Androgen deprivation therapy (ADT) is a mainstay of prostate cancer treatment, given the dependence of prostate cells on androgen and the androgen receptor (AR). However, tumors become ADT-resistant, and there is a need to understand the mechanism. One possible mechanism is the upregulation of AR co-regulators, although only a handful have been definitively linked to disease. We previously identified the Mediator subunit MED19 as an AR co-regulator, and reported that MED19 depletion inhibits AR transcriptional activity and growth of androgen-insensitive LNCaP-abl cells. Therefore, we proposed that MED19 upregulation would promote AR activity and drive androgen-independent growth. Here, we show that stable overexpression of MED19 in androgen-dependent LNCaP cells promotes growth under conditions of androgen deprivation. To delineate the mechanism, we determined the MED19 and AR transcriptomes and cistromes in control and MED19-overexpressing LNCaP cells. We also examined genome-wide H3K27 acetylation. MED19 overexpression selectively alters AR occupancy, H3K27 acetylation, and gene expression. Under conditions of androgen deprivation, genes regulated by MED19 correspond to genes regulated by ELK1, a transcription factor that binds the AR N-terminus to induce select AR target gene expression and proliferation, and genomic sites occupied by MED19 and AR are enriched for motifs associated with ELK1. Strikingly, MED19 upregulates expression of monoamine oxidase A (MAOA), a factor that promotes prostate cancer growth. MAOA depletion reduces androgen-independent growth. MED19 and AR occupy the MAOA promoter, with MED19 overexpression enhancing AR occupancy and H3K27 acetylation. Furthermore, MED19 overexpression increases ELK1 occupancy at the MAOA promoter, and ELK1 depletion reduces MAOA expression and androgen-independent growth. This suggests that MED19 cooperates with ELK1 to regulate AR occupancy and H3K27 acetylation at MAOA, upregulating its expression and driving androgen independence in prostate cancer cells. This study provides important insight into the mechanisms of prostate cancer cell growth under low androgen, and underscores the importance of the MED19-MAOA axis in this process. Prostate cancer is one of the most common cancers worldwide, and androgen hormones are essential for prostate cancer growth. Androgens exert their effects through a protein called the androgen receptor (AR), which turns on and off genes that regulate prostate cancer growth. Powerful drugs that block AR action by lowering androgen levels–so-called androgen deprivation therapy—are used to treat prostate cancer patients, and this yields initial success in reducing tumor growth. However, over time, tumors circumvent androgen deprivation therapy and patients relapse; in many cases, this occurs because AR becomes re-activated. The factors responsible for re-activating AR and promoting growth under androgen deprivation are not well understood. Here, we demonstrate that a subunit of the Mediator transcriptional regulatory complex, called MED19, promotes growth of prostate cancer cells under low androgen conditions, mimicking the ability of tumors to grow under androgen deprivation in prostate cancer patients. MED19 promotes androgen-independent growth by working with a transcription factor that interacts with AR, called ELK1, to induce the expression of genes regulated by AR that promote prostate cancer growth. This study provides important insight into how prostate cancer cells can maintain growth under androgen deprivation through MED19. Prostate cells depend on androgens and the androgen receptor (AR) for growth and survival, and AR is a key driver of prostate cancer from early to late stage disease . The maiin vitro and in vivo [One important mechanism of AR re-activation with the potential to advance treatment modalities is the upregulation of AR co-regulators ,7–9. For in vivo ,16. Furt in vivo .AR co-regulators implicated in prostate cancer progression also include transcription factors, such as members of the ETS domain family of transcription factors that recognize ETS binding motifs in the genome ,18. ETS However, of the hundreds of proteins that have been identified as AR co-regulators, only a small portion have been definitively linked to disease; furthermore, AR co-regulators function in multi-protein transcriptional complexes –9,25. ThTo this end, our lab previously performed an unbiased, genome-wide siRNA screen for novel AR co-regulators, and identified MED19, a subunit of the middle module of the Mediator complex that functionally bridges promoters and enhancers to connect transcription factors and RNA polymerase II (Pol II) ,27. We fIn this study we examined the ability of MED19 to confer androgen independence and its effect on gene expression and AR occupancy. We identified the specific gene target, MAOA, and cooperating transcription factor, ELK1, underlying MED19 regulation of androgen-independent growth.To determine if MED19 is sufficient to convert androgen-dependent prostate cancer cells to androgen independence, we stably overexpressed MED19 by lentiviral transduction in the prototypical androgen-dependent LNCaP cell line (MED19 LNCaP cells) . As a coIn contrast to control LNCaP cells, MED19 LNCaP cells showed robust proliferation in 2D culture, increased colony formation, and larger spheroid formation, when cultured in media depleted of steroids . This deWe also examined if upregulation of MED19 could promote the proliferation of other early stage prostate cancer cell lines. Indeed, we found that overexpression of MED19 increased proliferation in non-malignant RWPE-1 cells , but didAltogether, this indicates a role for MED19 in conversion of early stage cells to aggressive growth and androgen independence.As AR amplification is a common mechanism to achieve androgen independence, we evaluated the mRNA and protein levels of AR with MED19 overexpression in LNCaP cells. We found that AR mRNA and protein were unchanged in MED19 LNCaP cells compared to control LNCaP cells under androgen deprivation . MED19 oWe then examined the reliance of MED19 LNCaP cells on AR for their androgen-independent growth by determining their sensitivity to enzalutamide, an AR antagonist that reduces AR transcriptional activity in part by preventing AR nuclear accumulation . Enzalutversus MED19 LNCaP cells cultured under androgen deprivation and treated with vehicle  or R1881 (androgen), a synthetic AR agonist. MED19 overexpression upregulated both basal and androgen-dependent expression of the cell cycle regulators UBE2C and MYC  and ChIP sequencing (ChIP-seq) studies for FLAG-tagged MED19, AR, and H3K27 acetylation in MED19 LNCaP cells and control LNCaP cells cultured under androgen deprivation and treated with vehicle (androgen-deprived conditions) or R1881 (androgen-treated conditions).Global AR occupancy under androgen deprivation, as measured by total number of and individual level of occupancy at AR sites in published ChIP-seq studies, is low compared to androgen treatment. We speculated that MED19 may alter AR activity under androgen deprivation by modulating AR at low occupancy sites. Therefore, we included in our ChIP-seq study all AR-specific sites, including those with low occupancy in androgen-deprived conditions. We used rigorous quality controls to maximize capture of AR sites and ensure strict specificity to AR occupancy (see detailed ChIP-seq section under UBE2C and MYC fell below the 1.5-fold cut-off)  . This wacut-off) . As expecut-off) . There wcut-off) . There wcut-off) .Of these 309 genes, 82 were also differentially expressed in the absence of androgens   Tables. MED19 occupancy in the absence and presence of androgens corresponds almost entirely with AR occupancy, with virtually every gene differentially expressed in MED19 LNCaP cells occupied by AR, and many (the majority in the absence of androgens) also occupied by MED19, indicating direct regulation by MED19 . In factPSA and FKBP5 , and, as expected, AR was the top transcription factor from ChEA analysis for both  Tables. nd FKBP5 . Some gend FKBP5 .MED19 appears mainly to modulate the response of canonically androgen-regulated genes: the top 100 androgen-induced and androgen-repressed genes almost all overlapped between MED19 LNCaP cells and control LNCaP cells, with MED19 overexpression augmenting the response to androgen for some genes, and reducing the response to androgen for others . HoweverAlthough AR occupies unique sites in MED19 LNCaP cells, the majority of genes altered with MED19 overexpression contain AR sites shared by control LNCaP cells and MED19 LNCaP cells . What weFor example, LRRTM3  is one of the most upregulated genes upon MED19 overexpression under androgen deprivation, while androgen treatment suppresses LRRTM3 expression Figs  and S11AWe wanted to determine the specific gene targets altered by MED19 overexpression that were responsible for promoting androgen-independent growth. We decided to focus on genes upregulated by MED19 overexpression under androgen deprivation, which could be depleted to inhibit androgen-independent growth. Given that LRRTM3 appears to be a direct target of MED19, with changes in AR occupancy that correlated with a large upregulation in expression, we tested its effect on proliferation. However, LRRTM3 depletion had very little effect on androgen-independent growth . AlthougOne target that fulfilled these criteria is MAOA (monoamine oxidase A). MAOA is a mitochondrial enzyme that degrades monoamine neurotransmitters and dietary amines and produces hydrogen peroxide. It has a well-established role in promoting aggressive prostate cancer cell growth, invasion, and metastasis –38. MAOAIndeed, AR occupies the promoter and 5’ UTR of MAOA, with increased and reorganized occupancy when MED19 is overexpressed and when the cells are treated with R1881 Figs  and S13BSince increased or decreased AR recruitment corresponded to activation or repression of target genes as a function of MED19 overexpression, and given that AR works in concert with other transcription factors to control gene expression, we determined the identity of other transcription factor binding motifs associated with AR and MED19 occupancy. We were particularly interested in identifying any transcription factors uniquely enriched in MED19 LNCaP cells under androgen deprivation, that correlated with MED19 occupancy, and would have an established connection to prostate cancer and regulation of MAOA.FOXA1 and FOXM1 were the most enriched transcription factor motifs for AR occupancy in control LNCaP cells and MED19 LNCaP cells, in the absence or presence of androgens, as well as for MED19 occupancy in the absence or presence of androgens . This isuniquely in MED19 LNCaP cells. Under androgen deprivation, we found that the most enriched motif corresponded to ELK1  occupancy in MED19 LNCaP cells and in control LNCaP cells, in the presence and absence of androgens, were enriched for ARE and other canonical AR-related motifs , as expected, this enrichment was reduced under androgen deprivation . In addiWe next used Enrichr’s “Transcription Factor Perturbation” tool to compare published gene expression changes with transcription factor knockdown or overexpression to our RNA-seq study. Strikingly, under androgen deprivation, genes upregulated with MED19 overexpression corresponded consistently to genes downregulated with ELK1 knockdown (top hit) or with AR knockdown, including MAOA Figs  and S15BGiven the strong connection to ELK1, we tested its functional role in gene expression and androgen-independent growth. We depleted ELK1 by siRNA in MED19 LNCaP cells and measured the effect on MAOA mRNA expression and proliferation under androgen deprivation. ELK1 knockdown both greatly reduced expression of MAOA and inhibited androgen-independent growth .To determine if ELK1 occupied MAOA at sites where AR and MED19 were present, we performed ChIP-qPCR for ELK1 at the MAOA promoter region overlapping with the reported ARE, where AR, MED19, and H3K27 acetylation were present Figs  and S17.We have demonstrated that overexpression of MED19 in androgen-dependent LNCaP cells provides a growth advantage in the absence and presence of androgens. This is mediated by AR. The cells remain dependent on AR for growth under androgen deprivation, without increasing full-length AR abundance or splice variant AR-V7 expression. Therefore, increased expression of MED19 is sufficient to convert a cell that is androgen-dependent to one that is androgen-independent for growth.Consistent with this are reports from our lab and others that MED19 depletion reduced AR transcriptional activity and growth of LNCaP cells and LNCaP-abl cells, which are derived from LNCaP cells and are androgen-independent ,30. We aWe expected that the selective effect by overexpressed MED19 on AR-associated gene expression, as well as genome-wide occupancy of MED19, AR, and H3K27 acetylation under androgen deprivation, would illuminate mechanism. Indeed, we observed by RNA-seq a defined set of genes that were differentially expressed upon MED19 overexpression in LNCaP cells compared to control cells. It should be noted that the changes in androgen-dependent gene expression reflect both primary and secondary effects of AR on gene expression, given that hormone treatment was done overnight. In addition, we observed a large overlap between MED19 and AR occupancy under both androgen-independent and androgen-dependent conditions. There was also a unique set of loci with AR and MED19 occupancy under androgen deprivation in MED19 LNCaP cells compared to control cells, suggesting that MED19 can drive AR to new sites. Thus, we observed upon MED19 overexpression a selective alteration of the AR cistrome. This was reflected in changes in gene expression under conditions of low androgen levels. This is likely via MED19 acting as part of the Mediator complex, and is consistent with the MED19 subunit being used to select the entire mammalian Mediator complex from mouse B cells for cryo-EM studies . HoweverWe also found that MAOA was upregulated upon MED19 overexpression in LNCaP cells under androgen deprivation. This was associated with an increase in occupancy of AR and H3K27 acetylation at the MAOA promoter. We also observed a striking growth-inhibitory effect upon MAOA depletion in MED19 LNCaP cells under androgen deprivation. Although MAOA is likely not the sole mediator of MED19-induced androgen-independent growth, it is important, given the large reduction in androgen-independent growth upon its depletion. Furthermore, multiple studies have established the importance of increased MAOA expression in facilitating prostate cancer proliferation ,36,48,49An ELK1 motif was enriched at sites occupied by AR, as well as MED19, in MED19 LNCaP cells but not in control LNCaP cells. Furthermore, MED19-upregulated genes, including MAOA, were associated with ELK1, suggesting that ELK1 could be cooperating with MED19 and AR to promote MAOA expression and growth under androgen deprivation. Indeed, MED19 overexpression promoted ELK1 occupancy at the MAOA promoter, and ELK1 depletion reduced MAOA expression and androgen-independent growth. ELK1 is an ETS transcription factor that controls AR transcriptional activity and promotes prostate cancer progression ,23,24. EBased on our findings, we propose a model whereby under conditions of androgen deprivation and MED19 upregulation, MED19-containing Mediator cooperates with ELK1 to recruit and stabilize AR, via its N-terminal domain, to the promoter of MAOA, and increases H3K27 acetylation at the MAOA promoter, through recruitment of HATs. Recruitment of Pol II ensues, upregulating MAOA and licensing cell growth under low androgen . ConsistThe Mediator subunit MED1 has been described to promote androgen-dependent AR activity through interaction with the ligand-binding domain of AR, and is overexpressed in prostate cancer ,54–56. IOur study may also have implications for MED19 function in other cancers. For example, reduction of MED19 by siRNA has been reported to reduce the proliferation of certain breast, ovarian, cervical, and lung cancer cell lines, and increased abundance of MED19 protein is observed in tumors compared to benign tissue ,57–63. TLNCaP cell lines purchased from the ATCC  were used for stable transfections. MED19 overexpression and empty vectors were purchased from Origene Technologies . Cells were generated after lentiviral infection with the above constructs. Lentiviral particles were produced in the 293T/17 cell line (ATCC). LNCaP, RWPE-1, RWPE-2, and a mouse prostate stem cell (MSC) line expressing activated AKT were infected on two consecutive days with control or MED19 lentiviral particles and polybrene. Pooled clones were collected after selection with puromycin (1 μg/mL). MED19 expression was verified by western blot. Cells were routinely tested for mycoplasma, and only cells that were mycoplasma-free were used in the experiments.LNCaP cell lines were maintained in complete RPMI: RPMI-1640 supplemented with 10% fetal bovine serum (FBS)  and 1% penicillin-streptomycin  For assays under androgen deprivation, cells were cultured in androgen-depleted RPMI: phenol red- and L-glutamine-free RPMI-1640 supplemented with 10% FBS dextran charcoal-stripped of androgens  and 1% L-glutamine . Cells were cultured on poly-d-lysine-coated plates. RWPE-1 cells and RWPE-2 cells were cultured in keratinocyte SFM media supplemented with L-glutamine, BPE, and EGF (Thermo Fisher Scientific) . The mouCells were plated in the appropriate media in quintuplicate  in poly-d-lysine-coated 96-well plates. Cell proliferation was determined using the Cyquant-NF Cell Proliferation Assay (Invitrogen) or PrestoBlue Cell Viability Assay (ThermoFisher Scientific). Fluorescence was quantified with the SpectraMaxM5 Microplate Reader and SoftMaxPro software (Molecular Devices) and normalized to readings at Day 0 (day after plating).Cells were plated in the appropriate media in duplicate . Cells were fixed with 66% methanol/33% acetic acid solution and stained with 0.1% crystal violet solution.Cells were plated in the appropriate media in 96-well ultra-low attachment plates (Corning)  for 10–14 days. Cells were imaged with CellInsight CX7 LZR and spheroid area per well was analyzed by Cellomics Scan Version 6.6.1.6) were mixed with an equal volume of Matrigel and injected subcutaneously into the flank region of Nu/J (nude) male mice (Jackson Laboratories). Tumor volume was measured twice weekly. All animal studies were performed at NYU School of Medicine. The animal research was approved by the NYU School of Medicine Institutional Animal Care and Use Committee (IACUC), protocol number IA16-01775.For xenograft experiments, mouse prostate stem cells  , phospho-p44/p42 ERK1/2 (pERK) , Ki-67 , and AR , washed, and followed by a 1 hour incubation with a biotinylated rabbit secondary . An avidin-biotin complex was formed and developed using diaminobenzidine chromagen, followed by a counterstain with hematoxylin.Immunohistochemistry (IHC) was performed on paraffin-embedded tissue sections that were dewaxed in xylene, rehydrated, and washed in phosphate-buffered saline (pH 7.4). Antigen retrieval was performed by heating sections in a microwave oven (900 watts) for 5 min in 10 mM citrate buffer, followed by treatment with 3% HTotal RNA was extracted using RNeasy (Qiagen) according to the manufacturer’s instructions. RNA (1 μg) was reverse transcribed using the Verso cDNA Synthesis Kit (ThermoFisher Scientific) following the manufacturer’s instructions. Gene-specific cDNA was amplified in a 10-μL reaction containing Fast SYBR Green qPCR Master Mix (ThermoFisher Scientific). Real-time PCR was performed using the Applied Biosystems Quantstudio 6 Flex Real-Time PCR System with each gene tested in triplicate. Data were analyzed by the DDCT method using RPL19 as a control gene, and normalized to control samples, which were arbitrarily set to 1. The sequences of the primers used for real-time PCR are as follows.RPL19 –F:CACAAGCTGAAGGCAGACAA, R:GCGTGCTTCCTTGGTCTTAG; ELK1 –F:CACATCATCTCCTGGACTTCAC, R:CGGCTGAGCTTGTCGTAAT; MED19 –F:CTGTGGCCCTTTTTACCTCA, R:GCTTCTCCTTCACCTTCTTCC;AR–F:TACCAGCTCACCAAGCTCCT, R:GAACTGATGCAGCTCTCTCG; LRRTM3 –F:ATACGACCAGCCCACAATAAG, R: GCTCAGTCTCTAGGTGTGTTTC;MAST4 –F:GCCAAAGAAGGACAGGGTATTA, R:GCTGTCCCACTATCGTAGTTTC;MAOA–F:CCTGTGGTTCTTGTGGTATGT, R:CACCTACAAACTTCCGTTCCT; AR-V7 –F: CCATCTTGTCGTCTTCGGAAATGTTA, R:TTTGAATGAGGCAAGTCAGCCTTTCT;FKBP5 –F:CGCAGGATATACGCCAACAT, R:CTTGCCCATTGCTTTATTGG;PSA (KLK3)–F:CCAAGTTCATGCTGTGTGCT, R:GCACACCATTACAGACAAGTGG. The RT2 Profiler PCR Array (PAHS-135) for androgen-dependent targets in prostate cancer profiled in Three individual siRNAs  were pooled and transfected into cells using the Lipofectamine RNAimax transfection reagent (ThermoFisher Scientific) following the manufacturer's instructions. Each siRNA sequence was cross-referenced with siRNA-Check to confirm specificity . Scrambl# 2276S). Tubulin  was used as a loading control.Cells were lysed in RIPA buffer supplemented with protease inhibitor cocktail . Protein lysates were subjected to SDS/PAGE and immunoblotted with antibodies against AR  or MYC tag , with a HyperScale architecture developed by OnRamp BioInformatics, Inc. . Reads were trimmed using cutadapt. Quality scores were assessed using FastQC. Reads were aligned to the Homo sapiens genome build hg19 using STAR. Individual sample reads were quantified using HTseq and normalized via Relative Log Expression (RLE) using DESeq2 R library. Read Distribution percentages, violin plots, identity heatmaps, and sample MDS plots were generated as part of the QC step using RSeQC. DEseq2 was also used to calculate fold changes and p-values. Clustering of genes for the final heatmap of differentially expressed genes was done using the PAM (Partitioning Around Medoids) method using the fpc R library. Functional enrichment analysis of pathways, gene ontology, domain structure and other ontologies was performed using HOMER. Several database sources were referenced for enrichment analysis, including Interpro, NCBI, MSigDB REACTOME, WikiPathways. Enrichment was calculated relative to a set of background genes relevant for the experiment. Additional gene enrichment is available from the following partner institutions: Advaita (http://www.advaitabio.com/ipathwayguide). Differentially expressed genes were also analyzed using the Enrichr platform from the Ma’ayan Laboratory, including ChEA and “Transcription Factor (TF) Perturbations Followed by Expression” analyses [RNA was prepared as described above. Libraries were prepared with ribodepletion using Illumina TruSeq stranded total RNA with RiboZero Gold library preparation kit. Sequencing was performed using the Illumina HiSeq2500 Sequencing system (HiSeq 4000 Paired-End 50 or PE75 Cycle Lane). Data was analyzed by Rosalind  to preserve both protein-DNA and protein-protein interactions. A mixture of two antibodies for AR ChIP was used, one against the C-terminus and one against the N-terminus, to maximize AR enrichment and minimize epitope masking. Antibodies were tested and optimized. The ChIP-seq study was performed in independent biological triplicates . Inputs were used for normalization and additional IgG controls included to ensure any low occupancy peaks were specific to AR and not background. We discarded any peaks for AR and for H3K27 acetylation that scored above 0 for IgG; this was also done with peaks for FLAG-MED19 in MED19 LNCaP cells that scored above 0 for FLAG in control LNCaP cells (very few peaks scored above 0 and overlapped in either case) . Rigorouet al [Protocol for ChIP-seq was adapted from Fonseca et al . Brieflyet al . Sequenchttps://rosalind.onramp.bio/), with a HyperScale architecture developed by OnRamp BioInformatics, Inc. . Reads were trimmed using cutadapt. Quality scores were assessed using FastQC. Reads were aligned to the Homo sapiens genome build hg19 using bowtie2. Per-sample quality assessment plots were generated with HOMER and Mosaics. Peaks were called using MACS2 (with input controls background subtracted). Peak overlaps were analyzed using the DiffBind R library. Read distribution percentages, identity heatmaps, and FRiP plots were generated as part of the QC step using ChIPQC R library and HOMER. HOMER was also used to generate known and de novo motifs and perform functional enrichment analysis of pathways, gene ontology, domain structure and other ontologies.Data were analyzed by Rosalind . IgGs were included as negative controls. After DNA isolation, qPCR was performed as described above, with primers targeting the MAOA promoter region—F: TGTCAAGGCAGGCGTCTAC, R: GGACCCTTGTACTGACAC. Relative enrichment was calculated as a percentage of 10% input.Statistical analyses were performed using GraphPad Prism software. Data are reported as mean ± SEM . Number of experiments are described in the figure legends; unless otherwise noted, two-tailed unpaired Student’s t test was used when comparing two groups, with a p value < 0.05 being considered significant and levels of significance denoted as *p < 0.05; **p < 0.01; and ***p < 0.001.S1 FigMED19 LNCaP cells with stable overexpression of MYC- and FLAG-tagged MED19 and control LNCaP cells with stable expression of the empty vector were created by lentiviral transduction, with pooled clones selected with puromycin. After selection, stable overexpression of MED19 in MED19 LNCaP cells was confirmed. A) Scheme of the lentiviral expression construct MED19 (NM_153450) (adapted from Origene). B) RNA was extracted and qPCR was performed for MED19 in control LNCaP cells and MED19 LNCaP cells to confirm upregulation of MED19 mRNA . ***p < 0.001. C) MED19 LNCaP cells and control LNCaP cells were treated with MED19 siRNA or scrambled siRNA, and total protein lysates were probed by MYC tag. Tubulin was used as a loading control. D) Validation of MED19 knockdown, with MED19 mRNA measured as in B (with MED19 mRNA expression with scrambled siRNA treatment set as “1”).(PDF)Click here for additional data file.S2 FigA) Control LNCaP and MED19 LNCaP cells were cultured in complete media, fixed with paraformaldehyde, permeabilized, and stained with a mouse monoclonal antibody to MYC (Myc-Tag (9B11) Cell Signaling #2276), which is an epitope tag on the MED19 expression construct see , followe(PDF)Click here for additional data file.S3 FigTotal protein lysates from RWPE-1 and RWPE-2 cells stably expressing FLAG- and MYC-tagged MED19 (MED19 RWPE-1 and MED19 RWPE-2) or empty vector (control RWPE-1 and control RWPE-2) were probed for MYC tag, with tubulin used as a loading control.(PDF)Click here for additional data file.S4 FigImmunohistochemistry of formalin-fixed, paraffin-embedded tissue sections from control MSC and MED19 MSC using antibodies against A) phospho-AKT1 Ser473 (pAKT) , B) phospho-p44/p42 ERK1/2 (pERK) , C) Ki-67 , and D) AR . White arrow shows a cluster of cells with strong pERK staining in a tissue section from a MED19 MSC tumor.(PDF)Click here for additional data file.S5 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated overnight with ethanol vehicle. RNA was extracted and mRNA measured by qPCR for AR-V7 mRNA . LNCaP-95 cells that express AR-V7 were used as a positive control. *p < 0.05; **p < 0.01; and ***p < 0.001. ns = not significant.(PDF)Click here for additional data file.S6 FigMED19 LNCaP cells were cultured in A) androgen-depleted media or B) androgen-containing media, with control LNCaP cells. AR was depleted by siRNA and proliferation was evaluated after 7 days, normalized to proliferation with scrambled siRNA. KIF11 was used as a positive control. Experiment was performed in biological duplicate, with representative results shown. *p < 0.05; **p < 0.01; and ***p < 0.001. ns = not significant. C) Validation of AR knockdown .(PDF)Click here for additional data file.S7 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated for 16 h with ethanol vehicle or 10 nM R1881. RNA was extracted and mRNA measured by qPCR for the AR target genes indicated . Experiment was performed in biological triplicate, with representative results shown. *p < 0.05; **p < 0.01; and ***p < 0.001. ns = not significant.(PDF)Click here for additional data file.S8 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated with ethanol vehicle or 100 nM R1881 for 4 h. ChIP-seq for FLAG-MED19, AR, and H3K27ac was performed in biological triplicate with the exception of ChIP-seq for AR in control LNCaP cells + R1881, where one sample was excluded from the analyses because of low signal. A) ChIP-qPCR QC of AR, H3K27ac, and FLAG-MED19 ChIPs are shown, with normalization to inputs. AR occupancy and H3K27ac at PSA ARE III greatly increase in response to R1881 treatment. IgG is shown as a negative control. FLAG-MED19 shows high occupancy in MED19 LNCaP cells at PDZK1P1, identified as a site of strong FLAG-MED19 occupancy from a pilot ChIP-seq for FLAG-MED19. FLAG in control LNCaP cells is shown as a negative control. Experiments were performed in biological triplicate, with representative results shown. *p < 0.05; **p < 0.01; and ***p < 0.001. B) ChIP-seq tracks (representative results) for AR and H3K27ac at PSA and FKBP5 in control LNCaP cells with vehicle or R1881 treatment. AR occupancy and H3K27ac clearly increase in response to R1881 treatment (occupancy scores in (PDF)Click here for additional data file.S9 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated for 16 h with ethanol vehicle or 10 nM R1881. RNA-seq was performed in biological triplicate. Graphs represent fold changes from qPCR  and tables represent fold changes from RNA-seq. Upregulation of PSA and FKBP5 mRNA expression in A) control LNCaP cells and B) MED19 LNCaP cells in response to R1881 treatment, with consistency between RNA-seq and qPCR, and expected increase in expression with R1881 treatment. Experiments were performed in biological triplicate, with representative results shown. *p < 0.05; **p < 0.01; and ***p < 0.001.(PDF)Click here for additional data file.S10 FigControl LNCaP and MED19 LNCaP cells were cultured under androgen deprivation for 3 days and treated with ethanol vehicle or 100 nM R1881 for 4 hours. ChIP-seq for FLAG-MED19, AR, and H3K27ac were performed and in biological triplicate, with the exception of ChIP-seq for AR in control LNCaP cells + R1881, where one sample was excluded from the analyses because of low signal. A) Binding heatmaps for AR occupancy in control LNCaP and MED19 LNCaP cells. Brown = sites occupied by AR in both control LNCaP and MED19 LNCaP cells; blue = sites occupied by AR in control LNCaP cells only; orange = sites occupied by AR in MED19 LNCaP cells only. B) Binding heatmaps for H3K27 acetylation. Brown = sites of H3K27 acetylation in both control LNCaP and MED19 LNCaP cells; blue = sites of H3K27 acetylation in control LNCaP cells only; orange = sites of H3K27 acetylation in MED19 LNCaP cells only. C) Binding heatmaps for AR and FLAG-MED19 in MED19 LNCaP cells. Brown = sites occupied by AR and FLAG-MED19; blue = sites occupied by AR only; orange = sites occupied by FLAG-MED19 only. D) Binding heatmaps for AR, FLAG-MED19, and H3K27ac in MED19 LNCaP cells. Dark brown-green = sites occupied by AR and FLAG-MED19 and marked by H3K27 acetylation; medium green = sites occupied by AR and marked by H3K27 acetylation; medium brown-green = sites of FLAG-MED19 occupancy and marked by H3K27 acetylation; brown = sites of AR and FLAG-MED19 occupancy; light green = sites of H3K27 acetylation only; blue = sites occupied by AR only; orange = sites occupied by FLAG-MED19 only.(PDF)Click here for additional data file.S11 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated with ethanol vehicle or R1881 . RNA-seq and ChIP-seq for FLAG-MED19, AR, and H3K27ac were performed in biological triplicate, with the exception of ChIP-seq for AR in control LNCaP cells + R1881, where one sample was excluded from the analyses because of low signal. A) Fold change mRNA expression from RNA-seq and qPCR validation of changes in LRRTM3 mRNA expression . Greater fold changes by qPCR likely due to low abundance (raw counts in RNA-seq) of LRRTM3 in control LNCaP cells. B) ChIP-seq tracks (representative results) for FLAG-MED19, AR, and H3K27ac for androgen deprivation or R1881 treatment are shown for intronic regions of LRRTM3. Fold change (up (+) or down (-)) in occupancy scores for MED19 LNCaP cells compared to control LNCaP cells shown for each peak (see (PDF)Click here for additional data file.S12 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated with ethanol vehicle or R1881 . RNA-seq and ChIP-seq for FLAG-MED19, AR, and H3K27ac were performed and in biological triplicate, with the exception of ChIP-seq for AR in control LNCaP cells + R1881, where one sample was excluded from the analyses because of low signal. A) Fold change mRNA expression from RNA-seq and qPCR validation of changes in MAST4 mRNA expression . *p < 0.05; **p < 0.01; and ***p < 0.001. B) ChIP-seq tracks (representative results) for FLAG-MED19, AR, and H3K27ac for androgen deprivation or R1881 treatment are shown for promoter and intronic regions of MAST4. Fold change (up (+) or down (-)) in occupancy scores for MED19 LNCaP cells compared to control LNCaP cells shown for each peak (see (PDF)Click here for additional data file.S13 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated with ethanol vehicle or R1881 . RNA-seq and ChIP-seq for FLAG-MED19, AR, and H3K27ac were performed in biological triplicate, with the exception of ChIP-seq for AR in control LNCaP cells + R1881, where one sample was excluded from the analyses because of low signal. A) Fold change mRNA expression from RNA-seq and qPCR validation of changes in MAOA mRNA expression . B) ChIP-seq tracks (representative results) for FLAG-MED19, AR, and H3K27ac for androgen deprivation or R1881 treatment are shown for the promoter region of MAOA. Fold change (up (+) or down (-)) in occupancy scores for MED19 LNCaP cells compared to control LNCaP cells shown for each peak (see (PDF)Click here for additional data file.S14 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated with ethanol vehicle or 100 nM R1881 for 4 hours. ChIP-seq for FLAG-MED19, AR, and H3K27ac was performed in biological triplicate, with the exception of ChIP-seq for AR in control LNCaP cells + R1881, where one sample was excluded from the analyses because of low signal. A) Top 10 enriched transcription factor motifs under androgen deprivation, associated with MED19 sites in MED19 LNCaP cells (top), MED19 and AR occupied sites in MED19 LNCaP cells (middle top), AR sites in MED19 LNCaP cells (middle bottom), and AR sites in control LNCaP cells (bottom). B) Top 10 enriched transcription factor motifs with R1881 treatment, associated with MED19 sites in MED19 LNCaP cells (top), MED19 and AR occupied sites in MED19 LNCaP cells (middle top), AR sites in MED19 LNCaP cells (middle bottom), and AR sites in control LNCaP cells (bottom).(PDF)Click here for additional data file.S15 FigA) Top 10 enriched transcription factor motifs with R1881 treatment associated with sites of AR and MED19 occupancy in MED19 LNCaP cells where AR is present only in MED19 LNCaP cells, with SP1 as the top associated transcription factor. B) Gene changes associated with ELK1 knockdown and AR knockdown from Enrichr Transcription Factor Perturbation, compared to MED19 overexpression under androgen deprivation from the RNA-seq study. C) SRF knockdown is the top hit from Enrichr Transcription Factor Perturbation, associated with genes upregulated by MED19 overexpression with R1881 treatment from the RNA-seq study (top); corresponding genes changes associated with SRF knockdown compared to MED19 overexpression (bottom).(PDF)Click here for additional data file.S16 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated with ethanol vehicle or 100 nM R1881 for 4 hours. ChIP-seq for FLAG-MED19, AR, and H3K27ac was performed in biological triplicate, with the exception of ChIP-seq for AR in control LNCaP cells + R1881, where one sample was excluded from the analyses because of low signal. A) Top 10 enriched transcription factor motifs associated with AR sites in control LNCaP cells for R1881 vs. vehicle treatment, with enrichment of AR-related motifs in response to R1881 treatment. B) Top 10 enriched transcription factor motifs associated with AR sites in MED19 LNCaP cells for R1881 vs. vehicle treatment, with enrichment of AR-related motifs in response to R1881 treatment. C) Top 10 enriched transcription factor motifs associated with MED19 sites in MED19 LNCaP cells for R1881 vs. vehicle treatment, with enrichment of AR-related motifs in response to R1881 treatment.(PDF)Click here for additional data file.S17 FigMED19 LNCaP cells and control LNCaP cells were cultured under androgen deprivation for 3 days and treated with ethanol vehicle or 100 nM R1881 for 4 hours, and ChIP-qPCR for ELK1 was performed. ELK1 occupancy at previously published ELK1 sites was verified in control LNCaP cells (top) and MED19 LNCaP cells (bottom), with occupancy verified +/- R1881 for sites at A) Chr.1 and B) Chr. 6. Normalization to inputs was done and IgG is shown as a negative control. *p < 0.05; **p < 0.01; and ***p < 0.001. ns = not significant.(PDF)Click here for additional data file.S18 FigMED19 LNCaP and control LNCaP cells were cultured in androgen-containing media. Cells were lysed with RIPA buffer, input was collected, and FLAG-tagged MED19 was immunoprecipitated using FLAG antibody coupled to protein A/G agarose beads. Total protein lysates (input), FLAG immunoprecipitates, and flow through were collected and probed with antibodies for MYC (for overexpressed MED19), AR, and MED1. Tubulin was used as a loading control for input. Experiment was performed in biological duplicate, with representative results shown.(PDF)Click here for additional data file.S19 FigEach Mediator subunit or associated factor from the kinase module was depleted by siRNA and proliferation in androgen-depleted media was evaluated after 5 days, normalized to proliferation with scrambled siRNA . KIF11 knockdown is included as a positive control (black). MED19 depletion is highlighted in bold. A) Knockdown of Mediator subunits in MED19 LNCaP cells. B) Knockdown of Mediator subunits in LNCaP-abl cells. *p < 0.05; **p < 0.01; and ***p < 0.001. ns = not significant. Statistics denote comparison to scrambled siRNA. There is no statistically significant difference in growth between MED19 depletion and MED1 depletion in MED19 LNCaP cells or in LNCaP-abl cells. There is no statistically significant difference in growth between MED19 depletion and MED26/MED4/MED18/CDK19/MED12/MED27 depletion in MED19 LNCaP cells.(PDF)Click here for additional data file.S20 FigA) Full western blot from (PDF)Click here for additional data file.S1 Table76 genes are upregulated  and 75 genes are downregulated. P-values, p-adjusted values, fold changes, and gene descriptions are shown for each gene (sheet 1). Genes responsive to R1881 treatment from the RNA-seq (sheet 2), AR targets from ChEA (sheet 3), and androgen-responsive or AR targets from the literature (sheet 4) are shown.(XLSX)Click here for additional data file.S2 Table78 genes are upregulated  and 231 genes are downregulated. P-values, p-adjusted values, fold changes, and gene descriptions are shown for each gene (sheet 1). Genes responsive to R1881 treatment from the RNA-seq (sheet 2), AR targets from ChEA (sheet 3), and androgen-responsive or AR targets from the literature (sheet 4), are shown.(XLSX)Click here for additional data file.S3 Table2361 genes are upregulated and 2069 genes are downregulated. P-values, p-adjusted values, fold changes, and gene descriptions are shown for each gene.(XLSX)Click here for additional data file.S4 Table2727 genes are upregulated and 2314 genes are downregulated. P-values, p-adjusted values, fold changes, and gene descriptions are shown for each gene.(XLSX)Click here for additional data file.S5 TableTop 100 androgen-induced (sheet 1) and androgen-repressed (sheet 2) genes for control LNCaP cells and MED19 LNCaP cells, with fold changes for each shown in comparison. Genes with 1.5-fold or more change in mRNA expression in response to androgen only in control LNCaP cells (sheet 3) or only in MED19 LNCaP cells (sheet 4).(XLSX)Click here for additional data file.S6 TableOccupancy shown for androgen deprivation (sheet 1) and with R1881 treatment (sheet 2), with fold change in expression upon MED19 overexpression indicated. Androgen responsiveness of each gene in control LNCaP cells and MED19 LNCaP cells is also indicated. AR occupancy scores for LRRTM3 (sheet 3), MAST4 (sheet 4), and MAOA (sheet 5).(XLSX)Click here for additional data file.S7 TableAR occupancy scores for PSA and FKBP5 (sheet 1); and list of background peaks for IgG (sheet 2) and FLAG in control LNCaP cells (sheet 3) for ChIP-seq.(XLSX)Click here for additional data file.


However, methods for the specific mapping of m3C throughout the transcriptome are lacking. Here, we developed a m3C-specific technique, Hydrazine-Aniline Cleavage sequencing (HAC-seq), to profile the m3C methylome at single-nucleotide resolution. We applied HAC-seq to analyze ribosomal RNA (rRNA)-depleted total RNAs in human cells. We found that tRNAs are the predominant m3C-modified RNA species, with 17 m3C modification sites on 11 cytoplasmic and 2 mitochondrial tRNA isoacceptors in MCF7 cells. We found no evidence for m3C-modification of mRNA or other non-coding RNAs at comparable levels to tRNAs in these cells. HAC-seq provides a novel method for the unbiased, transcriptome-wide identification of m3C RNA modification at single-nucleotide resolution, and could be widely applied to reveal the m3C methylome in different cells and tissues.Cellular RNAs are subject to a myriad of different chemical modifications that play important roles in controlling RNA expression and function. Dysregulation of certain RNA modifications, the so-called ‘epitranscriptome’, contributes to human disease. One limitation in studying the functional, physiological, and pathological roles of the epitranscriptome is the availability of methods for the precise mapping of individual RNA modifications throughout the transcriptome. 3-Methylcytidine (m Xu et al. reported based on mass spectrometry-based (LC-MS/MS) analysis of isolated RNA fractions, that these four enzymes can be classified into two groups where METTL2A/2B/6 modify tRNAs while METTL8 modifies mRNA  contain m3C modification remains to be determined  (TRM140) responsible for tRNA mrevisiae ,19. Recereported . In humaies mRNA . Howevertermined . In addill lines ,29. This3C modification in normal cells and diseases, it is essential to have a reliable high-throughput method to site-specifically detect and quantify m3C RNA modification throughout the transcriptome. Unlike for certain other RNA modifications including m6A or m7G, due to lack of suitable anti-m3C antibodies, no antibody-based RNA immunoprecipitation and sequencing (RIP-seq) approaches have been successfully performed. Currently available approaches to detect and quantify RNA m3C levels can be classified into three types: The first approach is LC–MS/MS analysis. While this method is quantitative it cannot readily determine which sites are m3C-modified nor can it be used for the unbiased detection of the modified subset of RNA species within a bulk population. The second approach is the traditional primer extension assay. While this method can be helpful in identifying sites of modification, it can only be applied to candidate RNAs and this method is not high-throughput nor can it distinguish between m3C and other RNA modifications that disrupt base-pairing to terminate the reverse transcriptase. Another major limitation of this approach is that reverse transcription may also be arrested by RNA secondary structures and/or by other RNA modifications, both of which are very common on tRNAs. The third approach, is the next-generation sequencing based methods such as DM-tRNA-seq for mapping m1A, m1G, m2,2G and m3C RNA modifications , a chemically-based sequencing technique to unbiasedly map and quantify m3C methylomes on tRNA and other RNA species throughout the mammalian transcriptome at single-resolution.In order to better study the role of RNA mications , and Alkications . These ml-glutamine, 100 units per ml each of penicillin and streptomycin in a humidified atmosphere with 5% CO2 at 37°C.MCF7 cells were cultured in DMEM high glucose medium supplemented with 10% fetal bovine serum, 2.05 mM Small RNAs (<200 nt) were purified using the miRVana miRNA Isolation Kit (Thermo Fisher Scientific) following by the manufacturer's protocol. For RiboMinus Total RNA isolation, total RNA was first extracted using Trizol (Thermo Fisher Scientific). 10 μg total RNA was then used for ribosomal RNA depletion by using the RiboMinus™ Eukaryote Kit for RNA-seq (Thermo Fisher Scientific) following the manufacturer's protocol. For individual tRNA isolation, 10 μg small RNA was first annealed with a mix of three 1 μM biotin-labeled probes covering the entire tRNA sequence in 50 μl of annealing buffer ). The annealed RNA-probe mixture was purified using Dynabeads MyOne Streptavidin C1 magnetic beads (Thermo Fisher Scientific) following the manufacturer's protocol. RNA was released by boiling the beads in water at 95°C for 5 min and immediately cooled on ice. The isolated tRNA was further cleaned by using the Dynabeads MyOne Streptavidin C1 magnetic beads and collecting the supernatant. The following 5′ biotin-labeled probes were used: ValCAC1: 5′-AACCACTACACTACAGAAGC-3′; ValCAC2: 5′-CTTTCGCGTGTGAGGCGAAC-3′; ValCAC3: 5′-GCTTCTGCCCGGTTTCGAACC-3′; SerGCT1: 5′-CCACTCGGCCAGGCCTCTCC-3′; SerGCT2: 5′-ACAATGGATTAGCAGTCCAT-3′; SerGCT3: 5′-TCGAACCCAGGATCTCCTGT-3′. Final RNA concentrations were measured using Nanodrop. The RNA quality was confirmed using a A260/280 ratio of ∼2.0.5C and m3C) (258.1–126.1), and adenosine (268.1–136.1). Standard compounds for m5C  and m3C  were run on HPLC/MS-MS to optimize HPLC method and determine retention times for each nucleoside. For LC/MS-MS data collection and analysis, Agilent Mass Hunter LC/MS Data Acquisition Version B.08.00 and Quantitative Analysis Version B.07.01 software was used.250 ng to 500 ng RNA was digested with 100 U S1 nuclease (Thermo-Fisher # EN0321) at 37°C for 2 h and dephosphorylated with 1 U rSAP (NEB # M0371S) at 37°C for 1 h. The 100 μl samples were filtered with Millex-GV 0.22u filters (Millipore Sigma # SLGV033RS). 5–10 μl from each sample was injected into the Agilent 6470 Triple Quad LC/MS instrument with Agilent Zorbax Eclipse C18 reverse phase HPLC column. The samples were run at 500 μl/min flow rate in mobile phase buffer A (water with 0.1% Formic Acid) and 0–20% gradient of buffer B (acetonitrile with 0.1% formic acid). MRM transitions are measured for cytidine (244.1–112.1), 5-methylcytidine and 3-methylcytidine (m® BL21 Star™ (DE3) competent cells. A single transformant was cultured in LB medium containing antibiotic at 37°C overnight. Bacteria were inoculated and cultured in LB medium at 37°C until OD600 ∼0.5. Protein expression was induced by adding IPTG to a final concentration of 0.5 mM at 16°C overnight. Cells were harvested and lysed by sonication. After centrifugation, the cell lysates were collected. Recombinant His-tagged proteins were purified using Ni-NTA Agarose (Qiagen) following the manufacturer's protocol. AlkB-WT and AlkB-D135S proteins were checked by SDS-PAGE followed by colloidal blue staining and dialyzed into BC100 low salt buffer .pET30a-AlkB-WT and pET30a-AlkB-D135S plasmids from Tao Pan (Addgene plasmid # 79050 and #79051) were transfected into One Shot2, 0.1 mM (NH4)2Fe(SO4)2.6H2O, 0.6 mM 2-ketoglutarate, 4 mM l-ascorbic acid, 0.1 mg/ml BSA, and 0.1 M MES, pH 5.0. The demethylation reaction was performed at room temperature in the dark for 2 h, and quenched with 5 mM EDTA. RNAs were then purified by acid phenol–chloroform.For small RNA, 1 μg of isolated small RNA was demethylated in 100 μl of demethylation buffer containing 80 pmol of AlkB-WT, 160 pmol of AlkB-D135S, 5 μl of RNasin, and 50 μl of 2× demethylation buffer. For total RNA, 5 μg of total RNA was demethylated in 100 μl of demethylation buffer containing 160 pmol of AlkB-WT, 200 pmol of AlkB-D135S, 5 μl of RNasin, 50 μl of 2× demethylation buffer. The 2× demethylation buffer contains 0.6 M KCl, 4 mM MgCl2O, 25 μl of 3M sodium acetate, pH 5.5, and 0.75 ml of 100% ethanol. After centrifugation and washes, the RNA pellet was resuspended in 100 μl cleavage buffer  and incubated at room temperature in the dark for 2 h. RNA was then ethanal precipitated and dissolved in H2O. RNA concentration was measured by Nanodrop. 500 ng of small RNA were mixed with 2× TBE-Urea loading buffer (Bio-Rad), denatured at 95°C for 5 min, placed on ice before loading. RNA samples were then separated by 15% TBE–urea gels (Bio-Rad) at 200 V for 1 h. After electrophoresis, RNAs were transferred onto nylon membrane. Membranes were then UV-crosslinked, pre-hybridized, and blotted with p32-radioactive probes against the 3′ end of indicated tRNAs. The following probes were used: ArgCCT: 5′-CACCCCAGATGGGACTCGAA; LeuCAG: 5′-GTCAGGAGTGGGATTCGAAC-3′; SerCGT: 5′-TCGAACCCAGGATCTCCTGT-3′; ThrAGT: 5′-TCGAACCCAGGATCTCCTGT-3′; mt-ThrTGT: 5′-TGTCCTTGGAAAAAGGTTTTC-3′; ValCAC: 5′-GTTTCCGCCCGGTTTCGAACC-3′. Band intensities were quantified using ImageJ.2 μg small RNA was treated with 25 μl of ice-cold hydrazine buffer  on ice for 10 min to 4 h. Then RNA was precipitated by adding 225 μl of H2O:glacial acid:aniline = 7:3:1) and incubated at room temperature at dark for 2 h. RNA was then ethanal precipitated and dissolved in H2O. Before cDNA library preparation, HAC samples were demethylated by treating with 160 pmol of AlkB-WT and 200 pmol of AlkB-D135S in 100 μl of demethylation buffer at room temperature in dark for 2 h. RNAs were next purified by acid phenol–chloroform. Lastly, 100 ng of Ctrl, HAC, and DM-HAC RNA fragments were used for cDNA library construction by using the NEBNext Small RNA Library Prep Set (NEB) following the manufacturer's protocol. In the reverse transcription step, RNAs were incubated at 60°C for 1 h to increase the RT efficiency. cDNA libraries were sequenced with Illumina Nextseq 500. HAC-seq data analysis was performed following the m7G TRAC-seq protocol (i as the ratio of the number of reads starting at sitei+1 to the read depth of sitei+1. Position i was considered as the m3C site if it met the following three criteria: (i) the cleavage ratio in HAC group is at least 0.5; (ii) there is at least a significant (P< 0.05) 2.5-fold increase in cleavage ratio after HAC treatment (HAC) compared with untreated control (Ctrl); (iii) there is at least a significant (P< 0.05) 2.5-fold decrease in cleavage ratio if the RNAs were demethylated before HAC (DM-HAC) compared with HAC. These threshold values can be set case by case depending on the experiment. The number of cleavage sites and the cleavage ratio plots were exported for presentation in GraphPad Prism. The 7-nt sequences (3-nt upstream and 3-nt downstream of the m3C sites) were used to generate the conserved m3C motif on WebLogo  Ctrl: total RNA with demethylase treatment, without HAC; (ii) HAC: total RNA without demethylase treatment, with HAC; (iii) DM-HAC: total RNA with demethylase treatment, with HAC. 2 μg ribominus total RNA with or without demethylase treatment was fragmented by incubating in 10 μl of RNA fragmentation reagents  at 70°C for 10 min. The fragmentation reaction was stopped by adding 20 mM EDTA, pH8.0. After ethanol precipitation, RNA fragments were dephosphorylated with Antarctic phosphatase (NEB) and re-phosphorylated with T4 PNK (Thermo Fisher Scientific) following the manufacturer's protocols. After end repair, RNA was purified by using the Zymo RNA-Clean&Concentrator-5 (Zymo Research). Then hydrazine/aniline-induced cleavage (HAC) was performed for HAC and DM-HAC groups. Equal amounts of HAC and DM-HAC RNA samples were treated with 25 μl of ice-cold hydrazine buffer  on ice for 4 h. The reaction was stopped by ethanol precipitation. Next, RNA pellet was resuspended in 100 μl cleavage buffer  the reaction is specific for m3C  (< 200 nt). We first incubated smRNAs (<200nt) with 10% hydrazine with 3M NaCl for different periods of time. Then RNAs were subjected to aniline-induced cleavage of the RNA chain. LC–MS/MS analysis showed that hydrazine/aniline treatment was able to decrease m3C levels on RNA in a time-dependent manner whereas it did not affect m5C or unmodified C levels  technique for the identification of m3C modification sites on RNAs at single-nucleotide resolution  depletion, we fragmented (∼200 nt) these ribominus RNAs. We then repaired the 5′ and 3′ ends to make sure that the fragments contain the correct 5′ phosphate and 3′ OH groups. Fragmented RNAs were first treated with 10% hydrazine with 3M NaCl and then with aniline to induce the site-specific chemical cleavage. In order to get more sequence reads, we included a demethylation step  to remove other major types of methylation on small RNAs that can interfere with the reverse transcriptase production of cDNA. To validate that the cleavage sites detected by HAC-seq were due to methylation, total RNAs were pre-treated with demethylase AlkB to remove m3C on RNA before HAC treatment. Then cDNA libraries were prepared from untreated control RNA (Ctrl), HAC-treated RNA (HAC), and demethylated plus HAC-treated RNA (DM-HAC) samples with adaptor ligation and reverse transcription followed by PCR. If an RNA is m3C-modified, the hydrazine/aniline treatment cleaved the RNA and generated two or more cleaved fragments. The 3′ cleaved fragments contain the correct 5′ phosphate and 3′ OH groups for adaptor ligation for cDNA library construction. However the 5′ cleaved fragments have a 3′ damaged end which cannot be ligated with adaptors. Based on this, during the cDNA library preparation step only the 3′ cleaved fragments and the full-length RNAs could be sequenced. The sequence reads were aligned with reference RNA datasets to identify all chemically-induced cleavage sites. We next calculated the cleavage ratio at each nucleotide (i) throughout a RNA by dividing the number of reads starting at the following nucleotide (i+ 1) by the read depth at the following nucleotide (i+ 1) in the Ctrl, HAC and DM-HAC groups. m3C modification sites were determined by the following three criteria: (i) the absolute cleavage ratio in the HAC group was at least 50%; (ii) there was a significant 2.5-fold increase in cleavage ratio when comparing HAC with Ctrl; (iii) there was a significant 2.5-fold decrease in cleavage ratio when comparing DM-HAC with HAC since m3C was removed by AlkB before HAC treatment.In order to explore the global mn Figure . Our LC–n Figure  and nortn Figure  and D sh3C sites on tRNA by mapping the HAC-seq data to tRNA dataset. Our analysis revealed that C was the main nucleotide identified by HAC-seq as expected  prevented the chemical-induced cleavages  is less sensitive to the AlkB treatment than those on tRNA-Ser species with U36 (SerGCT). tRNA-Ser that decodes codons beginning with U are different from tRNA-Ser that decodes with codons that begin with A in many ways. Previous research reported that tRNA-Ser with A36 showed lower levels of m3C compared with tRNA-Ser with U36 in S. pombe  m3C32   . HoweverCAC C50)  did not ng noise .3C modification sites  of the A loop, C47d (LeuCAG) of the V loop, or C20 (MetCAT) of the D loop  could be influenced by cleavage at the downstream m3C site (e.g. m3C47d on tRNA-Ser). Absolute m3C quantification for different tRNAs might however be possible by using appropriate standards .In addition to single-nucleotide resolution mapping of me Figure . AlkB tr3C also exists on mRNA via LC-MS/MS analysis  but not the CTC1 mRNA  in their sequencing data under no-demethylase treatment condition . MI was  0.8–1.0 . Our HAC3C modification sites corresponding to 17 different m3C sites on 11 cytoplasmic tRNA isoacceptors and 2 mitochondrial tRNA isoacceptors in MCF7 cells (Figure 3C sites detected by HAC-seq and other methods including DM-tRNA-seq and HAMR are summarized in Figure 3C modification sites from Modomics database (3C modification of each modified RNA. LC-MS/MS is widely used to quantify the percentage of modifications on RNA. Xu et al. reported that m3C on tRNAs is 1000-fold greater than that on m3C on mRNA (3C-modified RNA species with high stoichiometry. Almost no mRNA or non-coding RNAs were identified as m3C positive by HAC-seq. Similarly, no m3C signals were detected on polyA-enriched yeast mRNA or non-polyadenylated yeast non-coding RNA by AlkAniline-seq (3C modification is either limited to tRNAs or is present at very low stoichiometries for other RNA species that are below the limit of detection using stringent criteria for HAC-seq analysis. Future studies combining antibody-based enrichment of m3C-containing RNAs with HAC-seq will likely help resolve this question.Our HAC-seq analysis identified 48 ms Figure . All m3Cdatabase . The cle on mRNA . Howeverline-seq . This suHAC-seq data accession number: GSE157682.gkaa1186_Supplemental_FilesClick here for additional data file.


KITLG. Genotyping with digital droplet PCR (ddPCR) confirmed a significant association between an increased copy number with the dark-red coat color in NSDTR (p = 6.1 × 10−7). The copy number of the CNV was also significantly associated with coat color variation in both eumelanin and pheomelanin-based Poodles  and across other breeds. Moreover, the copy number correlated with pigment intensity along the hair shaft in both pheomelanin and eumelanin coats. KITLG plays an important role in melanogenesis, and variants upstream of KITLG have been associated with coat color variation in mice as well as hair color in humans consistent with its role in the domestic dog.Dogs exhibit a wide variety of coat color types, and many genes have been identified that control pigment production, appearance, and distribution. Some breeds, such as the Nova Scotia Duck Tolling Retriever (NSDTR), exhibit variation in pheomelanin pigment intensity that is not explained by known genetic variants. A genome-wide association study comparing light red to dark red in the NSDTR identified a significantly associated region on canine chromosome 15 (CFA 15:23 Mb–38 Mb). Coverage analysis of whole genome sequence data from eight dogs identified a 6 kb copy number variant (CNV) 152 kb upstream of  Canis familaris, was domesticated from wolves (Canis lupus), although the exact time and place are still under scientific debate [The dog, c debate ,2,3,4. Ec debate ,6. A blac debate . Color vMC1R) and agouti signaling protein (ASIP) [MSH), promotes eumelanin synthesis [MC1R that results in the sole production of pheomelanin pigment [ASIP variant and a dominant gain-of-function variant in the gene encoding β-defensin 103 (CBD103) can result in black coat colors in dogs [The basis of the main coat color types in domestic dogs have been determined ,8,9,10. n (ASIP) . MC1R isynthesis . Disrupt in dogs ,9.MFSD12 gene encoding major facilitator superfamily domain-containing 12 [MLPH gene encoding melanophilin, resulting in abnormal clumping of pigment in the hair shaft and keratinocytes [TYRP1, which disrupt pigment production along the eumelanin pathway [MC1R and MFSD12 . By analyzing sequence coverage from whole genome sequence data, a previously identified copy number variant (CNV) on chromosome 15 upstream of the he NSDTR . PhenotyBlood and saliva samples were collected from privately owned dogs through the University of California, Davis Veterinary Medical Teaching Hospital (VMTH) or submitted directly to the study from owners and veterinarians. Collection was performed under the supervision of the UC Davis Institutional Animal Care and Use Committee (protocol #20356). Genomic DNA from blood samples was extracted using the Qiagen Gentra Puregene Whole Blood Extraction Kit . Saliva samples were collected with Performagene Animal Collection swabs  and extracted with the provided kit and protocol. Breed, date of birth, sex, and color were provided by owners.n = 23) or dark (n = 13) based on visual inspection of their coat colors. The dogs were inspected in person or from photographs of the dogs in diffused lighting and graded by a single individual. Quality control and a Chi-square association analysis was performed with PLINK 1.9 [p ≤ 4.7 × 10−7). Figures were created with R packages qqman [Single-nucleotide variant (SNV) genotyping of 35 NSDTR was performed on the Illumina Canine HD 170 K BeadChip , which is mapped to the CanFam2.0 reference genome ,19. DogsLINK 1.9 ,21. All es qqman , GenAbeles qqman .https://genome.ucsc.edu/index.html) [Whole-genome sequencing of 100 canine genomes was performed as described by Brown et al. . Thirteeex.html) ,28,29,30ex.html) ,32. Coveex.html) . The aveKITLG CNV were designed from the CanFam3.1 reference genome  with a 5′ 6-FAM (fluorescein) dye and quenched with 3′ Zen Iowa Black dye as designed by Integrated DNA Technologies . A control gene assay for ETS proto-Oncogene 1 (ETS1) was designed by BioRad with HEX (hexachloro-fluorescein) dye to a region conserved across mammalian Taxa chr5:6,166,572–6,166,747 (BioRad Assay ID: 10042961). Prior to PCR, DNA was digested with New England Biosciences AvaII restriction enzyme . The copy number was calculated using the BioRad QX200 software standard protocol as a ratio of positive KITLG droplets to positive ETS1 droplets over all accepted droplets. Sample and primer concentrations were optimized to produce single templates per droplets. Statistical analysis of the results and generation of figures was performed with GraphPad Prism Version 8 . A Mann-Whitney test was used to evaluate the differences between dark and light coat colors in NSDTRs and Poodles.Copy number quantification of samples was performed with a digital droplet PCR (ddPCR) assay on a BioRad QX200 Droplet Digital System . Primers for the In the Poodle, coat color information was provided by the owners . For breeds like the NSDTR, whose color variation is not recognized in the registered color of the dog, further quantification of the hair color from root to tip was performed by photographing the dogs’ coats under diffused light using a WhiBal G7 photography gray card . The photos were then color corrected and analyzed for the mean color depth at the root and tip of the hair shaft using the GNU Image Manipulation Program (GIMP version 2.10.8). The difference from root to tip was used to compare high and low copy number dogs and evaluated for statistical significance with a linear regression test in GraphPad Prism.MFSD12 ; p.(Arg51Cys))) associated with lighter pheomelanin coat color [Bonferronip of <0.05 on CanFam2 chr15: 32,383,555    B. Analysated SNV C.KITLG.In an initial screen for candidate causative variants, paired-end whole genome sequences of six light red NSDTR, one dark red NSDTR, and one dark red Irish Setter were investigated for variants in the critical region surrounding the best associated SNV. There were 81,560 SNV and 53,277 small insertion/deletion (indel) variants identified, and 112 of those variants segregated with coat color intensity (90 SNVs and 22 indels). None of these 112 variants were predicted to have protein coding effects, and none of the variants were in highly conserved regions of the genome. This region was also visually inspected on alignment files to screen for large indels or structural changes that were not identified by the variant calling software, and a literature search was performed to identify any known large structural variants in the region. Preliminary analysis of the aligned sequences of the abovementioned eight dogs plus an additional six medium red NSDTR showed that coverage over a previously identified 6 kb CNV at CanFam3.1 chr15:29,821,450–29,832,950 segregated with coat color, with dark red dogs showing an increase in the relative copy number compared to light red dogs A. Note tKITLG CNV with the dark red coat color in the NSDTR, 26 dark red and 32 light red dogs confirmed to be homozygous for the loss of function alleles at MC1R and wild type for the cream-associated allele at MFSD12 were genotyped at the CNV with a digital droplet PCR (ddPCR) assay. Dark red NSDTR (median = 5 genomic copies) had a significantly higher copy number of the CNV compared to light red NSDTR  (n = 50), a breed characterized by intense dark red pigment, had a median genotype of eight genomic copies. Another breed with dark red pigment, the Brittany (n = 4), had a median genotype of seven genomic copies. To validate the association of the high copy number at the  × 10−7) B. After n = 26) to red Poodles (n = 26) showed a significant increase in the copy number in red Poodles (median = 8 genomic copies) compared to cream   were not included in the analysis. Two other phenomenalistic breeds with non-functional MC1R that show coat color variation are the Golden Retriever (GR) and the yellow Labrador Retriever (LR). Their variation is not explained by the variant identified at MFSD12 [p = 0.9977, light n = 8, medium n = 19, dark n = 8; LR, p = 0.7377, light n = 8, dark n = 8). The breeds and number of dogs of each coat color are shown in In order to confirm the segregation of the high copy number with dark red coats, comparisons were made of cream- and red-colored Poodles A. the co × 10−9) B. Cream KITLG. Black Poodles (n = 25) had a significantly higher copy number (median = 6 genomic copies) compared to light grey  dogs (p = 1.5 × 10−8) (n = 7), and black breeds like the Border Collie , the Flat-Coated Retriever , and the Rottweiler . Additionally, grey wolves with unknown coat colors from North America (n = 2), Asia (n = 1), Europe (n = 1), and Africa (n = 1) were genotyped and all were found to have a very low copy number (approximately two genomic copies)  B. Breeds copies) . p = 0.0035) (p = 4.3 × 10−8)  B. Additi × 10−8) C.KITLG that was highly associated with pheomelanin intensity within the NSDTR (p < 0.0001) and in other breeds. The copy number of this CNV is significantly associated with pheomelanin and eumelanin intensity in the Poodle and across breeds. All tested wolves had two copies of this element, indicating that chromosomes with higher copy numbers and high pigment intensity represent a derived state. Pigment intensity is due to the differing distribution of pigment along the hair shaft, with low copy number individuals having lighter hair at the root while more intensely pigmented individuals have no difference in pigment intensity from the root to hair tip. In the Poodle breed, the hair is continuously growing, such that the difference in colors associated with the different CNV alleles are particularly striking. Segregation of pheomelanin intensity in the NSDTR enabled identification of a region on CFA 15 associated with pigment intensity in dogs. Variant analysis identified a 6 kb CNV upstream of KITLG and its receptor, KIT, have previously been shown to function in hematopoiesis, melanogenesis, and gametogenesis [KITLG is important for postnatal cutaneous melanogenesis [KITLG expression is essential in hair follicle epithelial cells for melanocyte terminal differentiation [KITLG plays in melanogenesis, both developmentally and in the hair shaft, is consistent with its identification as a pigmentation intensity locus in dogs.ogenesis . Pleiotrogenesis . In thisogenesis ,37. Thesogenesis . Specifintiation . The essKITLG and its receptor KIT cause pigmentation alternations in humans. Coding sequence variants in KITLG have been shown to cause familial progressive hyperpigmentation with or without hypopigmentation [KITLG was associated with hair color in a genome-wide association scan in Icelanders and Dutch [KITLG showing a strong signature of selection in humans [KITLG has diverse important developmental functions; however, the alternation of just hair follicle expression may be a means to limit the effects to pigmentation phenotypes.Variants of entation  as well entation . Additioentation ,43,44,45nd Dutch . This van humans ,47,48. Gn humans . KITLG hKITLG (or Kitl in the mouse) have been identified as causing pigment differences in other animal species. The original Kitl mouse mutants were called steel due to their uniformly diluted coat colors [Kitl, which also leads to diluted pigmentation [KITLG expression is lower in lighter-colored animals as compared to more darkly pigmented animals [KITLG were not evaluated due to challenges in obtaining skin samples from healthy pet dogs; however, based on the work done in other species, the CNV likely alters expression levels of KITLG in the hair follicle.Regulatory variants of t colors . The steentation ,51. In b animals ,53. In tKITLG CNV, some breeds with pheomelanin variation did not. Notably, the Golden Retriever and Labrador Retriever had variability at the CNV, but it did not correlate to their coat colors, indicating that there are still additional pigment intensity loci or variants to be identified in dogs. It is not surprising that many loci affect pigmentation since dogs are under such strong artificial selection for the variety in coat colors.While many dog breeds tested had a pigment intensity correlated to the KITLG was previously associated with susceptibility for digital squamous cell carcinoma (DSCC) in darkly colored dogs [MC1R. KITLG variants have been implicated in melanoma, testicular cancer, colorectal cancer, and general cancer risk in humans [KITLG CNV is another risk factor for breeds that are already more susceptible to this particular tumor type. Alternatively, some black dogs with high KITLG copy numbers may also have protective alleles. The canine CNV upstream of red dogs . The anan humans ,56,57,58n humans . Black-pn humans . Howevern humans . It is pKITLG that affect hair color are located in a similar region to the CNV identified in dogs. Conservation tracks within the CNV are 86.1% identical to the human sequence. The human homologous region to the canine CNV is located 200 kb upstream of human KITLG. The strongest selective signature for KITLG in humans lies 218 kb upstream of the coding sequence [KITLG [KITLG is under selective pressure as it was one of the top 20 regions of the genome to have a signature of selection within domestic dogs [Regulatory variants of human sequence  while the [KITLG . In dogstic dogs ,62. InteKITLG CNV is associated with coat pigment intensity in the domestic dog. An increase in the copy number is associated with a darker, more intense appearance to the coat and a more uniform pigment intensity across the hair shaft in many breeds. Wolves do not show copy number variation at this locus, and previous genome-wide screens have identified the KITLG CNV region as being under selection in domestic dogs. KITLG plays an important role in melanogenesis, both in development and in the adult hair follicle, and genetic variants upstream of KITLG in both mice and humans are associated with coat and hair color variation. This research suggests that the KITLG CNV is a new intensity locus in domestic dogs.The


Spatial patterns in the reef fish assemblage were primarily influenced by habitat-associated variables . The biomass of target reef fish was greatest in Ha’apai (~820 kg/ha) and lowest in Vava’u (~340 kg/ha), and was negatively associated with higher human influence and fishing activity. Overall mean reef fish biomass values suggest that Tonga’s reef fish fishery can be classified as moderately to heavily exploited, with 64% of sites having less than 500 kg/ha. This study provides critical baseline ecological information for Tonga’s coral reefs that will: (1) facilitate ongoing management and research; and (2) enable accurate reporting on conservation targets locally and internationally.Despite increasing threats to Tonga’s coral reefs from stressors that are both local (e.g. overfishing and pollution) and global (e.g. climate change), there is yet to be a systematic assessment of the status of the country’s coral reef ecosystem and reef fish fishery stocks. Here, we provide a national ecological assessment of Tonga’s coral reefs and reef fish fishery using ecological survey data from 375 sites throughout Tonga’s three main island groups , represented by seven key metrics of reef health and fish resource status. Boosted regression tree analysis was used to assess and describe the relative importance of 11 socio-environmental variables associated with these key metrics of reef condition. Mean live coral cover across Tonga was 18%, and showed a strong increase from north to south correlated with declining sea surface temperature, as well as with increasing distance from each provincial capital. Tongatapu, the southernmost island group, had 2.5 times greater coral cover than the northernmost group, Vava’u (24.9% and 10.4% respectively). Reef fish species richness and density were comparable throughout Tongatapu and the middle island group, Ha’apai (~35 species/transect and ~2500 fish/km Coral reefs are increasingly threatened by cumulative human-induced disturbances , 2. ThesAssessing the ecological status of a country’s coral reefs and associated fishery resources requires a comprehensive assessment of both benthic habitat structure, reef fish communities, and exploited species at large spatial scales , 12. WitCoral reef community structure and condition is likely to be determined by complex interactions between socioeconomic and environmental variables –28. ManaAs with many other South Pacific nations, coral reefs in Tonga are increasingly threatened . In the While several local-scale projects and reports exist , there iA clear gap exists in the information available to support a national-level assessment of Tonga’s coral reefs and reef fish fishery for government, managers, and other stakeholders. The aim of this study was therefore to compile and analyse the first national dataset on the current ecological status of Tonga’s coral reefs (375 sites) and provide baseline ecological information to facilitate ongoing management and research. In addition, we used boosted regression trees to test the relative association of eleven socio-environmental variables with seven key metrics of reef health. Specifically, we ask: i) What are the differences and similarities in benthic cover , fish diversity and abundance, and target fish biomass among the main island groups of Tonga? and ii) What are the most influential variables associated with reef status across Tonga’s coral reef ecosystem?From 2016 to 2018, 375 sites were surveyed across Tonga as part of four separate projects but using a standardized methodology  and reef slope were also collected for each site on a five-point scale from low and sparse relief (score = 1) to exceptionally complex with numerous caves and overhangs (score = 5), and from < 10o (score = 1) to 90o (score = 5), respectively [ectively .www.benthobox.com) to assist with the benthic annotations. BenthoBox automatically classifies points into benthic substrate categories from images based on training provided by a human annotator. The aim of the automated annotation method is to learn from human annotations and automatically analyze the remaining images to within an acceptable margin of error [Benthic community composition was estimated using image analysis of ten 1 x 1 m benthic photoquadrats per transect with 15 points randomly overlaid across each image . Given the large number of images and points required for annotation , we used the machine learning software BenthoBox . In addition, no benthic data were collected at 32 sites surveyed in the Vava’u island group. For these sites, only fish-related analysis was conducted.2) and the biomass of target species (kg/ha) larger than 20 cm. We selected this size cut off for biomass because it represents the fishable biomass of target reef fish species that is likely to be targeted by fishers.Seven metrics of reef status were used to assess the current ecological condition of Tonga’s coral reef ecosystem and reef fish fishery. Based on acceptable error rates in benthic annotations, benthic response variables were hard coral, soft coral, CCA and turf algae. For the status of coral reef fishes and Tonga’s reef fish fishery, we included total reef fish species richness (n/transect) and density  and seven were spatially continuous across Tonga’s coral reefs . Details of how these variables were calculated are available in the Eleven socio-environmental variables known to affect coral reef community structure across Tonga were selected as potential explanatory variables . Three oThe seven variables of reef condition were compared between island groups using generalized linear models with Tukey’s post hoc comparisons. Models were fitted with either raw data, log, or log(x+1) transformations and model performance assessed by i) comparing AIC scores ii) visual inspections of qqplots and plotted fitted vs. residuals and iii) calculating goodness of fit and overdispersion. Analysis outputs are available in gbm.step routine in the dismo package [ggBRT package [Variables potentially influencing reef condition in Tonga were then explored using boosted regression tree (BRT) models , 60. All package  within t package . BRTs fi package . Overfit package . BRTs ar package , the relOptimal model parameters  for each BRT were determined by running all iterations and selecting the one with the greatest explained deviance and a minimum of 1000 trees . Coral reef communities around many of the shallow, fringing reefs in the inner, enclosed areas of Vava’u appeared to be characterized by little or no hard or soft coral cover. Sites near the mouths of the two large estuarine lagoons in Vava’u often had 0% live coral cover and large numbers of Diadema sp. sea urchins, which appeared to be destroying the reef matrix. Coral cover in the Ha’apai island group increased gradually from north to south, with exposed sites in the southern islands  having the greatest cover of the sites assessed. Likewise, sites along the outer western islands  generally had greater coral cover than the sheltered sites along the margins of the ribbon islands in north eastern Ha’apai (e.g. Foa and Lifuka). There was widespread evidence of damage from multiple cyclones and bleaching events along the western, sheltered edges of the north-east ribbon islands . With the exception of southern Ha’apai, live coral cover in Tongatapu and near the capital Nuku’alofa was consistently greater than elsewhere in Tonga. Most sites within the central bay, and even fringing reefs adjacent to the city centre, had moderate coral cover. As in Ha’apai, there was evidence of bleaching damage along back reefs of the north-eastern ribbons from Tao to Nuku island. As in Vava’u, near the mouth of the Fanga’uta lagoon, Tongatapu, there were large numbers of Diadema sp. sea urchins and very low (often 0%) live coral cover.Overall, mean hard coral cover across the 343 sites in Tonga for which benthic data were available was 18% (+/- .625 SE). However, mean hard coral cover in Vava’u was less than half that of the other island groups , and thiPatterns of CCA cover did not vary significantly between island groups in Tonga. Conversely, there were substantial differences in the mean cover of turfing algae throughout Tonga. These patterns were largely the inverse of live coral cover, with the greatest cover in Vava’u and lowest in Southern Ha’apai.A total of 510 individual reef fish species  the spatial distribution of observed values across all surveyed sites; ii) the relative influence of each predictor variable; iii) the relationships of the most influential predictors; and, iv) significant interactions between influential variables.The five most influential predictors of hard coral cover in Tonga were SST, distance from the provincial capital, habitat rugosity, reef density, and wave energy . NegativThe three most influential predictors of soft coral cover in Tonga were SST, distance from the provincial capital, and wave energy . There w2). This relationship breaks down with greater terrestrial influence. As with coral cover, SST was negatively associated with the percent cover of CCA. Three variables had interactions with habitat rugosity , all of which predicted greater cover of CCA at higher rugosity levels. The model explained 51% of the cross-validated deviance.The six most influential predictors of CCA in Tonga were habitat rugosity, distance from the provincial capital, reef density, depth, land area, and SST . The two2) and plateaued at levels greater than this. For turf algae, depth displayed the opposite relationship to that for tuCCA, with greatest cover at 5 meters depth and lower levels in shallower and deeper water. There were five interactions that predicted turf cover. Taken together, the results suggested that turf algae was most dominant in shallow, low-complexity reefs that were close to human influence and in warmer waters. The model explained 54% of the cross-validated deviance.The five most influential predictors of turf algae cover in Tonga were distance from the provincial capital, habitat rugosity, SST, depth, and land area . The relThe four most influential predictors of reef fish species richness were habitat rugosity, hard coral cover, distance from provincial capital, and project . Specieso. More complex reefs, with rugosity scores above 3, also had greater densities of reef fish. The relationship between reef fish density and reef density was slightly negative. Three interactions between influential variables were present. Taken together, the partial plots and interactions predicted that reef fish density was greatest with high coral cover and mid-sloped reefs with relatively low reef density nearby. The model explained 30% of the cross-validated deviance.The four most influential predictors of reef fish density were hard coral cover, reef slope, habitat rugosity, and reef density . Reef fiThe six most influential predictors of target species biomass were habitat rugosity, distance from the provincial capital, wave energy, hard coral cover, land area, and fishing pressure . BiomassWe provide the first national description of the status of Tonga’s coral reef ecosystem and reef fish fishery. Clear differences exist in the structure of coral reef ecosystems along a latitudinal gradient that corresponds to the different island groups of the country. These differences appear to be well explained by a combination of both natural biophysical variables , and anthropogenic influences . Overall live coral cover was low (18%) but comparable to other regions throughout the Pacific affected by anthropogenic pressures . Reef fiThe strongest and most common two predictors of reef structure in Tonga were habitat rugosity and distance from the provincial capital. Habitat rugosity is a well-established driver of reef community structure, and is linked to both natural processes and anthropogenic disturbances , 68. GivThe negative effects of increased SST on scleractinian corals is well documented , 69. HowPatterns of reef fish diversity and density in Tonga appear to be associated primarily with natural biophysical variables, such as reef complexity (i.e. rugosity), slope, and hard coral cover. The importance of both structural complexity and live coral cover as influences on coral reef fish communities is well recognized  and, although often collinear, it is nonetheless important to distinguish the two. While scleractinian corals are the dominant habitat-forming organisms on reefs, larger-scale structural complexity can be more strongly associated with long-term patterns of reef accretion than the immediate presence of live coral, as well as habitat-forming organisms in addition to corals ,71. WhilBased on previous calculations of global baselines and benchmarks for fish biomass , 25, 67,Porites rus and Porites cylindrica. With these sites removed, mean coral cover for the rest of Vava’u was closer to 5%. Likewise, richness, density and biomass estimates for Vava’u were all lower than other island groups. The geography of Vava’u is unique and very different to reefs in Ha’apai and Tongatapu that are more typical of Pacific reefs. The reefs of Vava’u are generally sheltered, narrow fringing reefs below limestone cliffs and adjacent to deep (60–100 m) water. Most reefs are likely to have very little current flow and are sheltered from the open ocean and prevailing weather conditions. Reefs in Vava’u might therefore be more susceptible to impacts from both coral bleaching and local pollution. When reefs are subjected to heatwaves, coral bleaching in more open areas could be limited by flushing from cool oceanic waters, while the geography of Vava’u would limit flushing and result in pockets of warm water persisting for much longer. Likewise, pollutants from local sources are unlikely to wash away readily given the topography of the islands and instead might persist at greater concentrations. However, limited data are available on current regimes around Vava’u to investigate this hypothesis.The most prominent findings from these data are the poor results for Vava’u across all metrics of reef condition. Coral cover in Vava’u was exceptionally low (mean 10.4%), but even this was buffered by several lagoonal sites with high cover of While reefs in southern Ha’apai were generally in the best condition of those assessed, there was also extensive evidence of recent bleaching along the western edge of the northern Ha’apai ribbon reefs. As with Vava’u, part of this problem could be associated with prevailing wind, wave and current conditions, which generally move from east to west. Many of the sites in northern Ha’apai are sheltered from the east by the main islands and therefore could also trap pockets of warm water, exacerbating bleaching at a local scale. Conversely, the reefs of Southern Ha’apai are much more exposed, which might therefore promote flushing by prevailing winds, waves and currents.An additional point worth noting is that there are increasing numbers of fishers from Tongatapu travelling to Southern Ha’apai to fish, as well as fishers from Ha’apai transporting their catch to Faua Wharf in Tongatapu . Both of these factors could potentially confound the influence of local fishing pressure and distance from the provincial capital on target biomass. However, given the strength of these variables, it is still clear that fishing activities within island groups are strong predictors of fish biomass.The coral reefs around Tongatapu have experienced the greatest human pressures in Tonga, with 70% of the country’s population on this island. The Fanga’uta lagoon is highly polluted  and flowDiadema sp. sea urchins. These appeared to be scraping away the reef matrix extensively and inadvertently destroying any recruiting corals. It is possible that pollution from Tonga’s lagoonal areas might be causing outbreaks of Diadema sp. sea urchins. We recommend this possibility as a critical area for further investigation in Tonga. An additional caveat of this study was the inability to attribute reef condition to cyclone damage, which is known to affect reefs on a large scale. One possible explanation is Tonga’s small geographic extent and the small geographic extent of our surveys compared to the scale of most cyclones. Damaging wind levels in excess of 50 knots frequently affect most of the country during a single cyclone event, and this large proportional coverage may mask patterns of damage to Tonga’s reefs, given that most of the country is affected at the same time. The fine scale of surveys may therefore have been unable to detect impacts at larger spatial scales.Several sites of note were also identified with very poor coral cover (0%) in both Tongatapu and Vava’u, which warrant further investigation. In Tongatapu these were near the mouth of the Fanga’uta lagoon and in Vava’u in many of the inner island areas, particularly near the causeways. At these sites there were often no living corals and instead high densities of While fisheries management in Tonga has been historically open access, in recent years this has changed with the implementation of the Special Management Area (SMA) program. This locally driven initiative has now grown to include over 50 communities that each have at least one no-take marine protected area as well as an exclusive access zone . PreviouOur data and analysis deliver critical baseline ecological information for Tonga’s coral reefs that will both aid ongoing management and research and enable accurate reporting to local and international agencies. For example, future reports on Tonga’s coral reefs need no longer classify them as ‘data deficient’, and some degree of accountability should now be expected from governments regarding effective policies and management. There is also now a great deal more information available to support the SMA program, with data from these projects already being used to examine the impact of existing and potential new configurations of no-take reserves , 49. TheS1 File(DOCX)Click here for additional data file.