Augmented Reality (AR) is a technology that allows virtual elements to be superimposed over images of real contexts, whether these are text elements, graphics, or other types of objects. Smart AR glasses are increasingly optimized, and modern ones have features such as Global Positioning System (GPS), a microphone, and gesture recognition, among others. These devices allow users to have their hands free to perform tasks while they receive instructions in real time through the glasses. This allows maintenance professionals to carry out interventions more efficiently and in a shorter time than would be necessary without the support of this technology. In the present work, a timeline of important achievements is established, including important findings in object recognition, real-time operation. and integration of technologies for shop floor use. Perspectives on future research and related recommendations are proposed as well. This is a review article that aims to summarize the work carried out in the area of augmented reality to help with maintenance tasks using deep learning neural networks in object detection. We intend this review to help readers understand this area and its immense potential for future projects.The evolution of technology has been used in supporting, solving, and optimizing various problems in different contexts of daily life. Augmented Reality (AR) is one such technology, allowing virtual elements such as objects, texts, graphics and other types of data to be superimposed onto images of real environments.This technology can be found in the context of games, such as the popular POKEMON GO , and camAR can be applied in open or closed spaces using adequate hardware, and has many different uses in education, healthcare, and more. Previous literature reviews on the use of augmented reality in other areas have been presented in ,4,5.Achieving AR with a simple cell phone is often the cheapest and most practical approach . HoweverIn general, augmented reality contributes to standardization as well as to friendlier and more efficient workflows through the use of contextualized and personalized information . In areaCurrently, maintenance protocols require printed or digital manuals that explain maintenance procedures in detail, and these require careful training and study. Despite progress in human–machine interaction, newer and more complex equipment may contain digital systems that are highly complex and which require demanding and careful maintenance.If maintenance technicians are able to use augmented reality glasses to help with maintenance tasks, this can reduce costs, increase production capacity, reduce safety risks, and optimize the learning process, among other advantages . One impThus, AR is an important step towards the digitization of services taking place all over the world. On the other hand, it poses new challenges for energy supply and demand, as AR headsets and similar devices require large processing and communication capacities in real time, which draws considerable amounts of energy from portable batteries. Nonetheless, AR in maintenance is an area of active research for training and guidance of maintenance technicians, and interest is growing.Augmented reality initially used markers; however, the focus of this study is on the potential of integrating AR with deep learning algorithms in object detention, avoiding the use of markers.Modern deep learning algorithms have played a key role in the development of AR. For the first time in history, convolutional neural networks are capable of automatically recognizing objects in images with very good accuracy and precision. This capacity makes it possible to identify objects in the user’s field of view and take actions based on what is visible, as well as to assess the results of many actions that change the environment. Therefore, deep learning methods are taking the AR experience to a whole new level, expanding its areas of application while opening new lines of applied research.The present research identifies important steps in the evolution of augmented reality and provides a comparative analysis of several studies on the use of augmented reality in maintenance, including automotive, military, and other use cases. Important algorithms, methods, ideas, and trends are identified. In order to optimize this process, this review further aims to show how the use of deep neural networks can be combined with augmented reality to identify and locate objects automatically, and how this can be explored for better application of AR technologies.In the present work, we have conducted a systematic literature review (SLR) on augmented reality, focusing mostly on landmark developments and industrial applications.In the first phase, the two types of augmented reality are considered, namely, approaches with and without markers. A search was carried out for articles related to this topic through the “Google Scholar” platform using the following main keywords: “Augmented Reality”; “Maintenance”; and “Artificial Neural Networks”. The resulting articles were analyzed and compared in a number of aspects. This was a preliminary investigation, and was smaller than the full review, as it was only introductory in nature compared to the main focus on studies in which augmented reality techniques were used for maintenance tasks.In the second phase, because the research on neural networks is quite extensive, only those approaches deemed most relevant were reviewed in detail. A comparison of several studies involving different architectures for deep learning neural networks was conducted. In order to filter the articles, we decided to focus on articles published after 2019 up to the present. In specific cases, such as the use of AR in electric power systems, it was necessary to move the search date back to 2017 in order to find relevant studies, as described in The main sources we used were Web of Science, IEEE Xplore, and ScienceDirect.Augmented reality can be used in several ways. Estrada et al. evaluated several of these techniques in a project for an AR application in engineering lab training :Marker-based AR: This technique requires the use of visual markers such as QR codes or other patterns, which represent reference points for the virtual object. When the AR system recognizes these markers, for example through a camera, the virtual objects or information are superimposed on the real view.Markerless AR: Unlike the previous technique, these systems do not require specific markers. Instead, they use computer vision techniques such as localization or object recognition to detect the environment that the user is in and to place virtual objects in the user’s view.Location-based AR: This technique collects real-time information through the use of GPS or other device location systems to overlay AR content based on the user’s geographic position.Projection-based AR: Here, virtual content is applied to real-world objects or physical surfaces via projectors or projector-equipped glasses that project the virtual content, avoiding the need for screens or other equipment.Recognition-based AR: A technique that involves recognizing real-world objects or images and then overlaying relevant information or virtual content onto the identified objects.Outlining-based AR: This type of AR uses image recognition to obtain a better understanding of the current environment; users can create contours or shapes and highlight components of the real world using special cameras.Motion tracking: Motion tracking techniques allow AR systems to follow the user’s movements, and as such are able to adjust the position and/or orientation of virtual objects depending on the user’s movements. This allows for greater interaction with AR content, providing a more immersive and dynamic experience.Simulated Physics and Interactions: In this approach, virtual objects can interact with the real-world environment or with each other in order to enhance the realism of AR experiences.Along with the concept of AR, there are other terms that refer to the use of virtual objects. Virtual Reality (VR) and Mixed Reality (MR) are often confused with augmented reality. VR is a technology that allows users to experience a virtual environment through VR glasses. MR refers to the inclusion of virtual objects in a real environment or the inclusion of real elements in a virtual environment.There are different types of augmented reality, and each may be better suited for particular uses, although they all share common characteristics. Marker-based AR applications use target images (markers) to position objects in a given space. These markers determine where the application places the 3D digital content in the user’s field of view (FOV) . MarkerlThere are several techniques for presenting virtual data. One technique is based on the use of portable screens, for instance, mobile devices such as smartphones or tablets, providing users with a video view of the physical environment that is then amplified by corresponding virtual content. Another technique is a head-mounted visor which is worn on the user’s head, possibly as part of a helmet. These devices use optical visualization methods that allow users to view the real world environment with their eyes and see virtual content overlaid by holographic optical elements .One of the simplest ways of producing virtual or augmented reality effects is to use projection devices, sometimes called spatial displays. These devices directly display virtual content on the surfaces of real objects, and can be naturally enlarged to permit collaboration between a group of people ,15.In general, all types of displays have advantages and disadvantages. For handheld devices, the main disadvantages are that they constrain the use of the hands and that the information, is restricted to the size of the screen. On the other hand, the ability to easily take these devices anywhere represents a major advantage. Conversely, a form of spatial display that interconnects a camera and a projection device allows hands-free use and can maximize user immersion . HoweverSmart glasses are a type of intelligent augmented reality device that offer various opportunities in work environments . They inIndustry 4.0 proposes the use of predictive technologies for tasks such as manufacturing and maintenance in the industry of the future, with connected machines as part of a collaborative community. This evolution requires the use of advanced predictive tools to ensure that data can be systematically processed in order to extract information that can explain uncertainties and lead to more informed decisions ,20.The history of augmented reality started around the year 1957, when Morton Heilig invented the Sensorama, which delivered visuals, sounds, vibrations, and smells to the viewer. Despite not being controlled by a computer, this was considered the first example of an augmented reality experience . In 1968The term AR officially emerged in the year 1990 when it was coined by Thomas P. Caudell, from Boeing. Caudell created a system  based onIn 1992, Louis Rosenberg developed the first successful fully immersive AR system for the US Air Force . VirtualIn 1993, the KARMA  project was the first project conducted for maintenance applications using a see-through head mounted display to display instructions about how to perform a task on a printer .In 1996 Schmalstieg et al. developed Studierstube, the first collaborative AR system. With this system, multiple users could experience virtual objects in the same shared space. Each user had a tracked HMD and could see correct-perspective stereoscopic images from an individual viewpoint .In 1997, Feiner et al. developed the first outdoor AR system at Columbia University. This machine used an HMD with GPS and orientation tracking.The history of markers began in 1998. Jun Rekimoto  introducIn 1999, this technology began to be applied in industrial contexts. NASA  created a hybrid synthetic vision system of their X-38 spacecraft. In the same year, the first work about ARToolKit, the most popular open-source library for AR, was published .In 2001, an AR interactive maintenance guide was developed to replace paper-based instructions, and in 2004 Stuart Gooseet al.  created In 2007, several examples of systems integrating AR with CAD (Computer-Aided Design) models appeared ,29.In 2008, Schönfelder and Schmalstieg  proposedThe first Google Glasses model appeared in 2013, followed by the Microsoft HoloLens in 2016. Both brands have subsequently developed new models ,32. FiguSeveral companies in the automotive sector have been developing AR applications to permit users to interact with their vehicles. As early as 2010, Volkswagen presented an Augmented Reality version of the Golf model on its website, and in 2012 Volvo provided the opportunity to see the inside of a model, the Volvo V40, using 2D markers to transform 3D images through an augmented reality experience . The HyuBMW has developed augmented reality techniques to support maintenance work for complex technical innovations and vehicles service. This solution for AR-based repair guidance consists of a markerless CAD-based model from BMW that uses a virtual camera with a 90ough HMD . The sysough HMD .Boboc et al. have produced a systematic literature review on AR in the automotive industry AR has been widely studied over the years for military applications, where it has found uses in control, support, and especially equipment maintenance. The ARMAR  project  created Henderson et al.  analyzedThey experimented with two HWD (Head-Worn Device) screens. These displays are more recently referred to as head-worn displays, but historically have been referred to as head-mounted displays (HMD) . In the The study was intended to compare the prototype with the use of user manuals. During each task, the LCD  presented a single static 3D scene rendered in VR. Each static scene was presented using the same engine used to generate the virtual content for the AR condition, and described identical text instructions, 3D labels, close-up graphics, and animated sequences. Additional 3D models were added to the scene to represent the central component of interest and important surrounding context. To control the overall effects of using the HWD, a third condition was added which included an unmonitored version of the AR prototype. This Head-Up Display (HUD) condition used fixed graphics on the screen to present text instructions and close-up views identical to those in the AR condition. When experiencing the HUD condition, participants used the same HWD as in the AR condition to interact with the application using the same pulse controller as in the AR and LCD conditions.The authors selected eighteen tasks  performed as part of a larger maintenance sequence.The average task completion times for each condition were 42 s (AR), 55.2 s (HUD), and 34.5 s (LCD). The mean task location times were 4.9 s (AR), 11.1 s (HUD), and 9.2 s (LCD).The authors stated that their qualitative results provide additional incentives for the application of AR in maintenance tasks. They further noted that future AR systems should contain lighter and more comfortable screens with larger viewing areas and higher resolutions.LaViola et al.  presenteZhukovskiy et al.  and KoteIn another study , an AR sIn 2017, a number of studies were conducted; Peng et al.  used AR Henderson et al.  proposedThe prototype used an HWD with a diagonal field of view of 60The study was conducted by viewing information through th glasses and comparing the results to viewing the same information on an LCD. The object location time was slightly longer with the glasses; however, the task execution time was 21.31 s faster (46.8%) using AR. The authors stated that the localization time was faster using the LCD because because the participants easily knew where the object was located. The average difference between the ideal orientation and that achieved by the user was 0.08 radians for the AR and 0.36 radians for the LCD. The average accuracy rate in the AR condition was 95.3 percent, compared to 61.7 percent in the LCD condition.Moutriz et al.  developeThe results indicated that the functional requirements discussed during the problem modeling phase research work were successfully addressed and that the resulting zero-time AR content creation tool further minimized the MTTR (mean time to repair).Tang et al.  conducteAriasyah et al.  discusseThe development of an AR-CMMS system for maintenance was demonstrated through the example of a 3D printer. In this study, two types of failures were considered: an easy “level” failure and a more difficult one. Maintenance instructions can be presented through HoloLens glasses in text format or verbal messages followed by labels that identify the target location and animated 3D models or video clearly showing how the task should be performed. In this study, a marker was used to identifies the object of interest.The authors stated that the AR-CMMS system allows unexpected breakdowns to be dealt with immediately by the operator.Aschauer et al.  presentsThe results of the studies detailed above and summarized in It is important to realize that there are several studies that, although less detailed, demonstrate the potential applicability of augmented reality for teachers, educators, and trainers, smart mobile terminals, and even in the area of medicine, with a focus on visualization for surgical support ,54. OverModern machine learning algorithms are playing a key role in the development of new AR applications. The use of deep learning techniques can help to overcome several typical shortcomings of marker-based and markerless AR. Through deep learning, it is possible to obtain faster and more accurate detection . Figure This section reviews a number of the latest applications.To automatically trigger certain actions in an AR scene, it is necessary to detect related events, which ideally takes place automatically. Different types of events can be used, such as sounds or objects in the scene. The presence of objects in the field of view is one of the preferred methods. In the past, one of the most used solutions to overcome this problem was the use of fiducials, or artificial markers placed in the scene . The marUntagged augmented reality, known as location-based reality, provides location-based data that can be provided via sensors such as digital compasses, accelerometers, a speed meters, or GPS . Marker-As the use of artificial markers represents a major constraint on AR usage in industrial environments, target objects must be found based on their natural characteristics . To thisCV has changed traditional construction management by enabling automatic activity recognition, object tracking, and performance monitoring . AdvanceIn industry, computer vision is used as a tool to support quality control, object counting, identification of worn equipment, and more ,60. In rAhmadyan et al.  proposedRecent techniques based on deep learning are used in feature matching as well. This technique can be combined with AR, and involves identifying and tracking of points of interest in an image captured by a camera. Accurate matching of features between real-world images and virtual images is essential to ensure accurate alignment of virtual objects. Sarlin et al.  proposedAlgorithms based on convolutional neural networks and recurrent neural networks have been used in accurate real-time pose estimation. This method of recognition in the AR world is essential, as it allows virtual objects to be correctly superimposed on the real environment. Konstantinidis et al.  presenteDeep neural networks are very good at object detection, and their application is changing the way AR models operate. Several key works are reviewed in this section. Because of their importance for real-time operation, applications that use YOLO and MobileNet are reviewed separately.Mourtzis et al.  presenteFor development, the Microsoft HoloLens was used with a desktop PC. For the AR module, the Unity 3D game engine was used together with the MRTK library and Vuforia API. This API was used to enable the generation and viewing of AR scenes.To begin the procedure, the video is first processed by the CNN, which recognizes the equipment. After completing this step, the technician validates the recognition result, then communication to the third module is performed, which generates the AR instructions to be transmitted to the glasses that become visible to the technician. In the various tests performed, all technicians were able to perform the tasks; although in certain cases they were not confident due to lack of knowledge and experience, all followed the instructions and managed to complete the tasks. Regarding the process time, the average time for disassembly was 60 min, including familiarization with the structure. The average time for an experienced technician was around 25 min. The authors concluded that in order to successfully develop the proposed architecture it was necessary to a computing cluster, optimize the CNN architecture, and perform new experiments using the same framework within a real-world context.Zhao et al.  proposedThe artificial intelligence training subsystem was divided into client-side and server-side training. On the client side, the training model is used to directly perform recognition operations, greatly reducing the real-time calculation pressure and making the system faster and more responsive.This paper proposed and discussed a remote control tower maintenance and induction system. An algorithm was trained and a recognition model was generated. Resources such as description, fault type, and troubleshooting method were managed uniformly and exported to the resource management system, then received by the AR system, which used Microsoft’s HoloLens augmented reality glasses.In the proposed system, the intended functions are explained and displayed in the form of graphical and 3D models, description of steps, warning information, etc. Maintenance jobs can replay, pause, and rewind the installation, debugging, and disassembly steps.The authors concluded that the system improved maintenance efficiency and significantly improved the training process with respect to complex equipment, which is of great importance in improving the information level and combat capability of the military.Lai et al.  presenteZheng et al.  developePark et al.  proposedWang et al.  presenteCoscetti et al.  describeSun et al.  presenteBastos et al.  presenteLi et al.  proposedPerez et al.  aimed toYOLO (You Only Look Once) is a CNN architecture known for its remarkable performance and speed, making it advantageous for real-time AR systems. Malta et al.  used theSimilarly, Zhi-Hao Chenÿ presented an automatic mechanism based on YOLOv4 to recognize potential cracks during nondestructive testing (NDT) of civic aeroengines. In their paper , traininIn order to create a more accurate object detection model for Augmented Reality using the communication between deep learning processing and the Microsoft HoloLens, ref.  proposedEstrada et al.  designedAnother AR application was implemented in  to detecDespite the promising developments discussed above, there are a number of relevant disadvantages to using the combination of AR and deep learning technologies. While the many advances made are intended to simplify and improve workers’ daily lives, there are always additional points to be discussed and possibly improved. Among the potentially negative aspects are, for example, that very large diversity of objects in production environments, which may be difficult to identify and track in real time . Noise mHowever, published works have demonstrated great benefits as well. Kyeong-Beom et al. ,82 propoEtonam et al.  providedAugmented reality is a technology that has had and continues to have limitations. Current limitations include tracking techniques , which aSeveral studies, such as , have diThrough object detection, image processing, and computer vision, it is possible to enhance and improve AR efficiency and effectiveness and to develop more interactive and intelligent applications. In , EnglertA final issue is that there are issues with cost-effectiveness compared to traditional methods. The development of an AR system involves choice of hardware as well as the development of software, which usually involves custom algorithms and data flows. These selections are challenging due to the wide variety of available services and options, which results in fragmentation, leading to different development processes and different user experiences . HoweverAugmented reality has become one of the most promising technologies in maintenance thanks to the ease with which it can connect the physical environment to virtual content.We analyzed a number of articles proposing diverse scenarios applying AR to assist in maintenance tasks. From our review, it is clear that this technology can help technicians, engineers, operators, etc., in varied maintenance tasks, including learning, assistance, remote support, inspection, and more. The potential of this technology increases even more when combined with deep learning techniques for object detection, which is the primary focus of this paper. It is necessary to bear in mind that for the practical use of these systems it is necessary to carry out environmental and personal studies and analyses in order to optimize their use as much as possible.In addition to the proposals described herein, there are many others in the same area of study that are equally important in understanding the future of augmented reality and neural network training for the identification and location of objects.This article has provided contributions in several areas. We have reviewed state-of-the-art machine learning techniques in combination with new and existing augmented reality applications to to assist maintenance technicians while increasing safety and efficiency.Our future work will includes further literature research on object occlusion and augmented reality hardware.


Bacteroidete and Firmicutes remarkably increased in the AR group, but Bacteroidete reduced to CG level after SLIT. AR patients have different nasal microbiome composition, but we do not know how it happened and whether the AR condition affected nasal microbiome composition or nasal microbiome affected AR.The mechanism of allergic rhinitis (AR) remains unclear. Most researchers believe that AR is the result of a combination of environmental and genetic factors. Sublingual immunotherapy (SLIT) is a treatment that can change the natural course of AR through immunomodulatory mechanism and maintain efficacy after the treatment. Nasal cavity is the main site where AR patients contact with external allergens, produce inflammatory reactions and nasal symptoms. Therefore, in this study, we investigate the nasal microbiome in AR patients, and the changes after SLIT. In this cross-sectional study, nasal swabs for microbiome analysis were collected from 3 groups: SLIT-naïve AR patients (AR group), AR patients undergoing SLIT treatment over 2 years (SLIT group) and a control group (CG). The characteristics of nasal microbiome of each groups were produced by 16s-rDNA sequencing technology. The Simpson index of AR group was significantly higher than that of CG and SLIT groups, but not different between SLIT group and CG group. The abundance of  Epidemiological studies have revealed that the prevalence of AR has increased progressively in developed countries, and affects 10% to 40% of adults and 2% to 25% of children worldwide. It is also a critical public health, medical and economic problem in China, the prevalence of AR in the 18 major cities is17.6%, which increased gradually, especially in children.Allergic rhinitis (AR) is an immunoglobulin E (IgE)-mediated inflammatory response to inhaled particles in nasal cavity. which caused the autoimmune system overreacted to the normal flora of the body, thus increased the prevalence of allergic diseases year by year.The mechanism of AR is still unclear. Most scholars believe that AR is the result of the interactive effect of environmental and genetic factors. As the “health hypothesis” said, opportunities for children to be exposed to pathogenic microorganisms in the natural environment were significantly reduced as the human living environment improved, therefore the immune system cannot receive an effective stimulus and schedules,,6 Both subcutaneous immunotherapy (SCIT) and sublingual immunotherapy (SLIT) have been proved to be effective and safe by previous studies. SLIT is more acceptable for children since it has fewer and less severe adverse events compared with SCIT. AIT is applied to clinical practices for almost a century, clinical efficacy has also been widely recognized, but its specific mechanism is still unclear.Current evidences demonstrated that 3 to 5 years of allergen immunotherapy (AIT), which could cause allergen specific immune system tolerance, thereby reduced the nasal symptoms and blocked the natural processes of allergic diseases. Nasal cavity is the main place where AR children contact with external allergens, produce inflammatory reactions and nasal symptoms. What is its microbial community composition? Are there any differences between healthy controls or AR children after SLIT? Is the mechanism of SLIT related to the microbes in the nasal cavity? Further research is needed to fully comprehend the role of nasal microbiome dysbiosis in AR.There were few researches about the relationships between nasal microbiome dysbiosis and the developments of AR, whereas the nasal microbiome holds an important role in the modulation of localized immune responses, pathophysiology, and development of AR potentially.Therefore, in this study, we take the nasal microbiome as an entry point to compare the nasal microbiome differences between the healthy controls, AR children before and after SLIT. All AR patients received allergen skin prick test and/or allergen serum-specific IgE test, and the positive results included at least 1 mite allergen. Exclusion criteria were: rhinosinusitis with or without polyposis, cystic fibrosis, autoimmune conditions affecting nasal mucosa, sinonasal tumor, aspirin-exacerbated respiratory disease, allergic fungal rhinosinusitis, prior history of sinus surgery, and treatment with systemic or topical antibiotics or glucocorticoid in the preceding 4 weeks.This study was approved by the Ethics Committee of Children’s Hospital of Nanjing Medical University. Informed consents were obtained from the guardians of participants. A total of 45 children were randomly enrolled from the Department of Otorhinolaryngology, Children’s Hospital of Nanjing Medical University from July 2022 to August 2022. Include: Fifteen AR patients undergoing SLIT over 2 years (SLIT group); Fifteen SLIT-naïve AR patients (AR group); A control group with 15 heathy children (control group [CG]). The AR patients were confirmed by Guideline for diagnosis and treatments of pediatric allergic rhinitis .The age and gender of all children and the total nasal symptom score (TNSS) of AR were recorded. Samples of each children were collected from the common meatus under anterior rhinoscopy. The nasal swabs were inserted into each nostril, rotated for 3 times, applied constant pressure and placed immediately into a sterile tube. Samples were immediately transported to the microbiology laboratory and kept at −80°C for further analysis.DNA from samples was extracted by CTAB according to manufacturer’s instructions. The reagent which was designed to uncover DNA from trace amounts of sample has been proved to be effective for the preparation of DNA of most bacteria. Nuclear-free water was used for blank. The total DNA was eluted in 50 μL of elution buffer and stored at −80 °C until measurement in the PCR by LC-Bio Technology Co., Ltd, Hang Zhou, Zhejiang Province, China. were tagged with specific barcodes per sample and sequenced universal primers. PCR amplification was performed in a total volume of 25 μL reaction mixture containing 25 ng of template DNA, 12.5 μL PCR Premix, 2.5 μL of each primer, and PCR-grade water to adjust the volume. The PCR conditions to amplify the prokaryotic 16S fragments consist of an initial denaturation at 98 °C for 30 seconds, 32cycles of denaturation at 98 °C for 10 seconds, annealing at 54°C for 30 seconds, and extension at 72 °C for 45 seconds; and then final extension at 72 °C for 10 minutes. The PCR products were confirmed with 2% agarose gel electrophoresis. Throughout the DNA extraction process, ultrapure water, instead of a sample solution, was used to exclude the possibility of false-positive PCR results as a negative control. The PCR products were purified by AMPure XT beads  and quantified by Qubit . The amplicon pools were prepared for sequencing the size, and quantity of the amplicon library were assessed on Agilent 2100 Bioanalyzer  and with the Library Quantification Kit for Illumina , respectively. The libraries were sequenced on NovaSeq PE250 platform.The primers: 341F(5’-CCTACGGGNGGCWGCAG-3’) and 805R(5’-ACTACHVGGGTATCTAATCC-3’)Samples were sequenced on an Illumina NovaSeq platform according to the manufacturer’s recommendations, provided by LC-Bio. Paired-end reads was assigned to samples based on their unique barcode and truncated by cutting off the barcode and primer sequence. Paired-end reads were merged using FLASH. Quality filtering on the raw reads were performed under specific filtering conditions to obtain the high-quality clean tags according to the fqtrim (v0.94). Chimeric sequences were filtered using Vsearch software (v2.3.4). After dereplication using DADA2, we obtained amplicon sequence variants feature table and amplicon sequence variants feature sequence.T test and gender comparison among groups was compared by Chi-square test. P < .05 was considered as statistically significant. Bioinformation sequence were mainly analyzed using the QIIME2 and the diagrams were implemented using the R package (v3.5.2).SPSS 19.0 software was used for statistical analysis of the general data of patients. Age and the TNSS among groups were tested by QIME2 software was preferred to draw dilution curves for saturation analysis to evaluate whether the current sequencing depth was sufficient to include microbial diversity. Alpha diversity is applied to analyze complexity of species diversity for a sample through 5 indices, including Chao1, Observed-otus, Shannon, Simpson, and Goods-coverage. The nonmetric multidimensional scaling based on weighted UniFrac distances of the samples was used for beta diversity analysis by QIIME2 and R package classifier, feature abundance was normalized using relative abundance of each sample. The wilcox test was used to compare the differences between the 2 groups of samples with biological replicates, and the kruskal–Wallis test was used to compare multiple groups with biologically replicated samples. LDA effect size (LEfSe) analysis was used to find species with significant abundance differences among different groups (biomarkers). Blast was used for sequence alignment, and the feature sequences were annotated with SILVA database for each representative sequence.Then according to SILVA (release 138)P > .05). The TNSS of the AR patients in the SLIT (TNSS = 4.47 ± 2.696) were significantly lower than the AR group (TNSS = 7.07 ± 2.086) (P = .006), which confirmed the efficacy of SLIT treatment.A total of 45 pediatric subjects were enrolled,15 children in each group. There were 5 female and 10 male in the CG (mean age = 6.75 ± 2.12), 7 female and 8 male in AR group (mean age = 7.41 ± 1.58), and 5 female and 10 male in SLIT group (mean age = 8.01 ± 1.43). The mean ages and gender among the 3 groups have no statistically difference (P = .039), the simpson index had significantly increased in the AR group compared to the control group (P = .021) and the SLIT group (P = .045), but there is no difference between the control group and SLIT group  were significant higher in AR group and SLIT group than CG group, and Bacteroidota  was significantly higher in AR group, but reduced to CG level in the SLIT group  in AR and SLIT group had higher relative abundance than the CG group, but Moraxella  in the AR and SLIT group were significantly lower than CG group.The top 30 species in relative abundance among all samples had been shown at phylum living on the human body and playing a key role in health and immunity. Each body habitat harbors a characteristic bacterial community. We make a pairwise further comparison of the 3 groups, and revealed that the simpson index of AR group was significantly higher than the CG group and the SLIT group, but there is no difference between the CG group and SLIT group. These shown that the bacterial evenness of AR patients had increased in their nasal microbiomes, as prior studies reports,,20 but had an apparently fallen after 2 years SLIT treatment.The α-diversity across samples shown a statistically significant difference of simpson index among 3 groups, but no difference in Chao1 and Observed and shannon indices. These demonstrated that there was on significant group difference in richness but did show a difference in evenness, consistent with previous research reports.Firmicutes and Bacteroidetes in AR group compared to the CG group. Studies on intestinal microbes in adult asthma patients found that the relative abundance of Firmicutes and Bacteroidetes in asthma patients increased, which was significantly associated with the severity of allergy and lung function. Thus, we could infer that the plus of Firmicutes and Bacteroidetes in nasal cavity might be also related to the mechanism of AR. Bacteroidetes as the biomarker of nasal cavity in the AR patients based on the LEfSe analysis deserved a special attention. In our study, the richness of Bacteroidetes was significantly increased in the AR patients, but reduced after SLIT, which may be related to the mechanism of SLIT in AR. In addition, the significantly increased of Staphylococcus genus in AR group compared to control group had been reported in a national study yet. We also found a remarkably increase of Staphylococcus (member of Firmicutes) in AR patients. All these variations in nasal microbiota may contribute to the appearance and development of AR.About the composition of microbes, we found a significant increase of the abundance of  There are still some limitations to this study. First of all, this was just a small sample size cross-sectional study captured on 1 time point, a large sample longitudinal follow-up of AR patients undergoing SLIT treatment is needed, which allows for serial repeated measures of the nasal microbiome status. Then, our study can learn the different microbiota between the AR group and the control group, or between the AR group and the SCIT group, but we do not know the different microbiota has a positive or negative regulatory effect on AR inflammation or how does it happen. Therefore, further animal model studies and clinical correlation studies are needed.Although our study focusing on children greatly reduced the effects of demographic variables on the alterations in the nasal microbiome, such as smoking or age.Firmicutes and Bacteroidete were remarkably increased in the AR patients, and the Bacteroidete reduced in the patients undergoing SLIT over 2 years. All these variations in nasal microbiota may contribute to the mechanism of AR and SLIT.This study evaluated the changes about the bacterial nasal microbiome what occurred in AR children before and after SLIT. The bacterial evenness of nasal microbiome increased in the AR patients and reduced in the patients undergoing SLIT over 2 years. The abundance of Data curation: Zhi-Pan Teng.Investigation: Xiao-Fei Shen, Zhenkun Yu.Methodology: Xiao-Fei Shen, Zhi-Pan Teng.Writing – original draft: Zhi-Pan Teng.Writing – review & editing: Xiao-Fei Shen, Qi Li, Zhenkun Yu.


The clinically used androgen receptor (AR) antagonistsfor thetreatment of prostate cancer (PCa) are all targeting the AR ligandbinding pocket (LBP), resulting in various drug-resistant problems.Therefore, a new strategy to combat PCa is urgently needed. Enlightenedby the gain-of-function mutations of androgen insensitivity syndrome,we discovered for the first time small-molecule antagonists towarda prospective pocket on the AR dimer interface named the dimer interfacepocket (DIP) via molecular dynamics (MD) simulation, structure-basedvirtual screening, structure–activity relationship exploration,and bioassays. The first-in-class antagonist M17-B15 targeting theDIP is capable of effectively disrupting AR self-association, therebysuppressing AR signaling. Furthermore, M17-B15 exhibits extraordinaryanti-PCa efficacy in vitro and also in mouse xenograft tumor models,demonstrating that AR dimerization disruption by small molecules targetingthe DIP is a novel and valid strategy against PCa. Small molecules targeting the dimer interface pocket locatedat the ligand-binding domain of androgen receptor (AR) are capableof suppressing AR signaling and combating prostate cancer. AR is a member of the nuclear receptor(NR) family of ligand-dependent transcription factors, and it containsfour functional modules, including a large N-terminal domain (NTD),a DNA-binding domain (DBD), a short hinge region, and a C-terminalligand-binding domain (LBD).3 The bindingof testosterone or DHT to the ligand binding pocket (LBP) of the ARLBD triggers a conformational change of AR that in turn promotes thedissociation of AR from heat shock proteins, and AR dimerization,and transport of AR from the cytosol into the cell nucleus. Subsequently,the AR dimer binds to a specific region of DNA known as the androgenresponse element (ARE) to activate or repress specific gene transcription.5 Abnormal AR activity is associated with various diseases, such asprostate cancer (PCa), androgen insensitivity syndrome (AIS) and spinalbulbar muscular atrophy (SBMA).8Androgens are responsible for normal physiology,development, andmetabolism of reproductive and nonreproductive systems.11 Point mutations within the AR LBD are frequentlydescribed in thisdisease.12 The AR LBD possesses a three-layersandwich fold architecture .The two AR LBD monomers in the homodimer are arranged “head-to-head”and form a large dimer interface by centering the helix 5 (H5). Nearly40 different AIS-related mutations have been reported on the AR dimerinterface.13 However, so far, how the mutationson the AR dimer interface affect the biological functions of AR remainspoorly understood.AIS is the most common cause of the 46,XY disorder in sexdevelopment.14 At the beginning, we explored the structural ensemblesof the wild type AR LBD and two AIS-associated mutants with the mutations(W751R and F754V) located at the dimer interface by molecular dynamics(MD) simulations and small-angle X-ray scattering (SAXS). The simulationand experimental results illustrate that the AR LBD homodimer canbe easily disrupted by either W751R or F754V. Therefore, we speculatedthat blocking the AR dimerization by small molecule antagonists targetingthe interface is probably favorable to combat PCa. Interestingly,an AR LBD dimer interface pocket (DIP) was discovered by our theoreticalpredictions. To prove the druggability of the DIP, an integrativestructure-based virtual screening was performed and a novel hit compoundM17 was identified. Then through a four-step structure optimization,M17-B15 with >31-fold improved inhibitory activity on AR transcriptionalfunction was discovered. M17-B15 exhibited potent anti-PCa efficacywithout detectable toxicity, and it can specifically target to ARrather than its phylogenetically related nuclear receptors. Collectively,our findings demonstrate that AR dimerization disruption by smallmolecules targeting the DIP is a novel and valid strategy to developnew classes of AR antagonists against PCa.In this study, we report the discovery processof a series of novelthiadiazole-amide-based small molecules that block the AR LBD dimerizationas our granted patent “CN113444081B”.WT)and two AIS-associated mutants with mutations located in H5 , i.e., W751R (ARW751R withchanged hydrophobicity) and F754V (ARF754V with reducedhydrophobicity and bulkiness), by molecular dynamics (MD) simulations.16 Analysis of the time evolution of the root-mean square deviations(RMSDs) of the protein backbone atoms of the ARWT dimerconfirmed that the simulated system is equilibrated after ∼260ns  and the two monomers (monoA and mono B) are stable . However, the RMSDs for the ARW751R and ARF754V dimers are both dramatically fluctuant, though those for each monomerare stable . The alignmentof the initial structure (magenta) and the last snapshot (blue) extractedfrom the MD trajectory for the ARWT dimer shows a highstructural similarity with a RMSD of 2.24 Å (Movie S1), while those for the ARW751R and ARF754V dimers exhibit significant structural differences with RMSDs of11.94 and 14.86 Å, respectively (Movies S2–3). Based on the theoreticalpredictions, the mutations disrupt the AR LBD dimerization and leadto self-dissociation.We exploredthe structural ensembles of the wild type AR LBD  of the ARW751R or ARF754V mutant bound with DHT is much smaller than that of ARWT, suggesting a compaction of the protein system. As each AR monomeris rigid, the compaction can only be explained by the emergence ofthe AR LBD monomer. In addition, Dmax ofARW751R is almost identical to that of ARF754V, suggesting that neither ARW751R nor ARF754V stably exists in the form of a homodimer. Considering that the ARdimerization is essential for AR transcriptional activity, blockingthe AR dimerization by small-molecule antagonists targeting the dimerinterface can be a powerful way to combat PCa.The predictions were then confirmed by the small-angleX-ray scattering(SAXS) technique, which gives the overall dimension and shape silhouetteof the biomacromolecule. As shown in 17 Novel AR antagoniststoward the DIP are expected to offer a new approach to treat PCa.We thenanalyzed the dimer interface and successfully discovered a potentialsmall-molecule binding pocket located at each LBD monomer as illustratedin Figure S3A) and androgen-dependent cell line LNCaP viability . The serum level of prostate-specificantigen (PSA) is usually used for detecting and monitoring the PCaprogression and treatment response.18 Herein,the secreted PSA from LNCaP cells was evaluated, and the result showsthat M17 dose-dependently inhibits the secretion of PSA . Furthermore, the binding of M17to the conventional LBP was excluded by a competitive ligand-bindingassay . These results promptedus to investigate the cell viabilities of androgen-independent celllines  upon the treatment of M17.As shown in Figures S3E–H, M17 exhibitscomparable effects with the marketed second-generation AR antagonistenzalutamide (Enz) on the proliferation of the four cell lines. Inaddition, the cytotoxicity of M17 to a normal cell line murine embryonicfibroblast (NIH-3T3) was determined and both M17 and Enz show negligiblecytotoxicity against NIH-3T3 . These data support that M17 targets AR but not the LBP with highsafety profile, and it is worth chemical optimization.Next, the structure-based virtual screening (SBVS) procedure wasemployed toward the DIP 1F. ThrouTable S1. The AR antagonistic activities weredecreased or totally lost when methylfuran (A ring) was replaced bydifferent groups (M17-A2 and M17-A2). We also examined the role ofthe benzene ring (B ring) in 3,4-dichlorophenyl. The compound withphenyl (M17-B1) exhibits optimal AR antagonistic activity comparedwith the rest of the compounds (M17-B2 ∼ M17-B4). Subsequently,we designed compounds with various substituents at different positionsof phenyl (M17-B5 ∼ M17-B11) and found that the meta-substitutedcompound M17-B10 is more potent. We thus designed compounds with differentsubstituents at the meta-position of the phenyl (M17-B12 ∼M17-B23), which led to the discovery of the potent compound M17-B15(IC50 = 0.03 μM). This compound achieved >31-foldimprovement in AR transcriptional inhibition compared with the originalhit M17  of 1.32 × 10–5 M. Similar to M17, M17-B15 does not target the LBP as indicatedby the competitive ligand-binding assay .19 The ensemble refinement against the CXMS restraints indicatesthat 7 conformers in the ensemble can satisfy all the cross-linkingpairs (Biolayer interferometry(BLI) experiments were performed to determine the binding affinityof M17-B15 to the purified AR LBD. As shown in ng assay 3B. We th M17-B15 3C. Furthngpairs 3D.By trngpairs 3E. FurthTo explore whether M17-B15 can inhibit the AR dimerizationat thecellular level, the N-terminal yellow fluorescent protein (YFP)-labeledand C-terminal cyan fluorescent protein (CFP)-labeled AR-LBD plasmidswere constructed for the acceptor-bleaching fluorescence resonanceenergy transfer microscopy (FRET) assay. After treating with 10 nMDHT, the AR LBD dimerization occurred, which induced the YFP- andCFP-labeled AR LBDs to move closer to each other, thereby exhibitinghigh FRET efficiency. However, lower FRET efficiency was observedin the presence of 10 μM M17-B15 3F, indicFigures S4A–C), and M17-B15 does not show obvious inhibitionon the cell viability of 22RV1, PC3, and Du145 . The dose–response curves forM17-B15 and Enz on LNCaP cells are quite similar, which is consistentwith their anti-AR transcriptional activities , consistent with the data from thecell viability assays.Like Enz, M17-B15shows negligible cytotoxicity against NIH-3T3, human liver cells (Chang),and human gastric epithelial cell  and ARF876L/T877A (FL-ARF876L/T877A) on M17-B15 was evaluated using the dual-luciferasereporter assay. As a comparison, a recently approved AR antagonistdarolutamide, which targets the AR LBP and could significantly inhibitthe transcriptional activity of the AR F876L and F876L/T877A mutants,was also applied.21 As shown in 50 valuesof 0.16 μM and 0.10 μM for FL-ARF876L, and0.15 μM and 0.34 μM for FL- ARF876L/T877A,respectively. In addition, M17-B15 significantly inhibits the DHT-inducedtranscriptional and translational levels of PSA , Enz (2.5 mg/kg/week),and M17-B15 (2.5 mg/kg/week) were administered to the establishedsubcutaneous xenografts of LNCaP cells. As shown in Figures S5A–C). The Venndiagram displays that there are 14 shared differentially expressedgenes , which are down-regulatedin the antagonist-treated groups of Enz and M17-B15 while up-regulatedin the DHT-treated group. The corresponding heatmap shows their relativeexpressions . Among these DEGs,4 of them have been reported and their up-regulations are linked torecurrent PCa, including KLK3 (encoding PSA), CDC20, CENPF, and MKI67.22 Therefore, we validated the vitalgenes of CDC20, CENPF, and MKI67 by qPCR. It is shown that the treatment by Enz orM17-B15 significantly reduces the expressions of all these genes comparedwith those by DHT  for abbolactone is potent at the concentrationof 0.4–10 μM, while those for Enz and M17-B15 are notobserved even at a relatively high concentration of 10 μM. Thesecompounds do not show an obvious effect on the MR agonistic activities.We analyzed the LBDs of 48 human nuclear receptors(NRs), and theirphylogenetic tree was constructed. The LBDs of progesterone receptor(PR), glucocorticoid receptor (GR), and mineralocorticoid receptor(MR) show the highest evolutionary conservation 5C. That ectively 5D. It isectively 5E–F. Simtivities5G. TheseFigure S7, with the concentrationonly as high as 50 μM, mild to moderate inhibitory effects wereobserved for either Enz (except for U2OS) or M17-B15 (except for A549).Furthermore, both M17-B15 and Enz do not show an obvious impact onthe cell cycle and apoptosis of the LNCaP cell line . The above results highlight the potential highAR specificity of M17-B15 and good druggability of the DIP.In addition, the cell viabilityinhibition of various of humancancer cell lines for Enz and M17-B15 was evaluated, including osteosarcoma(U2OS), breast cancer (MCF-7), hepatocellular carcinoma (HepG2), leukemia(HL60), non-small cell lung cancer (H1299 and A549), colorectal cancer(SW480), glioma (U87), and cervical adenocarcinoma (Hela) cell lines.As shown in Investigation of the dynamic behavior ofAIS mutations at the ARhomodimer interface illuminated the strategy of small molecules targetingthe protein interface to disrupt AR homodimerization to treat PCaand AR-related diseases. Herein, we identified a potential pocketnamed the DIP located at the dimer interface near H5, and the druggabilityof the previously unexploited pocket was validated by a novel smallmolecule M17-B15. TM17-B15 exhibits potent anti-PCa efficacy in vitroand in vivo, and it specifically targets AR toward its homologuesPR, GR, and MR. However, structural optimization is still urgentlyrequired for M17-B15, especially to the chemical modification of thesolvent-exposed moiety of 3-(2-oxopropyl)-1,2,4-thiadiazol to improveits pharmacokinetic properties. In addition, we believe that the ARDIP will inspire interesting studies on the discovery of new classesof therapeutics including novel proteolysis-targeting chimeras (PROTACs)and molecular glues for treating PCa and AR-related diseases. Moreover,the DIP is as excellent as the conventional target of the LBP in ourstudy, providing a new option for the other NRs to develop variantNR modulators. Overall, this study provided a successful paradigmfrom gain-of-function mutations to a potential druggable pocket andnew therapeutic strategies.


The Allergic Rhinitis Clinical Management Pathway (AR-CMaP) was developed to overcome the challenge of implementing current AR guidelines in the Australian community pharmacy practice and support pharmacists in optimally managing patients' AR.To evaluate the impact of AR-CMaP on patients' behaviour and pharmacists' needs in managing AR in the pharmacy.This study used a cross-sectional, pre-post study design in which the primary outcome was the appropriateness of medications purchased from community pharmacies in Australia. Patient data were collected before and after the implementation of AR-CMaP. Pharmacist needs were recorded before and after AR-CMaP training. Data were analysed descriptively.Six pharmacies, 19 pharmacists and a total of 416 patients were included in the study; 206 pre-AR-CMaP implementation and 210 post-AR-CMaP implementation. Pre-AR-CMaP, 22.4% of patients purchased appropriate AR medication compared with 29.0% post-AR-CMaP implementation. Over half the patient cohort (52%) consulted a pharmacist pre-AR-CMaP and 37% consulted a pharmacist post-AR-CMaP implementation. Post-AR-CMaP, pharmacists reported increased awareness of barriers such as patients' lack of time, patients' perceptions about the pharmacist's role and patient choice to self-manage. Pharmacists also rated an increased desire to interact with other health care providers (HCPs) in caring for patients with AR.While there was a non-statistically significant increase in the proportion of patients purchasing optimal AR medication, AR-CMaP did empower patients to self-select their own medication without further detriment. Moreover, following the implementation of AR-CMaP, pharmacists developed a greater awareness of their role in AR management, exemplified by their increased desire to be actively involved in AR management and increased interaction with other HCPs. Future research needs to explore more effective tools to support pharmacists' clinical decision-making and target patients' self-selection of AR medications. This study highlights that there is an ingrained self-reliance of AR decision-making that has become a habit for people living with AR. All pharmacists employed within the enrolled pharmacies were invited to participate in the study and were provided with written study information. Written consent was obtained prior to pharmacist participation.2.3Patients, 18 years of age and over, who obtained AR medications for use in treating AR through self-selection or requesting medications from a pharmacist were eligible for study enrolment and were approached by a research assistant following purchase of their AR medication.At the time of approach by the research assistant, patients were provided with verbal and written information (if requested). Verbal informed consent was required before study participation.2.4Following the pre-AR-CMaP data collection period, all participating pharmacists received training in AR-CMaP in their pharmacy. They were also provided with a suite of patient management resources for AR  rulers and AR education posters) to help engage and manage people with AR in the pharmacy.After AR-CMaP implementation, patient recruitment continued until the sample size was reached (as per the processes involved in collecting pre-AR-CMaP data) using follow up data collection form .Patient data collection form: A research assistant-administered questionnaire Demographic characteristics ii)Clinical symptoms for which the product was being purchased , andiii)Medication(s) purchased to treat AR symptoms.Pharmacist needs assessment: The pharmacist needs assessment Optimal (AR medication(s) purchased matched the VAS score and was consistent with the latest ARIA guideline recommendationsb)Suboptimal (AR medication(s) under-medicated for AR severity)c)Inappropriate (medication(s) purchased not indicated for AR treatment)The appropriateness of AR medication purchased was based on the specific AR symptoms reported and categorised into:Pharmacist needs assessment: The mean scores for each item  were calculated.2.7Data collected from patient and pharmacist participants were exported from REDCaP into IBM SPSS version 28.0 , for analysis. Patient data collected through the baseline and follow up data collection forms were summarised using descriptive statistics. Data relating to patient demographics, AR , medication purchased and pharmacist interactions pre-and-post-AR-CMaP were compared using Chi-square tests for independence. Mean scores for each item in the pharmacist needs assessment were tested for normality, and the Wilcoxon signed-rank test was used for comparisons between paired data. A two-tailed significance level of 0.05 was used for all statistical procedures.2.8p = 0.8, p < 0.05, based on independent sample comparison and a cluster effect of 1.5.A minimum of 400 patient participants (200 pre- and 200 post-AR-CMaP) were required to detect a doubling of the proportion of people with AR purchasing appropriate medication following the implementation of AR-CMaP (i.e. from 15% to 30%), at 3Seven pharmacies initially agreed to participate; five were service-oriented pharmacies, one hybrid and one discount pharmacy; the latter withdrew at an early stage citing issues with availability of staff. Therefore, six pharmacies and 19 pharmacists were enrolled, with data collection occurring between September 2019 and November 2019. Patient data were collected as research staff were available during normal pharmacy business hours.n = 416, p = 0.489) (= 0.489) .Fig. 2Fl3.1There were no significant differences in the overall demographic and clinical characteristics of people with AR recruited into the study pre-AR-CMaP and post-AR-CMaP. The majority (61%) of participants were aged over 40 years and over 98% of this cohort were experiencing moderate-severe AR . The mos3.2p = 0.02) ( = 0.02) .Table 2Ap = 0.185, p = 0.511 and p = 0.306 respectively).n = 415, p = 1.000).There was no significant difference in the proportion of people with AR and wheeze/asthma, who purchased INCS pre-AR-CMaP compared with post-AR-CMaP .There was a statistically significant increase in the proportion of people with AR with itchy/watery eye symptoms, who purchased optimal medications pre-AR-CMaP compared with post-AR-CMaP .There was no significant difference between the proportion of people with AR purchasing optimal medication pre-AR-CMaP versus post-AR-CMaP, when subset analysis based on pharmacy type was conducted comparing service-oriented and hybrid-oriented pharmacy models. . Of those who consulted the pharmacist, there was no statistically significant difference between the proportion of people with AR who purchased optimal medication after consulting with a pharmacist pre-AR-CMaP compared to post-AR-CMaP .There was a statistically significant decrease in the proportion of people with AR who consulted a pharmacist pre-AR-CMaP compared to post-AR-CMaP . Responses across 23 items in Domain 1 indicated that overall, pharmacists agreed/strongly agreed that they had a role in AR diagnosis, medication management and supporting patient self-management. There was no statistically significant difference in pharmacists' responses pre-AR-CMaP versus post-AR-CMaP with regards to Domain 1 .Of the 8 potential pharmacist-related barriers in Domain 2, the 2 major barriers were lack of time by the pharmacist and language barrier. There was no statistically significant difference in pharmacists' responses pre-AR-CMaP versus post-AR-CMaP with regards to Domain 2 .Of the 6 potential patient-related barriers in Domain 2, the major barrier identified by pharmacists was patients' lack of AR knowledge. There was a significant increase in agreement of pharmacists that patients' lack of time and patients' choice to self-manage were barriers to addressing AR properly in the pharmacy, post-AR-CMaP compared with pre-AR-CMaP .Amongst the interprofessional relationship and interaction in Domain 3, there was a statistically significant increase in agreement of pharmacists wanting to work with other health care providers around AR management post-AR-CMaP compared with pre-AR-CMaP .4This study builds on the previous extensive work of the research team on the suboptimal management of AR in the community pharmacy. It once again confirms that a high proportion of people seeking AR medication do not have a doctor diagnosis, suboptimally self-select their medicines and even a highly specialised education and training program for community pharmacists, does little to address these major issues. This study aimed to improve AR management through the implementation and evaluation of AR-CMaP, a community pharmacy-specific AR intervention, responsive to the high self-selection health behaviour of people with AR who visit the community pharmacy and aimed at increasing the purchase of appropriate medication. As noted above, the results of this study indicate that AR-CMaP did not result in an overall increase in the proportion of people with AR purchasing optimal medication, however it did increase the proportion of people with AR and ocular symptoms purchasing optimal medication. There was also an increase in the proportion of people with AR who self-selected their own AR medication, without a decrease in those purchasing optimal medication, hence, AR-CMaP did appear to support the process of self-selection. In terms of assessing pharmacists' needs, AR-CMaP seemed to highlight the more complex nature of managing AR in the community pharmacy, with pharmacists being more aware of the need for more time and the importance in overcoming patient self-management as critical barriers, while expressing a desire to work more closely with other health care providers in managing AR.People with AR in this study showed both similarities and differences to previous AR populations studied within community pharmacy. Similarly, there was a high proportion (98%) of people with moderate-severe AR in this study.,A key feature of the AR-CMaP was addressing the high proportion of AR medication self-selection. For this reason, AR-CMaP contained both a consumer/patient component, while also specifically addressing the known perceptions and experiences of people with AR and their medication-selection behaviour.,,This study showed a relatively high level of pharmacist engagement pre-AR-CMaP implementation  consulted the pharmacist for AR). This is noticeably higher than the proportion of people with AR consulting with the pharmacist in previous studies.When it came to the pharmacists and perceptions of their role in AR management, overall and consistently, from the outset , pharmacists rated their role highly across all items from diagnosis to referral and everything in between. This did not change post-AR-CMaP and suggests a desire to be actively involved in AR management. Hence, when it comes to barriers to implementing AR management in the pharmacy, pharmacists essentially were either neutral or rated pharmacy-related barriers lowly . However, post-AR-CMaP, pharmacists reported higher agreement with three patient-related factors: patients' lack of time, patients' perceptions about the pharmacist's role and patient choice to self-manage. This suggests that either pharmacists became more aware or found it more difficult to engage with the patient post-AR-CMaP and this is reflected in the decreased proportion of people with AR who consulted with the pharmacist.Pharmacists also registered an increased desire for interacting with other HCPs in caring for patients with persistent AR; this suggests better identification of referral pathways and communication with other HCPs should be considered in the future. While international frameworks for AR management recognise the care pathway, connecting patients in pharmacy to GP and then to specialists,4.1There was no control group in this study due to the lack of resources for recruiting people with AR during high AR flare-up season. Hence a pre-post study design was implemented, which did not include matched pairs. Further, we can now hypothesise that people with AR who visited the community pharmacy pre-AR-CMaP may have a different phenotype to those who visited the pharmacy post-AR-CMaP as they were less likely to report pollen as a trigger to their AR. It would have therefore been important to recruit/sample our population based on AR phenotypes. Another limitation of this study is the lack of feedback post-AR-CMaP from people with AR. Prior to the implementation of this study, the process of getting feedback from people with AR was considered, however, being an implementation study, the burden of research data collection and the potential for this to hinder participation of people with AR was weighed against the benefits associated with getting feedback from people with AR. It was determined that participation was the priority objective. Therefore, moving forward, it will be critical to get feedback from people with AR, especially given the increased self-selection of AR medication observed in this study post-AR-CMaP. A last limitation to this study was that we were not able to apply all current global guidelines5AR-CMaP appeared to empower people with AR in self-selecting their own AR medication with the tools provided post-AR-CMaP, with a slight improvement in the appropriateness of optimal medication purchased and improvement only amongst individuals with the ocular symptom AR phenotype. This highlights the challenges of AR management in the community and points us to several important considerations for the future. Firstly, improved care pathways for AR in the community pharmacy are desperately needed. Secondly, care pathways need to address the desire of pharmacists to engage with patients and other HCPs but must better support the needs of people with AR who are clearly determined to manage AR more independently. Lastly and perhaps most urgent, this study, truly highlights the necessity for greater understanding of the role of the patient in AR management. In particular, patient health behaviours and all aspects of their perceptions and experiences which leads patients to AR self-management. Only once the perspective, experiences and challenges of people with AR have been truly understood, the needs can then be addressed within the context of community pharmacy. Until this is achievable, it will still be a struggle to reduce the burden of AR in the community.Investigator-Initiated Woolcock-Mylan Partnership Grant 001984.This study is partly supported through an 10.13039/100001003Boehringer Ingelheim, GlaxoSmithKline, for her contribution to advisory boards/key international expert forum.RH, BC, LC, OL declare no conflict of interest. VK has received honoraria from AstraZeneca, GlaxoSmithKline and Pfizer. JR declares no conflict of interest, has received sponsorship from GlaxoSmithKline, AstraZeneca, Sanofi, Stallergenes. KY has received honoraria for speaking and consulting from AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline, Meda, Mundipharma and Pfizer. JB reports personal fees from Chiesi, Cipla, Hikma, Menarini, Mundipharma, Mylan, Novartis, Purina, Sanofi- Aventis, Takeda, Teva, Uriach, other from KYomed-Innov, outside the submitted work. SBA is a member of the Teva Pharmaceuticals Devices International Key Experts Panel, has received research support from Research in Real Life, has received lecture fees and payment for developing educational presentations from Teva, GlaxoSmithKline, AstraZeneca and Mundipharma; and has received Honoria from AstraZeneca,


Overexpression of androgen receptor (AR) is the primary cause of castration-resistant prostate cancer, although mechanisms upregulating AR transcription in this context are not well understood. Our RNA-seq studies revealed that SMAD3 knockdown decreased levels of AR and AR target genes, whereas SMAD4 or SMAD2 knockdown had little or no effect. ChIP-seq analysis showed that SMAD3 knockdown decreased global binding of AR to chromatin. Mechanistically, we show that SMAD3 binds to intron 3 of the AR gene to promote AR expression. Targeting these binding sites by CRISPRi reduced transcript levels of AR and AR targets. In addition, ∼50% of AR and SMAD3 ChIP-seq peaks overlapped, and SMAD3 may also cooperate with or co-activate AR for AR target expression. Functionally, AR re-expression in SMAD3-knockdown cells partially rescued AR target expression and cell growth defects. The SMAD3 peak in AR intron 3 overlapped with H3K27ac ChIP-seq and ATAC-seq peaks in datasets of prostate cancer. AR and SMAD3 mRNAs were upregulated in datasets of metastatic prostate cancer and CRPC compared with primary prostate cancer. A SMAD3 PROTAC inhibitor reduced levels of AR, AR-V7 and AR targets in prostate cancer cells. This study suggests that SMAD3 could be targeted to inhibit AR in prostate cancer. Prostate cancer (PCa) is the second most common cancer in men worldwide. The androgen receptor (AR) is the primary target in treating advanced PCa. Upon androgen binding, AR translocates into nuclei, binds specific genomic locations, and regulates gene expression to promote PCa cell proliferation and survival. Thus, androgen deprivation therapy (ADT), which suppresses AR activity, is the first-line treatment for metastatic PCa. However, despite initial clinical remission after ADT, disease invariably re-occurs in 2–3 years and progresses to lethal castration-resistant prostate cancer (CRPC). Second-generation AR pathway inhibitors such as enzalutamide and abiraterone were developed to treat CRPC by repressing AR signaling. These treatments extend patient survival, but CRPC eventually becomes drug-resistant, driven by reactivated AR signaling ,2.Re-activation of AR transcriptional activity in CRPC occurs via multiple mechanisms, such as AR overexpression/mutation/splicing, overexpression of AR co-factors, and/or intratumoral androgen biosynthesis . Modest SMAD3, a key transcription factor mediating TGF-β signaling, consists of an N-terminal MH1 domain that interacts with the Smad Binding Element (SBE), a linker region, and a C-terminal MH2 domain that interacts with other proteins . In canoHere, we identify AR as a key downstream effector of SMAD3 in gene expression and PCa progression. We reveal a new role for SMAD3 in promoting expression of AR mRNA by binding to an enhancer in AR intron 3. Our results also implicate that SMAD3 cooperates with or co-activates AR in terms of AR target gene expression. Overall, our study supports the idea that SMAD3 may provide new therapeutic opportunity to inhibit AR for potential CRPC therapy.Antibodies were purchased from the following companies: AR (06–680), EMD Millipore ; AR (22089-1-AP), Proteintech ; AR-V7 (31-1109-00), RevMAb Biosciences ; SMAD3 (#9523), SMAD2 (#5339), phospho-SMAD3 (#9520) and phospho-SMAD2 (#3108), Cell Signaling Technology ; SMAD3 (ab28379), Abcam ; Cas9 (61957), Active Motif ; Rabbit IgG control (13–0042), EpiCypher ; Mouse IgG control (02–6300), Invitrogen ; Myc , Santa Cruz Biotechnology ; Flag  and actin (A5441), Sigma-Aldrich . Trueblot secondary antibodies  were purchased from Rockland Immunochemical . Enzalutamide was purchased from Abmole Bioscience . R1881 was purchased from Sigma-Aldrich . TGF-β was purchased from Cell Signaling Technology . SMAD3 phosphorylation inhibitor SIS3 (HY-13013) and SMAD3 PROTAC inhibitor (HY-147025) were purchased from MedChemExpress . All reagents were used according to manufacturers’ recommendations.Rv1 cells  were provided by Dr James Jacobberger . C4-2 and C4-2B cells were provided by Dr. Leland Chung . Enzalutamide-resistant C4-2B cells were generated by serial growth and passaging of parental C4-2B cells in sequentially increasing concentrations of enzalutamide (2–20 μM) for over 1 year. LN95 cells were provided by Dr. Alan Meeker . PC3 and VCaP cells were purchased from the American Type Culture Collection (ATCC). Rv1, C4-2, C4-2B and PC3 cells were maintained in RPMI 1640 media supplemented with 10% FBS and antibiotics. VCaP cells were maintained in DMEM media supplemented with 10% FBS and antibiotics. LN95 cells were maintained in phenol-red free RPMI 1640 media with 5% charcoal-stripped FBS and antibiotics. Cells were periodically checked for Mycoplasma by PCR analysis, and cells of <20 passages were used for experiments.Rv1 cells were transduced with sh-Control, sh-SMAD2, sh-SMAD3, or sh-SMAD4 lentivirus for 48 hours. Total RNAs were extracted using a total RNA miniprep kit . Three biological replicates of RNA samples were used for library preparation, sequencing , and differential expression analysis by Novogene Corporation . In brief, sequenced reads with low quality or adaptor contamination were removed. Clear reads were mapped to the human reference genome (hg19) using Hisat2 v2.0.5 software. To count the read numbers mapped to each gene, featureCounts v1.5.0-p3 was used. The fragments per kilobase of transcript per million mapped reads (FPKM) method was used to estimate expression levels. Differential expression analysis was performed using the DESeq2 R package (1.20.0).5 living cells per replicate) were used for ChIP-seq preparation, following the CUT&RUN kit protocol (EpiCypher). In brief, cell nuclei were extracted using nuclear extraction buffer  and incubated with ConA Beads to absorb nuclei onto beads. Antibody buffer and 0.5 μg of antibody were added to samples and incubated overnight at 4°C. After washing beads, 2.5 μl of pAG-NMase was added and incubated for 10 min at RT. After washing, 1 μl of chromatin digest additive was added and incubated for 2 hours at 4°C. Stop buffer and 0.5 ng of Spike-in DNA were then added and incubated for 10 min at 37°C. DNA was purified using the DNA purification kit (EpiCypher). 5 ng of purified DNA was subjected to the library preparation using the Illumina library prep kit (NEB). The library was purified with 1.0× AMPure beads (Beckman) and library fragment size was evaluated using an Agilent Bioanalyzer. Libraries were sequenced  at Novogene Corporation .Rv1 cells were transduced with sh-Control or sh-SMAD3 lentivirus for 48 hours. Two biological replicates , Androgen-Response Elements (AREs) and AR half-sites. For SBE motif scanning, we used FIMO default setting of MEME suite v5.4.1.Quality of ChIP-seq reads was checked using FastQC software, and Trim Galore was used to remove adaptor sequences. After trimming, reads with a score >28 were aligned to the human hg19 reference genome and Gene ontology (GO) term analysis was performed using the Enrichr web server with differentially expressed genes from RNA-seq or annotated peaks from ChIP-seq. The EnhancedVolano R package was used to create volcano plots. BART web server  was usedFlag-AR, myc-AR, Flag-AR mutants  in the pcDNA3 vector, AR or AR-V7 in the pLvx-IRES-zsGreen1 vector, and AR shRNA in pLKO.1 vector targeting the AR N-terminal region were described previously . The FlaLentiviral vectors harboring shRNAs, sgRNAs, or AR, AR-V7 cDNAs were packaged in 293T cells using calcium phosphate transfection. The supernatant containing lentiviral particles were collected 48 h after transfection. PCa cells were then transduced with supernatants in the presence of polybrene (8 μg/ml) for 24 h before replacement with fresh growth media. Cells were analyzed 48 hours post transduction. For CRISPRi studies, cells were selected for 7 days in puromycin (1 μg/ml) before analysis.Immunoprecipitation and western blotting procedures were detailed previously ,23.Total RNA from cells was prepared using a total RNA miniprep kit . cDNA was synthesized using random hexamers. SYBR Green qPCR analysis was performed using a Mx3005P QPCR system (Agilent Technologies). Primers for peptidylprolyl isomerase A (PPIA) served as an internal control. Biological triplicate samples were used for qPCR analysis, and independent experiments were repeated at least three times. Primers for qPCR analysis of human gene transcripts were: PPIA: 5’-GACCCAACACAAATGGTTC-3’, 5’-AGTCAGCAATGGTGATCTTC-3’; SMAD3: 5’-TGGACGCAGGTTCTCCAAAC-3’, 5’-CCGGCTCGCAGTAGGTAAC-3’; SMAD4: 5’-CTCATGTGATCTATGCCCGTC-3’, 5’-AGGTGATACAACTCGTTCGTAGT-3’; SMAD2: 5’-CGTCCATCTTGCCATTCACG-3’, 5’-CTCAAGCTCATCTAATCGTCCTG-3’; AR: 5’-CCATCTTGTCGTCTTCGGAAATGTTATGAAGC-3’, 5’-AGCTTCTGGGTTGTCTCCTCAGTGG-3’; AR-V7: 5’-CCATCTTGTCGTCTTCGGAAATGTTATGAAGC-3’, 5’-TTTGAATGAGGCAAGTCAGCCTTTCT-3’; KLK2: 5’-GGTCGGCACAACCTGTTTGA-3’, 5’-GCCCAGGACCTTCACAACAT-3’; KLK3: 5’-ACCAGAGGAGTTCTTGACCCCAAA-3’, 5’-CCCCAGAATCACCCGAGCAG-3’; NKX3-1: 5’-ACTTGGGGTCTTATCTGTTGGA-3’, 5’-CTCGATCACCTGAGTGTGGG-3’; SLC45A3: 5’-GACACTATGATGAAGGCGTTCG-3’, 5’-GAGAAGGTGAACCCGGTGAG-3’. TMPRSS2: 5’-CCTCTAACTGGTGTGATGGCGT-3’, 5’-TGCCAGGACTTCCTCTGAGATG-3’; PMEPA1: 5’-CTGAGCCACTACAAGCTGTCTG-3’, 5’-GGATTCCGTTGCCTGACACTGT-3’; STEAP1: 5’-CCCTTCTACTGGGCACAATACA-3’, 5’-GCATGGCAGGAATAGTATGCTTT-3; STEAP2: 5’-GGTCACTGTAGGTGTGATTGG-3’, 5’-ACCACATGATAGCCGCATCTAA-3’.Cells were crosslinked using 1% formaldehyde for 7 min at RT and then quenched with 5 M glycine. Cell nuclei were extracted with lysis buffer 1  and lysis buffer 2 . Nuclear extracts were solubilized in shearing buffer  and sonicated to obtain 500-bp chromatin fragments, using a Covaris M220 sonicator. To prepare antibody-conjugated Protein A/G magnetic beads, beads were washed and incubated with 3 μg primary antibody or control IgG overnight at 4°C. 500 μg of lysate proteins were diluted 10-fold and incubated with antibody-conjugated Protein A/G magnetic beads overnight. After four washes, crosslinking was reversed, and DNA was purified using spin columns and subjected to real-time PCR analysis. ChIP-PCR was performed in biological triplicates, and independent experiments were repeated at least three times. Data were calculated as percentage of input. Real-time PCR primers for various regions of the AR gene were: primer1: 5’-AGGTCACAAGTGCCTAGAAATACA-3’, 5’-TTTGGCAAAGGCAATCTGGG-3’; primer2: 5’-AGAAATTGTGGGGTCTGGGC-3’, 5’-CCAAAAGAAAAGTACAGCCACCC-3’; primer3: 5’-CCCACCTCCACCATTTTCTCT-3’, 5’-CCCTGAGAAGTGGAAAGAAGAGTC-3’; primer4: 5’-CACACAGCCAAGGCCCTATC-3’, 5’-GCTTGCCACTTCGAGAAACA-3’; primer5: 5’-TAATGTGGCTTGGCATTGGC-3’, 5’-GAAAAGCAAGCGACTGAGCC-3’; primer6: 5’-AGCTCCCACAGGGGTCTAAT-3’, 5’-CAAGGGGCTTTCCAGTCCAT-3’; primer7: 5’-AGCAAGCCTCATTGTCCAGG-3’, 5’-ACTCCATTCATTCTGCCGGG-3’; primer8: 5’-GTCAGTTCCAGGTTGTATCTTTTT-3’, 5’-ACCAAAGAGAGAATTTTATTGCTCA-3’; primer9: 5’-CAGGATGCTCTACTTCGCCC-3’, 5’-AGCTTCACTGTCACCCCATC-3’. AR exon 2: 5’-AAGACCTGCCTGATCTGTGG-3’, 5’-TGCATGTGCAAGACCCTTTA-3’. Real-time PCR primers for AR target genes were: KLK3 enhancer: 5’-CCTAGATGAAGTCTCCATGAGCTACA-3’, 5’-GGGAGGGAGAGCTAGCACTTG-3’; KLK2 promoter: 5’-GGGAATGCCTCCAGACTGAT-3’, 5’-CTTGCCCTGTTGGCACCTA-3’.The following oligos targeting SBE sites were synthesized and cloned into the pLV hU6-sgRNA hUbC-dCas9-KRAB-T2a-Puro plasmid: sg1 (GGAGTGGCCAGGAGTGAGAC), sg2 (CCACAGGGGTCTAATGCCCC), sg3 (TGCTCTGACCCAAGACTAAC), sg4 (ACTAGCAAGCCTCATTGTCC), sg5 (CAAGTCGTCTAGCAACATCC), and sg6 (CTCTGCTGCAGACAGAACAG); sg of AR exon 2 (CACTATGGAGCTCTCACATG). Rv1 cells were transduced with lentivirus carrying CRISPRi constructs, either individually or in combination. After 48 h, cells were treated with puromycin (1 μg/ml) and cultured for 7 days to remove non-transduced cells. The remaining cells were used for real-time RT-PCR or ChIP-PCR analysis.PCa cells were seeded at low density into 6-well plates in duplicate or triplicate. After 2 weeks, cells were fixed in 3.5% paraformaldehyde and stained with 0.2% crystal violet. Cell colony images were taken with a scanner. The number of colonies (>100 μm in diameter) was determined in 8 or 12 higher-power fields.PCa cells were mixed with agar to a final concentration of 0.4% and layered on top of 0.8% agar in six-well plates. Triplicate plates were incubated for 3 weeks at 37°C. The number of colonies (>50 μm in diameter) was scored in 12 high-power fields.n = 10 per group) were subcutaneously injected with Rv1 cells (1 × 106 cells/100 μl of 1:1 PBS:Matrigel). 5 weeks post injection, xenograft tumors were collected, and tumor weights measured using a digital balance.Athymic nude mice were purchased from the Jackson Laboratory and housed in the animal facility at the University of Maryland School of Medicine. All experiments were approved by the Institutional Animal Care and Use Committee (IACUC # 0122010) and conducted following university's animal policy in accordance with NIH guidelines. 8-week-old male athymic nude mice (n = 3). Sample size other than triplicate was indicated in the figure legend. Statistical analysis was performed using GraphPad Prism software. Student's t-test (2-tailed) was used to compare differences between two groups of datasets. One-way ANOVA was used to compare differences among more than two groups of datasets. For all statistical analyses, differences were labeled as ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001. P values <0.05 were considered statistically significant.Experiment was performed in biological triplicate each time. Experiment was independently repeated at least 3 times except RNA-seq, ChIP-seq and xenograft studies. Biological triplicate from one representative experiment was used for quantification and statistical analysis. Quantification was presented as mean ± SD  to BART  analysis, an algorithm that can predict key transcriptional regulators underlying altered gene expression. Significantly, AR was predicted to be the most significant transcription regulator altered by SMAD3 KD  was confirmed by both real-time RT-PCR  and RNA-D Figure .To validate RNA-seq results, we performed real-time RT-PCR analysis of AR and AR target genes  in Rv1 and C4-2 cells. SMAD3 KD in both lines decreased mRNA levels of both AR and target genes Figure , E. AR-V2 [fold change] < −0.5, Padj < 0.05, Figure 2 [fold change] > 0.5, Padj < 0.05, Figure Padj < 0.05, Figure 2 [fold change] > 0.5, Padj < 0.05, Figure To evaluate whether other SMADs regulate AR signaling, we knocked down either SMAD2 or SMAD4 in Rv1 cells for RNA-seq analyses. After confirming the knockdown of SMAD2 or SMAD4 by real-time RT-PCR , we subjSMAD3 is a mediator of the TGF-β signaling pathway . To deteTo identify mechanisms underlying SMAD3 regulation of AR expression, we examined the genome-wide chromatin binding of SMAD3 using Cut&Run ChIP-seq . 11779 STo identify SBEs that may functionally regulate AR mRNA expression, we blocked access to SBEs using the CRISPR interference (CRISPRi) approach , in whicin vitro binding of bacterially-generated fusion proteins . 48.9% of common peaks have at least one SBE motif, while 51.1% of commons peaks have no SBE motif . The forTo determine whether SMAD3 loss alters AR binding to chromatin, we knocked down SMAD3 in Rv1 cells and performed Cut&Run ChIP-seq analysis of AR. Among AR peaks, ∼74% (9637 of 13 073) showed a statistically significant reduction in peak size after SMAD3 KD, while 26% (3436 of 13 073) showed reductions in peak size that were not statistically significant Figure –C. For eWe have shown that SMAD3 KD in PCa cells decreases AR transcript expression Figure . To funcTo determine whether the SMAD3 peak in AR intron 3 functions as an enhancer of the AR gene, we analyzed the published datasets of H3K27 acetylation (H3K27ac) ChIP-seq or ATAC-seq, both of which are enhancer markers. The SMAD3 peak in AR intron 3 overlapped with the H3K27ac peak in AR-positive PCa cells , which also displayed H3K27ac peaks in AR upstream enhancer (650 kb upstream of the AR gene) Figure . By contTo determine the biological significance of SMAD3 regulation of AR expression, we analyzed and correlated SMAD3 and AR mRNA levels in human PCa datasets. SMAD3 and AR mRNA levels were positively correlated in TCGA PCa datasets Figure . In two SMAD3 PROTAC inhibitors have been developed to induce the degradation of SMAD3 by recruiting VHL ubiquitin ligase . We testAR overexpression is one of the main drivers in converting castration-sensitive to castration-resistant PCa. However, mechanisms underlying transcriptional upregulation of AR mRNA are not well understood. Several transcription factors reportedly bind to the AR promoter to positively or negatively regulate AR transcription . LigandeThe SMAD3 peak in AR intron 3 overlaps with H3K27ac peaks or ATAC-seq peaks in human PCa cells or PCa tissues. CRISPRi-mediated blocking of SMAD3-binding sites in AR intron 3 inhibited expression of AR mRNA in PCa cells. These findings indicate that the SMAD3 peak in AR intron 3 functions as an enhancer to promote AR mRNA expression. AR intron 3 enhancer and AR upstream enhancer show either comparable or differential pattern in various PCa datasets, supporting the importance and relevance of AR intron 3 enhancer in human PCa.We also show that SMAD3-mediated expression of AR mRNA is independent of TGF-β signaling in PCa cells. In addition to SMAD3-SMAD4 heterodimers, SMAD3 is known to function as a homodimer  or as a Previous studies reported that SMAD3 MH2 domain and AR N-TAD domain interact, overexpression of SMAD3 can increase the expression of AR target gene KLK3, and SMAD3 overexpression can activate or repress activities of androgen-responsive reporters . Thus, SAR-V7 generation is a key mechanism driving development of enzalutamide resistance. AR-V7 splicing occurs co-transcriptionally with transcription of AR pre-mRNA . Thus, iin vitro and in xenograft models. These results indicate that AR is a key downstream effector in SMAD3-mediated gene expression and PCa cell growth. Neuroendocrine prostate cancer (NEPC) is a lethal form of prostate cancer characterized by loss of AR expression and thus resist ADT. In the AR-negative PC3 cells, SMAD3 was reported to promote HIF-1α chromatin binding under hypoxia to promote neuroendocrine (NE) phenotype (Our study demonstrates a tumor-promoting role of SMAD3 in PCa cell growth dependent on the AR pathway. RNA-seq and subsequent bioinformatic analysis of SMAD3-KD Rv1 cells did not predict changes in SMAD or TGF-β signaling, potentially due to the lack of detectable TGF-β signaling in Rv1 cells. Instead, AR and AR signaling pathways were predicted to be altered by SMAD3 KD. AR re-expression in SMAD3-KD PCa cells partially rescued defects in AR target expression and PCa cell growth henotype . Thus, tIn Summary, we identified a previously uncharacterized function of SMAD3 in regulating transcription of AR mRNA and demonstrated AR as a key downstream effector in SMAD3-dependent PCa progression. We also provide evidence that a SMAD3 PROTAC inhibitor can be used to inhibit expression of AR, AR-V7 and AR target genes. The present findings support that SMAD3 could be targeted to modulate/inhibit AR expression and activity in advanced PCa.Raw data related to RNA-seq and ChIP-seq have been deposited in the GEO database with accession number GSE211946.gkad043_Supplemental_FileClick here for additional data file.