Transcription factors are among the most attractive therapeutic targets but are considered largely ‘undruggable’ in part due to the intrinsically disordered nature of their activation domains. Here we show that the aromatic character of the activation domain of the androgen receptor, a therapeutic target for castration-resistant prostate cancer, is key for its activity as transcription factor, allowing it to translocate to the nucleus and partition into transcriptional condensates upon activation by androgens. On the basis of our understanding of the interactions stabilizing such condensates and of the structure that the domain adopts upon condensation, we optimized the structure of a small-molecule inhibitor previously identified by phenotypic screening. The optimized compounds had more affinity for their target, inhibited androgen-receptor-dependent transcriptional programs, and had an antitumorigenic effect in models of castration-resistant prostate cancer in cells and in vivo. These results suggest that it is possible to rationally optimize, and potentially even to design, small molecules that target the activation domains of oncogenic transcription factors. Transcription factors are rich in intrinsic disorder and therefore hard to drug. The authors improve an experimental drug for castration-resistant prostate cancer by learning how the activation domain of the androgen receptor activates transcription. For example, TP53, the most frequently mutated gene in cancer, and MYC, the most frequently overexpressed gene in cancer, encode TFs3. The rewiring of transcriptional programs is a hallmark of cancer, and oncogenic transcriptional programs of numerous tumor types depend on small subsets of specific TFs4. Despite their appeal, TFs are considered largely ‘undruggable’ because of the intrinsic disorder of their protein regions that are essential for transcriptional activity, rendering them challenging targets for structure-based drug discovery6.The genes encoding transcription factors (TFs) are frequently mutated or dysregulated in cancer, and TFs are coveted targets in oncology8. However, approximately 20% of people with prostate cancer progress to castration-resistant prostate cancer (CRPC), a lethal disease that is associated with the emergence of constitutively active AR splice variants. Such splice variants lack a LBD and consist of only a DNA-binding domain (DBD) and an intrinsically disordered activation domain (AD), rendering them insensitive to LBD-targeting anti-androgens12. Insights into how the ADs of oncogenes function could thus facilitate the development of therapeutic approaches for some of the most lethal cancers.Nuclear hormone receptors, for example the androgen receptor (AR), are TFs that contain a structured ligand-binding domain (LBD), and anti-androgens targeting the LBD are a first-line therapy for the treatment of AR-driven prostate cancer14. Essentially all human TFs, including AR, contain an IDR, and these regions have recently been shown to contribute to the formation of TF condensates and the partitioning of TFs into heterotypic condensates with transcriptional effectors such as the co-activator Mediator (MED-1) or RNA polymerase II (RNAPII)19. The molecular basis of TF condensation has been dissected for a small number of TFs and, in all cases, substitutions of amino acids in the IDRs that altered phase separation also altered transcriptional activity22. On the basis of these findings, we hypothesized that gaining insights into the molecular basis of the phase-separation capacity encoded in the IDRs of oncogenic TFs could be exploited to develop small molecules that alter its activity.Recent studies have suggested that IDRs in many cellular proteins mediate liquid–liquid phase separation in vitro and the partitioning of proteins into biomolecular condensates in cells23), a small-molecular inhibitor of the AR AD discovered by phenotypic screening24, a derivative of which is being investigated in clinical trials for CRPC . On the basis of how this small molecule interacts with Tau-5, we introduced changes in its chemical structure that have led to substantially improved potency in cells and in human xenografts models of CRPC.To investigate this, we chose to study the sequence and structural determinants of AR phase separation. We discovered that this process is required for nuclear translocation and transactivation. The phase transition is driven by interactions between aromatic residues, distributed throughout the sequence of the AD but particularly concentrated near the C terminus. This region includes a subdomain of the AD that has high helical propensity . Tau-5, which plays a key role in transactivation by the splice variants associated with CRPC, harbors the binding site of EPI-001 . This technique provides residue-specific information in the absence of structure and is thus well-suited to studying intrinsically disordered proteinstic Fig. . These r23FQNLF27 motif of the AR AD increases its solubility in vitro30. To avoid interference from this aggregation-prone motif in our ability to study AR AD in vitro, we introduced a substitution in the motif (L26P) that decreases its propensity to aggregate  in vitro31. We substituted tyrosine residues, the most abundant aromatic amino acid in the AR AD, by serines, thus generating three mutants: 8YtoS, in which the 8 tyrosines closest to the DBD were substituted; 14YtoS, in which the other 14 tyrosines were substituted; and 22YtoS, in which all tyrosines were substituted , an in vitro model for RNAPII condensates33. WT* AR AD partitioned into both MED1 IDR and RNAPII CTD droplets, whereas the partitioning was reduced by the 22YtoS AR AD mutant , they decreased the nuclear translocation rate of the AR: when the amount of time that had elapsed since DHT treatment (tDHT) was 60 min, WT AR was localized within the nucleus, 8YtoS and 14YtoS were distributed roughly evenly between the cytosol and nucleus and 22YtoS remained in the cytosol -tagged wild-type full-length AR and mutants containing the 8YtoS, 14YtoS or 22YtoS substitutions in the AD in AR-negative PC3 cells. The expression levels of wild-type and mutant full-length AR were heterogeneous and, in certain cell populations, higher than those of endogenous AR in relevant cell lines  and a significance analysis of interactome quantification (SAINTq), a sampling method to assign confidence scores to protein-protein interactions, revealed that many proteins were enriched after DHT stimulation in cells expressing WT AR , a technique to systematically identify the interactions of a specific protein in living cells. The WT AR and the 22YtoS mutant were fused to a FLAG-tagged Mini-TurboID (MTID) enzyme and introduced into PC3 cells using a lentiviral vector. The addition of biotin for 1 h led to increased protein labeling, demonstrating that the MTID enzyme was functional  tract starting at position 58 and the 179LKDIL183 motif in the Tau-1 region , which confirmed the high helical propensity of the 397WAAAAAQ403 motif  and by 35 °C at 5% TFE  to obtain eGFP-AR-ΔNLS  = 2.29 ± 1.17 s) both 1 h and 24 h after DHT stimulation  formed fewer and smaller condensates in PC3 cells . We also studied its partitioning in the condensates formed by the 8YtoS mutant and obtained an approximately 40% lower partition coefficient (P8YtoSEPI-001 ≈ 32), indicating that the aromatic character of the AR AD liquid phase is a determinant of partitioning.EPI-001 is a small-molecule inhibitor of the AR AD, identified by phenotypic screening, that was investigated in clinical trials but was insufficiently potentues Fig. , we hypoues Fig.  and founR,19S) stereoisomer of EPI-001, was replaced by two carbon atoms separated by a single (compound 4aa), a double  or a triple (1aa) bond  was 1.4 ± 0.1 mM for 1aa versus 5.2 ± 0.4 mM for EPI-002, in agreement with the NMR and gene reporter data  group at R1 (achieved with compound 1ba) or R2 (compound 1ab) increased the potency, reflected by the half-maximal inhibitory concentration (IC50) changing from approximately 5 µM to approximately 1 µM; after introduction of this group in both positions (compound 1bb), the IC50 was 0.5 µM in the luciferase reporter system. In line with this, the introduction of a tert-butyl (C(CH3)3) group at R2, bearing three methyl groups (compound 1af), led to an IC50 of approximately 0.22 µM. Substitution of hydrogen by fluorine (compound 1ad) or a methoxy (CH3O) group (compound 1ac) at position R2 barely changed the IC50, but introduction of an additional aromatic ring (compound 1ae) at this position led to an IC50 of approximately 1.5 µM . As expected, 5 µM enzalutamide, which binds to the AR LBD, had no activity against AR-V7-induced V7BS3-luciferase activity, whereas 35 µM EPI-002 blocked luciferase activity, consistent with previous reports52 , whereas 1ab and 1bb had no inhibitory effects   on LNCaP prostate adenocarcinoma cells after treatment with approximately IC4 h Fig. . Six-houels Fig. , led to els Fig. . These r58 were xenografted into the mice. 1ae was administered at a daily dose of 30 mg per kg body weight for 28 d . In both models, neither enzalutamide nor 1ae had an effect on the housekeeping gene ALAS1 , and LNCaP95-D3 cells (expressing elevated levels of the AR-V7 splice variant)8 d Fig. . After 28 d Fig. : in the 8 d Fig. . Finally60. In the AR AD, these cluster in the 23FQNLF27 motif and in the C terminus of the domain, which includes a sequence region that is key for transactivation in the absence of androgens61  supplemented with either 10% (vol/vol) charcoal-stripped serum  or 5% FBS (vol/vol), as specified below, and antibiotics. Induction of transcriptional activation by the AR in experiments using 5% FBS cultured LNCaP cells  were maintained in DMEM containing 4.5 g L–1 glucose supplemented with 10% (vol/vol) charcoal-stripped FBS and antibiotics. LNCaP95 cells were obtained from S. R. Plymate (University of Washington) and cultured in phenol-red-free RPMI supplemented with 10% (vol/vol) charcoal-stripped FBS (Gibco) and antibiotics. Cells were cultured in a humidified atmosphere containing 5% CO2 at 37 °C. Cell cultures tested negative for mycoplasma contamination.PC3  and LNCaP clone FGC  cells were cultured in RPMI-1640 containing 4.5 g Llls Fig.  and 7a–e2 was used to euthanize the animals. Six- to eight-week-old male mice (NOD-scid IL2Rgammanull) were maintained at the Animal Care Facility at the British Columbia Cancer Research Centre. Five million LNCaP cells were inoculated subcutaneously in a 1:1 volume of matrigel (Corning Discovery Labware). Tumors were measured daily using digital calipers, and the volume was calculated using the formula for ovoid volume: length × width × height × 0.5236. When xenograft volumes were approximately 100 mm3, the mice were castrated, and treatment dosing started one week later. Animals were dosed daily by oral gavage with 30 mg per kg body weight of 1ae, 10 mg per kg body weight enzalutamide, or vehicle .All animal experiments adhere to regulatory and ethical standards and were approved by the University of British Columbia Animal Care Committee (A18-0077). Before any surgery, metaCAM  was administered subcutaneously. Isoflurane was used as an anesthetic. COThe primers and synthetic genes used in this work are listed in Supplementary Data Table 70 was removed from peGFP-C1-AR (Addgene no. 28235) using the Q5 site-directed mutagenesis kit and primer design tools (New England BioLabs). Any clones found to have expansion or shrinkage of either the polyQ or polyG site in the AR were corrected by replacing the 1510-base-pair (bp) KpnI-KpnI fragment with that of the WT AR sequence from peGFP-C1-AR.For peGFPC1-AR-∆NLS, the NLS sequence  of the eGFP-AR fusion proteinFor peGFPC1A-V7, the V7 variant of AR was generated from peGFP-C1-AR using the Q5 site-directed mutagenesis kit and primer design tools (New England Biolabs). Any clones that were found to contain expansion or shrinkage of either the polyQ or polyG site in the AR were corrected by replacing the 1510-bp KpnI-KpnI fragment with that of the WT AR sequence from peGFP-C1-AR.71. The sequence downstream of the AR activation domain in AR-V7, containing the DBD and NLS, was subcloned into an mEGFP plasmid (Addgene no. 18696) using Gibson assembly to create the mEGFP-AR-V7-ΔAD  constructs, mEGFP was subcloned into vectors containing human AR (Addgene no. 29235) and AR-V7 (Addgene no. 86856) using Gibson assembly to create mEGFP-AR-FL and mEGFP-AR-V7  by GenScript Biotech. To enable simple excision from pUC57 and insertion into plasmids derived from peGFPC1-AR, two HindIII sites were included as flanks on the fragments. After digestion using HindIII, the resulting 1,722-bp fragments were excised from TBE agarose gels, purified using the E.Z.N.A. MicroElute Gel Extraction Kit (Omega Biotech) and ligated into HindIII-cut, calf intestinal alkaline phosphatase (CIP)-treated and gel-purified peGFPC1-AR, peGFPC1-AR ∆NLS or peGFPC1A-V7 plasmids to produce the YtoS mutants.pDEST17 plasmids for bacterial recombinant production of AR AD YtoS mutants were synthesized by Thermo Fisher Scientific with open reading frame (ORF) sequences flanked with attB1 and attB2 sequences.The DNA sequence corresponding to the 1,558-aa fragment of AR-AD was synthesized and encoded in a pDONR221 vector by Thermo Fisher Scientific (flanked with attB1 and attB2 sequences).pDONR221-AR-AD-WT was subcloned into a pDEST17 vector using the LP clonase reaction (Thermo Fisher Scientific).L26P was introduced into a WT AR sequence (pDONR221-AR-AD-WT) using a Quickchange protocol with Pfu Turbo polymerase (Agilent), and the resulting plasmid with the L26P substitution (pDONR221-AR-AD-WT*) was subcloned into a pDEST17 vector using the LP clonase reaction (Thermo Fisher Scientific).L56P was introduced into the pDONR221-AR-AD-WT*  using a Quickchange protocol with Pfu Turbo polymerase (Agilent) to generate pDONR221-AR-AD-L56P*. The resulting plasmid with the L26P and L56P substitutions (pDONR221-AR-AD-L56P*) was subcloned into a pDEST17 vector using the LP clonase reaction (Thermo).The A186P, L192P and C238P substitutions were introduced in a step-wise manner into pDONR221-AR-AD-WT*  using a Quickchange protocol with Pfu Turbo polymerase (Agilent) to generate pDONR221-AR-AD-Tau-1*. The resulting plasmid with the L26P, A186P, L192P and C238P substitutions (pDONR221-AR-AD-Tau-1*) was subcloned into a pDEST17 vector using the LP clonase reaction (Thermo Fisher Scientific).The A356P, A398P and T435P substitutions were introduced in a step-wise manner into pDONR221-AR-AD-WT*  using a Quickchange protocol with Pfu Turbo polymerase (Agilent) to generate pDONR221-AR-AD-Tau-5*. The resulting plasmid with the L26P, A356P, A398P and T435P substitutions (pDONR221-AR-AD-Tau-5*) was subcloned into a pDEST17 vector using the LP clonase reaction (Thermo Fisher Scientific).The L56P, A186P, L192P and C238P substitutions were introduced in a step-wise manner into pDONR221-AR-AD-TAU-5*  using a Quickchange protocol with Pfu Turbo polymerase (Agilent) to generate pDONR221-AR-AD-L56P+Tau-1+Tau-5*. The resulting plasmid containing the L26P, L56P, A186P, L192P, C238P, A356P, A398P and T435P substitutions (pDONR221-AR-AD-L56P+Tau-1+Tau-5*) was subcloned into a pDEST17 vector using the LP clonase reaction (Thermo Fisher Scientific).30 using KOD polymerase (Merck Millipore) and the supplied buffer no. 2. The resulting fragment was purified using AmPure XT (Beckman) before InFusion (Takara Bio) into SalI and AflII-cut and gel-purified peGFPC1-AR ∆NLS plasmid.A 507-bp fragment with deletion of residues 21–35 was amplified from pCMV5-FLAG-AR deltaFQNLFThe A186P, L192P and C238P substitutions were introduced in a step-wise manner into the WT AR sequence encoded in pDONR221-AR-AD-WT using a Quickchange protocol with Pfu Turbo polymerase (Agilent). A 755-bp fragment was amplified from the resulting clone, incorporating the A186P, L192P and C238P substitutions (pDONR221-AR-AD-TAU1), using KOD polymerase (Takara Bio). The resulting fragment was digested with DpnI to remove the template and purified using AmPure XT (Beckman) before InFusion into AflII-BstEII-cut and gel-purified peGFP-C1-ARΔNLS plasmid.The A356P, A398P and T435P substitutions were introduced in a step-wise manner into the WT AR sequence (pDONR221-AR-AD-WT) using a Quickchange protocol with Pfu Turbo polymerase (Agilent). A 1,544-bp fragment was then amplified from the resulting plasmid, incorporating the A356P, A398P and T435P substitutions (pDONR221-AR-AD-TAU-5), using KOD polymerase (Takara Bio). The resulting fragment was digested with DpnI to remove the template and purified using AmPure XT (Beckman) before InFusion into KpnI-cut and gel-purified peGFP-C1-ARΔNLS plasmid.Constructs for expression of FLAG-MTID or its fusions with AR WT and 22YtoS were synthesized by Genscript and were either cloned into pcDNA3.1(–) and subsequently cloned into pLenti-CMV-MCS-GFP-SV-puro using XbaI and BamHI to replace GFP or cloned directly into pLenti-CMV-MCS-GFP-SV-puro by Genscript using the same sites. Sequences were codon optimized for mammalian expression and verified by sequencing. pLenti-CMV-MCS-GFP-SV-puro was a gift from P. Odgren (Addgene plasmid no. 73582).E. coli Rosetta (DE3) cells that were transformed with pDEST17 plasmid encoding His-AR-AD, as described previously72. Briefly, cell cultures at an optical density of 600 nm (OD600) of 0.5 were induced with 0.1 mM IPTG at 22 °C overnight. Cells were lysed in PBS buffer and centrifuged. The pellet was solubilized overnight in Tris buffer  containing 8 M urea and 500 mM NaCl at pH 8. The protein was captured on Nickel columns  and eluted with 500 mM imidazole. After urea removal by dialysis, the His-tag was cleaved by TEV protease at 4 °C overnight. Urea (8 M) was added to cleaved protein before reverse-nickel affinity chromatography to separate noncleaved protein and the His-tag. Protein in the flowthrough was concentrated, filtered and stored at −80 °C. To prevent protein aggregation or instability, an additional purification step was conducted, and the sample was run on a Superdex 200 16/600 column pre-equilibrated with AR AD buffer . Tau-5* (330–448 aa) was expressed and purified as previously described23, and an equivalent protocol was used to express and purify fragment AR AD (441–558 aa).WT and mutant AR AD (1–558 aa) were recombinantly produced in AR-LBD (663–919 aa) containing an amino-terminal His-tag and encoded in a pET15b plasmid (Addgene no. 89083) was expressed in Rosetta (DE3) cells with 1 mM IPTG at 16 °C overnight. Cells were resuspended in Ni-Wash buffer , lysed and centrifuged. Soluble protein was captured by IMAC and eluted with 500 mM imidazole. During an overnight dialysis, His-tag was cleaved by thrombin , and the NaCl concentration was reduced to 100 mM. Cleaved protein was captured by cation exchange  and eluted with 1 M NaCl gradient. LBD was injected in a Superdex 200 16/600 column pre-equilibrated with 25 mM HEPES, 250 mM NaCl, 10% glycerol, 1 mM TCEP, 10 μM DHT, 1 mM EDTA and 0.5% Tween-20 at pH 7.2.MED1 IDR (948–1573), encoded in a peTEC plasmid, was a gift from T. Graf. A 3C cleavage site was introduced by Q5 site-directed mutagenesis (New England Biolabs) between mCherry and the MED1 sequence, yielding peTEC-His-mcherry-3C-MED1-IDR plasmid. Protein was expressed in B834 (DE3) cells at 16 °C overnight with 0.1 mM IPTG. Upon cell lysis in Tris buffer with 100 mM NaCl, the soluble cell fraction was injected in a HisTrap HP column, and protein was eluted with 500 mM imidazole. The eluted protein was concentrated and separated by cation exchange chromatography. The collected fractions were cleaved by 3C protease, and MED1 IDR was separated from other protein fragments by size-exclusion chromatography (SEC) with Superdex 200 16/600 column pre-equilibrated with 20 mM sodium phosphate, 100 mM NaCl and 1 mM TCEP at pH 7.4.E. coli B834(DE3) cells transformed with the pDEST17 plasmid, which encodes H6-3C-RNAPII-CTD. The protein was expressed at 25 °C overnight with 0.1 mM IPTG and extracted from the insoluble cell fraction. The pellet was resuspended in Tris buffer with 8 M urea and loaded on a HisTrap HP column. Captured protein was dialyzed against 50 mM Tris-HCl, 50 mM NaCl and 1 M NaCl at pH 8 and was cleaved by 3C protease overnight at 4 °C. RNAPII CTD was injected in a Superdex 200 16/600 column pre-equilibrated with 20 mM sodium phosphate, 150 mM NaCl, 5% glycerol and 1 mM TCEP at pH 7.4.RNAPII CTD (1592–1970) was produced in AR-LBD, MED1-IDR and RNAPII-CTD fractions from SEC were concentrated, filtered and stored at −80 °C until further use.Tc measurements of protein solutions were monitored by measuring the absorbance of the solutions at 340 nm as a function of temperature on a Cary 100 Multicell UV-vis spectrophotometer, equipped with a Peltier temperature controller, at a heating rate of 1 °C min–1. The Tc values were obtained as the maximum of the first-order derivative of the obtained curves from three independent samples.Protein samples were prepared in AR AD buffer , with the indicated protein and NaCl concentrations, on ice. Samples were centrifuged at 21,130 r.c.f. for 20 min at 4 °C, and the supernatant was transferred to a quartz cuvette. Phase separation For in vitro condensation experiments, proteins were labeled with fluorescent dye instead of being tagged with fluorescent protein, to avoid nonspecific interactions in heterotypic condensates. AR AD and MED1 IDR were fluorescently labeled with Dylight 405 or Alexa Fluor 647, respectively, unless otherwise indicated in the figure legends. LBD and RNAPII-CTD were labeled with Oregon Green 488. In all cases, sulfhydryl-reactive dyes were used, which reacted to the naturally occurring cysteines of the protein, except for RNAPII-CTD in which an N-terminal Cys was added. Protein was labeled according to the manufacturer’s instructions for sulfhydryl-reactive dyes (Thermo Fisher Scientific). Briefly, protein and dye were mixed at a 1:20 ratio in each protein storage buffer, adjusted to pH 7.5 overnight at 4 °C. Then, 1 mM DTT was added to stop the reaction, and protein was separated from free dye with a pre-equilibrated PD-10 column. Protein was concentrated and filtered, and the concentration and conjugation efficiency were analyzed, following the manufacturer’s instructions for sulfhydryl-reactive dyes (Thermo Fisher Scientific).73. Images were taken using Zeiss LSM 780 Confocal Microscope with a Plan-ApoChromat ×63/1.4 Oil objective lens. Fluorescence recovery after photobleaching (FRAP) experiments were recorded using the same set-up on a 50 μM AR AD sample containing approximately 1% of protein labeled with DyLight 488 dye (Thermo Fisher Scientific) with 500 mM NaCl. The data were analyzed using the EasyFRAP software74 to extract the mobile fractions and recovery half-times.Each protein solution was prepared by adding approximately 1% of equivalent labeled protein. Solutions were stored on ice. Samples were prepared by mixing proteins at the indicated protein concentration with AR AD buffer  in low binding PCR tubes at RT. Once all proteins were mixed, the phase separation trigger was added: NaCl for AR samples, or Ficoll 70 for transcriptional component samples. Samples were homogenized, and 1.5 μl of sample was transferred into sealed chambers composed of a slide and a PEGylated coverslip sandwiching 3M 300 LSE high-temperature double-sided tape (0.34 mm). Coverslips were PEGylated according to the published protocolAll NMR experiments were performed at 5 °C (278 K) on either a Bruker 800 MHz (DRX or Avance NEO) or a Bruker Avance III 600 MHz spectrometer, both equipped with TCI cryoprobes, and versions 3.2 and 4.0.8 of TOPSPIN.15N,13C-double-labeled AR AD (441–558 aa) sample in NMR buffer (20 mM sodium phosphate (pH 7.4), 1 mM TCEP, 0.05% (wt:vol) NaN3) was used for backbone resonance assignment. The following series of three-dimensional (3D) triple resonance experiments were acquired: HNCO, HN(CA)CO, HNCA, HN(CO)CA, CBCANH and CBCA(CO)NH. Chemical shifts were deposited in the Biological Magnetic Resonance Bank (BMRB) (ID: 51476).A 300 μM 25607) and 142–448 (BMRB ID: 51479)). In addition, 3D HNCO and HNCA experiments were acquired for two 15N,13C-double-labeled AR AD (1–558 aa) samples (25 μM and 100 μM) dissolved in NMR buffer. For the 100 μM sample, additional 3D HN(CA)CO and HN(CO)CACB experiments were also recorded. Three-dimensional experiments were done using 25% non-uniform sampling. Chemical shifts were deposited in the BMRB (ID: 51480).The assignment of AR AD (1–558 aa) was guided by the assignments obtained for the smaller AR fragments that were first studied here (residues 441–558) or previously reported , with only local differences in residues around the position substituted (L26), which were assigned using non-uniform sampled 3D BEST-TROSY HNCO and HNCA experiments1H,15N correlation spectrum by adding increasing amounts of unlabeled sample on a 25 μM 15N-labeled AR AD to reach total concentrations of 57.5, 100.8, 122.5 or 155 μM. Spectra were recorded using 128 scans per increment  to ensure that intensities in the regions with weaker signals were quantified properly. Throughout the article, the term oligomer refers to intermolecular complexes formed through weak, and therefore reversible, site-specific interactions between monomers.The oligomerization of AR AD was monitored by recording the induced intensity changes on the two-dimensional 1H,15N correlation spectra and non-uniform sampled 3D BEST-TROSY, HNCO and HNCA experiments recorded in the presence of increasing TFE amounts .The effect of TFE on 50 μM WT* AR AD and Tau-5* secondary structures were monitored by a series of 15N chemical shifts in 2D 1H,15N CP-HISQC76 spectra at 37 °C (310 K), using 60 μM Tau-5* in the absence or presence of 60 μM compounds (1:1 ratio). Samples contained NMR buffer (above) at pH 6.6 with 200 mM NaCl and 2% DMSO-d6. The CP-HISQC pulse sequence and the pH level of 6.6 were chosen to reduce water exchange of labile amide protons at 37 °C (310 K).EPI-001 and 1aa binding to Tau-5* was studied by comparing 77 for non-uniform sampled data, and NMRPipe78 for all uniformly collected experiments. Data analysis was performed with CcpNmr Analysis79. Helix populations were extracted using the δ2D software67.Data processing was done using qMDDFQNLFQ and FQNPFQ synthetic peptides were obtained as lyophilized powders with >95% purity from GenScript with amidated C and acetylated N termini. The lyophilized peptides were solubilized at a final concentration of 5 mM in DMSO. Immediately before each experiment, the stock solutions were diluted to 125 μM in 20 mM HEPES buffer, pH 7.5, with 150 mM NaCl. For aggregation assays, the samples were incubated overnight at 37 °C at 600 r.p.m. agitation. The term aggregate refers to the quasi-irreversible formation of fibrillar species stabilized by strong intermolecular interactions, involving a large conformational change.–1 scan rate. The peptides were sonicated for 10 min in an ultrasonic bath (Fisher Scientific FB15052) before measurement.Synchronous light scattering was monitored using a JASCO Spectrofluorometer FP-8200. The conditions of the spectra acquisition were: excitation wavelength of 360 nm, emission range from 350 to 370 nm, slit widths of 5 nm, 0.5-nm interval and 1,000 nm min−1 within the 1,800–1,500 cm−1 range. All spectral data were acquired and normalized using the OPUS MIR Tensor 27 software. Data was afterwards deconvoluted using the Peak Fit 4.12 program. The buffer without peptide was used as a control and subtracted from the absorbance signal before deconvolution.Fourier transform infrared spectroscopy (FT-IR) experiments were performed using a Bruker Tensor 27 FT-IR spectrometer (Bruker Optics) with a Golden Gate MKII ATR accessory. Each spectrum consists of 16 independent scans, measured at a spectral resolution of 4 cmThe morphology of the aggregated FQNLFQ peptide was evaluated by negative staining using a JEOL JEM-1400Plus Transmission Electron Microscope. Five microliters of peptide solution was placed on carbon-coated copper grids and incubated for 5 min. The grids were then washed and stained with 5 μl of 2% wt/vol uranyl acetate for 5 min. Then, the grids were washed again before analysis. Images and videos were processed with ImageJ.2 in a temperature-regulated incubation chamber (EMBL). eGFP was excited with a 488 nm laser, and Z-stack images were acquired every 0.45 μm. Time-lapse images were compiled, processed and analyzed with Fiji (ImageJ)80. Intensity thresholds were set manually and uniformly to minimize background noise.PC3 cells were seeded in collagen-I-coated µ-slide four-well glass-bottom plates (Ibidi 80426) at 60% confluency 24 h before transfection. Then, 170 ng of expression vectors encoding WT AR tagged with eGFP (eGFP-AR) or mutant AR proteins were transfected per well using polyethylenimine (PEI) (Polysciences) at a ratio of 1 µg DNA to 3 µl PEI. Four hours after transfection, the medium was changed to RPMI supplemented with 10% charcoal-stripped FBS and cells were cultured for 16 h before imaging. Transiently transfected PC3 cells expressing eGFP-AR were imaged in 3D during 1 min, by taking one image every 15 s, to acquire a baseline readout of AR expression. Cells were then treated immediately with 1 nM of DHT and imaged during 1 h, also by taking an image every 15 s. Time-lapse imaging was performed in an Andor Revolution Spinning Disk Confocal with an Olympus IX81 microscope and a Yokogawa CSU-XI scanner unit. Images were acquired with an Olympus PlanApo N ×60/1.42 Oil objective lens. A stable temperature (37 °C) was maintained during imaging under COFLAG-MTID-AR-WT and PC3 FLAG-MTID-AR-WT-Y22toS cell lines were seeded in 24-well culture plates, on 12-mm sterilized coverslips. The next day, 50 μM biotin (or DMSO for a negative control) and 1 nM DHT were added for 2 h. The culture medium was removed and the cells were washed with PBS. Next, cells were fixed for 15 min with 4% paraformaldehyde. After fixation, cells were washed with PBS and then permeabilized with 0.1% Triton X-100 for 10 min. Coverslips were then washed and blocked with blocking buffer  for 1 h at 37 °C. Coverslips were incubated with primary antibody—anti-AR —overnight. The next day, coverslips were washed with PBS, and secondary antibodies were added (1:500): anti-streptavidin antibody conjugated to Alexa Fluor 488  or Alexa Fluor 488-conjugated goat anti-rabbit-IgG (H+L) . Fluorescence images were acquired using a Leica TCS SP8 confocal microscope. Images were taken with ×63 oil objectives, and standard LAS-AF software was used.2 incubation chamber set to 37 °C. Images were acquired across two biological replicates.HEK293T cells in DMEM with 10% FBS were seeded at a density of 40,000 cells per well on glass-bottom chambered coverslips (Ibidi 80827). Sixteen hours later, wells were refreshed with 280 µl seeding medium and transfected with 50 ng of mEGFP expression plasmids, as shown in Extended Data Fig. HEK293T and HeLa eGFP-AR cells in DMEM with 10% FBS were seeded at a density of 40,000 cells per well on glass-bottom chambered coverslips (Ibidi 80827). Sixteen hours later, wells containing HEK293T cells were refreshed with 280 µl seeding medium and transfected with 50 ng of mEGFP expression plasmids, as shown in Fig. LNCaP cells  were seeded in RPMI-1640 5% FBS onto PLL-coated 18-mm no. 1.5 thickness glass coverslips  at a density of 100,000 cells per coverslip on a 24-well plate. Sixteen hours later, the media was refreshed and cells were incubated further for another 24 h. For fixation, wells were washed with PBS, then fixed with 1 ml of 4% PFA in PBS for 20 min at room temperature. After a second wash in PBS, cells were permeabilized with 0.5% Triton X-100, PBS (vol/vol) (Sigma 93443) and then stained with anti-AR  and STAR 635P secondary antibody . Nuclear translocation of the AR signal was validated by staining LNCaP cells grown in RPMI-1640 5% CSS , following the same protocol. DNA was counterstained with 1:2,000 Spy555-DNA , and samples were mounted onto glass slides with vectashield .2 (5%). eGFP imaging was performed using a 473-nm stimulation wavelength laser at 20% power and a 592-nm depletion laser at 20% power. Images were taken using a HC PL APO CS2 ×63/1.40 oil objective, with a final resolution of 23 nm pixel–1.HEK293T and HeLa cells were imaged on a Leica Stellaris STED DMI 8 microscope equipped with an okolab incubation chamber set to 37 °C and a constant concentration of CO–1. Fluorescence-lifetime imaging microscopy cutoffs and fluorescence-lifetime stimulated emission depletion deconvolution strengths were determined automatically using Leica LAS-X software v2.5.6 to filter background photons with low fluorescence lifetimes  on top of a droplet, which was repeated five times; the maximum laser power intensity was 488 nm, and the dwell time for bleaching was 40 µs. Twenty pre-bleached images and 200 post-bleached images in total were taken at intervals of 180 ms. Post-bleached images were acquired immediately after the bleaching. Mean gray intensity measurements were quantified in three different ROIs in each FRAP experiment: a bleached region, a background region outside the cells and a region spanning the whole cell were drawn to allow normalization of the gray values. Fiji was used to measure it in each ROI using the plot 2O (9:1) were used as mobile phases, containing 0.1% TFA.Concentrations of EPI-001 in the dense and light phases of WT* AR AD and 8YtoS AR AD were determined using the Agilent Technologies 1200 HPLC instrument, using a Jupiter analytical C4 column from Phenomenex. H2O and ACN:H3. One equivalent of compound was added to 60 μM of protein from DMSO stocks. The final concentration of DMSO in all samples was 2%. Liquid–liquid phase separation of the protein was induced by adding 1.25 M NaCl, followed by incubation for 5 min at 37 °C and centrifugation at 2,000 r.p.m. for 2 min at 37 °C to separate the light and dense phases. The light phase was transferred to a new microcentrifuge tube, and the dense phase was diluted nine times by adding the buffer containing 4 M urea, which dissolvd the condensates. These solutions were injected in an HPLC system. The corresponding peaks of small molecules were integrated, and concentrations were determined using standard calibration curves that were obtained by measuring four concentrations for each compound.Samples were prepared on ice in 20 mM sodium phosphate buffer (pH 7.4), 1 mM TCEP and 0.05% (wt/wt) NaN3, 1 M NaCl and 2% DMSO.The effects of compounds on AR AD phase separation in vitro were assessed by turbidity (see ‘Turbidity measurements’) and microscopy (see ‘Fluorescence microscopy of in vitro protein condensation’). The samples contained 25 μM WT* AR AD with 1 molar equivalent of the indicated compounds in 20 mM sodium phosphate buffer (pH 7.4), 1 mM TCEP, 0.05% (wt/vol) NaN81.HEK293T cells were co-transfected with an androgen-response element (ARE)-luciferase construct containing a luciferase reporter gene under the control of three AREs (kindly provided by the M. Pennuto lab), along with an empty vector, an AR-expression vector (pEGFP-C1-AR or AR V7) or different mutants in the presence or absence of DHT. HEK293T cells were maintained in DMEM with 10% charcoal-stripped FBS during the assay. Transfections were carried out using PEI, and cells were treated with vehicle or 1 nM DHT 24 h after transfection. Cell extracts were prepared 48 h after transfection, when eGFP-AR mutants are mostly localized to the nucleus, and assayed for luciferase activity using the Promega luciferase detection kit. Luciferase activities were normalized to co-transfected β-galactosidase activity82. PSA(6.1 kb)-luciferase reporter plasmid (0.25 μg well–1) was transiently transfected into LNCaP cells that were seeded in 24-well plates. Twenty-four hours after transfection, cells were pretreated with compounds for 1 h prior to the addition of 1 nM R1881 and incubation for an additional 24 h. For the V7BS3-luciferase reporter, an expression vector encoding AR-V7 (0.05 μg well–1) and a filler plasmid  were transiently co-transfected with V7BS3-luciferase reporter plasmid (0.25 µg well–1) into LNCaP cells in 24-well plates. After 24 h, the cells were treated with the indicated compounds for additional 24 hours. Transfections were completed under serum-free conditions using Fugene HD (Promega). Luciferase activity was measured for 10 s using the Luciferase Assay System (Promega) and normalized to total protein concentration determined by the Bradford assay. Validation of consistent levels of expression of AR-V7 protein was done using western blot analyses.PSA(6.1 kb)-luciferase, V7BS3-luciferase and AR-V7 plasmids and transfections of cells have been described previously–1 into 96-well flat-bottom plates  that had been pre-coated with poly-l-lysine (Sigma P4707). Sixteen hours later, triplicate wells were refreshed with 100 µl of seeding medium spiked with 7× serial dilutions of EPI-001 from 200 µM (Selleckchem lot no. S795502), 7× serial dilutions of 1ae from 50 µM, or DMSO control, at a constant DMSO concentration of 0.5% (vol/vol). Ninety-six hours later, wells were washed with 200 µl PBS and then fixed with 100 µl of 4% PFA in PBS for 20 min at room temperature. After fixation, LNCaP nuclei in each well were counterstained using 100 µl of Hoechst 33342 (Abcam ab228551), diluted to 1:4,000 in PBS, for 20 min at room temperature. After a final wash in PBS, plates were imaged using a Celldiscoverer 7 microscope equipped with a ×20 air objective. Twenty-five tile regions (5 × 5 tiles) were imaged for each technical replicate well (triplicate wells for each dose and compound). Data were acquired across two biological replicates performed on different weeks.LNCaP cells  in RPMI-1640 with 5% FBS were seeded at a density of 4,000 cells well–1) were plated in 96-well plates in their respective media plus 1.5% dextran-coated charcoal (DCC)-stripped serum. LNCaP cells were pretreated with the compounds for 1 h before they were treated with 0.1 nM R1881 for an additional 3 d. Proliferation and viability were measured using the Alamar blue cell viability assay, following the manufacturer’s protocol (Thermo Fisher Scientific). LNCaP95 cells  were seeded in 96-well plates in RPMI plus 1.5% DCC for 48 h before the addition of compounds and incubation for an additional 48 h. BrdU incorporation was measured using BrdU Elisa kit (Roche Diagnostics).To compare the antiproliferative effects of 1ae and enzalutamide in LNCaP and LNCaP95 cells, LNCaP cells , Ct values from target regions were normalized to Ct values from control regions from the treatment sample, and were then normalized to the DMSO sample. Data were collected from three biological replicates performed on different days.Target primer sequences are listed in Supplementary Table –1 into 6-well plates. Sixteen hours later, wells were refreshed with seeding medium spiked with either EPI-001 or 1ae at the doses indicated in Fig. LNCaP cells  in RPMI-1640 with 5% FBS were seeded at a density of 300,000 cells wellg to separate soluble and pellet fractions. Total protein was quantified using a BCA assay (Pierce Biotechnology). Proteins were resolved by 4–12% gradient Bis-Tris SDS–PAGE (Invitrogen NP0323), transferred to PVDF membranes and blocked with 5% non-fat milk in TBST for 1 h at room temperature with shaking. The membranes were incubated with the following antibodies: anti-GAPDH  and anti-AR  as well as RD-680-conjugated anti-mouse  and CW-800-conjugated anti-rabbit  secondary antibodies. Membrane fluorescence was read with the Odyssey CLx infrared imaging system (LI-COR).To compare the levels of AR expression, cells were washed and collected in PBS ×1 and lysed in RIPA buffer ×1  containing phosphatase and protease inhibitors (Roche). Lysates were centrifuged at 15,000–1 into six-well plates . Forty-eight hours later, cycloheximide  was added to a final concentration of 50 µg ml–1 and incubated for 3 h before incubation with 1ae (at the indicated concentrations) for 21 h. Cells were washed in PBS  and lysed in RIPA buffer  containing protease inhibitor . Lysates were centrifuged at 15,000g to separate soluble and insoluble fractions. Soluble protein was quantified using a BCA assay . Protein extracts (10–70 µg) were electrophoresed in a Bolt 8% Bis-Tris gel  and transferred with PVDF transfer stacks . Membranes were blocked with 3% non-fat milk in TBST for 1 h at room temperature with shaking. Afterwards, membranes were incubated with the following antibodies: anti-GAPDH , anti-AR , 800-CW-conjugated anti-rabbit  and RD-680-conjugated anti-mouse . Imaging was done using the Odyssey CLx, and protein-band intensity was quantified with ImageJ.To determine the effect of 1ae treatment on AR levels, LNCaP cells  were seeded in RPMI-1640  supplemented with 5% FBS  and 1% penicillin–streptomycin  at a density of 150,000 cells well–1) for 48 h, before 1 μg ml–1 puromycin was introduced for 72 h to select for stable cell lines. pMDLg/pRRE was a gift from D. Trono . pCMV-VSV-G was a gift from B. Weinberg . pRSV-Rev was a gift from D. Trono .FLAG-MTID, FLAG-AR-WT-MTID or FLAG-22YtoS-MTID were subcloned from pcDNA3.1(–) (Genscript) into pLenti-CMV-MCS-GFP-SV-puro (Addgene no. 73582) by replacing GFP using XbaI-BamHI digestion. Vectors were co-transfected with lentiviral packaging plasmid vectors REV (cat. no. 12253), RRE (cat. no. 12251) and VSV-G (cat. no. 8454) into 293T cells with PEI (Sigma-Aldrich). Two days after transfection, virus-containing medium was collected and filtered through a 0.45-µm low-protein-binding filtration cartridge. The virus-containing medium was used to directly infect LNCaP/PC3 cells in the presence of polybrene  with 10% (vol/vol) charcoal-stripped FBS for 48 h. Cells were seeded, and the next day, 50 μM biotin  and 1 nM DHT were added for 2 h. For small-molecule inhibitors, cells were pretreated for 1 h with either EPI-001 or 1ae, then for 2 h with DHT and biotin. For MS, cells were collected through trypsinization, washed two times in PBS and snap-frozen on dry ice. Cell pellets were lysed in modified RIPA buffer  on ice and treated with 250 U benzonase (Millipore), and biotinylated proteins were isolated using streptavidin-sepharose beads . Proteins were washed in ammonium bicarbonate and digested with trypsin. Mass spectrometry was performed in the IRB Barcelona Mass Spectrometry and Proteomics facility, as described previously83. Data were analyzed using SAINTq84.Prior to BioID experiments, MTID-containing stable cell lines were generated through lentiviral infection and puromycin selection. They were subsequently grown in RPMI-1640 medium modified with Protein–protein interactions were studied using a Duolink In Situ Orange Starter Kit Mouse/Rabbit , following the manufacturer’s protocol. Briefly, transduced prostate cancer cells were seeded in coverslips and cultured overnight. The next day, they were treated with 50 μM biotin and 1 nM DHT for 2 h or were pretreated initially with small-molecule inhibitors. Slides were washed with cold 1× PBS and fixed in 4% paraformaldehyde for 15 min, washed in PBS and permeabilized using 0.1% Triton X-100 for 10 min and washed then blocked with blocking buffer  for 1 h at 37 °C. The coverslips were blocked with Duolink Blocking Solution in a pre-heated humidified chamber for 30 min at 37 °C. Primary antibodies to the following proteins were added and incubated overnight at 4 °C: androgen receptor (ER179(2)) , Nup153 , Med1  and ARID1a/BAF250A . Coverslips were then washed with 1× wash buffer A and subsequently incubated with the two PLA probes  for 1 h, then the ligation-ligase solution for 30 min, and the amplification-polymerase solution for 100 min in a pre-heated humidified chamber at 37 °C. Before imaging, slides were washed with 1× wash buffer B and mounted with a cover slip using Duolink In Situ Mounting Medium with DAPI. Fluorescence images were acquired using a Leica TCS SP8 confocal microscope. Images were taken with ×100 oil objectives, using standard LAS-AF software.−ΔΔCt method using the housekeeping gene SDHA. To analyze gene expression in PC3 cells expressing AR V7, 2 × 105 cells were plated in duplicate in 6-well plates. After 48 h, total RNA was extracted using the SV Total RNA Isolation System (Promega), following the manufacturer’s instructions.To analyze tumor gene expression, tumors were flash frozen, and ~100-mg samples were pulverized under liquid nitrogen. Samples were added to 1 ml TRIzol (Invitrogen) and homogenized using a FastPrep-24 tissue homogenizer . Total RNA was extracted using the RNeasy Micro Kit (Qiagen), cleaned using the DNase I Kit, amplification grade (MilliporeSigma), and reverse transcribed using the High-Capacity RNA-to-cDNA Kit (Thermo Fisher Scientific), according to the manufacturers’ protocols. Diluted cDNA and Platinum SYBR Green qPCR SuperMix-UDG with ROX (Invitrogen) were combined with gene-specific primers. Transcript quantification was completed using a QuantStudio 6 RT–qPCR machine, and calculation of the mean normalized expression of target transcripts was done using the 2–1 into 6-well plates . Forty-eight hours later, cycloheximide  was added to a final concentration of 50 µg ml–1 and incubated for 3 h before incubation with 1ae (at the indicated concentrations) for 21 h. Cells were washed in PBS  and lysed in RIPA buffer  containing protease inhibitor . Lysates were centrifuged at 15,000g to separate the soluble and insoluble fractions. Soluble protein was quantified using the BCA assay . Protein extracts (10–70 µg) were electrophoresed in a Bolt 8% Bis-Tris gel  and transferred using PVDF transfer stacks . Membranes were blocked with 3% non-fat milk in TBST for 1 h at room temperature with shaking. Afterwards, membranes were incubated with the following antibodies: anti-GAPDH , anti-AR , 800-CW-conjugated anti-rabbit  and anti-mouse . Imaging was conducted with Odyssey CLx, and protein-band intensity was quantified with ImageJ.LNCaP cells  were seeded in RPMI-1640  supplemented with 5% FBS  and 1% penicillin–streptomycin  at a density of 150,000 cells wellR2_R3 region  in the presence of 1aa was performed using GROMACS 2019.2 (refs. 86), patched with PLUMED v2.6.0 (ref. 87) as described previously51 and compared with previously reported simulation results of Tau-5R2_R3 in the presence of EPI-002 (ref. 51). In brief, an explicit solvent simulation was performed in a cubic box with a length of 7.5 nm and neutralized with a salt concentration of 20 mM NaCl by 8 Na+ ions and 5 Cl− ions. The AR Tau-5R2−R3 protein was parameterized using the a99SB-disp force field; water molecules were parameterized with the a99SB-disp water model88. 1aa was parameterized using the GAFF89 for ligand forcefield parameters. The replica exchange with solute tempering (REST2) algorithm90 was used to enhance conformational sampling. Sixteen replicas were run in parallel using a temperature ladder ranging from 300–500 K, with all protein and ligand atoms selected as the solute region. Tau-5R2−R3 with 1aa was simulated for 5.2 μs per replica, respectively, for a total simulation time of 83.2 μs. Convergence of simulated properties was assessed by a comparison of the conformational sampling of each simulated replica, as previously reported51, and statistical errors were calculated using a blocking analysis following Flyvbjerg and Peterson91. We define an intermolecular contact between a ligand and a protein residue as occurring in any frame where at least one heavy (non-hydrogen) atom of that residue is found within 6.0 Å of a ligand heavy atom. To calculate a simulated KD value for each compound, we defined the bound population of each ligand as the fraction of frames with at least one intermolecular contact between a ligand and Tau-5R2_R3.A molecular dynamics simulation of the AR Tau-5t-test or Mann–Whitney U test in base R or python. Differences were considered significant when adjusted P values were lower than 0.0001 (****), 0.001 (***), 0.01 (**) or 0.05 (*).Pairwise comparisons shown in Figs. 2d,g,4d,g,h,j,k6c,j,k,A custom-made macro in Fiji was developed to quantify the total number and the size of AR condensates into the cytoplasm as a function of time Fig. . This mazintensity projections of the 3D stacks. A manual step of drawing a ROI was integrated into the macro to select the nuclei to be removed so that only the cytoplasm area would be kept for the detection and quantification of the AR condensates. After filtering and thresholding steps, the cytosol area was segmented and quantified. Then a mathematical operation was done between the resulting mask of the cytosol without the nuclei and the z maximum intensity projection data to detect and quantify the total number and the area of AR condensates in the cytosol. The quantification was done at three time points after DHT exposure.The macro creates z-sum projection of the 3D stacks from the time-lapse to improve the quantitativeness of the results. A stackreg plugin is used in the macro to register and correct the xymovement of the cells over time; there is a manual step that involves drawing the nuclei area and the cytoplasm area to extract automatically the mean gray values of these ROIs over time.A custom-made macro in Fiji was developed to quantify the mean gray intensity value in the nuclei area over time Fig. . The macFor the transcriptional activity assay, reported in Fig. I(t)). Obtained values were further normalized to the initial fluorescence by dividing I(t) by the mean gray value of the initial pre-bleaching acquisition images.Mean intensities of bleached areas were corrected both for bleaching due to imaging over time and background noise. The corresponding calculations were performed with EasyFrap by calculating the fluorescence intensity over time  by simple median filtering, background subtraction and local thresholding. Nuclei exhibiting an insufficient or overly strong level of expression were excluded manually, and the s.d. of the intensity was estimated inside the remaining nuclei in the original images. For the granularity analysis, reported in Fig. 92 to create the dose–response curves shown in Fig. 50 and IC10 values for EPI-001 and 1ae , were used to construct dose–response curves for EPI-001 and 1ae Fig. . Segment1ae Fig. .For in vitro droplet analysis of AR AD in images with multiple components in Fig. https://github.com/BasuShaon/AR-RNA-seq-STED. In brief, the DNA counterstain was first used to identify and threshold nuclear objects. Clusters within nuclear objects were then detected using the rolling ball algorithm, with the size of the rolling ball set to eight times the limit of detection (48 nanometers), according to a standard protocol93. This enabled detection of nuclear AR clusters for cells imaged with the same τ-deconvolution strength. Nuclear AR clusters were pooled from 7 LNCaP nuclei and analyzed for mean intensity and size as indicated in Extended Data Fig. Composites acquired in τ-STED mode  and ACN were used, with 0.1% TFA. The chromatographic separation was optimized for a 5-min run, by using a linear gradient from 0% to 100% ACN in the first 3 min.In which CHI is a chromatographic hydrophobicity index. CHI values were determined from gradient retention times at pH = 7.4. Chromatograms were measured using the Agilent 1100 HPLC instrument, using a Luna C18 analytical column from Phenomenex. For mobile phases, 50 mM ammonium acetate  with standard settings. The first and fourth columns in ReadsPerGene.out.tab STAR output files (GeneIDs and reverse strand reads) were used to build raw count matrices for each sample library.Paired-end sequencing reads were first quality checked using FASTQC and then aligned to the 95. First, raw count matrices from sample libraries were merged into a single object using the ‘DESeqDataSetFromHTSeqCount’ function with the design set to the treatment condition . The merged count matrix was then fitted to the DESeq statistical model using the ‘DESeq’ function. The fit and merged matrix was then reduced using a variance-stabilizing transformation, ‘vst,’ to visualize principal components one and two | > 1 and P< 1 × 10–10 were used to determine differentially expressed genes in a given contrast  values for the genes in any given DESeq2 contrast by sorting log2(FC) values in descending order and filtering out duplicate entries. Ranked lists were then analyzed for the enrichment of 50 hallmark gene sets (collection H) obtained from the molecular signature database msigDB, maintained by the Broad Institute, using the ‘plotEnrichment’ and ‘plotfgseaRes’ functions in fgsea and the ‘GSEA’ function in DOSE .Gene set enrichment analysis was performed using R/bioconductor packages fgsea and DOSEPadj) of the calculated enrichment  to create a log2 normalized count matrix  values by contrasts indicated in Fig. 6d–f and Extended Data Fig. 7.RNA-Seq DESeq2 logSupplementary Table 4RNA-Seq DESeq2 raw count matrix and normalized count matrix used to calculate expression values plotted in Fig. 6 and Extended Data Fig. 7d.Supplementary Table 5Gene level RNA-Seq data analysis, GSEA datasets, AR up genes in PC3 AR-V7 and source data used to produce Extended Data Fig. 3j–l.Supplementary Table 6Sequences of primers and synthetic genes.Source Data Fig. 1Statistical source data.Source Data Fig. 2Statistical source data.Source Data Fig. 3Statistical source data.Source Data Fig. 4Statistical source data.Source Data Fig. 5Statistical source data.Source Data Fig. 6Statistical source data.Source Data Extended Data Fig. 1Statistical source data.Source Data Extended Data Fig. 2Statistical source data.Source Data Extended Data Fig. 3Statistical source data.Source Data Extended Data Fig. 4Statistical source data.Source Data Extended Data Fig. 5Statistical source data.Source Data Extended Data Fig. 6Statistical source data.Source Data Extended Data Fig. 7Statistical source data.Source Data Extended Data Fig. 8Statistical source data.Source Data Fig. 3 and Extended Data Figs. 3 and 6Unprocessed western blots.


Augmented reality head-up display (AR-HUD) interface design takes on critical significance in enhancing driving safety and user experience among professional drivers. However, optimizing the above-mentioned interfaces poses challenges, innovative methods are urgently required to enhance performance and reduce cognitive load.A novel method was proposed, combining the IVPM method with a GA to optimize AR-HUD interfaces. Leveraging machine learning, the IVPM-GA method was adopted to predict cognitive load and iteratively optimize the interface design.Experimental results confirmed the superiority of IVPM-GA over the conventional BP-GA method. Optimized AR-HUD interfaces using IVPM-GA significantly enhanced the driving performance, and user experience was enhanced since 80% of participants rated the IVPM-GA interface as visually comfortable and less distracting.In this study, an innovative method was presented to optimize AR-HUD interfaces by integrating IVPM with a GA. IVPM-GA effectively reduced cognitive load, enhanced driving performance, and improved user experience for professional drivers. The above-described findings stress the significance of using machine learning and optimization techniques in AR-HUD interface design, with the aim of enhancing driver safety and occupational health. The study confirmed the practical implications of machine learning optimization algorithms for designing AR-HUD interfaces with reduced cognitive load and improved occupational safety and health (OSH) for professional drivers. AR-HUD However, existing AR-HUD designs may not be optimal for professional drivers who are dependent on more specialized and customized information to perform their jobs effectively . ConsequThe significance of this study to occupational health and safety (OHS) is indicated in its potential in strengthening safety measures and improving professional drivers’ quality of life. The study leverages machine learning and optimization techniques to design Augmented Reality Heads-Up Displays (AR-HUDs), which can present more insights into improving driving tasks’ ergonomic aspects, such that accident risks can be mitigated, and driver well-being can be elevated. Notably, the optimization of AR-HUDs with the IVPM-GA method is conducive to reducing cognitive load, which when excessive, can lead to driver fatigue and higher accident rates. Furthermore, the IVPM method’s prowess in localizing eye-tracking hotspots and predicting extreme points on graphs is capable of enhancing AR-HUDs’ visual ergonomics, minimizing eye strain, and preventing health issues . Additionally, a well-optimized AR-HUD interface can enhance the overall user experience, reducing stress and discomfort during extended operation periods, which contributes to mental health benefits. The study’s findings can guide subsequent OHS policies and training programs, such that an evidence-based method can be provided to interface design that can be incorporated into professional drivers’ training. The optimized AR-HUD interface is promising in integrating with existing driver assistance systems, such that timely and relevant information can be presented, and cognitive load reduction and overall driving safety enhancement can be facilitated.1.2.The item of this study refers to optimizing AR-HUD visual interaction for professional drivers using machine learning techniques to reduce visual fatigue and cognitive load. To be specific, this study aimed at developing an optimized AR-HUD interface design using a genetic algorithm based on an Image Viewpoint Prediction Model (IVPM), comparing the effectiveness of the IVPM method and the conventional Backpropagation Genetic Algorithm (BP-GA) method in optimizing AR-HUD interface design, assessing the implications of optimized AR-HUD interfaces for the occupational health and safety (OHS) of professional drivers, employing machine learning to predict cognitive load in AR-HUD design and assess its impact on driving performance and user experience, and laying a solid basis for future Occupational Safety and Health (OSH) policies and training programs following the findings of this study. The above-mentioned research items can guide the exploration of AR-HUD interface design optimization, the comparison of different optimization methods, and the implications of the above-described methods for OHS in professional driving.The rest of this study is organized as follows. In Section 2, a comprehensive review of related research on AR-HUD interfaces, cognitive load, and optimization methods is presented. In Section 3, the methodology applied in this study is introduced, including the IVPM method and the GA optimization algorithm. In Section 4, the experimental setup, data collection, and the assessment of the proposed IVPM-GA method are presented, compared with the conventional BP-GA method. In Section 5, the interpretation of the results and the implications of the findings are discussed, and the limitations of the study are acknowledged. Lastly, in Section 6, the research findings are summarized, the innovation of the IVPM-GA method is emphasized, and future research directions in the field of AR-HUD technology and interface optimization are proposed.2.2.1.The research on cognitive load of drivers using an AR-HUD in assisted driving refers to a multidisciplinary field dedicated to designing and assessing interactive systems to enhance the driver experience while improving occupational safety and health . AR-HUD The design of AR-HUD interfaces poses several challenges for HCI (Human-Computer Interaction) designers: the design should consider the user’s cognitive load, as too much information presented on the display can overwhelm the user and lead to decreased performance and safety issues . Further2.2.Machine learning (ML) and deep learning (DL) have been confirmed as subfields of artificial intelligence that are highly promising in facilitating the design and assessment of interactive systems in HCI. ML refers to a method of teaching computers to learn patterns from data without being explicitly programmed . DL refeIn the research of AR-HUD human-computer interaction, ML and DL have been employed for tasks  . BesidesThe research on the optimization of AR-HUD interface design using machine learning in recent years is presented in the following: Optimization of AR-HUD interface design by machine learning. Conati et al.  using maThe following is the research on the optimization of AR-HUD interface design using deep learning over the past few years. Kang et al.  proposed2.3.Optimization techniques , particle swarm optimization (PSO), and simulated annealing (SA)) have been extensively employed in HCI to optimize the design and assessment of interactive systems. The above-mentioned techniques were adopted to find the optimal solution from a large set of possible solutions by iteratively assessing and modifying the design variables . GA is aAs revealed by the literature review, the backpropagation genetic algorithm (BP-GA) has been extensively employed to predict cognitive load based on eye-tracking data, such that AR-HUD and OSH of professional drivers can be better optimized , 37. TheThe main novelties of this study lie in the application of advanced machine learning techniques, specifically the Image Viewpoint Prediction Model (IVPM) and the Backpropagation Genetic Algorithm (BP-GA), to optimize the design of Augmented Reality Head-Up Display (AR-HUD) interfaces for professional drivers. This study is unique in its method to reducing visual fatigue and cognitive load, key factors that can impact the occupational health and safety (OHS) of professional drivers. By leveraging machine learning to predict cognitive load in AR-HUD design, this study offers a novel way to enhance driving performance and user experience.This study represents a contribution to the field of human-computer interaction, demonstrating how advanced technologies such as AR-HUD can be effectively adopted to enhance driver safety and occupational health. Thus, this study advocates for the development of safer and more ergonomic professional driving environments, a requisite consideration in the modern fast-paced world, substantiating its relevance to OSH.3.3.1.3.1.1.In the experiment, a self-designed integrated helmet with eye tracking and AR function was adopted to provide 360 immersive driving simulation and eye tracking experiments. The operating equipment refers to a high-performance desktop computer, and the steering wheel, pedals, and gears were connected to the notebook computer through relevant ports. To enable the test subjects to acquire prominent real driving experience and interactive experience, Unity3D engine was employed in the experiment to design and build a driving assistance test system on AR platform. The system compiled the logic codes of vehicle actions . Moreover, Tobii Pro adaptive eye movement analysis function was loaded in Unity3D environment. The eye movement analysis SDK exhibited the capability of providing eye movement data stream signals , displaying the gaze origin (3D eye coordinates), gaze point and space comfort distance L(GazeData), and synchronizing the external TTL event signals of the input port, with the aim of synchronizing eye movement data and other biometric data streams. The participants were a group of 40 professional drivers with normal or corrected vision. The group had a mean age of 31.36 years (standard deviation = 4.97). At the time of testing, the participants were in good mental condition and none of them experienced VR simulation sickness. They all held a driver’s license and had an average driving experience of over 5 years.The simulation platform was illustrated in 3.1.2.According to the design principles of AR-HUD and the visual characteristics of the human eye, AR-HUD is located in the lower left corner of the windshield, which is also the driver’s central visual area, as shown in 3.1.3.This study delineates AR-HUD information design patterns and tests varied visual colors, layouts, and components. Each driver executes 6 tasks involving random layout and component designs, with a 10-min rest between tasks. This process produces 240 samples for deep learning prediction. Each driving scenario lasts 60 s, with eye movement parameters extracted in 30-s intervals.The experiment was performed on city roads under good weather and moderate traffic conditions. A 15-min training familiarized participants with simulator operation, visual search, target warnings, and driving modes. After the respective task, participants employed the NASA-TLX form to assess their subjective cognitive load regarding AR-HUD use. .3.2.p < 0.05), meeting the assumption of parametric testing. Accordingly, using a paired t-test, eye-tracking variations in drivers were tested through visual search and target discovery. The descriptive statistics and matched t-test results for visual search are shown in Eye-tracking data segments were taken before and after visual search for 30 s. According to the Shapiro-Wilke test, the differences in the four groups of experimental data were normally distributed  represents the activation function of the hidden layer in the backpropagation (BP) neural network. A[0] represents the input layer, and A[1]-A[3] represent the hidden layers. A[4] represents the output layer. X represents the input variable, and Y represents the output variable.According to practical issues, the topological structure of neural network is determined, including three layers: 1 input layer, 3 hidden layer and 1 output layer. The number of neurons in the input layer is 26 and the number of neurons in the output layer is 1. In the hidden layer, the optimal number of neurons is determined by heuristic method , 51, and3.3.3.The activation function of neurons refers to an integral part of BP neural networks, which should be differentiable, and its derivative should be continuous , 52. Thu3.3.4.As depicted in To establish a neural network prediction model, this study constructed a training sample set. The input is chromosome coding of AR-HUD visual model, and the output is the average NASA-TLX of the 240 AR-HUD prototypes rated by the user in the experiment. After organizing the data, the input and output data of the neural network model were determined, as shown in 3.3.5.The initial population size was the total number of samples in the AR-HUD dataset 240. The mutation rates range of GA algorithm in this study was from 0.01 (1%) to 0.1 (10%). The crossover rate of GA reached 0.8 (80%). Roulette wheel selection method was adopted to select individuals from the population for reproduction .3.4.A deep learning-powered image view point prediction model (IVPM) was introduced in this study to improve AR-HUDs  design with a strong focus on occupational safety and health (OSH). Based on Bylinskii et al.’s work , the IVP3.4.1.IVPM uses the eye movement experimental dataset in section 3.1 for training, which contains 240 ground truth (GT) significance markers developed by AR-HUD HCI, and divides the training set and the test set according to 80–20%.3.4.2.The equation of importance for IVPM to predict the content of each pixel position in a bitmap image is shown in After continuous pooling, the model prediction turned out to be 1/32 of the input image resolution. To improve the prediction resolution and capture finer details, skip connections from earlier layers were introduced, following the steps proposed by Long et al. to form the FCN-16 s model , 57. As 4.4.1.Kullback–Leibler divergence  rating scale, which subjectively evaluates driving difficulty on a scale of ten levels, was used for comparison. A total of 15 participants assessed their driving experience and perceptions of the difficulty levels . The colThe results in the table indicate that 11 out of 15 participants  rated the IVPM-GA solution higher than the BP-GA solution. As indicated by the above result, the AR-HUD optimized using IVPM-GA outperforms the one optimized using only BP-GA in terms of the driving performance, such that the effectiveness of the algorithm adopted in this study is confirmed.5.5.1.As revealed by the results of this study, the AR-HUD interface optimized using the IVPM-GA method outperformed the BP-GA method in terms of the driving performance. The genetic algorithm based on the IVPM method was also found to be effective in optimizing the interface design. Furthermore, the Cooper-Harper rating scale results suggested that the IVPM-GA method was preferred by the majority of the participants.In terms of occupational health and safety, the findings of this study are significant as they provide evidence that the IVPM-GA method can enhance driving performance and user experience. The above finding is vital to professional drivers who face occupational health and safety risks . By reducing cognitive load and improving driving performance, AR-HUD interfaces optimized using the IVPM-GA method can contribute to reducing occupational health and safety risks for professional drivers.5.2.The results of the study not only confirmed the effectiveness of the genetic algorithm optimization methods for AR-HUD interface design while taking on great significance in the safety and health of professional drivers. The optimized AR-HUD interface using the IVPM method significantly improved driving performance compared with the BP-GA method. This finding suggests that optimized AR-HUD interfaces can potentially improve driver safety on the road. Moreover, the Cooper-Harper rating scale results indicated that the IVPM method was preferred by the majority of the participants, suggesting that the optimized interface could improve user experience and reduce visual fatigue, which can benefit the occupational health of professional drivers.5.3.In brief, future research on real-time image processing for driving applications should focus on performance and efficiency while consider the implications of OSH. It is imperative to assess the cognitive load, distractions, and overall effect on driver well-being. Furthermore, optimizing the system’s user interface and integrating it with driver assistance systems can further improve OSH in driving scenarios. By addressing the above-described aspects, researchers can develop real-time image processing systems that enhance driving performance and occupational safety and health. The implications of real-time image processing for Occupational Safety and Health (OSH) in driving scenarios should be considered . As the 6.6.1.This study confirmed the superiority of the IVPM-GA method over the conventional BP-GA method for classical HCI optimization design under AR-HUD interface optimization for professional drivers. The IVPM-based genetic algorithm is effective in optimizing the interface design while improving driving performance and enhancing user experience. The above-mentioned results take on vital significance in the safety and well-being of professional drivers since optimized AR-HUD interfaces are promising in lowering cognitive load, reducing visual distractions, and ultimately enhancing driver safety.6.2.The importance and necessity of this study lie in its potential to dramatically enhance the occupational health and safety (OHS) of professional drivers. By utilizing machine learning to predict cognitive load in AR-HUD design, this study can lay a solid basis for significantly enhancing driving performance, user experience, and above all, the occupational health of drivers, an often overlooked yet critically important facet of professional driving. The emphasis of this study on driver OHS was the need to reduce fatigue, mitigate risks of accidents, and improve overall health conditions, primarily through the optimization of visual ergonomics and the reduction of cognitive loads. The potential benefits are vast, such that the day-to-day experiences of professional drivers can be affected, and longer-term health outcomes and safety records can be facilitated.Future research inspired by this study could delve into the application of a broad range of machine learning methods and optimization techniques. It is imperative to expand sample sizes and engage diverse participant groups, such that the findings can be ensured to be generalizable and applicable across a wide spectrum of professional drivers. An important direction for future research is exploring how optimized AR-HUD interfaces can integrate with existing driver assistance systems, providing timely, relevant information and further contributing to the reduction of cognitive load, ultimately enhancing overall driving safety.The results of this study are critical to inform future Occupational Safety and Health (OSH) policies and training programs, offering an evidence-based method to interface design that centers on driver safety and wellness. This study represents a significant contribution to the expansive field of human-computer interaction, emphasizing the beneficial integration of advanced technologies . It elucidates how the above-described technologies can be efficiently adopted to enhance driver safety and occupational health. Accordingly, this study advocates for the development of safer and more ergonomic professional driving environments, a requisite consideration in the modern fast-paced world, substantiating its relevance to OSH.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by the Ethics Committee of Lingnan Normal University. The patients/participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.JT: conceptualization, methodology, validation, visualization, and writing—original draft. FW: funding acquisition. YK: software and resources. J-KK: conceptualization, supervision, and project administration. All authors contributed to the article and approved the submitted version.This study was funded by Experimental Management Research Fund of Guangdong Higher Education Society (GDJ 2016037); Industry-University-Research Innovation Fund of Science and Technology Development Center of Ministry of Education (2018C01051). 2023 Lingnan Normal University Education and Teaching Reform Project.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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There has been a resurgence of interest in psychiatric rehabilitation to cater to patients with chronic and complex mental illnesses.This study is aimed at examining patients' characteristics and the prevalence of psychiatric and non-psychiatric comorbidity in a local inpatient rehabilitation service, as well as to investigate the impact of the whole-system approach to rehabilitation on future utilization of mental health services and to analyze the cost-effectiveness and quality of this service.Patients managed over 3 years in a psychiatric rehabilitation inpatient unit were self-controlled; they were retrospectively (pre-rehabilitation) and prospectively (post-rehabilitation) examined for readmission rate, length of stay (LOS), and emergency room (ER) visits. Relevant information was retrieved from Discharge Abstract Database (DAD), Patient Registration System (STAR), and Emergency Department Information System (EDIS). The quality of care in the rehabilitation unit was examined using the Quality Indicator for Rehabilitative Care (QuIRC), and the cost analysis was conducted using data obtained from a single-payer government medical service insurance (MSI) billing system.P < 0.0001), respectively. There were substantial subsequent cost savings in the post-rehabilitation year.Of the 185 patients admitted over the study period, 158 were discharged. There was a significant reduction in readmission rate (64% decrease), LOS , and ER presentations (166 fewer visits) (In the 3-year study, an inpatient psychiatric rehabilitation service in Nova Scotia, Canada, resulted in the successful discharge of most patients with severe and persistent mental illness to more socially inclusive environments. It also reduced their post-rehabilitation mental health service utilization, hence greatly enhancing the effectiveness and efficiency of these services. For more than 15 years, there has been a renewal of interest in psychiatric rehabilitation using whole-system approaches at national and international levels  has explicitly incorporated psychiatry rehabilitation in its recent clinical guideline (NG181), 2020 as an important treatment for patients with complex psychosis that should be embedded in a local comprehensive mental health service. The rehabilitation treatment should be recovery-oriented, patient-centered, and be offered in the least restrictive environment with the recognition that some patients may require a long-term supported placement post-rehabilitation.The cost-effectiveness argument has been a very important part of making the case for ongoing investment in mental health rehabilitative services . In the Making a case for investment in psychiatric rehabilitation services must be based on evidence of their quality of care and clinical effectiveness . This maThe objectives of our study were, therefore, to (i) examine the demographics of a cohort of patients receiving recovery-oriented psychosocial psychiatric rehabilitation , (ii) assess the rate of psychiatric and non-psychiatric comorbidity in this cohort, (iii) investigate the impact of a psychiatric rehabilitation model (whole-system approach) on LOS, readmission rate, and ER visits, (iv) examine the quality of care of service provision using the Quality Indicator for Rehabilitative Care (QuIRC), –11, and The study retrospectively examined inclusively all the patients who had been discharged within 3 years and 1 month period between 1 June 2012 and 30 June 2015.The study hypothesizes that inpatient psychiatric rehabilitation is clinically effective and that the whole-system approach of the biopsychosocial rehabilitation model has a carryover therapeutic effect. This effect can be measured by comparing service utilization in the pre- and post-rehabilitation treatment in patients with severe and persistent mental illness.Each patient's service utilization of the acute psychiatric services (rate of admissions and LOS) and ER visits were examined for a 1-year period in a bi-directional manner for the pre- and post-exposure to the rehabilitation treatment in the unit.Information was retrieved from the Discharge Abstract Database (DAD) to examine the outcome of patients who were admitted and discharged over the study period. The service utilization information for each discharge was retrieved from the STAR practice management system in this context.Table 5) diagnoses, and information on the involuntary/voluntary nature of the admissions were obtained from the DAD. The ER visit data were obtained from the Emergency Department Information System (EDIS) for the same timeframe, and the presentations with mental health problems or diagnoses upon ER discharge were examined.Additional variables, such as the primary diagnosis for inpatient rehabilitation admission, other psychiatric and non-psychiatric  billing, which amounted to CAD 1,400 per day spent in acute mental health services and CAD 309 per ER visit.t-tests were conducted to compare the rate of admission and the total LOS in the pre- and post-rehabilitation treatment period. In addition, descriptive statistics such as mean, median, standard deviations, and 95% confidence intervals were also computed.Statistical analysis was conducted with a 95% confidence interval (α = 0.05) using SAS JMP version 12.0. Matched paired The Quality Indicator for Rehabilitative Care (QuIRC) was employed to examine the quality of care in the unit. QuIRC is a standardized, international quality assessment tool for inpatient and community rehabilitation units and although completed by managers of the service, during its development, the ratings were validated against service users' experiences of care and autonomy and found to correlate well .The QuIRC tool assesses the provision of care across seven domains: (1) living environment, (2) therapeutic environment, (3) treatments and interventions, (4) self-management and autonomy, (5) social interface, (6) human rights, and 7) recovery-based practice. The tool is the product of collaborative work involving 11 centers in 10 European countries and was determined based on the evidence on crucial components of care gleaned from different studies and systematic reviews  providing psychiatry rehabilitation services for the population of Nova Scotia in Canada. The unit is operating under the auspices of the Mental Health and Addictions Program (MHAP)/Recovery and Integration Services (R&I) which is part of the Provincial Health Care System in Nova Scotia, Canada .The unit is based in the community and was opened in 2012 with 40-beds capacity. The unit has no maximum length of stay, and the bed capacity was later reduced to 20 beds with a 75% occupancy.The unit has 45 full-time equivalent (FTE) staff, which includes one FTE psychiatrist, two psychiatric residents (~0.8 FTE each), one family physician, two FTE occupational therapists, 32 FTE nurses, five FTE unlicensed support workers, one FTE social worker, one FTE recreation therapist, and one FTE volunteer. Peer support workers with past mental illness experience are also employed as staff to support patients. The aforementioned staffing level was based on 40-beds capacity.The unit is diversified and has extended referral criteria and a recovery-oriented philosophy , 13; it Each patient admitted to the rehabilitation unit underwent a detailed assessment process involving an interprofessional team and included the patient, family, nurses, social workers, occupational therapy, recreation therapy, and psychiatrists, to collaboratively assess the nature and degree of their illness and to develop a comprehensive biopsychosocial management plan . The bioSimultaneously, patients' psychosocial rehabilitation goals were determined and worked upon with support from comprehensive occupational, psychological, recreational, and social assessments that consider current strengths and areas requiring treatment and skill development. Using a shared decision-making framework, recovery goals were defined with involvement from patients and families and were aimed to achieve recovery and integration into the community.During the study period, the unit received 185 admissions; there were 14 patients discharged to the community after the timeframe of the study, seven patients were transferred back to the acute services, and six patients remained in the rehabilitation unit waiting for a supported placement in the community. Hence, the 158 patients who were discharged within the timeframe of the study were subsequently examined for outcomes.Most patients were male (66%) subjects with a mean age of 38. The main diagnosis was schizophrenia which affected 84% of patients. One or two additional psychiatric and/or medical diagnoses, other than the primary diagnosis, were present in 45% of cases. The mean LOS was 165 days, median of 93.5, IQR (interquartile range) 160, and 31% of patients were involuntarily admitted under the Involuntary Psychiatric Treatment Act (IPTA) , 2.The median of the LOS on the rehabilitation unit was higher for patients with personality disorders, behavioral/emotional disorders, and schizophrenia .p < 0.0001). Patients with diagnoses of depression or substance misuse showed a significant reduction in their admission rates and LOS on the acute service but no change in their ER visits  for admission rate during pre- and post-rehabilitation was 1 (0) and 0 (1); for LOS 56 (54.75) and 0 (25.75); and for ER visits 1 (2) and 0 (1), respectively .Patients with diagnoses of personality disorders, emotional/behavioral disorders, bipolar, and adjustment disorder showed no significant changes in these domains  during the post-rehabilitation year. However, in general, patients with other psychiatric and medical co-morbidities , 5 had lp < 0.0001. The net change in the admission rate was reduced in 64% of patients, showed no change in 28%, and increased in 8% of the studied cohort.In total, there was a significant reduction in the rate of admission and LOS in the acute psychiatric services in the post-rehabilitation year , 4. The p < 0.0001  from asylum models of care has resulted in several major challenges for mental health service delivery. Following the deinstitutionalization, it was difficult to find cost-effective and patient-centered alternatives for patients with chronic and disabling mental illnesses. Many patients were discharged to community settings without the necessary support to sustain recovery in the community .Without a well-developed and cost-effective alternative to the asylum model, there was a gap in appropriate services for patients with chronic and disabling mental illness which left some vulnerable patients too functionally impaired to sustain recovery in the community.Enabling recovery and reducing revolving door admissions of patients with SPMI using the whole-system approach of the biopsychosocial model may not only resonate positively by reducing expenditure but also by improving the quality of life of this group of complex needs patients. Although the cost-effectiveness argument for psychosocial psychiatric rehabilitation may be appealing to stakeholders, as a society we should also value other outcomes, such as social integration/inclusion and quality of life.A sizable number of the cohort in this study was men with chronic schizophrenia which was consistent with similar studies that examined the demography of inpatient rehabilitation patients .In this study, most patients were discharged into the community, and a large number had no readmissions in the first post-rehabilitation year. There was a significant reduction in readmission rate, LOS, and ER presentation.In a previous study , a sub-cA similar study examining 5-year outcomes for mental health rehabilitation service users found that in a 140-patient cohort, 41% were discharged, not readmitted, and moved to less supported placements; 9% achieved independent living; 26% remained stable in the community; and 33% had poor outcomes  .Patients with diagnoses of personality disorders, emotional/behavioral disorders, and adjustment disorders showed no significant changes in these domains in the post-rehabilitation year; although the number of patients in each of these groups was relatively low, their inclusion could, nevertheless, confound the results.The estimated cost savings in inpatient expenditures in the post-rehabilitation year were ~CAD 9,270,000 equivalent to $58,670 saving per patient. Similarly, a retrospective study evaluating the clinical outcome and cost implications for 22 inpatient psychiatric rehabilitation service users in the UK found a significant reduction in bed cost 2 years after rehabilitation compared to the pre-rehabilitation period . FurtherIt is important to realize that the reduction of acute services utilization in the post-rehabilitation year might have a positive impact on patients' and carers' live that goes beyond monetary calculations.Similar to this study, a naturalistic prospective study examined clinical outcomes and costs for patients with SPMI across 50 rehabilitation service units in England in which a total of 329 patients were followed over 12 months (94% of those recruited). Although service quality was not associated with patients' social function or length of admission at 12 months, successful discharge of over half of complex-needs-patients was achieved within 18 months with an associated reduction in the cost of care. Factors associated with successful discharge were the recovery orientation of the service and patients' activity and social skills at recruitment .The Royal College of Psychiatrists' Center for Quality Improvement (CCQI) runs accreditation networks for inpatient mental health services that include rehabilitation services. The Accreditation of Inpatient Mental Health Services (AIMS) program has incorporated QuIRC as one of its standardized measures for Mental Health services to demonstrate that they meet national guidelines and standards for quality of care .In this study, QuIRC –11 was eNotably, the deinstitutionalization movement across the world has been followed by trans-institutionalization, i.e., patients with SPMI end up in different forms of institutions rather than living in their own homes, such as nursing homes, boarding houses, and homes for the elderly or even in prisons .In similar studies , 18–20, In this study, patients served as self-control during the pre- and post-rehabilitation period and this design would reduce possible biases that may result from comparing the heterogeneous group of patients. Hence, direct causality of the effectiveness of psychiatry rehabilitation intervention on the reported outcomes could be inferred when demonstrating the difference in service utilization in the pre- and post-rehabilitation. These findings were also in keeping with Dalton-Locke's systematic review in 2021.Limitations of the study could be the relatively small sample size and the presence of a high prevalence of other psychiatric and medical co-morbidities in the studied cohort; hence, the heterogeneity of the sample may impact the outcome. The non-randomized nature of the study design and the lack of a control group are also considered limitations.However, these limitations may also reflect the true nature of patients with complex co-morbidities and the ethical dilemma that one may face when randomizing patients who need rehabilitation treatment into “a treatment as usual arm”. Given the illness severity and complexity of patients with psychiatric rehabilitation needs, randomization of such patients to rehabilitation and non-rehabilitation services would be impractical and ethically questionable.Despite these limitations, our data and the findings of other studies indicate that inpatient psychiatric rehabilitation services are an effective and long-term investment in the mental healthcare of patients with complex psychosis.Further prospective studies on larger patient cohorts are recommended to establish the longer-term impact of psychiatric inpatient rehabilitation services over several years. Future research could also include other measures important to the concept of recoveries such as social participation and measures of quality of life in the community. Further studies are needed to elucidate which components of psychiatric rehabilitation most optimally support a recovery-oriented approach.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.The studies involving human participants were reviewed and approved by Research Ethics Board of Nova Scotia Review Exemption Approval Letter REB FILE #: 1028070. Consent to participate and to publish was not applicable as patients' information is anonymized and Research Ethics Board approved exemption for publication. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.The first draft of the manuscript was written by MA who along with LD, DE, NL, and JG contributed to the study conception and design, material preparation, data collection, and analysis. All authors commented on previous versions of the manuscript, and they all read and approved the final manuscript.