (p-adj < 0.05). 2 ≥ All fish Ensembl IDs were converted to Organization (HUGO) IDs (column 5) for Ingenuity Pathway Analysis (IPA) analysis and direct comparison of the zebrafish, medaka and mouse. HUGO IDs were then imported and mapped by Qiagen’s IPA software for functional and pathway analysis (column 6). Mouse Ensembl IDs were directly imported into IPA for functional and pathway analysis.

Species Organ Total Modulated Up-Modulated Down-Modulated HUGO IDs Mapped by IPA Skin 523 336 187 482 364 Zebrafish Brain 94 49 45 89 78 Liver 83 56 27 79 64 Skin 2304 1229 1075 2298 2284 Medaka Brain 592 433 159 589 587 Liver 3492 1684 1808 3483 3464 Skin 107 49 58 N/A 102 Mouse Brain 1174 699 475 N/A 1172 Liver 182 51 131 N/A 166

Genes identified as being differentially modulated in response to FL were further analyzed for organ specificity using Venny 2.1 [19] and for functional specificity with Ingenuity Pathway Analysis (IPA, Qiagen, Redwood City, CA, USA). IPA-based gene expression analysis yielded gene clusters, genetic pathways, functional classes, and potential up-stream regulators to aid in mechanistic interpretation. Herein, the term “pathways” is short for canonical pathways assigned by IPA based on the light exposure input differentially expressed gene (DEG) data. In IPA, known pathways are drawn as pictures with input DEGs overlaid onto them that are identified by symbols and colors indicating known functions and direction of modulation. A z-score algorithm is used to determine if a pathway is up- or down-regulated based on the genes that fall into that particular pathway and the direction of modulation. IPA assignment of DEGs into “functions” or “functional classes” relates the input DEGs to known disease states and biological functions, as published in the scientific literature. Functional classes are visualizations of the biological trends in the light-effected DEG dataset, and may be used to predict the effect of gene expression changes of the entire dataset on biological processes and known cellular functions. Functional assignment uses an algorithm to assess the dataset as a whole and predict what is collectively occurring on a larger down-stream scale. Genes 2019, 10, 271 5 of 25

2.6. Validation of RNA-Seq Gene Expression Results NanoString (NanoString Technologies, Inc., Seattle, WA, USA), with a custom panel for zebrafish (Table S1) and a separate panel for medaka [20], was used as an independent technology to confirm the DEGs identified using RNA-Seq. For mouse, a commercially available nCounter PanCancer Immune Profiling Panel (NanoString, https://www.nanostring.com, XT-CSO-MIP1-12) was used for RNA-Seq validation. Aliquots of the RNA (500 ng) used for RNA-Seq were also used for the NanoString nCounter assay. Hybridization protocols were strictly followed according to manufacturer’s instructions [21]. Samples were hybridized overnight at 65 ◦C with custom probes and transferred to the NanoString Prep Station. The NanoString cartridge containing the hybridized samples was immediately evaluated with the NanoString nCounter based on unique color-coded signals. Probe counts were quantified through direct counting with the nCounter Digital Analyzer. Data analysis was performed by lane normalization using a set of standard NanoString probes, followed by sample normalization using a set of 10 housekeeping genes. Fold changes were calculated on normalized counts and plotted using Microsoft Excel. All RNA-Seq short read sequence data utilized to prepare the differential expression analyses presented herein are deposited on the Xiphophorus Genetic Stock Center website (https://www. xiphophorus.txstate.edu) and will be made available upon reasonable request to the corresponding author. The final differential expression gene lists are published with the supplementary data files associated with this manuscript.

3. Results Adult male zebrafish, medaka, or hairless mice were exposed to FL (35 kJ/m2 or 52.5 kJ/m2) as previously detailed [1,13,22,23]. Isolated total RNA from all organ samples was subjected to Illumina sequencing, as previously described [1,3,12,13,22–24]. Table S2 shows RNA-sequencing (RNA-Seq) statistics for the reads utilized in this report. As shown, the RNA-Seq read lengths were quite deep, ranging from 3.42 109 (medaka sham, liver) to 12.9 109 (mouse FL, liver), post filtration. This equates × × to exome sequencing depths ranging from 147 to 215 , with most samples well over 120 (Table S2). × × × The numbers of DEGs for each of the organs after FL exposure, for each species examined, is shown in Table1. Table S3 presents a list of all DEGs for skin in zebrafish (S3a), medaka (S3b), and mice (S3c), while supplementary Tables S4a–c and S5a–c, present DEG lists for brain and liver, respectively. Medaka skin (2304 DEGs) and liver (3493 DEGs) showed the most robust genetic responses to FL, compared to zebrafish (23 and 83, respectively) or mice (107 and 182, respectively). However, the mouse brain exhibited the highest numbers of brain DEGs (1,174) compared to medaka (592) and zebrafish (94). For all three organisms, there was very little overlap in DEGs, when gene I.D.s for each organ from one of the test animals was compared to the other two (Figure1, top). Medaka showed the highest degree of overlap after FL exposure with 157 DEGs shared by all three organs (skin, brain and liver), while in zebrafish and mice showed no overlap among all three organ targets (Figure1). Genes 2019, 10, 271 6 of 25 Genes 2019, 10, 271 6 of 25

Figure 1. Following FL exposure, modulated genes compared to sham-treated samples were converted Figure 1. Following FL exposure, modulated genes compared to sham-treated samples were into HUGO IDs and imported into IPA. The mapped IPA genes from each organ tested (skin, brain, converted into HUGO IDs and imported into IPA. The mapped IPA genes from each organ tested and liver) were compared using Venny (top row). Gene expression was verified using NanoString (skin, brain, and liver) were compared using Venny (top row). Gene expression was verified using nCounter assay, and comparing fold changes back to RNA-Seq determined fold changes for each model NanoString nCounter assay, and comparing fold changes back to RNA-Seq determined fold changes tested (bottom row). In zebrafish (left) a total of 72 targets were tested using the nCounter assay on a for each model tested (bottom row). In zebrafish (left) a total of 72 targets were tested using the custom zebrafish panel (Table S1). A Mouse PanCancer Immune Profiling Panel was used, and 103 gene nCounter assay on a custom zebrafish panel (Table S1). A Mouse PanCancer Immune Profiling Panel targetswas were used, above and background103 gene targets (center). were Inabove medaka background (right), a(center). total of In 373 medaka targets (right), were tested a total using of 373 the nCountertargets assay were ontested a custom using the medaka nCounter panel. assay on a custom medaka panel.

DEGsDEGs identified identified by by RNA-Seq RNA-Seq after after FL FL exposure exposure were were validated validated by independent measurement measurement of of genegene expression expression using using the NanoStringthe NanoString n-counter n-counter platform platform [21 [21]] as previouslyas previously detailed detailed [1 ,[1,3,12,13,223,12,13,22–25– ]. Using25]. the Using NanoString the NanoString assay, 72assay, zebrafish 72 zebrafish targets targets (30 targets (30 targets in skin, in skin, 18 in 18 brain, in brain and, 21and in 21 liver) in liver) were testedwere and tested 69 were and above 69 were background. above background. All (100%) All of these(100%) matched of these the matched RNA-Seq the DEGs RNA-Seq in direction DEGs in of modulationdirection with of modulation an R2 = 0.84 with (Figure an R2 =1 0.84, left (Figure bottom). 1, left For bottom). medaka, For 373 medaka, targets 373 were targets tested were using tested the nCounterusing assaythe nCounter (117 in skin,assay 41(117 in in brain, skin, and 41 in 66 brain in liver), andand 66 in 100% liver) matched and 100% the m RNA-Seqatched the inRNA direction-Seq within an direction R2 = 0.87 with (Figure an R21 =, right0.87 (Figure bottom). 1, right bottom). ValidationValidation of RNA-Seq of RNA results-Seq results in mice in skin, mice brain, skin, and brain liver, and employed liver employed the commercial the commercial NanoString MouseNanoString PanCancer Mouse Immune PanCancer Profiling Immune Panel that Profiling simultaneously Panel that tested simultaneously the gene expression tested the profile gene for 800 geneexpressio targetsn profile including for 800 50 gene housekeeping targets including genes 50 [housekeeping26]. In total, 103genes gene [26]. targets In total, on 103 the gene PanCancer targets panelon were the PanCancer found informative panel were and found statistically informative valid and in statistically the mouse valid RNA-Seq in the dataset,mouse RNA with-S 97%eq dataset, of these with 97% of these targets confirmed in modulation direction (all but three) and an R2 = 0.73 (Figure targets confirmed in modulation direction (all but three) and an R2 = 0.73 (Figure1, middle bottom). 1, middle bottom). Two mouse liver targets, and one brain target, did not agree in direction among Two mouse liver targets, and one brain target, did not agree in direction among the 29 skin, 39 brain, the 29 skin, 39 brain, and 35 liver target genes tested. and 35 liver target genes tested. 3.1. Genetic Response of Skin to Fluorescent Light 3.1. Genetic Response of Skin to Fluorescent Light Zebrafish skin induced 364 genes (Table S3a), which Ingenuity Pathway Analysis (IPA, Qiagen, Zebrafish skin induced 364 genes (Table S3a), which Ingenuity Pathway Analysis (IPA, Qiagen, Redwood City, CA) clustered into 19 canonical pathways having |z-scores| > 2.0 (12 up-modulated Redwoodand seven City, CA,down USA)-modulated, clustered Table into 19 2, canonical top). In pathwayscomparison, having adult| z-scores male medaka| > 2.0 (12 exposed up-modulated to FL and sevenmodulated down-modulated, 2284 genes (Table Table S3b)2, top). in skin In comparison,associated with adult 24 malecanonical medaka pathways exposed (19 to up FL-modulated modulated 2284and genes five(Table down- S3b)modulated, in skin Table associated 2, middle), with while 24 canonical mature male pathways hairless (19 mouse up-modulated skin, exhibited and 102 five down-modulated,DEGs (Table S3c) Table that2 clustered, middle), into while 12 up mature-modulated male canonical hairless pathways mouse skin, (Table exhibited 2, bottom). 102 DEGs (Table S3c) that clustered into 12 up-modulated canonical pathways (Table2, bottom). Table 2. Modulated canonical pathways were predicted using IPA for zebrafish (top), medaka (middle), and mouse (bottom) skin. All pathways with a z-score ≥ |2|, p-value < 0.05, and five or more genes were included in the analysis. The column on the far right indicates if the pathway is involved

Genes 2019, 10, 271 7 of 25

Table 2. Modulated canonical pathways were predicted using IPA for zebrafish (top), medaka (middle), and mouse (bottom) skin. All pathways with a z-score |2|, p-value < 0.05, and five or more genes were ≥ included in the analysis. The column on the far right indicates if the pathway is involved in an immune (Im), inflammatory (If), acute phase (AP), cellular proliferation (CP), or cellular signaling (CS) response.

Zebrafish Skin Ingenuity Canonical Pathways z-score p-value Gene Category Acute Phase Response Signaling 4.12 4.27E-02 9 Im, If, AP, CP Production of Nitric Oxide and ROS in Macrophages 4.11 2.29E-02 11 Im, AP Oncostatin M Signaling 3.19 3.09E-03 10 Im, If, AP, CP Leukocyte Extravasation Signaling 3.07 2.88E-03 14 Im, If TREM1 Signaling 3.00 2.69E-02 12 Im, If, AP mTOR Signaling 2.90 2.09E-02 12 CP, CS Complement System 2.71 2.02E-02 6 Im, If, AP IL-6 Signaling 2.45 2.96E-02 11 Im, If, AP B Cell Signaling 2.00 3.98E-02 10 Im, AP ILK Signaling 2.00 4.27E-03 13 Im, If, AP, CP Nitric Oxide Signaling in the Cardiovascular System 2.00 4.16E-02 6 If NRF2-mediated Oxidative Stress Response 2.00 1.02E-02 9 If, AP Role of NFAT in Regulation of the Immune Response 2.00 4.33E-02 6 Im, AP Thrombin Signaling 2.00 1.28E-02 9 If, AP Gα12/13 Signaling 2.00 4.17E-03 10 If, AP, CP, CS − PPARα/RXRα Activation 2.37 2.46E-01 7 If, AP − Cardiac Hypertrophy Signaling 2.87 7.76E-03 14 If, AP, CP, CS − eNOS Signaling 2.97 4.79E-02 8 If, CP − CDK5 Signaling 3.56 2.09E-03 9 If, AP, CP − 3.58 7.94E-12 26 CP, CS − Actin Cytoskeleton Signaling 2.47 2.40E-03 15 AP, CP, CM, CS − Medaka Skin Ingenuity Canonical Pathways z-score p-value Genes Acute Phase Response Signaling 3.67 3.39E-09 45 Im, If, AP, CP Gα12/13 Signaling 2.79 4.68E-03 24 If, AP, CP Pancreatic Adenocarcinoma Signaling 2.79 1.10E-05 29 Im, AP, CP RhoA Signaling 2.79 4.27E-03 23 If, CP, CM Eicosanoid Signaling 2.73 9.33E-03 14 If IL-6 Signaling 2.68 1.82E-05 30 Im, If, AP ILK Signaling 2.49 7.59E-05 39 Im, If, AP, CP TREM1 Signaling 2.43 5.01E-02 13 Im, If, AP Endothelin-1 Signaling 2.41 5.75E-04 35 AP, CP, CM Ceramide Signaling 2.37 7.76E-05 23 If, AP, CP ERK5 Signaling 2.22 1.35E-02 13 CP NGF Signaling 2.20 5.25E-03 22 AP, CP p38 MAPK Signaling 2.20 2.45E-03 23 Im, If Activation of IRF by Cytosolic Pattern Recogn. Recep. 2.16 2.05E-02 9 Im, AP MIF Regulation of Innate Immunity 2.16 2.51E-02 9 Im, AP Cardiac β-adrenergic Signaling 2.10 1.45E-02 23 CS VEGF Signaling 2.10 7.41E-02 16 AP, CP, CM B Cell Receptor Signaling 2.06 1.86E-03 33 Im, AP NANOG in Mammalian Embryonic Stem Cell Pluripotency 2.00 1.51E-04 27 AP, CP ATM Signaling 2.00 3.98E-02 14 If, CP − Wnt/GSK-3β Signaling in the Pathos of Influenza 2.00 1.26E-02 15 Im − NFAT in Cardiac Hypertrophy 2.11 6.31E-03 32 Im, AP − PPARα/RXRα Activation 2.68 1.70E-02 17 If, AP − Antioxidant Action of Vitamin C 2.82 4.17E-02 17 If, AP, CP − Mouse Skin Ingenuity Canonical Pathways z-score p-value Genes TREM1 Signaling 4.20 2.00E-05 12 Im, If, AP Role of IL-17F Inflammatory 4.00 3.98E-05 5 If Leukocyte Extravasation Signaling 3.00 1.26E-02 5 Im, If Hepatic Stellate Cell Activation 2.30 1.58E-05 7 If AMPK Signaling 2.20 1.00E-02 5 CP Granulocyte Adhesion and Diapedesis 2.20 2.00E-11 12 If Genes 2019, 10, 271 8 of 25

Table 2. Cont.

Mouse Skin Ingenuity Canonical Pathways z-score p-value Genes IL-1 Mediated Inhibition of RXR Function 2.20 1.58E-02 5 Im, If, AP PPARα/RXRα Activation 2.20 7.94E-03 5 If, AP Acute Phase Response 2.10 3.98E-03 17 Im, If, AP, CP Inhibition of Matrix Metalloproteases 2.10 2.51E-05 5 CM Genes 2019, 10LXR, 271 /RXR Activation 2.10 2.00E-03 58 of 25 Im, If Agranulocyte Adhesion and Diapedesis 2.00 1.00E-08 10 Im, If Agranulocyte Adhesion and Diapedesis 2.00 1.00E-08 10 Im, If Abbreviations: Reactive oxygen species (ROS), Triggering receptor expressed on myeloid cells 1 (TREM1), Mammalian targetAbbreviations: of rapamycin Reactive (mTOR), oxygen Interleukin species (ROS), 6 (IL-6), Triggering Integrin-linked receptor expressed kinase (ILK), on myeloid Nuclear cells factor 1 erythroid 2-related factor(TREM1), 2 (NRF2), Mammalian Nuclear target factor of rapamycin of activated (mTOR), T-cells Interleukin (NFAT), 6 Peroxisome(IL-6), Integrin proliferator-linked kinase activated (ILK), receptor alpha (PPARNuclearα), Retinoid factor X erythroid receptor 2 alpha-related (RXR factorα ), 2 Endothelial (NRF2), Nuclear nitric fa oxidector of synthase activated (eNOS), T-cells (NFAT), Cyclin dependent kinase 5 (CDK5),Peroxisome Ras homolog proliferator gene activated family receptor member alpha A (RhoA), (PPARα Extracellular), Retinoid X signal-regulated receptor alpha (RXR kinaseα), 5 (ERK5), Nerve growthEndothelial factor (NGF), nitric Mitogen oxide synthase activated (eNOS), Cyclin kinase dependent (MAPK), kinase Interferon 5 (CDK5), regulatory Ras homolog factor gene (IRF), Migration inhibitory factorfamily (MIF), member Vascular A (RhoA), endothelial Extracellular growth signal factor-regulated (VEGF), kinase 5 (ERK5), transcriptionNerve growth factor (NGF), Nanog (NANOG), Ataxia-telangiectasiaMitogen activated mutated protein (ATM), kinase Liver (MAPK), X receptor Interferon alpha (LXR),regulatory Wingless factor integrated(IRF), Migration (Wnt), inhibitory Glycogen synthase kinase 3 betafactor (GSK-3 (MIF),β), Adenosine Vascular endothelial monophosphate growth factor activated (VEGF), protein homeobox kinase (AMPK), Interleukin-1 factor Nanog (IL-1). (NANOG), Ataxia-telangiectasia mutated (ATM), alpha (LXR), Wingless integrated (Wnt), Glycogen synthase kinase 3 beta (GSK-3 β), Adenosine monophosphate activated protein The top canonicalkinase (AMPK), pathway Interleukin in-1 zebrafish (IL-1) . skin (based on z-score and pathway coverage) was the acute phase response (APR, Table2, Figure2,[ 27,28]). Overall, based on the DEG clustering following FL exposure, zebrafishThe top canonical skin responded pathway in byzebr mountingafish skin (based a robust on z-score inflammation and pathway and coverage) immune was the response that acute phase response (APR, Table 2, Figure 2, [27,28]). Overall, based on the DEG clustering following principally involvedFL exposure modulation, zebrafish skin of respond sub-pathwaysed by mounting within a robust the APR inflammation (Table2 ,and Figure immune2,[ response27–30]). The APR is a large stressthat principally pathway involve thatd includes modulation many of sub sub-pathways,-pathways within the such APR as (Table Oncostatin 2, Figure 2, M, [27 mTOR,–30]). PPARα /RXRα, TREM1,The APR and is a others large stress (Figure pathway2). that All includes of the APRmany sub sub-pathways-pathways, such showed as Oncostatin significant M, mTOR, z-scores in PPARα /RXRα, TREM1, and others (Figure 2). All of the APR sub-pathways showed significant z- zebrafish skin (Table2, Figure2), and all but three of the 21 pathways modulated by FL were associated scores in zebrafish skin (Table 2, Figure 2), and all but three of the 21 pathways modulated by FL with inflammationwere associated and thewith APR inflammation (Table2 and). the APR (Table 2). Figure 2 TREM1, *IL-6, NRF2-mediated Oxidative Stress, NFAT in Regulation of the Immune Response, Pancreatic Adenocarcinoma Signaling, Ceramide Signaling, Activation of IRF by Cytosolic Pattern Recognition, MIF Regulation of Innate Immunity, NANOG in Mammalian Embryonic Stem Cell Pluripote, *IL-1 Mediated Inhibition of RXR Function, Signaling, HMGB1 Signaling, Type I IL-1 Diabetes Mellitus Signaling, Toll-like Receptor Signaling, IL-8, CD28 Signaling in T Helper Cells, JAK/STAT Signaling, GNRH Signaling, P2Y Purigenic Receptor Signaling, PKCθ Signaling, cAMP-mediated Signaling, Cholecystokinin/Gastrin-mediated Signaling, Gαq, Gα12/13, PPAR/RXR Activation, Cardiac Hypertrophy Signaling, p38 MAPK Signaling, PPAR Signaling, FLT3 Signaling in Hematopoeitic Progenator Cells, BMP Signaling Production of NO and ROS in macrophages, TREM1, *IL-6, B Cell Receptor Signaling, ILK, NRF2- mediated Oxidative Stress, NFAT in Regulation of the Immune Response, Pancreatic Adenocarcinoma Signaling, Ceramide Signaling, NGF Signaling, Activation of IRF by Cytosolic Pattern Recognition, VEGF Signaling, NANOG in Mammalian Embryonic Stem Cell Pluripote, *IL-1 Mediated Inhibition of RXR Function, PEDF Signaling, Renal Cell TNF-α Carcinoma Signaling, HMGB1 Signaling, Type I Diabetes Mellitus Signaling, Toll-like Receptor Signaling, CNTF Signaling, PI3K/AKT Signaling, IL-8, CD28 Signaling in T Helper Cells, Synaptic Long Term Potentiation, Prolactin Signaling, JAK/STAT Signaling, P2Y Purigenic Receptor Signaling, PKCθ Signaling, Cholecystokinin/Gastrin-mediated Signaling, CXCR4 Signaling, Gαq, Gα12/13, NFAT in Cardiac Hypertrophy, Antioxidant Action of Vitamin C, p38 MAPK Signaling, PPAR Signaling, FLT3 Signaling in Hematopoeitic Progenator Cells, BMP Signaling OSM Oncostatin M Signaling Production of NO and ROS in macrophages, TREM1, *IL-6, Thrombin Signaling, Melanocyte Development and Pigmentation Signaling, Chemokine Signaling, IL-2, Gαs, Tec Kinase Signaling, Endothelin-1 Signaling, Activation of IL-6 IRF by Cytosolic Pattern Recognition, NANOG in Mammalian Embryonic Stem Cell Pluripote, CREB Signaling, Thrombopoietin Signaling, eNOS Signaling, CDK5 Signaling, Actin Cytoskeleton Signaling, NFAT in Cardiac Hypertrophy, *Designates pathways that are in the mouse liver and expression is opposite of color designation in the table. Phospholipase C Signaling Figure 2. TheFigure acute 2. The phaseacute phase response response (APR) (APR) is is central central to the to FL the induced FLinduced response in response skin, brain, inand skin, brain, and liverofliver allthree of all three model model organisms. organisms. Four Four main main regulators regulators (IL1B, TNF (IL1B, OSM, TNF, and IL, OSM-6) are ,central and IL-6to this) are central response (top of pathway in dark orange). The expression of these regulators controls four major to this responsesignaling (top cascades of pathway, the MEK/MKK in dark cascade orange). (dark blue),The expression the NFkβ cascade of these (green), regulators the JAK/STAT controls four major signalingcascade cascades, (purple), and the the MEK Ras/Raf/MKK cascade cascade (light (dark blue). The blue), pathways the NFk fromβ cascade Tables 2–4 (green), that overlap the JAK/STAT cascade (purple),with the andAPR the(designated Ras/Raf APcascade in each table) (light all blue).fall into Theone of pathways these signaling from casc Tablesades under2–4 thethat overlap with the APRcontrol (designated of one of the four AP primary in each regulators table) all(Table fall on into the right). one The of theseproducts signaling following the cascades APR are under the highlighted in light orange and each helps induce an inflammatory and/or immune response in the control of oneorganism. of the Pathways four primary in the table regulators on the right (Table are in onred thefont right).if they are The up- productsmodulated and following green font the APR are highlightedif inthey light are down orange-modulated. and each Pathways helps labeled induce by an * inflammatoryare in the mouse liver and as/or well immune as other organs response in the organism. Pathwaystested. The color in the indicated table for on these the pathways right are follows in red the font primary if they response are up-modulatedand is; therefore, op andposite green font if in the mouse liver. they are down-modulated. Pathways labeled by an * are in the mouse liver as well as other organs tested. TheOf color the indicated 24 significantly for these modulated pathways pathways follows identified the primary in medaka response skin and after is; therefore, FL, 18 we oppositere in the mouseassociated liver. with inflammation, immune, or the APR (Table 2, middle). Seven of the FL significantly

Genes 2019, 10, 271 9 of 25

Genes 2019, 10, 271 9 of 25 Of the 24 significantly modulated pathways identified in medaka skin after FL, 18 were associated modulatedwith inflammation, canonical immune, pathways or in the medaka APR (Table skin 2(i.e.,, middle). PPARα/RXRα Seven of activation, the FL significantly B cell receptor modulated signaling,canonical TREM1 pathways signaling, in medaka ILK signaling, skin (i.e., IL PPAR-6 signaling,α/RXRα Gα12/13activation, signaling B cell, receptor and the signaling,APR) were TREM1 sharedsignaling, with ILKzebrafish signaling, skin and IL-6 we signaling,re modulated Gα12 in/13 the signaling, same direction, and the with APR) the exception were shared of Gα12/13 with zebrafish signaling.skin and were modulated in the same direction, with the exception of Gα12/13 signaling. ForFor the the hairless hairless mouse mouse FL induced FL induced DEGs, DEGs, 11 of the 11 12 of pathways the 12 pathways modulated modulated in mouse skin in mousewere skin alsowere observed also observed to be involved to be involved in inflammation, in inflammation, immune immune,, or the APR or the responses APR responses (Table 2, (Table bottom).2, bottom). PathwaysPathways shared shared with with the zebrafish zebrafish skin skin FL FL response response included include APR,d APR, PPAR PPARα/RXRαα/RXRα activation, activation, leukocyte leukocyteextravasation extravasation signaling, signaling, and TREM1. and TREM1. ZebrafishZebrafish medaka medaka and and mouse mouse skin skin exposed exposed to FL to share FL sharedd three threecanonical canonical pathways pathways (TREM1 (TREM1 signaling,signaling, APR APR signaling signaling,, and PPARa PPARa/RXRa/RXRa activation). activation). Of Of these, these, the the most most significantly significantly up-modulated up- modulapathwaytedfollowing pathway following FL exposure FL exposure in mouse in skin mouse was skin the TREM1was the signalingTREM1 signaling pathway, pathway, a sub-pathway a sub- of the pathwayAPR. For of thethe TREM1APR. For pathway, the TREM1 both pathway, zebrafish both and zebrafish mouse and showed mouse 12 show genesed modulated 12 genes modulated (41% coverage), (41% coverage), with z-scores of 3.0 and 4.2, respectively, while medaka modulated 13 genes with a with z-scores of 3.0 and 4.2, respectively, while medaka modulated 13 genes with a z-score of 2.4 z-score of 2.4 (Table 2, Figure 3). As an example of the conserved FL response in skin, the TREM1 (Table2, Figure3). As an example of the conserved FL response in skin, the TREM1 pathway for pathway for the two fish, and the mouse, is compared in Figure 3. Here we observed many of the the two fish, and the mouse, is compared in Figure3. Here we observed many of the same gene same gene targets modulated similarly in all three organisms after FL exposure. The TREM1 pathway,targets modulated part of the similarlyAPR (Figure in all 2), three lead organismsto the initiation after FLof the exposure. JAK/STAT The pathway, TREM1 pathway, ERK/MAPK part of the signaling,APR (Figure as well2), lead as tothe the production initiation of the many JAK of/ STAT the cytokines pathway, that ERK enhance/MAPK thesignaling, immune as andwell as the inflammationproduction of response many ofs ( theIL-8 cytokines, TNF, IL1B that, IL- enhance6). the immune and inflammation responses (IL-8, TNF, IL1B, IL-6).

Figure 3. TREM1 modulation in medaka (top), zebrafish (middle), and mouse (bottom) show Figureconsiderable 3. TREM1 overlap modulation following in medaka FL exposure. (top), zebrafish All three (middle) vertebrates, and up-modulatemouse (bottom) key show regulators, considerableincluding TNF overlapand IL1B following. Red genes FL exposure. are up-modulated All three and vertebrates green genes up-modulate are down-modulated. key regulators Predicted, includingdown-stream TNF and functional IL1B. Redeffects genes that are expected up-modulated to increase and green are in genes orange are and down to decrease-modulated. are in blue. Predicted down-stream functional effects that are expected to increase are in orange and to decrease areThe in blue. skin of zebrafish, medaka, and mice exhibited high similarity of DEG functional responses after FL exposure, which clustered into inflammation, immune, and acute phase responses. These responses

GenesGenes2019 2019, 10, 27110, 271 10 of10 25 of 25

The skin of zebrafish, medaka, and mice exhibited high similarity of DEG functional responses areafter known FL toexposure be regulated, which by cluster principleed into up-stream inflammation, transcription immune factors,, and acute such asphaseIL1B responses., with downstream These supportresponses through are known middle to regulators,be regulatedIL1A, by principle TNF, IFN upγ-stream, and othertranscription factors factors, such as suchNFk asβ, IL1B STAT3,, with JUN , anddownstreamRELA. Within support the skinthrough dataset, middle subsets regulators, of these IL1A, transcriptional TNF, IFN, and regulators other factors were such observed as NFkβ, to be up-modulatedSTAT3, JUN, amongand RELA all three. Within organisms the skin tested. dataset, Forsubsets example, of these FL-modulated transcriptional skin regulators pathways we inre all observed to be up-modulated among all three organisms tested. For example, FL-modulated skin three organisms contributed to an overall increase in the immune and inflammation responses, likely pathways in all three organisms contributed to an overall increase in the immune and inflammation controlled by the IL1B upstream regulator that is differentially regulated in mouse (up-modulated responses, likely controlled by the IL1B upstream regulator that is differentially regulated in mouse 4.1-fold, p-value, 2.36E-05), medaka (up-modulated 6.7-fold, p-value 2.07E-67), and zebrafish (up-modulated 4.1-fold, p-value, 2.36E-05), medaka (up-modulated 6.7-fold, p-value 2.07E-67), and (up-modulatedzebrafish (up- 2.7-fold,modulatedp-value 2.7-fold, 1.73E-09) p-value (Tables 1.73E-09) S3a–c). (Tables The S3a,b,c). numbers The numbers of skin DEGs of skin identified DEGs afteridentified FL exposure after wasFL exposure robust enough was robust to allow enough analyses to allow of predictedanalyses of upstream predicted regulators upstream ofregulators the genetic response,of the genetic as shown response, in Figure as 4shown. Here in we Figure see remarkable 4. Here we similaritysee remarkable in regulators similarity of in the regulators skin FL response of the amongskin allFL threeresponse animals. among In all medaka three animals. and zebrafish In medaka skin, and the zebrafish upstream skin, regulators, the upstream such regulators as IL1B and, TNFsuch, and as middle IL1B and regulators TNF, and ( SMAD3,middle regulators RELA, STAT3, (SMAD3, NFk RELA,β, EGR1, STAT3, JUN) NFkβ, were EGR1, part of JUN) the DEGwere dataset,part whereasof the inDEG the dataset, mouse, whereas the role ofin thesethe mouse, regulators the role was of predictedthese regulators by IPA wa baseds predicted on the direction,by IPA based levels, andon numbers the direction, of DEGs levels modulated, and numbers in the of datasetDEGs modulated (Figure4). in the dataset (Figure 4).

IL1B

IL1A TNF IFNG

RELA NFKB1 STAT3 NFkB NFkBIA JUN (complex) TP63 NOS2 SELP CCR1 CTSL CYBA NOS2 EIF4A1 MMP12 TNFAIP3 FFAR2 THBS1 GRN PLAU SELP IL1B B3GNT5 TNF STAT1 CD274 WT1 HAMP VCAN IGFBP6 ISG15 WT1 TNF EFNA1 STAT5A MMP13 NCF2 MMP13 HAVCR1 MMP13 TNFAIP2 CCND1 CYBB STAT5A ODC1 FN1 PLAU CLU PLAU IL4R PLAU CLU TIMP2 SAA1 FN1 SLC2A6 NOS2 NOS2 HMOX1 STAT1 LEP MMP12 ACOD1 USP10 PRDM1 ISG15 STAT4 IL1B IL1B SF3B3 CCND1 SAA1 CMPK2 CH25H KRT18 TNFAIP2 CCND1 TNFRSF9 C3 TNF CTSL NOS2 STAT3 PRF1 TNFAIP3 MMP13 TNFAIP3 C3 HAMP CCND1 FN1 THBS1 GBP1 TFDP1 RSAD2 CCR1 C3 HMOX1 GBP1 CD274 NCF2 CCND1 SAA1 CYBB FN1 STAT2 TFDP1 IL1B PLAU SOCS1 RRAS ANXA1 MYLPF IL1B PRDM1 STAT1 FN1 TNF RSAD2 CLU ASNS SVIL HMOX1 CD274 NOS2 GBP2 PARK7 GBP2 TIMP2 TXN ISG15 GBP2 IL1B HMOX1 CRYAB CYBB ISG15 TNF IFI35 ISG15 SAA1 FST SOCS1 TNF TNFAIP2 HMOX1 CCND1 ADIPOQ TNFAIP3 BMI1 HMOX1 FST PLAU PDIA4

Retnia SLC2A4 TDO2 CCL2 HK2 TIMP1 PPARGC1A SLC2A4 CCL2 HK2 ACOD1 CXCL6 TNFRSF11 CXCL3 SOX6 CCL2 CXCL3 CLEC4E CCL7 CXCL6 UCP1 TIMP1 CXCL3 ACOD1 CXCL6 HK2 CXCL3 IL1B KRT6B TNC S100A9 CA3 MMP13 IL1B CCL7 S100A9 S100A9 CXCL6 TNFRSF11 CCL7 TNFRSF11 MMP13 CCL2 CXCL3 TREM1 TNC MMP13 MMP12 TIMP1 SLPI TIMP1 SAA3 IL1B KRT16 IL1B SAA3 SAA3 IL1B MMP13 LILRB4 SAA3 MMP13 KCNN4 CCL2 MMP10 SPP1 TNFRSF11 KCNN4 CCL2 IL1B REG1A SPP1 CXCL6 TIMP1 CXCL3 TNC MMP12 PPARG RELA NFKB1 STAT3 NFkB NFkBIA JUN EGR1 SMAD3 (complex) RETN CCL2 TIMP1 TNFRSF11B CXCL3 CCL2 TNC GPD1 CXCL3 CXCL6 Ccl7 Saa3 MMP10 Ccl7 MMP13 UCP3 CXCL6 CCL2 CLEC4E Ccl7 KRT16 CXCL3 TIMP1 SPP1 IL1B LILRB4 KRT6B ACOD1 TIMP1 CXCL6 CCL2 AQP3 Saa3 S100A9 MMP13 TNC MMP13 IL1B IL1B CCL2 TNFRSF11B REG1A TIMP1 S100A9 MMP12 TIMP1 CXCL3 SPP1 TREM1 MMP13 CCL2 CXCL6 SPP1 SPP1 SPP1 IL1B SULT1E1 CXCL3 CXCL3 S100A9 CA3 SLC7A11 MMP12 CXCL3 THRSP MMP12 CXCL6 IL1B SLPI MMP10 CXCL6 CYP2E1 IL1B IL1B KCNN4 TNC PPARGC1A UCP1 PPARGC1A PPARGC1A TNFRSF11B TIMP1 INMT ADRB3 SOX6 SOX6 SLPI CCL2 VGLL2 AQP7 HK2 HK2 Saa3 MMP13 SLC2A4 UCP1 SPP1 CXCL3 PDE3B ADRB3 Retnla HK2 Retnla SLC2A4 TDO2 PPARGC1A SLC2A4 HK2

30 28 43 28 29 24 15 11 71 52 66 93 59 56 40 46

PPARG RELA STAT3 NFkB NFkBIA JUN EGR1 SMAD3 (complex)

IL6 TNF IFNG ERK1/2

IL1B . Figure 4. IL1B controls expression of many shared mid-level regulators in the zebrafish (top), medaka Figure 4. IL1B controls expression of many shared mid-level regulators in the zebrafish (top), medaka (bottom),(bottom) and, and mouse mouse (middle) (middle) skin skindatasets, datasets including, including the key the immune key immune and inflammation and inflammation APR regulator APR TNFregulator. Genes inTNF. red andGenes green in red are and modulated green are di ffmodulatederentially expresseddifferentially genes expressed (DEGs) genes and genes (DEGs in) orangeand andgenes blue in are orange predicted and byblue IPA are to predicted be up and by down,IPA to respectively.be up and down Due, respectively. to the robust Due dataset to the in robust medaka, thedataset number in of medaka genes up-, the and number down-modulated of genes up- areand listed down in-modulated red and green, are listed respectively. in red and green, respectively. 3.2. Genetic Response of Brain to Fluorescent Light 3.2.The Genetic brain Response FL-mediated of Brain to response Fluorescent of Light adult zebrafish was considerably lower than in skin (78 modulatedThe braingenes FL-mediated compared response to 364 of genes adult in zebrafish skin; Table was S4a), considerably and only lower 11% than (9 genes) in skin of (78 the DEGsmodulated were shared genes between compared brain to 364 and genes skin in (Figure skin; Table1, top). S4a) The, and FL only modulated 11% (9 genes) genes of in the zebrafish DEGs we brainre clusteredshared intobetween four brain significant and skin canonical (Figure 1, pathways top). The (TableFL modulated3, top), allgenes of whichin zebrafish were brain associated clustered with mounting inflammatory, immune and acute phase responses (Table3). Genes 2019, 10, 271 11 of 25

Table 3. Modulated canonical pathways were predicted using IPA for zebrafish (top), medaka (middle), and mouse (bottom) brain. All pathways with a z-score |2|, p-value < 0.05, and five or more genes ≥ were included in the analysis. The column on the far right indicates if the pathway is involved an immune (Im), inflammatory (If), acute phase (AP), cellular proliferation (CP), or cellular signaling (CS) response.

Zebrafish Brain Ingenuity Canonical Pathways z-score p-value Gene Category Synaptic Long-Term Potentiation 2.60 8.71E-03 13 Im, If, AP, CP Intrinsic Prothrombin Activation Pathway 2.20 1.17E-08 6 If Coagulation System 2.10 1.66E-08 6 If Acute Phase Response 2.00 7.24E-04 17 Im, If, AP, CP Medaka Brain Acute Phase Response Signaling 3.67 3.98E-08 20 Im, If, AP, CP Endothelin-1 Signaling 3.36 2.00E-04 15 AP, CP, CM B Cell Receptor Signaling 2.71 1.29E-02 11 Im, AP Prolactin Signaling 2.65 1.91E-03 8 Im, AP, CP ERK/MAPK Signaling 2.50 1.17E-03 14 CP IL-6 Signaling 2.50 4.47E-05 13 Im, If, AP CREB Signaling in Neurons 2.45 3.02E-02 10 AP, CP, CS JAK/Stat Signaling 2.45 2.57E-02 6 Im, AP, CP Thrombopoietin Signaling 2.45 8.51E-03 6 If, AP, CP P2Y Purigenic Receptor Signaling Pathway 2.33 1.02E-02 9 If, AP, CP GNRH Signaling 2.33 8.91E-03 9 If, AP, CP, CM Melanocyte Development and Pigmentation Signaling 2.24 4.57E-02 6 AP, CP, CS ERK5 Signaling 2.24 2.88E-02 5 CP Hypoxia Signaling in the Cardiovascular System 2.24 1.91E-03 7 If, CP NFAT in Regulation of the Immune Response 2.12 1.29E-02 11 Im, AP PKCθ Signaling in T Lymphocytes 2.12 2.82E-02 8 Im, AP Signaling 2.12 3.02E-03 12 CP, CM, CS Chemokine Signaling 2.12 6.92E-04 8 If, AP, CP, CM cAMP-mediated signaling 2.00 3.98E-04 16 If, AP, CS IL-2 Signaling 2.00 3.02E-02 5 Im, AP, CP, CS PPAR Signaling 2.83 3.89E-03 8 Im, AP − Synaptic Long-Term Potentiation 3.23 3.98E-04 34 Im, If, AP, CP − Mouse Brain Actin Cytoskeleton Signaling 3.50 1.66E-03 22 CP, CM Gαs Signaling 3.40 2.57E-03 11 If, AP, CS RhoA Signaling 3.10 1.82E-02 12 If, CS VEGF Signaling 2.60 4.68E-02 10 CP, CM Nitric Oxide Signaling 2.50 1.35E-03 11 If, CS Cyclins and Cell Cycle Regulation 2.40 4.37E-02 8 CP Gα12/13 Signaling 2.30 2.24E-02 13 If, AP, CS Cholecystokinin/Gastrin-mediated Signaling 2.10 3.89E-03 10 AP, CP, CM CXCR4 Signaling 2.10 2.09E-02 16 Im, AP, CP, CM B Cell Receptor Signaling 2.10 3.16E-02 18 If, Im, AP, CM Acute Phase Response Signaling 2.10 3.80E-02 19 If, Im, AP, CP Gαq Signaling 2.10 1.45E-03 15 If, AP, CM, CS IL-6 Signaling 2.00 4.79E-02 21 If, Im, AP Synaptic Long-Term Potentiation 2.00 1.95E-03 12 If, AP, CS ILK Signaling 2.00 8.32E-04 19 If, Im, AP, CP, CM Tec Kinase Signaling 2.00 4.79E-02 10 AP, CP, CM Calcium-induced T Lymphocyte Apoptosis 2.00 1.70E-02 6 Im − Glutamate Receptor Signaling 2.00 4.37E-06 11 If, CS − FLT3 Signaling in Hematopoietic Progenitor Cells 2.00 4.90E-02 9 Im, AP, CP − BMP signaling pathway 2.10 2.09E-03 8 AP, CP − Abbreviations: (JAK), Signal transducer and activator of transcription (STAT), Purinergic -coupled receptors (P2Y), Gonadotropin-releasing hormone (GNRH), C theta (PKCθ), Cyclic adenosine monophosphate (cAMP), Interleukin 2 (IL-2), G protein alpha subunit (Gαs), Ras homolog gene family member A (RhoA), Guanine nucleotide-binding protein alpha 12 and 13 subunit (Gα12/13), G protein subunit q (Gαq), FMS-like 3 (FLT3), Bone morphogenetic protein (BMP). Genes 2019, 10, 271 12 of 25

Genes 2019, 10, 271 12 of 25 2), G protein alpha subunit (Gαs), Ras homolog gene family member A (RhoA), Guanine nucleotide-binding protein alpha 12 and 13

subunit (Gα12/13), G protein subunit q (Gαq), FMS-like tyrosine kinase 3 (FLT3), Bone morphogenetic protein (BMP) The top regulator of the zebrafish brain FL responsive DEG dataset was OSM (z-score 2.77, p-value 4.97E-03),The known top regulator to be controlledof the zebrafish by TNF brain(2.38, FL responsive 9.85E-03). DEG Together dataset this was indicates OSM (z-score an FL-induced 2.77, p- up-modulationvalue 4.97E-03), in the known immune to be and controlled inflammation by TNF responses,(2.38, 9.85E as-03). observed Together in this the skin, indicates but via an FL use- of uniqueinduced genes up- andmodulation pathways. in the immune and inflammation responses, as observed in the skin, but via useIn of medaka unique genes brain, and 587 pathways. genes became differentially expressed after FL exposure and these genes were associatedIn medaka with brain, 22 587 canonical genes became pathways differentially (20 up-modulated expressed andafter twoFL exposure down-modulated, and these genes Table 3, middle).were associated Sixteen of with the 22 canonical pathways pathways (Table3) (20 were up directly-modulated associated and two with down inflammation,-modulated, Table immune, 3, middle). Sixteen of the 22 pathways (Table 3) were directly associated with inflammation, immune, and acute phase responses, with APR the top up-modulated pathway. and acute phase responses, with APR the top up-modulated pathway. FL exposure modulated substantially more genes (1172 DEGs) in mouse brain than in any other FL exposure modulated substantially more genes (1172 DEGs) in mouse brain than in any other organ in mouse or zebrafish. Like zebrafish, the mouse brain showed an organ specific gene set organ in mouse or zebrafish. Like zebrafish, the mouse brain showed an organ specific gene set expressedexpressed after after FL, FL, with with only only 9% 9% (nine (nine genes)genes) of the DEGs DEGs in in mouse mouse brain brain shared shared with with induced induced DEGs DEGs in mousein mouse skin skin (Figure (Figure1 1,, middle). While While many many more more genes genes were weredifferentia di ffllyerentially modulated modulated in the mouse in the mousebrain, brain, compared compared to mouse to mouse skin, or skin, zebrafish or zebrafish and medaka and medaka brain, the brain, same the pathway same pathway responses responses were wereobserved observed (Table (Table 3 and3 and Figure Figure 5). Of5). the Of modulated the modulated pathways, pathways, APR and APR long and-term long-term potentiation potentiation were wereshared shared between between mouse, mouse, zebrafish zebrafish,, and me anddaka medaka brain samples brain samples after FL. after The FL. DEGs The predict DEGsed predicted the same the sameupstream upstream regulators, regulators, and and direction direction of regulation,of regulation, between between zebrafish, zebrafish, medaka medaka,, and and mouse mouse brain brain followingfollowing FL FL exposure exposure (Figure (Figure5). 5).

IL1B

IL6 TNF

FOS NFkB JUN (complex) CNNM4 RPS5 ATR MST1R CRYZ MGLL FDPS HPRT1 SCG5 ABCC4 CELF1 DNAJB11 ANXA11 ABCC4 CNNM4 MGLL FLNA GAD1 RBM22 MGLL CSRP1 GAD1 ARHGAP35 CAMK1G PTPRN DNAJB11 BCAS3 B2M CDC27 B2M FDPS FLNA MIER1 MST1R MMP13 CYGB GATA2 DPM3 GTF2F1 HTATIP MST1R LPP HPRT1 MIER1 GTF2F1 SKIL NCAM1 DPM3 MATN4 FDPS ABCC4 PDXK HPRT1 CYGB NCAM1 QKI

NPTX2 AKR1C3 SDC1 WNT10B JUNB PKIB FAM19A1 WNT7B HOMER1 MET SPP1 BCL11A JUNB TRPC3 EOMES FRZB PLAU Prl2c2 COL24A1 NOV CDC20 CX3CL1 PLAU INSRR B3GNT5 VEGFD CRABP1 TH CX3CL1 KRT15 DUSP5 NGF EGR1 CALB1 MSX2 RGS4 OCM LTBP2 NGF Crip2 LGR5 CFTR TRPC6 ITPR1 CYBB MYO5B EGR1 IGFBP1 OXTR CYP11A1SDC1 IL12A CCK SLC2A4 A2M PTGS2 WNT4 A2M IGFBP4 JUNB PKIB CCND2 HES5 Tnfsf9 COL18A1 MET NPY COL18A1 CCND2 PLAU BTG1 PKP1 ALDH1B1 NFKBIE SPP1 KRT15 HMMR SPP1 Hrk WNT4 EVX1 WNT10A MYLK3 FCER2 DUSP5 LPL NGF FRZB IGFBP1 Prl2c2 PTGS2 FST TRAF1 ST18 EGR2 OXTR RUNX2 CD247 TH IDO1 PLK2 MTSS1 HLA-A WNT1 FOSB WNT9A NROB2 CD7 IL22 PAX7 COL2A1 3-OCT HRK PTGS2 A2M RELA SP1 FOS CEBPB NFkB NFkBIA JUN STAT3 (complex) FSCN1 PAX2 CYP11A1 PHOX2B CYP11A1 MUP1 FSCN1 CYBB CRIP2 EVX1 MAFB IL22 RORC E2F1 Akr1b7 E2F1 A2M CYP11A1 Prl2c2 FSCN1 OMP DGAT2 LHX5 JUNB PAX7 JUNB CYBB PLAU SLC6A2 PLAU let-7 JUNB TH JUNB EOMES E2F1 TFAP2A PLAU EOMES PLAU A2M TNNC1 SCT EGR1 COL2A1 PLAU NR0B2 PLAU PAX3 JUNB CYP2A6 EGR1 B3GNT5 EGR1 LGR5 EGR1 SLC5A1 HES5 CD59 WNT4 IGFBP1 NR4A2 IGFBP1 NR4A2 PAX2 TRPC6 MSX2 OPRD1 COL2A1 OPRD1 IGFBP1 SYTL1 ITPR1 HOMER1 CD247 EGR1 SLC9A3 PLXND1 ZIC2 CCK CYBB TRPC6 CD59 ADRA1D ATP2A3 CX3CL1 TRAF1 COL24A1 A2M HES5 LTF KLF5 LTF HES5 A2M SMOC2 SLC2A4 MPL SLC9A3 CFTR CX3CL1 SPP1 HTR2A RORC KLK8 MSX2 ALDH1B1 COL18A1 SYTL1 NFKBIE CHRNA5 SPP1 FANCD2 NGF CDC20 NPY A2M OXTR A2M MYLK3 SPP1 CX3CL1 TRAF1 IL12A CDC42BPG IL12A OXTR DUSP5 FST RET IGFBP4 SPP1 FST DUSP5 CFTR IGFBP4 COL2A1 CCND2 IGFBP4 LGR5 CX3CL1 PAX6 CCND2 GLI1 PLK2 ST18 IL12A CCND2 ANK1 KCNT2 CCND2 SDC1 SYNPO HLA-A HGF BTG1 BCL11A HLA-A ALOX5AP HGF SLC22A4 PTGS2 Hrk PKIB CCND2 ROR1 ALOX5AP PCSK6 CX3CL1 COL2A1 KCNT2 RUNX2 CD59 WT1 FRZB BTG1 HGF ITGB6 RUNX2 SLC2A4 NGF SLC22A4 PTGS2 WNT9A ALMS1 RUNX2 RPE65 ALDH1A1 PTGS2 TIMP4 CFTR TRPC3 KRT15 PTGS2 CHRNB4 WNT9A PCSK9 HK2 LPL IL12A INSRR WT1 MET NPR1 PTGS2 VIP Tnfsf9 EGR2 CCND2 CALB1 LPL HK2 MET PTGS2 TRPM3 RIN1 PKP1 ITPR1 STX1A TIMP4 NOV EGR2 CD7 WNT10A MTSS1 DRD1 KRT15 EGR3 AKR1C3 PTGS2 SLC2A4 PNMT NKX2-1 IDO1 Tnfsf9 CD7 NFKBIE IL22 FCER2 TRAF1 10 19 15 24 7 14 18 17 21 20 21 14 20 21

RELA SP1 CEBPB NFkB NFkBIA JUN STAT3 (complex)

IL6 TNF IFNG EGF

IL1B

FigureFigure 5. 5. IL1BIL1B controlscontrols expression expression of many of many shared shared mid-level mid-level regulators regulators in the zebrafish in the (top), zebrafish medaka (top), medaka(bottom) (bottom),, and mouse and mouse (middle) (middle) brain datasets brain datasets,, including including the key the immune key immune and inflammation and inflammation APR APRregulator regulator TNF.TNF Genes. Genes in redin red and and green green are are modulated modulated DEGs DEGs,, and and genes genes in orange in orange and and blue blue are are predictedpredicted by by IPA IPA to beto be up up and and down, down respectively., respectively. Due Due to to the the robust robust datasetdataset inin medaka,medaka, the number number of genesof genes up- and up- down-modulatedand down-modulated are are listed listed in redin red and and green, green respectively., respectively.

3.3.3.3. Genetic Genetic Response Response of of Liver Liver to to Fluorescent Fluorescent LightLight Following FL exposure, the zebrafish liver showed only 64 DEGs (Table S4a), sharing only 9% (six genes) of the response with skin and 3% (two genes) with brain (Figure1, left). There were five significant canonical pathways up-modulated in liver and four were associated with inflammation, immune, and acute phase responses (Table4). Genes 2019, 10, 271 13 of 25

Table 4. Modulated canonical pathways were predicted using IPA for zebrafish (top), medaka (middle), and mouse (bottom) liver. All pathways with a z-score |2|, p-value < 0.05, and five or more genes were ≥ included in the analysis. The column on the far right indicates if the pathway is involved an immune (Im), inflammatory (If), acute phase (AP), cellular proliferation (CP), or cellular signaling (CS) response.

Zebrafish Liver Ingenuity Canonical Pathways z-score p-value Gene Category Acute Phase Response 2.5 3.89E-02 18 Im, If, AP, CP Complement Signaling 2.3 5.62E-03 9 Im, If, AP IL-1 Mediated Inhibition of RXR 2.0 1.35E-02 22 Im, If, AP ILK Signaling 2.2 4.79E-02 19 If, Im, AP, CP, CM Calcium Signaling 2.0 2.24E-02 17 CP, CS Medaka Liver z-score p-value Gene # Category Acute Phase Response Signaling 3.2 9.77E-07 53 Im, If, AP, CP Tec Kinase Signaling 2.9 1.02E-02 39 AP, CP, CM mTOR Signaling 2.8 1.32E-02 44 CP, CS PEDF Signaling 2.7 2.40E-06 31 AP, CP, CM Insulin Receptor Signaling 2.6 1.70E-06 45 If, AP, CP IL-1 Signaling 2.5 1.82E-06 33 If, AP, CS Renal Cell Carcinoma Signaling 2.4 6.76E-03 22 Im, AP, CP, CM HMGB1 Signaling 2.4 7.08E-04 36 If, Im, AP NANOG in Mammal Embry Stem Cell Pluripotency 2.2 4.57E-03 31 AP, CP Type I Diabetes Mellitus Signaling 2.2 3.80E-02 25 If, Im, AP Toll-like Receptor Signaling 2.2 2.04E-03 22 If, Im, AP UVB-Induced MAPK Signaling 2.2 2.69E-02 17 If, AP, CS CNTF Signaling 2.2 1.74E-02 17 Im, AP, CP, CS Pancreatic Adenocarcinoma Signaling 2.2 5.25E-05 36 If, AP, CP IL-1 Mediated Inhibition of RXR 2.1 3.31E-03 51 Im, If, AP PI3K/AKT Signaling 2.1 4.68E-06 40 If, Im, AP, CP IL-8 Signaling 2.1 5.25E-07 59 If, Im, AP Macropinocytosis Signaling 2.1 2.69E-02 20 Im CD28 Signaling in T Helper Cells 2.1 2.14E-03 34 Im, AP ERK5 Signaling 2.0 1.74E-04 22 CP G-βγ Signaling 2.0 2.14E-03 25 If, AP, CP MIF Regulation of Innate Immunity 2.0 4.07E-02 11 Im, AP Chemokine Signaling 2.0 2.75E-03 21 If, AP, CP, CM − Ephrin B Signaling 2.1 2.57E-04 24 AP, CP, CM − Phospholipase C Signaling 2.1 2.04E-02 50 AP, CP, CM − LXR/RXR Activation 2.2 2.40E-06 40 Im, If − Signaling 2.2 7.59E-03 28 CP − CREB Signaling in Neurons 2.3 2.69E-04 48 AP, CP, CS − Wnt/Ca+ pathway 2.3 3.02E-05 22 CP − Signaling 2.4 1.45E-02 10 CP − Dopamine-DARPP32 Feedback in cAMP Signaling 2.7 8.71E-05 45 CS − Basal Cell Carcinoma Signaling 2.9 7.41E-03 20 CP, CM, CS − Calcium Signaling 3.2 8.32E-04 45 CP, CS − Mouse Liver z-score p-value Gene # Category IL-1 Mediated Inhibition of RXR 2.0 6.92E-04 23 Im, If, AP − LXR/RXR Activation 2.0 8.51E-03 13 Im, If − VDR/RXR Activation 2.0 1.48E-02 8 Im, CP − Granulocyte Adhesion and Diapedesis 2.1 1.15E-03 17 If − Coagulation System 2.1 1.58E-03 5 If − STAT3 Pathway 2.3 1.26E-02 7 If, Im, AP − p38 MAPK Signaling 3.0 7.59E-03 12 Im, If − Acute Phase Response Signaling 4.0 2.57E-02 19 − I Im, If, AP, CP m, If, AP IL-6 Signaling 4.0 1.55E-03 26 − Abbreviations: Pigment epithelium-delivered factor (PEDF), High-mobility group box 1 protein (HMGB1), Ciliary neurotrophic factor (CNTF), Phosphoinositide 3-kinase (PI3K), Protein kinase B (AKT), Interleukin 8 (IL-8), cAMP response element-binding protein (CREB), Vitamin D response (VDR). Genes 2019, 10, 271 14 of 25

GenesAbbreviations:2019, 10, 271 Pigment epithelium-delivered factor (PEDF), High-mobility group box 1 protein (HMGB1), Ciliary neurotrophic factor14 of 25 (CNTF), Phosphoinositide 3-kinase (PI3K), Protein kinase B (AKT), Interleukin 8 (IL-8), cAMP response element-binding protein (CREB),

Vitamin D response (VDR) Although liver was expected to be the most indirect FL light-receiving organ tested, the response in zebrafish liver very closely mimicked skin, the most direct light receiving organ. The most significantly Although liver was expected to be the most indirect FL light-receiving organ tested, the response up-modulated pathway was APR (Table4, 18 genes, z-score = 2.5), with the classical complement in zebrafish liver very closely mimicked skin, the most direct light receiving organ. The most systemsignificantly closely followingup-modulated (9 genes, pathway z-score was 2.3). APR Like (Table skin, 4, all18 modulatedgenes, z-score pathways = 2.5), with may the be classical controlled by TNFcomplement(z-score system 2.89, p-value closely 7.85E-03) following to (9 up-regulate genes, z-score transcription 2.3). Like skin, of genes all modulated involved withpathways the immune may andbe inflammation controlled by responsesTNF (z-score (Table 2.89,4). p-value 7.85E-03) to up-regulate transcription of genes involved withIn medaka,the immune analysis and inflammation showed a robust responses set (Table of 3464 4). DEGs in liver after FL exposure. This large DEG setIn corresponded medaka, analysis to show predicteded a robust modulation set of 3464 of DEGs 33 canonical in liver after pathways FL exposure. (22 up-modulated This large DEG and 11 down-modulated,set corresponded toTable predicted4, middle). modulation of 33 canonical pathways (22 up-modulated and 11 down- modulatedIn medaka, Table liver, 4, as middle). with skin and brain, the top modulated pathway was APR. Unlike zebrafish, each organIn medaka in medaka liver, as shared with skin approximately and brain, the 40% topDEG modulate gened identitypathway withwas APR. the other Unlike organs zebrafish, tested (Figureeach1 organ, right; in 44%medaka between shared skin approximately and liver, 40% 40% between DEG gene skin identity and brainwith the and other 47% organs between tested brain and(Figure liver). 1, right; 44% between skin and liver, 40% between skin and brain and 47% between brain and liver).Following FL exposure, mouse liver differentially modulated 166 genes ( 37% more genes than ≈ zebrafishFollowing liver), which FL exposure, are expected mouse toliver down differentially regulate ninemodulated canonical 166 genes pathways (≈37% (Table more genes S4c, Table than 4). zebrafish liver), which are expected to down regulate nine canonical pathways (Table S4c, Table 4). The most differentially expressed and suppressed pathways in the mouse liver dataset was the IL-6 The most differentially expressed and suppressed pathways in the mouse liver dataset was the IL-6 signaling pathway ( 4.0 z-score, 26 genes) and the APR (z-score 4.0, 19 genes). These canonical signaling pathway− (−4.0 z-score, 26 genes) and the APR (z-score −−4.0, 19 genes). These canonical pathways were oppositely modulated in direction between mouse liver and mouse skin or brain, pathways were oppositely modulated in direction between mouse liver and mouse skin or brain, as as well as opposite compared to all organs in zebrafish and medaka. well as opposite compared to all organs in zebrafish and medaka. TheThe mouse mouse liver liver exhibited exhibited modulation modulation ofof thethe same pathways pathways and and u upstreampstream regulators regulators observed observed uponupon FL FL exposure exposure in in skin skin and and brain brain (Figure (Figure6 ).6). In In addition, addition, mouse, medaka medaka,, and and zebrafish zebrafish liver liver appearedappeared all all tightly tightly regulated regulated by by the the same same corecore set of upstream upstream regulators regulators,, including including TNFTNF, IL1B,, IL1B, IFN IFNγ, γ, IL-6,IL NFk-6, NFkβ,β, and andSTAT3 STAT3; however,; however all, of all these of these genes genes were we modulatedre modulated in the in opposite the opposite direction direction in mouse in livermouse compared liver compared to the two to fishes the two (Figure fishes6 ().Figure 6).

IL1B

IL6 TNF IFNG

STAT3 NFkB NFkBIA (complex) HMOX1 UGP2 MYLK3 APOA4 PTK6 MAT2A MYLK3 CDX2 PPARGC1A CPA4 TG CDX2 GFPT2 IGFBP1 FASN PTPRB GFPT2 CD36 TNNC1 GNAI2B CD36 DBT TNNI1 SLC16A9 DBT MAT2A TSPAN13 HMOX1 DBP APOE DDX5 NR1D1 HMOX1 PPARGC1A MST1R DBP ZBTB16 NR1D1

LEPR BCL6 MMP7 GBP3 SLC39A10 MYC ADRA2A EREG ABCB1B SERPINE1 SERPINA3g IL27 LCN2 Mt2 GBP3 SERPINA3 PLSCR1 A2M SERPINE1 Mt1 MYC TIMP1 MAFB EPCAM HSPB1 IGFBP6 KRT8 CCNB1 IL1R1 SLPI CCNB1 A2M KRT18 A2M MT1 TIMP1 SERPINA3 CXCL2 TIMP1 CXCL2 IGFBP1 CXCL2 CGREF1 LCN2 CCNB1 FOS RELA STAT3 NFkB NFkBIA JUN (complex) VEGFD IGFBP1 BACH2 A2M BCL6 LEPR GBP3 LCN2 MYC LOR MYC MMP7 MYC CXCL2 GBP3 MMP7 MYC LCN2 MYC KRT8 ABCB1B SERPINE1 IL27 CHGA Abcb1b CD14 MYC SLC39A10 IL27 SERPINE1 ABCB1B KRT18 A2M CD14 A2M KRT8 IL27 KCNT2 A2M MT1 A2M HSPB1 A2M Mt2 SPRR1A IGFBP1 TIMP1 HSD3B2 SERPINA3g TIMP1 CCNB1 TIMP1 CCNB1 TIMP1 Mt1 TIMP1 MMP7 EREG SEMA3E CXCL2 MAFB CXCL2 SERPINA3 CXCL2 CCNB1 EREG CHGA MMP8 MT2 SERPINE1 IL1R1 ADRA2A CGREF1 EREG FHL2 MMP8 FABP4 SERPINE1 FABP4 IGFBP1 SERPINA3g EPCAM SLPI IGFBP6 CD14 SLPI PLSCR1 SERPINA3 51 38 42 54 44 45 77 45 65 85 64 58

FOS RELA STAT3 NFkB NFkBIA JUN (complex)

IL6 TNF IFNG EGF

IL1B

FigureFigure 6. IL1B6. IL1Bcontrols controls expression expression of of many many sharedshared mid mid-level-level regulators regulators in in the the zebrafish zebrafish (top), (top), medaka medaka (bottom),(bottom) and, and mouse mouse (middle) (middle) liver liver datasets, datasets, includingincluding the the key key immune immune and and inflammation inflammation APR APR regulator TNF. Genes in red and green are modulated DEGs, and genes in orange and blue are predicted by IPA to be up and down, respectively. Due to the robust dataset in medaka, the number of genes up and down-modulated are listed in red and green, respectively. Genes 2019, 10, 271 15 of 25

regulator TNF. Genes in red and green are modulated DEGs, and genes in orange and blue are Genes 2019, 10, 271 15 of 25 predicted by IPA to be up and down, respectively. Due to the robust dataset in medaka, the number of genes up and down-modulated are listed in red and green, respectively. Of the significantly modulated pathways, three were shared between mouse liver and skin Of the significantly modulated pathways, three were shared between mouse liver and skin (granulocyte adhesion and diapedesis, IL-1 mediated inhibition of RXR, and LXR/RXR activation), (granulocyte adhesion and diapedesis, IL-1 mediated inhibition of RXR, and LXR/RXR activation), one betweenone between liver and liver brain and (IL-6 brain signaling), (IL-6 signaling) and one, and between one between all three all organsthree organs (APR); (APR); however, however, all were all modulatedwere inmodulated the opposite in the directionopposite direction when compared when compared to mouse to mouse liver. liver. In addition, In addition, two two pathways pathways (APR and(APR IL-1 and mediated IL-1 mediated inhibition inhibition of RXR) of RXR) were we sharedre shared between between mouse mouse and and zebrafish zebrafish liver liver samples, samples, and threeand pathways three pathways (APR, LXR (APR,/RXR LXR/RXR activation, activation, and IL-1 and mediated IL-1 mediated inhibition inhibition of RXR), betweenof RXR), medaka between and mousemedaka liver, and though mouse all liver, pathways though were all pathwaysmodulated we inre the modulated opposite in direction the opposite in the direction mouse. in the mouse. 3.4. Conservation Among Fluorescent Light Responses 3.4. Conservation Among Fluorescent Light Responses When comparing the canonical pathways that were modulated in each organ for all three vertebrates,When the majority comparing of the the pathways canonical were pathways associated that with were induction modulated of cellularin each inflammation organ for all threeand immunevertebrates, responses. the For majority example, of inthe zebrafish pathways brain, were allassociated four canonical with induction pathways of lead cellular to an inflammation increase in the immuneand immune and inflammation responses. For response, example in, in mouse zebrafish brain brain, 70% (all all four but sixcanonical pathways) pathways were involved,lead to an and in medakaincrease in brain the 64%immune (14 pathways)and inflammation of the pathways response, aidedin mouse in the brain increased 70% (all modulation but six pathways) of immune were and inflammatoryinvolved, and responses. in medaka An brain overlap 64% (14 analysis pathways) of the of functional the pathways categories aided in modulated the increased in modulation each brain sampleof indicated immune thatand althoughinflammatory mice responses. modulated An over overlap 15 times analysis more of genesthe functional following categories FL exposure modulated than in each brain sample indicated that although mice modulated over 15 times more genes following FL zebrafish, and twice as many as medaka, the predicted functional effects were highly conserved among exposure than zebrafish, and twice as many as medaka, the predicted functional effects were highly all three vertebrates. conserved among all three vertebrates. Such transcriptional response similarities after exposure to FL, in three organs of three highly Such transcriptional response similarities after exposure to FL, in three organs of three highly divergeddiverged vertebrates, vertebrates, suggested suggest shareded shared regulatory regulatory circuitry. circuitry. Analysis Analysis of of expression expression for for upstream upstream regulatorsregulators derived derived from from the data the data itself, itself, or predicted or predicted by byIPA IPA based based upon upon the the DEG DEG sets, sets, showed showed remarkableremarkable similarity similarity for all for organs. all organs. Figure Figure7 shows 7 shows the expression the expression patterns patterns for for regulators regulators of of cell cell proliferation,proliferation, acute phase acute response, phase response, immune immune response, resp andonse, inflammatory and inflammatory response response for all three for animals all three and organs.animals It wasand organs. clear these It wa upstreams clear these regulators upstream all regulators tracked together all tracked after together FL exposure, after FL forexposure, both fish for speciesboth and fish the species mouse and in skin the mouse and brain in skin (Figure and 7brain, skin (Figure and brain). 7, skin Aand very brain). similar A very pattern similar of pattern e ffect withinof the eff sameect within functional the same classes functional of regulators classes was of observedregulators for wa liver,s observed with the for notable liver, with exception the notable that mouseexception liver upstream that mouse regulators liver upstream were modulated regulators inwe there modulated opposite directionin the opposite compared direction to the compared fishes (Figureto7, the liver). fishes (Figure 7, liver).

Figure 7. Zebrafish, medaka, and mouse shared up-stream regulators differentially modulated (z-score 2) in skin (left), brain (center) and liver (right) following FL exposure. Red represents regulators ± that are up-modulated in response to FL, and green represents down-modulation as predicted by IPA. Medaka and mouse have one regulator in liver, PRDX2, that is shared in the same direction. Genes 2019, 10, 271 16 of 25

Figure 7. Zebrafish, medaka, and mouse shared up-stream regulators differentially modulated (z-score ± 2) in skin (left), brain (center) and liver (right) following FL exposure. Red represents regulators that are up-modulated in response to FL, and green represents down-modulation as predicted by IPA. Genes 2019Medaka, 10, 271 and mouse have one regulator in liver, PRDX2, that is shared in the same direction. 16 of 25

The trends in zebrafish, medaka, and mouse brain observed following FL exposure could be tracedThe trendsfrom the in u zebrafish,pstream regulators medaka, to and the mouse functional brain effects observed (Figures following 4–6). A FLdirect exposure line could could be be traceddrawn from from the the upstream up-modulation regulators of TNF tothe (2.38 functional zebrafish, e 3.39ffects medaka (Figures, and4– 62.63). A mouse) direct through line could the be drawnuniquely from expressedthe up-modulation gene sets ofthatTNF were(2.38 regulated zebrafish, by 3.39 up-modulation medaka, and of TNF 2.63 mouse)(11 zebrafish, through 112 the uniquelymedaka expressed, and 129 gene mouse) sets, that leading were toregulated the up- bymodulation up-modulation of the of TH1TNF i(11mmune zebrafish, response 112 medaka, of T andlymphocytes 129 mouse), and leading cell proliferation, to the up-modulation as well as the of thesuppression TH1 immune of apoptosis response in zebrafish of T lymphocytes and mouse. and cellThis proliferation, highly shared as well response as the wa suppressions also observed of apoptosis in zebrafish in zebrafish, medaka and, and mouse. mouse This skin highly and liver, shared responsethough was in the also opposite observed direction in zebrafish, for the mouse medaka, liver. and In the mouse liver, skin33%, andor 93 liver, regulators, though of inthe the zebrafish opposite directionresponse for we there mouse shared liver. by identity In the liver,with medaka 33%, or and 93 regulators, mouse. Of ofthese the zebrafish93 shared responseregulators, were 62% shared (58 by identityregulators) with we medakare involved and with mouse. an immune Of these or 93 inflammation shared regulators, response 62% (Figure (58 regulators) 7, liver). The were immune involved and inflammation response in all organs from all three vertebrates could be traced through the top with an immune or inflammation response (Figure7, liver). The immune and inflammation response in modulated regulator, TNF, directly to IL1B (Figures 4–6). Many of the other regulators involved were all organs from all three vertebrates could be traced through the top modulated regulator, TNF, directly directly linked to the APR, which was the only shared canonical pathway modulated in all three to IL1B (Figures4–6). Many of the other regulators involved were directly linked to the APR, which organs and all three animals (Figure 2). was the only shared canonical pathway modulated in all three organs and all three animals (Figure2). 4. Discussion 4. Discussion Fluorescent light (FL) has only been commonly utilized as an inexpensive artificial light source forFluorescent homes, office light buildings (FL) has, and only research been commonly facilities since utilized the late as an 196 inexpensive0′s. Increasing artificial use of lightFL in sourceanimal for homes,and ohumanffice buildings, environments and research has led facilitiesto longer since daily the exposures late 1960 to0s. FL Increasing sources, yetuse careful of FL in studies animal of and humanpotential environments genetic consequences has led to longerthat may daily result exposures from increased to FL sources,FL exposure yet have careful not studies been performed. of potential geneticSince consequences the complex solar that mayspectrum result concurrently from increased provides FL exposure nearly all have visible not spectrum been performed. wavelengths Since in the complexsimilar solar intensities spectrum (Figure concurrently 8), organisms provides had the opportunity nearly all visible to conscript spectrum each wavelengthswavelength as incues similar to intensitiesregulate (Figure selected8), genetic organisms pathways had the, allowing opportunity interaction to conscript with their each environment. wavelength Thus, as cues significantly to regulate selectednarrowing genetic the pathways, complexity allowing of available interaction light wavelengths, with their environment. as occurs under Thus, FL significantly and other types narrowing of theartificial complexity light, of may available not incite light the wavelengths, total genetic asrespon occursse fine under-tuned FL over and otherevolutionary types of history artificial to the light, maysolar not spectrum. incite the total genetic response fine-tuned over evolutionary history to the solar spectrum.

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Herein, we extend previous observations of fish skin to investigation of FL-induced genetic effects within internal organs (i.e., brain and liver). We provide head-to-head comparisons of FL-induced modulation of gene expression among two fish species (i.e., zebrafish and medaka) with each other, and with the hairless mouse (Mus musculus). The fish species utilized (medaka and zebrafish) are diurnal vertebrates originally derived from Japan and India, respectively, with an estimated divergence of 115 My [31–33]. The mouse, a nocturnal rodent, has an estimated 450 My of divergence from ≈ ≈ Genes 2019, 10, 271 17 of 25 the common ancestor that led to the fishes [34,35]. All animals were similarly exposed to FL (4100 K), and the transcriptional response in skin, brain, and liver organs compared after processing of RNA-Seq data. Our findings suggest the primary response to FL is extraordinarily well-conserved among these three highly divergent species. This suggests the gene expression changes that occur after light exposure may be due to ancient genetic circuitry that has remained embedded within the vertebrate genome.

4.1. Alternate Pathway Analysis and Cross Validation As a secondary confirmation of the pathway and functional annotation categories identified by IPA, an independent enrichment analysis using Consensus PathDB was performed (http://consensuspathdb. org,[36,37]). Consensus PathDB uses a collection of published interaction databases including biochemical pathways, protein interactions, genetic interaction signaling, metabolism, gene regulation, and drug-target interactions, and an enrichment analysis to determine what biological functions are over-represented (enrichment p-value < 0.05) in a dataset. Consensus PathDB’s functional enrichment analyses showed genes related to innate immune response, immune system development, interferon production, cytokine production, and wound healing response, as well as oxidation-reduction process, response to oxygen containing compounds, and response to oxidative stress are over-represented (p < 0.05) among the genes modulated by FL exposure. This observation supports the results obtained from IPA in each of the zebrafish, medaka, and mouse organs (Figures S1–S3). Other functions that were enriched following Consensus PathDB analysis included cell–cell signaling, defense and stress response signaling, apoptosis, cell cycle regulation, lipid metabolism, cellular transport, and circadian rhythm regulation. As bioinformatics analyses showed that inflammation and immune related genes dominate the genetic response incited by FL, we hypothesized that gene expression pattern change after FL exposure should be similar to that after known treatments that lead to inflammation and infection. We compared the FL-incited DEGs to literature-reported organism and organ-matched transcriptomic data associated with an inflammatory condition, immune response, and acute phase response [38–40]. Mouse skin FL-incited DEGs were compared to DEGs modulated by Imiquimod (IMQ), a compound that is used to induce psoriasis-like skin inflammation [41,42]; mouse liver FL-incited DEGs were compared to DEGs modulated by Lipopolysaccharide (LPS) challenge; and zebrafish liver FL-incited DEGs were compared to DEGs modulated by Edwardsiella tarda vaccination. In mouse skin and zebrafish liver, the majority (100% in mouse skin and 63% in zebrafish liver) of DEGs exhibited consistent direction of modulation from FL exposure or from treatment performed by each response based on the published data. These observations match the functional analyses performed using the FL exposure dataset. In contrast, mouse liver showed opposite modulation in all but one gene between FL and LPS challenge, suggesting FL exposure led to an opposite genetic response compared to LPS induction. This result indicates the immune response is repressed by FL, while induced by LPS challenge administered over a six-day period (Figure S4).

4.2. Fluorescent Light Genetic Response Conservation in the Skin of Vertebrates We show that upon FL exposure of the intact animal, the skin of both fishes and mice exhibited APR pathway induction, as well as species-specific APR sub-pathways, as part of an overall inflammation and immune response. For example, in zebrafish skin, Oncostatin M is induced by FL (Table2, z-score 3.19, 10 genes). This APR sub-pathway is known to play a role in inflammation, promote production of IL-6 in epithelial cells, as well as regulate many key APR [43–45]. In addition, zebrafish showed activation of the nitric oxide and oxidative stress sub-pathway (Table2), suggesting that after FL, the skin has the perception of reactive oxygen species (ROS) and potential oxidative damage [46]. This may occur due to the light exposure itself; however, the lower wavelengths (i.e., UVA) that are expected to lead to ROS production are a very minor component of the FL spectrum (Figure8). Alternatively, activation of nitric oxide pathways may be an offshoot of FL effect on cell division, which is generally reduced when entering the circadian light cycle [25]. Genes 2019, 10, 271 18 of 25

Medaka exhibited a strong APR response that was more robust than observed in zebrafish or the mouse (Table2, z-score 3.47, 45 genes). Unique medaka FL-induced APR sub-pathways included RhoA signaling, Eicosanoid signaling, Ceramide signaling, p38MAPK signaling, and others. Induction of these pathways indicated medaka skin has perceived oxidative stress (i.e., p38MAPK and others), likely from initial cytokine induction by the APR, and has begun to reorganize cellular structure in response (i.e., RhoA, Eicosanoid, and Ceramide signaling pathways). Upon FL exposure, the mouse skin not only exhibited induced components of the APR response, but also showed up-modulation of immune cell activation (Table2, leukocyte extravasation, hepatic stellate cell activation, granulocyte adhesion, agranulocyte adhesion, etc.). In support of this cell-based response, the mouse NanoString PanAm panel, used to validate the RNA-Seq data, is able to assess changes in cell populations. In FL exposed mice, T-cells increased two-fold in skin and 2.3-fold in liver. In addition, CD45, a common leukocyte antigen, increased 1.3-fold, 1.2-fold, and 1.4-fold in skin, liver, and brain, respectively, following FL exposure, while macrophage increased 1.4- and 1.5-fold in both skin and liver. Taken together with the analyses of altered gene expression, these data suggest the mouse organs tested all up-modulate a cellular inflammation and immune response. In skin, four canonical pathways were shared between zebrafish, medaka, and mouse, and each of these has considerable genetic overlap. For example, 12 genes were differentially modulated in the triggering receptor expressed on myeloid cells 1 signaling pathway (TREM1, Figure3) for zebrafish and mouse, while medaka modulated 13 TREM1 genes. TREM1 is a key signaling pathway in the inflammatory response due to its role in TNF, IL-6, IL-8, JAK2, and ERK1/2 activation, JAK2 and ERK1/2 activation leads to increased expression of NFkβ, STAT3, and STAT5. Many of these cytokines were shared among all three organisms analyzed, including the upstream regulators STAT3, TNF, and IL1B (Tables S3a–c, Figures4–7). These transcription factors in turn activate an inflammatory response [47,48]. Overall, the primary response of FL-exposed skin indicated zebrafish, medaka, and mouse all increase cellular functions to activate an immune and inflammation response that is highly conserved. The highest up-stream regulator for zebrafish, medaka, and mouse skin was TNF (6.16-, 5.88-, and 5.39-fold, respectively) followed closely by IL1B (5.23-, 5.58-, and 4.51-fold, respectively). TNF and IL1B both increase pathway modulation of oncostatin M and TREM1 before activating the APR signaling pathway. Therefore, it is not surprising that the down-stream effects (increased inflammation and immune response) are conserved, since the upstream regulators are so tightly correlated among these three divergent biomedical research models.

4.3. Fluorescent Light Genetic Response Conservation in the Brain of Vertebrates The top dysregulated canonical pathway in the zebrafish brain, also up-modulated in the brain of mice, but down-modulated in medaka, was long-term potentiation. This pathway is known to increase and strengthen the synapses in the brain based on the activity of the animal. It is used primarily by animals to increase synaptic plasticity and; therefore, decrease the amount of signal necessary to generate a response [49]. In addition, to long-term potentiation, zebrafish brain induced three canonical pathways, coagulation system, intrinsic prothrombin activation, and the APR signaling pathway. Along with the up-stream regulators, up-modulation of these pathways also suggested the brain is increasing immune and inflammation responses, as well as making increased synaptic memories so the organism can properly respond quickly to the same physical stimulus in the future. In this way, the brain linked the physical light response through a conserved neuronal transcriptional response, leading to genetic induction of gene sets that produced an inflammation and immune response. The suppression of long-term potentiation ( 3.32-fold, 34 genes) in medaka brain was perplexing, − given the high genetic conservation observed in other pathways after FL exposure. Overall, the medaka brain showed a robust genetic response to FL; including APR as the top activated pathway (3.67 z-score, 20 genes) and activation of a large list of APR sub-pathways (IL-6 signaling, JAK/STAT, GNRH signaling, hypoxia signaling, IL-2 signaling, PPAR signaling, etc.). The extent of this list, compared to zebrafish Genes 2019, 10, 271 19 of 25 and the mouse, suggested medaka brain had developed a more mature response (i.e., forward gene expression movement into a full response) in the six hrs between FL exposure and RNA isolation, while the other two animals may require a longer time period to fully execute APR and APR sub-pathway activation. Medaka may have activated the APR at a lower threshold and more rapidly than zebrafish or mice, and thus, early steps in pathway activation in response to FL (e.g., long term potentiation) may have already peaked and are no longer needed to induce downstream cytokines. In any case, the overall response of medaka brain to FL involved the same conserved pattern of inflammation and immune function activation as presented by the other two animals, perhaps just later in the process. The FL response in mouse brain was very similar to the medaka skin response (Tables2 and3). Both mouse brain and medaka skin show up-regulation of APR, Gαs 12/13 signaling, RhoA signaling, IL-6 signaling, ILK signaling, VEGF signaling, and B cell receptor signaling. Thus, as with medaka skin, it appears the mouse brain has begun to reorganize cell structures and affect cell cycle progression in anticipation of real or perceived ROS damage. Suppression of the glutamate receptor signaling pathway (GRS) in the mouse brain after FL exposure was interesting. GRS has two primary functions within neural cells. One is cell communication associated with memory formation [50,51], by adjusting the number of ionotropic glutamate receptors to regulate long term potentiation [52,53]. Secondly, increased activity of the GRS pathway can suppress TNF- and ROS-mediated cascades that lead to inflammation and cell death in response to cellular oxidative damage [54,55]. Therefore, the observed suppression of GRS following FL exposure ( 2.00-fold, 11 genes) in mouse brain likely serves to increase TNF cytokine interaction, activating − caspase dependent apoptosis, inflammation, and cell death in neuronal cells. This was supported by the increased expression of ILK signaling (2.03-fold).

4.4. Fluorescent Light Genetic Response Conservation in the Liver of Vertebrates In zebrafish liver, the APR and classical complement pathways, one of the first steps in the inflammation cascade, were upregulated (Table4, 2.3-fold, 9 genes). This agrees with the FL response in other organs as the perception of eminent oxidative stress, with the resulting induction of genes and pathways to mount an inflammation and immune response. Again, the medaka liver exhibited a long list of activated APR sub-pathways, indicating a fuller or more mature inflammation and immune response then observed for zebrafish liver. Following FL exposure, the mouse liver was the only organ, among the organs tested, predicted to down-modulate upstream regulators IL1B, TNF, and TGFβ based on the DEG sets. This indicated the mouse liver was predicted to suppress inflammation and immune function in response to FL exposure. Key pathways, including IL-6 (z-score 4.0, 26 genes), APR pathway (z-score 4.0, 19 genes), − − and STAT3 (z-score 3.0, 7 genes), were down-modulated in mouse liver, but up-modulated in − zebrafish, medaka, and all other mouse organs tested. Mice are nocturnal animals and it is reported that metabolic processing and cellular turnover is reversed from diurnal animals, such as humans and fish [56–60]. Therefore, RNA isolated at the onset of the light cycle for both fish and mice reflect their entry into active and inactive periods, respectively. After FL exposure, transcriptional activity of the upstream regulators in zebrafish and mouse liver were exactly opposite; except for one regulator, PRDX2, that was up-regulated in both medaka and mice but suppressed in the zebrafish liver samples. PRDX2 is an antioxidant used to reduce hydrogen peroxides and protect cells from oxidative damage. The increase in PRDX2 gene expression in both medaka and mouse supported cellular perception of oxidative stress. The opposite FL response in the liver of mice, compared to the other organs or fishes, is consistent with reports indicating liver metabolism follows an animal’s “activity time” [57,61]. Metabolic processes in particular are reversed in nocturnal animals and more closely correspond to feeding times, which generally occur during the animal’s active period. For example, insulin is reported to oscillate in liver out of phase with the circadian light cycle, regardless of stimulation [62,63]. Our RNA-Seq data support insulin as an upstream regulator (Tables S5a–c, mouse liver insulin 2.02-fold, zebrafish 2.56-fold, − Genes 2019, 10, 271 20 of 25 and medaka 2.37-fold). It has been shown that feeding nocturnal animals during their natural resting period may result in diminished liver metabolism, leading to obesity, compared to the animals fed during their active periods [56,58,62,63]. Thus, although the mouse liver may perceive light-induced damage, the metabolic background at entry into the rodent’s sleep cycle may have prevented the “normal” light cycle genetic response observed in the other organs (brain and skin), as well as all organs in the diurnal fishes.

4.5. Evolutionary Comparisons Highlighting Transcriptional Activation of the APR The question of how FL serves to incite the APR, with the inflammation and immune responses, is of interest. APR is known to have multiple modes of initiation including local trauma, bacterial or viral infection, oxidative cellular stress, etc. [28,29,64]. Interestingly, neural signals initiated by opsonins initiate a neoplasia inflammatory response in the brain, that quickly signals to local pro-inflammatory cytokines IL1B and TNF throughout the body [65,66]. This in turn activates neutrophils that signal liver hepatocytes to modulate protein synthesis and increase APR throughout the body via transport in the blood stream, to increase inflammation and minimize oxidative damage [27,67–69]. Each response in the respective organs is controlled through the induction of key regulators TNF and IL1B (Figures4–6) that modulate 72%–88% of the zebrafish DEGs, 65%–74% of the medaka DEGs, and 61%–79% of mouse DEGs. Following gene modulation, an innate inflammatory response is up-modulated in skin and brain of all three animals, with over 30% of the functional categories being shared in any two-organ comparison. The functional categories modulated following this response can be grouped into four primary categories: immune cell trafficking, inflammatory response, cellular movement, and cell to cell signaling. Each of these categories is a branch of the APR signaling pathway, and together serve to stimulate the immune and inflammation responses to compensate for the perception of increased oxidative cellular stress. It seems likely the intense light exposure (35 and 52.5 kJ/m2) over a short time (40–60 min) may have saturated the normal light response, changing the light cycle for the animals in a matter of minutes, rather then gradually, as would be expected in more natural conditions. The evolutionary history may have entrained the genetic response to intense light (i.e., noon) to include protection against ROS, since anytime light was received under the solar spectrum, UVA, and shorter wavelengths would have been a major component. This explains why UV specific DNA photorepair proteins (i.e., cyclobutene pyrimidine dimers and 6–4 photoproduct DNA photolyases) become transcriptionally induced upon exposure to visible light, and not UV light for which their activity is needed. Animals simply never saw visible light without a UV component until they became housed in artificially-lit facilities. Therefore, it may not be surprising that exposure to the 4100 K FL spectrum leads to inflammation and immune responses, even though the UV wavelengths emitted, that induce ROS, are a very small fraction of the light received (Figure8). Recently, we reported that Xiphophorus exposed to specific light wavelengths induce and suppress select genetic pathways in a wavelength-specific manner [3]. In addition, different wavelengths were identified that could induce, or suppress, the same genetic pathways. This supports the concept that over evolutionary time, animals may have conscripted specific wavelengths of light for select genetic regulatory responses, and the summation of all solar wavelengths may be needed to properly respond to the environment. We also determined similar wavelength specific genetic responses occur in both zebrafish and medaka (in preparation). Given the high conservation of the FL response in fish and mice shown here, it will be interesting to determine whether mammals have retained wavelength-specific genetic regulation, and these experiments are currently underway.

5. Conclusions We present results that assessed changes in gene expression patterns due to FL exposure in zebrafish and medaka fishes, and in hairless mice. Following FL exposure, RNA from skin, brain, and liver was utilized for RNA-Seq, and gene expression was validated with NanoString nCounter Genes 2019, 10, 271 21 of 25 assays. Differentially expressed genes (DEGs), due to the FL exposure, were utilized in functional analyses to identify FL-affected biological pathways for comparison. All organs, in all three animals, respond to FL by modulating pathways leading to inflammation and immune responses. These conserved genetic responses involved induction of the acute phase response (APR) in all organs of the fish species, and mouse skin and brain. The pathways affected by FL are regulated primarily by TNF and IL1B and are predicted to induce APR, leading to inflammation and immune responses. The only exception was the mouse liver, that showed suppression in the same APR pathways that were activated in the other organs examined. APR suppression in the mouse liver may be due to a nocturnal metabolism keeping the liver out of phase with FL exposure. Collectively, the conserved FL genetic response in both fishes and mice appear due to cellular perception of oxidative stress. These data suggest the primary response to FL is extraordinarily well-conserved among highly divergent species, representing both diurnal and nocturnal lifestyles, and; therefore, deeply embedded within the vertebrate genome.

Supplementary Materials: The following are available online at http://www.mdpi.com/2073-4425/10/4/271/s1, Figure S1: Differentially expressed genes (DEGs) from fluorescent light (FL)-induced skin of zebrafish, medaka, and mouse were imported into Consensus PathDB. Dot size represents the number of DEGs represented in that function and dot color represents statistical significance measured by the p-value (red is a lower p-value and white is a higher p-value). Figure S2: DEGs from FL-induced brain of zebrafish, medaka, and mouse were imported into Consensus PathDB. Dot size represents the number of DEGs represented in that function and dot color represents statistical significance measured by the p-value (red is a lower p-value and white is a higher p-value). Figure S3: DEGs from FL-induced liver of zebrafish, medaka, and mouse were imported into Consensus PathDB. Dot size represents the number of DEGs represented in that function and dot color represents statistical significance measured by the p-value (red is a lower p-value and white is a higher p-value). Figure S4: Comparison of FL-incited DEG expression patterns to literature reported transcriptomics datasets. Mouse skin FL-incited DEGs were compared to DEGs modulated by Imiquimod (IMQ), a compound that is used to induce psoriasis-like skin inflammation, in mouse skin (dark blue); mouse liver FL incited DEGs were compared to DEGs modulated by Lipopolysaccharide (LPS) challenge-modulated genes in mouse liver (red); and zebrafish liver FL incited DEGs were compared to DEGs modulated by Edwardsiella tarda vaccination in zebrafish liver (light blue). Genes that are present in both the FL-modulated DEGs and each literature reported DEGs are plotted as a dot plot, with each color representing a particular dataset comparison, and each dot representing a shared gene between two datasets. The position of each dot is defined by X and Y values. The X value is the Log2(Fold Change) (Log2(FC)) following FL exposure, and the Y value is the Log2(FC) reported by a particular treatment performed in literature. Dots that fall into the X > 0, Y > 0 or X < 0, Y < 0 means FL exposure and treatment performed in the literature led to the same direction of modulation of a gene’s expression; dots that fall into the X > 0, Y < 0 or X < 0, Y > 0 means FL exposure and treatment performed in the literature led to an opposite modulation of gene expression. Table S1: Custom NanoString probes used to confirm the zebrafish RNA-Seq results. Table S2: Read depth and RNA-Seq statistics for FL-exposed and sham-treated zebrafish, medaka, and mouse samples. Table S3A–C: Differentially modulated genes for zebrafish (A), medaka (B), and mouse (C) skin samples as determined by the EdgeR with a |log (FC)| 1.0 (FDR < 0.05). Table S4A–C: Differentially modulated genes for zebrafish (A), medaka (B), 2 ≥ and mouse (C) brain samples as determined by the EdgeR with a |log2(FC)| 1.0 (FDR < 0.05). Table S5A–C: Differentially modulated genes for zebrafish (A), medaka (B), and mouse (C) liver≥ samples as determined by the EdgeR with a |log (FC)| 1.0 (FDR < 0.05). 2 ≥ Author Contributions: R.B.W. is the principal investigator who conceived of the project and designed the research. M.B. and R.B.W. wrote the main manuscript text. Y.L., M.B., W.B., C.W., and R.S. contributed to critical editing of the paper. M.B., W.B., M.S., and K.H. performed the animal exposures, dissections, and RNA isolations. Y.L., M.B., and W.B. performed bioinformatic analyses. Y.L., M.B., and W.B. contributed to functional clustering data analyses and developed the figures. W.B. performed NanoString validation studies. All authors have read and approved this manuscript. Funding: This research was funded by the National Institutes of Health, ORIP grants R24-OD-011120, R24-OD-018555 and R15-CA-223964. Acknowledgments: The authors would like to thank the staff of the Xiphophorus Genetic Stock Center, Texas State University, for maintaining the pedigreed fish lines and caring for the animals used in this study. We would also like to thank the University of Texas Health-San Antonio, for maintaining and caring for the mice used in this study. NIH, ORIP grants R24-OD-011120, R24-OD-018555 and R15-CA-223964. Conflicts of Interest: The authors declare no competing interests. Genes 2019, 10, 271 22 of 25

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