The long-lasting view of entanglement as the characteristic trait of quantum mechanics has been recently challenged by experimental demonstrations of non-quantum entanglement. This motivates a review of the meaning of Bell violations, which have been widely taken to prove the impossibility of a realistic interpretation of quantum mechanics and as a manifestation of its non-local character. This work provides new theoretical evidence for the need of reviewing the meaning of Bell violations, especially when they occur outside the quantum framework. We present a local-realistic model that reproduces quantum predictions concerning Bell tests. We claim that local-realism is fully compatible with correlations that are not of the Bell type and therefore lie outside the scope of Bell’s theorem. Most experimental Bell tests involve either spin vectors spanning the Bloch sphere or Stokes vectors spanning the Poincaré sphere. A suitable statistical tool that allows assessing correlations between vectors is given by inner-product-type correlations. Using them, it is possible to reproduce quantum predictions for all Bell states, thereby explaining experimental results of Bell tests within a local-realistic framework. In view of several experimental tests that violate Bell inequalities, it has been widely accepted that realism and locality cannot hold together. In recent times, however, such a conclusion has been challenged, because entanglement – a key feature of Bell violations – could be exhibited at the classical level10, and this led people to produce “non-quantum Bell violations”. From a practical point of view, these violations can be variously used: as an entanglement witness11, as a tool to detect hidden coherences16, as a means to simulate quantum information processing19, etc. From a foundational point of view, a key question remains open: How can a local-realistic model and its experimental implementation lead to a Bell violation? The present work addresses this question and explores the possibility that Bell violations do not prove wrong the conjunction of realism and locality alone, but the conjunction consisting of realism, locality and Bell-type correlations. These correlations were a natural choice from Bell’s original viewpoint, in which binary outputs played a central role. Indeed, if we consider the standard array in which Stern-Gerlach-type measurements are performed by two parties – “Alice” and “Bob” – at two distant sites, then at each site we have a binary output. Bell-type correlations were constructed so as to capture this binary nature of the output. They were also proved to be constrained by so-called Bell-type inequalities21, which may be violated by quantum correlations. Now, whereas realism and locality are necessary assumptions to establish Bell-type correlations, these correlations are not the only possible choice that is compatible with realism and locality. In other words, realism and locality are necessary but not sufficient conditions for deriving Bell-type inequalities. This fact is well known and has been repeatedly stressed in the past; but emphasis has been placed on more technical, additional assumptions, such as the “fair-sampling assumption”, “free will”, “non-signaling”, etc.23. Here we want to focus on the assumption that correlations must be of the Bell type. As we shall see, other type of correlations that are fully compatible with realism and locality can be used to address the task of explaining experimental observations. The correlations we will address have been widely used in classical, stochastic electrodynamics and also in standard statistics25. In Bell tests, Alice and Bob’s measurement outcomes are eventually encoded in terms of vectors, e.g., vectors on the Bloch sphere in the case of spin-1/2 particles, and vectors on the Poincaré sphere in the case of polarized photons. Generally, correlations between vectors are not properly described by quantities such as Bell-type correlations, which are constructed in terms of scalar quantities alone. Inner-product, Pearson-type correlations could be a more appropriate tool for this case. Our aim is to see how far we can go with a local-realistic, classical approach, by paralleling the quantum approach. This should help us assessing how much an approach departs from the other, thereby contributing to fix the yet undetermined quantum-classical border. A key question thus concerns the definition of statistical correlations by means of inner products. Is this definition allowed only within the quantum framework? This point will be discussed with the help of Gudder’s theorem26. To this end, we first discuss the relevant background, with special emphasis on the derivation of Born’s rule from Gudder’s theorem. As well known, Born’s rule is at the basis of quantum correlations. We will thus identify all quantum correlations that are relevant to standard Bell tests. These are the ones that should be reproduced by a local-realistic model. Such a model is presented and, finally, we discuss our results.Bell inequalities were originally derived to show the impossibility of “completing” quantum mechanics by means of a local realistic modelAB〉Bell and quantum correlations 〈AB〉QM. Representative examples of these are given byAs is well known, standard Bell tests involve two types of correlations: Bell-type correlations 〈A(λ) and B(λ) in the local-realistic case, in which “hidden variables” λ are assumed to determine whether the output is +1 or −1 in each case, i.e., A(λ) = ±1 and B(λ) = ±1. The probability distribution of λ is given by ρ(λ) ≥ 0, with ∫ρ(λ)dλ = 1.Unit vectors 20. It is given byVarious, so-called Bell measures can be constructed in terms of the above correlations. The Bell-Clauser-Horne-Shimony-Holt (Bell-CHSH) measure is a particularly suitable one for experimental testsA and A′ refer to two different settings: B, B′. In the case of 〈AB〉Bell, on using the identity (A(λ) + A′(λ))B(λ) + (A(λ) − A′(λ))B(λ) = ±2 together with |∫f(λ)dλ| ≤ ∫|f(λ)|dλ, one can readily prove that |S| ≤ 2 for any choice of states and observables, while one can get |S| = 2AB〉QM, under appropriate choice of states and observables28. The fact that |S| > 2 for 〈AB〉QM has led people to conclude that quantum correlations can be “stronger” than classical, Bell-type ones. Bell violations of constraints such as |S| ≤2 have also prompted the conclusion that quantum mechanics is non-local.Here, AB〉Bell, we should stress that it is a local quantity, in spite of involving two distant settings. Indeed, it derives fromα = ±1 and β = ±1 occurring when measuring settings  = m(A) + m(B), whenever A ∩ B = ∅. A measure essentially quantifies how many times a given unit fits into what is being measured. Very often, it is convenient to provide this measure with a sign, thereby defining a signed measure. This allows us to count not only how many times the unit fits into something, but also how many times the unit is missing when trying to reach some given amount. A probability measure is a non-negative measure. As such, it is a special case of a signed measure. Gudder’s theorem allows us to deal with signed measures that lead to the Born rule when properly restricted by physical requirements. Moreover, we can deal with correlations between measured quantities and be in accordance with experimental outcomes of Bell tests, without invoking any quantum concept. We next dwell extensively on these issues.Modulo some loopholes, experiments have shown that we must resort to correlations such as 〈ϕ|ψ〉|2 is the probability of finding a system in state |ϕ〉, when it was previously prepared in state |ψ〉. We should stress that Born’s formula applies irrespective of location. That is, it works the same when we have, say, |〈ϕ(x)|ψ(y)〉|2, with x ≠ y. This is because Born’s formula is just a measure for the overlap of two vectors, |ϕ〉 and |ψ〉, irrespective of any positional degrees of freedom that we may attach to them. Hence, a potential built-in non-locality is already there. It is thus no wonder that non-locality shows up in related expressions, such as the probability rules that we interpret as “correlations”.Eq.  is basedρϕ = |ϕ〉〈ϕ| and ρψ = |ψ〉〈ψ|. Here, we recognize the Hilbert-Schmidt inner product for operators A and B, namely Tr(A†B). The latter is just a reshaping of the standard, Euclidean inner product between vectors: a ⋅ b = ∑iaibi, which follows from “vectorializing” the matrix representations of A and B. By “vectorializing” a matrix we mean writing its rows as column vectors, one on top of the next. It is thus possible that Born’s rule by itself does not convey any feature that must be dubbed a “quantum mechanical” one. In order to elucidate this issue, we focus on two-level systems (qubits), the ones most relevant for measurements having a binary output, i.e., those entering the Bell tests we address here.We can make more obvious that Born’s formula requires only a vectorial scalar product – rather than the square of it – by writing this formula in the form m over the elements of some set . This map is required to satisfy m(A∪B) = m(A) + m(B), whenever A∩B = ∅. A particular case of a measure is the “probability measure” of quantum mechanics, which is defined over the projection lattice m(Pi + Pj) = m(Pi) + m(Pj), whenever PiPj = 0. We can relax the requirement that m be non-negative, in order to include those cases in which we find it convenient to add a sign to our measurement results. In such a case, the above definition of a measure may be replaced by another one that is the subject matter of Gudder’s theorem26. This theorem addresses an inner product vector space V and a continuous function f that is orthogonally additive and maps vectors r ∈ V to the reals. Such a function can be seen as a sign-carrying measure. The definition of orthogonal additivity reads as follows.As we shall see, the correlations entering Bell tests may be expressed as inner products. This is just a particular instance of a general statement regarding physical measurements. As we said before, measurements essentially consist on counting how many times some given unit fits into the observable being measured, and the mathematical tool that captures this basic notion is a non-negative map Definition 2.1.26 the following result:In terms of the foregoing definition, Gudder provedis orthogonally additive and continuous, then it is of the formk ∈ V.If V4, with elements ψ〉〈ψ|:σ0 is the identity and σμ = 1,2,3 the Pauli matrices.Let us now focus on the space fϕ relative to a given qubit fϕ is assumed to satisfy the requirements of Theorem 2.1 and the following, additional ones:fϕ as a probability measure, when applied to projectors We want to define a sign-carrying measure 34On applying Gudder’s theorem one readily obtainsV4, where it is given by the Euclidean inner product, or in the space of linear operators acting on the two-dimensional Hilbert space Tr(A†B). Indeed, on view of Thus, under the above conditions  see Eq. , and \dofϕ(r) on vectors fϕ as a probability measure. This is the measure entering Born’s rule, which was first derived from a somewhat different axiomatic in Gleason’s theorem30:Generally, In summary, a sign-carrying measure is the fundamental concept behind our quantification of correlations between vectors. This measure turns out to be an inner product. By itself, it does not contain information about any possible cause-effect relationship between the involved vectors. In order to pinpoint such a relationship, we should introduce the additional notion of “transport”, so that the two vectors can be brought to one and the same point of the underlying manifold where they are defined, be it the space-time manifold or any other one.35, which is used when one aims at comparing pairs of events , in order to assess whether they are correlated or not. These events often occur at two different locations, where they are repeatedly registered. One then constructs n-tuples  and  and defines the correlationIt is worthwhile to stress that correlations between vectors are a particular instance of correlations between n-tuples. An example of the latter is Pearson’s correlationCP ∈ , as it has been defined in Eq. , where Π refers here to a projector and ρ to the density matrix representing the state that is subjected to measurement. In a standard Bell test, one deals with projective measurements performed by two parties. The mathematics we use in this case does not encode a unique physical picture. This is because (quantum) correlations reproducing our measurements only refer to two observables. We may imagine them as being related to two distant parties, or else we can relate these two observables to one and the same physical object, e.g., a classical light beam. In such cases, the appropriate projectors are of the form, say, ρ = |Φ〉〈Φ|, with α = ±1 and β = ±1 as a result of measuring qubit A and qubit B along directions As already remarked, the quantum correlation 〈 see Eq. , stems fAB〉QM, see Eq. /±〉 = (|H〉|V〉 ± |V〉|H〉)/24. The quantum expressions for the relevant correlations readFor the sake of concreteness, we shall refer to an optical CHSH test being performed with horizontally (H) and vertically (V) polarized, classical light beams. The same test can be performed with horizontally and vertically polarized photons. Bell states are given by |ΦOur goal is to construct a local-realistic model that reproduces quantum expressions by starting from the following, classical correlations:36Generally, we can write Ccl and the marginals ψ〉〈H|, which transforms a horizontally polarized state into a conveniently chosen Jones vector |ψ〉. Consider for example the Jones matrixThe basic building blocks of our model will be operators of the form, e.g., |H〉,|V〉} ≡ {T,T}, where T = transpose) of the operator TA = |ψA1〉〈H| + |ψA2〉〈V|, with TA. One can implement TA and similar transformations with the help of optical elements, such as polarizing beam splitters and wave plates. The label A in TA refers to Alice’s measurements, as explained below. As for Bob’s measurements, these will be related to the transformationThis is the matrix representation  into (31), we reproduce the quantum prediction for the Bell state |Φ see Eqs  and , Eq. , therebyand Bell-type correlations. By rejecting last assumption and assuming correlations that are not of the Bell type, it becomes possible to construct a local-realistic model that leads to Bell violations. Bell’s theorem shows that quantum correlations cannot be of the Bell type. If realism and locality would imply Bell-type correlations, then Bell violations would prove wrong the conjunction consisting of realism and locality. However, as we have seen, realism and locality are compatible with correlations that lie outside the class of Bell-type correlations. They are inner-product-type correlations, having thus the same mathematical structure as the well known Pearson correlations of classical statistics. Quantum correlations stem from Born’s rule: Tr(ρϕρψ), which is formally nothing but an Euclidean inner-product in disguise. As we have seen, under quite general assumptions, Gudder’s theorem leads to a signed-measure that is also an Euclidean inner product. By conveniently restricting the range of the function entering Gudder’s theorem, this function can be used as a probability measure. We can recover in this way Born’s rule, thereby showing that inner-product-type correlations are not exclusively related to quantum phenomena. We are entitled to use this type of correlations in the classical context, as much as we are entitled to use Born’s rule in the quantum context.Bell violations prove wrong a conjunction that consists of three assumptions: realism, locality AB〉Bell and 〈AB〉QM, see Eqs.  Correlations are given by Born’s rule. (2) Physical systems can be produced in entangled states. Here we illustrate how these assumptions fit into a local-realistic description. To this end, let us consider the quantum correlation 〈 see Eq. . Last eqTrσi = 0 together with the Pauli algebra: σiσj = δij + iεijkσk, where i, j, k ∈ {1, 2, 3} and εijk means the completely antisymmetric Levi-Civita symbol. Eq. , refer to different values of field components that are conditioned to be taken at two given locations, P1 and P2. Thus, η engages two degrees of freedom, field polarization and spatial location, while keeping fixed a third one: ω . Such a procedure can be variously used to exhibit different sorts of so-called cross-sector coherences, some of which might be initially hidden37.The probability density functions entering the above definition, e.g., η, is a quantity that can be seen, similarly to the quantum case, as involving two vectors, η represents an entangled observable. This observable, being given by Eq. (AB〉Bell = ∫A(λ)B(λ)ρ(λ)dλ. Hence, η lies beyond the scope of Bell’s theorem and for this reason it may lead to Bell violations, in spite of being a full classical quantity.Finally, notice that the spectral degree of coherence, n by Eq. , has not


Standard treatment for metastatic prostate cancer (CaP) prevents ligand-activation of androgen receptor (AR). Despite initial remission, CaP progresses while relying on AR. AR transcriptional output controls CaP behavior and is an alternative therapeutic target, but its molecular regulation is poorly understood. Here, we show that action of activated AR partitions into fractions that are controlled preferentially by different coregulators. In a 452-AR-target gene panel, each of 18 clinically relevant coregulators mediates androgen-responsiveness of 0–57% genes and acts as a coactivator or corepressor in a gene-specific manner. Selectivity in coregulator-dependent AR action is reflected in differential AR binding site composition and involvement with CaP biology and progression. Isolation of a novel transcriptional mechanism in which WDR77 unites the actions of AR and p53, the major genomic drivers of lethal CaP, to control cell cycle progression provides proof-of-principle for treatment via selective interference with AR action by exploiting AR dependence on coregulators. Prostate cancer is the second leading cause of cancer deaths in men in the Western world. Almost all of these deaths happen when the main treatment for advanced prostate cancers stops working. The treatment, known as androgen deprivation therapy, targets a protein called the androgen receptor. This receptor is activated when it binds to signaling molecules and, once active, it switches on genes that encourage the cancer cells to grow. Androgen deprivation therapy blocks the androgen receptor from interacting with the signaling molecules; however, this treatment eventually fails because the receptor finds other ways to remain active in prostate cancer.Increasing the survival of patients with prostate cancer will depend on new treatments that can inhibit androgen receptors that no longer respond to androgen deprivation therapy. The androgen receptor’s ability to switch on genes could be another target for prostate cancer therapy – though not enough was known about the way this ability is regulated and how it controls the progression of prostate cancer. Liu, Kumari et al. set out to better define how this ability drives the growth of prostate cancer.The androgen receptor needs to interact with other proteins, known as coregulators, to work, and Liu, Kumari et al. developed an assay that examines, all at the same time, how important 18 such coregulators are for more than 400 genes that are regulated by the androgen receptor. This revealed that the coregulators did not all affect the same genes and that each coregulator tended to help activate sets of genes associated with a specific aspect of the biology of prostate cancer cells.Liu, Kumari et al. also discovered previously unknown interactions between androgen receptors, coregulators and other proteins that were responsible for the specific associations between genes and corregulators. The most important of these new interactions was one between the androgen receptor, the coregulator WDR77, and a protein called p53. These interactions are enriched in prostate cancers, including those that do not respond to androgen deprivation therapy, where they promote cancer growth.These findings lay the foundation to develop new drugs that interfere with the interactions between the androgen receptor and other proteins that are most important for the progression of advanced prostate cancers. Other researchers have already shown that it is possible to develop such drugs – though further testing is needed before any new treatments begin to help prostate cancer patients who no longer respond to androgen deprivation therapy. The androgen-activated androgen receptor (AR) is both the major driver of prostate cancer (CaP) progression and the main target for treatment of metastatic CaP. An initial remission after AR-targeting androgen deprivation therapy (ADT) almost inevitably results in recurrence because CaP cells acquire resistance to ADT and continue to rely on AR activity . With feNovel, alternative approaches to block the AR action that drives CaP to the lethal stage are highly sought. Current ADT prevents interaction between AR and its androgenic ligands, thus targeting the AR ligand-recognition function. AR’s effector function as a transcription factor controls expression of Androgen Response Element (ARE)-driven genes  and ultiInterfering with AR action at the post-receptor level  requiresCoregulators have long been of interest as therapeutic targets for CaP . ExpressThe true potential of coregulators as alternative targets to block AR action in CaP and their contribution to AR-dependent transcription that drives CaP progression, however, remains unknown. The few studies so far  have takbona fide AR target genes. Our results demonstrate a previously unrecognized level of gene specificity and context-dependence in reliance of AR target gene expression on coregulators, and the corresponding AR target gene sets contribute differentially to CaP initiation and progression. Analysis of the molecular basis and associated cell biology of coregulator-dependent AR target gene expression indicates transcriptional codes exist in which AR cooperates with select coregulator(s) and transcription factors to control transcription of a subset of its target genes. Our identification of a WDR77-dependent functional interaction between AR and p53 provides a rationale for a coregulator-dependent alternative to target for therapy the major drivers of lethal CaP progression.Here, we use an integrated approach to systematically define the contribution of 18 clinically relevant coregulators to androgen responsiveness of 452 A systematic analysis of the role for coregulators in regulation of AR function requires a sizable set of ARE-driven AR target genes that can be interrogated coordinately. System biology approaches per se do not provide an unambiguous signature of AR target genes. Expression profiles of androgen-regulated genes do not distinguish between direct AR target genes, which are androgen-responsive because of direct AR-ARE interaction, and indirect AR target genes, which are androgen-regulated secondary to the action of a direct AR target gene. ChIP-chip, ChIP-Seq and ChIP-exo studies document androgen-dependent recruitment of AR throughout the genome but are prone to artifacts, only a small fraction of isolated ARBSs undergo independent ChIP validation, and the association between an ARBS(s) and androgen regulation of an adjacent gene often remains elusive.bona fide direct AR target genes. ARBSs present within 300 Kb of TSSs of RefSeq genes after androgen treatment  position, and androgen-responsive gene expression would result in identification of reatment  of AR-poreatment  was defireatment . A custoreatment , panel Aand SCAP  verifiedand SCAP . Oligoarand SCAP , panel BCAP  ind RAB27A , panel Ad RAB27A . These dAR-associated coregulators that are most relevant to CaP aggressiveness will provide the most clinically useful insights. At the onset of this study, 181 coregulators that interact physically and functionally with AR had been identified . For 51 The relevance of the remaining 18 AR-associated coregulators  to androNext, the effect of loss of coregulator expression on the direction of androgen regulation of gene expression was analyzed. AR target genes for which loss of coregulator expression altered the absolute level but not the direction of androgen regulation  were considered as having consistent directionality whereas those in which silencing coregulator expression changed both the magnitude and the direction of androgen regulation  were considered as exhibiting ‘opposite’ directionality . The vasGNB4 and RAB27A was modified by 4 or 12, respectively, individual coregulators  were present at relatively high basal level at these AREs, but modification of the androgen regulation of the corresponding gene was not noted unless there were marked changes in recruitment of the coregulator to those AREs (for instance RAB27A)  .Time course studies were performed to determine the kinetics of representative coregulator recruitment to AREs in these genes. At 1 hr, 4 hr, 16 hr, and 48 hr after treatment, cells were harvested for ChIP analysis of WDR77, NCOA3, and AR. Robust androgen-induced recruitment of AR was seen after 1 hr, 4 hr, or 16 hr at AREs in both genes, which became less pronounced (RAB27A) or not detectable (GNB4) at 48 hr. Androgen-stimulated binding of WDR77 and NCOA3 occurred at all time points at AREs of both genes . The kinThe possibility that multiple coregulators work in concert to control androgen regulation of individual target genes was examined further. Pairwise comparison between all coregulator-dependent AR target gene sets was done to determine the overlap in number of genes. With few exceptions (e.g. EP300-BAG1) the overlap in genes between different coregulator-dependent signatures was less than 30%. Among the 136 pair-wise comparisons, only 42 (30.8%) and 31 (22.7%) have significant overlap at the level of p<0.05 and p<0.01, respectively . For insBecause of these findings, we analyzed whether AR target genes could be grouped based on the degree to which their androgen-responsiveness depends on specific coregulators. For this analysis, effect of coregulator loss on magnitude of androgen regulation, but not its direction, was considered. RCHY1 knock-down results were not included as loss of this coregulator did not affect androgen-regulation of any of the 452 AR target genes. Relatively small groups of AR target genes for which androgen-dependency was either decreased or increased clustered together . Mutual Since kinetics studies  did not The possibility that individual coregulator-dependent AR target gene sets control different aspects of androgen-dependent CaP cell biology was explored. Using Ingenuity Pathway Analysis (IPA), we determined the association of each of these individual gene signatures with biological functions. The entire 452 AR target gene set was associated with 36 categories, whereas individual AR target gene signatures were associated with 0 (e.g. CAV1) to 34 (for SMARCA4) categories . Also, cThe CaP-specificity and clinical relevance of different coregulator-dependent AR target gene signatures was evaluated using prostate and CaP gene expression profiles that are available in the public domain . First, MYC, a well-known p53 target gene with pivotal roles in CaP aggressiveness and progression. In addition, IPA identified p53 as an upstream regulator function of WDR77- and AR-regulated genes (p=1.37.e−7).The results above pointed to the existence of discrete coregulator-dependent mechanisms of AR action that may control select aspects of CaP cell biology, differ in clinical relevance, and be governed by specific coregulator-AR-TF interactions . An inteThat p53 and WDR77 may cooperate to regulate CaP cell response to androgens was examined first in Co-IP studies. Despite previous reports of functional interaction between AR and p53 , these pGNB4 and RAB27A was maintained in the presence of added mutant p53 , comparGNB4 and RAB27A, to which WDR77 and AR also bind   as part of the AR-WDR77-p53 immunocomplex. PGAM5, which has previously been reported to play a role in regulation of cell death  but was lso bind . Knock-d2.2E-16) , and for2.2E-16) , and sil2.2E-16) . Moreove2.2E-16) . In combGNB4 and RAB27A as well as HES6 and AGR2    were incre also) . Similarre also) . Except re also) . Westernre also) .bona fide AR target genes has revealed remarkable diversity in the molecular modulation of AR-dependent transcription. By integrating results from diverse analyses, important novel insights in AR action in CaP, including 2 novel AR-dependent transcriptional codes (AR-WDR77-p53 and AR-STAT3-IRF1) and a novel AR-associated coregulator (PGAM5), have been derived. These findings strengthen the concept and feasibility of selective ADT.This first systematic analysis of the individual contribution of multiple coregulators to androgen regulation of several hundred Previous efforts to target for therapy the heterogeneity in AR action have focused on enhancing AR action in a tissue-specific manner via development of selective AR modulators . The altThe finding that androgen action depends on different coregulation of transcription is consistent with previous studies that mostly involved silencing of one coregulator and a handful ARE-driven gene fragments or global androgen-dependent gene expression patterns . The scoThat heterogeneity in coregulator-dependent AR target gene subsignatures is associated with different cell biology processes and clinical CaP progression is novel. This result has important implications for therapeutic intervention as it lends further credence to the concept of selective, CaP-specific forms of ADT. Theoretically, blocking a CaP-specific segment of AR transcriptional output that controls aggressive CaP cell behavior and clinical progression will lead to CaP remission while bypassing resistance and avoiding side effects of conventional ADT . The AR-PSA), and, most often, exogenously overexpressed AR and/or p53. Context-dependence such as that observed above for coregulator contribution to AR-mediated transcription may explain these discrepancies. None of these studies detected direct interaction between AR and p53. Interaction between AR and WDR77 has been shown  mechanisms. Yet other models of androgen action may not have been captured because of the selection criteria of our assays. The cut-off of 300 Kb between ARBS and TSS, although sizable, may have prevented identification of genes which form AR transcriptional complexes over longer distances via chromatin looping . The reqIn conclusion, our alternative approach to define systematically the contribution of coregulators to AR-dependent gene expression in CaP revealed that AR action in CaP partitions according to defined fractions. These segments may be amenable to future development into alternative forms of ADT that inhibit only the most clinically relevant portion of AR action.CVCL_1379) and VCaP (RRID:CVCL_2235) cells were obtained from the ATCC and cultured as before . Antibodies that were used are listed below. siGenome On Target Plus SmartPools were purchased from Thermo-Scientific .Western blotting was done as described .Cells were seeded, transfected and treated as before .RNA isolation, cDNA synthesis and real-time RT-PCR were done as before . PrimersCells were harvested in PBS  and spun down. After resuspension of cells in 200 µl PBS, 2 ml 70% ethanol was added dropwise and cells were kept on ice before adding 50 µl RNAse A (10 mg/ml) and 50 µl propidium iodide . After incubation at 37C for 30 min, cells were sorted using a Becton Dickinson LSR II flow cytometer. Data was analyzed using ModFit software.Cell viability was assessed as before .2; 0.5% NP40; 1X EDTA-free protease inhibitor cocktail (Roche)] for 1 hr at 4°C. The protein content of the cell lysates was determined using a Bradford assay. The cell lysate was precleared with 50 µL of lysis-buffer-equilibrated Dynabeads protein G (Life Technologies) or protein G agarose , purified on RNeasy columns  and checked for integrity using an Agilent 2100 Bioanalyzer.http://research4.dfci.harvard.edu/brownlab) (A list of AR binding sites (ARBSs) across the human genome derived from LNCaP cells that had been treated for 4 hr with DHT (100 nM) was downloaded from the Brown laboratory website (rownlab) . ARBSs wrownlab) . This apThe eArray design features on the Agilent website were used to design a custom 8×15K Agilent expression array. Probes targeting genes of interest were chosen from the Agilent probe catalog using their gene symbol. A linker region was attached to the probes as recommended by the manufacturer. Selected probes were organized in probe groups. Probe groups were used to populate the custom Agilent array. Three major probe groups were generated:Probe group ‘AR target genes’: 898 genes, 2409 probes. For 898 of the 900 genes that were identified as putative AR target genes by cross-matching tiling array data and gene expression array data, Agilent catalog probes were available.Probe group ‘coregulator genes’: 180 genes, 541 probes. For 180 of the 181 genes that were identified as AR-associated coregulators by cross-matching tiling array data and gene expression array data, Agilent catalog probes were available.Probe group ‘housekeeping genes’: 58 genes, 184 probes. This probe group targets genes that are routinely used as housekeeping genes on microarray and real-time RT-PCR array platforms as well as genes for which expression has been shown previously not to change during CaP progression .As additional controls for overall assay performance, probes targeting genes of non-human origin were included . For all probe groups, 4 replicates were included on the array. As per the manufacturer’s recommendation, the human expression 50 Agilent replicate probe group was included (10-fold), as was an Agilent control grid (536 features).Labeling of RNA (One-color), hybridization of the labeled cRNA, washing and scanning of the array slides was done by Roswell Park Cancer Institute’s Genomic Shared Resource according to the manufacturer’s instructions.Array images were scanned using the G2565AA Microarray scanner (Agilent Technologies) with the gene expression signal extracted using Agilent’s Feature Extraction 5.1.1 software. The expression data were then normalized by quantile normalization following by log2 transformation.The Agilent oliogoarray chips were first used to profile RNA samples from LNCaP cells that had been treated with vehicle or androgens . An unsupervised hierarchical clustering based on the average linkage of Pearson correlation verified classification of probes and samples as vehicle- and androgen-treated. The limma program in the Bioconductor package under the R computing environment was used to identify androgen-regulated genes. To control the false positive rate (FDR), the resulting p-values were adjusted using the Benjamini and Hochberg algorithm. A total of 452 androgen-regulated genes were obtained at an FDR cutoff 0.05 plus two-fold change in expression intensities. A pie chart was produced to show the genomic distribution of AR binding sites of those 452 genes, using the genomic features obtained from the UCSC reference gene table.The Agilent custom oligoarray was then used to determine the relative contribution of 18 clinically relevant AR-associated co-regulators to androgen-regulation of AR target gene expression. RNA from appropriate coregulator siRNA transfections was processed as above.For each co-regulator specific siRNA transfection, we performed three separate comparisons for each gene based on the samples' characteristics: (1) level of androgen regulation by comparing control siRNA with androgen treatment versus control siRNA without treatment condition, (2) level of androgen regulation by comparing specific siRNA-silencing with androgen treatment versus specific siRNA-silencing without androgen treatment condition, and (3) fold androgen regulation in (1) compared to (2). The limma program was used to assess differential expression in each of the above-mentioned comparison. The genes with FDR <0.05 and at least 2.0 fold change are considered as differentially expressed genes.To visualize the androgen regulation signatures upon loss of each co-regulator, we generated a heatmap by using the R package gplots. R package WGCNA was further used to generate the heatmap to display the Pearson correlation among co-regulators. Functional GSEA was conducted using the java GSEA application, version 2.0. The detailed GSEA parameters were as follows: the number of permutations was 1000, and gene set size filters with minimum of 15.The Aligent oligoarray data have been deposited in Gene Expression Omnibus under accession number GSE66722 in MIAME-compliant format.RNA was obtained and subjected to quality control as above. HumanHT-12 v4 Beadchip analysis was done by Roswell Park Cancer Institute Genomics Shared Resource as per the manufacturer’s recommendations. The raw intensity of Illumina HumanHT-12 v4 gene expression array was scanned and extracted using BeadScan, with the data corrected by background subtraction in GenomeStudio module. The lumi module in the R-based Bioconducter Package was used to transform the expression intensity into log2 scale . The logIllumina BeadChIP data have been deposited in GEO under accession numbers GSE66977 and GSE81780 in MIAME-compliant format.Datasets containing Gene Symbol identifiers and corresponding expression values were uploaded into the application. Each gene symbol identifier was mapped to its corresponding human/mouse/rat orthologue cluster in the Ingenuity Knowledge Base . An absoThe IPA Downstream Effects Analysis (DEA) was used to identify the biological functions and/or diseases that were most significant to the dataset. A right-tailed Fisher’s Exact test was used to calculate a p-value determining the probability that each biological function and/or disease assigned to these data sets is due to chance alone. Furthermore, DEA was used to predict increases or decreases of these biological functions and/or diseases occurring after androgen activation by integrating the direction change of the DEGs into a z-score algorithm calculation. Functions and/or diseases with z-scores ≤ −2 or ≥2 were considered significant.Canonical pathways analysis identified the canonical pathways from the IPA library that were most significant to the data set. The significance of the association between the data set and the canonical pathway was measured in 2 ways: (1) the ratio of the number of molecules from the data set that map to the pathway divided by the total number of molecules that map to the canonical pathway, and (2) calculation of a p value using Fisher’s Exact test to determine the probability that the association between the genes in the dataset and the canonical pathway is explained by chance alone.Upstream Analysis was used to identify the cascade of upstream transcriptional regulators  that could explain the observed gene expression changes in these datasets, by measuring an overlap in p-value with Fisher’s Exact test and by measuring the activation z-score as well to infer the activation states of the predicted transcriptional regulators.Cistrome project tools were used to analyze the ARBS genomic regions of different coregulator-dependent AR target gene expression signatures for the presence of consensus TF binding sites. A heatmap was generated using the R gplots package to visualize the association of the gene signatures with the presence of TF binding sites in ARBS genomic regions from Cistrome.After 16 hr of treatment with either R1881 (5 nM) or vehicle, nuclear extracts were prepared from LNCaP cells using the nuclear extract kit (Active Motif) and samples were immunoprecipitated as described above. The eluted samples were run on 10% Bis-Tris Novex NuPAGE gels (Life Technologies) and were silver-stained according to the manufacturer’s protocol . The bands were cut into smaller pieces to minimize excess polyacrylamide and were first reduced with 100 mM DTT and further alkylated with 150 mM iodoacetamide. All bands were digested in-gel by adding 50 ng trypsin in 50 mM ammonium bicarbonate to each gel band for 16 hr at 37°C. Peptides were extracted in 50% acetonitrile with 5% formic acid and dried using speed vac. The dried pellet was resuspended in buffer A (1% acetic acid) for LC-MS analysis.ftp://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/) with the programs Mascot and Sequest, The search results were further analyzed using the search program X! Tandem which is bundled into the program Scaffold. The Scaffold data was filtered based on a 1.0% FDR at the protein level and 2 positively identified peptides with a peptide threshold at 0.1% FDR. The relative abundance of protein in the IP and control samples was determined by comparing the total spectral counts identified for each protein.The mass spectrometric analysis was performed using a Dionex Ultimate 3000 LC system coupled to Finnigan LTQ-Obitrap Elite hybrid mass spectrometer (Thermo Fisher Scientific) equipped with nanoelectrospray ion source. Five μL volumes of the peptide mixture was resolved on a 15 cm Dionex HPLC column (75 μm) filled with 2 µm C18-resin. The peptides were loaded with buffer A and eluted with a 2% to 70% acetonitrile gradient of Buffer B (acetonitrile/0.1% formic acid) at a flow rate of 300 nl/min for 110 min. The digest was analyzed using the data dependent multitask capability of the instrument acquiring full scan CID mass spectra to determine peptide molecular weights and product ion spectra to determine amino acid sequence in successive instrument scans. The data was processed and analyzed by searching the human reference sequence database  per the manufacturer’s instructions with minor modifications. After crosslinking, cell pellets were lysed and shearing using a Diagenode Bioruptor Plus Sonicator using 3 × 10 cycles of 1 min on and 1 min off at the medium setting at 4C. Protein G agarose was replaced by Protein G DynaBeads (Thermo-Scientific) . For p53 ChIP, 3 p53 antibodies (DO1 (Santa Cruz), FL393 (Santa Cruz), and pAB421  were used in combination at equal ratios and at a total amount of 2 µg antibody per reaction. Real-time RT-PCR on ChIP’ed DNA and data analysis were done as described . Primer 5 per well in 6 well plates in medium supplemented with charcoal-stripped FBS. Two days later, cells were treated with 5 nM R1881 or ethanol vehicle for 48 hr. After 48 hr, cells were harvested in PBS and equal ratio cells to 0.4% trypan blue dye was mixed and cells were counted using a Countess II FL cell counter (Life Technologies).LNCaP cells were cultured at a density of 3 × 105 in medium supplemented with charcoal-stripped FBS and treated in the same way as for the trypan blue dye exclusion studies. For immunofluorescence, cells were washed twice with PBS and fixed with cold methanol for 4 min at −20C. Fixed cells were incubated in blocking solution (1% BSA in PBS) for 1 hr. The cells were then incubated for 1 hr at room temperature with 1:500 dilution of Ki67 antibody (Abcam) in 1% BSA in PBS in a humidified chamber. After washing with PBS, the cells were incubated with the fluorescently labeled Alexa fluor 488  secondary antibody for 45 min at room temperature in a humidified chamber. After incubation, cover slips were washed 5 times with PBS, dipped once in distilled water, DAPI stained and mounted in Vectashield medium (Vector laboratories). Images were acquired using an inverted EVOS FL imager (Life Technologies) at 10x magnification. For presentation purposes, images were merged and contrast enhanced using Image J 1.43.LNCaP cells were seeded on coverslips at a density of 1.5 × 10Unstained sections of tissue microarrays (TMAs) PR954, PR753, PR483 were obtained from US Biomax. Sections were baked overnight at 58°C in a dehydration oven. Deparaffinization, sections were hydrated in three separate solutions of Xylene (20 min in the first and 20 s each in the last two solutions), followed by three separate solutions of 100% ethanol (4 min in the first and 20 s in each of the last two solutions), followed by 95% ethanol for 1 min and then, rinsed in distilled water for 5 min. Sections were incubated in 3% hydrogen peroxide for 5 min to prevent endogenous peroxidase activity and washed in distilled water for 15 min. Vectastain, Universal Elite ABC kit was used for immunostaining (Cat# PK-6200). Epitopes were retrieved by boiling slides for 30 min in a citrate-based buffer provided as antigen unmasking solution (Vector Laboratories). Non-specific binding was blocked with normal horse serum for 30 min. Affinity-purified, polyclonal rabbit antibody against PGAM  was used as the primary antibody. The incubation time for primary antibody was overnight at 37°C for 1 hr. A diaminobenzidine (DAB) substrate kit  was used for visualization according to manufacturer’s instruction. The slides were counterstained with hematoxylin QS solution  and rehydrated by immersion into 80%, 95% and 100% ethanol followed by dipping in Xylene twice each time for two minutes.The intensity and distribution of positive staining was evaluated. A standard 4-point scale was employed for intensity, with cores being scored as negative (no staining), 1 + (weak staining), 2+ (moderate staining) and 3+ (strong staining). The distribution of positive staining was evaluated as percentage of positive cells (0–100). The vast majority of cores stained homogeneously (100%).For pair-wise point comparison of categorical variables, Fisher’s exact test was used. For pair-wise comparisons involving continuous variables, Student’s t-test was used. For populations with unequal variances, Welch's t test was used. If normality is not satisfied (Kolmogorov-Smirnov test) even after log-transformation, wilcoxon rank sum test was used. For multi-group comparisons, ANOVA analysis was performed. To derive the statistical significance of the overlaps between lists of genes, hypergeometric tests were used. All tests were two-tailed, with p=0.05 significance cutoff.Each microarray experiment was performed in triplicate . To confirm the data obtained by microarray analysis, we verified a subset of the target gene changes by independent real time RT-PCR analysis. To eliminate potential batch effects, the samples were randomly assigned to different plates using the OSAT program to ensure that the distribution of sample groups was even across plates.Antibodies used for these studies include AR , AR , AR , AIB1 , BRG1 , β-actin , IRF1 , MEP50 , p300 , p53 , p53 , p53 , PGAM5 , pHistone H2a.z , PKN1 , STAT3 , TIF2  and Ki67 .Multiple primers were designed and used for these studies. For real-time RT-PCR, primers include AGR2 (F: TGTTTGTTGACCCATCTCTGACA and R: TCTTCAGCAACTTGAGAGCTTTC), AOF2 (F: CCACAACAGACCCAGAAGGT and R: CTGGGTGGACAAGCACAGTA), ARHGAP11A (F: GGTTCCCTTGGATGATCTGA and R: TGGTCTCCTAAGGACCCTGTT), ATP11A (F: AGGGAGAACCACATCGAAAG and R: CGAAGAATCTGCTCCTTTGC), BAG1 (F: GCAGCAGTGAACCAGTTGTC and R: CAACGGTGTTTCCATTTCCT), CAMKK2 (F: GTCTCACCACGTCTCCATCA and R: GCCAACTTGACGACACCATA), CAV1 (F: CCACCTTCACTGTGACGAAA and R: CCCAGATGTGCAGGAAAGAG), CTNNB1 (F: GCTTGGTTCACCAGTGGATT and R: GTTGAGCAAGGCAACCATTT), FHL2 (F: GGTACCCGCAAGATGGAGTA and R: CTCATAGCAGGGCACACAGA), GAK (F: CAGCAGAAGGTGTGGAGTCA and R: CTCGGGGACAGGTTGTAGAC), GNB4 (F: GGGAAGGGTAAACGTGTTAGATT and R: GCCACTGTACAAATAGAGGAATGA), GNL1 (F: CACCCCACAGGACCCTAGTA and R: GCTGCTCAAGTCCACTTTCC), GUCY1A3 (F: GATTCTTCCCGGCATCATAA and R: GATTCACAAACTCGCTGCAA), HES6 (F: CTGCCGGCTACATCCAGT and R: ATGGACTCGAGCAGATGGTT), HIP1 (F: CCAGCGGAAGACTCAAGAAC and R: CTGACTGGGCAGAAGTTTCC), HTATIP2 (F: GAAACAGAAGCCCTGTCGAA and R: CAATGAGCGTGACTTTGGAA), IRF1 (F: GGATTCCAGCCCTGATACCT and R: CACCTCCAAGTCCTGCATGT), KAT5 (F: CAGATCACACTCCGCTTCAA and R: CACTGGAGTTGCTGGTGAAA), MEF2A (F: AGCACATTGTGGGAGAGAGACTGA and R: TGGCTTGGCCATTTTTCCTGAGCA), MPRIP (F: GGTTTGCAGCAATGGAAGAA and R: CTTCGATGGCTGAGATGGTG), NCOA1 (F: CTCTGGATTCAGGGCTTCTG and R: GTTCGGCAGTTGTTGTCAAA), NCOA2 (F: GGCAAGAAGAGTTCCCATGA and R: CTGCTCTCATGGTGCTGGTA), NCOA3 (F: CACATGGGAGTCCTGGTCTT and R: GGTTCCCAGTATTGCCAGAA), NET1 (F: CTGTGGTCAGAGATGCTGGA and R: GGGTCATGGTAGGCCTTTCT), PARK7 (F: TGGCTAAAGGAGCAGAGGAA and R: ATGACCACATCACGGCTACA), PGAM5 (F: GCAAAGTCAGCACAGATCTG and R: CATCTGCGCGGTGGATGTAG), PKN1 (F: GCCATCAAGGCTCTGAAGAA and R: GTCTGGAAACAGCCGAAGAG), RAB27A (F: CTGCCAATGGGACAAACATA and R: CCGTAGAGGCATGACCATTT), RAD9A (F: GTGCGGAAGACTCACAACCT and R: CAGGAGAGAAGGGCAGAACA), RALB (F: CTACGCAGCCATTCGAGATA and R: CGGAGAATCTGTTCCCTGAA), RCHY1 (F: CCGTGTTGTTGCTCATGTCT and R: CATCATCCAGCTGTCTCCAA), SASH1 (F: TCCGAAAGAACCAGAAAGGA and R: TAGCTGAATCCGCTCCTCAT), SDC4 (F: CCACCGAACCCAAGAAACTA and R: GCACAGTGCTGGACATTGAC), SERPINB5 (F: CCCTATGCAAAGGAATTGGA and R: CAAAGTGGCCATCTGTGAGA), SMAD3 (F: CTCCAAACCTATCCCCGAAT and R: CGCTGGTTCAGCTCGTAGTA), SMARCA4 (F: CCTGAATGAGGAGGAAACCA and R: GCAGACATGTCGCACTTGAT), SMARCC1 (F: GCGGATGCTCCTACCAATAA and R: CACTTTGCAGGGAGTTTGGT), STAT3 (F: GGCCATCTTGAGCACTAAGC and R: CGGACTGGATCTGGGTCTTA), TP53 (F: GAAGACCCAGGTCCAGATGA and R: CTGGGAAGGGACAGAAGATG), WASF3 (F: CAGCTGAGCAGTCTGAGCAA and R: CTGGGTGACTTTGACAGCAA), WDR77 (F: GTCTTGAGCTCTGGCACACA and R: CAGCATGAGCTCGGTATGAA), and ZIC2 .For ChIP assays, primers were CAMKK2 (F: AGAACACTGTAGCTCACACAGGCA and R: GGGCACTTCCCAACCTTTCTTACT), GNB4 (F: TATGAGTCCGTCTCAGTGTTG and R: TTTGAATGCACCTAATCAGCC), MEF2A (F: TTGTTCTGTTTCTAGTGCTGTG and R: GCCAAATCTTTCCAAGTAGC), NET1 (F: CCGAAAGTCAGCTCAGATCA and R: TTGCCTGTTCCTTCTCTCTGA), RAB27A (F: TCCTGACCACAATCATAGGTTA and R: CGTTAAAAGCAAAGTCAAGGTC), RALB (F: TAGGTGGTGGTGCTTGAGTG and R: TCTTCAGTCACAATCCTTGGAA) SASH1 (F: CATTTCAGAACAACAGGCTCAG and R: TTGTTCATTGAGGTCAACGTG) and PSA (F: ACAGACCTACTCTGGAGGAAC and R: AAGACAGCAACACCTTTTT).ATGTACTCCCCTGCCCTCAACAAG and R: CTTGTTGAGGGCAGGGGAGTACATGCAAGTCACAGACTTGGC), F134L (F: CCTGCCCTCAACAAGATGTTATGCCAACTGGCCAAGAC and R: GTCTTGGCCAGTTGGCATAACATCTTGTTGAGGGCAGG), C135Y (F: GCCCTCAACAAGATGTTTTACCAACTGGCCAAGACCTGCC and R: GGCAGGTCTTGGCCAGTTGGTAAAACATCTTGTTGAGGGC), P152L (F: TGGGTTGATTCCACACCCCTGCCCGGCACCCGCGTCCG and R: CGGACGCGGGTGCCGGGCAGGGGTGTGGAATCAACCCA), R174W (F: ACATGACGGAGGTTGTGTGGCGCTGCCCCCACCATGAG and R: CTCATGGTGGGGGCAGCGCCACACAACCTCCGTCATGT), V272L (F: GGACGGAACAGCTTTGAGTTGCATGTTTGTGCCTGTCCTG and R: CAGGACAGGCACAAACATGCAACTCAAAGCTGTTCCGTCC), R273C (F: GAACAGCTTTGAGGTGTGTGTTTGTGCCTGTCCTGGG and R: CCCAGGACAGGCACAAACACACACCTCAAAGCTGTTC), N239T (F: CTACAACTACATGTGTACCAGTTCCTGCATGGGCGGCATG and R: CATGCCGCCCATGCAGGAACTGGTACACATGTAGTTGTAG), and P359S (F: TGCCCAGGCTGGGAAGGAGTCAGGGGGGAGCAGGGCTCAC and R: GTGAGCCCTGCTCCCCCCTGACTCCTTCCCAGCCTGGGCA).For site-directed mutagenesis of p53, primers used were: T125M .Thank you for submitting your article "Coregulator-dependency differentiates between distinct mechanisms of androgen receptor action in prostate cancer" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:General assessment: The Reviewers felt that this work was a comprehensive and ambitious tour de force assessing the role of individual co-regulators in the androgen responsiveness of genes in prostate cancer and normal prostate. In this analysis the authors discover and characterize a novel complex consisting of AR-p53 and WDR77 that is influential in the regulation of a subset of androgen-responsive genes. Overall there was enthusiasm for the novelty of the conclusions of this work:Central conclusions:1 A subset of AR co-regulators appears to be broadly required for most androgen responsive genes.2) There is considerable AR target gene preference and context-dependence among co-regulators for their impact on androgen responsiveness of target genes.3) An important new complex in AR is described, consisting of AR, p53 and WDR77. The authors find that androgen responsiveness after p53 knock down or WDR77 knockdown affects the same subset of genes in the same direction, supporting the premise that a complex of AR-WDR77-p53 exists to control the expression of these genes.Overall, the Reviewers felt that this study describes very novel findings regarding the complexity of the mechanisms of action of the AR in prostate cancer. In particular, it was felt that this manuscript would serve as an invaluable template for all future studies in this important and clinically-relevant area. The reviewers raised a number of concerns that must be adequately addressed before the paper can be accepted. Some of the required revisions will likely require further experimentation within the framework of the presented studies and techniques.Essential revisions:1) The authors state, but do not actually formally prove, that a subset of androgen-response genes is controlled by the p53-WDR77-AR complex. Specifically, in 2) Since the p53-WDR77 complex has not previously been reported, it seems that IP-westerns in both directions  in a minimum of two different cell lines is warranted, in the interest of rigor and reproducibility.3) Appropriate statistical analyses are at times lacking. Statistical analysis for the overlapping of genes in 4) It appears that much of the cell line derived data was generated by exposing cells in stripped serum containing media with and without 5nM R1881. However, numerous publications document that cells can have a bimodal dose response to androgens, with optimal growth promotion at 0.1-1nM R1881. In contrast, in some studies 5nM R1881 has been reported to inhibit growth of cells. Thus, the authors need to document that, at least in their hands, 5nM R1881 is actually growth stimulatory. If this is not the case, their conclusions are compromised. Essential revisions:1) The authors state, but do not actually formally prove, that a subset of androgen-response genes is controlled by the p53-WDR77-AR complex. Specifically, in Thank you for this suggestion. We have performed ChIP-re-ChIP experiments for p53 and AR on these RAB27A and GNB4 gene regions. LNCaP cells were transfected with non-targeting control siRNA or siRNA targeting WDR77. 72 hours after transfection cells were treated with 5nM or vehicle, and 16 hours later cells were harvested and processed for ChIP-re-ChIP.ChIP using p53 antibody followed by re-ChIP with AR antibody showed androgen-dependent enrichments on GNB4 and RAB27A genes under control conditions, confirming co-recruitment of AR and p53. This androgen-regulation was decreased after silencing of WDR77, indicating involvement of WDR77 in co-occupancy of AR and p53 on GNB4 and RAB27A genes. Similar results were obtained when ChIP was done with an antibody targeting AR, followed by re-ChIP for p53.These results have been described in the Results section, and are included as novel panel J in 2) Since the p53-WDR77 complex has not previously been reported, it seems that IP-westerns in both directions  in a minimum of two different cell lines is warranted, in the interest of rigor and reproducibility.We have performed these reciprocal Co-IP experiments in LNCaP cells and VCaP cells. The experiment in LNCaP cells in which IP was done for p53 and WB for WDR77 was shown in the original All 4 experiments corroborate the presence of WDR77 and p53 in same immunocomplex, in both LNCaP and VCaP cells. They have been described in the Results section of the revised manuscript, and the legends of In light of minor comment 6, we have moved the original panels D-F of 3) Appropriate statistical analyses are at times lacking. Statistical analysis for the overlapping of genes in We apologize for this oversight. The requested statistical analyses have been performed and For For A description of the statistical methods used has been included in the legends to 4) It appears that much of the cell line derived data was generated by exposing cells in stripped serum containing media with and without 5nM R1881. However, numerous publications document that cells can have a bimodal dose response to androgens, with optimal growth promotion at 0.1-1nM R1881. In contrast, in some studies 5nM R1881 has been reported to inhibit growth of cells. Thus, the authors need to document that, at least in their hands, 5nM R1881 is actually growth stimulatory. If this is not the case, their conclusions are compromised.Thank you for the suggestion. We have addressed this comment by first performing trypan blue exclusion assays on LNCaP cells that had been treated for 48 hours with either 5nM R1881 or vehicle. A ~2-fold increase in cell proliferation was found after androgen treatment. These data have been included in a novel


Nitrogen (N) is important for the growth of crops. Leaf nitrogen content (LNC) serves as a crucial indicator of the growth status of crops and can help determine the dose of N fertilizer. Laser-induced fluorescence (LIF) technology and the reflectance spectra of crops are widely used to detect the biochemical content of leaves. Many vegetation indices (VIs) and fluorescence parameters have been developed to estimate LNC. However, the comparison among VIs and between fluorescence parameters and VIs has been rarely studied in the estimation of LNC. In this study, the performances of several published empirical VIs and fluorescence parameters for the estimation of paddy rice LNC were analyzed using the support vector machine (SVM) algorithm. Then, the optimal VIs  and fluorescence parameters (F735/F460 and F685/F460), which were suitable for LNC monitoring in this study, were chosen. In addition, the combination of the VIs and fluorescence parameters was proposed as the input variables in the SVM model and used to estimate the LNC. Experimental results exhibited the promising potential of the LIF technology combined with reflectance for the accurate estimation of LNC, which provided guidance for monitoring the LNC. Paddy rice is an important crop and a daily necessity to one-third of the world population. In China, approximately 30 million hectares of farming land are utilized each year to cultivate paddy rice. China is the leading producer of paddy rice in the world , 2. NumeIn passive remote sensing, several research groups reported that reflectance spectra are closely related to LNC  in that Laser-induced chlorophyll fluorescence, which differs from the reflectance spectra of remote sensing and LiDAR, was proposed by Chappelle et al.  to monitSeveral VIs based on reflectance spectra and fluorescence parameters based on fluorescence spectra have been designed to estimate LNC in crops on a leaf or canopy scale. However, the performances of published empirical VIs and fluorescence parameters have rarely been systematically tested in monitoring LNC in paddy rice. Comparisons between VIs and fluorescence parameters in terms of their estimation of LNC on a leaf scale are still rare. In addition, few studies have investigated the estimation of paddy rice LNC on the basis of the combination of VIs and fluorescence parameters. Therefore, the present study aims to (1) systematically analyze the performance of published empirical VIs and fluorescence parameters in the estimation of LNC using the support vector machine (SVM) algorithm, (2) compare the performances of VIs and fluorescence parameters in monitoring LNC, and (3) discuss the effectiveness of the proposed combination of VIs and fluorescence parameters in improving the monitoring accuracy of LNC.Permission to access private lands, on which several sites were located, was obtained from landowners. Species surveys were conducted in accordance with the laws of the People’s Republic of China.Yongyou 4949 of the three-line indica/japonica hybrid rice and Yangliangyou 6 hybrid indica rice were planted on April 27, 2014 and April 30, 2015, in experimental stations established in Junchuan County, Suizhou City, and Huazhong Agricultural University (HAU) in Wuhan City, China, respectively. During the entire growth period, six  and four  different doses of N fertilization of urea were utilized in 2014 and 2015, respectively. The most optimal doses of N fertilization were 270 and 180 kg/ha in 2014 and 2015, respectively, in accordance with the advice of the local farm extension service. N fertilization was divided into four splits  in 2014 and into three splits  in 2015. The experimental field had a block design with three replications for each treatment under the same cultivation conditions . The leaThe LIF system was built in a laboratory and consisted of three parts, namely, an excitation light source, optical receiver assembly and data collection system, and treatment part. The excitation light source is a neodymium-doped yttrium aluminum garnet laser and a third-harmonic generation. The emitted wavelength was 355 nm, and the width per pulse and output power were 5 ns and 1.5 mJ, respectively. The excitation light was transmitted perpendicular to the targets after passing several completely reflecting mirrors. To collect the back-emission fluorescence signal, a Maksutov-Cassegrain telescope was utilized in this system. An additional long-pass filter (Semrock BLP01-355R-25 with edge of 361 nm and 93% transmittance at 364.9–900 nm) was placed behind the telescope and used to eliminate the reflected light from the laser entering the optical fiber. Then, a single-mode optical fiber with a diameter of 200 μm was utilized to transmit the fluorescence collected between the telescope and spectrograph . An intensified charge coupled device camera was utilized to detect the excited fluorescence signals passing through the spectrograph. A personal computer was utilized to store and post-process these fluorescence data conveniently. In this study, the fluorescence spectra ranged from 360 nm to 800 nm, and the sampling interval was 0.5 nm. To eliminate the oscillation of the excitation light, each leaf sample was measured five times to calculate the average fluorescence spectral curve for each sample.RL(λ) and RR(λ) represent the leaf and reference standard white board radiances at wavelength λ, respectively.In this study, leaf reflectance spectra were obtained using an ASD FieldSpec Pro FR  which is a commercial passive instrument. The spectral acquisition process was conducted following the study of Pu et al. . A 100 WLeaves were destructively sampled by randomly cutting six fully expanded the second leaves from the top with three replicates in each experimental field. These paddy rice leaves were sealed in plastic bags, stored in an ice chest, and immediately transported to the laboratory for reflectance and fluorescence spectral measurements . All sam740/F685, F740/F460, F685/F460, F685/F525, F740/F525, (F460-F685)/(F460+F685), and (F460-F740)/(F460+F740). F740, F685, F525, and F460 denoted the intensity of fluorescence at 740, 685, 525, and 460 nm, respectively) [In this study, 67 published empirical VIs (formula and detailed description in Appendix) , 30–34 wctively) , 35, 36 γ denotes a kernel parameter, Yi represents the training output, Xi stands for the training inputs, and d is a constant.SVM, which is a classical supervised learning algorithm that has the capacity to construct linear and nonlinear inversions, was implemented in this investigation. In comparison with the artificial neural network, SVM has a strong theoretical foundation in statistical theory and exhibits remarkable performance (accuracy on test sets) in practice. Furthermore, SVM is insensitive to the dimension number of training samples and requires a small number of training samples. Detailed description of SVM can be found in the references , 38. In R2), root mean square error (RMSE), and relative error (RE) in the prediction were utilized to discuss the performance of the model on the basis of different spectral characteristics. RMSE and RE can be written as follows:n denotes the number of samples, Xp,i represents the predicted values, Xo,i corresponds to the measured values, and RMSE and RE and high R2 indicate a high accuracy and precision of a model in predicting LNC [Wavelet transform is similar to Fourier transform but uses a completely different merit function. The capability of wavelets to provide multiresolution low entropy makes them an ideal tool for studying spectra . Before ting LNC , 42.The fluorescence spectra ranged from 360 nm to 800 nm and exhibited three main fluorescence peaks at 440–465 nm, 680–690 nm, and 730–740 nm. According to previous studies , 44, theThe red solid line shown in R2 values were 0.79, 0.77, 0.78, and 0.70 for 2014 and 0.72, 0.73, 0.65, and 0.66 for 2015) than the other VIs. The correlation between the fluorescence parameters and LNC was investigated in our previous study [As shown in us study . The invus study .R2 of the linear regression of predicted and measured LNC.To analyze the performances of all VIs and fluorescence parameters in estimating LNC, each of the parameters was used as a single input variable to predict LNC through SVM. R2 values for the two growing years (2014 and 2015). A detailed analysis of the performance of fluorescence parameters in estimating LNC in paddy rice is found in reference [eference . In our To compare the performances of the three different types of characteristics parameters , SVM was utilized to estimate LNC. The measured dataset was automatically divided into four equal parts, and four-fold cross validation was used. R2 and low RMSE and RE, R2 = 0.83 and 0.81 for 2014 and 2015, respectively) were superior to the VIs . In addition, the combination of VIs and fluorescence parameters provided higher accuracy in LNC monitoring than either fluorescence parameters or VIs alone. The R2 of the regression analysis between the predicted and measured LNC reached 0.89 and 0.88 for 2014 and 2015, respectively.R2 and lower RMSE and RE than other VIs and therefore were suitable for estimating LNC in paddy rice [At present, the amount of VIs based on reflectance and LIF spectral data has already been utilized to analyze the growth status of vegetation, but only a few studies have compared VIs and fluorescence parameters in term of estimating LNC in paddy rice. , 47, 48.ddy rice , 50. Theddy rice . In addiddy rice . HaboudaR2 = 0.83, 0.81 for 2014 and 2015, respectively) were superior to VIs  in the estimation of LNC. The probable interpretation is that the fluorescence parameters were influenced by the re-absorption of leaf internal fluorophore and were relatively unsusceptible to ambient factors in comparison with the VIs [R2 = 0.89 and 0.88 for 2014 and 2015, respectively) [In this study, fluorescence spectra exhibited three main fluorescence peaks at 440–465 nm, 680–690 nm, and 730–740 nm. According to previous studies , 44, the the VIs , 53. Mea the VIs , 45. Malctively) . The satIn this investigation, the performances of VIs and fluorescence parameters in estimating LNC were compared in detail. In addition, the combination of VIs with fluorescence parameters was proposed to estimate LNC in paddy rice. However, this preliminary investigation only compared the VIs and fluorescence parameters in terms of monitoring LNC in paddy rice using the SVM model. Certain limitations should be considered in future studies. For the SVM algorithm, the kernel function is a key factor, and the effect of different kernel functions on LNC monitoring needs to be discussed in future work. Furthermore, to obtain a solid conclusion and improve the generalization capability of the proposed approach, additional paddy rice cultivars, growth seasons, and other crops should be considered in future studies.R2 = 0.83 and 0.81 in 2014 and 2015, respectively) were superior to the VIs . Finally, the combination of VIs and fluorescence parameters was proposed to estimate LNC. The experimental results demonstrated that the proposed combination could effectively improve the accuracy of LNC estimation . Thus, the LNC in paddy rice could be accurately evaluated by implementing the LIF technology combined with hyperspectral reflectance. Nevertheless, further studies using additional crops cultivars and growth years are still required to obtain a solid conclusion and improve the generalization capability of the proposed approach.This research investigated the performances of VIs and fluorescence parameters ub the estimation of LNC in paddy rice. The experimental results demonstrated that TVI, MTVI1, MTVI2, and MSAVI were superior to the other VIs in monitoring LNC. In addition, the comparison of the VIs and fluorescence parameters for the estimation of LNC through SVM demonstrated that the fluorescence parameters (Vegetation indices (VIs) based on reflectance spectra and the corresponding calculation formula in this paper , 30–34.Normalized difference vegetation index:Normalized difference index:Simple ratio vegetation indexZarco-Tejada&Miller:Optimized vegetation indexChlorophyll absorption ratio index:Modified chlorophyll absorption ratio index:Transformed chlorophyll absorption ratio index:Triangular vegetation index:Modified Triangular vegetation index:Red edge position linear:Optimized soil adjusted vegetation index:Red edge model index:Green model index:


Simple sequence repeats (SSRs) have become widely used as molecular markers in plant genetic studies due to their abundance, high allelic variation at each locus and simplicity to analyze using conventional PCR amplification. To study plants with unknown genome sequence, SSR markers from Expressed Sequence Tags (ESTs), which can be obtained from the plant mRNA (converted to cDNA), must be utilized. With the advent of high-throughput sequencing technology, huge EST sequence data have been generated and are now accessible from many public databases. However, SSR marker identification from a large in-house or public EST collection requires a computational pipeline that makes use of several standard bioinformatic tools to design high quality EST-SSR primers. Some of these computational tools are not users friendly and must be tightly integrated with reference genomic databases.A web-based bioinformatic pipeline, called EST Analysis Pipeline Plus (ESAP Plus), was constructed for assisting researchers to develop SSR markers from a large EST collection. ESAP Plus incorporates several bioinformatic scripts and some useful standard software tools necessary for the four main procedures of EST-SSR marker development, namely 1) pre-processing, 2) clustering and assembly, 3) SSR mining and 4) SSR primer design. The proposed pipeline also provides two alternative steps for reducing EST redundancy and identifying SSR loci. Using public sugarcane ESTs, ESAP Plus automatically executed the aforementioned computational pipeline via a simple web user interface, which was implemented using standard PHP, HTML, CSS and Java scripts. With ESAP Plus, users can upload raw EST data and choose various filtering options and parameters to analyze each of the four main procedures through this web interface. All input EST data and their predicted SSR results will be stored in the ESAP Plus MySQL database. Users will be notified via e-mail when the automatic process is completed and they can download all the results through the web interface.http://gbp.kku.ac.th/esap_plus/.ESAP Plus is a comprehensive and convenient web-based bioinformatic tool for SSR marker development. ESAP Plus offers all necessary EST-SSR development processes with various adjustable options that users can easily use to identify SSR markers from a large EST collection. With familiar web interface, users can upload the raw EST using the data submission page and visualize/download the corresponding EST-SSR information from within ESAP Plus. ESAP Plus can handle considerably large EST datasets. This EST-SSR discovery tool can be accessed directly from: The online version of this article (doi:10.1186/s12864-016-3328-4) contains supplementary material, which is available to authorized users. Microsatellites or simple sequence repeats (SSRs) are small sequence motifs comprising 1–6 base pairs (bp) in length that are highly conserved in the genome . TraditiAn EST is generated by a single-pass (from 5′ or 3′ end) sequencing of a clone randomly selected from cDNA libraries . Using ETo improve the biological information of ESTs both in the yield and quality, multi-step procedure is required to quality control ESTs (EST cleaning), clustering and assembly, match ESTs to pertinent databases (database matching), and annotate the results  . PresentEST sequences represent only a short portion of their corresponding mRNA (approximately 100–800 bp). These derived EST sequences are error sensitive during their sequencing processes. Therefore, the pre-processing step must be carried out to reduce the overall noise in EST data in order to improve the efficacy of the subsequent analyses . A compuESTs are highly redundant and they need to be grouped together to reduce redundancy. TGICL is a tool for clustering EST sequences based on a stringent pairwise comparison between two input EST sequences. The ESTs in these clusters contain significant common regions and they will be assembled to produce consensus sequences . AnotherDesigning primers for the candidate SSR loci is the final step of EST-SSR development. Many online and offline primer designing tools are available to predict SSR primers including Primer3 , SSR priThe entire workflow for designing EST-SSR primers must utilize various bioinformatic tools. A batch process/workflow for developing large scale EST-SSR primers requires these tools to be in one single pipeline yet the data formats shared among these tools may not be fully compatible.http://gbp.kku.ac.th/esap_plus/.In this work, we developed an efficient web-based EST analysis pipeline, called ESAP Plus, to facilitate the batch EST-SSR development. ESAP Plus can automatically generate EST-SSR primers directly from raw EST data. ESAP Plus incorporates bioinformatic scripts and software tools necessary for all four important procedures in the development of EST-SSR primers including 1) pre-processing, 2) clustering and assembly, 3) SSR identification and 4) SSR primer design. Since different tools may offer varying performance depending on their inputs, ESAP Plus also offers users with different software choices to include in the pipeline during the clustering/assembly and SSR identification steps. ESAP Plus was implemented as a web-based bioinformatic pipeline to reduce problems with complicated installation procedures and storage requirements. EST-SSR primer results generated by ESAP Plus are shown and can be download from the web interface with e-mail notification when the automatic task is completed. ESAP Plus can be freely used from ESAP Plus architecture is based on the three-tier architecture model, containing a user interface, processing logic and database tiers. The user interface tier provides graphical interface of ESAP Plus via a familiar web interface, whereas the processing logic tier carries out bioinformatic tasks in the proposed analysis pipeline. We wrote a set of Perl and bash shell scripts to string all standalone bioinformatic routines together in the proposed pipeline. MySQL server (version 5.5) is used to manage raw EST data, intermediate and final output data produced during the automated processing of ESAP Plus. We also keep the reference database required by both RepBase  and UniVESAP Plus was constructed to identify SSR markers and design the primers from a large EST collection. ESAP Plus includes four main processes to be executed: EST pre-processing, EST clustering and assembly, SSR identification and SSR primer design Fig. . We downEST pre-processing is the first process in the proposed pipeline, developed to screen for high-quality ESTs. EST pre-processing has four sub-processes, (1) EST formatting, (2) Length and %N detection, and EST removal, (3) Vector detection and removal and (4) Low-complexity masking. EST formatting module is responsible for converting multiple raw data formats and merge them into a text file with multiple FASTA entries. A Perl script, called xtract.pl, parses raw EST input sequences and converts them into a combined FASTA text file with a “.txt” extension. The second module takes care of screening high quality EST (sequence with ≥100 bp with < 5% of unknown nucleotides) . A Perl High quality EST data from the pre-processing stage will be passed to EST clustering and assembly stage. In this part, there are two alternative workflows using two different algorithms, including CD-HIT-EST  and TGICWe offer two different algorithms, namely MISA  and RepeEST-SSR sequences obtained from SSR mining of both MISA and RepeatMasker will be sent to BatchPrimer3  that utiSaccharum spp.) from 26 cDNA libraries of SUCEST project .The dataset of ESTs supporting the result of this article was published by SUCEST projects  and it iAdditional file 1:Supplementary materials. Lists of in-house Shell script in ESAP Plus. Nine in-house Shell scripts including: (1) est1_formatting.sh, (2) est2_length_N.sh, (3) est3_vector.sh, (4) est4_lowcom.sh, (5) est5_est_clustering_cdhitest.sh, (6) est5_est_clustering_tgicl.sh, (7) est6_ssr_misa.sh, (8) est6_ssr_repeatmasker.sh, and (9) est7_primer.sh. Format: The above shell scripts were compressed in one zip file. (ZIP 10 kb)Additional file 2: Table S1.A list of 15 SSR primers used to amplify DNA fragments by PCR reaction with genomic DNAs extracted from 15 sugarcane cultivars. (DOCX 15 kb)Additional file 3: Figure S1.Saccharum spp.). (PPTX 894 kb)PCR amplification results of primer SU018 for 15 commercial cultivars of sugarcane (


Physcomitrella patens following pathogen infection. Sequence analysis revealed that there is only one NPR1/NPR3/NPR4-like protein in P. patens. This agrees with the phylogenetic study that showed the divergence of NPR1 and NPR3/NPR4 from the same ancestor during the evolution of higher plants. Intriguingly, expression of the P. patens NPR1/NPR3/NPR4-like gene in Arabidopsis does not complement the constitutive defense phenotype of the npr3 npr4 double mutant, but can partially rescue the mutant phenotypes of npr1-1, suggesting that it functions as an NPR1-like positive regulator of SA-mediated immunity and P. patens does not have an SA receptor functioning similarly as NPR3/NPR4. Future characterization of the P. patens NPR1-like protein and analysis of its functions through knockout and biochemical approaches will help us better understand how SA is perceived and what its functions are in P. patens.Salicylic acid (SA) is a key signaling molecule in plant immunity. Two types of SA receptors, NPR1 and NPR3/NPR4, were reported to be involved in the perception of SA in Arabidopsis. SA is also synthesized in the non-vascular moss  PATHOGENESIS RELATED (PR) gene expression as two key features of SAR  is a plant immune response induced after primary infection, which leads to enhanced resistance to a broad spectrum of pathogens in distal tissue . Early ss of SAR . Clear es of SAR . In addiised SAR . On the nfection . These snpr1 alleles  domain at the N-terminus and an ankyrin-repeat domain in the middle of the protein  in the PR1 promoter that are required for the induction of PR gene expression by SA, suggesting that they are important for regulating PR1 expression . In Arabidopsis NPR1, Cys521, and Cys529 in the C-terminal domain were reported to be required for SA-binding, but they are not universally conserved in NPR1 orthologs . It is not obvious whether Pp3c21_7570 is more related to Arabidopsis NPR1 or NPR3/NPR4 simply from the phylogenetic tree.The predicted Pp3c21_7570 protein based on the cDNA sequence shows 34% identity and 53% similarity with Arabidopsis NPR1, 37% identity and 55% similarity with NPR3, and 36% identity and 56% similarity with NPR4. Similar to NPR1 and NPR3/NPR4, it contains a conserved N-terminal BTB/POZ domain, a central ankyrin-repeat domain and a C-terminal domain with nuclear localization signals . These data suggest that the 35S::Pp3c21_7570 transgene does not complement the mutant phenotypes of npr3-2 npr4-2.To determine whether Pp3c21_7570 has similar functions as NPR3/4 or NPR1, we generated a construct expressing 35S::Pp3c21_7570 transgenic lines in the npr1-1 background, INA-induced PR1 expression is partially restored . Similarly, INA-induced resistance to H.a. Noco2 is largely restored in the 35S::Pp3c21_7570 transgenic lines, although it is not as strong as in wild type (WT) plants . These data suggest that Pp3c21_7570 can partially complement the mutant phenotypes of npr1-1. Thus Pp3c21_7570 is orthologous to Arabidopsis NPR1 and we named Pp3c21_7570 as PpNPR1.In the npr1 mutant phenotypes by PpNPR1 indicates that it functions as a positive regulator of SA-induced defense gene expression, suggesting that similar mechanisms are used to promote defense gene expression and pathogen resistance by SA in P. patens. Since there is only one NPR1-like protein in P. patens, it probably does not use NPR3/NPR4-like SA receptors to negatively regulate defense responses. As NPR3/NPR4 evolved from the same ancestor as NPR1 in higher plants, they probably diverged from NPR1 to take a different role in fine-tuning SA-induced defense responses. Whether P. patens has a simpler immune system which does not need negative regulators like NPR3/NPR4 or it uses an alternative approach to prevent auto-activation of defense responses in the absence of SA remains to be determined.The partial complementation of the Arabidopsis P. patens. Future analysis of the potential SA-binding activity of PpNPR1 and characterization of the function of PpNPR1 by knockout analysis will help us to better understand its roles in SA perception and defense against pathogens. In Arabidopsis, NPR1 interacts with TGA transcription factors and regulates defense gene expression through TGA2/TGA5/TGA6 (P. patens. It will be interesting to determine which TGA transcription factors in P. patens interact with PpNPR1 and analyze their roles in SA-induced defense gene expression.Bryophytes are remnants of early diverging lineages of embryophytes and occupy an ideal phylogenetic position for reconstruction of ancient evolutionary changes . The ideGA5/TGA6 . It is lYP performed the experiments and wrote the paper; TS performed the experiments; YZ designed the experiments and wrote the paper.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.