However, in a recent study, we found an unexpected and generalized phenotype of high bone mass in female mice with chronic sympathetic hyperactivity, due to double gene inactivation of adrenoceptors that negatively regulate norepinephrine release, α2A-and α2C-AR (α2A/2C-AR-/-). These findings suggest that β2-AR is not the single adrenoceptor involved in bone turnover regulation and show that α2-AR signaling may also mediate the SNS actions in the skeleton. In addition, we found that α2A/2C-AR-/- animals are resistant to the thyrotoxicosis-induced osteopenia, suggesting that thyroid hormone (TH), when in supraphysiological levels, interacts with the SNS to control bone mass and structure, and that this interaction may also involve α2-AR signaling. In the present study, to further investigate these hypotheses and to discriminate the roles of α2-AR subtypes, we have evaluated the bone phenotype of mice with the single gene inactivation of α2C-AR subtype, which mRNA expression was previously shown to be down regulated by triiodothyronine (T3). A cohort of 30 day-old female α2CAR-/- mice and their wild-type (WT) controls were treated with a supraphysiological dose of T3 for 30 or 90 days, which induced a thyrotoxic state in both mouse lineages. The micro-computed tomographic (μCT) analysis showed that α2C-AR-/- mice present lower trabecular bone volume (BV/TV) and number (Tb.N), and increased trabecular separation (Tb.Sp) in the femur compared with WT mice; which was accompanied by decreased bone strength (determined by the three-point bending test) in the femur and tibia. The opposite was observed in the vertebra, where α2C-AR-/- mice show increased BV/TV, Tb.N and trabecular thickness (Tb.Th), and decreased Tb.Sp, compared with WT animals. In spite of the contrasting bone phenotypes of the femur and L5, thyrotoxicosis negatively regulated most of the micro architectural features of the trabecular bone in both skeletal sites of WT, but not of α2C-AR-/- mice. T3 treatment also decreased biomechanical properties (maximum load and ultimate load) in the femur and tibia of WT, but not of knockout mice. The mRNA expression of osteocalcin, a marker of mature osteoblasts, and tartrate-resistant acid phosphatase, which is expressed by osteoclasts and is involved in collagen degradation, was increased by T3 treatment only in WT, and not in α2C-AR-/- mice. Altogether, these findings suggest that α2C-AR subtype mediates the effects of the SNS in the bone in a skeletal site-dependent manner, and that thyrotoxicosis depends on α2C-AR signaling to promote bone loss, which sustains the hypothesis of a TH-SNS interaction to modulate bone remodeling and structure.A series of studies have demonstrated that activation of the sympathetic nervous system (SNS) causes osteopenia via β It is well known that hyperthyroidism is one of the major causes of secondary osteoporosis ,2. Thyro2-AR signaling, leading to bone loss , the ultimate load [the load at the fracture point (J)], Young´s modulus (mPa), resilience [a measure of the ability of a bone to suffer elastic deformity (J)] and stiffness [a measure of the extrinsic rigidity of the bone tissue (N/mm)].TCT GCA GCT CTT CCA TGA CAC T and R: CGA TGA GAC TGG GCA GGT AAG (NM_009399), RANK-L_F: GGC CAC AGC GCT TCT CAG and R: GAG TGA CTT TAT GGG AAC CCG AT (NM_011613.2), OPG_F: AGT CCG TGA AGC AGG AGT G and R: CCA TCT GGA CAT TTT TTG CAA A (NM_U94331), 18S_F: GTA ACC CGT TGA ACC CCA TT and R: CCA TCC AAT CGG TAG TAG CG (NM_11188), TRAP_F: F: TGC ACA GAT TGC ATA CTC TAA GAT CTC,R: TTT TGA AGC GCA AAC GGT AGT (NM_007388), OC_F: F: CTC ACA GAT GCC AAG CCC A,R: CCA AGG TAG CGC CGG AGT CT (NM_U11542) were synthetized  specifically for real-time PCR using Primer Express software (Applied Biosystems). The amplification reaction was carried out for 40 cycles, with denaturation at 95°C for 5 seconds, and annealing/extension at 60°C for 31 seconds. Melt curve analysis was conducted after each run. Each pair of primer generated a single peak. The relative abundance of each target was calculated as 1,000 × 2(Ct target gene–Ct Gapdh), in which Ct represents the threshold cycle for each transcript, and Cyclophilin or 18s is the reference.Expression of receptor activator of nuclear factor kappa-B (RANK), RANK ligand (RANKL), osteoprotegerin (OPG), osteocalcin (OC) and tartrate-resistant acid phosphatase (TRAP) were determined by real time PCR in the whole femur and fourth lumbar vertebra (L4), as described previously . The tot-1 x day-1) on bone of 30-day old female WT and α2C-AR-/- mice for 30 and 90 days. We first evaluated the induction of a thyrotoxic state by analyzing serum levels of TH, heart mass and body mass. Both 30- and 90-day-long T3 treatments increased by 8-fold the serum concentration of T3, and, as expected, decreased by half the T4 serum levels both in WT and KO mice , which is an indirect measure of cardiac hypertrophy. Surprisingly, the heart mass of KO mice was not affected by the thyrotoxic state  presented lower BV/TV  and Tb.N  than their WT controls  by T3 treatment in the femur KO animals, it occurred only after 90 days of treatment  and in a much lower magnitude than in WT animals. Although less evident, the cortical bone was also differentially affected, in some way, by TH excess in the femur and vertebra of KO animals. In the femur, T.Ar decreased 16% and Ec.Pm increased 25% only in WT animals. All the other cortical parameters responded similarly to thyrotoxicosis in WT and KO animals  and lower in the vertebra (56%) of 30-day saline-treated α2C-AR-/-mice (60-day old mice), as compared with WT controls  present a phenotype of HBM [2A-AR and/or α2C-AR signaling mediate actions of the SNS in the skeleton and that TH, when in toxic levels, interact with this system to cause bone loss. To gain insights regarding the specific roles of the different α2-AR subtypes in these processes, we evaluated, in the present study, the bone phenotype of mice with the global single gene inactivation of α2C-AR subtype (α2C-AR-/- mice), and the skeletal responses of these KO animals to chronic conditions of TH excess (30 or 90 days of thyrotoxicosis). Similarly to α2A/2C-AR-/- mice, α2C-AR-/- animals also present sympathetic over activity with increased circulating and urine levels of epinephrine [We recently showed that young adult mice with global double gene inactivation of αe of HBM  and are e of HBM . These fnephrine .2C-AR-/- mice showed to be different from that of α2A/2C-AR-/- mice. While the latter present a generalized phenotype of HBM [2C-AR-/- mice present lower trabecular bone mass in the femur compared with WT mice, which was accompanied by decreased bone strength in the femur and tibia. The opposite was observed in the vertebra, where α2C-AR-/- mice show increased trabecular bone mass compared with WT animals, likewise α2A/2C-AR-/- mice. This heterogeneous bone phenotype presented by α2C-AR-/- mice reinforces the hypothesis that the SNS regulates bone remodeling and structure, via α2C-AR signaling, but also implies that this regulation occurs in a skeletal site-dependent way. It is important to consider that the lower bone mass observed in the femur of α2C-AR-/- mice may be the result of local activation of β2-AR, since these animals present a hyperadrenergic state [2C-AR-/- mice and in the whole skeleton α2A/2C-AR-/- mice. The fact that the double KO animals (α2A/2C-AR-/-) present a generalized phenotype of HBM, regardless of the elevated sympathetic tone, suggests that α2A-AR and/or α2C-AR also mediate osteopenic actions of the SNS. Therefore, the lack of both receptors could result in anabolic effects that could overcome the negative effects of β2-AR activation in the bone [2C-AR-/-and α2A/2C-AR-/-mice phenotypes suggests that the lack of only α2C-AR is sufficient to surpass the osteopenic effects of β2-AR activation in the vertebra, while both α2A-AR and α2C-AR absences, or the single α2A-AR absence, may be necessary to overcome the osteopenic effects of β2-AR activation in the femur.Interestingly, the bone phenotype of αe of HBM , α2C-AR-ic state . An intrthe bone . Followi2C-AR-/- mice, leptin deficient mice (ob/ob mice) also exhibit a heterogenic skeletal phenotype, with higher cancellous bone mass in lumbar vertebra and lower cancellous bone mass in distal femur metaphysis when compared to WT animals [Similarly to α animals . A possi animals ,49.2-AR action in the bone are still unknown. The presence of α2 adrenoceptor subtypes in mouse and human bone cells suggest direct actions of the SNS, via α2-AR signaling, in the skeleton. This hypothesis is supported by the responsiveness of in vitro mouse calvaria-derived osteoblasts and mouse bone marrow-derived osteoclasts to the selective α2–AR agonist clonidine and to the nonspecific α-AR antagonist phentolamine [2 receptors in non-neuronal cells, and specially in the peripheral and CNS (as autoreceptors and heteroceptors), it is much more likely that α2-AR mechanisms of action involve both central and local pathways, which are important points to be investigated in future studies.The mechanisms of αtolamine ,39. Howe2-AR signaling in the neuro-endocrine regulation of bone remodeling was shown in humans. Mkalar et al, 2010 identified expression of α2A-AR in osteoblasts and lining cells and showed associations of the α2A-AR gene locus with bone mineral density and with important bone remodeling markers, such as serum C-terminal cross-linking telopeptide of type I collagen,serum cathepsin K and plasma osteocalcin. In addition, α2A-AR was shown to be upregulated in osteoblasts derived from osteoporotic patients relative to osteoblasts derived from non-osteoporotic patients [Further evidence for the involvement of αpatients .2C-AR-/- mice presented resistance to the thyrotoxicosis-induced bone deterioration in both skeletal sites, similarly to α2A/2C-AR-/- mice [2C-AR-/- mice.. This resistance was very evident in the vertebra of KO animals, where no negative effects of toxic levels of T3 were detected in any μCT parameter, after 30 or 90 days of T3-treatment. On the other hand, two trabecular parameters of the femur (BV/TV and Tb.N), which is a skeletal site known to be very sensitive to TH [2C-AR subtype signaling. Once again, it is still unknown if the TH-SNS interaction occurs locally (in the skeleton) or at the CNS level. Previous in vitro studies with calvaria-derived osteoblasts, showed that the known negative effects of T3 on cell growth [2A/2C-AR-/- cells. Additionally, the combination of T3 with clonidine had an additive effect on the inhibition of WT cell growth, whereas T3 attenuated the positive effect of clonidine on α2A/2C-AR-/- cell growth. Altogether, these findings suggest that a TH-SNS interaction involving α2-AR signaling may occur in osteoblasts to locally regulate bone remodeling [In spite of the heterogeneous bone phenotype, low bone mass in the femur and high bone mass in the vertebra, α-/- mice . As expeve to TH ,46, werel growth ,51 were modeling .2C-AR-/- mice and their resistance to the T3-induced bone weakening, we evaluated the effect of T3 on gene expression of the RANKL-RANK-OPG system, which plays an essential role in the differentiation and activity of osteoclasts [2C-AR-/-mice, as compared with WT controls. This substantiates the lower and higher trabecular bone volume in the femur and vertebra of KO mice, respectively. These mRNA differences, however, were not observed in 120-day-old mice. We also observed that 30 days of T3 treatment increased RANK and RANKL mRNA expression and decreased OPG mRNA expression in the femur of WT mice, but not in the femur of KO mice. These effects, however, were no longer observed when T3 treatment was increased to 90 days. More interestingly, the RANKL/OPG ratio was significantly increased by both 30 and 90 days of T3 treatment in the femur of WT but not KO animals. The responses of the RANKL-RANK-OPG system to T3 treatment were quite different in the vertebra and were fairly similar between WT and KO mice. Altogether, these findings suggest that the RANKL-RANK-OPG system may partially mediate the effects of T3-treatment only in the femur and that this system may need an intact α2C-AR signaling to work properly. We also evaluated the mRNA expression of bone turnover-related genes, such as osteocalcin (OC), which is expressed by mature osteoblasts [2C-AR-/-mice. TRAP mRNA expression was also significantly increased by T3-treatment both in the femur and vertebra of WT animals, and this effect was cleared impaired by the lack of α2C-AR as well. The increases in OC and TRAP mRNA expression reflect an increase in bone turnover. It is well known that TH excess increases both bone formation and resorption, but the latter is favored leading to bone loss [2C-AR considerably impairs the thyrotoxicosis-induced activation of both osteoblasts and osteoclasts and, once more, support a TH-SNS interaction to control bone remodeling in a α2C-AR-dependent manner.To further investigate the basis of the complex bone phenotype of αeoclasts . RANKL ieoclasts . OPG is eoblasts , and tareoblasts . As expeeoblasts . This efone loss ,55. The 2C-AR subtype in young adult mice results in a heterogeneous bone phenotype, with decreased bone mass in the femur and increased bone mass in the vertebra. In spite of these differences, the global absence of α2C-AR subtype also results in resistance to the deleterious effects of thyrotoxicosis on bone structure and strength of both skeletal sites. Altogether, these results bring new evidence that (i) α2-AR signaling mediates sympathetic actions in the skeleton; that (ii) these actions might be dependent on the skeleton site; and that (iii) α2C-AR signaling, but not α2A-AR signaling, is relevant to the thyrotoxicosis-induced osteopenia. It is important to consider, however, that the KO mice of the present study lack global α2C-AR isoform since conception, which may promote indirect developmental defects or adaptations that could explain, at least partially, the bone phenotypes of these mice and the differential responses to thyrotoxicosis. Nevertheless, the findings of the present study show that the SNS actions in the skeleton are much more complex than initially proposed.In summary, we have shown that the global gene inactivation of αS1 Fig-1 day-1). Significance between groups was determined by two-way ANOVA followed by Tukey’s test. Values are expressed as means ± SEM (n = 10–12 per group). *P <0.05 vs. the respective saline-treated animals . ++P< 0.01 for differences between WT and KO mice, as indicated. T.Ar, tissue area; B.Ar, bone area; Ma.Ar, medullary area; Ec.Pm, endocortical perimeter.(A–D) Effect of 30 days of T3 treatment. (E-H) Effect of 90 days of T3 treatment. Animals were treated with saline or a supraphysiological dose of T3 (7 μg 100 g body wt(TIF)Click here for additional data file.


Partitioning involves estimating independent models of molecular evolution for different subsets of sites in a sequence alignment, and has been shown to improve phylogenetic inference. Current methods for estimating best-fit partitioning schemes, however, are only computationally feasible with datasets of fewer than 100 loci. This is a problem because datasets with thousands of loci are increasingly common in phylogenetics.We develop two novel methods for estimating best-fit partitioning schemes on large phylogenomic datasets: strict and relaxed hierarchical clustering. These methods use information from the underlying data to cluster together similar subsets of sites in an alignment, and build on clustering approaches that have been proposed elsewhere.We compare the performance of our methods to each other, and to existing methods for selecting partitioning schemes. We demonstrate that while strict hierarchical clustering has the best computational efficiency on very large datasets, relaxed hierarchical clustering provides scalable efficiency and returns dramatically better partitioning schemes as assessed by common criteria such as AICc and BIC scores.These two methods provide the best current approaches to inferring partitioning schemes for very large datasets. We provide free open-source implementations of the methods in the PartitionFinder software. We hope that the use of these methods will help to improve the inferences made from large phylogenomic datasets. Choosing an appropriate model of molecular evolution (model selection) is an important part of phylogenetics, and can affect the accuracy of phylogenetic trees, divergence dates, and model parameters -11. One Partitioning is one of many methods to account for variation in substitution processes among sites. Some approaches automatically assign sites to different substitution models e.g. ,16), and, and16])It is important to note that all of the commonly used methods to account for variation in substitution patterns among sites (including partitioning) assume that sequences evolved under a stationary, reversible, and homogeneous process. These assumptions are necessary to make the methods efficient enough to allow for searches of phylogenetic tree space, although they are far from guaranteed to hold for empirical datasets (e.g. ). It is a priori knowledge of the variation in patterns of molecular evolution among sites [The biggest challenge in partitioning is to select the most appropriate partitioning scheme for a given alignment, i.e. to divide the alignment into groups of sites that account for variation in patterns of molecular evolution, while avoiding over- or under-parameterisation ,4. To seng sites ,10. The ng sites . For smang sites . Howeverng sites -25. Currk-means clustering) have been used to select partitioning schemes in a number of previous studies with datasets of various sizes [O(N), while the greedy algorithm is O(N2). For example, with an alignment of 1000 protein-coding genes, the strict hierarchical clustering approach we describe below requires the analysis of only 1999 subsets of sites , which is more than 3 orders of magnitude more efficient than existing approaches.Hierarchical clustering is a statistical method that has some attractive properties for optimising partitioning schemes for phylogenomic datasets. To use hierarchical clustering to optimise partitioning schemes, molecular evolutionary parameters (such as base frequencies and rates of molecular evolution) are first estimated for each initial data block, and data blocks are then combined based on the similarity of their parameter estimates. Hierarchical clustering and related methods  a parameter to describe the overall substitution rate of that data block ; (ii) one or more parameters to describe the relative rates at which nucleotides replace each other  model, known as the rate matrix); (iii) parameters to describe the proportions of nucleotides or amino acids in the data block (base or amino acid frequencies); and (iv) one or two parameters to describe the distribution of substitution rates among sites . In principle, data block similarity can be defined using any combination of these parameters. However, different studies have used different parameter combinations, and there has been no attempt to systematically understand the best way to define the similarity of different data blocks when estimating partitioning schemes ,26-30.In this study, we set out to investigate the performance of hierarchical clustering approaches for optimising partitioning schemes for phylogenomic datasets. We first developed a generalised strict hierarchical clustering method that allows the user to define relative importance of different model parameters when defining the similarity of subsets. We found that the choice of weighting scheme can have very large effects on the performance of the algorithm, and that regardless of the weighting scheme strict hierarchical clustering always performed substantially worse than the existing greedy algorithm. To remedy this, we developed a new method, which we call relaxed hierarchical clustering, that incorporates many of the benefits of strict hierarchical clustering while avoiding many of its disadvantages. We show that relaxed hierarchical clustering outperforms strict hierarchical clustering on all of the datasets that we examined. The computational demands of this method can be scaled to the dataset and computational resources available. It is therefore a pragmatic approach to estimating best-fit partitioning schemes on phylogenomic datasets, where more rigorous methods are computationally infeasible.http://www.robertlanfear.com/partitionfinder. The PartitionFinder source code is available from https://github.com/brettc/partitionfinder/.We have implemented all of the methods described in this study in the open-source software PartitionFinder, which is available for download from st codon positions of a protein coding gene). A “subset” is a collection of one or more data blocks. Therefore, all data blocks are also subsets, but the converse is not true. A “partitioning scheme” is a collection of subsets that includes all data blocks once and only once. We do not use the term “partition” because it has conflicting meanings in phylogenetics and set theory – in phylogenetics a “partition” is used colloquially to denote what we call a “subset” here, whereas in set theory it defines what we call a “partitioning scheme” [Following previous studies ,4, we de scheme” .We developed a strict hierarchical clustering algorithm inspired by a popular previous implementation , with so1. Estimate a phylogenetic tree topology from the sequence alignment;2. Start with a partitioning scheme that has all user-defined data blocks assigned to independent subsets;3. Calculate the ML model parameters and log likelihood of each subset in the current partitioning scheme;4. Calculate the similarity of all pairs of subsets in the current partitioning scheme;5. Create a new partitioning scheme by combining the two most similar subsets in the current partitioning scheme;6. Return to step 3, until a partitioning scheme with all sites combined into a single subset is created (i.e. terminate after N iterations);7. Choose the best-fit partitioning scheme based on information theoretic metrics.In principle this algorithm could be applied to DNA or amino acid alignments, but for simplicity we focus only on DNA alignments in this study.https://github.com/brettc/standard-RAxML because RAxML is the most widely-used and computationally efficient software for analysing extremely large alignments. We substantially modified the PartitionFinder code (https://github.com/brettc/partitionfinder) to enable it to perform model selection and partitioning scheme selection by calling RAxML, and parsing the output produced by RAxML.All ML calculations in this algorithm are performed with a modified version of RAxML  availablIn step 1 of the strict hierarchical clustering algorithm, we estimate a maximum parsimony (MP) starting topology in RAxML which is then fixed for the rest of the analysis. Fixing the topology is crucial in increasing computational efficiency when searching for best-fit partitioning schemes . AlthougIn step 2 we calculate the log likelihood and parameters of a GTR + G model on each new subset of sites using RAxML. A new subset of sites is defined as a subset that the algorithm has not yet encountered. The log likelihood and ML parameters of each subset are then stored in memory so that they do not have to be recalculated in subsequent iterations of the algorithm. We use the GTR + G model rather than the GTR + I + G model because the ‘I’ parameter, which describes the proportion of invariant sites, is not independent from the ‘G’ parameter, which describes the gamma distribution of rates across sites, making it impossible to estimate both parameters accurately . This deIn step 3 we calculate the similarity of subsets based on their ML model parameters. To do this, we group parameters into four categories and use a distance metric that allows users to specify the relative importance of different parameter categories. The four parameter categories are: (i) the overall rate of evolution of the subset, calculated as the sum of the maximum likelihood branch lengths for that subset; (ii) the 6 parameters of the General Time Reversible (GTR) model; (iii) the four base frequencies; and (iv) the alpha parameter that describes the gamma distribution of rates across sites. The parameters from categories (ii) and (iii) are not independent of each other, but we include both because we do not have prior information on which parameters are more important, or which may be most useful for optimising partitioning schemes. To calculate the similarity of all pairs of subsets, we first calculate a pairwise Euclidean distance matrix for each of the four parameter categories. We then normalise each distance matrix so that the maximum distance is one, and then scale each matrix by a user-specified weight (set using the ‘--weights’ command line option in PartitionFinder v1.1.0). The similarity of a pair of subsets is then calculated as the sum of the distances across the four matrices, which gives the Manhattan or city block distance between a pair of subsets. In this approach, the user-specified weights have a natural interpretation as the relative importance of different parameters in defining subset similarity.This approach to calculating subset similarity has a number of advantages over previous methods. Many previous approaches have used fewer than the four categories we define to calculate subset similarity, and most have implicitly assumed that all parameter categories are equally important in determining subset similarity . In contIn each iteration of our algorithm, we find the most similar pair of subsets from the focal partitioning scheme (step 4), and then merge these subsets to create a new subset and a new partitioning scheme (step 5). In this manner, the algorithm iteratively merges subsets to create a set of N partitioning schemes from N initial data blocks. These N schemes contain from 1 to N subsets. The final step of the algorithm (step 7) simply involves comparing the information theoretic score  of all N partitioning schemes, and choosing the scheme with the best score. Choosing the best partitioning scheme does not involve any further ML calculations, because the log likelihood of each partitioning scheme can be calculated from the sum of the log likelihoods of the subsets contained in that scheme .The strict hierarchical clustering algorithm is computationally efficient, but it has some obvious drawbacks. First, it can merge subsets that make the information theoretic score of a partitioning scheme worse, rather than better. This is because there is no guarantee that any given measure of ‘similarity’ will translate into an improvement in the information theoretic score. Second, even if a given similarity measure does translate into robust improvements in the information theoretic score, the algorithm may be misled when the accuracy of ML parameter estimates is limited, as can be the case with small subsets .To overcome these limitations, we propose a relaxed hierarchical clustering algorithm. This algorithm has eight steps:1-4. Identical to strict hierarchical clustering5. Select the top P% of most similar subset pairs;6. Create S new partitioning schemes, each of which includes one of the subset pairs from step 5;7. Choose the partitioning scheme from step 6 with the best information-theoretic score ;8. Return to step 3, until no further improvements in the information theoretic score are found;Steps 1–4 proceed precisely as in the strict hierarchical clustering algorithm. In step 5 we create a ranked list of all possible subset pairs from the current partitioning scheme, where the rank is defined by the similarity of the subsets. We then use a user-defined percentage, P (‘--rcluster-percent’ in PartitionFinder), to choose the S most similar subsets pairs. In step 6 we create a new partitioning scheme for each of the S subset pairs, by merging the two subsets in each pair and calculating the new log-likelihood and maximum likelihood parameter estimates. In step 7, we calculate the information theoretic score  of each of the S new partitioning schemes, and select the partitioning scheme with the best score. The algorithm then iterates (step 8) until no further improvements in the information theoretic score can be found.The key difference between the relaxed and strict hierarchical clustering algorithms is the ability to set the parameter P, which controls the thoroughness of the heuristic search algorithm. When P is set to 0%, the relaxed clustering algorithm will behave similarly to the strict hierarchical clustering algorithm, and only evaluate the single partitioning scheme that includes the most similar pair of subsets . When P is set to 100%, the relaxed clustering algorithm will behave similarly to the existing greedy algorithm in PartitionFinder , and evaAs described above, we expect both of the new methods we describe here to perform worse than the greedy algorithm implemented in PartitionFinder, simply because they are less thorough heuristic searches. The true utility of the new methods is to find partitioning schemes for datasets that are too large to analyse with existing methods . Neverthhttp://dx.doi.org/10.6084/m9.figshare.938920). Removing taxa does not reduce the complexity of the task of selecting partitioning schemes, but simply reduces the computational burden of analysing each subset. Note that this is done to provide a suitable set of test datasets for comparing new and old methods, and we do not mean to imply that partitioning schemes estimated from reduced-taxon datasets should be used on the full-taxon dataset. All of the datasets, as well as the associated input files for PartitionFinder, are available from figShare (http://dx.doi.org/10.6084/m9.figshare.938920), and references for the datasets and the studies that they are associated with are provided in Table We used 10 publicly available datasets , although this is known to be an important source of variation in patterns of substitution ,11. In pFor every analysis, we recorded: (i) the best partitioning scheme and it’s information theoretic score (i.e. AICc or BIC score); (ii) the information theoretic score of each partitioning scheme visited by each algorithm during the heuristic search; and (iii) the time taken to complete the analysis on a desktop a Mac Pro with 2 2.26 GHz Quad-Core Intel Xeon processors and 32GB RAM. The details of the absolute computational times are not important, but a comparison of the analysis times is informative (see below) because it allows us to empirically compare the computational efficiency of the different methods.For the phylogenomic dataset from birds we first removed all sites in the alignment that were removed by the original authors , and theWe performed 2 searches for optimal partitioning schemes using the greedy algorithm [We performed 2000 searches for optimal partitioning schemes using the strict hierarchical clustering algorithm described above. The 2000 searches comprise 1000 searches using the BIC and 1000 using the AICc, where each search used one of 1000 distinct clustering weights (the ‘--weights’ commandline option in PartitionFinder). The clustering weights are defined by a vector of four numbers that specify the relative importance of four parameter categories . Analysing 1000 sets of weights allows us to empirically compare the performance of different weighting schemes, and to determine the relative importance of the different parameter categories when searching for partitioning schemes, as well as the variation in the algorithm’s performance under different weighting schemes. The first 15 sets of weights comprise all possible combinations of setting at least one weight to 1.0, and other weights to 0.0 . These represent 15 of the 16 corners of a four dimensional hypercube, and allow us to compare the 15 cases where either all parameter categories are given equal weight  or where one or more parameters are given zero weight . The other 985 points were chosen using Latin Hypercube Sampling in the ‘lhs’ package, version 0.1 in R . This prWe performed 10,000 searches for optimal partitioning schemes using the relaxed clustering algorithm described above. These 10,000 searches comprised 5000 searches using the AICc, and 5000 using the BIC, each of which was performed with 1000 different clustering weights, and at 5 different values of the parameter P. The 1000 weighting schemes we used were identical to those used above, and the values of the parameter P  that we used were 1%, 2%, 5%, 10%, and 20%.http://dx.doi.org/10.6084/m9.figshare.938920).The results of all 12002 analyses presented here are available at figShare (http://dx.doi.org/10.6084/m9.figshare.938920).Based on the results of our analyses of the phylogenomic bird dataset, we set some pragmatic default values for the clustering weights and the P parameter (see below). We then analysed the performance of the greedy algorithm, the strict hierarchical clustering algorithm, and the relaxed hierarchical clustering algorithm across all 10 datasets in Table All three algorithms we discuss in this paper start with a user-defined set of data blocks, and progressively merge data blocks to improve the information-theoretic score of the partitioning scheme. Better algorithms will lead to larger improvements in the information theoretic score. We discuss algorithm performance below in two ways: in terms of the amount (in AICc or BIC units) that they improve the score of the partitioning scheme relative to the starting scheme which has each data block assigned to an independent subset; and in terms of the percentage improvement that an algorithm achieves relative to the existing greedy algorithm in PartitionFinder. Thus, a good algorithm will score highly on both counts.The strict hierarchical clustering algorithm performed substantially worse than the greedy algorithm on the phylogenomic bird dataset Figure , Table 1The performance of the strict hierarchical clustering algorithm also varied substantially depending on the way in which subset similarity was defined. Across all 1000 different sets of clustering weights analysed, the worst-scoring partitioning schemes found by the strict hierarchical clustering algorithm improved the AICc and BIC scores by 42 and 862 units respectively , the strict hierarchical clustering algorithm is likely to perform very poorly. Although some methods of defining subset similarity performed better than others, our results suggest that there is no one method of defining subset similarity that works well for the duration of the algorithm. This is likely to be because the parameters of molecular evolution that we are able to measure  are not sufficient to determine whether clustering a given pair of subsets will result in an improvement of the AICc or BIC scores. As a result, the algorithm often clusters together subsets that result in a worsening of the AICc or BIC score. This problem is compounded by the fact that it is difficult to predict, either from first principles or empirical tests Figure , the besThe relaxed hierarchical clustering algorithm performed better than the strict hierarchical clustering algorithm, and its performance approached that of the existing greedy algorithm Figure . When 20The performance of the relaxed hierarchical clustering algorithm improved as the percentage of schemes examined was increased Figure . When 1%These results suggest that although our estimates of subset similarity are highly imperfect, they do contain information that can be used to help optimise partitioning schemes more efficiently. Unlike the strict hierarchical clustering algorithm, the relaxed hierarchical clustering algorithm does not rely solely on the estimated similarity of subsets in order to decide whether to cluster them together. Instead, it considers a collection of the most similar pairs of subsets and then chooses the pair that gives the largest improvement in the AICc or BIC score. This approach circumvents the limitation of the strict hierarchical clustering method by reducing the reliance of the algorithm on the estimates of subset similarity.To ensure that the results we obtained on the phylogenomic dataset of birds were not idiosyncratic to a single dataset, we compared the strict and relaxed clustering algorithms to each other and to the greedy algorithm on a collection of 10 datasets Table , and on The relaxed clustering algorithm found better partitioning schemes than the strict clustering algorithm on all 10 of the datasets we examined Figure , Table 2Both the relaxed clustering algorithm and the strict clustering algorithm took less computational time than the greedy algorithm, but the identity of the fastest algorithm depended on the size of the dataset Figure , Table 3These differences in the speed of the strict and relaxed clustering algorithms result from two effects: search space and stopping conditions. The relaxed clustering algorithm analyses many more partitioning schemes than the strict clustering algorithm, which tends to make it slower. However, the relaxed clustering algorithm stops when the information theoretic score stops improving, whereas the strict clustering algorithm always computes the likelihood of N partitioning schemes for a dataset with N data blocks. The interplay of these two effects determines which algorithm will be quicker on any given dataset. Although the fastest algorithm depends to some extent on the number of data blocks in the optimal partitioning scheme, a general rule of thumb is that the strict clustering algorithm will be quicker on very large datasets, but will produce poorer results.Partitioning is an important part of many phylogenetic analyses, and can dramatically improve the fit of models to data for almost all datasets. This is particularly true of very large datasets, which contain more genomic regions, and thus more variation in rates and patterns of molecular evolution than smaller datasets. As the analysis of very large datasets becomes more common, methods to infer partitioning schemes need to keep pace so that we can make the best possible inferences from the datasets we have.In this study, we compared three methods for estimating partitioning schemes: an existing greedy algorithm ; a stricPreference should always be given to using the greedy algorithm in PartitionFinder over the two algorithms developed here . The subWhen using the relaxed hierarchical clustering algorithm, the percentage of schemes analysed at each step of the algorithm (--rcluster-percent option in PartitionFinder) should be set as high as practically possible. Determining what is practical for a given dataset on a given computer may require some trial and error, but we suggest first running the analysis using the default setting of 10%. If this run finishes quickly, the percentage should be increased and the analysis re-run. If it runs too slowly, the analysis can be cancelled and re-started with a smaller percentage. Subsequent runs will be much faster than the initial run, because PartitionFinder saves and reloads the results of previous analyses. Determining whether a given percentage of schemes analysed will produce a partitioning scheme of a similar score to the greedy algorithm may be possible by examining the results of at least three runs of the relaxed clustering algorithm using different percentages . This is because as the percentage of schemes analysed is increased, the results of the relaxed clustering algorithm will asymptotically approach those of the greedy algorithm .The strict hierarchical clustering algorithm should be used only if an analysis using the relaxed hierarchical clustering algorithm is computationally infeasible. The strict hierarchical clustering algorithm is still likely to provide large improvements in the fit of the model to the data when compared to not attempting to optimise the partitioning scheme, but it may be sensible to try a number of different methods of defining subset similarity in order to ensure the best possible results . For example, one option would be to optimise partitioning schemes under all possible combinations of setting at least one weight to 1.0, and other weights to 0.0. The best-fit partitioning scheme could then be chosen from the set of 15 estimated partitioning schemes. For simplicity, this set of 15 weights can be found in the figShare repository that accompanies this paper (In the future, it would be interesting to explore more complex partitioned models of molecular evolution. For example, our study considers only partitioning schemes in which each subset of sites has an independent model of molecular evolution from all other subsets. This decision was results from the practical consideration that this is the only partitioned model available in RAxML, the primary software for analysing extremely large phylogenomic datasets. However, the most recent version of other maximum-likelihood phylogenetic software, PhyML , allows http://dx.doi.org/10.6084/m9.figshare.938920[The data sets supporting the results of this article are available in the figShare repository, re.938920. This rehttp://www.robertlanfear.com/partitionfinder.All of the methods we have developed and described here of the latest version of PartitionFinder are available from: The authors declare that they have no competing interests.RL and BC conceived the study. RL, BC, CM, DK, AS developed, coded, and tested the methods. RL collated and analysed the empirical datasets. RL wrote the manuscript. All authors read and approved the final manuscript.


After rehabilitation, we found a fair and significant correlation between 8-OHdG and FMA (r = −0.34) and 8-OHdG and pain . Baseline 8-OHdG was significantly correlated with post-treatment FMA, MRC, and pain scores , indicating its ability to predict functional outcomes. 8-OHdG levels were significantly decreased, and functional outcomes were improved after rehabilitation. The exploratory study findings conclude that 8-OHdG is a reliable and promising biomarker of oxidative stress and could be a valid predictor of functional outcomes in patients. Monitoring of behavioral indicators along with biomarkers may have crucial benefits in translational stroke research.We evaluated the reliability of 8-hydroxy-2′-deoxyguanosine (8-OHdG), and determined its ability to predict functional outcomes in stroke survivors. The rehabilitation effect on 8-OHdG and functional outcomes were also assessed. Sixty-one stroke patients received a 4-week rehabilitation. Urinary 8-OHdG levels were determined by liquid chromatography–tandem mass spectrometry. The test-retest reliability of 8-OHdG was good (interclass correlation coefficient = 0.76). Upper-limb motor function and muscle power determined by the Fugl-Meyer Assessment (FMA) and Medical Research Council (MRC) scales before rehabilitation showed significant negative correlation with 8-OHdG ( Stroke Stroke is a leading cause of death and permanent disability worldwide ,7,8. AltOn the other hand, stroke survivors generally exhibit lower physical capacity and higher oxidative stress than healthy individuals ,14. Some2.2.1.r = −0.14 to 0.23, p > 0.05). Similarly, the reported stroke types (ischemic and hemorrhagic) were not significantly correlated with functional outcomes in our study (p > 0.05).2.2.i.e., Baselines 1 and 2) was good , which indicates a good agreement between two baseline measures. The differences are symmetrically distributed within a 95% confidence interval limit, which further indicates no systematic bias between the measures . The negative correlation indicates that patients with higher 8-OHdG levels had lower upper-limb motor function and muscle power. No significant correlation with 8-OHdG was found for fatigue (r = 0.21) and pain (r = 0.13) before rehabilitation; however, positive correlation values for fatigue and pain suggest that patients who rated severe fatigue and pain had higher 8-OHdG levels , as well as between 8-OHdG and pain rating scales  during the rehabilitation intervention. We also observed that patients with severe pain had higher baseline 8-OHdG levels and that those with lower 8-OHdG levels before treatment reported reduced pain scores after treatment. Reasonable relationships between 8-OHdG and pain were observed in this study, but the role of DNA damage in pain of stroke survivors needs further investigation.The results obtained on upper-limb motor function and reduced pain after rehabilitation has confirmed the close association with decreased DNA damage. Regardless of the rehabilitation, our findings of decreased 8-OHdG levels and increased clinical outcomes are in agreement with a previous study . A notewTo establish a strong correlation between biomarkers of oxidative stress and functional outcomes, further evaluation of antioxidant status and other oxidative stress markers  in blood or urine samples is necessary. In addition, advanced research techniques, such as magnetic resonance imaging, could provide the additional direct evidence to explain the vascular remodeling, cerebral angiogenesis, and neural plasticity that are associated with the decreased oxidative stress biomarkers after rehabilitation intervention in stroke survivors. Lack of a control arm and other clinical scales, including the modified Rankin Scale and Barthel Index, are the additional limitations in our study, which are considerable outcomes to include in future rehabilitation studies.4.4.1.In this secondary analysis study, the effects of stroke rehabilitation programs were investigated in 61 stroke patients. All patients were carefully screened for the following inclusion criteria: (1) a unilateral ischemic or hemorrhagic stroke; (2) able to follow the study instructions and perform study tasks; (3) no upper-limb fractures within the last 3 months or painful arthritis or injuries of the joints; (4) no acute inflammatory or infectious disease; (5) no participation in any experimental rehabilitation or drug studies during the study period; (6) medically stable; and (7) willing to provide written informed consent. Institutional review board approval was obtained from the participating hospitals, and written consent was obtained from each patient before inclusion. Stroke type, smoking and drinking behavior, and intake of antioxidant supplements were recorded along with anthropometric variables. Participants were asked to keep the same intake of antioxidant supplements  during the study period.4.2.Patients received their rehabilitation programs, including robot-assisted therapy, combined rehabilitation therapy, and customary rehabilitation, for 20 training sessions during the 4-week study period. Each training session comprised 90 to 105 min/day, 5 days/week. The rehabilitation intervention was provided by licensed occupational therapists experienced in the administration of training protocols. Functional outcomes were administered to the patients before and after the rehabilitation intervention by a blinded rater who was well trained to appropriately conduct these functional assessments.4.3.i.e., baseline 1) and after rehabilitation . To determine the test-rest reliability of 8-OHdG, baseline sample collections (before rehabilitation) were done twice  with a 3-day interval from 45 of the 61 patients. The collected samples were properly labeled and stored at −20 °C until the 8-OHdG analysis.A urine sample (10 mL) was collected from the 61 patients before (4.4.15N5-8-OHdG solution (42.6 ng/mL) as the internal standard. The prepared sample (10 μL) was injected into the ultra-performance LC-MS/MS system, which was equipped with an Accela autosampler and micropump , an L-2100 pump  and two reverse-phase columns  and 2.1 × 100 mm, 3 μm; Atlantis T3 . Creatinine levels were used to correct for variations in urine concentration.Highly-sensitive liquid chromatography with tandem mass spectrometry (LC-MS/MS) was used to analyze the urinary 8-OHdG levels . Briefly4.5.4.5.1.The 33-item FMA was used to determine the upper-limb motor impairments of the stroke patients . The FMA4.5.2.Muscle power of affected arm was examined by the MRC scale, a reliable assessment in stroke patients, with a score range from 0 to 5 . Grade 04.5.3.Pain and fatigue, two common complications after stroke that affect the patient’s well-being were also measured ,33. Seve4.6.y-axis) against the mean values (x-axis) [The 8-OHdG data from the two baseline measurements were used to examine the test-retest reliability by the ICC. The ICC is considered high at >0.75, moderate at 0.4 to 0.74, and poor at <0.40 ,36. The (x-axis) . The 95%r) was used to determine the associations between 8-OHdG and functional outcomes. An r value between 0 and 0.25 indicates low, between 0.25 and 0.5 indicates fair, between 0.5 and 0.75 indicates moderate to good, and greater than 0.75 indicates good to excellent correlations [The Pearson correlation coefficient . Values are expressed as mean ± standard deviation, and the significance level was set at p < 0.05.The changes in 8-OHdG levels and functional outcomes after rehabilitation were examined with the paired 5.Our study has delineated for the first time the correlation between 8-OHdG and functional outcomes in stroke rehabilitation. The findings demonstrated that higher baseline 8-OHdG content represents poor functional outcomes, whereas lower 8-OHdG after rehabilitation resulted in improved functional outcomes. The results indicate that 8-OHdG can be used as a reliable and valuable biomarker to predict functional outcomes in stroke rehabilitation. Our study further demonstrates the promising prognostic values of 8-OHdG and its associations with functional outcomes in stroke survivors. In addition to rehabilitation therapy, future studies may consider examining the effects of complementing rehabilitation with the use of free radical-scavenging drugs in patients with stroke.


With the rapid growth of ageing population, rehabilitation for neurological disorders such as Alzheimer's, Parkinson's, and stroke is one of the grand challenges faced for the coming years. Knowledge and new technologies are needed for effective rehabilitation to release the increasing demands for long-term medical treatments and healthcare, as well as help the patients regain or maintain independency in their daily lives. Successful rehabilitation depends on the understanding of the pathological mechanisms, effective methods in the treatment, and accurate evaluation of the recovery progress. Advances in neural computation provide solutions to brain modeling, quantitative neural information processing, and neural imaging. New findings in these areas also inspire novel techniques for diagnosis, rehabilitation treatments, and development of novel training devices. Considering the aforementioned trends, neural computation in the area of rehabilitation is a natural choice for the theme of this special issue. In this issue you will find thirteen high-quality, peer-reviewed articles that will provide researchers in diverse backgrounds such as engineering, neuroscience, rehabilitation, and computational sciences with the current state-of-the-art knowledge of this emerging interdisciplinary research area.Neural coding for effective rehabilitation” by the special issue editors X. Hu et al. covers a wide range of the latest breakthroughs in neural coding, neural network imaging, and neural informatics techniques with potential applications for more effective rehabilitation, as well as the advancements using electroencephalographic (EEG) and electrocorticographic (ECoG) signals in human patients for clinical applications and in intelligent robotic systems designed for interactive rehabilitation.The review paper “DWI-based neural fingerprinting technology: a preliminary study on stroke analysis” by C. Ye et al. proposes a new methodology to identify subtypes of ischemic stroke using diffusion weighted imaging to facilitate the efficient clinical diagnosis. P. Liu et al. introduce a cost-effective ultrasonic method for poststroke muscular evaluation and monitoring the outcomes of rehabilitation training in their paper entitled “Change of muscle architecture following body weight support treadmill training for persons after subacute stroke: evidence from ultrasonography.”Rehabilitation usually is a long-term process. Its clinical success heavily depends on the accurate diagnosis and follow-up evaluations. Furthermore, for effective rehabilitation a better understanding of the relationships between physical exercise therapies and the motor outcome is essential. This special issue presents two imaging studies for enhancing diagnosis of stroke subtypes and evaluation of rehabilitation outcomes. The paper “Gradually increased training intensity benefits rehabilitation outcome after stroke by BDNF upregulation and stress suppression” by J. Sun et al. illustrates the effects of rehabilitation, namely, fixed training regiments versus increased training intensity on the cerebral neuroplasticity. Studying the effects of rehabilitation on the process of neuroplasticity coupled with motor outcome is extremely important for the design of therapies that are likely to expedite recovery. To this end, brain-machine interfaces (BMIs) have been a promising tool, which enables users to learn to modulate their neural activity through real-time feedback. In this issue, we highlight several articles that focus on the design of BMIs that could have significant applications for rehabilitation. The paper “Circuit models and experimental noise measurements of micropipette amplifiers for extracellular neural recordings from live animals” by C. H. Chen et al. presents a study on noise modeling for novel hardware designs utilizing micropipettes for extracellular recordings for BMI applications. “Neural decoding using a parallel sequential Monte Carlo method on point processes with ensemble effect” by K. Xu et al. introduces a new sequential Monte Carlo estimation on point processes that can accurately predict movement from neural activity in real time on GPU, providing up to 10 times faster decoding speeds compared to serial implementations. S. Ryun et al. show that it is possible to predict and delineate hand grasping from elbow flexion using ECoG signals prior to movement execution in humans. The novelty of this work is that signals utilized for the prediction were delimited to the prefrontal cortex with no input from sensorimotor areas. The next two papers focused on noninvasive recording methodologies. In “A study on decoding models for the reconstruction of hand trajectories from the human magnetoencephalography,” H. G. Yeom et al. present a Kalman filter approach to continuously reconstruct hand trajectories using MEG. The paper “Nonlinear EEG decoding based on a particle filter model” by J. Zhang et al. describes a novel nonlinear particle filter model that achieves decoding accuracies comparable to those in the literature and requires smaller training datasets. Y. Qi et al. present a BMI study for clinical applications in epilepsy in the paper “Robust deep network with maximum correntropy criterion for seizure detection.”The paper “Optogenetic activation of the excitatory neurons expressing CaMKIIα in the ventral tegmental area upregulates the locomotor activity of free behaving rats” S. Guo et al. successfully and selectively upregulated the locomotor activity of free behaving rats. The authors will further study if they can have similar results in stroke models. The paper “A blood pressure monitoring method for stroke management” by H. T. Ma introduces cuffless blood pressure monitors, which can be used for monitoring stroke survivors, since blood pressure is an important risk factor for stroke prognosis. H. T. Ma et al. also present a “Muscle-based pharmacokinetic modeling of marrow perfusion for osteoporotic bone in females,” which is a novel muscle-based pharmacokinetic modeling approach to monitor marrow perfusion.There are three papers that provide novel methodologies demonstrating promising potential applications for stroke therapy and monitoring. In “We hope that this special issue will help to promote the further development of neural computation methodologies for rehabilitation. Improved understanding between neural plasticity and rehabilitation, as well as improved methodologies for the diagnosis of disorders and the evaluation of progress in behavior and physiology, may lead to better performance outcomes in neurorehabilitation. Such methods can also reduce the cost, duration, and overall impact of neurological disease. In addition to reducing suffering and improving quality of life, neurorehabilitation when combined with novel neural computation methods has the potential to advance our knowledge about the mechanisms of the nervous system.


FKBP52 and β-catenin have emerged in recent years as attractive targets for prostate cancer treatment. β-catenin interacts directly with the androgen receptor (AR) and has been characterized as a co-activator of AR-mediated transcription. FKBP52 is a positive regulator of AR in cellular and whole animal models and is required for the development of androgen-dependent tissues. We previously characterized an AR inhibitor termed MJC13 that putatively targets the AR BF3 surface to specifically inhibit FKBP52-regulated AR signaling. Predictive modeling suggests that β-catenin interacts with the AR hormone binding domain on a surface that overlaps with BF3. Here we demonstrate that FKBP52 and β-catenin interact directly in vitro and act in concert to promote a synergistic up-regulation of both hormone-independent and -dependent AR signaling. Our data demonstrate that FKBP52 promotes β-catenin interaction with AR and is required for β-catenin co-activation of AR activity in prostate cancer cells. MJC13 effectively blocks β-catenin interaction with the AR LBD and the synergistic up-regulation of AR by FKBP52 and β-catenin. Our data suggest that co-regulation of AR by FKBP52 and β-catenin does not require FKBP52 PPIase catalytic activity, nor FKBP52 binding to Hsp90. However, the FKBP52 proline-rich loop that overhangs the PPIase pocket is critical for synergy. Androgen receptor (AR)-mediated gene expression contributes to the progression of prostate cancer (PCa), and, for patients with advanced PCa, standard treatments block AR signaling through androgen deprivation or by use of classic AR antagonists. While patients initially respond to treatment, tumors often recur and develop into castration-resistant prostate cancer (CRPC) . As a refkbp52-deficient mice display characteristics of partial androgen insensitivity syndrome including dysgenic prostate [in vitro and in vivo, FKBP52 has emerged as an attractive target for the treatment of prostate cancer.AR folding and hormone-dependent activation is dependent upon the dynamic, ordered assembly of heteromeric complexes involving multiple chaperone and cochaperone proteins (reviewed in ), many oprostate , 11. GivWhile the FKBP52 peptidyl-prolyl cis/trans isomerase (PPIase) catalytic activity is not required for FKBP52 potentiation of AR activity, the PPIase domain (FK1) is essential. Previous studies demonstrated that the proline-rich loop that overhangs the PPIase catalytic pocket within the FK1 domain is critical for function and likely represents an interaction surface that, at least, transiently contacts the receptor hormone binding domain within the AR-chaperone complex , 12. In In addition to FKBP52, β-catenin, which is a well-documented regulator of AR-mediated transcription –20, has Previous studies demonstrated that β-catenin interacts directly with AR to potentiate receptor activity. While the AR ligand binding domain (LBD) and N-terminus were shown to be important for this functional interaction in yeast , mammaliin vitro in the absence of other proteins  cell line provides a true FKBP52-negative background in which to assess the ability of β-catenin to potentiate AR activity. Thus, we assessed the ability of β-catenin or the degradation-resistant β-catenin (S33A) mutant to potentiate hormone-dependent and hormone-independent AR-mediated luciferase reporter gene expression in the presence or absence of FKBP52 , which express both full length AR and a constitutively active, truncated AR protein that supports AR-dependent growth in the absence of full length AR activity. The fact that we were able to achieve knockdown in this cell line suggests that FKBP52 does not regulate activity from the truncated AR protein, which is expected given that FKBP52 is known to act through the receptor LBD [top panel), but did significantly reduce hormone-dependent expression of an AR-mediated luciferase reporter gene  in FKBP51, which does not potentiate AR activity alone, conferred the ability to fully potentiate receptor activity similar to that of FKBP52 . Given tOur data suggest that FKBP52 promotes β-catenin interaction with AR, and that the β-catenin interaction site overlaps with the AR BF3 surface. Thus, we aimed to determine if the FKBP52-specific AR inhibitor MJC13, which is thought to target the AR BF3 surface [While previous studies suggested that β-catenin binds the AR AF2 , a co-crOur data demonstrate that FKBP52 not only binds directly to β-catenin, but also promotes β-catenin interaction with the AR LBD . Since AOur data suggest that we have discovered a novel mechanism by which FKBP52 and β-catenin regulate the androgen receptor independent of the well-characterized role of FKBP52 within the Hsp90 complex. This is supported by the fact that the receptor specificity for FKBP52 potentiation of receptor function alone does not mirror that for FKBP52 co-regulation of receptor activity with β-catenin. While FKBP52 is known to functionally interact with AR, GR and PR –8, β-catin vitro in the absence of other proteins. However, evidence suggests that FKBP52 at least contacts the receptor within the context of the Hsp90 multi-chaperone complex. Previous studies in yeast demonstrated that FKBP52 binding to Hsp90 is required for regulation of AR activity [The regulation of AR activity by the FKBP52 cochaperone alone has been extensively studied and much has been learned about the FKBP52 domain and residue requirements for function (reviewed in ). FKBP52activity . In contactivity  and FKBPWe previously demonstrated that mutations within the AR BF3 surface result in increased dependence on FKBP52 for function, and MJC13, which specifically inhibits FKBP52-regulated AR activity, is predicted to target the AR BF3 surface . In addiOur data also suggest a role for FKBP52 and β-catenin in promoting hormone-independent AR activity given that FKBP52 synergized with β-catenin to promote AR-mediated expression of a reporter gene in the 52KO MEF cells in the absence of hormone addition Figs , althougRegardless of the manner in which FKBP52 and β-catenin interact to regulate AR activity, our data demonstrate a clear synergistic relationship that is abrogated by treatment with MJC13 . MJC13 iMammalian expression vectors for human FKBP52, β-catenin wild type, β-catenin constitutive active mutant (S33Y), Gal4-AR LBD, Gal4-tk-luc, and TOP-luc were described previously , 25. Thefkbp52-deficient mouse embryonic fibroblasts (52KO MEF) were previously described [2 in the presence of 10% fetal bovine serum (FBS) unless otherwise indicated. 52KO MEF cells were maintained in Minimal Essential Medium (MEM)/Earl’s Balanced Salt Solution (EBSS) with 2 mM L-glutamine (Thermo Scientific). LNCaP cells were maintained in RPMI 1640 supplemented with L-glutamine. 22Rv1 cells were maintained in RPMI-1640 medium with 25mM HEPES and L-Glutamine (Thermo Scientific), and supplemented with the following reagents, when not provided in medium: 1% Pyruvate , 1% MEM-non essential amino acids , and 1% Pen-Strep . HeLa cells and 293T cells were maintained in Dulbecco's modified Eagle's medium (DMEM). 24 hours prior to transfection all cells were switched to medium containing Charcoal/Dextran Treated Fetal Bovine Serum (FBS) (HyClone). For transfection of 52KO MEF, 22Rv1 and 52KD 22Rv1 cells, cells were plated in 6-well plates and allowed to reach 80% confluence prior to transfection. Cells were transfected with Lipofectamine 2000 (Invitrogen Life Technologies) at a DNA to lipofectamine ratio of 1:3 in MEM-EBSS lacking FBS according to the manufacturer’s instructions. Typically, transfections included a constitutive β-galactosidase reporter plasmid  as a transfection control, a firefly luciferase reporter plasmid  driven by the androgen-dependent probasin promoter, a human AR expression plasmid, and plasmids expressing the indicated FKBP and β-catenin variants, or empty parent vector control. For transient transfection of HeLa, LNCaP, and 293T cells, cells were plated in 24-well plates and transfected with expression plasmids, reporter plasmid, and the control β-galactosidase expression plasmid (pCMV-β) using Fugene HD Transfection reagent (Roche) according to manufacturer’s instructions. Total amounts of expression vector plasmids were kept constant by the addition of appropriate amounts of empty pCMV vector. The siRNA for FKBP52 (ID no. s48) was purchased from Ambion. HeLa cells and LNCaP cells were transfected with siRNA (100 nM) using Lipofectamine RNAiMAX reagent (Invitrogen Life Technologies) according to manufacturer’s instructions.22Rv1 prostate cancer cells, 293T fibroblasts, and HeLa cells were obtained commercially (American Type Culture Collection). LNCaP cells were a kind gift from Don Tindall  and were originally obtained from the American Type Culture Collection. The escribed . All celTo generate stable FKBP52 knockdown in 22Rv1 prostate cancer cells, an shRNA specific for FKBP52 and scrambled shRNA control  were subFor all reporter assays systems, the single hormone concentrations used were determined from hormone dose-response curves. For assessing FKBP52 and/or β-catenin regulation we chose a dose near the bottom of the curve to maximize the observed AR potentiation. Twenty-four hours after transfection, medium was replaced with medium containing the indicated concentrations of dihydrotestosterone (DHT). After approximately 16 hours of incubation with hormone, cells in each well were lysed using 100μL mammalian protein extraction reagent (M-PER) (Pierce) supplemented with Complete ethylenediaminetetraacetic acid (EDTA)-free Mini Protease Inhibitor (Roche) and clarified in a microcentrifuge. Approximately 100 μl of lysate from each experimental condition was used for the luciferase assay and the remainder was used for Western Immunoblots as described below. AR-mediated luciferase expression was quantified by mixing 40 μL of cell lysate with 100 μL of luciferase assay reagent (Promega) in a 96-well plate. β-galactosidase expression was quantified by adding 20 μL cell lysate with 100 μL of Tropix Gal-Screen (Applied Biosystems). The 96-well plates were incubated at room temperature , followed by quantification of luminescence in a microplate luminometer . Luminescence was measured in Relative Light Units (RLU) and normalized to β-galactosidase activity  to control for transfection efficiency. Finally all data were normalized to a percentage of the maximum luciferase activity. All figures are representative of at least three independent experiments with similar results. Where indicated, statistically significant differences were determined by one-way analysis of variance followed by pair-wise comparisons using Bonferroni multiple comparisons tests with a 95% confidence interval. For assays with the Gal4-tk-Luc reporter in HeLa cells, cells maintained in 10% charcoal-stripped serum were treated with or without 10 nM dihydrotestosterone (DHT) and 30μM MJC13 for 24 h following transfection. Luciferase and β-galactosidase activities were assayed as previously described . The levμM MJC13 for 24 h following transfection. Luciferase and β-galactosidase activities were assayed as previously described [Mammalian two-hybrid assay was performed with 293T cells. Plasmids for Gal4-DBD fused to the AR LBD and plasmids for VP16-AD fused to the full-length β-catenin with Gal4-tk-luc reporter were cotransfected into 293T cells. Cotransfected cells maintained in 10% charcoal-stripped serum were treated with and without 10 nM DHT and 30escribed . The levμM MJC13 following transfection, and harvested in RIPA cell lysis buffer ). Whole-cell lysate was incubated with 2 μg of anti-β-catenin or anti-Gal4 antibody (Santa Cruz Biotechnology) for 4 hours at 4ºC, and was further incubated for another 12 hours after the addition of 30 μl of protein A/G agarose bead slurry (Santa Cruz Biotechnology). Agarose beads were washed three times with RIPA buffer at 4ºC, and bound proteins were separated by SDS-PAGE. Proteins on the gels were transferred to a PVDF membrane, subjected to Western blot analysis with anti-AR (Santa Cruz Biotechnology), anti-FKBP52 , anti-β-catenin (Santa Cruz Biotechnology), and anti-GAPDH  antibodies, and then detected with an ECL kit (Amersham Pharmacia).Co-immunoprecipitations were performed from extracts of LNCaP cells that were transfected with FKBP52 siRNA or 293T cells that were co-transfected with Gal4-AR LBD and β-catenin expression plasmids. Transfected or co-transfected cells maintained in 10% Charcoal-stripped serum were treated with and without 10 nM DHT and 30μL of Glutathione-Sepharose 4B  suspended resin per assay condition was added to microcentrifuge tubes, centrifuged at 4°C, and the supernatant was removed. Resin beds were washed in ice-cold Binding Buffer  supplemented with EDTA free, Halt protease inhibitor cocktail  and resuspended in 400 μL of Binding Buffer. Recombinant GST-Tagged β-catenin  or buffer control was preloaded onto the resin by gently mixing by inversion for 1.5 hours at 4°C. All tubes were centrifuged at 4°C and the supernatant was removed. The resin beds were then washed three times with ice-cold Wash Buffer  supplemented with EDTA free, Halt protease inhibitor cocktail (Pierce). Samples were resuspended in 400 μL Binding Buffer and an equimolar amount of recombinant C-terminal 6xhistidine-tagged FKBP52 protein or buffer control was added where indicated. The purification of functional 6xhistidine-tagged FKBP52 was previously described [μL of beta-mercaptoethanol/4 x sodiumdodecylsulfate solution was added to each sample, including input samples of FKBP52 and β-catenin protein, and all samples were heat denatured at 95°C for 5 minutes. Proteins were separated by electrophoresis, transferred to Immobilon polyvinylidene fluoride (PVDF) membranes (Millipore), and Western immunoblots were performed as detailed below.For the GST pull-down assays 20 escribed . All samWestern immunoblots were performed using the prepared lysates from assays as described above. Total cellular protein concentrations were determined by Coomassie Plus (Bradford) Protein Assay . Equivalent amounts of protein were loaded for each sample on 10–20% Criterion gels (BioRad), proteins were separated by electrophoresis, and proteins were transferred to Immobilon PVDF membranes (Millipore) following standard procedures. The following primary antibodies were used to detect proteins of interest: Rabbit polyclonal anti-β-catenin (Millipore), mouse monoclonal anti-FKBP52  , rabbit


Severe traumatic brain injury (TBI) is a great economical and logistic problem in the health care system which reduces the quality of life and productivity of the patient. The purpose of this study is to evaluate the outcome of patients after severe brain trauma according to the course of their rehabilitation.n = 16), B; following a standard rehabilitation procedure after work accidents (n = 34) and C; undergone standard rehabilitation procedure after accidents at home (n = 12). Glasgow Coma Scale (GCS), Post traumatic amnesia (PTA) during acute care, Glasgow Outcome Scale Extended (GOSE) and Functional Independence Measurement (FIM) were measured before and after rehabilitation. Long-term outcomes (12 months post injury) were measured with the Community Integration Questionnaire (CIQ).Patients with TBI were divided into three groups. Group A; after early rehabilitation (p < 0.001). PTA was significantly lower in group B than in group A (p = 0.038). GOSE of patients within group C was significantly lower (p = 0.004) at hospital discharge. FIM was significantly higher in B (p = 0.005) at the time of admission to rehabilitation center. At the time of discharge FIM showed no significant differences between the groups. CIQ showed a trend to improving scores in group A.Group A showed a significantly shorter time span from hospital admission until rehabilitation center admission than B and C (Despite the similar level of severity of TBI and outcome prognosis group A showed the best rehabilitation effect and long-term outcome. Patients who suffer traumatic brain injury (TBI) need a demanding and expensive care over a long period of time, which includes intensive care management and rehabilitative treatment. In Austria the incidence of traumatic brain injury is estimated in 312/100 000 inhabitants per year. TBI is not only the main cause of death in a population <45 years, [Enhancements in emergency medical aid and intensive care could improve survival rates and its outcomes. However, the increasing survival rate also implies a rise in the number of patients submitted to rehabilitation, which the health care system has to deal with. Besides traffic accidents, which are the main cause of TBI in Austria, falls below 3 meters are the second most common cause . Low-levWith the need of a prolonged intensive care treatment, economic costs rise and strain the health care system. This called attention within the public health sector for TBI and its treatments in the last 15 years. However there are no international guidelines for early rehabilitative treatment available yet.Early rehabilitation is a process which requires various specialists. A team of the intensive medical care unit, neurologists, physiotherapists, logopedics, occupational therapists, psychologists, social workers and nurses work together to achieve the best therapy for each individual patient . The cooAndelic et al.  and SörbIn Austria workers compensation, the Austrian Workers’ Compensation Board (AUVA), is split from general health insurance and keeps its’ own trauma hospitals and rehabilitation centers. One of these facilities is the trauma hospital Meidling, which has an exceptional position since it is directly next to a rehabilitation center of the insurance company. Because of the close collaboration early rehabilitation and strictly organized transfer is possible with less administrative barriers than in other hospitals and rehabilitation centers. Other patients undergo different administrative processes and therefore suffer from different delays depending on the hospital they are primary admitted and if their accident is listed as workplace accidents or outside work accidents.The goal of this study was to demonstrate that patients who are admitted to early rehabilitation as soon as possible at the intensive care unit and complete following rehabilitation programs without any delay reach better short and long-term outcomes as well as a reduced length of stay in the facilities.Between 2006 and 2008 this study was conducted by the cooperation of the trauma hospital Meidling  and the rehabilitation center Meidling (Rehabilitationszentrum Meidling) in Vienna, Austria and composed of a prospective early rehabilitation group and two retrospective control groups with a total number of 62 patients.The early rehabilitation group, group A, included 16 patients with severe TBI who were admitted directly to the trauma hospital Meidling or were transferred from other trauma centers after consultation. They fulfilled exclusion and inclusion criteria.n = 12). The controls, groups B and C were retrospectively recruited from the rehabilitation center Meidling.According to the Austrian health care system the control groups were defined as follows. Group B contained 34 patients with TBI after accident at work, treated in other hospitals and fulfilling the criteria. Members of group C suffered a TBI from other causes and were treated in other hospitals (Included were patients 1) with severe traumatic brain injury and a Glasgow Coma Scale (GCS) score ≤8 before intubation and/ or pathological CT findings, 2) with no more than two concomitant injuries, that have no impact on the progress and for the 2 next higher organ systems equivalent an Injury Severity Score (ISS) ≤13 independent of the brain injury’s Abbreviated Injury Scale , 3) who are haemodynamically stable (even though under catecholamines), 4) whose intracranial pressure (ICP) and cranial perfusion pressure (CPP) are stable , 5) who are between 16 and 70 years old, 6) with possible assessment of findings for prognosis, 7) whose prolonged hospital/rehabilitation center stay was assessable because of the severe injury.Exclusion criteria were 1) failure to perform inclusion criteria, 2) more than two concomitant injuries with impact on the progress of the patient, that are independent for the brain injury’s AIS with an ISS ≥13, 3) contact for transfer >7 days after trauma, 4) transfer within 14 days after trauma not possible, 5) not curative treated malignant tumor, 6) complete paraplegia, 7) stroke with defaults and/or severe dementia and/or severe degenerative CNS disease, 8) younger than 16 or older than 70 [, facio-oral therapy, and speech therapy if possible [Patients of group A were admitted either immediately after trauma and surgery or after consultation from other hospitals to the intensive care unit (ICU) as soon as possible, if a free bed was available. In the ICU after the acute phase of trauma an interdisciplinary team, consisting of neurologists, intensive care specialists, traumatologists, physiotherapeutics and nurses started with a rehabilitation oriented intensive care treatment comprising basal stimulation including occupational therapy , physiotherapypossible . Initialpossible  and the possible . The trepossible . All thrThe following period of times of stay in hospital and rehabilitation center were documented: duration until admission to the rehabilitation center (days to rehab. center), duration of the whole period of rehabilitation (days of rehab.) as well as their stay in the special rehabilitation unit of the rehabilitation center (urgent tract).The following scores were evaluated: GCS was evaluated prehospitally or in the emergency room. The Glasgow Outcome Scale Extended (GOSE) was recorded before the patient was transferred to the rehabilitation center (group A) or discharged from the ICU, if there was a waiting period for a therapeutic bed in the rehabilitation center , to characterize hospital outcome. The Functional Independence Measurement (FIM) score was used at admission (AFIM) and discharge (DFIM) from rehabilitation center. Also the duration of Post traumatic amnesia (PTA) was recorded. The Community Integration Questionnaire (CIQ) was sent to the patients 12 months after trauma.Scores of GOSE, CIQ and FIM were categorized in unfavorable (reached points <50 % of possible points), moderate (reached points between 50 % and 75 % of possible points) and favorable (reached >75 % of possible points).The GCS is an acute trauma score, which is used to evaluate the state of awareness at the time of the accident and to predict possible neurological physical injuries.PTA represents the severity of the brain injury. In doing so a duration less than 24 hours is comparable to a GCS of 12–15 and correspond to a mild TBI, a duration of 1 to 7 days is comparable with a GCS of 9–11 and is called a moderate TBI and 1–4 weeks equivalent a GCS of 3–8 and speak for a severe TBI. A duration of over 4 weeks implies a very severe TBI. Additionally PTA is the best acute and long-term predictor of the extent of deficits after TBI. It also serves as an indicator for the length of stay and as a predictor for the treatment cost –19.The FIM is a scoring system to measure the severity of limitations, caused by a TBI or other diseases and accidents. It can also be used to review the progress of the patient’s rehabilitation .The CIQ evaluates the reintegration of the patient in the daily life and his capability to cope with work, household, shopping and social life. The score consists out of the three sub-items: home integration, social integration and integration into productive activities. It was especially developed for people who have suffered a TBI .Because of the retrospective- partly prospective epidemiological study design and no additional intervention in the prospective group than the hospital´s normal procedures there was no ethics committee involved. Consent and permission wasn’t obtained since the study was performed in an acute setting with patients in a state of highly decreased consciousness.The statistical analysis was performed using SPSS version 22. Comparability of groups and outcome variables were analyzed by one-way ANOVA followed by post-hoc analysis (Mann-Whitney U-Test). A p-value of less than 0.05 was established as the level of significance. Descriptive data is presented with mean and SD.p = 0.038, Table There were no significant differences in age and GCS between the groups. Post traumatic amnesia (PTA) showed to be significantly shorter in patients of group B than in A (p = 0.031) and C  and B (p = 0.002).GOSE showed significant differences between the groups Table . Group Cp = 0.016) and C (p = 0.005). The DFIM score exposed no significant differences between groups .Out of 62 CIQ questionnaires 50 % returned, 29 could be analyzed. Of group A 8 patients replied. To establish a more meaningful comparison group the control groups were united , group A showed a favorable outcome on the same level as control group B, though A included more severe injuries, which has been indicated by PTA, and had prolonged stays at the urgent rehabilitation tract. Kunik et al.  demonstrAnother finding of our study was that group A showed a better long-term outcome measured by CIQ than the control group BC Table  ThereforOther studies verified already positive effects of early rehabilitation. Barnes  illustraHorn et al.  investigFurther studies suggest that also patients in vegetative state or older patients may benefit from early rehabilitation treatment and may also reach better outcomes , 25.One of the main limitations of our study was the low response of CIQ questionnaires. On the one hand it was not possible to suggest all patient families or nursing homes to cooperate with our investigation on the other a great number of subjects moved and were lost to our follow up. As a result only 29 of 62 questionnaires could be analyzed reducing the statistical value of the parameter and making only descriptive statistics possible.Another limitation was the major difference in trauma severity between the groups. Therefore results must be interpreted as only preliminary.After collecting all the data concerning patients who have suffered a traumatic brain injury, we conclude that the strategy of early rehabilitation may result in better short and long term outcomes. Further controlled studies are necessary to prove this approach in other specialized rehabilitation centers.