Serious gaming has increasingly gained attention as a potential new component in clinical practice. Specifically, its use in the rehabilitation of motor dysfunctions has been intensively researched during the past three decades.The aim of this scoping review was to evaluate the current role of serious games in upper extremity rehabilitation, and to identify common methods and practice as well as technology patterns. This objective was approached via the exploration of published research efforts over time.The literature search, using the PubMed and Scopus databases, included articles published from 1999 to 2019. The eligibility criteria were (i) any form of game-based arm rehabilitation; (ii) published in a peer-reviewed journal or conference; (iii) introduce a game in an electronic format; (iv) published in English; and (v) not a review, meta-analysis, or conference abstract. The search strategy identified 169 relevant articles.The results indicated an increasing research trend in the domain of serious gaming deployment in upper extremity rehabilitation. Furthermore, differences regarding the number of publications and the game approach were noted between studies that used commercial devices in their rehabilitation systems and those that proposed a custom-made robotic arm, glove, or other devices for the connection and interaction with the game platform. A particularly relevant observation concerns the evaluation of the introduced systems. Although one-third of the studies evaluated their implementations with patients, in most cases, there is the need for a larger number of participants and better testing of the rehabilitation scheme efficiency over time. Most of the studies that included some form of assessment for the introduced rehabilitation game mentioned user experience as one of the factors considered for evaluation of the system. Besides user experience assessment, the most common evaluation method involving patients was the use of standard medical tests. Finally, a few studies attempted to extract game features to introduce quantitative measurements for the evaluation of patient improvement.This paper presents an overview of a significant research topic and highlights the current state of the field. Despite extensive attempts for the development of gamified rehabilitation systems, there is no definite answer as to whether a serious game is a favorable means for upper extremity functionality improvement; however, this certainly constitutes a supplementary means for motivation. The development of a unified performance quantification framework and more extensive experiments could generate richer evidence and contribute toward this direction. Motor rehabilitation in various parts of the body such as the upper or lower limbs aims to help patients restore dysfunctions that affect their mobility. In this scoping review, we focus on motor disabilities related to the upper extremities. The motivation behind this review was first introduced within one of our group’s research projects related to upper limb rehabilitation, termed “Modern Interface Platform for Motor Control and Learning on People With Motor Disorders” -3. Our pTherapists have developed several clinical methods to indicate motor ability, such as range of motion (ROM) or range of force. In addition, specialized evaluation tests such as Fugl-Meyer Motor Function Assessment (FMA), Action Research Arm Test (ARAT), and Melbourne Assessment of Unilateral Upper Limb Function (MAUULF) aim to estimate the improvement of a patient’s motion condition. The usual rehabilitation scheme consists of repeated motion exercises for a specific body part, with the aim of restoring ability as close to the normal condition as possible.The idea to introduce gamification to the therapeutic protocol of upper limb rehabilitation was born as a means to motivate patients during the rehabilitation schemes but also represents a new method for monitoring the upper limb motion for further analysis. The first attempts of the introduction of gamification in upper limb rehabilitation appeared in 1999 by a team at Rutgers University , making These rapid technological developments led to more elaborate devices regarding motion capture, challenging researchers in this field to investigate this type of rehabilitation.Over the last few decades, there has been an increasing amount of studies regarding the enhancement of rehabilitation with the introduction of new technologies. A systematic review on the implementation of serious games and wearable technology in rehabilitation practices for patients recovering from traumatic bone and soft tissue injuries was published by Meijer et al . AnotherThis effort will contribute to the detection of gaps or limitations in this area, and may lead to new research paths and ideas.The rest of the paper is organized as follows. The Methods section depicts the procedure that was followed regarding the literature search, data management, and eligibility criteria of this review. The Results section presents the statistical results, including figures, after reviewing the included studies. Finally, the Discussion section comments on the results and delineates possible limitations of this study, along with highlighting the importance of this review for further development of this research area.In this scoping review, we followed the PRISMA-ScR   guidelinThis review included articles published from 1999 to June 2019. The PubMed and Scopus databases were used for the literature search. The keywords utilized in the literature search were: “rehabilitation,” “hand,” “upper limb,” “upper extremity,” “upper arm,” “game,” “serious gaming,” and “serious game,” which were investigated in titles and abstracts of articles published in the English language. The following search query was used: rehabilitation AND (hand OR upper limb OR upper-limb OR upper extremity OR upper-extremity OR upper arm OR upper-arm) AND (game OR serious gaming OR serious game). Subsequently, duplicated articles were removed, and the remaining studies were screened for eligibility.Two individual researchers (EK and IL) conducted the literature search and the removal of duplicates, and one author (IL) screened the titles and abstracts for eligibility under advisement by IC. The remaining studies were reviewed by EK, IL, and DF, guided by a set of inclusion and exclusion criteria to extract information from the selected articles. The extracted information followed a structure defined by IC, EK, and IL, as follows: (i) year of publication, (ii) purpose of the study, (iii) part of the upper limb for rehabilitation, (iv) sensors used, (v) disease that led to the patients’ condition, (vi) game type, (vii) game scenario, (viii) game target, (ix) clinic- or home-based application, (x) supervised or unsupervised, (xi) software used for creation of the rehabilitation game, (xii) hardware development, (xiii) system limitation, (xiv) use of a pilot study or not, (xv) number of patients in the pilot study, (xvi) evaluation methodology, and (xvii) features extracted from the game.The literature search was conducted in July 2019 with the requirements described above, and a total of 682 studies were identified, including 151 from the PubMed database and 531 from the Scopus database. After removal of duplicates, 557 studies were screened with the criteria set, resulting in a total of 244 articles. In addition, 75 studies were excluded due to meeting one or more exclusion criteria, and 169 studies were finally included in the scoping review about upper limb rehabilitation based on serious gaming technology. The most common reasons for a study to be excluded were the absence of a serious game from the rehabilitation procedure and the development of a system that did not focus on upper extremity rehabilitation. Inclusion criteria for eligibility of the selected articles were: (i) any form of game-based arm rehabilitation  and (ii) published in a peer-reviewed or conference journal. Exclusion criteria for this review were: (i) a nonserious game–based scheme of rehabilitation with a sensor ; (ii) a trial of a serious game rehabilitation scheme without any technical description of the game; (iii) medical article based on a health care professional’s perspective for arm rehabilitation without any technical description of a game; (iv) not published in English; or (v) a review, meta-analysis, or conference abstract.device development, which was a Boolean factor. In some cases, studies could belong to more than one category . Descriptive statistics were used for these factors to present an aggregated view of the studies and percentages.Based on the extracted information, we created 11 factors of categorization for the data. The extracted information is presented in the Data Management subsection above. The factors were determined based on the combination of the extracted information. All analysis factors were categorical, except for The results extracted from the included studies are presented according to the following structure: (i) statistics depicted in charts, (ii) descriptive statistics that provide information regarding the tendencies of research efforts, and (iii) conclusions extracted not only from the statistics but also from the general picture formed from the analysis of all included studies.Based on our literature search, the first study was published in 1999; however, only a few relevant papers were published in this field up to 2006. In 2007, researchers showed greater interest in upper extremity rehabilitation using new technologies based on serious games, and the number of publications has continued to rise up to the present day. medical condition, the categories were created based on the references used by the authors of the included papers regarding the medical condition that caused the motor dysfunction. For example, stroke and cerebral palsy are subcategories of hemiparesis or neurological motor deficits. However, some studies mentioned only hemiparesis or neurological motor deficits as a cause of motor dysfunction without any further explanation, while others specified that stroke was the cause of motor disability. Owing to this heterogeneity, we created the categories based on the references for the studied medical conditions. According to our literature search, stroke was the most common reason for upper extremity motor dysfunction and the need for rehabilitation using technology. More than half  of the studies introduced a system for upper limb rehabilitation after stroke, followed by general motion deficits . Additional categories with lower frequency in the retrieved literature were cerebral palsy, hemiparesis, and neurological motor deficits. Furthermore, one study was related to Parkinson disease [To specify the  disease , one stu disease , two stu disease ,22, thre disease -25, and  disease  or wrist disease ,27 injurMost of the included studies  referred to rehabilitation of the upper extremity/limb in general, whereas others  focused on a specific part such as the fingers, palm, wrist, elbow, forearm, and shoulder.The results of our search indicated that researchers in this field are showing more interest in commercial sensors that continue to evolve. Less interest is placed on the development of new devices designed for motion of a specific upper extremity part. This may be due to the more costly and time-consuming development of such specific devices. Studies on hardware development accounted for 37.3% (63/169) of the total studies, whereas there was double the number of studies related commercial sensors, representing 62.7% (106/169 studies) of the total. Despite the fewer attempts to address hardware development, there seems to be continuous interest in this research area. However, it is evident that there are more fluctuations of publications over time in the case of hardware development due to the difficulty of the task , whereas commercial sensor–related studies showed a consistent increasing trend over time .device development categories, the results regarding representation of the studied medical condition and upper extremity part factors did not vary in general. Stroke and upper extremity/limb accounted for more than half of all studies related to commercial sensors  and hardware development . Nevertheless, these two categories have several differences concerning the game type and game target approaches, as described below.Between the two Game types were classified into different categories of virtual reality (VR), augmented reality (AR), video game, electronic board game, and mobile health  apps. The included studies generally used the term “VR” to refer to games simulating the real world in 3D virtual environments generated by computer graphics (not necessarily using VR headsets), whereas the term “AR” is generally used to refer to two types of games: (1) games showing a real environment, but some objects are enhanced by computer-generated perceptual information; and (2) games representing a virtual world, including the real upper limb of the user . Furthermore, “video games” refer to the creation of 2D games, whereas “electronic board games” refer to an interactive table or board. Finally, the “mHealth apps” category includes studies that describe the games as VR or AR, which are health apps using mobile or tablet games.game type offers an alternative reality to the patient, transforming a repetitive exercise of a rehabilitation routine into an amusing and appealing game to spend their time.Based on these definitions, the majority of studies included in the review  approached the rehabilitation problem by developing VR games. This game type was video games , along with some efforts to develop AR systems . With respect to AR, most of these studies used cameras and markers on the hand to recreate objects on the screen [The next most common e screen -39, althe screen ,40,41.device development regarding game type, the ratio of VR and video games was proportionally equal  movements , with noticeable interest in the force that the user exerts [game type is the time . Overall, 16.6% (28/169) of the studies aimed at achieving time reduction of the specific task, thereby motivating the user to compete with themselves.The r exerts ,47,54-61commercial sensor is the Kinect depth sensor, an accessory developed for the gaming platform Xbox. Kinect seems to be the most preferred sensor for capturing body parts and following their movement in space, which was used by 15.4% (26/169) of the studies included in the review. Some of the studies used only the Kinect sensor for their systems [A variety of commercial or noncommercial devices have been proposed for rehabilitation of the upper extremities combined with serious gaming based on the researchers’ ideas and accessible technologies at the time of publication. The most commonly used by 15.4% /169 of t systems -73, wher systems ,41,74,75 systems  to gain  systems  and a Wi systems  or other systems -80, Tyro systems , Xsen 3D systems , body ma systems , and a c systems . Further systems ,85.commercial sensor that has attracted researchers’ interest is Leap Motion, a hand-tracking sensor, which is most commonly used alone [With respect to commercial gaming accessories, a few studies focused on individual sensors such as VR headsets -88, Wii ed alone ,98-103. ed alone  in an ated alone  combinedcommercial sensors. In some cases [Additionally, some studies have attempted to create their own tracking system using me cases ,106-110,Overall, 17.8%(30/169) of the studies included webcams and cameras in their systems. Half of them ,111-115 Besides interest in developing robotic devices identified in the hardware development category, some research teams have also focused on robotic devices that are already available on the market. Several studies used haptic devices such as Phantom Omni ,120-122,In addition, some studies attempted to either control or monitor patients’ movements using medical devices such as EMG ,134-142.hardware development have been put forward in the developed devices. There are simple approaches that included objects such as a cup [Several different ideas of as a cup , combinaas a cup , custom-as a cup , a mouseas a cup , a foam as a cup , and an as a cup . Additioas a cup ,150-156,as a cup ,157-161.as a cup ,162-173 as a cup -177. A sDespite the use of advanced technology, many studies that used commercial sensor devices mentioned limitations regarding the used hardware. For example, various studies ,69,80,82hardware development category, including the need for enhanced calibration or upgraded components for better monitoring of accurate data. One study reported the need to improve a control strategy [Limitations were also mentioned with respect to the strategy , while ostrategy ,156,166.strategy ,160 or tstrategy .supervision level involved with each system was not always evident. Approximately half of the articles did not mention the home or clinical use of their systems, while some mentioned various supervision levels. It is worth noting that only 42.6% (72/169) of the included studies mentioned any supervision level for their proposed system; 3%(5/18) of the unsupervised cases belonged to the hardware development category and 8%(13/18) belonged to the commercial sensors category.In general, the All of the included studies described a system that has been developed by the corresponding research team. Some of them included small pilot or limited-range trials, while others were complemented by subsequent studies reporting the results of pilot surveys. It is notable that the most frequent limitation in cases with no pilot study was the absence of clinical trials and the deficient testing of the system. Overall, 33.7%(57/169) of the studies tested their system with healthy subjects and therapists, whereas 27.1%(46/169) did not test the system at all. Although the remaining one-third of the research teams conducted clinical trials with patients, the majority of them included a very small number of patients, and also included healthy subjects in some cases to enlarge the sample. Among the 169 papers included in this scoping review, there were 2291 participants in the pilot and clinical trials. However, from the total number of participants, 689 were patients and 1602 were healthy volunteers, clinicians, therapists, and researchers.system evaluation methods could not be easily categorized. This is because the research teams chose vastly different approaches for evaluation of their proposed system based on the target of the study and the available means at the time of publication. In 11.8% (20/169) of the included studies, no means of evaluation were mentioned regarding the introduced system, whereas 23.7% (40/169) of the studies seemed to focus only on users’ or therapists’ feedback about user experience via questionnaires and interviews. In these attempts, therapists and clinicians were given the opportunity to try the system with respect to the rehabilitation goals, the game’s environment, and devices’ safe use before being tested by patients.The extracted game features of the studies.Furthermore, 29.6%(50/169) of the studies used metrics regarding functional recovery via standard tests such as FMA, ARAT, and MAUULF; scores such as ROM for the elbow and forearm; and the Jamar strength test for strength of the hands. By using standard tests and scores as evaluation methods, researchers can measure the progress of a patient regarding motion dysfunction before and after the rehabilitation scheme. In addition, several studies tried to extract game features to introduce quantitative measurements for evaluation of patient improvement. Among these studies, 65%(49/75) did not analyze the evaluation methods, referring to them more generally as “data analysis” and providing descriptive statistics or as “monitoring data” in which the sessions were recorded using several sensors. In the next section, we discuss an extended analysis regarding the Quantitative measurements of the treatment outcome are critical for clinical rehabilitation practice, which constitute an objective method for evaluating the patient’s medical progress. With these measurements, physicians can closely monitor the therapy process and adjust the treatment protocol individually. Among the studies included in this review, only a few described an assessment process of the patient’s recovery status based on extracted features.game target, which was used to define metrics. These metrics were mainly classified into categories of hand movements or the duration, task, session completion of gestures, and reaction time.Some of these studies ,155,164 Game performance was another consistent feature among the studies. In some cases, performance was associated with the score, and in other cases it was associated with task completion of the game target. Several studies ,126,155 game target classes for describing the patient’s overall improvement [It is worth noting that several studies monitored other kinematic indicators of the patient’s health condition that are not included in the rovement ,137,164.rovement ,137,164 rovement ,68 mentirovement , whereasrovement  to quantrovement ,124,126 rovement ,137 focuextracted game features.ROM and data regarding the angles of the hand during its motion constitute another significant group of features that have been commonly used by researchers and health professionals to quantify therapy progress. Several studies ,103,152 It should be noted that this scoping review does not report every metric for each study, as our purpose was not to elaborate on how each study implemented the assessment of the patient’s improvement but rather to outline and categorize the features extracted from the motion analysis process, excluding metrics of patients’ engagement and motivation. Furthermore, in this attempt of feature extraction categorization, no distinction was made between studies that evaluated these features with a group of patients and those that conducted trials with healthy subjects.With this scoping review, we aimed to explore the trends associated with deploying technologies for functional rehabilitation of the upper extremities. The results indicate that there has been increasing interest in these applications over time. The rapid evolution of technology contributes to new approaches concerning clinical practice and personalization of therapies. There is currently a wide range of sensors available for capturing motion  along with attempts to translate these technologies into an environment projected on computer screens, in VR headsets, or in AR image processing. The capabilities of various sensors related to motion, in conjunction with serious gaming, are used by research teams to develop contemporary systems for both doctors and patients.One of the main advantages of this study is the overview of the current state of the field of upper extremity rehabilitation using serious games. As part of our research interest, we tried to investigate this topic to better understand the various approaches used to date. Based on this summary, we present a set of characteristics that depict a common direction and provide a complete picture of the sensors and technologies utilized to achieve the therapy purpose in terms of standard clinical practice.medical condition for many research teams to trigger a study about upper limb mobility. This is understandable considering that stroke is the third leading cause of disability worldwide [The results indicate increasing research interest in the domain of serious gaming deployment in upper extremity rehabilitation. Based on the descriptive analysis, we can examine different aspects of this field of research. In particular, stroke seems to be a common orldwide .upper extremity part, we found only one study published in 2016 by Hung et al [commercial sensors for their proposed rehabilitation system.With respect to the factor ng et al  that pregame type as “mixed reality” [While reviewing the surveys included in our literature search, it is a safe assumption that computer graphic development in the last few decades has led to generalization of the term “VR.” In 1992, Coates  defined reality” ,62,89, wgame target, every choice of game scenario was related to different aspects of rehabilitation schema. The game scenario combines the targeted upper extremity part, the moves that the patient needs to repeat for training, and the researcher’s goal for an outcome that entertains and motivates the user. Increases in task completion and score are the most common scenarios differing in the content of the task based on the rehabilitation scheme.With respect to the sensors and their combinations, as presented in upper extremity parts for rehabilitation using mobile or tablet app–based games, mainly because of the touchscreens.Additionally, there is a broad selection of commercial sensors and hardware development categories. Comparing the two device development categories, both showed differences in the proposed implementations. The larger number of publications related to the commercialsensors compared to the hardware development category implies greater interest in growing an idea of a game based on an advertised device. This higher interest may occur because it is more time-consuming or expensive to develop a new device than to explore the applications of already existing brands. Nevertheless, in the commercial sensors category, some studies reported poor hand part detection during sensor use, as elaborated upon in the Hardware Use Limitations subsection, deploying problems in practice. In addition, in the hardware development category, researchers have used the opportunity to develop a device based on the targeted hand part; however, depending on the case, a customized device could raise problems such as difficulties in use and adjustments for every hand size. Furthermore, many of the rehabilitation schemes included in the commercial sensors category targeted a home-based system using portable devices. However, for the hardware development category, the fragile and limited customized devices require supervised use, which poses a challenge for home-based trials.In this study, we classified the used devices according to the hardware development or commercial sensors category. The aim of this figure is to present the use of every individual sensor in the research on upper limb rehabilitation over time. Since 1999, researchers have extensively studied glove sensors and robotics, a category including arms and handles. In addition, it is evident that since the first release of Kinect in 2010, there has been continuous interest in its use in upper extremity rehabilitation over the years. It is worth noting that until the release of Kinect, several studies were using the Wii remote, with the majority published in 2011, whereas after this point, there were only a few such attempts reported in 2013 and 2016. By contrast, Kinect gained increasing interest from 2011 to 2019. The Leap Motion sensor was first released in 2010, but the first attempts to use it in upper limb rehabilitation were only reported 4 years later in 2014, and the highest number of papers published in this field appeared 3 years later in 2017. Moreover, an inverse relation was observed between studies published on the Kinect sensor and the use of cameras over the years. Finally, the category biosignals includes biosensors such as EMG and electroencephalogram. Since 2011, these sensors have been consistently used in many studies  of the studies that included some form of assessment for the introduced rehabilitation game mentioned, among others, user experience as a factor for the system’s estimation. In addition to user experience assessment, the most common system evaluation method involving patients was the use of standard clinical tests. Efforts for the creation of quantitative measurements of game-based treatment constitutes an attempt to provide evidence about the efficiency of the rehabilitation scheme and to personalize clinical practice.Concerning the Game features such as visual feedback of user actions and reward mechanisms via score/points or goal achievement were present in all rehabilitation gamification attempts. These features, which are an essential part of one of the biggest industries of the present day , are known to induce user engagement and are a core part of the rationale behind the gamification of health treatment protocols. In this regard, an increase in patients’ interest as a motive for investing in gamified approaches was taken for granted in most cases, although some studies also provided results from questionnaires that confirmed the above assumption.The broadness of the field of upper limb rehabilitation using serious games constitutes a limitation leading to many potentially included studies for this review. There was a significant number of studies, each suggesting different ways to approach the rehabilitation scheme but with poor sources or minimum attempts. Many conference papers have been published over the years introducing thoughts and preliminary results, but with no further analysis and implementation of their idea for rehabilitation. Although our exclusion criteria limited the range of the existing literature to some extent, this review includes several uncompleted attempts. Moreover, since we used specific keywords such as “upper-extremity,” “rehabilitation,” and “serious game” in different combinations, in an attempt to focus on the area of interest, we concede that some surveys in the field may have been excluded. Nevertheless, we are confident that the remaining studies that met all of the inclusion criteria can reflect the state of the field of upper extremity rehabilitation employing serious games, thereby assuring the reliability of our conclusions.In addition, a limitation of this study is the lack of categorization based on gross motor vs fine motor or testing usability vs testing effectiveness. The many differently structured papers in combination with the heterogeneity in the provided information made such categorization very complicated and led us to the decision not to include these categories. It may be worth analyzing these categories separately to obtain an overview of this field in a different study.Finally, due to the rapid technological progress, we consider another limitation to be the fact that this review includes studies only published up to June 2019.Upper extremity motor dysfunction is a common problem that requires rehabilitation. Researchers studying the engagement of patients to the rehabilitation schemes have established several ways to develop more amusing training sets to better motivate patients. Technological progress constitutes an ally of these attempts, allowing for the combination of a traditional rehabilitation routine with serious games. In the last two decades, there has been a significant number of publications regarding upper limb rehabilitation using serious games, which is a field that continues to evolve based on user experience. Our goal regarding this review was to provide a complete overview of the field based on published studies over the years. Overall, this scoping review highlights several facts that point to the usefulness of serious games in rehabilitation in future medical procedures, as well as several weaknesses and challenges that have to be addressed. Despite the numerous attempts for establishing and evaluating game-based rehabilitation systems, more evidence is needed considering such systems not only as a means for patient motivation but also as an actual means for achieving upper extremity functionality improvement. In this vein, despite the challenges in the generalization and comparability of specific game decisions and implementations, it is important to support the efforts for the creation of quantitative measurements of game-based treatment, performance and outcome, and build evidence of its clinical value. In this direction, it would be important to work toward creating a framework for the therapeutic use of such gamified approaches, including the optimal dosage, personalization means, adaptations over time, session performance assessment, and therapeutic outcome. Such a therapeutic framework could enable the synthesis of more solid clinical evidence around game-based treatment, and eventually its incorporation in the clinical routine.


Castration resistant prostate cancer (CRPC) continues to be androgen receptor (AR) driven. Inhibition of AR signaling in CRPC could be advanced using state-of-the-art biophysical and biochemical techniques. Structural characterization of AR and its complexes by cryo-electron microscopy would advance the development of N-terminal domain (NTD) and ligand-binding domain (LBD) antagonists. The structural basis of AR function is unlikely to be determined by any single structure due to the intrinsic disorder of its NTD, which not only interacts with coregulators but likely accounts for the constitutive activity of AR-splice variants (SV), which lack the LBD and emerge in CRPC. Using different AR constructs lacking the LBD, their effects on protein folding, DNA binding, and transcriptional activity could reveal how interdomain coupling explains the activity of AR-SVs. The AR also interacts with coregulators that promote chromatin looping. Elucidating the mechanisms involved can identify vulnerabilities to treat CRPC, which do not involve targeting the AR. Phosphorylation of the AR coactivator MED-1 by CDK7 is one mechanism that can be blocked by the use of CDK7 inhibitors. CRPC gains resistance to AR signaling inhibitors (ARSI). Drug resistance may involve AR-SVs, but their role requires their reliable quantification by SILAC–mass spectrometry during disease progression. ARSI drug resistance also occurs by intratumoral androgen biosynthesis catalyzed by AKR1C3 (type 5 17β-hydroxysteroid dehydrogenase), which is unique in that its acts as a coactivator of AR. Novel bifunctional inhibitors that competitively inhibit AKR1C3 and block its coactivator function could be developed using reverse-micelle NMR and fragment-based drug discovery. The Standard of care for CRPC uses AR signaling inhibitor (ARSI) therapy to block steps in this pathway. The first class of agents includes the use of P450c17 (17α-hydroxylase-17/20 lyase) inhibitors such as abiraterone acetate , 7, 8 toA number of mechanisms may be responsible for ARSI drug resistance, which are not mutually exclusive and some of the mechanisms are highlighted in More effective methods to block AR signaling are urgently needed, but the development of these approaches is hindered by significant knowledge gaps in how the AR functions. For example, the rational development of AR antagonists that target the LBD of the full-length receptor (AR-FL) or the NTD of AR and its variants is hindered by a lack of structural information that could be addressed by cryo-electron microscopy (cryo-EM) of AR and its complexes.It is also clear that the domains of the AR do not work independently and that there is interdomain coupling that can be driven by the NTD, which is intrinsically disordered. The intrinsic disorder of the NTD needs to be probed using different domain constructs to elucidate how one domain affects the binding partners  of another using the tools of fluorescence and CD spectrometry coupled with forced-folding techniques.The AR also does not act alone but is further activated by coregulators and by phosphorylation to lead to transcriptional complexes on the chromatin. Elucidation of the mechanisms that lead to chromatin looping and enhanced gene transcription can lead to the identification of new therapeutic targets for the eradication of AR signaling.The AR can evade ARSI therapy by the emergence of AR-SVs. The AR-SVs have been promoted as biomarkers of disease progression and ARSI drug resistance , 24. HowARSI drug resistance also occurs due to adaptive intratumoral androgen biosynthesis catalyzed by AKRC13 (type 5 17β-hydroxysteroid dehydrogenase) . AKR1C3 AR function could be stopped in its tracks with superior AR antagonists that target the LBD and prevent its binding to chromatin and by NTD antagonists that prevent its interaction with coregulators. Numerous structures have been reported for the AR LBD with agonist or antagonist bound , 28, 29.The AR-LBD forms a bundle of 12 α-helices, where the central core of half of the domain makes up the androgen-binding pocket . The bouThe AR-NTD recruits transcriptional coregulators, typified by members of the SRC/p160 family . The NTDThe largest gap in our structural understanding of AR involves the NTD. It seems likely that at least some of the NTD will be structured in an AR-FL•ARE complex. Both the FQNLF motif and a related WHTLF motif in the NTD can interact with the LBD , and theSeveral splice variants of the AR have been identified in patient-derived cancer cells and each of these lacks a functional LBD and their activity is driven by the NTD .The most2-ARE complex with R1881 (agonist) bound and of the same complex bound to its coregulators SRC-3 and p300 were reported at 13 Å and 20 Å resolution, respectively  or possess ID regions (IDRs) , 54, 55.via several mechanisms. First, the addition of coregulators of GR, AR, and progesterone receptor (PR) induces secondary and tertiary structure within their respective AF-1 regions  GR activity and modulated DNA affinity in an isoform-specific manner , as shown schematically in i.e., binding, mutation, posttranslational modification, etc.) to a protein is either agonistic or antagonistic is determined by which subensemble is affected to the greatest degree. In the case of GR, this model demonstrates that DNA binding directly activates transcription, but at the same time indirectly represses transcription. The overall effect that is observed is based on the relative strengths of the activation and repression interactions, which vary for each isoform. The importance of this finding with regard to the SHR family is that it suggests that any ligand, coregulator, or even DNA may not obligatorily be either an activator or a repressor. To the contrary, the specific thermodynamic architecture of GR renders all molecules that bind GR to be “functionally pluripotent” dominates,  canripotent”, 71, meates, see .Figure 3e.g., steroid, coregulator, or DNA) to be tuned by the environment for activation or repression. This EAM may help clarify the origins of the activation and repression differences in AR-FL and the AR-SVs and illuminate the molecular basis of ligand-independent signaling in CRPC.Can the results from GR be leveraged into an understanding of AR signaling? Clearly AR and GR share a common structural architecture, as do all of the SHR family . HoweverBiophysical approaches can be used to determine the basis of interdomain coupling in the AR. For example, using two domain constructs of the AR, which contain the NTD and DBD, permits the influence of one domain on the other to be studied in the absence of ligand. In these constructs the NTD can contain both Tau1 and Tau5 or only Tau5. Forced-folding experiments using the osmolyte trimethylamine oxide (TMAO) coupled with changes in intrinsic tryptophan fluorescence and CD spectroscopy can be used to determine the effect of NTD truncations on folding. As tryptophan residues become more buried, there is a blue shift in their emission spectrum. Knowing the maximal fluorescence change at any one concentration of TMAO, it is possible to determine the fraction of the construct that is fully folded. Using the same two domain constructs, the influence of NTD truncations on DNA binding to fluorescently labeled ARE half-sites can be measured by fluorescence anisotropy. Variable binding affinity to the ARE with different NTD constructs would illustrate allosteric modulation of DBD-binding affinity to the ARE. Functional consequences of this allosteric modulation can be measured in AR constructs in which the NTD is truncated in luciferase reporter gene assays in the presence and absence of ligand. Transitions observed in the TMAO and Ka associated with DNA binding give Gibbs free energies associated with them allowing a quantitative thermodynamic model to be established. Moreover, forced folding using the confinement of RM encapsulation of the different constructs followed by NMR allows structure of the ensemble of different folded states to be illuminated. Such an approach could determine how EP1-7836 acts as an NTD antagonist.AR does not act alone but functions in a complex with other nuclear cofactors and chromatin-associated proteins to drive transcription of genes essential for tumor growth and drug resistance , 77, 78.Targeting of the proteins functionally associated with AR offers a promising new approach to block dysregulated AR signaling. Disruption of the AR interaction with coactivators such as the BET bromodomain family protein, the BRD4 methyltransferase complex, and the MLL-Menin, DNA repair enzymes—DNA-PKcs, have shown promising results in attenuating AR signaling and tumor growth in CRPC , 79, 80.An important and critical coactivator of AR is MED1 , which forms a multiprotein Mediator complex that is required for enhancer-promoter looping and transcription pause release , 82. TheMED1 binding to SHRs is mediated by LxxLL motifs in MED1 and the AF2 activation domain generated by ligand binding in SHRs . Additioin vitro kinase assay, cyclin-dependent kinase 7 (CDK7) was identified as the direct kinase that targets T1457 for phosphorylation, while the T1457D phosphomimetic associates with AR on chromatin and is resistant to CDK7 inhibition . Loss of CDK7 activity either through genetic inhibition or through treatment with THZ1, a small-molecule covalent inhibitor, leads to a loss of recruitment of MED1 to the chromatin, loss of AR-target gene expression, increased apoptosis, reduced cell proliferation, and inhibition of tumor growth in mouse xenograft CRPC models. Importantly, AR activity that was restored in enzalutamide-resistant prostate cancer cells was associated with increased MED1 pT1457 and demonstrated continued sensitivity to THZ1. The efficacy of THZ1 in curtailing hyper-AR signaling and promoting cell death even in the enzalutamide resistant setting points to the pivotal role of CDK7-MED1 axis in driving AR addiction and an approach to extinguish this activity .Studies have been performed to determine whether ligand-dependent AR activation leads to MED1 phosphorylation to promote its recruitment into the Mediator complex and interact with AR to form an active transcription complex . First, hibition    where phosphorylation on S81 can lead to stabilization of the AR and constitutive activation of AR , 101 andor S515) . It is aon of AR . The SRChttps://clinicaltrials.gov). However, only one of these trials includes CRPC patients.CDK7, along with cyclin H and MAT1, comprises the CDK-activating kinase (CAK), which provides the T-loop phosphorylation required for activation of CDK1, CDK2, CDK4, and CDK6, which drive cell cycle progression . AdditioIn light of the preclinical data that supports a role of CDK7 to enhance AR activity, at least partly through phosphorylation of MED1, and increased MED1 phosphorylation contributes to the restoration of AR activity in enzalutamide refractory prostate cancer, CDK7 becomes a potential therapeutic target in advanced CRPC. Therefore, excitement exists in using CDK7-specific inhibitors in the clinic for advanced CRPC in which AR activity has been restored, where increased phospho-MED1 could serve as a biomarker to identify potential responders to such treatment.AR may evade the effects of ARSIs by the appearance of AR-SVs that lack the LBD and are constitutively active. Examination of mRNA from patients in the international Stand Up 2 Cancer study demonstrated that 22 AR-SVs exist at the mRNA level . Howeverin situ hybridization (RISH) also showed low levels of mRNA in primary tumors without concomitant protein expression ; it is highly doubtful that these data accurately reflect the role of the AR-SV in disease  offers a new approach to quantifying protein biomarkers , 125. ThIP-LC-HRMS has been employed in proteomic approaches to detect ERα and ERβ, and peptide coverage showed that the majority of ERβ antibodies did not detect the receptor , 131. ThB, bottom panel). AR-FL would be quantified using the Glu-C AR-FL-specific peptide , with stable isotope labeling by amino acids in cell culture (SILAC)-A623GMTLGARKLKKLGNLKLQE642 derived from concomitant Glu-C digestion of SILAC-AR-FL as the internal standard . Individual AR-SVs could be quantified from their specific Glu-C-peptides. For example, K630FRVGNCKHLKMTRP644 and R711QLVHVVKWAKALPDCE727 (B and C) would be used to quantify AR-V7 and AR-V12, respectively, using appropriate SILAC-AR-SV protein internal standards.The SID-IP-LC-MS method in combination with endoproteinase Glu-C (V8 protease) digestion  could beFig. 5AKR1C3 promoter. In support of this model ChIPseq experiments demonstrated recruitment of ERG to the AKR1C3 promoter/enhancer  plays an essential role in intratumoral biosynthetic pathways to T and DHT, which are potent ligands for the AR. AKR1C3 is the most upregulated steroidogenic enzyme in CPRC . It is ienhancer .in vitro and in vivo  cells that are resistant to enzalutamide and abiraterone, and this resistance can be surmounted by indomethacin, a potent and selective competitive inhibitor of the enzyme,  in vivo , 148. Th in vivo . While tNonetheless, AKR1C3 remains an attractive target since it has other unexpected functions that impact its role in CRPC. It stabilizes the Siah2 ubiquitin ligase preventing its self-ubiquitination and permits degradation of AR-NCoR complexes  and it s222QRDKRW227, whereas in AKR1C1 and AKR1C2, the sequence corresponds to 222HREEPW227. Mapping this region to the AKR1C3 crystal structure shows that this peptide is in a disordered loop. Although p160 coactivators contain an LxxLL motif that binds to the LBD  on the disordered loop. Recent advances in solution NMR of proteins will provide detailed characterization of the structural and dynamic perturbations responsible for the bifunctionality of GTx-560. As a companion to structural techniques such as crystallography and cryo-EM, solution NMR provides access to otherwise hidden attributes such as transient populations of critical minor states . However, the PROTAC derivative based on enzalutamide,  the LBD .As the AR does not act alone, targeting specific coregulators that interact with AR to restore its activity in ARSI-resistant tumors offers a promising approach and could involve targeting MED1 with CDK7 inhibitors. Several CDK7 inhibitors are in clinical trials for solid tumors, but only one trial is enrolling CRPC patients so that the efficacy of this approach remains to be determined.The AR uses a number or escape mechanisms to evade the benefit of ARSI therapies. This includes the appearance of AR-SVs. AR-SVs may well be important biomarkers of disease progression and response to ARSI therapy but without a reliable assay to measure protein mass, these biomarkers cannot be assessed in terms of their sensitivity and specificity or clinical utility. If this were achieved using mass spectrometry, then the appearance of AR-SVs could be used to predict lack of response to ARSI therapy and promote the use of other therapies such as taxanes in their place. Another escape mechanism involves increased AR ligand synthesis catalyzed by AKR1C3. AKR1C3 is not only involved in ligand synthesis but AR coactivation as well. Bifunctional agents based on GTx-560, which target both functions of AKR1C3, would be an approach provided the protein is confirmed as a major AR coactivator in CRPC.CRPC is a heterogenous disease and that different phenotypes can exist. For example, small-cell neuroendocrine prostate cancer can emerge after ARSI therapy in about 20% of patients that progress . DiverseT. M. P. is the founder of Penzymes, LLC; he receives sponsored research funding from Forendo and serves on the Expert Panel for the Research Institute for Fragrance Materials. I. A. B is a founder of Proteoform Bio and a paid consultant for Calico, Chimerix, PTC Therapeutics, Takeda Pharmaceuticals, and Vivo Capital. S. R. P is the president of ProsTech, Inc. All other authors declare no competing interests.


Allergic rhinitis/rhinosinusitis (AR) is the most common allergic disease. It affects patients’ quality of life and may influence the severity of lower airway disease such as asthma. Therefore, its treatment is of great importance. AR is treated by a combination of effective approaches; however, in some patients, the disease is uncontrolled. In the last several years, the concept of AR has shifted from increased T helper 2 (Th2) cell signaling and downstream inflammation to disease phenotypes with non-Th2-mediated inflammation. AR is a largely heterogenous group of airway diseases, and as such, research should not only focus on immunosuppressive agents  but should also include targeted immunomodulatory pathways. Here, we provide an overview of novel therapies, focusing on the role of phosphodiesterase-4 (PDE4) inhibitors in AR. PDE4 inhibitors are potent anti-inflammatory agents that are used for the treatment of inflammatory airway diseases including AR. The PDE4 inhibitor roflumilast was shown to effectively control symptoms of AR in a randomized, placebo-controlled, double-blinded, crossover study in patients with a history of AR. However, only a few PDE4 inhibitors have proceeded to phase II and III clinical trials, due to insufficient clinical efficacy and adverse effects. Research is ongoing to develop more effective compounds with fewer side effects that target specific inflammatory pathways in disease pathogenesis and can provide more consistent benefit to patients with upper airway allergic diseases. Novel specific PDE4 inhibitors seem to fulfill these criteria. Allergic rhinitis (AR) is an inflammatory disorder of the nasal epithelium. Anterior and posterior rhinorrhea, nasal congestion, sneezing, and itching are typical clinical symptoms, which are reversible spontaneously or in response to therapy .AR is not considered to be the most serious medical condition compared with other respiratory diseases. However, patients with AR have a reduced quality of life, lack of or worsened quality of sleep, cognitive function impairment, irritability, and fatigue, especially during peak pollen season . The mosThe phenotypes of this disease appear to actively change, with about 50–70% of patients suffering from mixed rhinitis (non-AR and AR) and approximately 10–20% suffering from the pharmacologically resistant phenotype. AR with localized nasal allergic response in the absence of systemic atopy has also been identified .Standard treatment of AR includes the combination of oral antihistamines and glucocorticoids (GCs), which may not provide sufficient relief from symptoms; thus, novel therapies are needed . In recePDEs are responsible for the hydrolysis of intracellular cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), which have emerged as novel therapeutic targets. PDE inhibitors allow the elevation of cAMP and cGMP, which leads to a variety of cellular effects such as relaxation of airway smooth muscle and inhibition of cellular inflammation. Special attention has been paid to PDE4 inhibitors, which are potent anti-inflammatory agents that have shown encouraging results in the treatment of chronic obstructive pulmonary disease (COPD) and asthma. Their potential in the management of upper airway diseases has also been suggested. This review summarizes current evidence on the beneficial as well as harmful effects of the clinical use of PDE4 inhibitors in allergic rhinitis/rhinosinusitis .AR is clinically defined as a symptomatic disorder of the nose induced after allergen exposure by immunoglobulin E (IgE)-mediated inflammation of the nasal membranes . AR is aAs aforementioned, the most causal antigens for AR are inhalant allergens such as house dust mites, animal dander, and pollen. Many allergens have protease activity and are able to impair the epithelial barrier and penetrate into nasal mucosa. Allergen-specific Th2 cells are produced in patients with AR, whereas allergen-specific Th1 cells are produced in healthy individuals . The resBasophil cells produce cytokines such as interleukin 4 (IL-4) and thymic stromal lymphopoietin in response to allergens with protease activity, and may contribute to Th2 differentiation . Th2 celDysregulation of dendritic cells (DCs) plays an important role in the pathogenesis of AR. Understanding DC biology and its functions in physiologic and pathologic conditions may provide an understanding of the role of DCs in the initiation and perpetuation of inflammatory sinonasal diseases, and also lead to the development of potentially novel therapeutic strategies targeting DCs . DCs inf+ T cells protects against intracellular pathogens, but can also lead to tissue injury. DCs produce IL-23, and together with IL-6 and IL-1β, are involved in the expansion of Th17 cells, which are responsible for strong pro-inflammatory responses against extracellular pathogens, possibly leading to chronic inflammation and autoimmune diseases . PDE4A, PDE4B, and PDE4D are expressed by neutrophils, eosinophils, B cells, T cells, DCs, monocytes, and macrophages. Expression of PDE4C is typically minimal or absent  gene in patients with no history of nasal polyposis (a type of AR) and patients with nasal polyposis, indicating the potential of using a PD4 inhibitor for the management of patients with nasal polyposis (In a prospective study, genetic analysis revealed significant differences between the olyposis .The effects of PDE inhibitors on ciliary beating frequency in rabbit maxillary sinus and trachea have been also investigated. The authors compared rolipram, a cAMP-specific PDE4 inhibitor with bronchodilator and anti-inflammatory activities with milrinone, a cGMP-specific PDE3 inhibitor with good bronchodilator activity but weak anti-inflammatory activity and zaprinast, a cGMP-specific PDE5 inhibitor with weak bronchodilator effects. All of them stimulated ciliary beating frequency in explants from the maxillary sinus as well as from the tracheal mucosa .AR is a common health problem with a rising incidence. Inadequate treatment may cause chronic AR and contribute to lower respiratory tract diseases such as bronchial asthma. In general, the disease can be well controlled with adequate treatment with the exception of AR, which is not mediated by the Th2-inflammatory pathway and does not respond to current available treatment modalities such as corticosteroids and antihistamines. PDE4 inhibitors can suppress a variety of inflammatory cell functions that are known to contribute to AR. However, use of systemically delivered PDE4 inhibitors, such as roflumilast, are limited by systemic side effects. Topical treatment with inhaled PDE4 inhibitors may be a viable alternative to increase tolerability and determine the maximum therapeutic potential of PDE4 inhibition in AR.VJ and HP wrote the substantial part of the manuscript. EB contributed to the epidemiology and pathophysiology of allergic rhinitis. VC contributed to the final version of the manuscript. AC conceived of the presented idea. AC and AH reviewed the article and made contributions according to their expertise.VEGA 1/0055/19 and APVV-17-0250.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Previously, 3% of the human genome has been annotated as simple sequence repeats (SSRs), similar to the proportion annotated as protein coding. The origin of much of the genome is not well annotated, however, and some of the unidentified regions are likely to be ancient SSR-derived regions not identified by current methods. The identification of these regions is complicated because SSRs appear to evolve through complex cycles of expansion and contraction, often interrupted by mutations that alter both the repeated motif and mutation rate. We applied an empirical, kmer-based, approach to identify genome regions that are likely derived from SSRs.The sequences flanking annotated SSRs are enriched for similar sequences and for SSRs with similar motifs, suggesting that the evolutionary remains of SSR activity abound in regions near obvious SSRs. Using our previously described P-clouds approach, we identified ‘SSR-clouds’, groups of similar kmers (or ‘oligos’) that are enriched near a training set of unbroken SSR loci, and then used the SSR-clouds to detect likely SSR-derived regions throughout the genome.Alu , validating the sensitivity of the approach. Poly-A’s annotated by SSR-clouds also had a length distribution that was more consistent with their poly-A origins, with mean about 35 bp even in older Alus. This work demonstrates that the high sensitivity provided by SSR-Clouds improves the detection of SSR-derived regions and will enable deeper analysis of how decaying repeats contribute to genome structure.Our analysis indicates that the amount of likely SSR-derived sequence in the human genome is 6.77%, over twice as much as previous estimates, including millions of newly identified ancient SSR-derived loci. SSR-clouds identified poly-A sequences adjacent to transposable element termini in over 74% of the oldest class of  SSRs arneration , many orneration . In addineration . Numerouneration –14 and aA proposed life cycle for SSRs includes intertwined stages of birth, adulthood, and death –18. De nIt is thought that during SSR “adulthood”, slippage-induced expansions and contractions  can rapidly alter the length of SSR loci, but mutations that disrupt the composition of tandem repeats also accumulate and slow or stop the slippage process , 24. TheSSR death presumably occurs after either sufficiently large deletions at a locus have occurred or after enough mutations have accumulated so that there are no longer uninterrupted tandem motif stretches above the threshold length . After tThe abundance of active SSRs in the genome and their finite lifetime suggest that dead SSRs may also be abundant, although their high slippage mutation rate and complex, motif-dependent evolution makes modeling their evolutionary outcomes difficult. The identification of dead SSRs remains important if for no other reason than because their presence in the genome can confound the detection and annotation of other genomic elements . SeveralP-clouds) [P-clouds method has been applied to identify various repetitive structures in the human genome [P-clouds both fast and particularly well-suited to annotate regions resulting from the complex mutational processes associated with SSR loci. We obtained sets of p-clouds in regions flanking perfect live SSRs under the hypothesis that such regions will be enriched in the mutated detritus of the SSRs [Here, we report a new method to detect SSR-derived sequence using a probability-clouds (-clouds) , 33 basen genome , 33, incthe SSRs . These Sperfectly repeated contiguous SSR locus  at least 12 bp in length. A total of 4,551,080 perfect (uninterrupted) SSR annotations were found, covering 68.8 Mb (~ 2.2% of the genome). These perfect repeats constitute over three-quarters (77.8%) of the 88.4 Mb SSR sequence (2.85% of the human genome) annotated using standard TRF settings.Uninterrupted perfect SSR loci abound in the genome. SSR sequence motifs of 1–6 bp were grouped into motif families comprised of a motif, its reverse complement, and any possible alternate phase of the motif or its reverse complement  to create a total of 501 separate SSR motif families. If a longer motif was a repeated multiple of a shorter motif , that motif was assigned to the shorter motif. The unmasked human genome (hg38) was annotated  overlap with any annotated transposable element. Some caution is warranted in interpreting this result, both because the poly-A tail and the A-rich region in the center of many Alus may or may not contain a perfect repeat, and because RepeatMasker is inconsistent about whether it includes a poly-A tail in a repeat annotation. Nevertheless, this result indicates the minimum extent to which transposable elements contribute to the frequency of poly-A loci in the genome. Other than poly-A, the next most represented motif is CA/TG with 170,729 separate annotations, only 3,206 (1.88%) of which are found in an Alu element. Although all possible SSR motifs families have at least one locus in the genome, the most common motif families tend to have much simpler motifs than the least common  method [extended regions in the genome made up of such oligos by chance alone is improbable. For example, if 50,000 oligos were distributed evenly across the genome, one might expect to find only about one oligo every 100,000 bp.To characterize and detect oligos in SSR detritus fields, we used the probability clouds  method , 33, whiAlu transposition efficacy increases with poly-A tail length up to 50 bp [entire ancient sequence derived from the poly-A tail at the time of insertion. However, it should be recognized that some of the detected length could be due to slippage in either direction post-insertion and prior to degradation. The length distributions of other SSR loci are similarly expanded, but with tails often extending to much larger regions  of the human genome. Of these loci, 46.92% intersect a transposable element, which includes poly-A regions annotated as part of the transposable element. A total of 3,085,675 of the loci, comprising 62 Mb  do not overlap with any previous repetitive element (including SSRs annotated by TRF), and thus represent novel repetitive sequence. Accounting for false discoveries adjusted for cloud stringency and locus length see , we concThe average false discovery rate is 5.31%, but the probability of being a false discovery varies widely among loci, depending on length. Most loci have a high positive predictive value , but 3,423,735 loci covering 53.8 Mb (~ 25% of the SSR-clouds annotation) have a false discovery rate > 10% (maximum FDR = 0.175). The majority  of these less certain SSR loci are either 16 bp or 17 bp in length, while the remainder are comprised of short perfect SSR loci under 13 bp in length. Although these loci have high false discovery rates because they are short, there are millions more of these loci than expected by chance based on dinucleotide frequencies. This abundance of short SSRs indicates that simple sequences of this length may often originate during evolution but die quickly through mutation accumulation before they have a chance to extend to create longer loci. It is also worth noting that regardless of their origin, these short loci are identical in sequence to areas that have potentiated SSR expansions and likely good spawning grounds for future SSRs.Although the purpose of this research was not to replace Tandem Repeats Finder (TRF), we nevertheless compared the SSR-cloud annotations with TRF annotations using the same parameters as in , which yAlu transposable elements and poly-A SSRs [Alu has over a million copies in the human genome, and their relative ages can be accurately determined [Alus into three age groups approximately representing the main families of Alu and assessed how frequently poly-A loci detected by SSR-clouds of different stringencies could be found in the poly-A regions of Alu elements. While 63% of young poly-A tails tend to be annotated by uninterrupted poly-A clouds, older poly-A tails from the oldest group of Alus  are unsurprisingly the most difficult to detect and are often annotated only by low stringency SSR-clouds  had some sort of SSR-derived locus in the expected poly-A region. It is possible that the 9,500 old Alus without detected SSR-clouds had their tails deleted or moved through genomic rearrangements over time or they degenerated to the point of being unidentifiable. The oldest group of Alus is 1.60 times older than the average age for all Alus, while the unannotated Alus are 1.64 times older , supporting the idea that loss of tails increases with age.About 25% of old loci were not detected by poly-A clouds of any stringency level, but an additional 11,821 annotations were found using SSR-clouds from any SSR family, not just poly-A. Thus, almost 90% of the oldest P-clouds for finding SSRs and SSR-derived regions in the genome. SSR-clouds finds numerous previously undiscovered SSR loci whose overlap with poly-A regions of known ancient transposable element loci provides compelling evidence that these loci are indeed SSRs or are SSR-derived. SSR-clouds analyses reveal that SSR-derived regions comprise a larger portion of the human genome than previously appreciated, increasing the SSR-derived percentage from about 3% to at least 6.77%. This increase is due to increased annotation length of previously annotated loci as well as newly annotated loci  have intact poly-A tails, and only a small fraction (< 5%) have different motifs or no SSR at all in their poly-A tail region. Notably, the remaining nearly 40% have already begun to degenerate, even after relatively recent successful retrotransposition. However, although the poly-A appears to rapidly degenerate, these degenerate regions are detectable in many of even the oldest of Alu elements, demonstrating both a surprising longevity of SSR character in ancient simple repeats, and the sensitivity of SSR-clouds method.A large fraction of recent (4–6 million years old) Alus spawned new SSRs with different motifs  of poly-A’s from the oldest group of ifs Fig. . Spawnin% of polyThe high degree of overlap between transposable elements and SSR loci we present here supports the hypothesis that transposable elements play a substantial role in the generation of SSR loci , 37, 41.Perl script that clusters alternate phases of the same SSR motif (ACT = CTA = TAC) and reverse complements of each phase into a single motif family. Perfect SSR repeat loci were defined as uninterrupted tandem repeats of a single motif family ≥12 bp in length, and perfect stretches separated by 1 bp or more non-motif nucleotides were considered different loci. Perfect SSRs, as defined above, were annotated in an unmasked version of hg38. To identify sequence bias in regions near perfect SSR loci, each kmer (k-length oligonucleotide sequence) within 1000 bp of a perfect repeat locus was compared with the kmers from different phases of the perfect motif. Mean similarities to the closest repeat kmer were calculated versus distance from locus boundaries, and distances between perfect SSR repeat loci were also recorded.Oligonucleotide sequences representing all possible SSR sequences were created in silico using a P-clouds formation protocol [Alu in humans) were not our targets but are highly represented in regions flanking SSRs, and so all transposable elements annotated by RepeatMasker [http://www.repeatmasker.org/species/hg.html) were removed prior to cloud formation. Because clouds were formed separately for each family, individual oligonucleotides, including those representing perfect repeats, can belong to cloud sets for multiple families.SSR-clouds were constructed similarly to cloud construction methods outlined in , 33 withprotocol , p-cloudatMasker  (as founn), annotation was performed in the same way except that only oligonucleotides created from that family were used.Annotation with SSR-clouds was performed in an unmasked version of hg38 by simultaneously mapping oligonucleotide clouds from all motif families, and then merging loci within 5 bp of each other into a single locus. Annotations with merge distances of 0 bp and 30 bp were also performed and are presented as supplements . The simulation proceeded by randomly selecting nucleotides conditional on dinucleotide frequencies. When the previous nucleotide was absent or undetermined, a starting nucleotide was selected based on independent single nucleotide frequencies. Prior to creation of the simulated genomes, all regions annotated as either a perfect SSR or annotated as transposable elements or other repeat regions by RepeatMasker were masked so that nucleotide and dinucleotide frequencies used in simulation would be representative of non-repetitive portions of the genome. Because we expect that some SSR and SSR-derived sequences may occur only rarely using this simulation approach, the final simulated genome used to determine false positive rates consists of fifteen genomes that were simulated as described.With decreasing SSR-cloud stringency settings, there are more oligonucleotides and they are increasingly diverse see Fig. ; becauseSSR clouds were annotated in the simulated genomes exactly as done for the actual genome. For each stringency setting, false positive rates for each locus length (or longer) were calculated as the cumulative amount of simulated sequence annotated using that stringency of SSR-clouds, divided by the amount of sequence analyzed. The length of a locus annotated by a given stringency was considered to be the longest stretch of the locus that was consecutively annotated by oligonucleotides from that stringency. The false positive rates calculated from the simulated genome for each locus length and cloud stringency category were then applied to SSR loci in hg38   version Alu elements were obtained by filtering RepeatMasker Alu annotations from the hg38 assembly of the human genome. Relative ages of each element (measured in inferred number of substitutions since retrotransposition) were then estimated by applying the AnTE method to this dataset [Alu elements, with each element having an estimated age or retrotransposition relative to the mean age of retrotransposition of all Alu elements. To maximize the chances that the Alus tested still contained their poly-A tail, we removed all Alus that were < 275 bp or > 325 bp in length as well as those Alus that were within 50 bp of another TE. After filtering, 407,438 Alus remained.Full-length and non-concatenated  dataset . We begaAlu annotations were split into three groups by age and roughly based on the major expansions of AluY, AluS, and AluJ. The youngest group consisted of 57,873 Alu elements, ~ 97% of which are classified as AluY by RepeatMasker, with a mean age of 0.51 relative to the mean age of all Alus. The second and largest group, 99% of which are classified as AluS elements, consisted of 265,219 elements with a mean age of 0.92 relative to the mean age of all Alus. The third group consisted of all Alu elements older than those included in the first two groups, 90% of which are classified as AluJ and 10% as AluS, and had 84,346 elements with a mean age of 1.6 relative to the mean age of all Alus.The remaining Alu rather than other SSR-rich regions in Alu, we used the 30 bp directly 3′ to each Alu tested for intersection. We used BEDTools intersect (v2.19.1) [Alu elements that intersected each of the poly-A SSR annotations, beginning with the highest stringency poly-A annotations and proceeding to the lowest stringency annotations.To ensure detection of only the poly-A region of v2.19.1)  to countAdditional file 1: Figure S1. Enrichment of SSRs in the human genome. The mean enrichment of perfect repeats is shown relative to expectation from single nucleotide frequencies. All SSR motifs of a given length were clustered into groups, except that the Poly-A and poly-C single nucleotide repeats are shown as separate lines. The enrichment is shown for the number of repeats of a given size observed in tandem, and the gray dashed lines indicate 10x, 100x, and 1000x enrichments.Additional file 2: Figure S2. Separation distance between perfect SSRs in the human genome. The frequency of pairs of perfect SSRs ≥12 bp long with a given separation distance is shown. The separation distances were binned into groups of 5. The results in A) are for a masked version of the human genome, while B) shows results for an unmasked genome, demonstrating the strong effect and particular features of transposable element SSRs.Additional file 3: Figure S3. Asymmetric similarity to poly-A. The frequency of adenine nucleotides (A) at every site within 200 bp of perfect poly-A repeats. The solid line shows the frequency of A in a human genome where all transposable elements have been masked and the dotted line shows the frequency in an unmasked human genome. As a reference, the gray box represents a range of 3 standard deviations from the mean frequencies of A calculated in 700 bp windows from 300 to 1000 bp away from both ends of all perfect repeats. The strongly varying frequencies in the unmasked genome are mostly a symptom of the high copy number of retroelements such as Alu and Line1. The asymmetric frequency of A’s adjacent to perfect A repeats in the masked genome likely reflects incomplete masking of transposable elements and the existence of other unmasked retrotransposed sequences in what would have been the 5′ region of the retrotransposed poly-A mRNAs.Additional file 4: Figure S4. Cloud extension length distributions of training and test loci. Locus length density plots of SSR loci containing perfect repeats (black) and lengths after extension by mid- (red) and low-stringency (blue) cloud sets. Solid lines depict the distributions of lengths for training loci and dashed lines depict the almost perfectly overlapping distributions of lengths for test loci.Additional file 5: Figure S5. Genomic SSR content annotated with different merge distances and false discovery thresholds. The number of bp in the human genome that were annotated by SSR-clouds under various conditions are shown. With different merge distances and false discovery thresholds. Three lines are shown for merge distances of 0 bp (black), 5 bp (red), and 30 bp (blue), with the per-locus maximum false discovery criterion on the X axis.Additional file 6: Table S1. Summary statistics of perfect SSR loci in hg38 for each SSR family.Additional file 7 : Table S2. SSR-clouds recovery of Tandem Repeats Finder (TRF) loci. SSR-clouds loci with a merge distance of 0 bp were divided into 3 nested sets based on the most stringent oligo used to annotate each locus and compared to TRF loci. Comparisons were also made for SSR-clouds loci with FDR ≤ 5%. Cells in the table report the number of SSR-Clouds loci that overlap TRF loci and the number of bp within overlapping loci. We also report the number of novel SSR-clouds loci and bp. Recovery percentages are reported relative to the total number of TRF loci in each comparison category n: 148,607; All Motifs: 2,212,424) and total length in bp of the TRF loci n: 7,403,867; All Motifs: 88,485,889).Additional file 8: Table S3. SSR-clouds recovery of Tandem Repeats Finder (TRF) loci. SSR-clouds loci with a merge distance of 30 bp were divided into 3 nested sets based on the most stringent oligo used to annotate each locus and compared to TRF loci. Comparisons were also made for SSR-clouds loci with FDR ≤ 5%. Cells in the table report the number of SSR-clouds loci that overlap TRF loci and the number of bp within overlapping loci. We also report the number of novel SSR-clouds loci and bp. Recovery percentages are reported relative to the total number of TRF loci in each comparison category n: 148,607; All Motifs: 2,212,424) and total length in bp of the TRF loci n: 7,403,867; All Motifs: 88,485,889).Additional file 9: Table S4. SSR-clouds construction summary.