In this article, we tried to reveal the relationship between personality traits and escape behavior in traffic accidents. Different from common computer simulations, this study, for the first time, established a real database recording the escape behavior and personality traits of subjects when watching a first-person-view driving video with explosion. Then, we used a modeling method of general linear to establish a quantitative model of the influence of personality traits, explosion, and their interaction on escape behavior. In the model, we introduced escape response time, escape time, escape direction, escape speed, escape trajectory, and other motion characteristics to study individual escape behavior in accidents. Through the analysis, we concluded several conclusions, including that high neurotic individuals tend to escape with shorter response time and slower speed by choosing doors far from the explosion source. These conclusions may provide some references for the effective escape of the crowd and the successful escape of the individual under traffic accidents. The research on the escape behavior of people in traffic accidents is a hot topic in the development of transportation. With the rapid growth of economy, the transportation industry, which is convenient for people to travel, has also been explosively developed. At the same time, the frequency of extreme traffic accidents is also getting higher and higher, such as multivehicle collision on the fast road, automobile explosion in the tunnel, or tire explosion accidents. In extreme and traffic accidents, the escape ability of an individual is very important. The effective, rapid, and safe escape of individuals has become indispensable basic theoretical research for the sustainable development of traffic. The successful escape of individuals and the successful escape of crowds under extreme events are inseparable from the revelation of the escape behavior rules of people in traffic accidents.In fact, behavior of humans in a disastrous situation has been studied after the fact and has been documented by sociologists and a psychologist . BecauseOur contributions are as follows:1.Different from common computer simulation, this study adopts the experiment of designing extreme traffic accidents without or with explosion scenes to establish a real database of the escape behavior and individual personality traits of 200 subjects.2.Most of the existing studies on escape behavior only analyze one or two of the motion characteristics, such as response time, escape time, escape direction, escape speed, and escape trajectory, while ignoring the integrity analysis of the motion characteristics of escape behavior. In our study, we introduced escape response time, escape time, escape direction, escape speed, escape trajectory, and other motion characteristics to study the individual escape behavior in traffic accidents.3.As the first related study, to obtain the mapping relationship between individuals with different traits and escape behavior, we adopted the modeling method of general linear model and established a quantitative model of the influence of personality traits, explosion, and their interaction on escape behavior. We found that high neurotic individuals tend to escape with a shorter response time and slower speed by choosing doors far from the explosion source. This will provide some reference for the effective escape of the crowd and the successful escape of the individual under traffic accidents.Every year, a large number of casualties and property losses are caused by traffic accidents, fires, explosions, and other emergencies worldwide, and the processing cost of these emergencies is very huge. Escape behavior in unexpected situations has always been a problem that researchers pay more attention to. Emergency escape involves moving a large number of people in response to natural disasters . If not planned, escape may cause congestion at the exit. At the same time, to minimize casualties and property losses , it is nIn summary, the existing research on escape behavior mainly focuses on the study of group escape behavior in traffic accidents and rarely studies individual escape behavior, especially on the analysis of individual escape behavior with different personal traits. Therefore, this article focuses on the escape behavior of individuals with different personal traits in traffic accidents.In this study, a 2 × 2 factor experiment with low or high personality dimensions and with or without explosion scenarios is used to construct a database. The independent variable is personality traits , and the dependent variable is escape behavior . Experimental subjects and experimental scenarios are discussed below:A total of 200 healthy college students were included in the analysis of this study, including 68 males and 132 females, with an average age of 23.1 years, standard deviation of 3.03, and age range of 18–34 years. All subjects had normal visual acuity or corrected visual acuity, normal hearing, normal color vision, right-handedness, no state-trait anxiety, no previous and current manic episodes, no history of alcohol, drug abuse, developmental delay, and other organic diseases. This study was approved by the Ethics Committee of Southwest University. Before the formal experiment, all subjects signed informed consent.The plane diagram of the experimental scene in this study is shown in All participants first participated in the Big Five Personality Inventory, State Anxiety Inventory, Trait Anxiety Inventory, and Beck Depression Inventory. First, according to the score, subjects without state anxiety, trait anxiety, and depression participated in the experiment. Second, the experimenter would not tell them the process of the experiment and asked the subjects to sign informed consent voluntarily. Finally, the subjects were told to watch a video of the traffic driving scene. When the subjects sat on a stool 2 m away from the screen to watch the driving video, the subjects watched the video and recorded the whole behavior process of the subjects through the high-definition wide-angle camera . To simulate the real traffic accidents, two experimental videos were used as experimental stimulus materials. The two videos were real road driving videos and had a good ecological validity. Among them, the first video was a 2 min self-driving tour video, and the whole process was silent. The second video material of 2 min was homogeneous with the first video. The only difference was that it inserted a 4 s explosion video and audio in 1 min 56 s. The two video materials were played in the experiment in a reverse balanced order to balance the confusion caused by the playback sequence of video materials.According to the completed Big Five Personality Inventory, the personality traits data of all participants were recorded in the database.The research of human activity recognition through video attracts increasing attention . In the f1 when the participants watched in the video in calm, the frames f2 when the participants got up after watching the experimental video, and the frames f3 when the participants walked to the safety zone at normal speed. For the escape behavior trajectory, two coders recorded frame f4 when the explosion image appeared in the experimental video, frame f5 when the participants made up their posture and the frame f6 when the participants ran to the safe zone. Each participant corresponds to a set of arrived data from f1 to f6. For the arrived data of each participant from f1 to f6, if the difference of the frame number marked by two coders was greater than 5 frames, the two coders negotiated to recalibrate until the two coders reach agreement. If the difference between the frame number marked by two coders is less than or equal to 5 frames, the average number marked by two coders is taken as: fn1 represents the number of frames labeled by the first coder, fn1 represents the number of frames labeled by the second coder, and nf represents the average of the serial number labeled by the two coders. If the value of nf is not an integer, the nearest integer of nf is taken.Through this method, we can capture the moving coordinate information of the participants from starting to leave the scene, i.e., the moving trajectory of individual, which can be used to extract the motion characteristics such as the direction of escape, the response time of escape, the escape time, and the escape speed in the next step. After obtaining the moving trajectory of each participant, the two annotators independently recorded the ID of participants and calibrated the key frames for the trajectory of the nonescape behavior and the escape behavior. For the trajectory of the nonescape behavior, the two coders recorded the frames First, the escape direction can be obtained according to the trajectory of the participants. There are two escape directions in the experimental scene. The escape direction 1 corresponds to the door with a distance of 3.3 m, and the escape direction 2 corresponds to the door with a distance of 4.4 m.Then, according to the statistical key frames in 4.2, we can first calculate the response time of the nonescape and escape.rf represents the frame rate of the video and f2 − f1 represents the frame number difference between the frame number of the completion of the nonexplosion experimental video and the frame number of the participants to make the posture. The data obtained by dividing the frame number difference by the frame rate can represent the time from the completion of the experimental video to the participants to make the posture, that is, the response time of the subjects under normal conditions.The nonescape response time is rf represents the frame rate of the video and f5 − f4 represents the frame number difference between the frame number of explosion image in the experimental video and the frame number of the participants to make the posture. The data obtained by dividing the frame number difference by the frame rate can represent the time from the explosion image in the experimental video to the subjects to make the posture, that is, the response time of the participants to the stimulation of the explosion image.The escape response time is mt represents the time spent by individuals escaping from the location of a traffic accident to a safe zone. For nonescape behavior: Similarly, we can calculate the escape time of nonescape and escape behavior. S and mt have been given.Finally, we can calculate the escape speed by the following formula: The data in the final database include the Big Five Personality scores  of individual participants, the experimental scene data with and without explosion, the emotional impact scores  of individuals, the data of individual escape behavior [i.e., escape response time (s), speed (m/s), and direction  , collected the relevant data of 200 samples, and used the modeling method of the general linear model to establish the theoretical model of escape behavior affected by personality, explosion, and interaction. In the view of the regular trend of exploring the individual escape behavior affected by personality and explosion, the general linear model itself has a wide range of application, simple, and difficult to over-fit. On the basis of the experimental data, the optimal mathematical model is established. To apply to the diversity of the value of escape behavior and emotional dependent variable in this project , the general linear model of the project divides the dependent variable y into its probability distribution and its expected value to describe the escape behavior model. This article takes the neuroticism dimension of personality as an example to explain the modeling process.The main effect models of personality neuroticism and experimental scene on escape behavior  are as follows:1, v2) is the grouping vector of personality neuroticism dimension P or explosive experiment E. The first component is 1, and the second component is 0, indicating low neuroticism individuals and vice versa. The first component is 1, and the second component is 0, indicating that there is no explosion scenario and vice versa; c1 and c2 represent the effect of personality neuroticism dimension P and explosion scene E on dependent variables, respectively. The statistical significance of c2−c1 represents the main effect of personality traits or explosion experiment conditions. b is the dependent variable, which refers to one of the escape response time, direction, and speed variables in the experiment. When the value of behavioral variable b  is continuous, it is generally assumed that the probability distribution of the variable is normal distribution, and μ represents its mean. When the behavior variable b  has two choices, it is generally assumed that the probability distribution of its variable obeys is binomial distribution, and μ denotes the probability of its value. Considering the interaction effect between the emotion and experimental scene, the project extension model 2 is as follows:Among them,  of personality neuroticism and experimental scene on escape behavior, which is used to describe the trend between personality traits and experimental influence on escape behavior. To further understand and show the effect of each factor in the model, this article further expands the effect of each item and the corresponding coefficient and obtains the following full variable model with independent variables and their interaction items.PE is the interaction matrix of personality neuroticism and experimental scene. CTo explore the influence of personality neuroticism and explosion scene on the escape behavior tendency of an individual, a general linear model is used to establish a full-variable impact model of low or high neuroticism with or without explosion scene and their interaction on the escape behavior  of subjects. The full-variable impact model is as follows:L and NH are low and high variables of neuroticism of subjects . ENandNY are nonexplosion and explosion scene variables . N∗E∗ represents the interaction between the neuroticism and the test scenario groups. c∗ and c∗∗ are the effects of their variables on individual escape behavior b. b is one of the experimental dependent variables, which refer to the escape behavior . When b takes the response time of escape and escape speed, its probability distribution is assumed to be a positive distribution, and μ represents its mean value. When b takes the escape direction, the probability distribution is assumed to be binomial distribution, and μ denotes the probability of choosing a distant gate far from the explosion source. Based on the experimental data of the project, models 1–3 are taken as the theoretical guidance, and the theory and algorithm of general linear modeling are used to estimate the specific values of model parameters and the high-density interval of parameters. The independent variables that have a significant impact on the escape behavior are retained to obtain the optimal quantitative model.Among them, NBased on the experimental data, through the general linear model theory and parameter estimation algorithm, the project obtains the model of emotional impact and escape behavior tendency of individuals in neuroticism and explosion scene: low or high neuroticism with explosion scene and high neuroticism and explosion interaction. These four items have significant effects on emotional impact and escape behavior, so the optimal model of the project has the following forms:The other four dimensions  of personality traits and experimental explosion scenarios affect the emotional experience and escape behavior of subjects. The project replaces the N representing the neuroticism in the model 4 in turn to represent extroversion Ex, openness O, agreeableness A, and conscientiousness C and then obtains a full-variable model of the interaction between extroversion, openness, agreeableness, conscientiousness, and experimental explosion factors. Through the parameter estimation and variable optimization theory of the general linear model, on the basis of the experimental data, the optimal mathematical model of the influence of personality and explosion on the emotional impact and escape behavior of subjects is finally obtained, and the regular trend of the influence of different personality traits on the emotional impact and escape behavior of subjects is revealed.Using the general linear modeling method, based on the experimental data of 200 subjects, this study obtained the mathematical models of personality neuroticism N, extroversion Ex, openness O, agreeableness A, conscientiousness C, and experimental explosion E affecting the escape behavior  and emotional impact , including the main effect model and interaction effect model. The main effect model contains six and the interaction effect model contains five specific models.Personality neuroticism N, extroversion Ex, openness O, agreeableness A, conscientiousness C, and experimental explosion E affect the escape behavior  of subjects and emotional impact  of the significant main effect mathematical models are as follows: The interaction effect model between personality traits and experimental explosion on escape behavior  and emotional impact  is shown in P < 0.001), and the significant effect is shown in According to the main effect model from The main effect of explosion scene or high neuroticism on escape behavior and emotional impact is as follows: when it is negative to escape response, it is positive to escape speed, negative to escape direction, negative to emotional valence, and positive to emotional arousal. For the escape behavior mode, the results show that the subjects tend to shorten the escape response time, accelerate the escape speed, and reduce the probability of subjects choosing to escape in the explosion scene and high neuroticism. For the emotional impact of individuals, the results show that subjects tend to reduce the emotional valence score and enhance the emotional experience under explosive scenes or high neuroticism. In other words, participants tend to have a shorter response time, a faster escape rate, and a behavioral tendency to choose to escape near the door, all of which tend to obtain a relatively negative and strong emotional experience.The main effect of high extroversion on escape behavior and emotional impact is: it has a negative effect on escape response, negative effect on escape speed, positive effect on escape direction, positive effect on emotional valence, and negative effect on emotional arousal. The results show that in the case of high extroversion, the participants tend to shorten the escape response time, slow down the escape speed, and increase the probability of the participants choosing the distant door to escape. They tend to increase the emotional valence score and reduce the emotional experience excitement. In other words, under high extraversion, participants tend to have a shorter response time, slower escape speed, a behavioral tendency to choose to escape near the door and to get relatively positive, and weak emotional experience.The main effect of high openness and high agreeableness on individual escape behavior and emotional impact is: it has a negative effect on escape speed, negative effect on escape direction, positive effect on emotional valence, and negative effect on emotional arousal. For the escape behavior, the results show that under the condition of high openness and agreeableness, participants tend to slow down the escape speed and reduce the probability of participants choosing to escape from the distant door. They tend to increase the emotional valence score and reduce the emotional experience excitement. In other words, under the condition of high openness and agreeableness, subjects tend to have a slower escape speed, choose the behavior tendency of near-door escape, and tend to obtain relatively positive and weak emotional experience.The main effect of high rigor on individual escape behavior and emotional impact is: it has a negative effect on escape response, positive effect on escape speed, positive effect on escape direction, positive effect on emotional valence, and positive effect on emotional arousal. The results show that under the condition of high conscientiousness, subjects tend to reduce the escape response, accelerate the escape speed, increase the probability of subjects choosing to escape from the distant door and tend to increase emotional valence score, and increase emotional experience excitement.According to the interaction effect model from The interaction effect of high neuroticism or low agreeableness of personality trait and explosive experimental scene is: it has a negative effect on escape speed, positive effect on escape direction, positive effect on emotional valence, and positive effect on emotional arousal. For the escape behavior pattern, the results show that the interaction term of high neuroticism, low agreeableness, and explosive scene tend to slow down the escape rate and increase the probability of participants choosing to escape from the distant door. In other words, subjects with high neuroticism and low agreeableness tend to have a slower escape speed and a tendency to choose to escape far away in an explosion scenario. For high neuroticism, low agreeableness subjects are subject to emotional impact; the results show that the high neuroticism, low agreeableness subjects, in the explosion scene, tend to increase the emotional valence, arousal score, that is, making subjects get a more positive, more intense, excited emotional experience.The interaction effect of personality with low extroversion, low openness, low conscientiousness, and nonexplosion experimental scene is as follows: it has a positive effect on escape reaction, positive effect on escape direction, negative effect on emotional valence, and negative effect on emotional arousal. For the escape behavior pattern, the results show that low extroversion, low openness, low conscientiousness, and interaction with explosion scenes tend to increase the probability of participants choosing to escape from the distant door. In other words, participants with low extroversion, low openness, and low conscientiousness tend to escape from the distant door when they have a longer response time in the nonexplosion condition. For subjects with low extroversion, low openness, and low conscientiousness, the results show that they tend to reduce emotional valence and arousal scores in nonexplosion scenarios, that is, making subjects obtain neutral and less excited emotional experience.In conclusion, by comparing the results of main effect and interaction effect, the following conclusions are obtained:1.Participants with high neuroticism, in the explosion scenario, tend to have a slower escape speed, choose the distant door to escape, and get more positive, stronger, excited emotional experience.2.Participants with low extroversion, low openness, and low conscientiousness tend to choose the distant door to escape when they have a longer response time in the nonexplosion scenario. They get a neutral, less excited emotional experience.3.If only considering the explosion scene or high neuroticism, participants tend to react quickly, escape quickly, choose the near door to escape, and tend to get relatively negative, strong emotional experience.4.If only considering the high extraversion, participants tend to react quickly, escape slowly, choose the distant door to escape, and tend to get relatively positive, weak emotional experience.5.If only considering high openness and high agreeableness, participants tend to slow down, choose the near door to escape, and tend to get relatively positive, weak emotional experience.6.If only considering high conscientiousness, participants tend to react quickly, escape quickly, choose the distant door to escape, and tend to get relatively positive, strong emotional experience.To summarize, neuroticism refers to incapacity to maintain emotional stability, i.e., groups with high neuroticism are more emotionally susceptible and, therefore, feel more intense emotional experiences. The high neuroticism group shows susceptibility to high-intensity negative stimuli from the explosion, reacting faster and experiencing more intense negative emotions. Extraversion reflects optimistic, adventurous traits. And low extraversion groups have a more stable emotional experience. Groups with high openness and high agreeableness are imaginative and forthright and, therefore, slow to escape, with weak but positive emotional experience. Conscientiousness, meanwhile, is a trait of organization, caution, and restraint, so highly conscientiousness participants have fast reaction and escape times with relatively positive emotional experiences.In this study, the modeling method of the general linear model is used to design the simulation experiment of traffic accidents under different personal traits and explosion scenarios. The new problems of the influence of different personal traits and traffic accidents on individual escape are modeled, and the highly neurotic individuals are obtained. In the explosion scenario, they have the tendency to choose the door, which is far from them with a shorter response time and slower speed to escape. This will lay a foundation for revealing the individual escape law in traffic accidents and provide a theoretical model reference for the timely and effective escape of people.c_tong@swu.edu.cn or the corresponding author WA.The datasets presented in this article are not readily available because participants only gave ethical consent for this project, and not for further distribution outside the research team. Requests to access the datasets should be directed to The studies involving human participants were reviewed and approved by the School of Electronic and Information Engineering, Southwest University. The patients/participants provided their written informed consent to participate in this study.YX: modeling and writing. WA: supervising. XX: building database and experiments. All authors contributed to the article and approved the submitted version.YX and WA were employed by the company China Merchants Chongqing Communications Technology Research and Design Institute Co., Ltd. The remaining 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Higher ELMO1 expression of AS-MSC is found in vivo in AS patients, and inhibiting ELMO1 in SKG mice produces therapeutic effects in this spondyloarthritis model. This study may provide insight into not only the pathogenesis but also clinical therapy for AS.Ankylosing spondylitis (AS) is a type of rheumatic disease characterized by chronic inflammation and pathological osteogenesis in the entheses. Previously, we demonstrated that enhanced osteogenic differentiation of MSC from AS patients (AS-MSC) resulted in pathological osteogenesis, and that during the enhanced osteogenic differentiation course, AS-MSC induced TNF-α-mediated local inflammation. However, whether TNF-α in turn affects AS-MSC remains unknown. Herein, we further demonstrate that a high-concentration TNF-α treatment triggers enhanced directional migration of AS-MSC in vitro and in vivo, which enforces AS pathogenesis. Mechanistically, TNF-α leads to increased expression of ELMO1 in AS-MSC, which is mediated by a METTL14 dependent m Abnormal functions of mesenchymal stem cells (MSC) contribute into the pathogenensis of ankylosing spondylitis (AS). Here, the authors show that TNF-α at high concentration induces enhances migration of AS-MSC through METTL14 mediated m6A modification of the ELMO1 3′ UTR. As the major component of spondyloarthropathy, AS is characterized by chronic inflammation and ectopic ossification in the entheses, which distinguishes AS from other rheumatic diseases2. However, both the mechanisms of chronic inflammation and ectopic ossification and the relationship between these two features still require further investigation.Ankylosing spondylitis (AS) is a common kind of rheumatic disease that affects 0.1–0.5% of the population worldwide3. As both a major source of osteoblasts and a critical regulator of immune cells, MSC play an important role as a bridge connecting bone metabolism and immune homeostasis6. Previously, our study, as well as Chin-Hsiu Liu’s results, indicated that the abnormally enhanced osteogenic differentiation capacity of MSC from AS patients (AS-MSC) resulted in pathological osteogenesis and syndesmophyte formation8. Moreover, we further demonstrated that AS-MSC secreted a relatively large amount of CCL2 during the enhanced osteogenic differentiation course, which augmented monocyte migration, increased proinflammatory macrophage polarization and enhanced TNF-α secretion at the site of enthesis9. However, whether TNF-α secreted by macrophages, in turn, affects AS-MSC is still an open question.Mesenchymal stem cells (MSC) are a kind of pluripotent adult stem cell found in vivo10. Since normal MSC migration contributes to tissue regeneration and immune regulation, it is unsurprising that directional migration of dysfunctional MSC results in disease development. The migration process is under the regulation of various factors11. Engulfment and cell motility protein 1 (ELMO1), which is known as CED12 in Caenorhabditis elegans, is a core molecule involved in cell migration12. ELMO1 functionally binds dedicator of cytokinesis (DOCK) proteins and then regulates cytoskeletal rearrangement via Rac1 activation, which participates in various biological functions, such as directional migration, engulfment of apoptotic cells, nervous system development and cancer cell invasion15. Previous studies have shown that the abnormal expression of ELMO1 is greatly involved in several diseases, including diabetic nephropathy, inflammatory arthritis and inflammatory bowel disease18. Both the role of ELMO1 in MSC directional migration and its effect on AS pathogenesis are still ambiguous.Directional migration to a specific location ensures that MSC exert powerful functions6-methyladenosine (m6A), present at approximately three to five sites per mRNA transcript (0.1–0.4% of adenosines) in mammalian cells, is the most abundant internal mRNA modification19. m6A modification regulates gene expression and is widely involved in cell function and development21. Differential expression of methyltransferases such as METTL3 and METTL14 or demethylases including FTO and ALKBH5 results in abnormal expression of specific genes, which ultimately contributes to diseases development24. However, the effect of m6A modification on AS pathogenesis has never been investigated.N6A modification of the ELMO1 3′UTR in AS-MSC after TNF-α treatment. A higher expression of ELMO1 in AS-MSC was also found in the enthesis of AS patients in vivo. Moreover, injecting Av-ELMO1 significantly improved inflammation and ectopic ossification in SKG mice serving as a spondyloarthritis model. This study may not only contribute to the elucidation of the pathogenesis of AS but also provide insight into clinical therapy for AS.In this study, we demonstrate that a high level of TNF-α induced stronger directional migration of AS-MSC compared to MSC from healthy controls (HC-MSC) by increasing ELMO1 expression. Further research showed that this phenomenon resulted from the METTL14 mediated m9. In this study, the migration assays showed that the numbers of stained migratory AS-MSC were much greater than those of HC-MSC when cocultured with macrophages  with and without TNF-α and between TNF-α-treated AS-MSC and HC-MSC were considered to be critical genes. Through Venn diagram analysis, 489 critical differentially expressed mRNAs were identified Fig. . As deteConsistent with the sequencing data, PCR results showed that ELMO1 expression in MSC increased after TNF-α treatment and that its expression in TNF-α-treated AS-MSC was much higher than that in TNF-α-treated HC-MSC Fig. . These r17. The active Rac1 level in MSC was also enhanced with increasing TNF-α concentrations. Notably, the active Rac1 level in AS-MSC was significantly higher than that in HC-MSC treated with 100 ng/ml TNF-α , indicating a pathological rather than physiological level of TNF-α in vivo. Therefore, we suggest that this enhanced directional migration potential may exist only in local inflammatory positions with higher concentration of TNF-α, rather than in the whole body of AS patients. This speculation could be one of the reasons for specific site involvement such as enthesitis in AS.The levels of several inflammatory cytokines including TNF-α are elevated locally in the pathological tissue of AS36. Specifically, ELMO1 was reported to bind to DOCK2 and then activate Rac1, which regulate intracellular actin dynamics37. In addition to taking part in physiological processes, ELMO1 is involved in pathological conditions such as tumor invasion and local inflammation when abnormally expressed18. In our study, inhibiting ELMO1 in AS-MSC rectified the enhanced directional migration of these cells in vitro and in vivo, and elevating ELMO1 expression in HC-MSC resulted in migration dysfunction similar to that of AS-MSC. Hence, we concluded that ELMO1 is involved in the central mechanism of enhanced directional migration of AS-MSC induced by TNF-α.How TNF-α mediates the enhanced directional migration of AS-MSC was the critical question we aimed to answer. To address this issue, RNA sequencing of HC-MSC and AS-MSC was performed. After treatment with 100 ng/ml TNF-α, the gene expression profiles of AS-MSC were significantly different from those of HC-MSC, with a total of 489 differentially expressed mRNAs identified. Further bioinformatic analysis determined that these 489 key mRNAs were enriched in the biological process chemotaxis and the molecular function chemokine activity, and the chemokine signaling pathway was one of the most remarkably enriched pathways identified by KEGG analysis. Therefore, although TNF-α affects various aspects of MSC function, enhanced directional migration of AS-MSC was the most significant dysfunction caused by the high concentration of TNF-α. In addition, among the 489 differentially expressed mRNAs, we determined that ELMO1 was the most important molecule with the largest fold change, which was further confirmed at the gene and protein levels, as well as at the cellular and AS patient tissue levels. ELMO1, which functions through binding to DOCKs, is a core intersectional molecule in chemotaxis and chemokine signaling pathways38. However, we focused on ELMO1 because it had not only the largest fold change but also a central intersectional role in the chemokine signaling pathway. This means that targeting one of these chemokines barely affected the others‘ functions, but regulating ELOM1 could kept within limits of most chemokines. This hypothesis was confirmed by the in vivo experiments presented in our study. Using SKG mice, the AS feature was induced by an intraperitoneal injection of curdlan, and we found that the incidence time was delayed and that the disease activity was significantly decreased after antagonizing ELMO1. In addition, local inflammation and histological changes were ameliorated in both the spine and the ankles of SKG mice after Av-ELMO1 injection. Although we could not conclude whether Av-ELMO1 affected cells other than MSC, Av-ELMO1 markedly reduced the numbers of migratory CD105+ MSC and CD68+ macrophages and their secretory TNF-α levels in local tissues of SKG mice. We suggested that targeting ELMO1 could restrain the enhanced migration of AS-MSC, which therefore ameliorated macrophage-mediated inflammation and subsequent pathological changes in tissues. These results not only emphasize the critical roles of ELMO1 in the enhanced directional migration of AS-MSC and the pathogenesis of AS, but also foreshadow the promising application of ELMO1 in the clinical treatment of AS.Notably, several other chemokines, such as CCL4 and CCL13, were also differentially expressed in AS-MSC after TNF-α stimulation. These chemokines are reported to regulate MSC migration40. In our study, we noticed that the increasing ELMO1 expression in AS-MSC occurred first at the mRNA level after high-concentration TNF-α stimulation, suggesting that RNA modification may be involved in this issue. Recently, TNF-α was determined to inhibit MSC differentiation by mediating m6A modification of specific mRNAs41. In addition, m6A modification regulates gene expression and thus affects cell migration42. m6A, the most abundant internal modification on mRNA, can accelerate mRNA degeneration and negatively affect gene expression and is widely involved in cell function and development21. Furthermore, abnormal m6A modification plays important roles in the pathogenesis of rheumatic diseases, including rheumatoid arthritis and systemic lupus erythematosus44. After treatment with 100 ng/ml TNF-α, the m6A level of ELMO1 in AS-MSC was obviously lower than that in HC-MSC. In addition, the degeneration rate of ELMO1 mRNA in AS-MSC was slower than that in HC-MSC, which was consistent with the increasing level of ELMO1 mRNA expression in AS-MSC after TNF-α stimulation. Herein, we demonstrated the critical role of m6A modification in the elevated expression of ELMO1 and the subsequent enhanced directional migration of AS-MSC.Another key question remains: how does TNF-α induce elevated expression of ELMO1 in AS-MSC. Recent studies have shown that cytokines at relatively high concentrations can lead to abnormal changes in RNA modification and the subsequent gene expression profile, which results in tissue inflammation and autoimmune diseases6A modification is regulated by specific methyltransferases and demethylases45. These m6A-related proteins participate in the regulation of MSC functions41. Herein, although the expression of most m6A-related proteins was altered after TNF-α stimulation, a difference between AS-MSC and HC-MSC was found for only METTL14 after TNF-α treatment at a concentration of 100 ng/ml. Additionally, METTL14 expression in MSC from AS enthesis tissue was abnormally decreased compared with that from the control tissue. Previously, METTL14, an m6A methyltransferase, was proven to regulate breast cancer cell migration through m6A modification46. Furthermore, inhibiting METTL14 in MSC decreased the m6A level of ELMO1 and then upregulated ELMO1 expression at the protein level, thereby promoting the migratory ability of MSC in vitro and in vivo. Overexpressing METTL14 produced inverse results in MSC, confirming that METTL14 negatively regulates ELMO1 expression through m6A modification in MSC stimulated with TNF-α. Therefore, METTL14 was the key mediator of ELMO1 leading to enhanced directional migration of AS-MSC.m6A modification regulates specific gene expression by increasing or decreasing mRNA stability. The different effect of m6A modification depends on the predominant m6A methyltransferases or demethylases and their binding m6A reader enzymes47. A previous study demonstrated that METTL14 affected the mRNA stability of MYB and MYC through m6A modification, which ultimately inhibited hematopoietic stem cell differentiation26. In our study, we found that the ELMO1 mRNA half-life was negatively correlated to its m6A level and the METTL14 expression level. Additionally, in our experiment system, we determined that METTL14 mainly bound YTHDF2 and YTHDF3, which are the two m6A reader enzymes mainly promoting mRNA degradation47. We concluded that METTL14 along with YTHDF2 and YTHDF3 decreased the ELMO1 mRNA stability and increased its degradation rate after m6A modification, which led to a lower expression of ELMO1 in the TNF-α treated MSC.m2. However, it is well known that MSC are located in the bone marrow. Although we have demonstrated that multiple dysfunctions in AS-MSC contribute to the pathogenesis of AS9, it is still unclear how MSC migrate to and participate in enthesis lesions. As normal MSC migrate to certain sites and participate in tissue regeneration and immune regulation (42), the migration of MSC with abnormal capacities result in pathological lesions and contributes to the development of diseases, such as rheumatoid arthritis48. Therefore, it is important to illuminate the migratory ability of AS-MSC under certain conditions. Previously, microscopic holes connecting entheses with the bone marrow were observed in the cortical bone, and bone marrow tissues were shown to spill into entheses through these holes, which provides the anatomical basis for MSC migration49. In this study, we demonstrated that under high-concentration TNF-α stimulation, AS-MSC exhibited an enhanced directional migration ability facilitated through METTL14-mediated m6A modification of ELMO1. Summarizing our previous and current results9, we suggest that a vicious three-step cycle that is hard to break exists in the entheses of AS patients: (1) TNF-α at a relatively high concentration in the enthesis induces the directional migration of AS-MSC, recruiting AS-MSC from the bone marrow to the site of enthesis. (2) These migratory AS-MSC show an enhanced osteogenesis capacity to be involved in ectopic ossification. (3) AS-MSC subsequently secrete a large amount of CCL2 during the osteogenic differentiation course, which augments monocyte migration, increases proinflammatory macrophage polarization and enhances TNF-α secretion at the site of enthesis. Despite controversy regarding the relationship between chronic inflammation and ectopic ossification in AS50, our study adds support to the close relationship of these two features through the vicious cycle of MSC and TNF-α. However, current available evidence does not reveal the origin of this vicious cycle, but a study showing that early application of TNF-α blockers inhibits spinal radiographic progression in AS implies a possible upstream role for TNF-α51. Recent studies have indicated that various factors, including mechanical strain and the gut microbiota, may trigger the elevation in TNF-α levels52, but this needs further investigation.As features distinguishing AS from other rheumatic diseases, chronic inflammation and ectopic ossification in entheses are the central pathological features of AS that exist outside the bone53. This inhibitor targeting ELMO1/DOCK/Rac1 showed great ability in releasing inflammation55, and may have great application potential in AS. The therapeutic effect of ELMO1 requires further studies in the future.Our study may provide insight for the clinical treatment of AS. Successfully breaking the above-mentioned vicious cycle by targeting key molecules could markedly improve AS. Based on our in vivo experiments, we found that targeting ELMO1 could significantly ameliorate the clinical manifestations and tissue lesions in SKG mice. Recently, a small molecule inhibitor of DOCK named CPYPP was used to antagonize ELMO1/DOCK Rac1 GEF activity6A modification of ELMO1. This study may contribute to understanding not only the pathogenesis but also the diagnosis and treatment of AS. Some issues, such as the trigger of the vicious cycle and the sequential order of inflammation and ossification, remain unclear and need to be addressed in the future.In summary, we demonstrated that TNF-α accelerated the directional migration of AS-MSC through METTL14-dependent mThis study conforms to the Declaration of Helsinki and was approved by the Ethics Committee of the Eighth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. The experiments on mice were approved by the Institutional Animal Care and Use Committee of Sun Yat-Sen University, Guangzhou, China. All experimental procedures on mice were carried out in strict adherence to the rules and guidelines for the ethical use of animals in research.56. After being informed of the possible risks and complications of bone marrow puncture, all the AS patients and healthy controls signed informed consent forms. The characteristics of the study subjects are presented in Supplementary Table 2 flasks and cultured at 37 °C in 5% CO2. The cells in suspension were removed, and the medium was replaced every 3 days thereafter. When the culture reached 90% confluence, MSC were digested using 0.25% trypsin containing 0.53 mM EDTA and reseeded in new flasks; these cells were expanded and used for experiments.A total of 15 patients with AS and 15 healthy controls were recruited for this study. All the AS patients were diagnosed according to the 1984 New York modified criteria2. At 80–90% confluence, HEK293T cells were digested with 0.25% trypsin containing 0.53 mM EDTA and reseeded in new flasks.HEK293T cells were cultured in high-glucose DMEM supplements with 10% FBS at 37 °C under 5% CO® Inserts . Briefly, a total of 2 × 104 MSC in 100 μl DMEM were seeded in the upper chambers, and 600 μl DMEM without or with TNF-α at a concentration ranging from 10 to 100 ng/ml was added to the lower chambers. In the assays of Fig. 5 macrophages differentiated from CD14+ monocytes isolated from peripheral blood samples were suspended in 600 μl DMEM and then seeded in the lower chambers without or with 0.5 μg/ml anti-TNF-α, 0.05 μg/ml anti-IL-17 or 0.5 μg/ml anti-IL-23 neutralizing antibody. After coculturing for 24 h, the upper chambers were washed three times with phosphate-buffered saline (PBS) and fixed with 4% paraformaldehyde. Then, the upper chambers were stained with 0.1% crystal violet for 15 min. The migratory MSC on the lower side of the chamber were photographed and counted as the mean number of cells per 10 random fields for each chamber in two separate experimenters.MSC migration assays were performed using Polycarbonate Membrane Transwell® culture-Inserts 2 Well for self-insertion. Each insert was placed in a well of a 12-well plate. MSC (5 × 104) in 200 μl DMEM were seeded in a well of the insert. The inserts were then removed, and DMEM without or with TNF-α at a concentration ranging from 10 to 100 ng/ml was added. After 12 h of migration, the migratory area was calculated using ImagePro Plus 6.0. The percentage of the migratory area was defined as the ratio of the MSC migratory area at 12 h to the primary wound area created by the insert at 0 h.Wound-healing assays were performed using IBIDI® μ-slide chemotaxis chambers. An MSC suspension  was seeded into the center channel of μ-slide chambers. After 12 h of incubation, DMEM containing TNF-α was added to the left medium reservoir, and DMEM without TNF-α was added to the right one reservoir. The cell migration track was imaged every 10 min over 24 h with the BioTek Lionheart™ FX Automated Live Cell Imager with Augmented Microscopy™. Cell tracking analysis was performed using the Manual Tracking plugin for ImageJ 1.51 and the Chemotaxis and Migration Tool according to the IBIDI protocol.Directional migration assays were performed using IBIDI4 MSC in 1 ml DMEM were seeded in each well of a 12-well plate and then treated with or without 100 ng/ml TNF-α for 24 h. Then, the cells were washed with PBS, fixed with 4% paraformaldehyde and incubated with 1% Triton X-100. MSC were blocked in goat serum and then incubated with an anti-ELMO1 antibody (1:500) overnight at 4 °C. The cells were washed and incubated with a fluorescein-labeled secondary antibody  for 1 h, followed by staining with DAPI (Thermo Fisher) for 10 min. All images were obtained using an LSM 5 Exciter confocal imaging system (Carl Zeiss).For the cell immunofluorescence assay, 5 × 105 MSC/Fluc in 20 μl DMEM were injected subcutaneously into the dorsal sides of the BALB/c-nu/nu mice. Twenty microliters of TNF-α at a concentration of 500 ng/ml was administered at four sites around the MSC injection point at a distance of 0.5 cm. The injected MSC/Fluc were observed using a Xenogen IVIS Spectrum system  on days 0, 1, 3, and 5 after intraperitoneal injection of 3 mg D-Luciferin potassium. The bioluminescence area was analyzed using Living Image 2.12 and ImagePro Plus 6.0. On day 7 after injection, the mice were sacrificed, and the tissues at the TNF-α injection sites were obtained for immunohistochemical analysis as described below.MSC were transfected with a lentivirus encoding luciferase (OBiO Technology) as described below (MSC/Fluc). Mice were housed at the Laboratory Animal Center of Sun Yat-Sen University under specific pathogen-free conditions, with a 12-h light/dark cycle in a temperature ((22 ± 2 °C) and humidity-controlled room (60%) with free access to water and food. Eight-week-old BALB/c-nu/nu female mice  were anesthetized via intraperitoneal injection of ketamine and xylazine. A total of 5 × 10TM RT reagent kit according to the protocols. Quantitative real-time PCR was performed on the LightCycler®480 PCR System (Roche) using TB Green® Premix Ex TaqTM. The relative expression levels of each gene were analyzed using the 2−△△Ct method. The forward and reverse primers for each gene are shown in Supplementary Table Total RNA was isolated from MSC using TRIzol and transcribed into cDNA using a PrimeScriptP value ≤0.001. The sequencing data analysis, including heatmap clustering, principal component analysis (PCA), Venn diagram creation, gene ontology (GO) analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, were performed using BGI Dr. Tom 2.0.MSC were plated and treated with or without 100 ng/ml TNF-α for 24 h. RNA was extracted as described above. cDNA library construction and sequencing were performed by the Beijing Genomics Institute using the BGISEQ-500 platform. The sequencing data were filtered with SOAPnuke 1.5.2, and the clean reads were mapped to a reference genome using HISAT2 2.0.4. After alignment using Bowtie2 2.2.5, the expression level of each gene was calculated by RSEM 1.2.12, and differential expression analysis was performed using DESeq2 1.4.5 with the parameters fold change ≥2 and adjusted g at 4 °C for 30 min, and the lysate protein concentrations were determined using a Pierce BCA protein assay kit. The immunoprecipitants was obtained in Co-IP assay as described below. After boiling with sample loading buffer, equal amounts of protein extracts or the immunoprecipitants were separated using sodium dodecyl sulfate-polyacrylamide gel electrophoresis and subsequently transferred to polyvinylidene fluoride (PVDF) membranes (Millipore). The PVDF membranes were blocked and incubated overnight at 4 °C with primary antibodies against GAPDH, ELMO1, ELMO2, ELMO3, METTL3, METTL14, FTO, ALKBH5, WTAP, DOCK1, DOCK2, DOCK4, DOCK5, DOCK8, or Rac1 (1:1000). After washing three times, the PVDF membranes were incubated with a horseradish peroxidase (HRP)-conjugated secondary antibody (1:3000). Specific antibody-antigen complexes were detected using Immobilon Western Chemiluminescent HRP Substrate. The mean intensity ratio was determined and analyzed using a UVP ChemStudio PLUS system and ImagePro Plus 6.0.MSC were lysed in RIPA lysis buffer containing protease inhibitors and phosphatase inhibitors. The lysates were centrifuged at 1100 × Rac1 activation in MSC was detected with the Active Rac1 Detection Kit through a GST pulldown assay according to the kit protocol. Briefly, protein was extracted from MSC treated with or without TNF-α as described above. Glutathione resin containing agarose beads was added to the spin cup with a collection tube and washed three times. GST-Human PAK1-PBD was added to the spin cup, followed by protein lysis and incubation for 1 h at 4 °C with gentle rocking. After washing, the spin cup containing agarose beads was incubated with reducing sample buffer at room temperature for 2 min, and the eluted sample was collected by centrifugation. GTP-Rac1 in the eluted protein fraction was detected using Western blot analysis as described above. MSC-derived protein without GST pulldown was used to detect the total Rac1 and GAPDH levels.https://www.uniprot.org/).The Co-IP assay was performed using the Dynabeads™ Protein A Immunoprecipitation Kit according to the kit protocol. Briefly, an MSC protein extract was incubated with an anti-ELMO1 antibody (1:100) or IgG control at 4 °C overnight, followed by the addition of protein G agarose beads and incubation for another 4 h. Then, the immunoprecipitants were collected for LC-MS/MS and western blotting. LC-MS/MS was performed by Shanghai Applied Protein Technology Co., Ltd. Data were analyzed using Proteome Discoverer 1.4 against the UniProt database (9 TU/mL) and 5 μg/mL polybrene were added to DMEM and incubated with MSC for 24 h at a multiplicity of infection of 50. Experiments were performed on day 4 after transfection. Adenoviruses encoding an shRNA specific for ELMO1 (Av-ELMO1) or control adenoviruses (Av-NC) for mouse experiments were also constructed and purchased from OBiO Technology.Three ELMO1, METTL14, DOCK1, DOCK4, DOCK5, YTHDF2, and YTHDF3-specific siRNAs were designed and synthesized by OBiO Technology. The sequences are shown in Supplementary Table MSC transfected with Lv-ELMO1 (or OE-ELMO1) and its NC control were seeded in 96-well plates. Cell proliferation ability was detected using Cell Counting Kit-8 according to the protocol. Medium without cells were used as negative controls.6A RIP assay was performed using the Magna MeRIP™ m6A Kit according to the manufacturer’s instructions. Briefly, RNA was extracted and then chemically fragmented into fragments of 200 nucleotides or less. The RNA fragments were incubated with anti-m6A antibody- or IgG-conjugated Protein A/G magnetic beads at 4 °C overnight. Then, the magnetic beads were collected, and the bound m6A-modified RNA was eluted for qRT-PCR analysis as described above. Equal amounts of nonimmunoprecipitated RNA fragments were used as the input control. The fold enrichment of each target gene was calculated with the formulas below.The mThe relative fold enrichment was calculated by normalizing to the fold enrichment of HC-MSC without TNF-α stimulation.57.MSC were seeded in a 12-well plate and treated with actinomycin D at a concentration of 20 μg/ml for 0, 1, 2, and 3 h. After treatment, MSC RNA was immediately extracted, and qRT-PCR assays were performed as described above. The turnover rate and half-life of each target gene were calculated as described in a previous study2 UVC irradiation at 254 nm for 40 s. The CLIP assay was then performed using the EZ-Magna RIP™ RNA-Binding Protein Immunoprecipitation Kit according to the manufacturer’s instructions. Briefly, MSC were lysed using RIP lysis buffer, and the lysate was incubated with antibodies against METTL14, YTHDC1, YTHDC2, YTHDF1, YTHDF2, YTHDF3 (1:50) or IgG control with protein A/G magnetic beads at 4 °C overnight. The magnetic beads were immobilized, and then the immunoprecipitant containing specific protein with its bound RNA was eluted and treated with proteinase K. The remaining RNA was extracted for qRT-PCR analysis as described above. Equal amounts of nonimmunoprecipitated RNA fragments were used as the input control. The %Input was calculated with the formulas below.MSC were treated with 150 mJ/cm6A modification sites (A replaced by T), were synthesized by OBiO Technology. The possible m6A modification sites were predicted by the m6AVar database58. HEK293T cells were seeded in 12-well plates, and then 1.5 μg luciferase reporter vector, 1.5 μg METTL14 or its mutant vector, and 1.5 μg Renilla luciferase reporter vector were cotransfected into HEK293T cells using the Lipofectamine 3000 Transfection Kit according to the manufacturer’s instructions. After 48 h of transfection, the luciferase activity was detected using the Dual-Luciferase® Reporter Assay System Kit (Promega) according to the protocol. Relative Fluc/Rluc activity was calculated by normalizing the activity of firefly luciferase to that of Renilla luciferase.A METTL14 expression vector and its mutant vector (METTL14-R298P), as well as luciferase reporter vectors containing the C-terminal DNA fragment of ELMO1 or its mutant with mutations in m10 Av-ELMO1 via intravenous tail vein injection at the time of disease induction, and the SKG mice in the Av-NC group or PBS group were separately treated with equal amounts of control adenoviruses or PBS. Clinical features in the mice were monitored weekly by two independent observers who were blinded to the treatment groups. The score criteria were defined as previously reported59: 0 = no swelling or redness, 0.1 = swelling or redness of the digits, 0.5 = mild swelling and/or redness of the wrist or ankle joints, and 1 = severe swelling of the larger joints. At week 8 after induction and treatment, the mice were sacrificed, and the tissues were obtained for micro-CT examination, hematoxylin and eosin (H&E) staining, immunohistochemical analysis, and immunofluorescence as described below.Male SKG mice were purchased from CLEA Japan, Inc. and housed as described above. All mice were handled in accordance with the guidelines for animal care approved by the Institutional Animal Care and Use Committee of Sun Yat-Sen University. Disease was induced at 8 weeks of age using 3 mg curdlan administered by intraperitoneal injection. The SKG mice were randomly divided into three groups: a PBS group, an Av-NC group, and an Av-ELMO1 group. The SKG mice in the Av-ELMO1 group were treated with 5 × 10Micro-CT was performed to analyze the structures of the spine and ankle. Obtained tissues were fixed with 4% polyoxymethylene and then scanned using a Siemens Inveon CT scanner with a resolution of 19 μm. The figure data were analyzed using RadiAnt DICOM Viewer 5.0.2 software.Harvested spine and ankle tissue samples were fixed with 4% polyoxymethylene for 24 h, decalcified in 20% EDTA for 14 days and then embedded in paraffin for sectioning. The sections were stained with hematoxylin for 5 min. After washing with PBS for 10 min, the sections were stained with eosin for 3 min. Then, the sections were dehydrated and observed using a microscope.2O2 for 20 min and blocked with 5% normal goat serum for 1 h. Then, the sections were separately incubated with anti-CD68, anti-TNF-α or anti-CD105 (1:100) antibodies at 4 °C overnight. Secondary antibody (1:500) incubation and color development were performed using the SP Rabbit & Mouse HRP DAB Kit according to the kit protocol. For mouse tissue from the in vivo migration assay, the immunohistochemical assay was performed as described above except that an antibody specific for human HLA Class 1 ABC was used.For the SKG mouse tissue immunohistochemical assay, sections were incubated in 10 mM citrate buffer and microwaved at 750 W for 30 min for antigen retrieval. The sections were treated with 3% HFor the human enthesis tissue immunofluorescence assay, nine AS patients and 9 non-AS patients were recruited. Sites of ossifying enthesis were confirmed via presurgical image analyses and visual observations during surgery. Ossifying tissues were obtained during lumbar spine surgeries. The characteristics of the study subjects are presented in Supplementary Table t-test, and comparisons of three or more different groups were performed by a one-way ANOVA, followed by Bonferroni’s post hoc comparisons. Data are expressed as the means±standard deviations. Statistical analysis was performed with SPSS (SPSS Inc.). The n values indicate the numbers of individuals in each experiment. P-values less than 0.05 were considered statistically significant.All the results were determined based on at least three separate in vitro experiments containing at least triplicate samples. The Shapiro–Wilk normality test was used to check the normality of the data, and data with a Shapiro–Wilk test P > 0.05 were considered to fit a normal distribution. The two group comparisons were performed using a 2-tailed Student’s Further information on research design is available in the Supplementary Information FilePeer Review FileReporting Summary


TNF-α gene promoter region and increased levels of TNF-α were found to be associated with IS in different ethnic backgrounds. However, the published results are inconsistent and inconclusive. The primary objective of this meta-analysis was to investigate the concordance between rs1800629 polymorphism and IS. A secondary aim was to explore circulating levels of TNF-α and IGF-1 with IS in different ethnic backgrounds and different sourced specimens.Accumulated studies have explored gene polymorphisms and circulating levels of tumor necrosis factor (TNF)-α and insulin-like growth factor (IGF)-1 in the etiology of ischemic stroke (IS). Of the numerous etiopathological factors for IS, a single-nucleotide polymorphism (SNP) rs1800629 located in the In this study, we examined whether rs1800629 genetic variant and levels of TNF-α and IGF-1 were related to the etiology of IS by performing a meta-analysis. Relevant case-control studies were retrieved by database searching and systematically selected according to established inclusion criteria.A total of 47 articles were identified that explored the relationship between the rs1800629 polymorphism and levels of TNF-α and IGF-1 with IS risk susceptibility. Statistical analyses revealed a significant association between the rs1800629 polymorphism and levels of TNF-α and IGF-1 with IS pathogenesis.Our findings demonstrated that the TNF-α rs1800629 polymorphism, the increased levels of TNF-α, and decreased levels of IGF-1 were involved in the etiology of IS. Ischemic stroke (IS) is the second leading cause of mortality and physical disability following the onset of neuropathological complications worldwide . PresentPro-inflammatory cytokines play a major role in the process of activation of various cellular mechanisms, including differentiation and proliferation processes. If the pro-inflammatory cytokine storm occurs in the cerebral tissue , it willTNF-α gene polymorphism and peripheral blood levels of TNF-α and IGF-1 with IS onset in different ethnic-specific backgrounds. However, the findings from such studies have been inconsistent or inconclusive due to their highly diverse patients’ populations, differentially sourced specimens, small sample sizes, and most importantly, little influence of single-nucleotide polymorphisms (SNPs) on IS pathobiology. To overcome these roadblocks, an updated meta-analysis was performed to further testify whether (1) the TNF-α genetic SNPs could increase the susceptibility of different populations to IS and (2) the peripheral blood TNF-α and IGF-1 levels could be used as the biomarker for assessing the risks for IS in ethnic-specific background.Previous studies have also explored the association between the For this meta-analysis, full-length relevant articles and case studies were selected based on the following inclusion criteria: (1) The diagnosis of IS patients was based on the CT or MRI examinations; (2) selected studies were all case-control studies; (3) data on SNP frequency of circulating TNF-α and IGF-1 levels were clearly obtained; and (4) the frequency of each allele and genotype met the criteria for Hardy-Weinberg disequilibrium in both the case and control groups. In contrast, some studies were excluded based on the criteria that they were either case-only studies based on a family design or abstract-only reviews without the full case report.We used the keywords “tumor necrosis factor-alpha,” human recombinant tumor necrosis factor-alpha, “cachectin/tumor necrosis factor,” tumor necrosis factor ligand superfamily member 2, “TNF alpha,” “TNF-alpha insulin-like somatomedin peptide I,” “insulin-like somatomedin peptide I,” “IGF-I-SmC,” “IGF-1 insulin-like growth factor I,” “ischemic stroke,” “cryptogenic stroke,” and “cryptogenic embolism stroke” to search for the full-length case studies in the PubMed, Web of Science, Embase, Elsevier, Cochrane, Medline, and APA databases published until September 9, 2021. We thoroughly reviewed the retrieved literature and the corresponding reference lists. To avoid including duplicate articles, we selected studies from the larger sample size in the analysis.Three of the authors independently extracted the following data from each publication: Author, country of origin, racial descent of the study population, the number of eligible cases with proper controls, circulating levels of TNF-α, IGF-1 (mean and standard deviation), rs1800629 (G-308A) genotype, and allele frequencies.The data analysis using statistical methods was carried out according to the methods described in our previous study .n = 10; Asian, n = 15). PCR screening was used to detect genotypes, and all genotypes met Hardy-Weinberg disequilibrium criteria. Similarly, the TNF-α circulation levels were measured in 1,497 cases and 1,444 control subjects. The distribution of articles based on the ethnic specificity was Caucasian (n = 6), Asian (n = 6), and American (n = 2) and that based on the laboratory findings was plasma data (n = 3), serum data (n = 9), cerebrospinal fluid (CSF) data (n = 1), and gingival fluid data (n = 1). We identified 11 articles concerning the relationship between TNF-α circulation levels and IS, three articles for plasma data . The heterogeneity test in each group indicated the presence of high heterogeneity among the samples. Heterogeneity models I–V were randomly selected for testing, which showed results for each model, namely, GG vs. GA + AA model [odds ratio (OR) = 1.22, 95% confidence interval (CI) = 1.05–1.42, To further study the source of heterogeneity and whether there were differences among races, we conducted a racial stratification analysis on the same dataset. Among them, positive results were found in the Asian race with the models such as GG vs. GA + AA .z = 1.24, p = 0.22 for rs1800629; z = 0.73, p = 0.47 for TNF-α; z = 0.96, p = 0.34 for IGF-1).Begg’s test was used to verify publication bias, which showed that publication bias was negligible . Among the several known TNF-α gene polymorphisms, studies have extensively focused on the G-308A (rs1800629) mutation in the etiology of IS. The polymorphisms are reportedly located in the TNF-α gene promoter region, which contains a number of regulatory elements that can influence the transcriptional activity of the gene , and the circulating levels of TNF-α were elevated in the Caucasian and Asian patients as compared with controls. At the same time, a positive association was found between serum IGF-1 levels and IS in the Asian population but not in the Caucasian population. Therefore, based on previous meta-analysis results and those combined with ours, we proposed that therapeutic strategies to decrease the circulating levels of TNF-α and increased levels of IGF-1 might be considered as a promising therapeutic target with potential neuroprotective effects for the treatment of IS.The original contributions presented in the study are included in the article/XZ and LL equally contributed to the study design of this review. RD, NW, and YS equally performed the literature search, interpreted the data, and wrote the manuscript. QL and HL profoundly enriched the manuscript by adding important intellectual content. All authors contributed to the article and approved the submitted version.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Neuronal death is a major hallmark of Alzheimer's disease (AD). Necroptosis, as a programmed necrotic process, is activated in AD. However, what signals and factors initiate necroptosis in AD is largely unknown.Methods: We examined the expression levels of critical molecules in necroptotic signaling pathway by immunohistochemistry (IHC) staining and immunoblotting using brain tissues from AD patients and AD mouse models of APP/PS1 and 5×FAD. We performed brain stereotaxic injection with recombinant TNF-α, anti-TNFR1 neutralizing antibody or AAV-mediated gene expression and knockdown in APP/PS1 mice. For in vitro studies, we used TNF-α combined with zVAD-fmk and Smac mimetic to establish neuronal necroptosis models and utilized pharmacological or molecular biological approaches to study the signaling pathways.Results: We find that activated neuronal necroptosis is dependent on upstream TNF-α/TNFR1 signaling in both neuronal cell cultures and AD mouse models. Upon TNF-α stimulation, accumulated p62 recruits RIPK1 and induces its self-oligomerization, and activates downstream RIPK1/RIPK3/MLKL cascade, leading to neuronal necroptosis. Ectopic accumulation of p62 is caused by impaired autophagy flux, which is mediated by UVRAG downregulation during the TNF-α-promoted necroptosis. Notably, UVRAG overexpression inhibits neuronal necroptosis in cell and mouse models of AD.Conclusions: We identify a finely controlled regulation of neuronal necroptosis in AD by coordinated TNF-α signaling, RIPK1/3 activity and autophagy machinery. Strategies that could fine-tune necroptosis and autophagy may bring in promising therapeutics for AD. Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by clinical symptoms of memory loss, personality changes, and general cognitive decline Another process that may be involved in AD pathology is the age- or genetic-dependent compromisation of autophagy We have turned to investigation of possible action of a candidate critical proinflammatory cytokine in these processes: tumor necrosis factor-α (TNF-α). It is already known to be involved in neuroinflammation and to contribute to AD pathology TNF-α acts through a classical signaling pathway initiated by binding to its receptor TNFR1. TRADD, TRAF2/5, RIPK1, cIAP1/2 and other subunits are then recruited to form complex I, which in turn leads to ubiquitination of RIPK1 and thus to NF-κB activation We now report that elevated TNF-α signaling can indeed induce the activation of neuronal necroptosis in AD. TNF-α-triggered neuronal necroptosis is further modulated by impaired autophagic flux, in which a key regulator UVRAG tightly regulates signaling transduction. These findings thus implicate crosstalk between neuronal necroptosis and autophagy machinery in AD pathology, and may exemplify a more general paradigm for cell death regulation in other NDs.We hypothesized that elevated TNF-α signaling in AD brain may lead to neuronal necroptosis. To test this hypothesis, we first examined the levels of MLKL and phosphorylated-MLKL (p-MLKL), a marker of cell necroptosis, in the brains of AD patients. Immunohistochemical (IHC) staining demonstrated upregulation of both MLKL and p-MLKL in samples of cerebral cortex from AD patients at Braak VI stage compared to age-matched healthy controls (Ctrl) Figure . We thenTo assess whether TNF-α signaling is involved in neuronal necroptosis in the hippocampus, we carried out lateral ventricle injection of murine TNF-α as described To further study the function of TNF-α signaling in necroptosis, we used adeno-associated virus (AAV) vector-mediated small hairpin RNA (shRNA) to knockdown (KD) TNFR1 A, a TNF-In sum, we infer TNF-α/TNFR1 signaling is indispensable for the neuronal necroptosis activated in AD patients and AD mouse models.To explore the molecular mechanism of TNF-α-mediated neuronal necroptosis, we performed cell culture experiments using various neuronal cell types including SH-SY5Y, PC-12 and primary cortical neurons. We examined the effects of TNF-α on cell viability in all above cell types. The pan-caspase inhibitor zVAD-fmk  and cIAPs inhibitor Smac mimetic (S), two inducers of cell necroptosis Propidium iodide (PI) staining and cell morphology images showed that TNF-α, especially combined with Smac mimetic and zVAD (TSZ) treatment, indeed triggered the characteristic features of necrosis/necroptosis To confirm the involvement of necroptosis in TNF-α-induced cell death, we used Acridine Orange (AO) staining, a unique technique used to distinguish necroptosis from apoptosis Taken together, our data show that TNF-α alone can mildly activate neuronal necroptosis; but combined with Smac mimetic and zVAD (TSZ), it triggers strong activation of neuronal necroptosis.We aimed to clarify whether TNF-α-induced neuronal necroptosis is dependent on RIPK1, a known crucial control center in regulating TNF-α-induced NF-κB signaling pathway as well as apoptosis and necroptosis Previous studies have found that RIPK1 interacts directly with p62/SQSTM1, a molecule responsible for the clearance of aggregated proteins To evaluate the function of p62 in necroptosis, we transduced SH-SY5Y cells with recombinant adenoviral particles encoding GFP-tagged p62 (Ad-p62-GFP) or encoding LacZ as a control (Ad-LacZ) C. IntereThe ZZ domain (amino acid 122-167) of p62 is the binding domain with RIPK1 To test the possible promotion role of p62 for neuronal necroptosis in AD, we then assessed p62 levels in the brains of AD patients. IHC staining showed higher p62 levels in samples of cerebral cortex from AD patients compared to healthy controls Figure F. SimilaOverall, our results demonstrate that p62 elevates neuronal necroptosis by interacting with RIPK1 and promoting phosphorylation of RIPK1/RIPK3/MLKL cascades, both in TNF-α-treated neuronal cultures and in AD mouse brain.p62 is a critical molecule participating in several processes of autophagic flux including the initiation/assembly of autophagosome, docking/fusion of autophagosomes with lysosomes, and cargo degradation To confirm this notion, we used recombinant adenoviral particles encoding GFP and mCherry tagged LC3 (Ad-GFP&mCherry-LC3) to evaluate autophagic flux Our data thus clearly show that impaired neuronal autophagic flux contributes to TNF-α-induced neuronal necroptosis.We extended our study to the molecular mechanism of autophagy in TNF-α-mediated neuronal necroptosis. We examined mRNA expression levels of a series of genes responsible for the recognition and fusion of autophagosome and lysosome cIAP1/2 facilitates TNF-α-induced transcription factor NF-κB activation Uvrag gene across species using ConTra v2 software and found a high-scoring NF-κB binding site  signaling as well as viral infection o Figure . Our datTurning to details of the inferred pathway, Figure To further explore the mechanism of RIPK1-dependent neuronal necroptosis in AD, we focused on p62/SQSTM1, a RIPK1 binding partner that participates in both the ubiquitin-proteasome system (UPS) and autophagic lysosomal degradation process in many diseases p62 is also implicated in the interacting pathway of autophagy. Impaired autophagic flux has been found to be able to induce cell death by disabling degradation of protein aggregates in NDs Overall, in the picture of TNF-α/TNFR1-dependent neuronal necroptosis in AD, there appear to be several key successive events, including UVRAG downregulation and autophagic flux impairment and p62 accumulation, which further leads to RIPK1 activation and subsequent neuronal necroptosis Figure H. It remParaffin sections of the human cerebral cortex were kindly provided by NIH NeuroBioBank. These materials include samples from 8 AD patients (Braak VI) and 7 age-matched healthy elderly.APP/PS1  and 5×FAD  transgenic mice were purchased from The Jackson Laboratory and generously provided by Dr. Guobing Chen . In this study, we crossed the APP/PS1 or 5×FAD with C57BL/6 mice  to obtain daughter mice for further study. All mice were maintained under standard laboratory conditions with free access to food and water unless otherwise indicated. All experiments were conducted in accordance with the regulations for the Administration of Affairs Concerning Experimental Animals (China) and approved by the China Pharmaceutical University Animal Ethics Committee. Mice were genotyped by One Step Mouse Genotyping Kit , according to manufacturer's instructions.For DAB staining of paraffin sections, 5 µm slices were fixed in 10% formaldehyde followed a protocol as described Images of cellular autophagic flux analysis were captured by the Olympus FV1000 laser scanning confocal microscope. For quantitative analysis of the fluorescence intensity, the integral optical density was measured by using Image-Pro Plus 6.0 software.12~1013 v.g./ml) was injected into the hippocampal CA1 region at -2.2 mm from bregma, mediolateral 1.7 mm, depth 2.4 mm of mouse brains. All mice were placed on a heating pad until they recovered from the surgery. One week after injection of TNF-α (or) TNFR1 antibody, while three weeks after virus injection, mice were sacrificed for further experiments.Brain stereotaxic injection was performed as described 2. The cells were pretreated with or without zVAD-fmk , Necrostatin-1  or Chloroquine  for 1 h followed by adding recombinant human or murine TNF-α (or PBS)  in combination with (or without) SMAC mimetic  for 8 h for protein extraction, RNA/DNA extraction, IHC and cellular autophagic flux analysis. In some cases, cells were cultured for 24 h for cell viability analysis, Propidium Iodide (PI) staining, and Acridine Orange (AO) staining.Human SH-SY5Y cells were cultured in DMEM with 10% FBS. Rat PC12 cells were cultured in DMEM with 5% FBS (Gibco) plus 10% horse serum (Hyclone). The primary mouse cortical neuron culture was performed as described 12~1013 v.g./ml) were packaged and purified by Vigene Biosciences for further use.The AAV vector expressing two shRNA sequences and a separated RFP (pAV-2in1shRNA-CMV-RFP) was obtained from Vigene Biosciences, Inc. The structure of AAV vector can be found in Uvrag genes were cloned into indicated vectors, respectively . The structure of lentiviral vectors (Yeasen) used were listed in ectively . SH-SY5YThe adenoviral particles expressing mCherry-GFP-LC3B protein (Ad-mCherry-GFP-LC3) and GFP-p62 fusion protein (Ad-GFP-p62) were purchased from Beyotime (Ca# C3011 and C3015). The control adenoviral particles expressing β-glactosidase (Ad-LacZ) were purchased from Sciben Biotech (Ca# AD1002). SH-SY5Y cells or primary neurons were transduced with the individual adenoviral particles for 24 h at 5 of multiplicity of infection (MOI=5). Ad-LacZ was used as a control.Cells were seeded in 96-well plates before indicated treatment. The cells were then loaded with Enhanced Cell Counting Kit-8 (CCK-8) . Subsequently, the cell viability was determined by measuring the absorbance at 460 nm.Cells were seeded in 6-well plates before indicated treatment. Then cells were incubated with 2 μg/ml propidium iodide (PI)  for 10 min or 200 μM Acridine orange (AO)  for 15 min as described Immunoblotting and immunoprecipitation were performed as described Cells were homogenized in Trizol reagent  for extraction of total RNA based on the manufacturer's protocol. Purified RNA was reverse transcribed to cDNA using the HiScript III 1st Strand cDNA Synthesis Kit . mRNA levels were determined by qPCR using AceQ qPCR SYBR Green Master Mix kit . All primers used are listed in Uvrag gene was performed using a computational program ConTra v2 (http://bioit.dmbr.ugent.be/contrav2/index.php). Chromatin was extracted and immunoprecipitated using a Chromatin-IP (ChIP) kit  and an anti-RelA antibody  according to the manufacturer's protocol. Then separated and purified DNA was amplified by qPCR. All primers used are listed in Core promoter analysis of Morris water maze (MWM) test was performed as described t-test for nonpaired replicates was used to identify statistically significant differences between treatment means. Group variability and interaction were compared using either one-way or two-way ANOVA followed by Bonferroni's post-tests to compare replicate means. Significance was accepted at p < 0.05.All data were expressed as mean ± SEM. Student's Supplementary figures and tables.Click here for additional data file.