We consider the task of determining the number of chances a soccer team creates, along with the composite nature of each chance—the players involved and the locations on the pitch of the assist and the chance. We infer this information using data consisting solely of attacking events, which the authors believe to be the first approach of its kind. We propose an interpretable Bayesian inference approach and implement a Poisson model to capture chance occurrences, from which we infer team abilities. We then use a Gaussian mixture model to capture the areas on the pitch a player makes an assist/takes a chance. This approach allows the visualization of differences between players in the way they approach attacking play (making assists/taking chances). We apply the resulting scheme to the 2016/2017 English Premier League, capturing team abilities to create chances, before highlighting key areas where players have most impact. Within this article, we look to explain an English Premier League team's style of attacking play from data that consist of attacking events only: the details of a chance and the corresponding assist. We determine the number of chances a team creates, along with identifying the players involved and from where on the pitch the chance took place.2 It consists of 20 teams, who, over the course of a season, play every other team twice (both home and away), giving a total of 380 fixtures. It is the top division of English soccer, and every year the bottom three teams are relegated to be replaced by three teams from the next division down (the Championship). In recent times, the Premier League has also become known as the richest league in the world,3 through both foreign investment and a lucrative deal for television rights.5The English Premier League is an annual soccer league established in 1992 and is the most watched soccer league in the world.7To compete in the English Premier League, teams employ different styles of play, often determined by the manager's personal preferences and the players who make up the team. Examples of attacking styles of play include counter attacking  or passing build-up (many short passes to find a weakness in the opposition's defense). For further discussion of styles of play, we direct the reader to References.8 to determine a team's style of play, with the aim of showing that a team strives to “win home games and draw away ones.” Occupancy maps are also used by Bialkowski et al.,9 who take spatiotemporal player tracking data and develop a method to automatically detect formation and player roles.Methods to model a soccer team's style of play/behavior have been explored previously by a number of authors. Occupancy maps defined using a given metric  have been used by Lucey et al.10 to create spatiotemporal maps of a team's playing style just before they take a shot. Gaussian processes are utilized by Bojinov and Bornn11 to form a spatial map to capture each team's defensive strengths and weaknesses. Pass locations are used by Brooks et al.12 to determine teams based on their passing styles, before whether pass locations can be used to predict the importance of shots is investigated. All of these methods are applied to dense and expensive data (both in price and in collection expenditure), which consists of all events in a soccer match, with some further utilizing GPS data for the location of the players. Such data are not available to us here.Player and ball tracking data are used by Lucey et al.13–15 to draw conclusions about a team/player's use of possession. How the players on a team interact is discussed in Grund16 and Kim et al.17 estimate the global movements of all players to predict short-term evolutions of play. Outside of soccer, Miller et al.18 investigate shot selection among basketball players in the NBA, combining matrix factorization techniques with an intensity surface, modeled using a log-Gaussian Cox process. Defensive play in basketball is captured by Franks et al.,19 who take player tracking data and apply spatiotemporal processes, matrix factorization technique,s and hierarchical regression models. Finally, spatiotemporal data are used by Wei et al.20 to predict shot locations in tennis.Methods from the network analysis toolbox are employed by References21–23More generally, the statistical modeling of sports has become a topic of increasing interest in recent times, as more data are collected on the sports we love, coupled with a heightened interest in the outcome of these sports due to the continuous rise of online betting. Soccer is providing an area of rich research, with the ability to capture the goals scored in a match being of particular interest.24 which aims to represent a player's worth in a single number, whereas McHale and Szczepański25 identify the goal scoring ability of players. Which game-related statistics or performance indicators characterize success (or failure) are determined within References.27A player performance rating system (the EA Sports Player Performance Index) was developed by McHale et al.,28 where more successful teams had shorter possessions. Players are rated for a number of abilities, before using them to aid the prediction of goals scored in Whitaker et al.29 Finally Kharrat et al.30 develop a plus–minus rating system for soccer.The length of possession as a predictor of win, lose, or draw was investigated by Jones et al.,In this article, we propose a method to capture the number of chances a team creates during a given section of a match, along with determining the players involved in a chance, where on the pitch the chance was created and where it was taken from. Our work differs from previous studies in this area in a number of ways. First, previous work has used complete touch data  to model a team's attacking play. In this study, we use only the location of the assist and the chance. Thus, our proposed method is less computationally intensive and allows inferences from coarser and significantly cheaper data.Previous work has also focused on modeling the spatial dynamics of a team as a whole, whereas our method identifies the individual spatial contributions of players. Where specific players have been modeled in the past, this is often not accompanied by spatial analysis. Instead, player-to-player relationships are considered. We note that the model proposed within this article has a wide variety of applications, of which we illustrate a few.The remainder of this article is organized as follows. The data are presented in The Data section. In The Model section, we outline our model to capture a team's chances, before discussing an approach to identify the players involved with each chance and from which spatial locations. Applications are considered in Applications section and a discussion is provided in 11 to address the types of problems considered in this article typically consist of ∼600,000 events.The data available to us are Stratagem Technologies' Analyst data. This is a collection of data that marks the significant events during a soccer match, including goals, cards (both yellow and red), and chances created. For each of these events, a time is recorded (in minutes), the team and player involved with the event, and for the goals/chances the location on the pitch is marked. If the event is a goal/chance, both the player taking the chance and the player assisting the chance are recorded . From here on, we consider goals and chances to be the same for our purposes —we refer to them collectively as chance. A section of the data is shown in x-axis running between the two touch lines (width of the pitch) and the y-axis representing the length of the pitch between the goalposts. The spatial location is always recorded from the perspective of the attacking team, meaning the coordinate system does not need to be rotated to account for the second team, or to accommodate the fact that teams switch ends at halftime. The point Locations on the pitch are represented by game state is the number of goals a team is winning or losing by at that point in time, for example, a team winning 1-0 would have a game state of +1, a team losing 1–3 would be −2, and, if the game is currently a draw, both teams would have a game state of 0. The red card state is defined similarly, and is the difference in the number of players on each team. To elucidate, if a team has a player sent off, its red card state would be −1, whereas the opposition would be +1.Further to that mentioned, it is possible to extract additional statistics from the data set. These include the game state and the red card state for a team at a given time point. The In this section, we define our model to capture a team's chances, followed by our approach to determine the composite nature of each individual chance. Each chance consists of an assist player, a player taking the chance (chance player), the spatial location from which the assist was made, and the location of the chance.N) is sampled using a Poisson model. Then for each chance (E), we draw an assist player (A) and a chance player (C) from discrete distributions, with an assist location First, the number of chances a team has in a given period  and impose the constraint that the rt). Specifically,For identifiability purposes, we follow Karlis and NtzoufrasThe thinking behind this model construction is that if a team is creating chances, the other team cannot. Although this assumption is limiting by construction, given defensive tactics and other tangential aspects of play, it is the easiest (and possibly most meaningful) setup derived from the data, which consists of attacking instances only. From 29 . However, we find little or no difference in the sum-of-squares, bias, or empirical predictive distributions under the two setups. Thus, we proceed with the simpler model (in terms of the number of parameters) given by We note that it is possible to model the number of chances a team creates using an approach similar to that implemented by ReferencessE is a composition of the assist player (A), the chance player (C), the Once the number of chances created by a team is determined as mentioned, we break with First, let us consider the task of determining the assist and chance players involved with each event. We make the assumption that a player cannot assist a player on an opposing team , and neither can they take a chance created by a player from the opposition . In the context of soccer, these events are reasonably rare, and by implementing this assumption, we can consider the players of one team to be independent from the players of another team (in relation to assisting and taking chances).A player can switch teams part way through a season (in January) or at the end of a season by means of a transfer. However, we consider them to be a new player to be learned, as they may have different dynamics with their new teammates and possibly play in a different system . We model the probability of each assist player (and chance player) using a “Multinoulli”  distribution.i representing the assist player for event s, in a given block rt, with Let aZ as the vector defined by Defining withSimilarly, for the chance playerwhere31 Given the nature of the spatial locations, we implement a Gaussian mixture model, with M components. We denote the weighting of the mixture components  by Next, we consider the spatial locations, which we model using a mixture model. For a general discussion of mixture models, we refer the reader to McLachlan and Peel.Furthermore, let the observations for a given player, in a specific block, be m and variance V. Similarlywhere per se.To simplify our approach, we choose to predetermine the number of components that make up our mixture model. After discussion with inhouse expert soccer analysts, we decided upon eight components, whose locations we determine through k-means clustering. Thus, we set The locations of the cluster centroids are shown in To add some context to the cluster centroids, for the assist locations, the furthest right centroid x and a smaller magnitude of y represent a cross into the box. The extremes on the x-axis  indicate that the ball traveled across nearly all of the width of the pitch in creating the chance; whereas a y value of −400 indicates a long ball forward, for example, from a goalkeeper. A positive value of y occurs when a chance is created by passing the ball backwards down the pitch, for instance, a player passing the ball back from the opposition's goal line to the edge of the box.The We note that, although the confines of the pitch are bounded, the mixture of Gaussians has infinite support. Here, we ignore this aspect for computational simplicity and fit flexible covariance matrices to mitigate the effect—through our explorative analysis we believe this effect to be negligible.Having outlined the two components of our model, namely, the number of chances a team generates and the composition of these chances, we must consider an appropriate way to fit the model, which is the subject of the next section.To estimate the parameters in the model, we use a Bayesian inference approach. The joint posterior is given bywhere To fully specify the model, we implement the following priorsq, qI is the identity matrix with dimension q, and a priori, we are modeling the assist and chance players as a mixture of multinomials within a hierarchical Bayesian model, which is in line with techniques used in topic modeling. Where topic models  capture the words for a particular topic, here, we determine the players for an assist or chance.where rt. We fit the model in Python using the package PyMC3.The form of In the previous sections, we described our model for the spatial composition of goal chances according to latent player abilities. In this section, we test the proposed method in real-world scenarios. Given the independence between the components that constitute the model, we consider two applications. In the first, we learn a team's ability to create chances and, in the second, we examine which players are involved, and where on the pitch these events occur.For both applications, we use the data described in The Data section, namely the 2016/2017 English Premier League. We note that Chelsea won the league, with Tottenham Hotspur, Manchester City, and Liverpool getting UEFA Champions League places. Therefore, we may expect these four teams to be the best. In contrast, Sunderland, Middlesbrough, and Hull were relegated at the end of the season, meaning these three teams were perhaps the worst.We fit the model defined by t5 and t6, when compared with other teams. This suggests that they are able to find a way to win (by creating more chances) in the closing moments of a game (or a way to recover if they are losing), which is perhaps why they had a successful season.The posterior means for a team's ability to create chances . Chelsea does not have as high values as some of the other top teams (even though they won the league), suggesting they did not create as many chances as other teams, but they were more clinical with those they did create.Manchester City has the highest value in t1 and t6, with Middlesbrough having a similar ability across all blocks, leading to them being the two lowest scoring teams in the league.The teams that were relegated at the end of the season  have some of the lowest values in the table. Sunderland has the worst ability to create chances in t3 , this is possibly due to fan pressure to perform well. If a team is losing going into half time, fans want to see their team trying to get back into the game (by creating more chances), if they are drawing, they want to try to gain an advantage, or if they are winning, they want to see them press home their advantage. This level of home effect carries into the second half t6. The rise at the end of the game may correspond to a home team's need to achieve a positive result to please their fans. It is also possible that the home team is able to draw more energy from the crowd, and, therefore, outperform the away team toward the end of a match.There is a rise in the home effect in 8 which a team will play more defensively away from home, with the suggestion that if an away team is winning or drawing in the final 15 minutes of a game (t6), they will attempt to hold onto what they have (by defending more and creating less chances).The trend seen in By fitting the model to a certain point in the season and combining parameters, we are able to construct out-of-sample predictive distributions for the number of chances home and away teams create in a given block of a specific fixture. In particular, we fit the model up to a given week and predict the next week's fixtures. The predictive distributions for three randomly selected fixtures and blocks are presented in There is a variety of shape to the distributions, suggesting that the model is able to capture the differences between teams, as well as the differing opposition, and the different blocks in which chances can take place. Overall, the results presented in this section suggest the model is able to capture a team's number of chances reasonably well, as well as offering sensible predictions for the future.Having determined the number of chances a team will create, we now fit the model defined through t1 and t2.Christian Eriksen created the most chances in the 2016/2017 English Premier League. As we are implementing the model within the Bayesian paradigm, we can fit the model to a certain point in the season, before updating our beliefs once more data become available (more matches are played). To this end, we learn the model parameters using data up until January 1, 2017 , and then proceed to update our beliefs after each subsequent month.t5 after each of these months  are shown in Voronoi diagrams for Riyad Mahrez's assists in Utilizing the posterior densities, we can assess the uncertainty surrounding the weighting of each mixture component. The top row of We note that we observe only a small reduction in the variance here, however, this is due to the small number of observations available for each player. Fitting the model through time, over multiple seasons, would help to reduce the uncertainty surrounding individual player's mixture weights (subject to the data being available). This approach through  illustraIntegrating over the posterior uncertainty of the spatial locations gives the marginal posterior densities for t1 over the entire 2016/2017 English Premier League. Note, these surfaces can be constructed at any point in the season and updated once more data become available. From the figure, it is easy to see where this player had most influence, and we observe a pattern similar to that seen in the top left plot of By marginalizing over the mixture weights t3 and t5. Therefore, when approaching a game, Liverpool may want to know which of the opposition players are most likely to be involved in chances at these locations for each block, so that they can attempt to reduce their impact.During the 2016/2017 English Premier League, many pundits questioned the ability of Liverpool's defense, highlighting a weakness on the left-hand side. Looking at the data, this criticism appears fair. Of the goals Liverpool concede, the assist leading to the goal is most likely to come from the left-hand side of the box (LB), with a t3 and t5, we expect Crystal Palace to have one chance against Liverpool (in the match they had two chances in both t3 and t5).Let us consider the match Liverpool versus Crystal Palace —Crystal Palace is a team that in recent years has caused Liverpool problems. We fit our model using all data available before the match is played. From the model, in both t3 to have a chance at the t5, although there is little difference between the probability of Townsend and Benteke. Assists are likely to come from James McArthur in t3, or Yohan Cabaye or Jason Puncheon in t5 .By integrating over t3 is shown in t5 given in t3 and t5, Cabaye assisting in t3, and Puncheon assisting in t5.The 8 and Bojinov and Bornn,11 we can create maps to represent how a team plays overall. Our maps differ from those of References11 as they use complete touch data, whereas in this study we just use the locations of the chance and the assist. Our maps are, however, conceptually similar.In a similar style to Lucey et al.Integrating over 11 but from much coarser data.Chelsea and Manchester City are more free flowing in their assist making, and create more chances than Burnley. They have better players who can create chances all over the pitch, and as they are known for a passing style, they rarely rely on a long ball forward. Chelsea appears to favor the left box position for assists, whereas Manchester City uses the right corner and left box. The contours of both Chelsea and Manchester City are less tightly ringed than those of Burnley. We believe that these plots show that it is possible to make inferences about the way in which teams attack, distinguishing between teams, in a way similar to References,In this article, we provided a framework to infer how goal chances are created by a team, characterized by spatiotemporal player-level behavior on assisting a play or receiving assists. All is done within a Bayesian inference setting that by construction provides uncertainty measures and the ability to incorporate prior knowledge. We illustrated its value to professional analysts by showing how it can be used to visualize team behavior.We envisage that such modeling and inference methods can be easily incorporated into the toolbox of any organization with an interest in soccer analytics, due to its simplicity of implementation that can be accomplished by off-the-shelf tools such as the Python package PyMC3. Our approach is computationally efficient and utilizes the combination of a Poisson and Gaussian mixture model.We have shown in Applications section that inferences under the model are reasonably accurate and have close ties to reality, along with implementable applications, of which we only illustrate a few. In contrast to previous work, we exploit coarser data (consisting solely of attacking events) to identify individual player contributions, rather than modeling the spatial dynamics of a team as a whole.There are a number of ways in which this work can be extended. First, smoothing techniques can be applied to A and C needs incorporating into the model, which could also allow some network analysis techniques to be implemented. Finally, as an extension to the applications for the proposed method, an interesting area of future work is anomaly detection. This would allow us to highlight a player's patterns that are unusual in given fixtures and blocks. Techniques discussed in Heard et al.32 could be used as inspiration for methods to detect these changes.There is some dependence between the player assisting the chance and the player taking the chance. That is, it is expected that some players link up better with some players than others, further determined by the areas on the pitch in which they play. This dependence between


Salicylic acid (SA) acts as a major signaling molecule in plant immunity and systemic acquired resistance triggered by bacterial or viral pathogens. SA signal results in the activation of the master immune regulator, Nonexpressor of pathogenesis-related genes 1 (NPR1), which is thought to be recruited by transcription factors such as TGAs to numerous downstream PRs. Despite its key role in SA-triggered immunity, the biochemical nature of the transcriptional coactivator function of NPR1 and the massive transcriptional reprogramming induced by it remain obscure. Here we demonstrate that the CBP/p300-family histone acetyltransferases, HACs and NPR1 are both essential to develop SA-triggered immunity and PR induction. Indeed HACs and NPR1 form a coactivator complex and are recruited to PR chromatin through TGAs upon SA signal, and finally the HAC−NPR1−TGA complex activates PR transcription by histone acetylation-mediated epigenetic reprogramming. Thus, our study reveals a molecular mechanism of NPR1-mediated transcriptional reprogramming and a key epigenetic aspect of the central immune system in plants.Plant immunity depends on massive expression of  NPR1 ac1 (NPR1) . SA regu1 (NPR1) ,13; (ii)1 (NPR1) ; (iii) S1 (NPR1) .Arabidopsis has five CBP/p300-family genes: HAC1, HAC2, HAC4, HAC5 and HAC12  are well-known transcriptional coactivators that control a variety of differentiation and developmental processes. They facilitate transcription by diverse functions: relaxing chromatin structure through histone acetylation , modulatnd HAC12 ,32. Althignaling ,33.pathogenesis-related genes (PRs) during plant defense, the molecular mechanism of its transcriptional coactivator role remains elusive. In this study, we show that the CBP/p300-family HATs, HAC1 and HAC5 (HAC1/5), are essential to develop SA-triggered immunity and PR induction. HAC1/5 and NPR1 form a coactivator complex and are recruited to PR chromatin through NPR1–TGA interaction, finally relaxing repressive local chromatin and facilitating transcription. In sum, our study demonstrates a mechanism of NPR1-mediated transcriptional activation and proposes epigenetic reprogramming as central part of plant immune system.Despite the essential role of NPR1 in SA-triggered transcription of Arabidopsis mutants  and transgenic plants used in this study are in the Columbia-0 (Col) background. Details of the HAT mutants (npr1-1 (3), and tga2 tga5 tga6 (−2 s−1 cool white fluorescence light under short-day (8-h light/16-h dark photoperiod) or day-neutral (12-h light/12-h dark photoperiod) condition at 22°C. For 2,6-dichloroisonicotinic acid (INA) treatment, 4-week (w)-old plants were sprayed with DW or 300 μM INA (Sigma-Aldrich 456543) as previously described  with HAC1-gate-F/HAC1-R7 /NPR1 ORF-R (NPR1 promoter covering 1.7 kb upstream of the start codon generated by PCR with NPR1 P-F (NotI)/NPR1 P-R (NdeI) /TGA2-R (w/o stop) (TGA2 promoter covering 1.5 kb upstream of the start codon generated by PCR with TGA2 P-F (NotI)/TGA2 P-R (NdeI)   was inseyGate301  was replyGate301 . For the/o stop)  was clonR (NdeI)  was insempGWB510 , resultip method  via AgroProteins from nuclear and non-nuclear fractions were prepared as previously described ,40. Prot2 and homogenizing in extraction buffer , 10% glycerol, 60 μM MG132, 100 mM β-glycerophosphate, 20 mM sodium fluoride, protease inhibitors and phenylmethylsulfonyl fluoride (PMSF)). After preclearing with protein-A agarose beads, proteins were incubated with α-HA agarose beads (Sigma-Aldrich A2095) or α-GFP (Roche 11814460001) coupled to protein-A agarose (Santa Cruz sc-2001) at 4°C for 3 h. For protein elution, the beads were boiled in 2× SDS sample buffer, and the supernatant obtained after centrifugation was saved and used for protein detection.Co-immunoprecipitation (co-IP) assay was performed as previously described  with minNPR1 cDNA fragments encoding NPR1 full length, NPR1 BTB/POZ-ANK (1-369 aa), NPR1 Δ370 (370-593 aa) and NPR1 Δ513 (513-593 aa) were amplified by PCR with primers NdeI-NPR1-F/BamHI-NPR1-R, NdeI-NPR1-F/BamHI-NPR1-ANK-R, NdeI-NPR1 Δ370-F/NPR1-Stop-R and NdeI-NPR1 Δ513-F/NPR1-Stop-R, respectively , HAC1-C1 (875-1335 aa), HAC1-C2 (991-1536 aa), HAC1-C3 (1356-1697 aa), TAZN (624-716 aa) and TAZC (1575-1667 aa) were generated by PCR with primers NdeI-HAC1-F/SalI-HAC1-Stop-R, SmaI-HAC1-N-F/SalI-HAC1-N-R, NcoI-HAC1-C1-F/BamHI-HAC1-C1-R, NcoI-HAC1-C2-F/BamHI-HAC1-C2-R, NdeI-TAZN-F/TAZN–R and NdeI-TAZC-F/TAZC-R, respectively (NPR1 and HAC1 cDNA fragments were cloned into pGADT7 (Clontech 630442) and pGBKT7 (Clontech 630443) vectors, respectively, and introduced into yeast strain AH109 by lithium acetate method as described in the Clontech yeast protocol handbook. Interactions were assessed by yeast growth on synthetic drop-out medium lacking leucine, tryptophan, adenine and histidine in the presence of 1 or 3 mM 3-AT. Protein extraction from yeast was carried out as described previously  followed by 20 min of incubation at 4°C. After centrifugation at 13 000 rpm for 5 min, the supernatant was saved and filtered through a 0.45 μm filter (Millipore SLHP033RS). About 1.5 mg of total proteins were injected on the Superdex 200 10/300GL column  and fractionated by the AKTA fast protein liquid chromatography system (Amersham Biosciences). Proteins in each fraction were concentrated using acetone, separated by SDS-PAGE, and transferred onto nitrocellulose membranes (Millipore HATF00010) for immunoblot analysis.Proteins were prepared by homogenizing 4-w-old plant tissues in extraction buffer  assay was performed as previously described ,35. AntiT method  was used260/280 ratio of 1.8 to 2.2. RNAs obtained from three biologically independent experiments were combined and used for RNA-seq preparation. RNA-seq library was constructed and sequenced on the Illumina HiSeq™ 2000 at Beijing Genomics Institute (Hong Kong). Reads were aligned to the Arabidopsis reference genome using SOAPaligner/soap2 allowing mismatches of no more than 2 bases. Gene-expression level was calculated by using RPKM (reads per kb per million reads) method. Differentially expressed genes (DEGs) were selected with False discovery rate (FDR) ≤ 0.01 and |log2 Ratio| ≥ 1 as thresholds.Total RNA was isolated from leaves of 4-w-old short day-grown plants treated with DW or INA for 12 h using Tri Reagent (MRC TR118) and further purified with RNeasy MiniKit (QIAGEN 74106) to obtain OD™ 2000 were performed at Beijing Genomics Institute (Hong Kong). Reads were aligned to the TAIR10 Arabidopsis genome by using SOAP2 aligner and BWA, and uniquely mapped reads were used for further analysis. Using MACS2 version 2.1.0, normalized signals respective to Col input were obtained, and H3Ac-enriched peaks were identified (P < 1.00e-02). The wiggle files obtained from peak scanning were visualized and analyzed by using Integrative Genomics Viewer (IGV). Differential peaks between genotypes and/or treatments were identified by using MACS2 bdgdiff (10 likelihood ratio = 1) and annotated by PAVIS (https://manticore.niehs.nih.gov/pavis2/). H3Ac-distribution analysis was performed by using computeMatrix and plotprofile installed in the public server at the Galaxy   .3). DNA was isolated by reverse-crosslinking and proteinase K treatment and purified using QIAquick PCR Purification Kit (QIAGEN 28106). Quantification of immunoprecipitated DNA and the evaluation of the relative amount of amplified products in samples were performed as described in the ChIP assay section.Sequential ChIP was performed as previously described  with minArabidopsis PR1 locus is increased by pathogen attack or SA treatment, and this increase is tightly associated with PR1 transcription   treatment. The mutants lacking HAG1 (hag1-6)   in the promoter regions but reduced in the gene body (We and others have found that histone H3 acetylation (H3Ac) at the cription ,49–51. IGA2/5/6)   . These iGA2/5/6) . As H3Ac(hag1-6)  or HAC1  hac1/5)  showed ss Figure . Further5 Figure  as in npene body , suggestPseudomonas syringae pv. tomato DC3000 (Pst DC3000), the PR1/2 induction and H3Ac increase were all severely impaired in hac1/5 and to lesser extents in hac1 and hac1/12  assays. The HAC1–NPR1 interaction was not affected by TGA2/5/6 deficiency Figure  and D, nPR1 chromatin. INA-induced targeting of NPR1 and HAC1 to PR1 chromatin was completely abolished in tga2/5/6 triple mutants . Strikingly, in contrast to the HAC1/5-independent NPR1–TGA2/5 interaction , 21% (584) also showed HAC1/5-dependency /Col] ≥ 2, log2[(npr1+INA)/(Col+INA)] ≤ -2, log2[(hac1/5+INA)/(Col+INA)] ≤ -2 and FDR ≤ 0.05  and independent (Group 2) NPR1-regulated genes suggests that NPR1 might act in different modes depending on target chromatin contexts. For example, the degree of chromatin compaction could be a factor in the HAC requirement. Our finding of higher basal H3Ac levels at the Group 2-gene loci compared to the Group 1-gene loci Figure  supportsThe ChIP-seq and RNA-seq data have been deposited in the Gene Expression Omnibus (GEO) under the SuperSeries accession number GSE101572. All other data are available from the authors upon reasonable request.Supplementary DataClick here for additional data file.


The androgen receptor is a major driver of prostate cancer and inhibition of its transcriptional activity using competitive antagonists, such as enzalutamide remains a frontline therapy for prostate cancer management. However, the majority of patients eventually develop drug resistance. We propose that targeting the androgen receptor for degradation via Proteolysis Targeting Chimeras (PROTACs) will be a better therapeutic strategy for targeting androgen receptor signaling in prostate cancer cells. Here we perform a head-to-head comparison between a currently approved androgen receptor antagonist enzalutamide, and its PROTAC derivative, ARCC-4, across different cellular models of prostate cancer drug resistance. ARCC-4 is a low-nanomolar androgen receptor degrader able to degrade about 95% of cellular androgen receptors. ARCC-4 inhibits prostate tumor cell proliferation, degrades clinically relevant androgen receptor point mutants and unlike enzalutamide, retains antiproliferative effect in a high androgen environment. Thus, ARCC-4 exemplifies how protein degradation can address the drug resistance hurdles of enzalutamide. Jemilat Salami et al. develop a proteolysis targeting chimera ARCC-4, which inhibits prostate tumor cell proliferation via degradation of the androgen receptor. They show in cells that ARCC-4 is more effective than the prostate cancer drug enzalutamide and can degrade androgen receptor variants resistant to enzalutamide. For example, androgen deprivation therapy (ADT), via either surgical or chemical castration, has been the standard of care for decades3. Nonetheless, a castration-resistant form of the disease eventually develops, whereby tumor cell proliferation resumes despite sub-castration levels of serum testosterone2. More recently, the addition of anti-androgen therapy to ADT, either in the form of AR inhibitors 4 or CYP17 inhibitors, has improved overall survival6, although resistance ultimately still develops. As tumors progress, the majority of patients demonstrate AR gene amplifications or mutations, and these two events appear mutually exclusive7. Almost all patients also demonstrate a rise in serum prostate specific antigen (PSA) levels8, suggesting that AR remains the principal driver of metastatic disease.Androgen receptor (AR) signaling is crucial for normal prostate development, but also drives the growth and survival of prostate cancer cells. As such, AR signaling suppression is a common strategy for treating prostate cancer10. Unfortunately, this need to achieve and maintain high systemic concentrations is one of the major challenges in drug development today. However, in recent years, an alternative potential therapeutic approach, i.e., induced protein degradation13, has emerged and is based on event-driven (as opposed to occupancy-driven) pharmacology. This therapeutic approach is best exemplified by Proteolysis Targeting Chimeras (PROTACs), which are heterobifunctional molecules that work by creating a trimeric complex between a target protein and an E3 ubiquitin ligase, thus facilitating target ubiquitination and subsequent degradation16. The fact that PROTAC engagement leads to target protein degradation offers a potential advantage over occupancy-based inhibitors.Like other occupancy-based inhibitors, the antiandrogen enzalutamide requires high saturating drug concentrations to achieve its clinical benefitTo explore the potential advantages of a degrader versus an inhibitor, we synthesized a variety of enzalutamide-based von Hippel-Lindau (VHL)-recruiting AR PROTACs and selected our most potent compound ARCC-4 to compare with its parent inhibitor enzalutamide. We demonstrate herein that compared to its parent inhibitor, ARCC-4 is better at overcoming resistance in cellular models of castration-resistant prostate cancer (CRPC). Specifically, ARCC-4 is better than enzalutamide at inducing apoptosis and inhibiting proliferation of AR-amplified prostate cancer cells. Furthermore, ARCC-4 effectively degrades clinically relevant AR mutants associated with antiandrogen therapy, and maintains its potency to degrade AR and inhibit cell proliferation in a high androgen environment in which enzalutamide has no activity. We expect that PROTAC-mediated AR degradation can potentially address a number of AR-dependent mechanisms of drug resistance that are characteristic of castration-resistant prostate cancer but currently not addressed by enzalutamide-mediated inhibition.17 via different linkers  by appending enzalutamide to our previously reported VHL E3 ligase ligand22 in these experiments for comparison. All three AR PROTACs degraded AR, however to different extents; 2a was the least potent, 2b showed better potency, and 2c was the most potent, achieving 50% AR degradation (DC50) at 5 nM and a maximum degradation (Dmax) of over 95%  for further experiments.We first treated VCaP cells with increasing concentrations of different AR PROTACs 2a–c) for 20 h and assessed AR levels post-treatment  and a breast cancer cell line (T47D) and found that ARCC-4 efficaciously degrades AR in all of these cell lines Fig. . These r26, the lysosomal inhibitor bafilomycin27 or both , we found that ARCC-4 had no effect on glucocorticoid receptor and estrogen receptor levels, while there was a modest decrease in progesterone receptor (PR) A and B levels in these cells  R1881. We found that the IC50 values for ARCC-4, enzalutamide, and epimer were comparable at 36 nM, 70 nM, and 41 nM, respectively ds Table . These r36. Because enzalutamide competes with androgens for binding to AR ligand binding domain, elevated androgen levels lead to enzalutamide resistance21. Even though ARCC-4 shares the enzalutamide moiety, we speculated that an AR PROTAC can induce AR degradation since PROTACs only require momentary target interaction to tag a protein for degradation. We were thus interested in comparing the efficacy of ARCC-4 and enzalutamide in inhibiting CRPC cell growth within the context of competing androgen levels. First, we demonstrate that ARCC-4 continues to degrade the AR in VCaP cells in the presence of increasing concentrations of the synthetic androgen R1881 up to 1 nM  and its PROTAC degrader (ARCC-4) using cellular models of drug resistance. Here we demonstrate that AR PROTACs can degrade AR at low nanomolar concentrations in a manner that is independent of the cell line tested. Moreover, we demonstrate that AR PROTACs are particularly suitable in abrogating AR signaling. We show that PROTAC-mediated AR degradation can address the limitations of current AR antagonists used in CRPC therapy. Some of the escape mechanisms associated with resistance to current AR antagonists include AR amplifications, AR mutations, and intra-tumoral androgen synthesis that can out-compete AR antagonists and provide a local source of androgens to propel cell proliferation.The tool compound ARCC-4 reduces AR protein levels by greater than 95%, even in cells expressing high levels of AR protein. Furthermore, since AR degradation leads to loss of protein function, ARCC-4 is less susceptible to the agonist activity observed upon mutation of its AR binding moiety . Our results also show that AR PROTACs can be employed across a wide spectrum of mutations that have emerged due to previous therapies.38. We believe these three factors are at play in the improved biological potency of ARCC-4 over that of enzalutamide. ARCC-4 is approximately 10-fold more potent than enzalutamide in functional activity assays. Despite sharing the AR binding moiety, the epimer of ARCC-4 is much less potent than enzalutamide, likely reflecting its lower cell permeability, and suggesting that the 10-fold higher potency of ARCC-4 over enzalutamide occurs despite lower intracellular concentrations of ARCC-4. Thus, future PROTACs with enhanced permeability could widen the potency differential with enzalutamide even further.Another benefit of PROTACs stems from their remarkable potency, which is due to three factors: a catalytic mode of action, ability to induce favorable protein–protein interactions between the target protein and E3 ligase, and the need to form the active trimer for only brief periods of time41 suggesting that physiological  androgen levels block enzalutamide from inhibiting AR. Considering that both enzalutamide and ARCC-4 bind to the AR ligand binding domain in competition with high-affinity androgens, our data suggest that a PROTAC approach is better able to ablate AR signaling in the presence of androgens.Despite high plasma concentrations of enzalutamide in mouse models and in a clinical setting, enzalutamide is more efficacious in a castration settingin vitro cellular mechanisms of diseases that rely on AR, such as prostate cancer, breast cancer and spinal bulbar muscular atrophy (Kennedy’s disease)43.ARCC-4 will be useful as a tool compound to probe AR biology and dissect the Future work to generate compounds with optimal pharmacodynamic and pharmacokinetic properties will allow us to investigate whether the AR PROTAC activity observed in these studies is reflected in in vivo models. In summary, we have designed a potent enzalutamide-based AR PROTAC that allows for a direct comparison between protein degradation and inhibition in CRPC cell lines and moreover demonstrated the advantages of this PROTAC over its parent inhibitor in a number of cell-based models of enzalutamide resistance.44. R1881 (R0908) and bafilomycin (19-148) were purchased from Sigma. Enzalutamide (S1250) was purchased from SelleckChem. Charcoal-stripped FBS (FB-04) was purchased from Omega scientific and FBS (04-001-1A-US) was purchased from Biological Industries.VCaP, LNCaP, 22Rv1, PC3 and T47D cells were purchased from ATCC. LNCaP/AR and LNCaP/F876L cells were a kind gift from Donald McDonnell at Duke University, Durham. Stable LNCaP cell lines expressing wt-AR (LNCaP-AR) and F876L mutation (LNCaP-F876L), were generated using pQC-XIP retrovirus vector (Promega). AR-expressing HEK293 cells were generated by cloning AR into pDNA/4TO vector (Invitrogen). The plasmids were transfected into HEK293 cells and a pool of stable clones was selected for each construct. Cell lines purchased from ATCC are thoroughly authenticated using short tandem repeat DNA profiling. All cell lines tested negative for mycoplasma contamination. VCaP and AR-expressing 293 T cells were cultured in DMEM medium supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin. LNCaP, LNCaP/AR, LNCaP/F876L and T47D cells were cultured in EMEM medium supplemented with 10% FBS and 1% penicillin-streptomycin. PC3 cells were cultured in F-12K medium supplemented with 10% FBS and 1% penicillin-streptomycin. AR (5153), GAPDH (2118), GR (12041), ER (8644) and PR-A/B (8757) antibodies were purchased from Cell Signaling Technology. Tubulin antibody (T9026) was purchased from Sigma and PSA antibody (A0562) was purchased from Agilent Technologies. GAPDH and tubulin antibodies were diluted 1:3000, AR antibody was diluted 1:2000, GR, ER and PR-A/B antibodies were diluted 1:1000 and PSA antibody was diluted 1:500. Epoxomicin synthesis has been previously describedCells were lysed in RIPA buffer (50 mM Tris (pH 7.5), 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS) supplemented with protease  and phosphatase inhibitors . Lysates were centrifuged at 15,000 rpm and the supernatants were analyzed by SDS/PAGE. Western blotting was performed by transferring samples onto a nitrocellulose membrane, incubating in 5% milk in TBST  at room temperature for 1 h and probing with indicated antibody overnight at 4 °C. Membranes were visualized using the ECL prime western blotting detection reagent .TM RNAiMAX transfection reagent . Subsequent experiments were performed 48 h post transfection.VCaP cells were transfected with endo-ribonuclease prepared siRNAs against VHL  or Firefly Luciferase  as a negative control. Cells were seeded to 60–80% confluency, then forward transfected using LipofectamineVCAP cells were treated with 1 µM of enzalutamide, ARCC-4, epimer or vehicle for 2.5 h at 37 °C. Cells were then lysed in 50 mM Tris-HCl (pH 7.5), 150 mM NaCl, 1 mM EDTA, 1% NP-40, 10% glycerol, supplemented with protease , phosphatase,  and deubiquitinase inhibitors  for 10 minutes. Lysates were cleared by centrifugation at 15,000 RPM for 10 minutes. Equal amounts of supernatants were then incubated with 25 µL TUBE1 agarose beads at 4 °C for 2 h. Beads were then collected by centrifugation (5000 RPM for 1 minute), washed and re-suspended in 2 × SDS buffer. Beads were then boiled for 5 minutes and analyzed by SDS/PAGE. Western blotting was performed according to standard protocols.45.Agonist radioligand receptor binding assays were performed using LNCaP cell lysates. Experiments were performed by Eurofins Pharma Discovery Services in France according to the previously published AR receptor assay protocolCells were seeded in 96-well plates at 5000 cells per well in 150 µL of media and incubated at 37 °C for 3 days. VCaP cells were seeded in phenol red free DMEM + 10% charcoal-stripped FBS (Omega), PC3 cells were seeded in F-12K + 10% FBS and LNCaP, LNCaP/AR, LNCaP/F876L cells were seeded in phenol red free RPMI + 5% charcoal-stripped FBS. Cells were treated with 50 µL of 4 × concentrated compound to yield indicated concentrations for each experiment. Treated cells were incubated at 37 °C for 6 days (PC3 cells were incubated for 4 days) after which CellTiter-Glo reagent  was added to plates. Plates were shaken for 2 minutes to lyse cells and incubated at room temperature for 10 minutes. Plates were read on a luminometer and data was analyzed and plotted using GraphPad Prism software.oC for 2 days. Cells were treated with 50 µL of 4 × concentrated compound to yield indicated concentrations for each experiment. Treated cells were incubated at 37 °C for 48 h after which Caspase-Glo 3/7  was added to plates and incubated at room temperature for 1 h. Plates were read on a luminometer and data was analyzed and plotted using GraphPad Prism software.VCaP cells were seeded in 96-well plates in phenol red free DMEM + 10% charcoal-stripped FBS at 5000 cells per well in 150 µL of media and incubated at 37 Detailed procedures of AR PROTACs syntheses can be found in Supplementary Methods. See Supplementary Figure 1H NMR spectra of the compounds described in this study  are presented in Supplementary Figures All data supporting the findings of this study are available within the paper and Supplementary information files. Supplementary Information


One of the greatest obstacles to clinical translation of bone tissue engineering is the inability to effectively and efficiently vascularize scaffolds. The goal of this work was to explore systematically whether architecture, at a scale of hundreds of microns, can be used to direct the growth of microcapillary-like structures into the core of scaffolds. Biphasic bioceramic patterned architectures were produced using silicone molds of 3D printed parts. Grooves and ridges were designed to have widths of 330 μm and 660 μm, with periodicities respectively of 1240 μm and 630 μm. Groove depth was varied between 150 μm and 585 μm. Co-cultures of human dermal microvascular endothelial cells (HDMECs) and human osteoblasts (hOBs) were used to grow microcapillary-like structures on substrates. Bioceramic architecture was found to significantly affect microcapillary-like structure location and orientation. Microcapillary-like structures were found to form predominantly in grooves or between convexities. For all patterned samples, the CD31  signal was at least 2.5 times higher along grooves versus perpendicular to grooves. In addition, the average signal was at least two times higher within grooves than outside grooves for all samples. Grooves with a width of 330 μm and a depth of 300 μm resulted in the formation of individual, highly aligned microcapillary-like structures with lengths around 5 mm. Extensive literature has focused on the role of nano- and micro-topography  on cellular response. However, the idea that architecture at a scale much larger than a cell could be used to modulate angiogenesis has not been systematically investigated. This work shows the crucial influence of architecture on microcapillary-like structure self-assembly at the scale of hundreds of microns. Elucidating the precise correspondence between architecture and microcapillary-like structure organization will ultimately allow the engineering of microvasculature by tuning local scaffold design to achieve desirable microvessel properties. Gamsjäger et al. [Architecture across a wide range of length-scales has a crucial role in modulating biological response to scaffolds by affecting properties such as degradation behavior , nutrienr et al.  have extr et al. .Currently, one of the greatest challenges faced by tissue engineering is the inability to efficiently vascularize tissue engineering constructs . The faiin vitro, which could act as functional vasculature, as demonstrated by the presence of a lumen [in vitro vascularization has been proposed to be both a useful model for in vivo vascularization and a clinically useful tool for pre-vascularizing biomaterial constructs prior to implantation using patient-derived cells [in vivo [in vivo with promising prospects for clinical use.Co-cultures of endothelial cells and osteoblasts, or osteoblast-like cells, have been shown to result in the self-assembly of endothelial cells into microvessel-like tubular structures  a lumen . These s a lumen . This meed cells . Multipl[in vivo , 17. HowThe purpose of this study is to explore the effect of macro-architecture on microcapillary-like structure formation and organization on architecturally controlled biphasic  bioceramic substrates using co-cultures. Modulating the formation of self-assembled microcapillary-like structures using macro-architecture would benefit the production of pre-vascularized biomaterials for clinical use. We show that concavities allow the patterning of angiogenesis by controlling the position and direction of microcapillary-like structures on bioceramic surfaces.Desired architectures were designed using freely available computer aided design (CAD) software SketchUp . An overview of the chosen architectures can be seen in Hydroxyapatite powder with a 10 μm particle size was purchased from Sigma Aldrich (UK). The original hydroxyapatite powder used was phase-pure but was found to decompose into α-TCP upon heat treatment. The powder was calcined at 900°C for one hour with a heating rate of 5°C /min and a cooling rate of 4°C /min. Calcination was performed in humidified argon atmosphere.The particle size distribution of the suspension was analyzed using a MasterSizer . A slurry was made by adding gelatin from bovine skin  to a 50 weight% hydroxyapatite suspension in water. The gelatin was added as a binder to the hydroxyapatite suspension at a concentration of 0.03 g/mL and dissolved by briefly heating the slurry at 70°C and carefully mixing. The resulting slurry was used to cast the silicone molds, dried overnight and sintered at 1200°C for one hour with a heating rate of 5°C /min and a cooling rate of 4°C /min in humidified argon atmosphere.For scanning electron microscopy (SEM) imaging, hydroxyapatite powders and sintered samples were gold-coated and imaged using a 5800LV scanning electron microscope  at a 10 kV operating voltage. X-ray diffraction (XRD) was performed on calcined powders and final sintered samples. XRD was performed using a Philips PW1820 Theta/2-Theta diffractometer with Bragg-Brentano para-focusing geometry.Human dermal microvascular endothelial cells (HDMEC) and human osteoblasts (hOBs) were purchased from PromoCell GmbH (Germany). HDMECs were cultured in Endothelial Basal Medium MV (PromoCell) supplemented with 15% FBS , 10 μg/mL heparin sodium salt from porcine mucosa , 2.5 ng/mL human recombinant bFGF  and penicillin/streptomycin  according to the protocol of Unger et al. . hOBs weAll samples were used in triplicate and all co-cultures were grown in HDMEC media. Samples were sterilized by heating at 200°C, placed in low adhesion 24-well plates and washed once with PBS . Samples were pre-incubated in endothelial media before seeding to avoid cell localization being affected by capillarity. 71,250 of each HDMECs and hOBs at passage 6 were seeded in each well and allowed to attach overnight. Total cell densities were chosen based on the co-culture protocol by Unger et al. (2007). A 1:1 cell ratio was established to be optimal with the specific cells being used in previous optimization experiments. Higher passage numbers were avoided to prevent phenotype loss. Samples were subsequently moved to fresh wells after which media was changed every 2–3 days.At day 7, samples were washed once with PBS and fixed using 4% paraformaldehyde solution in PBS  for 10 minutes at room temperature. Samples were washed three times with PBS. Samples were blocked in 1% BSA  for one hour and stained using anti-human CD31 pre-conjugated Alexa 488 antibody  at a 1:100 dilution for one hour and DAPI  at a 1:5000 dilution. Samples were washed with PBS and immobilized on 24 well plate wells using Vectashield antifade mounting medium .TM High-Content Screening System  using a 5x objective. This imaging system allows for high throughput imaging of entire 24-well plates.All imaging was performed using an Opera Phenixhttps://github.com/gurdon-institute/Wagner/releases/tag/v1.0). Whole sample fluorescent image stacks were analyzed to quantify microcapillary-like structure alignment using the ImageJ Directionality plugin.Image stacks were reconstructed using Wagner  to exclude sample edges. The background signal was subtracted from maximum intensity projections using a rolling ball radius of 50 pixels. For alignment quantification, Directionality plugin  results using Fourier component analysis were compared with groove orientation. The degree of alignment (DOA) was defined as the ratio of the signal along the grooves (0°) to the signal perpendicular to the grooves (90°).2 were removed. Images rotated to have vertical grooves were analyzed to produce vertically averaged pixel intensity data across the sample length.Rotated stacks were cropped to a size of 5740 μm x 5023 μm (2400 x 2100 pixels) to exclude sample edges and the background signal was subtracted from maximum intensity projections using a rolling ball radius of 50 pixels. The image thresholds were adjusted by auto-contrasting before converting them to 8-bit grayscale. Phansalkar thresholding was applied using a radius of 5 pixels. Finally, all particles with sizes below 400 μmhttps://github.com/gurdon-institute/GrooveJ/releases/tag/v1.1.0). A line width for depth profiles of 300 μm, a curvature of 0.4, a rolling Z window of 100 μm, a Z threshold of 0.5, a slope factor of 0.35, and a depth weighting of 0.01 were used. Lines for groove detection were drawn across the sample in the central region of the cropped sample stacks. Groove detection data and vertically averaged pixel intensity data were imported and analysed using Matlab . The degree of containment (DOC) was defined as the ratio of the average signal intensity in grooves  to the average signal intensity outside of grooves . For convex ridges, grooves were considered as the regions between convexities.In order to detect grooves, we developed the image plugin GrooveJ  of 3.77 μm, a d(0.5) of 10.91 μm and a d(0.9) of 33.04 μm. While the original powder was found to be phase-pure hydroxyapatite according to XRD analysis, it progressively decomposed into α-TCP upon heat treatment. The ratio of the highest α-TCP peak and the highest hydroxyapatite peak, respectively at 2θ values of 30.75° and 31.77°, were used as an estimation of the relative weight ratio of the two phases . CalcineMacro-architecture significantly influenced the location, shape and alignment of self-assembled microcapillary-like structures . For conDeep concavities resulted in more contained microcapillary-like structures  comparedFor both large and small convexities , microcaThe results from the degree of alignment (DOA) and the degree of containment (DOC) analysis are shown in Details of the image processing procedure, including image binarization and filtering procedure results, groove detection and signal analysis, can be found in the Supplementary Material. The image processing procedure resulted in low background images with no visible loss of signal  Figs. ExWe show for the first time the effects of systematically varying macro-architecture on microcapillary-like structure self-assembly using co-cultures by varying convexity and concavity radius of curvature and concavity depth. The methods developed can be viewed as a toolkit that can be extended to other 2.5D architectures and other castable biomaterials to further explore architectural effects on microcapillary-like structure self-assembly, leading towards a predictive model for architectural optimization for angiogenic control similar to models developed by Rumpler et al.  and Guyoin vivo studies performed by Scarano et al. [Our findings on the positive role of concavities are in line with o et al. , who fouo et al. , 20. TheMechanistic explanations for observed architectural effects can be divided into biochemical and biomechanical hypotheses. Concavities and spaces between convexities can provide locally higher concentrations of pro-angiogenic factors by acting as physical barriers, as suggested by Chiu et al.  in theirin situ cell self-assembly allows the production of more extensive microcapillary-like structures by overcoming the higher time-dependence of sprouting angiogenesis.Combining architectural control with cellular self-assembly allows the production of more extensive networks in shorter time-scales than achievable through sprouting angiogenesis. Chiu et al.  reportedWe showed that architecture can be used to control both the localization and orientation of self-assembled microcapillary-like structures. Architecture alone is sufficient to regulate microcapillary-like structure growth, thus allowing for a more elegant and biologically controlled way of directing vascularization into the core of a scaffold than methods such as biomolecule encapsulation. The very promising results here have been obtained by systematically comparing six architectures. This process can be viewed as a toolkit that can easily be extended to systematically screen more 2.5D architectures in a high throughput manner by changing the original 3D printed part design.Further, this work has important implications for additive manufacturing techniques used for biomaterial production. Extrusion technology is commonly employed as a way of producing bioceramic and soft biomaterial constructs. This method inherently produces convex surfaces, which would not be favorable for microcapillary-like structure self-assembly. More importantly, this work has important clinical implications, with architecture allowing more efficient vascularization by directing vascular structures to the core of biomaterials, thus overcoming the issue of core degradation in implanted constructs.Six architectures consisting of concave grooves and convex ridges were used to systematically screen structural effects at a scale of hundreds of microns on microcapillary-like structure self-assembly by varying radius of curvature and depth. Architecture at a scale much larger than individual cells was found to significantly affect microcapillary-like structure self-assembly down to individual microcapillary-like structure control in small deep grooves. Microcapillary-like structure networks were found to form predominantly in concavities and between convexities, with convex structures inhibiting network continuity. Both ridges and grooves were found to affect both CD31 signal localization and directionality.in vivo biomaterial vascularization and as a tool to better control in vitro pre-vascularization of constructs for later implantation. This could lead towards the ability to engineer vasculature by locally optimizing architecture for desirable microcapillary properties.The work presented constitutes an accessible toolkit that can be employed for the production of high-resolution bioceramic architectures and the screening of architectures for their angiogenic potential. A more comprehensive screen of architectural effects on microcapillary-like structure self-assembly with systematic parameter variation will allow uncovering the precise mechanism for architectural effects. Results obtained showing architectural control of microcapillary-like structure self-assembly can be viewed both as a model for S1 FigImages show self-assembled microcapillary–like structures on (A) large concavities, (B) small concavities, (C) large deep concavities, (D) small deep concavities, (E) large convexities, (F) small convexities and (G) flat controls. Scalebars are 1 mm.(TIF)Click here for additional data file.S2 FigDemonstration of the developed binarization procedure’s ability to detect microcapillary-like structures in two distinct architectures: large concavities (top) and small deep concavities (bottom). Images are 2.58 mm x 2.58 mm.(TIF)Click here for additional data file.S3 FigExample of the binarization pipeline applied to an individual tile illustrating the effect of each step on the vertically averaged signal profile. Overall signal is maintained with a lowering of baseline background to levels close to zero for images after particle removal, demonstrating no significant loss of information. Images are 2.58 mm x 2.58 mm.(TIF)Click here for additional data file.S4 FigImages show sample results for (A) large concavities, (B) small concavities, (C) large deep concavities, (D) small deep concavities, (E) large convexities and (F) small convexities.(TIF)Click here for additional data file.S5 FigVertically averaged signal results superimposed to detected grooves in pink for one sample of each (A) large concavities, (B) small concavities, (C) large deep concavities, (D) small deep concavities, (E) large convexities and (F) small convexities.(TIF)Click here for additional data file.


Detection of androgen receptor (AR) variant 7 (AR-V7) is emerging as a clinically important biomarker in castrate resistant prostate cancer (CRPC). Detection is possible from tumor tissue, which is often inaccessible in the advanced disease setting. With recent progress in detecting AR-V7 in circulating tumor cells (CTCs), circulating tumor RNA (ctRNA) and exosomes from prostate cancer patients, liquid biopsies have emerged as an alternative to tumor biopsy. Therefore, it is important to clarify whether these approaches differ in sensitivity in order to achieve the best possible biomarker characterization for the patient. In this study, blood samples from 44 prostate cancer patients were processed for CTCs and ctRNA with subsequent AR-V7 testing, while exosomal RNA was isolated from 16 samples and tested. Detection of AR and AR-V7 was performed using a highly sensitive droplet digital PCR-based assay. AR and AR-V7 RNA were detectable in CTCs, ctRNA and exosome samples. AR-V7 detection from CTCs showed higher sensitivity and has proven specificity compared to detection from ctRNA and exosomes. Considering that CTCs are almost always present in the advanced prostate cancer setting, CTC samples should be considered the liquid biopsy of choice for the detection of this clinically important biomarker. Advanced prostate cancer (PC) tends to be initially hormone sensitive and is treated with androgen deprivation therapy (ADT). However, resistance to first line therapy usually develops in approximately 20–40% of patients, referred to as castrate resistant prostate cancer (CRPC) . Most coDue to its role in ADT resistance several therapies, such as Galeterone and EPI-506 have been developed to effectively target and reduce AR-V7 levels as shown in cell line studies, with clinical trials underway . ConsequWith AR-V7 emerging as a molecular biomarker of clinical importance, it is essential to determine the best strategy to detect this biomarker for predictive and prognostic purposes. Moreover, with blood biopsies emerging as a potential source to determine AR-V7 status, it is imperative to define which blood-based approach is the most sensitive and reliable. Herein we analyzed AR-V7 and AR-FL detectability in 44 PC patients using parallel blood samples for CTC isolation and ctRNA isolation. For 16 patients, parallel evaluation of AR-V7 and AR-FL status from exosomes was also possible see . Our datThe study was undertaken in accordance with the Declaration of Helsinki with human ethics approval, HREC/13/LPOOL/158, from the South Western Sydney Local Health District Ethics Committee . A total of 44 patients with prostate cancer were recruited at Liverpool Public and St George Private Hospitals in Australia. Patient information is summarized in Peripheral blood mononuclear cells (PBMCs) including CTCs were isolated in parallel from two blood samples using Lymphoprep™ with SepMate™ tube-based gradient centrifugation  as per the manufacturer’s protocol. PBMCs were recovered and washed once with 5 mL phosphate-buffered saline (PBS) , resuspended in 800 µL binding buffer with 40 µL FC buffer and 40 µL anti-EpCAM antibody coupled beads  and incubated for 90 min at 4 °C under gentle rotation. CTCs were then enriched using the standard IsoFlux CTC isolation protocol . One CTC isolate underwent CTC enumeration and the other was immediately frozen at −80 °C until RNA extraction and gene expression analysis.IsoFlux enriched CTCs were enumerated with an IsoFlux CTC enumeration kit containing all the staining antibodies . Briefly, after 10 min fixation with fixation buffer, cells were blocked for 10 min with 10% normal donkey serum (NDS) and probed with rabbit anti-human CD45 antibody (1:100) followed by detection with tetramethylrhodamine (TRITC) conjugated donkey-anti rabbit IgG (1:200). Cells were then washed twice with binding buffer and permeabilized using 0.2% Trition 100 before probing with fluorescein (FITC) conjugated anti-human Pan-cytokeratin antibody (1:10). After two further washes, cells were transferred to SensoPlate™ 24-well glass bottom plates  with Hoechst dye included in the mounting media. Fluorescent microscopy  was used for CTC enumeration.g for 10 min and the supernatant spun again using a microfuge at 16,000× g for 10 min at 4 °C. The supernatant was transferred and frozen at −80 °C until RNA extraction.All plasma was processed within 4 h of blood draw. Whole blood was spun at 280× RNA from CTC samples was extracted with Total RNA Purification Micro Kit  and eluted in 30 µL elution buffer. RNA from plasma was extracted with QIAamp circulating nucleic acid kit  and eluted in 65 µL elution buffer. Samples processed with this kit are referred to as ctRNA even for healthy controls that would only have normal cell free RNA. RNA from exosomes was extracted with Qiagen exoRNeasy serum/plasma Midi kit  and eluted in 30 µL. A total of 15 µL eluted RNA was reverse transcribed using SensiFAST cDNA synthesis kit .2O) were included in all ddPCR experiments.A total of 7 µL of cDNA of either CTC, ctRNA or exosomal samples was used for detection of AR-FL and AR-V7 transcripts by ddPCR as previously described . Glycerap < 0.05 represents statistical significance. Unpaired comparison of CTC numbers between hormone sensitive prostate cancer (HSPC) and CRPC and AR copy numbers between healthy controls and patient samples was performed with the non-parametric Mann–Whitney test.Data were analyzed with GraphPad Prism8 . As a normal distribution was not assumed, non-parametric statistics was utilized. A Fisher exact test was used to test the significance between two categorical variables; Twelve HSPC and 32 CRPC patients at various stages of treatment were recruited, all patients had been originally diagnosed with subtype adenocarcinoma PC. Patient baseline characteristics are listed in CTCs were detected in 97.7% (43/44) of patients. CTC counts varied from 0 to 184 per 9 mL blood with no significant difference in CTC counts between HSCP and CRPC patients .As expected, AR-FL and AR-V7 was only found in CTC processed blood samples with detectable CTCs. AR-FL was detected in 22 out of 44 patient CTC samples (50%). AR-FL copy number detected for CTC samples ranged from 0 to 13,714 copies/mL blood. A 62.5% share (20/32) of CRPC samples had AR-FL positive CTC samples, and only 2 HSPC patients, 16.7%, had low copy numbers of AR-FL . For AR-p = 0.13). The detected AR-FL copy numbers found in ctRNA ranged from 0 to 180 per ml plasma and were overall less abundant compared to CTC samples. Detection of AR-FL in ctRNA samples was similarly common in HSPC patients at 66.7% (8/12) compared to CRPC patients at 71.9% (23/32), although copy numbers tended to be higher in CRPC samples ranging between 0 and 15.2 vs. 0–180, for HSPC and CRPC patients, respectively.Interestingly, AR-FL detection from plasma-derived ctRNA was higher, with 70.5% (31/44) patients testing positive, although detection frequency was not significantly different from CTC samples . AR-V7 tends to be more frequently found in CRPC patient ctRNA 18.8% (6/32) vs. 8.3% (1/12) in HSPC patient ctRNA, but detectability and copy numbers are generally low for both CRPC and HSPC patients (range 0–8 per mL plasma). This data indicates that both AR-FL and AR-V7 are more readily detectable in CTC samples.In contrast, AR-V7 detection in ctRNA was lower in comparison to CTC samples with only 15.9% (7/44) of patient ctRNA samples testing positive. The difference in AR-V7 detectability in CTCs vs. ctRNA was significant (p = 0.02) while AR-V7 detection did not reach a significant correlation with CRPC (p = 0.32). In contrast, no correlation was found for AR-FL in ctRNA (p = 0.73) or AR-V7 (p = 0.65)  .The concordance of AR-FL positivity between CTC and ctRNA was 41% (18/44) while concordance of AR-V7 positivity was only 9.1% (4/44), indicating lack of sensitivity and possibly specificity for AR-V7 detection in ctRNA.From 16 of the analyzed patient samples, enough plasma was available to additionally extract exosomal RNA and analyze for AR-FL and AR-V7 transcripts.Overall, 12.5% (2/16) of patients were positive for AR-V7 when analyzed using exosomal RNA, compared to 6.3% (1/16) being considered AR-V7 positive according to ctRNA analysis in that patient sub-cohort. AR-V7 was also detectable in all CTC samples from the 16 patient cohort that were found to have detectable AR-V7 either by ctRNA or exosomal. Importantly however, overall more  patients tested positive for AR-V7 by CTC RNA analysis. In contrast, AR-FL was detected in 68.8% (11/16) ctRNA samples, in 50% (8/16) CTC samples and 37.5% (6/16) of exosomal RNA samples .We previously have demonstrated that AR-FL and AR-V7 detection from CTC samples is not only highly sensitive but also highly specific. When establishing the ddPCR assay we found AR-V7 undetectable even in large numbers of healthy donor PBMCs and AR-FL detection was very low  . To evalAdvancements in molecular technology have aided the accurate detection and quantification of novel blood-based biomarkers paving the way for their use in clinical environments. AR-V7 is emerging as a biomarker with the potential as a predictive tool in treatment selection. Patients that have detectable AR-V7 are considered to be resistant to abiraterone and enzalutamide but respond to taxane chemotherapy, and potentially eligible for clinical trials of new generation ADT drugs. Advanced stage PC tissue samples are generally unavailable for testing and liquid biopsies are explored as a surrogate. Thus far, studies on AR-V7 have utilized various methods of detection, resulting in different degrees of correlation between data and disease parameters, highlighting that a clear “gold standard” still needs to be found . While pHowever, our data indicate CTC-based detection is superior in sensitivity and specificity for AR-V7 with a detection rate of 48% as compared to 16% with ctRNA. Nevertheless, both assays demonstrated some association of AR-V7 detection with patients classified as castrate resistant, although our cohort was too small to find significant correlation. Previous studies which utilize CTC enrichment-based techniques reported broad detection rates ranging from 27% to 75% ,20,22,23Interestingly, the few patients detected positive for AR-V7 by either ctRNA or exosomal RNA testing were also found to be AR-V7 positive by CTC testing. Some CTC AR-V7 positive patients were however not detected as positive by ctRNA or exosomal RNA testing, suggesting higher sensitivity of the AR-V7 testing in CTC samples. We also detected AR-V7 in one healthy donor exosomal RNA sample. The implications of that for the individual remain unclear and further follow up is not possible because the individual consented as a healthy volunteer. Since the AR-V7 levels were not negligible and ddPCR data showed convincing detection, we have to assume poorer specificity of AR-V7 detection from exosomes at this stage, in comparison to that shown in our previous study for CTC samples . HoweverNot all CTC samples tested positive for AR-FL when either parallel ctRNA or exosomal RNA testing detected it. However, this finding has to be interpreted together with the fact that we found quite high levels of AR-FL transcripts in ctRNA and exosomal RNA samples from age- and sex-matched healthy control individuals. This may not be surprising as exosomes and cell free nucleic acids are thought to be released during normal tissue homeostasis, and it is quite conceivable that AR-FL transcript is released into the blood stream in that way from organs like the testis and prostate in male subjects. By analyzing AR-FL from CTC samples we seem to largely avoid such transcripts from non-tumor sources as AR expression in normal blood cells is known to be minimal or null .There may be other issues underpinning how, in comparison to ctRNA-based detection, CTC-derived AR-V7 provides for a more reliable assay. Given that high CTC counts on their own have proven to be of prognostic value in PC and detection of CTCs indicate higher disease burden, CTC detection can be interpreted hand-in-hand with AR detection to exclude potential false positives, and in our hands our ddPCR assay never detected AR-FL or AR-V7 in the absence of CTCs in a parallel enumerated sample . ctRNA, Since exosomes in blood are similarly believed to protect the intravesicular content including mRNAs it was important to also test AR-V7 and AR-FL detection from exosomes. We detected AR-V7 less frequently than in parallel CTC samples in only two patients. While both patients with detectable AR-V7 from exosomes are classified as castrate resistant, we also detected similar AR-V7 copy numbers in one healthy subject, challenging reliability of the finding in PC patients.While our small patient cohort was enough to determine the best liquid biopsy entity for AR-V7 and the study was not intended to answer questions of AR-V7 biology, there are a few issues worth highlighting. Firstly, our sensitive assay detected AR-V7 in some HSPC patient CTC samples. This is not entirely unexpected and has been reported previously ,28. It wAlthough the small patient cohort allowed us to answer the main question of which liquid biopsy entity is the better for detection of AR-V7 and AR-FL, a limitation of the study is that correlation with disease parameters was not as informative as a bigger cohort would have been. Larger cohorts of healthy control comparisons would be able to better define background AR-FL and AR-V7 in plasma.This study compared AR-V7 and AR-FL detection in liquid biopsy (blood)-derived CTC samples, ctRNA samples and exosomes. Our data show that testing of these clinically highly relevant biomarkers for PC patients is most reliable performed from CTC samples in regards to sensitivity and specificity. It also should be noted that a recent report shows that at the protein level correlation with PC disease parameters is linked to the nuclear localization of AR-V7 protein in CTCs . We usua


NON EXPRESSER OFPATHOGENESISRELATED 1 (NPR1) is the master regulator of SA perception. In Arabidopsis thaliana there are five paralogs of NPR1. In this work we tested the role of these paralogs in SA perception by generating combinations of mutants and transgenics. NPR2 was the only paralog able to partially complement an npr1 mutant. The null npr2 reduces SA perception in combination with npr1 or other paralogs. NPR2 and NPR1 interacted in all the conditions tested, and NPR2 also interacted with other SA-related proteins as NPR1 does. The remaining paralogs behaved differently in SA perception, depending on the genetic background, and the expression of some of the genes induced by SA in an npr1 background was affected by the presence of the paralogs. NPR2 fits all the requirements of an SA receptor while the remaining paralogs also work as SA receptors with a strong hierarchy. According to the data presented here, the closer the gene is to NPR1, the more relevant its role in SA perception.Salicylic acid (SA) is responsible for certain plant defence responses and  Plants use several pathways for their defence against pathogens. Broadly speaking, necrotrophic pathogens are dealt with by the methyl jasmonate pathway , while NON EXPRESSER OFPATHOGENESISRELATED 1 (NPR1) is an important SA perception gene. When mutants of Arabidopsis thaliana (Arabidopsis) were found that did not recognize SA or its analogues, the results were three alleles of NORESPONSE TOBTH 4 , othersYDRASES, ), and otYDRASES, ). NPR1 bYDRASES, , althougYDRASES, . NPR1 thYDRASES, .NPR1 has five paralogs in Arabidopsis , and BOP2). NPR3 and NPR4 (NPR3/4) have been shown to negatively regulate defences . T. TNPR1 pescribed ; [13]. ISequence data from this article can be found in the Arabidopsis Genome Initiative or GenBank/EMBL databases under the following accession numbers: NRB4, At1g15780; NPR1, At1g64280; NPR2, At4g26120; NPR3, At5g45110; NPR4, At4g19660; BOP1, At3g57130; BOP2, At2g41370; αDOX1, At3g01420; GRX480, At1g28480; OPR1, At1g76680; PR1, At2g14610; ANAC102, At5g63790; UGT1, At1g05560; NIMIN1, At1g02450; TGA2, At5g06950; TGA5, At5g06960; TGA6, At3g12250; TGA7, At1g77920; CA1, At3g01500; CA2, At5g14740; CA3, At1g23730; CA4, At1g70410.NPRs of Arabidopsis in Nicotiana benthamiana under the control of the promoter 35S. In order to check if there was any NPR1-like activity, our readout was the expression of the PR1 protein ). F). Fnpr1   in seve GRX480 , Fig 3b)npr1 muta control . The res 1 (OPR1 ). Fig 3d, GRX480  and OPR180  is introduced, the interaction is independent of SA . We testN. benthamiana with transient expression. We checked by Bimolecular Fluorescence Complementation , and FiGFP-NPR2 in N. benthamiana , biocherception . Third, rception .npr1-1 mutation ,,bop1 bopNPR2 can complement npr1, the lack of NPR2 produces a measurable phenotype, NPR2 interacts with some of the proteins with which NPR1 interacts, and binds SA. Therefore NPR2 acts as a SA receptor in the same way as NPR1.To sum up, our results show that in planta? NPR2 reaches peak expression in senescent leaves and later stages of fruit formation . In teran NPR1 . The danpr2 has a phenotype with SA in combination with npr1-1, we reasoned that perhaps the genes induced by SA in an npr1-1 mutant (SdNi) would alter their expression in an npr1-1 npr2 background. When we performed the experiment, the results were not as clear-cut as expected (npr1-1 (either in induction or repression), while in two the sextuple is closer to wt than to npr1-1. In the four cases where npr1-1 npr2 was tested alongside the sextuple, there was no significant difference between npr1-1 and npr1-1 npr2. Thus, instead of some genes being NPR1 and NPR2 dependent or independent, it would be more accurate to describe them as NPRs dependent or independent. It is clear that part of the SdNi genes are NPRs dependent. In the case of the gene GRX480, which we could call NPRs independent in the short term, it is more difficult to categorize in the long term , in the sextuple mutant the levels were even lower. In the rest of the genes and conditions, the sextuple mutant behaved differently than npr1-1 in all but in GRX480 at short times. Note that in some cases this difference took the sextuple closer to wt than to npr1-1  are able to trigger SA signalling.This contradiction between a positive and a negative role for S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.S6 Fig(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Here we propose a new method for data partitioning based on relative evolutionary rates of the sites in the alignment of the dataset being analysed. The rates are inferred using the previously published Tree Independent Generation of Evolutionary Rates (TIGER), and the partitioning is conducted using our novel python script RatePartitions. We conducted simulations to assess the performance of our new method, and we applied it to eight published multi-locus phylogenetic datasets, representing different taxonomic ranks within the insect order Lepidoptera (butterflies and moths) and one phylogenomic dataset, which included ultra-conserved elements as well as introns.Multiple studies have demonstrated that partitioning of molecular datasets is important in model-based phylogenetic analyses. Commonly, partitioning is done k-means. Different partitioning strategies were evaluated using BIC scores as calculated by PartitionFinder.We used TIGER-rates to generate relative evolutionary rates for all sites in the alignments. Then, using RatePartitions, we partitioned the data into partitions based on their relative evolutionary rate. RatePartitions applies a simple formula that ensures a distribution of sites into partitions following the distribution of rates of the characters from the full dataset. This ensures that the invariable sites are placed in a partition with slowly evolving sites, avoiding the pitfalls of previously used methods, such as Simulations did not highlight any misbehaviour of our partitioning approach, even under difficult parameter conditions or missing data. In all eight phylogenetic datasets, partitioning using TIGER-rates and RatePartitions was significantly better as measured by the BIC scores than other partitioning strategies, such as the commonly used partitioning by gene and codon position. We compared the resulting topologies and node support for these eight datasets as well as for the phylogenomic dataset.We developed a new method of partitioning phylogenetic datasets without using any prior knowledge (e.g. DNA sequence evolution). This method is entirely based on the properties of the data being analysed and can be applied to DNA sequences , protein sequences, as well as morphological characters. A likely explanation for why our method performs better than other tested partitioning strategies is that it accounts for the heterogeneity in the data to a much greater extent than when data are simply subdivided based on prior knowledge. Phylogenetic analysis of DNA sequences is based on models of molecular evolution that estimate parameters such as base frequencies, substitution rates among nucleotides, as well as among-site rate variation. To reduce the heterogeneity in the data, datasets are often partitioned into subsets that are deemed to have undergone more similar molecular evolution. A number of studies have demonstrated that partitioning of data is important , especiaa priori by the user, commonly by gene, gene and codon position, stems vs. loops in ribosomal RNA, or another feature of the sequence that the user believes to be important. In several studies, partitioning of protein-coding genes by gene and codon position was demonstrated to be a better option when compared to not partitioning or partitioning by gene .Recently, a different way of using TIGER together with h better . Howeverartition , which cHere, we describe a simple and objective method for partitioning using TIGER-rates. TIGER-rates is used for sorting of sites based on their relative evolutionary rates, but now we introduce an algorithm—RatePartitions—for dividing the sites among partitions in an objective way. This method has already been used in several published studies . We furtk-means algorithm  that determines the rate-spans for a variable number of partitions based on a user-specified division factor and the range of rates calculated by TIGER-rates, and subsequently defines character sets for each partition. The rate-spans are calculated for the first (and slowest) partition with the following function: z is the lower limit of the rate-span, x is the upper limit of the rate-span , y is the minimum value of rates for the entire dataset, d is a user defined division factor  and p is the partition number (when >1), which is multiplied by a fixed value of 0.3. The latter reduces the rate-span exponentially as partition number grows, which we found leads to partitions with more uniform rate variation for model-based analyses. Thus, for a dataset with rates ranging from 1 to 0.2 and with d set to 1.5, the first partition will consist of all characters with rates between 1 and 1−((1–0.2)/1.5) = 0.4667. For partition 2, x= 0.4667 and this partition will include characters with rates between 0.4667 and 0.4667−((0.4667–0.2)/(1.5 + 2*0.3) = 0.3397, and so on until less than 10% of all characters are remaining. At this point the iterations are stopped and the remaining characters are placed into their own partition (which becomes the last and fastest-evolving partition).RatePartitions is a PYTHON script (10.5281/zenodo.1251684), as well as To test the behaviour of our partitioning approach, we simulated DNA sequences on simple trees, then we re-estimated the trees using either TIGER-rates and RatePartitions or the standard partitioning approaches, and we compared the results. We simulated DNA sequences on two different topologies: one asymmetrical )))))))))))))) and one symmetrical , ), , )), , ), , ))). Each tree had 16 terminal branches and all terminal branches were set to a length of 0.1. Using INDELibleV1.03 , we simuFrom each of the resulting simulated datasets we re-estimated the tree using maximum likelihood implemented in RAxML 8.4  either bMorpho, and Pieridae; final amount of missing data is given in 10.5281/zenodo.1252828). The datasets varied in base pair length from 4,435 to 6,372 and in number of taxa from 31 to 164  , a greedy search may result in a total of nine partitions because some of the original partitions were combined into a larger subset of data with similar parameter values. BIC was chosen as a statistical model evaluation metric because it usually selects simpler models than AICc and it has been shown to perform well in model selection for phylogenetic analysis  . We compingless) , includinder 1.1 . We did analysis . We refeMorpho; and TIG4.5Gr for Calisto, Choreutidae, Noctuidae, and Pieridae. We used RFDs to quantify topological differences in resulting trees and we carried out a visual comparison of the trees to determine where the topological differences are located.We also conducted RAxML analyses of all the eight empirical datasets to compare the resulting trees between those from partitioning by gene and codon position (CodonGr) and the TIGER partitioning strategy with the best BIC score: TIG4.0Gr for Arctiina, Coenonymphina, Geometridae, and ca. 1,500 UCEs, nuclear introns from 15 loci, and three mitochondrial gene regions, totalling over 600 kilobases for 18 taxa of gallopheasants and is a difficult dataset because of a presumed rapid evolutionary radiation that occurred in this group  shows that they performed slightly better than those with trees resulting from TIGER-rates and RatePartition analyses (TIG) as measured with RFD: across all the simulations on asymmetrical trees (AS) the RFDs for the four-part analyses have a mean of 2.8 ± 4.6, while TIG analyses have a mean of 3.9 ± 6.2; and across all the simulations on symmetrical trees (SS) for the four-part the mean was 4.3 ± 7.1, while for TIG it was 5.7 ± 8.1 , Fig. 1.Our partitioning method performed equally well even when 25% of the data were missing for three taxa in the alignment , Fig. 2.Morpho and Calisto), subtribes (Arctiina and Coenonymphina), two small to medium-sized families , to two very large families   . The amoctively) .d equalling 4.5, Morpho, the dataset with fewest taxa, was split into only seven partitions, Pieridae into 20, Geometridae into 24, while all the other datasets ranged 10–14 in their number of partitions.TIGER partitioning resulted in a different number of partitions for each dataset, with Geometridae and Pieridae being split into many more partitions than the other datasets . For exaCalisto, Choreutidae, Noctuidae, and Pieridae, whereas TIG4.0Gr was the best strategy in Arctiina, Coenonymphina, Geometridae, and Morpho ) in A. rufa, ). Two other nodes in When we applied TIGER-rates and RatePartitions on the phylogenomic dataset from a priori based on some feature(s) of the DNA sequences such as genes, codon positions, stems, loops, introns, exons, etc., but this can be problematic because the properties of the sequence data are not fully known to the user to begin with. To avoid a priori partitioning, we developed a method of partitioning based on relative evolutionary rates of sites in an alignment.Many studies have shown that partitioning of DNA sequence data for phylogenetic analysis is important because it affects the resulting tree topology, branch support, as well as branch lengths . When the parameter conditions were more difficult, both partitioning strategies failed to recover the correct topology, although, as expected, TIGER performed slightly worse than the four-partition strategy. Hence, our simulation tests did not highlight any misbehaviour of our method, even under extreme parameter conditions.Comparing the fit of different partitioning strategies to empirical datasets, we demonstrated that our proposed method using TIGER and RatePartitions outperformed in all datasets that we tested other commonly used partitioning strategies, such as partitioning by gene and codon position.A possible explanation for why TIGER partitioning performed better than partitioning by codon position is that there are significant differences among sites belonging to the same codon position of the same gene in their relative evolutionary rate , S2, andIn all of our analyses, partitioning by gene was much worse than the other strategies. A protein-coding gene, with its first, second, and third codon positions, each of which evolve differently, is highly heterogeneous, and applying the same model to such a sequence most likely leads to an underparameterized model. It has been demonstrated that underpartitioning can result in in a more severe error in most datasets than overpartitioning , and ourComparisons of phylogenetic trees resulting from analysing differently partitioned empirical datasets again demonstrate that partitioning does affect the topology, nodal support, and branch lengths, as has already been demonstrated in other studies e.g., . Most ofAdditionally, our partitioning method has been applied in analyses of several other lepidopteran datasets: (1) the subfamily Acronictinae (Noctuidae)  analyzeda priori partitioning is difficult, as one does not need to provide user-defined partitions. However, our method has not yet been tested in timing of divergence analyses so we recommend careful examination of results in such cases.The TIGER method is entirely based on the alignment—not on trees or some features of the data deemed important by the user. It can be applied to any kind of categorical data , to protein-coding genes, RNA, introns, exons, as well as UCEs. It can be especially useful for sequences derived from introns or UCEs, where a priori.An issue we would like to stress with our approach, however, is that it should only be applied to studies where concatenation of data is justified, i.e., where gene tree/species tree problems are minimized. This is because our approach of partitioning by specific properties of each character removes any connections between characters belonging to the same gene region. This reshuffling of characters based on relative rates of evolution does have a biological basis to it (sites evolving at a similar rate are modelled together), but at the risk of losing other biologically relevant information . We do feel that for studies looking at deeper relationships, such as among genera, tribes, families, and orders, our approach is very useful and overcomes problems of overpartitioning for large multigene datasets that might be partitioned by codon position, as well as underpartitioning when users might be inclined to analyse their data unpartitioned because they are uncertain of how to partition Here we present a way of partitioning data based on relative rates of evolution as calculated by TIGER-rates. Analyses of simulated data over a wide range of parameter space did not reveal any misbehaviour of the method. Furthermore, we demonstrate that this approach works better than the traditional approaches based on the model BIC scores in eight empirical phylogenetic datasets and that its performance was similar in a phylogenomic dataset. Further utility of TIGER calculated rates and RatePartitions needs to be ascertained on other datasets. The program could certainly be used on amino acid  data in the same way as done here for nucleotides. We have tested the method using one phylogenomic dataset , but it 10.7717/peerj.5498/supp-1Script S1Python script for grouping sites in the alignment based on the relative evolutionary rate assigned by the program TIGER.Click here for additional data file.10.7717/peerj.5498/supp-2Data S1The datasets are provided in the PHYLIP format.Click here for additional data file.10.7717/peerj.5498/supp-3Data S2The datasets are provided in the PHYLIP format, together with the RAxML style partition definitions for the best partitioning strategy.Click here for additional data file.10.7717/peerj.5498/supp-4Table S1Click here for additional data file.10.7717/peerj.5498/supp-5Table S2Gene refers to partitioning by gene fragment, Codon to partitioning by codon position, and TIG to partitioning by relative evolutionary rate as estimated with the program TIGER with different values for the d, division factor in the RatePartitions script. See Click here for additional data file.10.7717/peerj.5498/supp-6Table S3A BIC value is provided for each partitioning strategy for each of the eight datasets analysed.Click here for additional data file.10.7717/peerj.5498/supp-7Table S4The number of internal branches for a fully resolved tree from each dataset is stated together with the percentage of internal branches that are different between the analyses of datasets from the two different partitioning strategies.Click here for additional data file.10.7717/peerj.5498/supp-8Figure S1Calisto (b), Choreutidae (c), Coenonymphina (d), Geometridae (e), Morpho (f), Noctuidae (g), and Pieridae (h).In each panel the tree resulting from the gene and codon partitioned analyses is shown on the left and the one from the TIGER partitioned analysis on the right: Arctiina (a), Click here for additional data file.10.7717/peerj.5498/supp-9Figure S2Calisto (b), Choreutidae (c), Coenonymphina (d), Geometridae (e), Morpho (f), and Pieridae (g).Plots showing the assigned TIGER relative evolutionary rates for codon positions of each of the gene fragments analysed: Arctiina (a), Click here for additional data file.10.7717/peerj.5498/supp-10Figure S3Comparison of the tree topologies resulting from the Meiklejohn et al. and TIGER partitioning strategies. The tree from analysis with the same partitioning strategy as in the original study is shown on the left and the tree from the TIGER partitioned analysis is shown on the right.Click here for additional data file.