AR) is an established mechanism of antiandrogen resistance in prostate cancer. Here, we show that the magnitude of AR signaling output, independent of AR genomic alteration or expression level, also contributes to antiandrogen resistance, through upregulation of the coactivator GREB1. We demonstrate 100-fold heterogeneity in AR output within human prostate cancer cell lines and show that cells with high AR output have reduced sensitivity to enzalutamide. Through transcriptomic and shRNA knockdown studies, together with analysis of clinical datasets, we identify GREB1 as a gene responsible for high AR output. We show that GREB1 is an AR target gene that amplifies AR output by enhancing AR DNA binding and promoting EP300 recruitment. GREB1 knockdown in high AR output cells restores enzalutamide sensitivity in vivo. Thus, GREB1 is a candidate driver of enzalutamide resistance through a novel feed forward mechanism.Genomic amplification of the androgen receptor ( The prostate is a roughly walnut-sized gland that makes up part of the reproductive system in men. The normal development of this gland depends on a protein known as the androgen receptor. This protein also plays an important role in driving the growth of prostate cancers, and doctors routinely treat such cancers with drugs that block the androgen receptor. While these treatments often shrink the tumors at first, the prostate cancer cells commonly become resistant to the existing “antiandrogen” drugs, highlighting the need to find new drugs for this cancer.The main way that prostate cancers become resistant to antiandrogen drugs is by making more of the androgen receptor. As such, a better understanding of this protein’s activity may prove vital to discovering new treatments. Together with other proteins called co-factors, the androgen receptor binds to DNA and switches on a set of target genes that drive the growth of prostate cancers. The activity of these genes, referred to as “androgen receptor output”, varies between different patients with prostate cancer and even between different cells from a single patient’s tumor. This variation may occur even when the level of the androgen receptor is constant, which suggests that other factors affect the output of the androgen receptor.Lee et al. set out to discover if cells with different androgen receptor outputs, but constant androgen receptor levels, respond differently to antiandrogen drugs. First, human prostate cancer cells were separated according to their androgen receptor output. Lee et al. then treated all the cells with an antiandrogen drug known as enzalutamide: tumors grown from cells with a high output became resistant to the drug faster than cells with low output. Next, a large-scale experiment revealed the differences in gene activity between cells with high and low outputs. On average, the cells with a high androgen receptor output had more of an androgen receptor co-factor called GREB1 than the cells with a low output. Biochemical experiments showed that the GREB1 protein interacts with the androgen receptor and amplifies the expression of the receptor’s target genes. When the levels of the GREB1 protein were experimentally decreased in prostate cancer cells with a high androgen receptor output, the cells became less resistant to the antiandrogen drug.Future work will be needed to know if GREB1 levels are a good proxy for patients with high androgen receptor output. The current work predicts that those patients will respond less well to current antiandrogen drugs. A better understanding of how GREB1 and androgen receptor cooperate may also be useful for developing new drugs to treat prostate cancer. AR) targeted therapy is highly effective in advanced prostate cancer but is complicated by the emergence of drug resistance, called castration-resistant prostate cancer (CRPC) , is correlated with reduced clinical benefit from the next generation AR inhibitors abiraterone or enzalutamide (Androgen receptor (r (CRPC) . The moson of AR . The implutamide .AR transcriptional output, measured by an AR activity score  upregulated in high AR output cells, all of which promote AR transcriptional activity through a feed-forward mechanism. Of these, we prioritized GREB1 for further characterization because GREB1 mRNA levels are increased in primary prostate tumors that have high AR activity. GREB1 amplifies AR transcriptional activity through a two-part mechanism: by promoting EP300 recruitment and by enhancing AR binding to chromatin. Importantly, GREB1 knockdown converted high AR output cells to a low AR output state and restored enzalutamide sensitivity in vivo. Collectively, these data implicate GREB1 as an AR signal amplifier that contributes to prostate cancer disease progression and antiandrogen resistance.Much of the work to date has focused on inter-tumoral heterogeneity. Here, we address the topic of intra-tumoral heterogeneity in KLK3 promoter/GFP reporter (PSAP-eGFP) showed that LNCaP prostate cancer cells display varying levels of eGFP expression. Characterization of low GFP cells in this analysis revealed reduced AR levels and increased expression of stem cell and developmental gene sets  and ARsig-lo (low AR output) cells, respectively   , and havanalysis . In addianalysis . Interesanalysis . Of notepulation .AR expression in conferring sensitivity to low doses of androgen  because this model has a track record of revealing clinically relevant mechanisms of enzalutamide resistance (AR output despite similar levels of AR expression (We next asked if isolated ARsig-lo and ARsig-hi populations have different responses to ligands such as dihydrotestosterone (DHT) or antagonists such as enzalutamide. ARsig-hi cells showed enhanced sensitivity to DHT in a dose-dependent manner . This reandrogen , but nowsistance . As we dpression . Remarkapression .AR output within prostate cancer cell lines, we asked if similar, intra-tumoral heterogeneity is observed clinically by immunohistochemical analysis of KLK3 and AR expression in several primary cancers. Consistent with previous reports  inhibited AR activity when knocked down in ARsig-hi cells, with successful knockdown confirmed by qRT-PCR  served as a negative control. Interestingly, all three hits are transcriptional upregulated by DHT simulation  between GREB1 RNA level and AR output score  versus low AR scores (bottom 5%)  and highly reduced KLK3 expression in LNCaP ARsig-hi sublines expressing different sgRNAs targeting GREB1, without detectable changes in AR protein level  . To be shi cells  and redunockdown . We furtin level .GREB1 was first reported as an estrogen-regulated gene in breast cancer  into ARsig-lo LNCaP and CWR22PC-EP cells and derived stably expressing sublines  was identical in intact versus GREB1 knockdown cells and there were no differences in consensus binding sites  GREB1 knockdown impaired the induction of >70% of all DHT-induced genes  are present in over 50% of CRPC patients  androgen concentrations, the biologic consequence of high GREB1 levels could be the same growth suppression seen with high androgen concentrations. This model predicts that high AR output cells would gain a fitness advantage under conditions of androgen deprivation or pharmacologic AR inhibition, as demonstrated by the enzalutamide resistance observed in xenograft models.One curious observation is the fact that prostate cancers can maintain transcriptional heterogeneity as a stable phenotype, despite the fact that trations . BecauseGREB1 levels in CRPC patients are predictive of response to next generation AR therapy. While we show that GREB1 levels are elevated in the tumors of CRPC patients who have progressed on enzalutamide, it will be important to address this question prospectively, prior to next generation AR therapy. It is also important to note that the positive correlation of GREB1 levels with high AR activity is largely based on the hormone-naïve TCGA cohort. It is also possible that the LNCaP cell line used for functional studies has an AR point mutation could potentially influence response to GREB1 expression, but we obtained similar results in 22PC cells that lack this mutation  and maintained in RPMI (LNCaP) or DMEM (MCF7) +10% FBS . LNCaP/AR cell line was generated and maintained as previously described . CWR22PcAR reporter cells were collected using Accumax dissociation solution , and dead cells were counterstained with DAPI . For FACS-sorting of ARsig-lo and ARsig-hi cells, 5% of the entire population with lowest and highest eGFP expression was sorted out using BD FACSAria cell sorter. The 5% cutoff was used because it generates at least a 100-fold difference in median AR-GFP reporter signal between ARsig-lo and ARsig-hi cells and also allows us to have sufficient numbers of sorted cells to conduct various assays. For flow cytometric analysis of reporter activity, eGFP expression was measured using the BD-LDRII flow cytometer and analysis was done using FlowJo software.Rapidly cycling eGFP AR reporter (ARR3tk-eGFP/SV40-mCherry) was generated by switching 7xTcf promoter of 7xTcf-eGFP/SV40-mCherry  with probasin promoter containing 3xARE (ARR3tk)  . For shR(ARR3tk)  with mChshAR.177: TAGTGCAATCATTTCTGCTGGCshGREB1-1: TTGTCAGGAACAGACACTGGTTshGREB1-2: TTTCAGATTTATATGATTGGAGshGREB1-3: TTGACAAGATACCTAAAGCCGAshKLF8.3467: TTGAGTTCTAAAGTTTTCCTGAshKLF8.2180: TATTTGTCCAAATTTAACCTAAshKLF8.2684: TTATAAAACAATCTGATTGGGCshGHRHR.544: TAAAAGTGGTGAACAGCTGGGTshGHRHR.1571: TTTATTGGCTCCTCTGAGCCTTshGHRHR.1583: TTCATTTACAGGTTTATTGGCTshEP300-1: TCCAGAAAGAACTAGAAGAAAAshEP300-2: TTAATCTATCTTCAGTAGCTTGshNCOA1-1: TTCTTCTTGGAACTTGTCGTTTshNCOA2-1: TTGCTGAACTTGCTGTTGCTGAshNCOA2-2: TTAACTTTGCTCTTCTCCTTGCGREB1, KLF8 and GHRHR in a small-scale shRNA screen, and shGREB1-1 was used for further studies. For CRISPR/Cas9 experiments, lentiCRISPRv2 vector gifted by F. Zhang  was used with the following guide sequences designed using http://crispr.mit.edu/ website:shRenilla was previously described as Ren.713 targeting Renilla luciferase . Pools oSgGREB1-7: AGGCATGTCCTGCGTGCCGCGREB1 cDNA plasmid was constructed by cloning GREB1 cDNA from pCMV6-GREB1 into Tet-inducible pLV-based lentiviral expression vector with HA-tag.SgGREB1-8: TCACGGGCATACGAGCAGTA sgNT was previously described . pCMV6-GAR reporter cell line, cells were infected with ARR3tk-eGFP/SV40-mCherry at low multiplicity of infection (MOI) to enable each cell has one copy of reporter construct, and the transduced cells were sorted by mCherry flow cytometry. To inactivate GREB1 gene, we single-cell cloned the cells infected with lentiCRISPRv2 vector containing SgGREB1-7 or SgGREB1-8, and isolated a clone that had genomic alteration at target sequence. Three clones were generated by using SgGREB1-7  and one clone was generated by using SgGREB1-8 (SgGREB1-8-2).Lentiviral transduction of cells was performed as described previously . To make5 cells per well, Corning, 353043) and each well was infected with pool of 3 SEPC shRNAs against each gene on the following day. Cells with stable integration of hairpins were selected with 2 μg/ml puromycin. 9 days after infection, half of the cells in each well was used to analyze eGFP AR reporter activity using flow cytometry, and the other half was subjected to qRT-PCR to determine knockdown level of the gene. We performed the screen in duplicate and each replicate included wells infected with shRenilla or shAR as controls. The median fluorescence intensity (MFI) of eGFP was measured using FlowJo software. The shRNAs decreased eGFP MFI more than 1.5 fold compared to shRenilla  in both duplicate were considered as hits. The list of 33 genes used in the screen and the summary of median eGFP intensity can be found at FACS-based small-scale shRNA screen with 33 selected genes was performed as follows: FACS-sorted ARsig-hi cells were plated in 12 well plate  and regular culture medium , and enzalutamide treatment was initiated on the day of injection. To test the effect of GREB1 knockdown on development of enzalutamide resistance, FACS-sorted ARsig-hi population derived from LNCaP/AR was infected with control or three different shGREB1 constructs 2 days after sorting. Cells with stable integration of hairpin were selected with 2 μg/ml puromycin. 5 days after infection, 2 × 106 cells were injected subcutaneously into the flank of castrated CB17 SCID mice , and enzalutamide treatment was initiated on the day of injection. The same cell populations used for injection were also used to test eGFP AR reporter activity using flow cytometry, and qRT-PCR to test knockdown level of GREB1. Measurements were obtained weekly using Peira TM900 system . All animal experiments were performed in compliance with the approved institutional animal care and use committee (IACUC) protocols (#06-07-012) of the Research Animal Resource Center of Memorial Sloan Kettering Cancer Center.To compare time to acquire enzalutamide resistance in vivo, FACS-sorted bulk, ARsig-lo and ARsig-hi populations derived from LNCaP/AR were cultured for 6 days after sorting to obtain enough number of cells for xenograft assay. 2 × 10Protein was extracted from cells using Triton lysis buffer and quantified by BCA Protein Assay . Nuclear/cytoplasmic fractionation was achieved with Subcellular Protein Fractionation Kit . Protein lysates were subjected to SDS-PAGE and immunoblotted with the following antibodies against: AR , KLK3 , FKBP5  TRPM8 , tubulin , Cyclophilin B , BRD4 , TOP2B , HA .For AR immunoprecipitation, at least 1.5 mg of total protein was incubated with AR antibody  overnight at 4°C followed by the addition of Protein A/G agarose beads  for 2 hr. Immune complexes were extensively washed with Triton buffer and solubilized using Laemmli sample buffer .For immunofluorescence staining, cells were fixed with 4% formaldehyde, permeabilized with 0.2% Triton-X, blocked with 5% normal goat and 5% normal horse serum, stained with anti-AR  primary and Alexa Fluor 647 (Invitrogen) secondary antibodies, and mounted with DAPI mounting solution . For Immunohistochemistry, tumor sections were stained with anti-AR  and KLK3  antibodies using Leica Bond RX .AR target gene upregulation, cells were hormone-deprived in 10% charcoal-stripped dextran-treated fetal bovine serum (Omega Scientific) media for 2 days and then treated with indicated concentration of DHT for 24 hr. Triplicate measurements were made on at least three biological replicates. The primer sequences used for q-PCR are listed at Total RNA was isolated using the QiaShredder kit  for cell lysis and the RNeasy kit (Qiagen) for RNA purification. For quantitative PCR with reverse transcription (RT–qPCR), we used the High-Capacity cDNA Reverse Transcription Kit  to synthesize cDNA according to the manufacturer's protocol. Real-time PCR was performed using gene-specific primers and 2X SYBR green quantfast PCR Mix . Data were analyzed by the DDCT method using GAPDH as a control gene and normalized to control samples, which were arbitrarily set to 1. To test DHT-induced http://broadinstitute.github.io/picard/). Normalization of feature counts was done using the DESeq2 package (http://www-huber.embl.de/users/anders/DESeq/). Differentially expressed genes were defined as a 1.5 fold difference, p<0.05 of DESeq-normalized expression. For GSEA, statistical analysis was performed with publicly available software from the Broad Institute (http://www.broadinstitute.org/gsea/index.jsp). The basal and luminal gene signatures used for GSEA  with 50 base pair (bp) reads. Partek Genomics Suite software  was used to analyze differentially expressed genes between ARsig-lo vs. ARsig-hi . To analyze RNA-seq data from ARsig-hi cells with shRenilla vs. shGREB1, reads were aligned to the NCBI GRCh37 human reference using STAR aligner . Quantiffor GSEA  were genfor GSEA . Full deChIP experiments were performed as previously described , using SFor ChIP–seq, library preparation and RNA-seq were performed by the NYU Genome Technology Center. Libraries were made using the KAPA Biosystems Hyper Library Prep Kit , using 10 ng of DNA as input and 10 PCR cycles for library amplification. The libraries were sequenced on a HiSeq 2500, as rapid run v2 chemistry, paired-end mode of 51 bp read length.http://broadinstitute.github.io/picard/index.html) and removed. The software MACS2 .The ChIP-seq reads were aligned to the human genome  using the program BWA  within the PEMapper. Duplicated reads were marked by the software Picard re ChAsE , and there ChAsE . The MEMre ChAsE  was applhttp://www.cbioportal.org). Tumor purity content was estimated computationally using the ABSOLUTE method   and PCF/E method , based oE method .http://www.broadinstitute.org/gsea/index.jsp). The sample size estimate was based on our experience with previous experiments ; Myles Brown (Reviewer #3).Thank you for submitting your article "The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:GREB1 amplifies androgen output in prostate cancer cell lines and does so by 2 mechanisms including enhancing AR binding and co-activator activity has implications for resistance of PC to enzalutamide and other AR targeting agents.The finding that Essential revisions:1) Statistical tests/results should be added to the figure panels and should be more clearly specified throughout the text and in the figures.2) The authors use both LNCAP and LNCaP with stably over-expressing AR (for xenograft work and based on previous studies). The use of these models needs to be clarified in the figures  as it is currently hard to follow.GREB1 expression across the full data set should be shown and the ARsig-hi and ARsig-lo marked on this?3) Figure 2F. Please indicate what AR score was used and what is the y axis? Ideally the distribution for GREB1 in CRPC tumors correlate with a poor clinical response to enzalutamide, analogous to the prognostic impact of AR gene amplification". This is inaccurate. The authors show increased expression in tumors collected at progression vs. baseline. Have these been controlled for tumor fraction and other potential biases?4) Regarding Figure 4H, in the Discussion the authors conclude "we show that elevated levels of 5) There are a few grammatical errors in the figure legends, such as in that of Figure 4H. Please correct. Essential revisions:1) Statistical tests/results should be added to the figure panels and should be more clearly specified throughout the text and in the figures.Thank you for noting this. We have added statistical results in revised Figure 1D, 1H, 1I, Figure 1—figure supplement 1B, 1E, Figure 1—figure supplement 3B, Figure 2E, Figure 2—figure supplement 1A, Figure 3A, 3B, 3E, 3F, 3G, 3H, Figure 3—figure supplement 1A, 1B, Figure 3—figure supplement 2A, 2C, Figure 3—figure supplement 3A, 3B, 3D, 3E, Figure 4A, 4C, Figure 4—figure supplement 1A and 1E. We have also edited the figure legends to specify the statistical analysis used in each figure.2) The authors use both LNCAP and LNCaP with stably over-expressing AR (for xenograft work and based on previous studies). The use of these models needs to be clarified in the figures  as it is currently hard to follow.We appreciate the feedback and have specified the cell lines throughout in the figures and figure legends.3) Figure 2F. Please indicate what AR score was used and what is the y axis? Ideally the distribution for GREB1 expression across the full data set should be shown and the ARsig-hi and ARsig-lo marked on this?GREB1, GHRHR and KLF8 expression across the 333 TCGA tumors. We have marked ARsig-lo and ARsig-hi cases to show that the expression of GREB1, but not GHRHR or KLF8, is increased in ARsig-hi cases . We have also analyzed the Pearson’s correlation (r) between RNA level of each gene and AR score across the TCGA tumors and observed that only GREB1 expression shows significantly positive correlation with AR score .We used the same AR score as in the TCGA study . The original Figure 2F did not have y-axis. As suggested by the reviewers, we’ve combined the original Figure 2F and 2G and now show the distribution of 4) Regarding Figure 4H, in the Discussion the authors conclude "we show that elevated levels of GREB1 in CRPC tumors correlate with a poor clinical response to enzalutamide, analogous to the prognostic impact of AR gene amplification". This is inaccurate. The authors show increased expression in tumors collected at progression vs baseline. Have these been controlled for tumor fraction and other potential biases?GREB1 is a prognostic biomarker .We thank the reviewers for pointing this out and have edited the Discussion to clarify this. We did not intend to claim that GREB1 expression is not caused by different tumor content between samples. These analyses are presented in new Figure 4—figure supplement 1F.Regarding tumor purity, we have analyzed tumor content and stromal signature scores of patient samples as described previously . We observed no significant change in both tumor purity content and stromal signature score between pre- vs. post-enzalutamide setting, suggesting that increased 5) There are a few grammatical errors in the figure legends, such as in that of Figure 4H. Please correct.We appreciate the feedback and have corrected grammatical errors in figure legends.


Drosophila, reduced insulin/IGF signalling (IIS) extends lifespan by coordinating activation of Aop, an ETS transcriptional repressor, and Foxo, a Forkhead transcriptional activator. Aop and Foxo bind the same genomic loci, and we show that, individually, they effect similar transcriptional programmes in vivo. In combination, Aop can both moderate or synergise with Foxo, dependent on promoter context. Moreover, Foxo and Aop oppose the gene-regulatory activity of Pnt, an ETS transcriptional activator. Directly knocking down Pnt recapitulates aspects of the Aop/Foxo transcriptional programme and is sufficient to extend lifespan. The lifespan-limiting role of Pnt appears to be balanced by a requirement for metabolic regulation in young flies, in which the Aop-Pnt-Foxo circuit determines expression of metabolic genes, and Pnt regulates lipolysis and responses to nutrient stress. Molecular functions are often conserved amongst ETS TFs, prompting us to examine whether other Drosophila ETS-coding genes may also affect ageing. We show that five out of eight Drosophila ETS TFs play a role in fly ageing, acting from a range of organs and cells including the intestine, adipose and neurons. We expand the repertoire of lifespan-limiting ETS TFs in C. elegans, confirming their conserved function in ageing and revealing that the roles of ETS TFs in physiology and lifespan are conserved throughout the family, both within and between species.Ageing populations pose one of the main public health crises of our time. Reprogramming gene expression by altering the activities of sequence-specific transcription factors (TFs) can ameliorate deleterious effects of age. Here we explore how a circuit of TFs coordinates pro-longevity transcriptional outcomes, which reveals a multi-tissue and multi-species role for an entire protein family: the E-twenty-six (ETS) TFs. In  Pnt and Aop, and Foxo, a forkhead TF with evolutionarily-conserved pro-longevity functions. This leads us to demonstrate that the activity of the majority of ETS TFs in multiple tissues and even different animal taxa regulates lifespan, indicating that roles in ageing are a general feature of this family of transcriptional regulators.Understanding the basic biology of ageing may help us to reduce the burden of ill-health that old age brings. Ageing is modulated by changes to gene expression, which are orchestrated by the coordinate activity of proteins called transcription factors (TFs). E-twenty six (ETS) TFs are a large family with cellular functions that are conserved across animal taxa. In this study, we examine a longevity-promoting transcriptional circuit composed of two ETS TFs,  Foxo) orthologues, have been studied extensively in this context. This effort has been driven by the association of Foxo3a alleles with human longevity[Caenorhabditis elegans, the pro-longevity activity of Daf-16 is orchestrated with further TFs including Hsf, Elt-2, Skn-1, Pqm-1 and Hlh-30/Tfeb [Drosophila, two members of this family, namely Aop (a.k.a. Yan) and Pnt, have been linked to ageing via genetic interactions with Foxo and IIS[C. elegans [Ageing is characterised by a steady systematic decline in biological function, and increased likelihood of disease. Understlongevity; and thelongevity–11. Foxo-30/Tfeb –15. Examo and IIS, and sim elegans . These fAop is one such repressor in Drosophila. Aop and its human orthologue Tel are thought to repress transcription by competing with activators for binding sites[Aop's role in physiology must be explored in the context of its interactions with additional TFs, especially activators. Foxo is one such activator[Foxo and Aop are required for longevity by IIS inhibition[in vivo. Whilst activating either in the gut and fat body extends lifespan, the effect of activating both is not additive. Furthermore, if Aop is knocked down, activating Foxo not only ceases to extend lifespan, but even becomes deleterious for lifespan[Aop and Foxo, and that there is a redundancy in the function of the two TFs, even though Foxo is a transcriptional activator and Aop a transcriptional repressor. We started this study by characterising Aop and its relationship with relevant transcriptional activators, including Foxo. This led us to uncover that roles in ageing are widespread throughout the ETS TF family, extending across multiple fly tissues and diverse animal taxa.The ETS TFs are conserved across animals, including 28 representatives in humans,19. Theiing sites,23, recring sites,24,25, aing sites–30. Consactivator. Both FoAop relate to that triggered by Foxo? We sought to identify genes that were differentially regulated in response to activation of either TF. We focused on adult female fly guts and fat bodies , since these are the organs from which Foxo and Aop promote longevity[Foxo, AopACT (encoding a constitutively active form of AOP) or both under the control of the S1106 driver by feeding flies with the RU486 inducer. We profiled genome-wide transcriptional changes in dissected guts and abdominal fat bodies (as associated with the cuticle) with RNA-Seq and identified genes responding to RU486 within each genotype at a False Discovery Rate (FDR) of 10%  enrichment analysis suggested that, in the gut, this combined set of Aop- and Foxo-regulated genes tended to be involved in translation and energy metabolism, whilst the equivalent analysis in the fat body showed enrichment for regulators of gene expression . Hence, Aop and Foxo not only promote longevity, but also individually effect equivalent transcriptional programmes.How does the transcriptional programme triggered by longevity. We indue unions , suggestt of IIS , tobi , ETS-binding motifs (EBMs: GGAA) or both, and examined for their response to FOXO and AOPACT in Drosophila S2 cells .What are the outcomes of combining  fat body. Since AS2 cells , S2 Fig.ACT to attenuate activation by FOXO, revealing that AOP can moderate FOXO’s activity when brought onto the same promoter. By striking contrast, in the absence of EBMs, AOPACT synergised with FOXO to stimulate induction to an order of magnitude greater than FOXO alone, indicating that AOPACT can indirectly accentuate FOXO’s ability to activate transcription  was comeferences,37), sugPntP1 either alone or in combination with AopACT and Foxo . For each of the gut and the fat body, we assembled sets of genes that were differentially regulated upon induction of any of the three TFs or their combinations  that Pnt, alongside Aop and Foxo, may regulate metabolism in young flies.We assessed the transcriptomic outcomes of induction of te group . In the te group , despitete group . OverallAop and Foxo appeared to drive a transcriptional programme opposed to that of Pnt, we hypothesised that directly limiting physiological levels of Pnt would be sufficient to recapitulate their effect on gene expression. We first assessed the transcriptome-wide changes in the gut and fat body induced by RNAi-mediated knockdown of Pnt. The sets of genes differentially regulated by Pnt knockdown (FDR 10%) significantly overlapped those regulated by AopACT or Foxo in both the gut and the fat body , into an outbred, wild-type background for ten generations. The mutation was homozygous lethal. However, heterozygote females exhibited a 20% increase in median lifespan (Pnt locus extended lifespan. The HMG-box repressor capicua (cic) represses expression of Pnt[PntRNAi, overexpressing cicΔC2 (a cic mutant lacking a known MAPK phosphorylation site) in the gut and fat body also substantially extended lifespan  in the transcriptome data suggested that Pnt modulates lipid metabolism. Therefore, we applied nutritional stresses to alter triacylglyceride (TAG) storage, and assessed how PntP1 altered the response to these stresses. We quantified TAG after a week of PntP1 induction, and then after a subsequent six days of starvation. PntP1 accentuated the loss of TAG per unit weight induced by starvation , but not overall weight loss , the adipogenic effect of sugar was opposed by Pnt, such that TAG levels on a high-sugar diet with Pnt induction were not different from those on a low-sugar diet without Pnt induction . Moreover, PntP1 induction spared flies from the full extent of the early death induced by dietary sugar, increasing median survival time by 26%, despite having no effect on the low-sugar diet (486:sugar p = 6.2e-3). Altogether, these results indicate that while Pnt activity is detrimental during ageing, in youth it predisposes flies to leanness, which correlates survival of nutritional stress. This may suggest that metabolic regulation is an adaptive function of the Foxo-Aop-Pnt circuit in early life, but that the configuration which is optimal for metabolism is deleterious for later survival.Our data show that each member of the mes Figs  and 4, wght loss , suggestulate TAG,40. Howegar diet . Whilst Drosophila the ETS family comprises the repressor Aop and activators: Pnt, Eip74EF, Ets21C, Ets65A, Ets96B, Ets97D and Ets98B, each of which is expressed with its own unique tissue-specific pattern , using the GS5966 driver, was sufficient to extend lifespan, as was activating either AopACT or Foxo  proliferation[Pnt functions in neurogenesis[PntRNAi induction, achieved with the ElavGS driver, did not affect lifespan, while over-expressing either AopACT or Foxo was deleterious and in contrast to their benefits in gut and fat body  in the gut and fat body enhanced lifespan, expressing the same construct in ECs did not  pathways. The insulin receptor  and Foxo, and red and Foxo. Whilst  did not , S8 Fig,C via cic, suggestlifespan , S8 Fig.Drosophila. ETS TFs are conserved throughout multicellular animals, and the extensive conservation of roles in lifespan amongst the ETS family in the fly suggested that this lifespan modulation may be a fundamental property of these TFs, that extends to other species. The genome of the nematode C. elegans encodes 11 ETS TFs in total. At least one of these, Ets-4, has been reported to limit lifespan in the worm intestine[C. elegans ETS TFs for roles in lifespan by feeding worms RNAi from egg or L4 onwards  consistently extended C. elegans lifespan, in multiple independent trials from L4 stage or egg  was dissolved in absolute ethanol and added to the base medium to a final concentration of 200 μM. Ethanol was added as a vehicle control in RU-negative food. For lifespan experiments, flies were transferred to fresh food and survival was scored thrice weekly. Feeding RU486 to driver-only controls did not affect lifespan  were used at 20°C on NGM plates supplemented with 15μM FUDR to block progeny production. RNAi treatment was started from egg or late larval stages . Animals that died from internal hatching were censored.Worms were maintained by Brenner’s protocol, at 20°CpGL3Basic-4xFRE-pADH-Luc construct  before recombination into the pAW expression vector.The eference ) was useeference ), flankeDrosophila S2 cells were cultured in Schneider’s medium (Gibco/Thermo Scientific 21720024), supplemented with 10% FBS (Gibco/Thermo Scientific A3160801) and Penicillin/Streptomycin (Thermo 15070063). Cells were split into fresh media 24h before transfection, then resuspended to a density of 106 ml-1 and transfected using Effectene reagent (Qiagen 301425) in 96-well plates, according to the manufacturer’s instructions. Reporters and TF expression plasmids were co-transfected with pAFW-eGFP to visually confirm transfection, and pRL-TK-Renillaluc as an internal control for normalisation of reporter-produced Firefly luciferase. Reporters and pRL-TK-Renillaluc were transfected 1:1. When multiple TF expression plasmids were transfected, it was done 1:1. Each TF expression plasmid was transfected 4:1 relative to reporters or pRL-TK-Renillaluc . The total amount of DNA transfected was then topped up to a standard quantity across all experimental conditions with pAFW-eGFP, in equal volumes of TE buffer. Reporter activity was measured 18h after transfection using Dual-Luciferase reagents (Promega E1960). pAHW-Foxo and/or pAW-AopACT were co-transfected with promoters bearing combinations of FREs and EBMs. pAW-AopACT and pAW-PntP1 were co-transfected with a promoter bearing EBMs.UAS-Foxo, UAS-AopACT and UAS-PntP1, or UAS-PntRNAi in an 106-GSS background were dissected after six days adult feeding on RU486. Tissues were dissected in ice-cold PBS. Guts were dissected by cutting off the head and last abdominal segment, pulling on the crop through an incision at the abdomenal-thoracic junction, then removing tubules. Reproductive anatomy was then removed from the abdomen and the remainder of the abdomen taken as fat body. Dissected tissues were placed directly into ice-cold Trizol (Ambion 15596026). In the Foxo-AopACT-PntP1 epistasis RNA-Seq experiment, four experimental replicates were sampled per condition, each comprising a pool of 12 fat bodies or guts. In the PntRNAi experiment three replicates were sampled per condition, also each comprising organs from 12 flies. RNA was extracted by Trizol-chloroform extraction, quantified on a NanoDrop, and integrity was assessed on an Agilent Bioanalyzer. Poly(A) RNA was pulled down using NextFlex Poly(A) beads (PerkinElmer NOVA-512981) and integrity was re-assessed. In the Foxo-AopACT-PntP1 epistasis RNA-Seq experiment, only samples with the highest RNA yields and integrity were included in library preparation, leaving 2–3 samples per experimental condition. All three replicates were prepped and sequenced in the PntRNAi RNA-Seq experiment. RNA fragments were given unique molecular identifiers and libraries were prepared for sequencing using NextFlex qRNAseq v2 reagents,  and 16 cycles of PCR. Individual and pooled library quality was assessed on an Agilent Bioanalyzer and quantified with a Qubit spectrophotometer. Sequencing was performed by the UCL Cancer Institute, using an Illumina HiSeq 2500 instrument (paired-end 50bp) for the Foxo-AopACT-PntP1 epistasis experiment, and a NextSeq 500 (paired-end 75bp) for the PntRNAi experiment.Flies bearing combinations of cDNAs were made from the polyA RNA preps that were prepared for sequencing, using SuperScript II Reverse Transcriptase (Thermo 18064014) and OligoDT. qRT-PCR was performed on an Applied Biosystems QuantStudio 6 Flex real-time PCR instrument with Fast SYBR Green PCR Master Mix (Thermo Fisher), with primers supplied by EuroFins Genomics  and homogenised by shaking with glass beads (Sigma G8772) for 30s in a ribolyser at 6500 Hz. Aliquots of homogenates were heated to 72°C for 15m to neutralise enzymatic activity, then spun 1m at 4500g and 4°C to pellet debris. Triglyceride was measured by treating 5 μl sample with 200 μl Glycerol Reagent (Sigma F6428) for 15m at 37°C and measuring absorbance at 540 nm, then incubating with 50 μl Triglyceride Reagent (Sigma F2449) for 15m at 37°C and re-measuring absorbance at 540 nm, calculating glycerol content in each reading, then quantifying triglyceride content as the difference between the first and second measurement.TAG was measured as in  in wholeD. melanogaster genome 6.19 with HiSat2 2.1. Alignments were enumerated with featureCounts 1.6. All downstream analyses were performed in R 3.3.1. The gut and fat body were analysed in parallel. In the RNA-Seq experiment analysing Foxo-AopACT-PntP1 epistasis, genes with no counted transcripts were excluded  of 10%. To identify correlated effects amongst genotypes, sets of shared targets were formed as unions of DE gene sets from individual genotypes. Log2 fold-change values  for each sample. To assess the interaction of FOXO and AOP with promoters’ complements of TF-binding motifs, these normalised data were analysed by fitting a linear model of the formy was the natural log of fold-change+1, FRE and EBM represented the TF-binding complement, and FOXO and AOPACT represented co-transfection with pAHW-Foxo or pAW-AopACT. By the same approach, the interactive effect of PNTP1 and AOPACT were assessed by fitting a linear model of the formLuciferase reporter data were normalised by taking the ratio of firefly luciferase to renilla luciferase signal and, for each promoter, taking the median reporter signal in the absence of FOXO and AOPPntP1 overexpression on TAG and lifespan responses to nutrient stress (starvation or high-sugar diet) were analysed by a model of the formThe effect of S1 FigNumbers represent set and intersection size.(EPS)Click here for additional data file.S2 FigACT both moderates and synergises with transcriptional activation by FOXO on synthetic promoters containing combined ETS-binding motifs (EBMs) and FOXO-responsive elements (FREs), upstream of a basal Adh-Fireflyluciferase reporter. Activity is shown following normalisation to internal Renillaluciferase controls, and calculation of fold-change over the median expression of each reporter in the absence of FOXO and AOPACT. A significant four-way interaction of promoter and TF combination (i.e. FRE:EBM:FOXO:AOP) was detected in each experiment and in the pool. Full statistical analysis for each replicate experiment and their pool in Results were qualitatively consistent in each of the three replicates: AOP(EPS)Click here for additional data file.S3 FigDESeq2's variance-stabilising transformation to read counts, taking medians per transcript, and mean-sweeping values. Principal component values are shown in Expression values were derived by applying (EPS)Click here for additional data file.S4 Fig486, F1,19 = 4.9, p = 0.04; starvation F1,19 = 465.55, p = 8e-15; RU486:starvation F1,19 = 2.45, p = 0.13).Linear model: RU(EPS)Click here for additional data file.S5 Fig(A-F) Expressing RNAi against each ETS TFs other than Aop and Pnt. Ets21C or Ets97D knockdown extended lifespan. (G-L) Expressing RNAi against the same set of ETS with the ubiquitous DaGS driver. Ets21C or Eip74EF knockdown extended lifespan. (M-N) Heterozygous and homozygous mutants of Ets21C were long-lived. Note that results from one experiment, with the same control population, are plotted in the two separate panels. (O-P) Knocking down Pnt with the ubiquitous DaGS or ActGS drivers limits lifespan, noting that an equivalent result is observed in (Q-R) Confirmation with ActGS that neither Ets65A nor Ets98B limit lifespan.Corresponding percentage changes in lifespan and associated p-values are given in (EPS)Click here for additional data file.S6 FigFoxo(A-C) or limiting ETS activation (AopACT(D-F),PntRNAi(G-I),Ets21CRNAi(J-L)) has cell type-specific effects in the gut. TFs were chosen for experimentation based on strength of lifespan extension with S106, excluding Ets97DRNAi due to its relatively mild effect therein.Expressing (EPS)Click here for additional data file.S7 Fig(A) Neuronal Eip74EFRNAi extends lifespan. (B) Neuronal PntRNAi does not impact lifespan. (C-D) Neuronal AopACT or Foxo are toxic.(EPS)Click here for additional data file.S8 Fig(A-B) Expressing dominant-negative forms of the insulin receptor InR or epidermal growth factor receptor EGFR in gut and fat body with S106, extended lifespan. (C-D) Expressing the same constructs using GS5966 reveals differentiation between impacts of insulin and EGF signalling in the gut and fat body. (E-F) Neither RTK construct extended lifespan when expressed using the ISC-specific driver GS5961. (G-H) Dominant-negative InR, but not EGFR, mildly extends lifespan when expressed under control of TiGS.(EPS)Click here for additional data file.S1 Supplementary Information(XLSX)Click here for additional data file.


The androgen receptor (AR) is a key target for the development of drugs targeting hormone-dependent prostate cancer, but has also an important role in endocrine disruption. Reliable prediction of the binding of ligands towards the AR is therefore of great relevance. Molecular docking is a powerful computational method for exploring small-ligand binding to proteins. It can be applied for virtual screening experiments but also for predicting molecular initiating events in toxicology. However, in case of AR, there is no antagonist-bound crystal structure yet available. Our study demonstrates that molecular docking approaches are not able to satisfactorily screen for AR antagonists because of this reason. Therefore, we applied Molecular Dynamics simulations to generate antagonist AR structures and showed that this leads to a vast improvement for the docking of AR antagonists. We benchmarked the ability of these antagonist AR structures discriminate between AR antagonists and decoys using an ensemble docking approach and obtained promising results with good enrichment. However, distinguishing AR antagonists from agonists with high confidence is not possible with the current approach alone. The androgen receptor (AR) is the main biomolecular target involved in the development and progression of the hormone-dependent prostate cancer . FurtherOne major challenge associated with the computational prediction of binding modes and affinities of compounds towards the androgen receptor by structure-based methods is the lack of a published structure of antagonist-bound AR and experimental and theoretical studies aimed at elucidating the mechanisms of AR antagonism ,18,19,20apo state of the protein [In its unbound state, the AR is complexed with heat-shock and chaperone proteins that stabilize the  protein . Agonist protein . A widel protein . This me protein ,15,16. F protein .Additional insights into to structural determinants for androgen antagonism were gained by structure-activity relationship studies, where the systematic influence of scaffold change on androgen agonism or antagonism was investigated ,19,20. BRegarding B-ring containing AR binders, the chemical nature of the linker seems to be the determining factor in converting agonists to antagonist and vice versa. The antagonist behavior of bicalutamide versus the agonist behavior of compound of S-22, an AR agonist containing an oxygen linker, can be explained by the presence of the much bulkier sulfonyl linker in the former .Molecular Dynamics simulations (MD simulations) were applied in various studies to investigate structural determinants of androgen receptor antagonism ,15,16,17However, MD simulations of ligand-receptor complexes need a valid input structure of the bound ligand in the binding site and furthermore, due to their computational expense, cannot be applied to large libraries of compounds that are usually used in virtual screening campaigns. Structure-based virtual screening (SBVS) ,23 basedFurthermore, molecular docking can be applied to toxicology profiling of chemical compounds by assessing how well a chemical entity fits into the binding site ,26. MoleBisson et al. used a biased probability Monte Carlo procedure and iteratively refined receptor conformations that are biased to yield good discrimination of agonists and antagonists . We usedTo validate the ability of MD simulations to generate antagonist-conformations of the AR, we ran several simulations of antagonist-bound WT AR and used a clustering approach to generate a structural ensemble for docking towards the AR. We then docked androgen antagonists towards this antagonist-ensemble and calculated the performance metrics compared to a set of decoys. Furthermore, we also constructed an agonist ensemble by simulating agonist-bound AR complexes and to assess if the performance for agonist screening is improved compared to the ensemble obtained from crystal structures.A generally similar strategy was applied by Swift and coworkers  where ARAll evaluations of the docking accuracy were done target the wild type AR, but the strategy can be extended to include AR mutants as well.The agonist and antagonist structural ensemble presented in our study have the potential to improve virtual screening campaigns and toxicology predictions for AR antagonism.A diverse ensemble  of five experimental AR structures  was used for an initial evaluation of the docking performance of Glide SP at the AR .3, 3B66: 189 Å3, 3G0W: 157 Å3, 3V49: 171 Å3, 4HLW: 168 Å3). The importance of the induced fit for binding at the AR was confirmed in a previous study [All ligands showed the highest predicted affinity towards their corresponding native  protein conformation in the cross-docking experiment . Furtherus study .In a next step, an agonist and antagonist set, taken from the NRlist BDB , and a sTaking experimentally validated decoys can be an alternative to the DUD-E, as described by Lagarde . In caseThe docking score distributions  for the As a third evaluation, we used the antagonist set as the active set and the DUD-E set as decoys. In this case, the ROC AUC reached 0.65 . This shWe used crystal structures of four different AR WT antagonists that act as agonists towards AR receptors with either a T877A or W741L mutation (PDB IDs: 2AX6 (T877A), 2OZ7 (T877A), 3RLL (T877A), 4OLM (W741L)). The antagonist ligand of 2AX6  correspoWe mutated the corresponding amino acids back to obtain the WT receptor. These structures will be termed as 2AXA_WT, 4OLM_WT, 3RLL_WT, and 2OZ7_WT in the following. We then employed induced fit docking (IFD) to dock the antagonists back into the corresponding WT structures and applied binding pose metadynamics  . Furthermore, the phenyl ring can have two different orientations as well as the amide group. These four poses were then assessed by the binding pose metadynamics protocol and ranked by their corresponding PoseScores, which is an indication of the stability of the pose . More deThe same protocol was applied to the other antagonist ligands. For 4OLM_WT, six different poses were identified by IFD, mainly differing by the orientation of the B-ring. The orientation of the B-ring in the most stable pose  differs Four distinct poses were found for 3RLL_WT by IFD. The bulkiness of the B-ring doesn’t allow alternative orientations and the identified poses are differing in the orientation of the A-ring. The best-scored pose  has the For 2OZ7, seven poses could be identified by IFD, differing in the orientation of the terminal acetate and keto groups. Due to the presence of a hydrogen bond to Thr877, the orientation of the keto group for the identified pose at the WT AR differs from the one that is observed in the crystal structure at the T877A AR .The structures with the most stable poses for all complexes were used as input structures for 100 ns MD simulations. We also conducted 100 ns MD simulations of the original PDB complexes (AR mutations) to get an ensemble of agonist structures for comparison and assessment of how the mutation affects the structural dynamics of the receptor.A comparison of the representative structures (cluster centroids) from the 100 ns MD simulations started from the original PDB structures (agonist activity towards mutants) and from the stable docking poses from the IFD/binding pose metadynamics towards the WT (antagonist activity) is shown in Structure 2AX6 corresponds to Hydroxyflutamide (HFT) bound to T877A AR. Towards this mutant, HFT acts as an agonist, whereas it has antagonist activity towards the WT AR. The terminal isopropanol group of HFT shows different orientations for the representative MD structures of the wild-type and the T877A structure , panel AFor cyproterone acetate CPA, B, similaLigand Duke-7 C has a bThe biggest structural differences between WT simulation and mutant simulation are observed for the binding of R-bicalutamide D. The W7The analysis of the antagonist-bound MD trajectories of the AR clearly indicated structural differences compared with trajectories of mutated AR, for which the antagonists are converted to agonists. However there remains the question if these antagonist-structures created by MD simulations can be used for improved prediction of AR antagonism by molecular docking.As a first assessment, a set of antagonists and decoys was docked to the antagonist ensemble obtained from the MD simulations. An exhaustive search was then applied to pick a subset of the ensemble, consisting of five structures (see Methods Section). The number was chosen since it provides a good trade-off between covering the necessary conformational flexibility and still achieving results with little computational resources. Analyses with varying ensemble sizes confirmed this choice (results not shown). The ensemble was picked that obtained the best AUC ROC for discrimination between antagonists and agonists  and it will be termed as the MD_antagonist_1 ensemble in the following.As a second evaluation, the agonist ligand set was docked towards the MD_antagonist_1 ensemble and the AUC ROC against the decoy set was determined. The obtained value of 0.92 indicates that this ensemble is not selective for AR antagonists, but also shows high promiscuity for the agonist ligands. This can be seen in In order to have consistent structural ensembles for docking of both agonist and antagonist, we also conducted classical MD simulations of four structurally diverse agonists bound to WT AR . The inpThe antagonist ligands reached an AUC ROC of 0.69  at the MIn order to verify if the improved AUC ROC values for the MD-derived ensembles compared to the crystal structures really stem from the sampling of agonist and antagonist-like conformations and not just the better sampling of the receptor conformational space in general, we also determined the AUC ROC against the decoy set of docking the agonist ligands into the antagonist ensemble and vice versa by selecting the best performing agonist ensemble for the docking of the antagonists (ensemble MD_agonist_2) and the best performing antagonist ensemble for the docking of the agonists (ensemble MD_antagonist_2) . The besThe above results indicate that ensemble-docking towards MD-derived structures can be used to distinguish agonists from decoys and antagonists from decoys, which is definitely a step forward for structure-based design at the AR. Yet, there remains the question if this strategy can distinguish agonists from antagonists. Therefore, a new search for well-performing ensembles was conducted. For the selection of the antagonist ensemble, the objective function was the summed AUC-ROC for (i) docking the antagonist ligands and the DUD-E decoys and (ii) docking the antagonists and taking the agonists as decoys. The same search was performed for the agonist ensemble, where the agonists were docked and the AUC-ROC was evaluated with taking (i) the DUD-E decoys and (ii) the antagonists as decoys. The best antagonist ensemble with best discrimination against decoys and agonists had AUC-ROC values of 0.79 (decoys) and 0.56 (agonists)   was downwww.rcsb.org). The structures were refined with the Protein Preparation Wizard [All androgen WT crystal structures with a resolution of better or equal than 2.5 Å were downloaded from the protein data bank  (ii) 12 ps simulation in the NVT ensemble, keeping the restraints and temperature at 10 K (iii) 12 ps simulation in the NPT ensemble, keeping restraints and temperature at 10 K (iv) 12 ps simulation in the NPT simulation with solute heavy atom restraints at 300 K and (v) 24 ps simulation in the NPT ensemble at 300 K without restraints. Production runs were performed in the NPT ensemble at 300 K and 1.01 bar for 100 ns. The TIP4P-EW water model was used and counterions were added to neutralize the net charge of the system. The temperature and pressure were controlled using a Langevin thermostat (relaxation time 1.0 ps) and a Langevin barostat (relaxation time 2.0 ps). Electrostatic interactions were treated using the particle mesh Ewald summation. The OPLS3 force field was employed [Receptor-ligand complexes of AR antagonists bound to mutants and AR WT as well as AR agonists bound to AR WT were refined with the Protein Preparation Wizard   . BindingFlexible-ligand rigid-protein docking was performed using the Glide program from Schrödinger . The appIFD was performed using the induced-fit docking protocol from Schrödinger . The detEnsemble docking usually involves flexible ligand—fixed protein docking towards a representative ensemble of protein structures ,34,52,53The chosen metric for the docking performance was the ROC AUC, the area under the curve of the receiver operator characteristic curve. For docking and scoring, the AUC gives the probability that a randomly chosen true active is ranked higher than a randomly chosen decoy. A ROC AUC of 1.0 would thereby indicate that all actives are ranked before the inactives/decoys. The ROC AUC metric was criticized for an insufficient incorporation of early enrichment . HoweverMolecular Dynamics simulations in combination with molecular docking was applied to study the ability of structure-based screening methods to predict AR agonism and antagonism. Docking against an ensemble derived from experimental crystal structures of AR in agonist conformation was shown to yield good enrichments for the screening of agonists, but is insufficient for antagonist docking. This was circumvented by using a workflow that combines induced-fit docking of known antagonists towards the AR, assessing the most stable poses and generate structural ensembles by running 100 ns MD simulations from these structures followed by a clustering procedure. To our knowledge, this the first study that benchmarks a docking approach at the AR aiming at prediction of agonism and antagonism.This MD-derived ensemble of antagonist-like protein conformations was shown to yield superior docking performance in terms of discriminating antagonists from decoys, compared to the ensemble from the crystal structure.Furthermore, an ensemble of agonist-like protein conformations was created by running MD simulations of bound agonists. This ensemble also showed better enrichments than the ensemble from the experimental crystal structures. A remaining challenge is the discrimination between agonists and antagonists. The antagonist ensemble shows high promiscuity towards both agonists and antagonists, whereas the agonist ensemble is selective for agonists. As a result, screening selectively for agonists is possible, by docking the library only towards the agonist set and taking the highest-scoring compounds as leads. However, selectively screening for antagonists based only on docking is not feasible.Lagarde et al. recently showed that nuclear receptor agonists and antagonists can be discriminated using a 3D pharmacophore approach . TherefoWe think that this study improves the potential of SBVS and structure-based design at the androgen receptor and leads to more reliable toxicology prediction of environmental chemicals by better accounting for AR antagonist. We could successfully create an ensemble of antagonist-like AR structures that can accommodate AR antagonists. These structures are made publicly available see  and are We furthermore showed the potential of MD simulations to generate meaningful protein conformations with desired objectives in the absence of experimental crystal structures.To promote further use of our findings, a link for downloading the agonist ensemble, antagonist ensemble and ligand sets is provided in the


Spinal and bulbar muscular atrophy (SBMA) is a neuromuscular disease caused by a polyglutamine (polyQ) expansion in the androgen receptor (AR). Despite the fact that the monogenic cause of SBMA has been known for nearly 3 decades, there is no effective treatment for this disease, underscoring the complexity of the pathogenic mechanisms that lead to a loss of motor neurons and muscle in SBMA patients. In the current review, we provide an overview of the system-wide clinical features of SBMA, summarize the structure and function of the AR, discuss both gain-of-function and loss-of-function mechanisms of toxicity caused by polyQ-expanded AR, and describe the cell and animal models utilized in the study of SBMA. Additionally, we summarize previously conducted clinical trials which, despite being based on positive results from preclinical studies, proved to be largely ineffective in the treatment of SBMA; nonetheless, these studies provide important insights as researchers develop the next generation of therapies. Progressive proximal spinal and bulbar muscular atrophy (SBMA) was first described by Dr. William R. Kennedy et al. in 1968 following the examination of 2 families in which 11 members, all male, presented with a late onset, slowly progressive neuromuscular disorder [The clinical manifestations of SBMA are summarized in Fig. In addition to neuromuscular symptoms, SBMA patients also present with signs of androgen insensitivity, such as gynecomastia, infertility, and testicular atrophy , 13–15; Numerous studies have also noted electrophysiological dysfunction and axon loss in the sensory neurons of SBMA patients, highlighting the fact that disease pathology extends beyond the neuromuscular system , 19–27. Metabolic disturbances have also been reported in SBMA patients, with a number of recent studies greatly expanding our understanding of and appreciation for the system-wide effects of mutant AR. For example, impaired glucose homeostasis is now understood to be a common feature of SBMA , 32, 33.higher in the Prose Memory test than controls, suggesting that earlier findings are not reproduced in a larger cohort and, thus, likely do not represent substantive changes [As the number of known clinical presentations of SBMA continuously expands, it is worth noting that even rarely reported symptoms, when taken together, provide clues for the fundamental mechanisms of cellular toxicity that contribute to SBMA. Although not considered a primary aspect of disease manifestation, some studies have noted subtle cognitive impairment in SBMA patients, finding diminished working memory and executive function , 37. Mor changes .Additionally, although there is no evidence of cardiomyopathy in SBMA patients , BrugadaUnique symptoms are also associated with a highly expanded CAG repeat. In a patient with 68 CAGs (currently the longest repeat identified in an SBMA patient), disease onset occurred at the age of 18, and by age 29, symptoms included autonomic nervous system dysfunction and abnormal sexual development . Taken tAlthough a highly expanded CAG repeat length may be associated with additional symptoms , there iThe androgen receptor gene (GenBank: M20132.1) is located on the X chromosome at position q11-12 and contains 8 exons spanning more than 90 kb, encoding a 919 amino acid protein  that participates in intra- and intermolecular amino-carboxyl (N/C) terminal interactions via charge-clamp hydrogen bonding with residues within the LBD [The length, sequence, and structure of the NTD vary between the steroid hormone receptors but typically contain at least one transactivation domain, referred to as the activation function 1 (AF-1) domain. The largest domain of the AR, the NTD spans amino acids 1 to 538 and contains a polymorphic polyglutamine tract as well as polyproline and polyglycine tracts. Although the AR NTD is intrinsically disordered in the absence of binding partners , over 15 the LBD , 58. Rec the LBD . The dis the LBD –61.The AR DNA-binding domain extends from amino acids 539 to 627 and contains two zinc finger domains composed of 3 α-helices . Consens617RKCYEAGMTLGARKLKKL634), encompassing the end of the DBD and the first 8 residues of the hinge region, regulates AR nuclear import [629RKLKKL634, as mutation of K630, K632, or K633 reduces this interaction and mutation of all 3 lysines completely prevents the interaction [versus noncanonical AREs [The hinge domain of the AR (residues 628-670) is so-called because it serves as a flexible bridge between the DBD and LBD. Although this region is poorly conserved, it nonetheless serves a number of important functions. A bipartite nuclear localization signal (NLS) , p23, and immunophilins  , 89. TheTestosterone is primarily produced by Leydig cells in the testes in response to secretion of luteinizing hormone (LH) from the pituitary gland . ExtraceFollowing nuclear translocation, the AR binds to DNA, canonically as a homodimer , but canThere is evidence that the AR is exported from the nucleus and subsequently degraded in the cytoplasm by the ubiquitin–proteasome system . In addiThe length of the polyQ tract in the AR NTD causes structural changes with important implications for AR function. Although the NTD is relatively unstructured in the absence of coregulator binding , expansiDrosophila homolog of PRMT6 (DART8) suppressed the degenerative eye phenotype of flies expressing AR52Q [in vivo [The AR is known to be regulated by phosphorylation, ubiquitination, methylation, SUMOylation, palmitoylation, and acetylation, with some of these posttranslational modifications altered by the polyQ expansion. For example, reduced phosphorylation of the mutant AR at S650 may contribute to its impaired nuclear export . Additiong AR52Q . Other sng AR52Q , 125. Sing AR52Q . Furtherng AR52Q . Blockin[in vivo . Taken tDrosophila model of SBMA [c oxidase subunit Vb (COXVb) interacts more strongly with soluble wild-type AR than with polyQ-expanded AR, but importantly, also colocalizes with AR aggregates [Considering that the AR interacts with approximately 250 different proteins , it is u of SBMA . Cytochrgregates . This algregates , 132, asgregates . Moreovegregates , contribAlthough polyQ-expanded AR translocates to the nucleus with comparable kinetics as wild-type AR , 134, boPolyQ-expanded AR exhibits not only a partial loss of its normal transcriptional function (as described above), but also a gain of new, toxic properties. Indeed, it is clear that the neurologic symptoms of SBMA are not caused by a loss of AR function, as these are not observed in patients with complete androgen insensitivity syndrome . Insteadin vitro, with both biochemical and imaging assays revealing that mutant AR aggregates first as a full-length protein, becoming proteolyzed over the course of inclusion maturation [Drosophila model of SBMA [Early studies of AR aggregation in SBMA patient tissue failed to detect AR aggregates with antibodies directed to the C terminus of the AR , 143, whturation . Moreoveturation . Althoug of SBMA —in agree of SBMA —it is cl of SBMA ; the cha of SBMA , 156–158 of SBMA , 160. ThIn the decades since the genetic cause of SBMA was identified, a number of cell models have been developed to recapitulate aspects of the disease Table . Both poA PC12 cell model of SBMA, in which rat pheochromocytoma-derived cells express full-length human AR under the control of a tetracycline-inducible promoter, is unique among SBMA cell models in that PC12 cells readily form intranuclear inclusions of aggregated AR in a polyQ length- and hormone-dependent manner . AlthougAnother widely studied immortalized cell model of SBMA is the motor neuron-derived 1 (MN-1) model, in which AR24Q, AR65Q, or AR100Q is expressed under the control of the CMV promoter , 163. ThSimilarly, mouse neuroblastoma-spinal cord (NSC)-34 cells have been utilized as a motor neuron-like cell line in the study of SBMA . NSC-34 Corresponding with a growing recognition in the SBMA research community that muscle plays a primary role in disease pathogenesis , 9, 169,It is important to note that in the aforementioned immortalized SBMA cell models, AR is overexpressed and contains a polyQ tract that far exceeds what is typically found in peripheral blood lymphocytes of patients (the longest patient polyQ tract length reported to date being 68Q) . In ordeDrosophila models allow for high-throughput genetic and pharmacologic screening in vivo. Using the GAL4/UAS system to drive cell-type–specific expression of mutant AR, Drosophila models of SBMA have been shown to display hormone-dependent toxicity in photoreceptor neurons, motor neurons, and pan-neuronally [Drosophila expressing wild-type AR also exhibit toxicity in response to DHT, although to a lesser extent than those expressing polyQ-expanded AR [Transgenic uronally , 176. Upuronally , 176. Itanded AR . This isA number of SBMA mouse models have been generated using a variety of genetic strategies, each with a unique set of strengths and weaknesses in their ability to recapitulate the human disease Table . TransgeRecently, a new transgenic mouse model was generated in which expression of AR121Q  is similarly driven by a CMV enhancer and chicken β-actin promoter, but with expression levels in the muscle comparable to endogenous mouse AR  . These mA more slowly progressive phenotype is observed in the prion protein (PrP) promoter-AR112Q mouse model of SBMA . In thisThe fact that disease progression in the previously described transgenic mouse models is hormone dependent , 178 supversus overexpression of the AR. Indeed, overexpression of the wild-type form of ataxin-1, another protein implicated in polyQ disease, also causes disease in mice [The importance of AR expression level in generating a phenotype in mice is highlighted by the ‘myogenic model’ of SBMA. In these mice, wild-type rat AR (22Q) is highly overexpressed in skeletal muscle, with expression driven by the human skeletal α-actin promoter . Myogeni in mice .Although many aspects of SBMA are recapitulated in transgenic models of SBMA, the knock-in model is the only tool available to SBMA researchers in which AR is expressed at precisely endogenous levels and regulated by endogenous regulatory elements. A knock-in model of SBMA was created in which a large part of exon 1 of the mouse AR gene was replaced with corresponding exon 1 sequences of the human AR containing 21Q or 113Q , 184. MiAltogether, there are a number of SBMA mouse models available to researchers, each with a unique set of advantages and disadvantages. However, important caveats remain in the use of any mouse model. With particular focus on the study of SBMA, it should be noted that the AR polyQ tract length in each of these models far exceeds the longest found in patients . The lenThe requirement for hormone in the pathogenesis of SBMA has been demonstrated in multiple animal models of the disease , 185 andAlthough the neurologic phenotypes associated with SBMA are caused by a gain of toxic function of the mutant AR, loss of AR function causes a number of deleterious symptoms in patients . Thus, tclinicaltrials.gov—NCT02156141).Exercise is well-known to be beneficial to overall health, but there is rationale for caution in the use of exercise as a therapy for neuromuscular disorders, as it has been hypothesized that exercise may increase the rate of muscle atrophy in ALS. This is primarily based on 2 observations: first, that high-level athletes are at a greater risk for developing ALS ; and secIGF-1 is known to promote growth and regeneration of skeletal muscle  and to pIt has been previously shown that SBMA patients exhibit reduced serum creatinine levels and that there is an inverse relationship between serum creatinine levels and motor dysfunction . CreatinGiven the lack of an effective treatment for SBMA following multiple clinical studies, fundamental aspects of the disease are continuing to be studied in an effort to more carefully design the next clinical trials. Indeed, new discussions regarding the role of hormone in SBMA have been driven by the fact that clinical trials aimed at suppressing hormone production in SBMA patients have had modest effects on disease progression , 187, deThe requirement for androgens in SBMA pathogenesis is exemplified by the fact that expression of mutant AR in heterozygous females primarily results in mild or subclinical symptoms , 31. AltDrosophila model [Another intriguing case study may hold additional insights. This report describes a male-to-female transgender SBMA patient who developed full disease phenotype despite 15 years of treatment with an anti-androgen (leading to undetectable levels of androgens) . Althougla model . Althougla model .These two case studies could indicate that there is a developmental aspect to SBMA, wherein androgen exposure that occurs in males prenatally or during puberty sets the disease in motion before symptoms appear. Additional changes to the cellular environment that occur with aging may ultimately lead to motor neuron death later in life, even if circulating androgen levels are reduced. Hormone is clearly a requirement for disease, as even homozygous females exhibit only mild symptoms; however, it is also clear that reducing androgen levels in postsymptomatic patients has limited effects on disease progression. Although 14 years of androgen deprivation slowed disease progression of 1 SBMA patient relative to historical data , it shouAdditionally, the question of which cell type(s) drive disease is of vital importance for the next generation of therapies. Some studies have suggested that peripheral knockdown of the AR is sufficient to rescue disease phenotypes in SBMA mouse models , 169. ShThus far, all of the studies conducted in SBMA mouse models suffer from the caveat that, in order to drive an observable behavioral phenotype, AR with a supraphysiological polyQ tract length is expressed. In HD, long polyQ tracts are associated with juvenile onset HD, which is characterized by dysfunction of different brain regions  than adult onset HD . Given tRelated to the question of the tissue involved in disease initiation, the extent to which tissue-specific somatic instability of the expanded CAG tract of mutant AR contributes to disease remains to be determined. Somatic mosaicism is primarily observed in cardiac and skeletal muscle of SBMA patients—tissues with high levels of AR expression . AdditioIt remains to be determined which direction(s) of study will ultimately lead to a treatment for SBMA, but until effective treatments exist, further exploration in all directions is imperative. The studies summarized here reflect broad efforts to advance our understanding of SBMA on multiple fronts, with the ultimate goal of achieving a clinically meaningful outcome in patients.


Asphalt rubber (AR) is a sustainable paving material with merits including waste tire consumption, low traffic noise, and enhanced mechanical performance. However, the poor workability and storage stability limited its further application. This study attempted to alleviate these two concerns of AR simultaneously by incorporating heavy bio oil (HBO). To achieve this goal, bio-AR binders with three different mixing sequences were prepared. A series of rheological and chemical tests were conducted. Test results prove that the bio-AR binders exhibited superior rutting and fatigue resistance compared to AR binder. The viscosity values of bio-ARs were closed to AR modified with commercial warm mix additive, which indicates enhancement in workability. Due to the relatively high density of HBO, the density difference between the asphalt liquid phase and crumb rubber in the bio-AR system narrowed, which brought improved storage stability. Among bio-ARs prepared with different mixing sequences, the direct mixing one (ARB) had the most satisfied overall performance. The early incorporation of HBO had limited negative influence on binder performance, but allowed for more energy saving during the bio-AR binder production. Future study will be conducted on performance of bio-AR mixtures and quantitative estimation of its energy saving during the blending and compacting process. Asphalt mixture, composed of bituminous binder and mineral aggregates, is the most common paving material for highways and urban roads. The durability of asphalt pavement highly depends on the rheological properties of the bituminous binder. Due to the growing traffic loading and aggravation of axis, incorporating polymer modifiers  in virgin bitumen for superior adhesive and cohesive performance has become a common practice ,2,3,4. ADespite the previously mentioned metrics, two main concerns of AR, the poor workability, and storage stability limit its further application. The highly viscous behavior of the AR binder causes the workability concern. In recent years, warm mix asphalt (WMA), which is a clean production technology, has been used with AR to address the workability concern . It is r3) of heavy bio oil (HBO), it is promising to use it as a performance enhancer for the AR binder, which may simultaneously alleviate the workability and storage stability concern of AR. In addition, when used as an additive rather than an alternate of bitumen, the dosage of HBO can be limited to 5–10% by weight of virgin bitumen, which makes the modified binder more stable compared to traditional bio-asphalt materials.Recently, using bio-asphalt from bio-waste to replace part of the petroleum-based bitumen has been a hot topic for pavement researchers. It is reported that the heavy bio-oil can significantly decrease the viscosity of asphalt and bring better workability . The comThe objective of this study was to evaluate the feasibly of incorporating heavy bio-oil to improve both the workability and storage stability of the AR binder. To achieve this goal, rheological and chemical tests were conducted on modified bitumen. The virgin bitumen, CRM and HBO, were blended by three different mixing sequences. Physical and rheological properties, including penetration, softening point, Superpave rutting/fatigue factors, and low temperature stiffness of asphalt rubber modified with HBO (HBO-AR) binders were characterized. Gel permeation chromatography (GPC) analysis and Fourier Transform Infrared Spectroscopy (FTIR), which characterizes the molecular weight distribution and chemical functional groups respectively, were conducted to reveal the compound modification mechanism of CRM and HBO. This study is anticipated to provide useful information to pavement researchers who are interested in mitigating the workability and storage stability concerns of rubberized asphalt binders.Pen60/70, which was provided by Guangzhou Xinyue Transportation Technology Co., Ltd., Guangzhou, China, was selected as virgin bitumen to prepare AR and HBO-AR binders. The hot AR binder was prepared by high shear mixing  crumb rubber  and virgin bitumen for 1 h at 176 °C, which allows the rubber particles to swell and dissolve in bitumen fractions. The CRM content was set as 18 wt % by weight of the hot AR binder, which is consistent with some previous publications ,19,20.3. The mass loss ratio of HBO after rolling thin film oven (RTFO) aging is 41.13%.The HBO additives were provided by a new energy company named Handan Zhenfei Trade Limited Company in the Hebei province, China. It is caramel viscous liquid (containing 3 wt % water) with a density of 1.07 g/cmThree mixing sequences were applied to prepared rubberized bituminous binder containing HBO. For all warm asphalt rubber (WAR) binders, the HBO or Evotherm-DAT  content was set as 5 wt % by weight of the hot AR binder. Specifically, AR-B refers to the WAR prepared by a conventional sequence, mixing HBO and the hot AR binder for 10 min at 160 °C. ARB was prepared by directly blending the same amount of virgin bitumen, CRM and HBO together at 160 °C for 60 min. BR-A used a pretreatment process in which CRM were soaked into liquid HBO for 48 h. This makes the HBO additive completely absorbed by the CRM particles. After that, the CRM containing HBO (B-CRM) was incorporated to virgin bitumen by 1 hour high shear mixing at 160 °C. For all rubberized binders with HBO, the mass ratio was set as 61:220:1000 for HBO, CRM, and virgin bitumen, respectively. In addition, the WAR binder with Evotherm-DAT (labelled as ER-A) was prepared as a control group to evaluate the performance of the HBO additive. ER-A is prepared by the pretreatment method like BR-A. According to Yu’s study, the pretreatment method provides the optimal performance for the WAR binder with Evotherm-DAT [The penetration and softening point test were conducted to evaluate the general performance of test binders. Rotational viscosity was conducted for workability characterization at three different temperatures , using a Brookfield rotational viscometer  with 20# spindle. A dynamic shear rheometer  was used to conduct rheological tests.*/sin δ, for both unaged and RTFO aged samples) and non-recoverable creep compliance . The standard RTFO procedure takes unaged asphalt binder samples in cylindrical glass bottles and places these bottles in a rotating carriage within an oven. The carriage rotates within the oven while the 163 °C temperature ages the samples for an additional 85 min. The G*/sin δ test started at 64 °C, and the temperature was increased automatically to the next PG temperature if the measured rutting factor was larger than the values, i.e., 1.0 kPa for unaged binder and 2.2 kPa for the RTFO binder. Jnr was evaluated by the multiple stress creep recover (MSCR) test at 64 °C. During the test, a creep load was applied for 1 s followed by 9 s of a recovery. Each specimen was subjected to 10 cycles with a creep stress of 0.1 kPa, which is followed by 10 cycles with a creep stress of 3.2 kPa.The high temperature performance of prepared binders was characterized by both the Superpave rutting factor (G*sin δ) test and linear amplitude sweep (LAS) test with pressurized aging vessel (PAV) aged specimens. During the PAV aging procedure, the RTFO aged asphalt was placed in a chamber with 100 °C and 2.1 MPa for 20 h. The G*sin δ test was started at 25 °C with a decrement of 3 °C until the G*sin δ value was larger than 5000 kPa. The LAS test was started with a frequency sweep followed by a linear amplitude strain sweep to determine the cycles to failure denoted as fN. The fatigue failure is defined as the 35% reduction of the initial modulus in the LAS test, according to the viscoelastic continuum damage (VECD) model.The fatigue resistance was evaluated by the Superpave fatigue factor  test was conducted for a low temperature performance evaluation. It characterized the stiffness and m-value of the test binder at a PAV aging state. The tests were implemented in a fluid bath with a constant load (980 ± 50 mN) at −12 °C, −18 °C, and −24 °C, respectively.*) based on the time-temperature superposition principle. Master curves (reference temperature: 60 °C) were obtained through a series of frequency sweeps at a temperature range from 76 to 4 °C with various frequencies between 0.01 and 30 Hz. The best fit of frequency sweep test data was conducted to obtain a single master curve based on the Williams-Landel-Ferry (WLF) formula (Equation (1) and Equation (2)) and the sigmoidal function (Equation (3)) [T, ΔT is the different value between the test temperature and reference temperature, and C1 and C2 are model constants.ξ is the reduced frequency at reference temperatures and f is the test frequency at specific temperatures.β, γ are the shape parameters, α is the span of G* values, and δ is the minimum modulus value.In addition to the standardized performance tests, the overall rheological performance of test binders was evaluated by master curves of a complex shear modulus (Gion (3)) .(1)log and Fourier transform infrared spectroscopy  were performed on prepared binders. The GPC test was conducted to evaluate the effect of crumb rubber and HBO on molecular weight distribution of liquid asphalt fractions, while FTIR tests characterized the chemical bonds and functional groups of test binders. For GPC tests, the test binders were dissolved in Tetrahydrofuran (THF) solvent and then filtered through a 0.45 μm Polytetrafluoroethylene (PTFE) syringe filter. The insoluble particles were removed by this process, while all the residual fractions can be completely dissolved in THF. By the filtering process, about 18–20 wt % of samples was filtered out. The asphalt-THF solution was drained through columns and allowed to flow at a rate of 0.5 ml/min, and the temperatures of the columns were maintained at 40 °C.−1 was obtained by scanning using an FTIR spectrometer. For FTIR tests, the test binder was first pressed to pellets with a thickness of approximately 1 mm, and then placed in a transmission holder and scanned. Infrared spectroscopy ranging from 4000 to 400 cmThe detailed information of conducted tests in this study was summarized in nr difference of all modified binders did not meet the requirement of AASHTO TP70-13 i.e., < 75%. This is attributed to the extremely low Jnr values at the applied stress of 0.1 kPa [nr3.2 values, Evotherm-DAT had a negative effect on the rutting resistance while HBO showed a positive influence. Consistent with the results of the Superpave rutting factor test, BR-A exhibited the lowest Jnr3.2 value, which indicates the best rutting resistance among three HBO-AR binders. 0.1 kPa . Based o*sin δ value and the test temperature. AASHTO M320 specified that the fatigue factor, G*sin δ, should be less than 5 MPa to pass a performance grade test at a specific temperature. The higher the fatigue failure temperature is, the poorer the fatigue resistance the test binder has. As depicted in fN) refer to better resistance of fatigue cracking. According to fN values of AR were more than 11 times of virgin bitumen at 2.5% and 5% strain levels. Inconsistent with Superpave fatigue test results, the HBO-ARs had higher fatigue lives compared to AR based on LAS evaluation. Since previous studies have proven that LAS is a more reliable fatigue characterizing methods for bituminous specimens, it is believed that the HBO additive has a limited negative effect on the fatigue performance of AR [ce of AR ,23.−2–10−8 Hz). To obtain the master curves, a series of complex mathematical calculations were conducted. The WLF formula (Equation (1) and Equation (2)) was first substituted into the sigmoidal function (Equation (3)) as Equation (4).Master curves were drawn to evaluate the overall rheological properties of asphalt binders at a wide angle of frequencies (10C1 and C2 with log(f) and ΔT as independent variable using Equation (4). Different loading frequencies were shifted at a given temperature (60 °C) to obtain a single master curve based on Equation (2). Lastly, the fitting master curves were obtained by a best fit based on Equation (3). The parameters of the WLF formula and sigmoidal function were presented in Then a nonlinear surface fit was conducted to obtain parameters * of test binders at the reference temperature of 60 °C. Based on sigmoidal function, scatters and smooth curves were obtained in term of the lg|G*| versus reduced frequency (lgrf). Based on the time-temperature superposition principle of viscoelastic material, low frequency refers to high temperature and vice versa. As expected, the increased frequency resulted in an increase of complex shear modulus. According to p), number-average molecular weight (Mn), weight-average molecular weight (Mw), and polydispersity (PDI = Mw/Mn). It is noted that the PDI values of HBO and Evotherm-DAT were lower than that of asphalt binder samples, which indicates more concentrated distribution of molecular weight. AR had a lower Mn value but higher Mw value than Pen 60/70, which is likely caused by the modification of CRM dissolved in asphalt fractions. This indicates that the dissolution of CRM varied the molecular weight distribution slightly. Among three preparation procedures, the PDI of AR was 1.8068 and 1.3126 higher than those of BR-A and AR-B, respectively, while 0.2173 was lower than that of ARB. However, the Mw values of all HBO-ARs were lower than those of AR. Therefore, different preparation procedures led to a different molecular weight distribution. Yet, the large molecules’ content of all HBO-ARs was lower than that of AR. By contrast, incorporating Evotherm-DAT decreased both Mw and Mn of AR. One possible reason is that the dissolution process of CRM was promoted by the incorporation of Evotherm-DAT, which affected the molecular weight distribution significantly.−1 (O-H or N-H stretching vibration of the hydrogen-bonded hydroxyl group and amino groups), 1651 cm−1 (C=O stretching vibration of secondary amides), and 1356 cm−1 (symmetric SO2 stretching of sulphone group), which indicated the existence of amines, amino ions, and sulfur-containing organics, respectively [−1 (O-H or N-H stretching vibration of hydrogen-bonded hydroxyl group and amino groups), 3049 cm−1 (=CH stretching vibration of the benzene ring), and 1600 cm−1 (C=C stretching vibration of the aromatic ring). These absorbance peaks were most likely caused by the presence of phenol, cresol, and xylenol. HBO did not peak at 724 cm−1 (–(CH2)n– rocking vibration of alkane groups, n > 4). This may lead to a smaller average molecular weight than neat bitumen and Evotherm–DAT, which is consistent with the results of the GPC test.ectively . As show−1 showed up in the spectra of E–CEM compared to that of both CRM and Evotherm–DAT. This indicates the decrease of the –COOH group of Evotherm–DAT and the –OH group of rubber caused by a chemical reaction between the two groups. Approximately, it can be seen in −1 (C–O–C and C=O stretching) were observed in B–CRM, which did not occur in either CRM or HBO. This may demonstrate the chemical reaction between the phenol of HBO and residual acetone of CRM during its regenerative process.−1 (mainly C–H stretching) and weakened the absorbance of 1639 cm−1 (C=O stretching). One possible reason is that the unsaturated functional groups of CRM and base binder was oxidized by high shear mixing. Therefore, the modification of CRM is not a single physical process. The modification can also result in the variation of functional groups and chemical bonds. As shown in The FTIR spectra of AR were shown in The mechanism of HBO contributing to the modification of AR can be illustrated as follows. During the modification process through high shear mixing, chemical bonding and physical absorbing may result in redistribution of hydrocarbon chains ,32. ThenCompared to a conventional AR binder, the bio–ARs had superior rutting and fatigue resistance but slightly poorer low temperature performance.The bio–ARs exhibited better performance in both workability and storage stability compared to AR. Specifically, the warm mix effect of HBO additive is comparable to the commercial liquid WMA additive.The methods that incorporate bio–oil in earlier stages (direct mixing and pretreatment methods) had a very marginal negative effect on the performance of bio–AR. Moreover, they are more sustainable since they reduce both the temperature of the mixing AR binder with aggregate, and that of blending CRM with virgin asphalt.This study evaluated the feasibly of using heavy bio–oil to improve both the workability and storage stability of the AR binder. Rheological and chemical tests were conducted to characterize the effects of HBO and its different preparation procedures for modifying AR. According to test results, the findings were obtained as follows.The findings of this study have proven that HBO is a promising modifier that simultaneously alleviates enhancing the workability and storage stability of AR. Future study will focus on effect of HBO on AR mixtures and quantitative estimation of energy saving during blending and the compacting process.