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We then examined whether mice can distinguish more complex visual targets and whether they require more training for more complex visual targets. Accordingly, we trained a group of mice to distinguish a set of black/white bars moving toward four different directions (Fig 1D, bottom). Similar to the color/luminance recognition, mice are able to distinguish targets moving in different directions after training. Fig 4B shows representative tracing results of a mouse that has just started training during the Acclimation phase (B1) and a mouse that was at the Data Acquisition phase (B2). After initial training, the mice were able to correctly distinguish the rewarding downward moving target from targets moving in the other three directions. Mice increased the number of times they reached the rewarding target from 7.43±0.87/session during the Baseline phase to 14.36±1.58/session during the Data Acquisition phase. A paired Student t-test shows that the difference between these two groups is statistically significant (Fig 4C, df = 41, t = 3.76, P<0.001). In addition, a chi-squared test was used to examine the difference in the ratio of rewards versus failures between these two groups and showed that the ratio of rewards is significantly higher in the Data Acquisition phase (4.67:1) than the Baseline phase (1.63:1, Fig 4D; F = 63.9, P<0.001).
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A) The outline of the training schedule for the recognition of moving bars. B) Representative tracing results of a mouse that has just started training (Acclimation, B1), and a mouse that has been trained to recognize the downward moving target for 21 total days (Data Acquisition, B2). C) The average number of rewards per session during the Baseline phase (day 6.5–9), and the Data Acquisition phase (day 19–21.5). A paired Student t-test shows that the difference between these two groups is statistically significant (df = 41, t = 3.76, P<0.001). D) The average number of rewards and failures during the Baseline and Data Acquisition phases. A Chi-squared test shows that the difference between these two groups is statistically significant (F = 63.9, P<0.001). E) The average distance traveled for each reward during the same two phases as in Panel C. A paired Student t-test shows that the difference between these two groups is not statistically significant (df = 37, t = 1.28, P>0.05). F) The average time required for each reward during the same two phases as Panel C. A paired Student t-test shows that the difference between these two groups is statistically significant (df = 37, t = 3.13, P<0.01). The number in each column indicates the number of test sessions from 7 mice.
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Similar to color/luminance recognition, mice trained to distinguish the moving direction of moving bars also improved their performance over the course of training. The mice reduced the average time required for reaching each reward from 98.93±4.7 sec in the Baseline phase to 78.40±4.5 sec in the Data Acquisition phase. A paired Student t-test shows that the difference between these two groups is statistically significant (df = 37, t = 3.13, P<0.01) (Fig 4F). However, the average distance required reaching each reward did not change significantly (Fig 4E, paired Student t-test, df = 37 t = 1.28, P>0.05).
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Finally, we examined whether mice could retain the capability of visual recognition and VR performance after they were trained. Accordingly, we established a 3-stage training/testing protocol (Fig 5A). During the first stage, a group of mice were trained for 21 days to recognize a downward moving target following the same schedule illustrated in Fig 2A. As shown in Fig 5B, the number of rewards per session increased after this training period, but did not reach a plateau. During the second stage, the mice were held in their home cages without any training and given free food access for 22 days. During the third stage, the mice were reintroduced to VR training for an additional 16 days and then we evaluated their performance. When the mice were reintroduced to training during the third stage, there was no Acclimation, or Target Identification phase (Fig 5A). To determine how well the memory was retained over the three-week break period (the second stage), the number of rewards per session during the first six sessions of the third stage (9.56±2.2) was compared to that of the Acclimation phase (day 1–3.5; 0.03±0.03 rewards/session), Baseline phase (day 6.5–9; 7.43±0.87 rewards/session), the initial Data Acquisition phase (day 19–21.5; 14.36±1.6 rewards/session) of the first stage, and the final six sessions of stage 3 (Final phase, 23.39±2.4 rewards/session). The average number of rewards during the initial six sessions of the third stage (Retrain phase) is statistically higher than that of the Acclimation phase of the first stage (Fisher’s PLSD, P<0.0001), and the average number of rewards during the final six sessions (Final) is significantly higher than any other training phase. However, the average number of rewards during the first six sessions of the third stage (Retrain phase) is not statistically different from that of the Baseline (Fisher’s PLSD, p>0.05) but is statistically different from that of the Data Acquisition phases of the first stage (Fisher’s PLSD, P<0.05) (see S1 Table for detailed results of Fisher’s PLSD tests). Fig 5D shows the cumulative distributions of the frequency of rewards/session of stage 1 and stage 3 of training. It is evident that the mice had a more rapid increase in rewards per session during stage 3, than they did during stage 1. A Kolmogorov-Smirnov (K-S) test confirmed that the difference of the distributions of the rewards/session of these two stages is statistically significant (P<0.001). Therefore, the non-maximum training during stage 1 facilitates the learning during stage 3.
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A) The outline of the training/testing schedule of the memory test of VR recognition. B) The average number of rewards per session as a function of training time during stage 1 (left, teal) and stage 3 (right, black). A linear regression analysis shows a linear fitting for both phases with a R2 = 0.61 and 0.45, respectively. C) The number of rewards per session of the Acclamation phase (Stage 1), Baseline phase (Stage 1), Data Acquisition phase (Stage 1), the first six sessions of Stage 3 of Additional Training (Retrain), and the final six sessions of Stage 3 Final Acquisition (Final). An ANOVA test shows that the differences among these five groups are statistically significant (F(4,145) = 29.66, P<0.001). Fisher’s PLSD post-hoc analysis shows that the number of rewards of the final six sessions (Final) is significantly higher than any other training phase. In addition, the number of rewards of the initial six sessions of the third stage (Retrain phase) is statistically higher than that of the Acclimation phase of the first stage (Fisher’s PLSD, P<0.0001) but is not statistically different from that of the Baseline (Fisher’s PLSD, p>0.05). However, it is statistically different from that of the Data Acquisition phases of the first stage (Fisher’s PLSD, P<0.05). The number in each column indicates the number of test sessions for 7 mice during stage 1, and 3 mice during stage 3. D) Cumulative distributions of the frequency of rewards/session in stage 1 and stage 3. A Kolmogorov-Smirnov (K-S) test confirmed that the difference of the distributions of the rewards/session of these two stages is statistically significant (P<0.001).
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In this study, we report a computerized and quantitative VR behavioral test to evaluate visual perception of mice. We emphasized the time requirement for the recognition of visual targets with different complexity. In comparison with commonly used visual behavioral tests, such as the lever press test [23–25], Morris water maze [26–28] and OKR [19–21], the VR test provides several advantages. First, this test can use virtually any type of visual targets, from simple displays such as color, luminance and moving targets, to complex visual displays such as a natural scene. Therefore, this test is readily applicable for use to analyze complex visual questions. Although the mice are able to quickly learn the task and differentiate the target of choice, the type of visual target makes a difference, with the more complex targets requiring a longer training paradigm to reach peak performance. Second, the computerized VR test allows the recording of highly detailed testing parameters, such as tracing the movement of the animals during the entire course of the test in high temporal resolution. Third, the computerized VR system runs the test with minimal human-animal interaction during the test (except for the initial training during Acclimation and Target Identification) and, therefore enhances the consistency of the tests and minimizes test variation. Forth, unlike the OKR, which records a reflex-based response to visual stimulation [19–21], the VR visual tests examine the visual perception of the animal. Finally, this VR behavioral test could be combined with in vivo image recording of CNS neurons in awake animals for correlated study of cellular activity of brain and behavior , and therefore, provides a platform to study the cellular mechanisms of visual perception.
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There seems to be two major learning components for optimized VR performance. One is to learn the recognition of the correct rewarding visual target, and the other is to learn to navigate through the VR arena. The capability of recognizing visual targets can be evaluated by the ratio of successful arrivals at the designated targets, while the ability of navigating the VR arena can be evaluated by measuring the proportion of distance that mice used to reach the rewarding target, the time required to reach a rewarding target, and the frequency of arriving at the rewarding target. For both the color/luminance targets and moving bars, mice can correctly identify the rewarding targets in a short training period. This is similar to, or less than, the amount of training time required for other behavioral tests, such as the Morris water maze or lever pressing tests [14,23–25,27,29,31,32] but not the reflex-based behavioral tasks, such as OKR [19–21]. However, additional training significantly improved the performance by increasing the frequency of arriving at rewarding targets, and shortening the time required to reach a rewarding target, indicating that the improvement in navigation requires more training than that of target recognition. Therefore, improvement in the maneuverability of the treadmill for navigation could potentially reduce the required training time, and more precisely measure visual perception. In addition, the type of target makes a significant difference in the training requirements, with more visually complex objects requiring more training to reach optimized performance than simple ones.
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It is worth pointing out that the four panels in the color/luminance test have a disparity in light intensity in power and, possibly, luminance. Therefore, the recognition and perception of the green panel against other color/luminance panels might not be solely due to spectral recognition per se, but also the difference in perceived luminance. Mice have two opsins that are expressed in cone photoreceptors, a short (S), and middle (M) opsin with peak sensitivity at the 360 nm and 508 nm respectively. These opsins are expressed at different proportions throughout the retina, with M-opsins dominating the ventral retina, and S-opsins are predominant in the dorsal retina, and some cones express both opsins . This would seemingly make it difficult for them to distinguish color, however it has been shown that with training that mice can detect differences in UV and green, and also color variations across the visual space . In addition, it has been suggested that full immersion into a visual environment is required to fully engage rat spatial navigation within the brain and to prevent the animal from thinking of the VR display as a physical object . However, Gaffen raised the possibility that the VR displays could be used by the animals as either a part of the environment or a large object within the actual environment and it may not matter for certain experimental questions. Several more recent studies demonstrated that a complete “immersion” might not be required for mice to identify and approach a visual target in a VR environment . Our results also support the notion that mice can recognize visual cues displayed in partially immersive visual environment and use it for spatial guidance. Because none of these previous studies investigated the properties of visual recognition and the training requirement of visual guided VR tests, our study provides useful information to address the questions of how quickly an experimental animal can learn to recognize certain visual cues, how significantly the complexity of visual cues affects the training requirement and performance of animals, and how long the memory of the visual guided task will last once animals are trained.
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Visual stimulation based VR behavioral tests have been extensively used for memory evaluation [10–16,30]. Our results confirmed that the visual memory could last for at least 3 weeks after 3 weeks of training. When mice were reintroduced to the VR behavior test after a 22 day “gap,” they were able to achieve a similar level of performance as before the no-training break. In addition, they were able to quickly surpass the performance of the previous training session after additional training. Therefore, not only could the mice maintain some of their skill set before the break, but also the previous experience could facilitate the additional training. This memory capability is particularly useful to evaluate visual function of animals before and after the induction of a disease or treatment.
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In conclusion, we have developed a VR test system, which can essay a variety of different aspects of visual perception. Using this system, it is possible to quantitatively characterize the behavioral responses of mice towards virtually any type of visual cues with minimal human-animal interaction to enhance the consistency of the tests and minimize test variation. Our results provide important details regarding the training requirement of different visual cues. If combined with a head-fixed in vivo imaging recording approach, it could provide a platform for correlated in vivo study of neuronal activity in the brain and visual perception.
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Osteoarthritis (OA), the most common joint pathology worldwide, causes structural and functional failure of synovial joints with loss and erosion of the articular cartilage [3, 20]. The prevalence of OA (symptomatic and non-symptomatic) reaches up to 80 % of the Western European population over 75 years of age, and it is estimated that approximately 10 % of the world’s population of 60 years and older have physical and/or pain symptoms that can be attributed to OA [3, 29]. OA has major consequences for an individual’s functioning during daily activities: it is the tenth leading cause of disability in high-income countries, and it is expected to become the world’s fourth leading cause of disability by 2020 [10, 20, 25]. The socio-economic impact of OA is substantial with the total annual disease costs being estimated at $5700 per individual per year. OA is also responsible for losses in work, social activities and difficulties in performing self-care [4, 22, 26].
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The impact of OA is not limited to physical symptoms only; several studies have shown that OA has a negative effect on a person’s mental well-being and their health-related quality of life [4, 13]. A recent US study measured the health-related quality of life of patients with OA using a generic quality of well-being scale. The quality of well-being scale score of these patients was 0.64, which was lower than that of the community-matched cohort (0.71) and similar to scores from patients with depression (0.64) or advanced cancer (0.63) [4, 17].
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Two recent systematic reviews showed the prevalence of OA was high among former elite athletes. Among retired professional footballers, the prevalence of knee OA ranged between 40 and 80 %, and the prevalence of ankle OA between 12 and 17 % . Among former elite athletes from various sport disciplines (ice hockey, basketball, handball, track and field athletes, power sport athletes, weight lifters, shooters and non-impact sport athletes), the prevalence of hip OA ranged from 2 to 60 %, and from 16 to 95 % for knee OA [12, 21]. OA in retired professional footballers causes pain, discomfort and functional limitations. It has been also suggested that OA could have an adverse impact on mental health: 37 % of the retired professional footballers suffering from OA reported moderate or severe problems related to anxiety/depression . Recent studies have shown that symptoms of common mental disorders (CMD) such as distress, anxiety/depression, sleeping disturbance and adverse alcohol behaviour are largely reported after a career in elite sports. However, the extent of the association between OA and symptoms of CMD in former elite athletes has not been established yet [13, 15, 18].
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Consequently, the primary aim of this study was to explore the association between OA and the occurrence and comorbidity of symptoms of CMD (distress, anxiety/depression, sleep disturbance, adverse alcohol use) among former elite athletes from rugby, football, ice hockey, cricket and Gaelic sports (football and hurling). The hypothesis was that former elite athletes suffering from OA were more likely to report symptoms of CMD than former elite athletes without OA. The secondary aim was to explore whether this association differed between the various subgroups of sports.
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The cross-sectional analyses were performed on the baseline questionnaires from five prospective cohort studies conducted among former elite athletes from the rugby, football, ice hockey, Gaelic sports (football and hurling) and cricket. The participants fulfilled the following inclusion criteria at recruitment: (I) age of 50 years or younger, (II) male, (III) able to read and comprehend texts fluently in either in English, French or Spanish, and (IV) a member of the national rugby union players’ associations, the national footballers’ unions, national ice hockey players’ associations, South African cricketers’ association or Gaelic players’ association from Finland, France, Ireland, Norway, South Africa, Spain, Sweden and/or Switzerland. In our study, the definition for a former elite athlete was that he had trained to improve performances, to have competed in the upper national division or league and to have had training and competition as major activity (way of living) or focus of personal interest, devoting several hours in all or most of the days for these activities, and exceeding the time allocated to other types of professional or leisure activities .
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The presence and location of OA diagnosed by a medical professional was examined through a single question (‘Have you been diagnosed with osteoarthritis by a medical professional?’). In the questionnaire, the following definition of OA was given to participants ‘Osteoarthritis is the damage of the joint’s cartilage that might lead to symptoms such as pain, stiffness or swelling in the given joint and that might impact functioning in either sport, work or daily life’.
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Distress in the previous 4 weeks was measured using the distress screener (three items scored on a three-point scale), which is based on a four-dimensional symptom questionnaire (4DSQ) [14, 30]. This is a self-rating questionnaire and is used as a convenient tool to assess common psychosocial symptoms. An example of a question is ‘Have you recently felt tense?’. Possible answers are no (0 points), sometimes (1 point), regularly (2 points) or (very) often (2 points). The 4DSQ, has been validated in several populations and languages including English, French and Spanish (internal consistency: 0.6–0.7; test–retest coefficients: ≥0.9; criterion-related validity: area under ROC curve ≥0.8) [14, 30]. A total score ranging from 0 to 6 was obtained by summing up the answers on the three items, a score of 4 or more indicating the presence of distress [14, 30].
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The 12-item General Health Questionnaire (GHQ-12) is a self-rating questionnaire designed for detecting individuals with a diagnosable psychiatric disorder related to anxiety/depression in the previous 4 weeks. The original version had 60 items, and these were reduced to 12 items . An example of an asked question in this questionnaire is ‘Have you recently been able to enjoy your normal day-to-day activities?’ with the following possible answers: better as usual, same as usual, less than usual, much less as usual. This 12-items questionnaire is validated in several populations and languages including English, French and Spanish (internal consistency: 0.7–0.9; test–retest coefficients: 0.8; criterion-related validity: sensitivity ≥0.7, specificity >0.7, area under ROC curve ≥0.8) [14, 28]. Based on the traditional scoring system, a total score ranging from 0 to 12 was calculated by summing up the answers on the 12 items, with a score of 4 or more indicating the presence of anxiety/depression (0 for favourable answers, 1 for unfavourable answers) [14, 28].
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Based on the PROMIS (short form), sleep disturbance in the previous 4 weeks was assessed through four single questions scored on a four-point scale (0 for favourable answers, 1 for unfavourable answers) [14, 32]. An example of a given question ‘In the past 4 weeks, I had difficulty falling asleep’. Possible answers: not at all (0 points), a little bit (0 points), somewhat (0 points), quite a bit (1 point), very much (1 point). A total score ranging from 0 to 4 was obtained by summing up the answers to the four questions, a score of 2 or more indicating the presence of sleep disturbance. The PROMIS has been validated in several populations and languages including English, French and Spanish (internal consistency: >0.9; test–retest coefficients: 0.8; construct validity: product–moment correlations ≥0.9) (for detailed information, see www.nihpromis.org) [14, 32].
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Level of alcohol consumption was assessed using the three-item AUDIT-C. The AUDIT-C test has been validated in several populations and languages including English, French and Spanish (test–retest coefficients: 0.6–0.9; criterion-related validity: area under ROC curve 0.70–<1.0) [8, 14]. An example of a question is ‘How often do you have a drink containing alcohol?’ with possible answers: never, once a month or less, 2–4 times a month, 2–3 times a week, 4 or more times a week [8, 14]. A total score ranging from 0 to 12 was obtained by summing up the answers on the three items, a score of 5 or more indicating the presence of adverse alcohol use [8, 14].
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Based on the dependent and independent variables, an electronic anonymous questionnaire available in English, French and Spanish was compiled (FluidSurveys™). In addition, the following descriptive variables were retrieved: age, stature, body mass, duration of professional sports career, years since retirement, nature of retirement (voluntarily or not), current occupation and number of working hours per week. Information about the study was emailed to potential participants by their respective association involved in the study. The invitation procedures were blinded to the responsible researchers for reasons of privacy and confidentiality of the former elite athletes. The participants were asked to give their informed consent and to complete the online questionnaire within 2 weeks while reminders were sent after 2 and 4 weeks. The questionnaire took about 20 min to complete. Ethical approval of the study was provided by the board of St. Marianna University School of Medicine (2014/04/16; Kawasaki, Japan), the Faculty of Health Sciences Human Research Ethics Committee of the University of Cape Town (642/2014, 843/2014; Cape Town, South Africa) and the Medical Ethics Review Committee of the Academic Medical Center (W15_060#15.0072, W15_171#15.0207; Amsterdam, The Netherlands). The present study was conducted in accordance with the 2013 Declaration of Helsinki and its later amendments or comparable ethical standards.
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Data were analysed using the statistical software IBM SPSS Statistics 23.0 for OS X. Descriptive data analyses (mean, standard deviation, frequency, range) were calculated for all variables in this study, including the frequency of participants that reported OA and symptoms of CMD. Univariate logistic regression analyses (expressed as odds ratio OR and related 95 % CI) were used to explore whether former elite athletes who suffered from OA (dichotomous independent variable) were more likely to report (simultaneously) symptoms of CMD (dichotomous dependent variables) than former elite athletes who did not suffer from OA (non-causal association). Under the assumption of at least 58 participants within a particular sport, similar univariate logistic regression analyses were conducted within each subgroup for sports, which met this criterion . In accordance with a sample size calculation for testing the relationship between independent and dependent variables (N > 50 + 8 m where m is the number of independent variables), sample size of at least 58 participants was needed . With regard to the five subgroups, i.e. five sports involved in our study, we strived to recruit a total sample size of minimum 290 participants (minimum 58 per sport) .
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A total of 2218 former elite athletes were contacted to participate in this study (1230 in rugby, 307 in football, 420 in ice hockey, 180 in cricket, 81 in Gaelic sports), from which 624 gave their written informed consent and completed the online questionnaire: 295 former rugby players (47 %), 220 former football players (35 %), 61 former ice hockey players (10 %), 27 former cricket players (4 %) and 21 former Gaelic sport athletes (3 %). From those, 22 participants did not answer the question about OA, and their data were consequently discarded. The mean age of the respondents was 36.7 (SD 6.3) years. Participants had an elite sports career with a mean duration of 10 years (SD 5) and were retired for 6 (SD 5). Approximately 42 % did not end their careers as elite athletes voluntarily. About 60 % of the participants were working in a paid position for about 37 h a week. All characteristics of the participants are presented in Table 1.Table 1Characteristics of the participantsVariablesOverallWith OA (n = 200)Without OA (n = 402)Age (in years; mean ± SD)37 ± 638 ± 536 ± 6Height (in cm; mean ± SD)183 ± 8184 ± 7183 ± 8Weight (in kg; mean ± SD)93 ± 1794 ± 1693 ± 17Duration of sports career (in years; mean ± SD)10 ± 511 ± 410 ± 5Duration of retirement (in years; mean ± SD)6 ± 57 ± 556 ± 5Involuntary retired from sports (%)424442Currently (self-) employed (%)605264Working hours per week (mean ± SD)37 ± 1442 ± 1242 ± 14Prevalence of osteoarthritis (%)33.2 %–– Prevalence of symptoms of CMD (%)Distress22.128.618.8Anxiety/depression27.130.825.3Sleep disturbance26.933.223.9Adverse alcohol use26.233.822.5Comorbidity (≥2)27.233.525.6 SD standard deviation, CMD symptoms, symptoms of common mental disorders, OA osteoarthritis; comorbidity, having two or more than two symptoms of common mental disorders
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Around 33 % (N = 200) of the respondents reported that a medical professional diagnosed them with OA. The most reported anatomical site to have OA was the knee with 50 % of those diagnosed with OA reporting it in one or in both of their knee joints. The distribution of OA by anatomical site is presented in Fig. 1. The prevalence of symptoms of CMD ranged from 22 % for distress to 27 % for anxiety/depression. In the group of former elite athletes with OA, this range was from 29 to 34 % for distress and adverse alcohol use, respectively. In the group of former elite athletes without OA this ranged from 19 to 25 % for distress and anxiety/depression, respectively (Fig. 2).Fig. 1Distribution of osteoarthritis (OA) by anatomical site Fig. 2Prevalence of symptoms of common mental disorders among former elite athletes with and without OA
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OA was statistically (not causally) associated with distress (OR = 1.7, 95 % CI 1.2–2.6), sleep disturbance (OR = 1.6, 95 % CI 1.1–2.3), adverse alcohol use (OR = 1.8, 95 % CI 1.2–2.6), comorbidity ≥2 (OR = 1.5, 95 % CI 1.0–2.1). The relative strength of the relationship between OA and symptoms of CMD (including across sports) is presented in Table 2.Table 2Association (OR and 95 % CI) between osteoarthritis and symptoms of common mental disorders among former professional athletes and among former professional athletes from football, rugby and ice hockeyDistressAnxiety/DepressionSleep disturbanceAdverse alcohol useComorbidity (2 ≥)Total 1.7 (1.2–2.6) 1.3 (0.9–1.9) 1.6 (1.1–2.3) 1.8 (1.2–2.6) 1.5 (1.0–2.1) Rugby 2.7 (1.5–4.7) 1.6 (0.9–2.7) 2.4 (1.4–4.1) 1.6 (0.9– 2.9) 2.1 (1.2–3.5) Football1.0 (0.5–2.1)0.8 (0.4–1.5)0.9 (0.4–1.6)0.5 (0.3–1.1)0.7 (0.3–1.4)Ice hockey 1.2 (1.1–1.4) 1.4 (1.2–1.8) 1.3 (1.1–1.6) 3.7 (2.2–6.9) 1.5 (1.2–2.0) Statistically significant values are given in bold
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OA is a prevalent joint disease in the general population, ranging between 10 and 20 % in the male general population (from age 35 and above) in many countries [19, 23]. Among former elite athletes from various sport disciplines (ice hockey, basketball, handball, track and field athletes, power sport athletes, weight lifters, shooters and non-impact sport athletes), the prevalence of OA reaches up to 60 % for the hip and ranges from 16 to 95 % for the knee [12, 22, 23]. In our study, 33 % of the former elite athletes reported that a medical professional had diagnosed them with OA (mostly in the lower limbs), which is in accordance with the prevalence found in previous studies [9, 13, 24]. Also in our study, there was a significant association between OA and symptoms of CMD among the former elite athletes. Consequently, it seems necessary to promote the appropriate management of this joint disease in former sportsman, not only for its physical consequences such as pain, discomfort and functional impairments, but also for its consequences related to symptoms of CMD.
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A previous cross-sectional study, investigated the prevalence of OA and anxiety and depression via questionnaires, which were sent to 515 former professional football players in the UK . A total of 284 players participated by returning the questionnaire (response rate 55 %), from which 138 (49 %) reported a diagnosis with OA on at least one site . Significantly more respondents with OA reported problems with anxiety/depression than those without OA: 37 % of the participants with OA reported problems on the questionnaire dimension anxiety/depression compared to 19 % of the participants of the group without OA (χ2 = 10.48, df = 1, p = 0.001) . Similar results were found in our study; namely that 31 % of the former elite athletes with OA reported problems related to anxiety/depression compared to 25 % of the former elite athletes without OA. As far as we know, this cross-sectional study is the first international study to explore the association of OA and symptoms of CMD among a large group of former elite athletes from different sports.
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Our study confirms that both OA and symptoms of CMD are two major health problems that are prevalent among former athletes. This impacts on the long-term health consequences of a career in the elite sport. Our study also shows that OA and its related physical consequences (pain, discomfort, impairments) may be seen as a risk factor for the occurrence of symptoms of CMD. In a previous study, we advocated for an interdisciplinary approach to the clinical care and support of current professional athletes during their career . The present study justifies such an interdisciplinary approach among former elite athletes after their careers, because the interaction between the physical and mental health issues occurring on the long term is complex.
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The preventive and supportive measures for reducing OA or symptoms of CMD for former elite athletes are lacking. As previously advocated by active and former professional athletes as well as by sports physicians, the development of self-management interventions directed towards OA and symptoms of CMD should be explored . Self-management strategies for chronic physical and mental health conditions, such as rheumatic diseases, diabetes and depression, are effective. These self-management strategies aim at engaging and promoting a healthy and active behaviour of patients by covering aspects such as self-awareness (information provision and patient education) and cognitive and behavioural therapy [5, 6, 27]. Another potential step forward in the medical care and support of former elite athletes is the development and implementation of an exit-career medical assessment (ExCMA). Such an ExCMA aims to raise self-awareness (information provision) about the long-term consequences of an elite sport career and counselling especially about OA’s remission, coping skills related to symptoms of CMD and promotion of healthy life style would be beneficial. Such an ExCMA is intended to be tested in professional football by the World Players’ Union (FIFPro) before a larger implementation across sports.
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Our study has a number of limitations. Firstly, a cross-sectional design was used. Such a design allowed us to explore the association between OA and symptoms of CMD, but it did not allow any causal relationships to be established. It remains unknown when the former elite athletes were diagnosed with OA and if they have coped with having symptoms of CMD on previously in their life. A longitudinal study design following a group of elite athletes through their careers until several years after retirement is needed to confirm this observation. Secondly, the recruitment procedures were blinded for the researcher for privacy and confidential reasons. Consequently, it was not possible to conduct a non-response analysis. Lastly, an additional aspect worth discussing is that the participants were from different countries and cultures. It would be possible that there is a difference in the frankness about answering questions about symptoms of CMD based on the social desirability. This heterogeneous sample might have implications for determining the true prevalence symptoms of CMD in former elite athletes.
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Several main strengths of our study can be acknowledged. First, the topic being studied, namely the long-term consequences of an elite sports career, is exponentially under scrutiny in medical sciences. Second, a large group of former elite athletes retired from rugby, football, ice hockey, Gaelic sports and cricket was secured, which provided a unique and innovative insight in this special population. Third, the assessment of patient-reported outcomes measures (PROMs) by validated questionnaires gives us real-time information about each participant having symptoms of CMD. If PROMS would be systematically used during and after a career in elite sports, it could be of great additional help for clinicians to easily identify relevant information, about the condition of the mental health of the elite athlete and for elite athletes themselves this could empower the clinicians in their clinical decisions and the athlete in seeking help .
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The clinical relevance of this study is that an interdisciplinary approach to the clinical care and support of former elite athletes after their careers is advocated as the interaction between the physical and mental health issues occurring on the long term is complex. Our findings contribute to advancing knowledge about the long-term consequences of a career in elite sports. With more knowledge about the medical issues that occur after a career in sport, specific preventive and supportive measures can be developed and implemented to empower the durable health of former elite athletes.
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| 99.25 |
OA might be a risk factor for developing symptoms of CMD in former elite athletes. Therefore, monitoring OA among former elite athletes should be empowered while strategies to prevent symptoms worsening should be developed and implemented. The self-awareness, prevention and care of mental health problems that might occur after a professional sports career should also be addressed.
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other
| 99.9 |
One of the key questions of evolution is to answer how the morphological complexity takes place among extant animals including vertebrates. The subphylum Vertebrata is characterized by the appearance of critical morphological innovations such as an elaborate segmented brain, neural crest cells, neurogenic placodes, and endoskeleton (Shimeld and Holland, 2000). Several decades of developmental genetic studies have led to the important discovery that most animals from different taxa share a bunch of regulatory and tissue-specific genes known as “genetic toolkit,” which controls animal body pattern (Carroll, 2000). Therefore, the molecular phylogeny of these toolkit genes will provide us an insight into the evolutionary origin of morphological novelties. Whole genome duplication (WGD) could not only enrich the genomic complexity, but also generate new functions of duplicated genes by co-option (Ohno, 1970; True and Carroll, 2002; Dehal and Boore, 2005; Conant and Wolfe, 2008; Cañestro et al., 2013). WGD occurred around the origin of vertebrate lineage, has been shown to be related to vertebrate morphological innovations (Manzanares et al., 2000; True and Carroll, 2002). For example, the origin of vertebrate cartilage was found to be attributable to the duplication of chordate fibrillar collagen genes (Wada et al., 2006; Zhang et al., 2006), and the emergence of vertebrate vascular vessels to the duplication of kank genes (Hensley et al., 2016).
|
review
| 99.7 |
The family of KHDRBS protein (KH Domain-containing, RNA Binding, and Signal transduction associated protein), is characterized by the GSG (GRP33/SAM68/GLD-1) domain, also named STAR domain, which includes a single KH domain flanked by the conserved N-terminal QUA1 and C-terminal QUA2 (Di Fruscio et al., 1999; Lukong and Richard, 2003). KHDRBS proteins bind RNA through their KH domains, and participate in signal transduction (Frisone et al., 2015; Ehrmann et al., 2016). In nematodes, GLD-1, the ortholog of KHDRBS, was found to be localized in the germ cell cytoplasm, and indispensable for oogenesis and meiotic prophase progression, while it shows little roles in the male germ line or soma, although it can stimulate sex determination of males in the hermaphrodite germ line (Jones and Schedl, 1995; Lee and Schedl, 2001, 2010). In contrast, NSR, the ortholog of KHDRBS of fruit fly, was found to be predominantly localized in the nuclei of primary spermatocytes, and necessary for male reproduction by regulating some male fertility genes (Ding et al., 2010). In vertebrates, there are three members of KHDRBS, KHDRBS1 (also called Sam68), KHDRBS2 (also called SLM1), and KHDRBS3 (also called SLM2) which share many features such as RNA binding and signal transduction. Interestingly, khdrbs1 knockout mice showed impaired fertility in males as a result of mRNA translational regulation defect during spermiogenesis (Paronetto et al., 2009; Ehrmann and Elliott, 2010; Frisone et al., 2015), and KHDRBS3 was also involved in spermatogenesis via directly binding to the genes essential for male gametogenesis (Zhang et al., 2009). This function of KHDRBS1 and KHDRBS3 in mice is apparently analogous to their invertebrate orthologs GLD-1 and NSR in term of regulation of gametogenesis. On the contrary, the three members of vertebrate khdrbs family appear to be expressed more specifically in the brain. In mouse, khdrbs1 and khdrbs3 were predominantly expressed in the brain and testis, but khdrbs2 was exclusively expressed in the brain (Ehrmann et al., 2016). Moreover, although khdrbs1 KO male mice showed male infertility, behavioral deficits and poor motor control (Ehrmann et al., 2013), khdrbs2 KO mice as well as khdrbs1 and khdrbs2 double KO mice both exhibited defects in cerebellar morphogenesis (Iijima et al., 2014), and khdrbs3 KO mice displayed synaptic plasticity and behavioral defects (Traunmüller et al., 2016). These data together suggest that khdrbs gene family, as a member of genetic toolkit, may be linked to vertebrate brain development. However, the evolutionary relationship between khdrbs gene family and vertebrate brain development is still a mystery.
|
review
| 99.7 |
The aim of this study is thus to answer this question by taking advantage of the zebrafish (Danio rerio) model, which has a brain resembling that of humans in both basic structures and functional capacities (Tropepe and Sive, 2003). We first analyzed the molecular evolution of khdrbs gene family in representative metazoan taxa, and then examined the expression patterns of khdrbs during early development and in adulthood of zebrafish. We found that khdrbs gene family was expanded by WGD in zebrafish, and all zebrafish khdrbs genes were predominantly expressed in the substructures of brain during early development. Given that these substructures are vertebrate-specific trait, the distinct expression domains of khdrbs genes in zebrafish suggested that khdrbs gene family was co-opted for vertebrate brain development after WGD events around the split of Vertebrata.
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study
| 100.0 |
The AB strain zebrafish were cultured at 28 ± 1°C. Embryos were cultured in E3 medium consisting of 5 mM NaCl, 0.17 mM KCl, 0.33 mM CaCl2, and 0.33 mM MgSO4. For whole-mount in situ hybridization (WISH), 0.0045% 1-phenyl-2-thiourea was added into E3 medium to prevent embryos from pigmentation started 24 h post-fertilization (hpf). Different stages of embryos were sorted and fixed following the guide of Kimmel et al. (1995).
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study
| 99.94 |
KHDRBS protein sequences of most major metazoan taxa were obtained from NCBI and Ensembl database. Other KHDRBS protein sequences were identified by a BLASTp search with a query sequence (human KHDRBS1 protein). The sequences used were listed in Supplementary Table 1. Sequence alignment was performed by ClustalW method in MegAlign v7.1.0. To acquire the best evolutionary model for phylogenetic inference, we used default parameters in MEGA7 to conduct a best-fit protein model test. According to calculated BIC (Bayesian information criterion) scores for every model, we chose LG + G model which had the lowest score for further phylogenetic analysis. After that, rooted phylogenetic trees were constructed by Bayesian analysis and maximum likelihood (ML) method. These two methods were conducted with MrBayes v3.2.6 and PHyML website, respectively. ML phylogenetic analysis used LG + G model and set bootstrap as 1,000 replicates. Bayesian analysis was performed with following parameters: ngen = 2,000,000, nruns = 2, nchains = 4, aamodel = fixed (LG), rates = gamma, samplefreq = 1,000, burninfrac = 0.25. Finally, phylogenetic trees obtained were viewed and modified with FigTree v1.4.2. Synteny data of khdrbs genes were collected using tools available from Ensembl and NCBI database. Gene structural features of those chosen transcripts were analyzed using data from Ensembl database. Characteristics of the protein domain were predicted by SMART website.
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study
| 100.0 |
Fragments of zebrafish khdrbs genes were amplified with specific primers (Table 1) that were designed using Primer Premier 5.0 based on existing sequences from NCBI. The purified PCR products were sub-cloned into vector pGEM-T, which was sequenced to verify inserts orientation.
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study
| 99.94 |
Digoxigenin (DIG)-labeled khdrbs antisense riboprobes were synthesized with linearized vectors (digested by NcoI restriction enzyme) and Sp6 RNA polymerase through in vitro transcription, while synthesis of sense riboprobes used SalI restriction enzyme and T7 RNA polymerase. WISH experimental procedures followed the protocol described by Thisse and Thisse (2008). After staining, the embryos were fixed with 4% paraformaldehyde, rinsed with 70% ethanol and then mounted in glycerin for imaging. The embryos were also washed with PBS, soaked in 30% sucrose (diluted in PBS) overnight and cryosectioned.
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study
| 64.2 |
Quantitative real-time PCR (qRT-PCR) was used to test the expression patterns of khdrbs in the different tissues of adult zebrafish. Total RNAs were extracted from the tissues gill, eye, brain, intestine, liver, heart, muscle, skin, spleen, testis, and ovary with TRIzolTM (Invitrogen) and purified using Total RNA Kit I (OMEGA Bio-Tek). For each tissue, 1 μg RNA was used for next reverse transcription. The cDNAs were reverse transcribed using M-MLV reverse transcriptase (TaKaRa) and Oligo (dT) primers as guided by the manufacturer’s instructions. β-actin and EF1-α were chosen as control to standardize the results by eliminating variations in mRNA and cDNA quantity and quality. qRT-PCR was conducted using the gene-specific primers (Table 2) on the ABI 7500 real-time PCR system (Applied Biosystem) with the 2× SYBR Premix Ex TaqTM Kit (TaKaRa). A total of 0.5 μl cDNA was used as template in each replicate. Reaction conditions were: 95°C for 15 s as stage 1, then 40 cycles of 95°C for 15 s, 60°C for 15 s, and 72°C for 35 s as stage 2. The expression level of khdrbs genes relative to that of the housekeeping genes β-actin and EF1-α was calculated by the comparative threshold cycle (CT) method (2-ΔΔCt). The experiments were performed in triplicate, and each replicate was from three experiments, i.e., the indicated replicates were both technical and biological replicates.
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study
| 100.0 |
Recent studies have revealed that changes of key genetic toolkit at cellular and developmental level can generate novel characters in morphological structures. To better understand, it we need to dig into the evolutionary history of these critical genes. Considering that DNA sequences are more vulnerable and variable to selection pressure during long evolutionary history, we used KHDRBS protein sequences of major metazoan taxa for later analyses. We acquired multiple KHDRBS protein sequences mainly from NCBI and Ensembl. These sequences were aligned by ClustalW algorithm for next phylogeny analyses. We constructed phylogenetic trees by MrBayes (Figure 1A) and ML methods (Figure 1B). These two phylogenetic trees were generally consistent with each other. The analyses revealed that KHDRBS was traced back to Trichoplax adhaerens and Hydra vulgaris, the basal metazoans.
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study
| 100.0 |
Phylogenetic analyses of KHDRBS proteins constructed by (A) Bayesian method and (B) maximum likelihood (ML) method. Numbers at each node suggest Bayesian posterior probability (pp) values based on two million runs and ML bootstrap values based on 1,000 replicates, respectively. Both trees are rooted with Trichoplax.
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study
| 99.94 |
The invertebrate KHDRBS proteins formed a single clade, positioned at the base of vertebrate KHDRBS proteins. The overall phylogenetic relationship of KHDRBS well reflects the classic phylogeny of metazoan taxa. In contrast to only one KHDRBS group in invertebrates, there were three distinct clades of KHDRBS proteins in vertebrates, KHDRBS1, KHDRBS2, and KHDRBS3 (Figure 1), consistent with recent reports that humans, mice, and birds have three KHDRBS genes (Artzt and Wu, 2010). Apparently, the emergence of these three clades occurred around the split of subphylum Vertebrata, implicating that the three KHDRBS clades originated from sequential WGD events. Notably, absence of KHDRBS3 was also observed in Amazon molly (Poecilia formosa), suggesting that gene loss event may have happened during evolution process. In addition, KHDRBS protein sequences alignment of major vertebrates (Figure 2) showed that KHDRBS2 and KHDRBS3 shared higher identity (range from 44.5 to 69.8%) than that between KHDRBS1 and KHDRBS2 (range from 43.6 to 65.9%) or KHDRBS1 and KHDRBS3 (range from 39.6 to 61.3%), which meant KHDRBS2 and KHDRBS3 formed a more close evolutionary relationship.
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study
| 100.0 |
KHDRBS protein sequences alignment of major vertebrates. Red area represents the percent identity between KHDRBS1 and KHDRBS2. Yellow area means the percent identity between KHDRBS1 and KHDRBS3. And blue area shows the percent identity between KHDRBS2 and KHDRBS3. Protein sequences are aligned by ClustalW method in MegAlign.
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other
| 89.3 |
We found that khdrbs1 genes in teleost were clustered into two sub clades, as khdrbs1a and khdrbs1b. Compared to tetrapod khdrbs1 genes, khdrbs1a and khdrbs1b formed a more close branch with a sister clade spotted gar (Figure 1). Spotted gar has an important evolutionary status because it diverged from teleost before gene duplication happened in teleost (Braasch et al., 2016). Therefore, this indicated that khdrbs1a and khdrbs1b emerged simultaneously within teleost lineage, i.e., they were generated by teleost-specific genome duplication (Hoegg et al., 2004; Jaillon et al., 2004). As phylogenetics, albeit widely analyzed, may be still short of enough reliability to elucidate gene orthologous relationships due to sophisticated situations like gene loss or gene expansion in specific lineage, we thus performed a syntenic analysis among zebrafish khdrbs1, spotted gar khdrbs1a and human khdrbs1 genes, which represented teleost and tetrapod, respectively (Figure 3), to further confirm the relationship between khdrbs1a and khdrbs1b. The results revealed that the flanking genes beside both khdrbs1a and khdrbs1b could be found in the neighboring region around spotted gar and human khdrbs1, which meant that khdrbs1 in zebrafish was orthology to spotted gar and human khdrbs1 and remained an evolutionarily conserved synteny.
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study
| 100.0 |
Syntenic map of the genomic segment where khdrbs1 resides in the chromosomes of zebrafish, spotted gar, and humans. khdrbs1 genes are highlighted in red and the synteny are linked with straight line. Other genes are noted with blue. The illustration of genes and their sizes are not proportional to the actual length of chromosome. Dr, zebrafish; Hs, human; Lo, spotted gar; Chr, chromosome.
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other
| 99.7 |
To pinpoint the relationships between the duplications of teleost khdrbs1 genes, we conducted protein domain analysis by SMART. We found that when compared with human KHDRBS1, both zebrafish KHDRBS1a and KHDRBS1b possessed similar protein domains, though they shared more similarities to spotted gar KHDRBS1a (Figure 4A). Additionally, the differences between genomic intron–exon structures of zebrafish khdrbs1a and khdrbs1b were also analyzed. Both khdrbs1a and khdrbs1b had an intron–exon structure similar to that of other species (Figure 4B), with the highly conserved region within the KH domains (Figure 4C).
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study
| 100.0 |
Bioinformatic analysis of KHDRBS1 proteins in cavefish, human, spotted gar, and zebrafish. (A) Protein domain structure predictions of KHDRBS1 by SMART. Purple bars represent low complexity regions. Vertical colored lines indicate the intron positions. KH domains are shown in oval shapes with “KH”. (B) Intron–exon structures of khdrbs1. The solid boxes denote the gene coding regions, and the empty boxes mean untranscribed regions. Dotted lines represent the introns. (C) Protein sequences alignment of KHDRBS1. Protein sequences are aligned by ClustalW method in MegAlign. The highly conserved part is noted by red rectangles.
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study
| 100.0 |
WGD commonly leads to the functional specialization of paralogous genes, or gene co-option, and contributes to vertebrate morphological innovations. Recently, khdrbs1a and khdrbs1b were found both mainly expressed in the brain primordium of zebrafish embryos, hinting at the clue that khdrbs genes may play critical roles in vertebrate brain development. We thus hypothesized that investigating the gene expression patterns of the four zebrafish khdrbs genes might illuminate the origin of the vertebrate brain during evolution.
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study
| 100.0 |
Zebrafish have two khdrbs1 genes, khdrbs1a and khdrbs1b. Different from control group (Supplementary Figures 1A–F), both khdrbs1a and khdrbs1b were pan-expressed in the blastoderm and the embryo per se (Figures 5A–E,A′–E′). At 24 hpf, khdrbs1a expression was detected in the brain (Figures 6A–D), eye, spinal cord, lateral line primordium, gonad primordium, pectoral fin, and caudal fin (Figures 5F–I), while khdrbs1b expression was relatively weak in the brain (Figures 6A′–D′), eye, lateral line primordium, and spinal cord (Figures 5F′–I′). By 48 hpf (Figures 5J–M) and 72 hpf (Figures 5N–Q), khdrbs1a expression in the brain (Figures 6E–L) and pectoral fin enhanced, and the signal at the lateral line neuromast was evident. Additional expressions were also observed in the pharyngeal arch, pronephric ducts (Figure 7) and otic vesicle, but the expression signals in the spinal cord and caudal fin apparently faded. Compared to khdrbs1a, khdrbs1b was not expressed in the lateral line by 48 hpf (Figures 5J′–M′); instead, its expression was restricted to the brain (Figures 6E′–H′), eye, pharyngeal arch and pectoral fin, and this expression pattern remained till 72 hpf (Figures 5N′–Q′, 6I′–L′).
|
study
| 100.0 |
Gene expression patterns of zebrafish khdrbs1a and khdrbs1b genes during early development detected by WISH. Stages of embryonic development: 0.45 hpf (A,A′), 3 hpf (B,B′), 6 hpf (C,C′), 10 hpf (D,D′), 14 hpf (E,E′), 24 hpf (F–I, F′–I′), 48 hpf (J–M, J′–M′), and 72 hpf (N–Q, N′–Q′). Expression sites are labeled with abbreviation as follows. cf, caudal fin; e, eye; gp, gonad primordium; hb, hindbrain; lln, lateral line neuromast; llp, lateral line primordium; mb, midbrain; ov, otic vesicle; pa, pharyngeal arch; pd, pronephric ducts; pt, pectoral fin; sp, spinal cord.
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study
| 99.94 |
Gene expression domains of zebrafish khdrbs1a and khdrbs1b genes in brain detected by WISH and cryosection. Transverse sections of brain structures: telencephalon (E,I,E′,I′), diencephalon (F,J,F′,J′), midbrain (G,K,G′,K′) and hindbrain (C,D,H,L,C′,D′,H′,L′). Brain structure of zebrafish at 24 hpf (A,B,A′,B′). Straight lines, representing slice positions, are marked by English letters corresponding to the section images. Expression sites are labeled with abbreviation as follows. lln, lateral line neuromast.
|
study
| 99.94 |
We then examined the expression patterns of zebrafish khdrbs2 and khdrbs3 expression. At the early developmental stages, both khdrbs2 and khdrbs3, like khdrbs1, were pan-expressed in the blastoderm and the embryo per se (Figures 8A–E,A′–E′ and Supplementary Figures 1G–L). However, khdrbs2 and khdrbs3 genes both later generated more specific expression patches in the brain, forming a sharp contrast to that of khdrbs1. By 24 hpf (Figures 8F,I,L, 9A–D), khdrbs2 was expressed in the telencephalon, epiphysis, hypothalamus, hind rhombomeres, and spinal cord. Signals in these regions gradually expanded in the midbrain and hindbrain with larva development. At 48 hpf (Figures 8G,J,M, 9E–H) and 72 hpf (Figures 8H,K,N, 9I–L), expression of khdrbs2 was primarily restricted to the tegmentum and tectum, respectively, and expression in the spinal cord disappeared. More interestingly, khdrbs3 signal was observed in the telencephalon, diencephalon, midbrain, and hindbrain at 24 hpf (Figures 8F′,I′,L′, 9A′–D′), and then became localized intensely in the telencephalon, hypothalamus, tegmentum, and hind rhombomeres (Figures 8G′,J′,M′, 9E′–H′). By 72 hpf (Figures 8H′,K′,N′, 9I′–L′), khdrbs3 expression spread out around the midbrain and hindbrain, with additional signals emerging in the retina and tectum.
|
study
| 100.0 |
Gene expression domains of zebrafish khdrbs1a gene in pronephric ducts detected by WISH and cryosection at 48 hpf. Straight lines represent slice positions (A,B). Transverse sections of zebrafish pronephric ducts at 48 hpf (C–E). Expression sites are labeled with abbreviation as follows. pd, pronephric ducts.
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study
| 99.94 |
Gene expression patterns of zebrafish khdrbs2 and khdrbs3 genes during early development detected by WISH. Stages of embryonic development: 0.45 hpf (A,A′), 3 hpf (B,B′), 6 hpf (C,C′), 10 hpf (D,D′), 14 hpf (E,E′), 24 hpf (F,I,L,F′,I′,L′), 48 hpf (G,J,M,G′,J′,M′), and 72 hpf (H,K,N,H′,K′,N′). Expression sites are labeled with abbreviation as follows. di, diencephalon; ep, epiphysis; hb, hindbrain; hr, hind rhombomeres; hy, hypothalamus; mb, midbrain; re, retina; sp, spinal cord; tec, tectum; teg, tegmentum; tel, telencephalon.
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study
| 99.94 |
Gene expression domains of zebrafish khdrbs2 and khdrbs3 genes in brain detected by WISH and cryosection. Transverse sections of brain structures: telencephalon (A,E,I,A′,E′,I′), diencephalon (B,F,J,B′,F′,J′), midbrain (C,G,K,C′,G′,K′), and hindbrain (D,H,L,D′,H′,L′). Straight lines, representing slice positions, are marked by English letters corresponding to the section images. Expression sites are labeled with abbreviation as follows. ep, epiphysis; hy, hypothalamus; tec, tectum; teg, tegmentum; tel, telencephalon.
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study
| 99.94 |
Finally, we carried out qRT-PCR to explore the expression profiles of khdrbs in adult zebrafish (Figure 10 and Supplementary Figure 2). As shown in Figures 10A,B, both khdrbs1a and khdrbs1b shared similarities in their expression profiles. They were both detected in all the tissues tested, with the highest expression levels in the brain. The obvious difference between khdrbs1a and khdrbs1b was that the latter gene had higher expression in the gonads. In contrast, the expression of khdrbs2 and khdrbs3 became more distinct: both khdrbs2 and khdrbs3 signal was predominantly detected in the brain, with lower expression in the eye and testis (Figures 10C,D). These indicated that these genes are mainly expressed in the brains of adult zebrafish in a tissue-specific fashion, consistent with their expression patterns in embryogenesis.
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study
| 100.0 |
Gene expression patterns of zebrafish (A) khdrbs1a, (B) khdrbs1b, (C) khdrbs2, and (D) khdrbs3 in different tissues. β-actin is used as internal control for normalization. Relative expression data is calculated by the method of 2-ΔΔCt. Vertical bars represent the mean ± standard deviation (SD) (n = 3). Data are from three independent experiments which were performed in triplicate.
|
study
| 99.94 |
One goal for evolutionary biology is to decipher the mystery underlying morphological novelties and complexity of extant organisms, especially vertebrates. As the largest and most diverse lineage in chordate, Vertebrata is characterized by several key morphological characteristics such as a tripartite brain. Such morphological characteristics are usually originated from variation of the related genetic toolkits. Hence, expression pattern and evolutionary analyses of key genes in these toolkits can deepen our understanding of the emergence of morphological innovations during evolution. In this study, we first analyzed the molecular evolution of khdrbs gene family, and then investigated zebrafish khdrbs genes expression patterns during embryonic development and in adulthood of zebrafish. The results indicated that after vertebrate WGD, khdrbs gene family was co-opted for brain development, and the duplication and diversification of khdrbs genes may promote the origin of vertebrate elaborate brains during evolution.
|
study
| 99.94 |
The khdrbs genes are evolutionarily conserved. We found that khdrbs date far back to the basal metazoans such as placozoa and cnidarian. Except for fruit fly, all the invertebrates analyzed possess a single khdrbs gene in each species. In fruit fly, there are five khdrbs genes, including qkr54B, qkr58E-1, qkr58E-2, qkr58E-3, and nsr (Ding et al., 2010; Volk, 2010), which are all localized on a region of the chromosome 2R, and clustered into one branch (Supplementary Figure 3), suggesting that they were generated by local tandem gene duplication of the ancestral khdrbs gene. Based on their expression patterns and tandem duplicated gene traits (Fan et al., 2008; Zhou et al., 2008; Volk, 2010), we speculated that these five genes perform similar biological functions. In contrast, three khdrbs genes, khdrbs1, khdrbs2, and khdrbs3, are identified in all the vertebrates examined, including lamprey (Xu et al., 2016). The birth of these three khdrbs gene clades coincides with the origin of vertebrates. As there existed two rounds (2R) of WGD happened in the vertebrate ancestors, it is thus expected that four khdrbs genes would be generated by these WGD. Interestingly, there are only three khdrbs genes identified so far in vertebrates. It is thus highly likely that only one copy out of the two khdrbs genes generated by the first round WGD underwent further duplication, thereby leading to the appearance of three khdrbs genes in vertebrates. This seems supported by the facts that KHDRBS2 and KHDRBS3 were grouped together, forming a sub-clade in the phylogenetic trees (Figure 1), and shared higher identity (Figure 2). However, the possibility cannot be ruled out that this may be due to gene loss as observed in Amazon molly. Additionally, we found duplication of khdrbs1 in teleost lineage. This duplication happened between the split of spotted gar and bony fish, agreeing with the third rounds (3R) of WGD in bony fish (Braasch et al., 2016).
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study
| 99.94 |
On the basis of our analyses on khdrbs gene family evolution and the mainstream opinions in WGD, we propose an evolutionary model for khdrbs gene family, as depicted in Figure 11. In brief, an invertebrate khdrbs gene underwent duplication once, and one copy of the resulting genes, as exemplified by khdrbs1, remained unchanged, while the other copy underwent duplication once more, thereby creating the vertebrate khdrbs1, khdrbs2, and khdrbs3. In teleost, the khdrbs1 was further duplicated through bony fish specific WGD, forming khdrbs1a and khdrbs1b. In some species like Amazon molly, khdrbs3 is lost in a lineage-specific manner.
|
study
| 100.0 |
Molecular evolution of khdrbs genes and the origin of vertebrate tripartite brain. The evolutionary relationships of metazoan and Vertebrata are based on our analyses above and recent phylogenomic analyses. Two red bars represent the 2R WGD events happening around the origin of vertebrate linage. The yellow one indicates teleost specific WGD event. Those blue rectangles represent the process of khdrbs gene duplication and diversification. The branch lengths are not proportional to the time of diversification.
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study
| 99.94 |
Expression pattern of genes is often associated with their functions in early development. We found all the zebrafish khdrbs genes were primarily expressed in the brain, while khdrbs1a was expressed in extra domains like the lateral line primordium, gonad primordium, and pronephric ducts. In adult fish, all the khdrbs also showed strong expression in the brain. Additionally, khdrbs1a, khdrbs1b, and khdrbs3 were found expressed abundantly in the gonads. These overlapping but distinct expression patterns may be a reflection of the sub-functionalization and/or neo-functionalization of gene duplicates after WGD. The split of bony fish from tetrapod happened around 450 million years ago. The fact that all the four khdrbs genes are retained in bony fish since the split may be because the KHDRBS proteins are signal-transduction related molecules (Putnam et al., 2008). Retained khdrbs genes may enrich their functions through changes in their expression patterns, which may then be co-opted in regulation network to generate physiological and morphological evolution.
|
study
| 100.0 |
Species diverges from common ancestors through changes in their DNA. Duplicated genes that impart new roles from their ancestors can be co-opted by changing their regulation process or altering their coding proteins to promote developmental and physiological features (Carroll, 2000; True and Carroll, 2002; Conant and Wolfe, 2008). A complex tripartite brain especially telencephalon is a key trait of vertebrates (Murakami et al., 2005; Sugahara et al., 2013, 2016). The highly plastic vertebrate brain influences all the basic biological functions, advance order processing and behavior control (Koscik and Tranel, 2012). As the most important part in organisms, however, it is hard to trace brain evolution because brains themselves are rarely fossilized. Hence, to explore the evolutionary origin of vertebrate brain needs to rely on molecular evolution and developmental studies.
|
review
| 99.56 |
Currently, mouse khdrbs genes were found expressed primarily in the brain, differing from that of the invertebrate orthologs, and khdrbs KO mice exhibited brain defects, indicating that khdrbs genes may be in the genetic toolkit correlated to vertebrate brain development (Ehrmann et al., 2013). In invertebrates, khdrbs genes were found expressed specifically in germ line (Lee and Schedl, 2001; Ding et al., 2010), so as zebrafish khdrbs1a, which was also expressed in the gonad primordium. Thus we surmise that one of the functions about khdrbs genes is to regulate gonad genesis. In addition to expression in gonads, zebrafish khdrbs genes were also found expressed in new domains such as the substructures of brain, especially in telencephalon. Telencephalon is an utterly vertebrate innovation (Murakami et al., 2005; Šestak and Domazet-Lošo, 2015). Considering that KHDRBS proteins act as splicing regulator molecules to control alternative splicing, and the brains have the largest amount of alternative splicing in the body, this distinct expression pattern of khdrbs genes gives a hint that the duplicated khdrbs genes were co-opted for motivating the evolutionary origin of vertebrate brain. Further studies on the expression and functions of khdrbs genes in other representative species within vertebrate lineage will better clarify the roles of KHDRBS played in brain evolution.
|
study
| 99.94 |
All the zebrafish used in the experiments were treated in accordance with the guidelines of the Laboratory Animal Administration Law of China, with the permit number SD2007695 approved by the Ethics Committee of the Laboratory Animal Administration of Shandong Province.
|
other
| 99.94 |
The amygdala is central to our emotional responses1. In particular, anxiety and fear learning rely on neural circuits and synaptic plasticity within the amygdala12. These anxiety and fear responses are modulated by our endogenous opioid system. Deleting one family of endogenous opioids, the enkephalins, increases behavioural measures of fear and anxiety34, whereas inhibiting enkephalin breakdown reduces these behaviours5. This suggests that endogenous enkephalin is anxiolytic. However, enkephalin is an agonist at both the μ-opioid receptor (MOR) and δ-opioid receptor (DOR)67 and the consequence of activating each receptor results in opposing behavioural outcomes. Indeed activation of DOR is anxiolytic589, while activation of MOR is anxiogenic910. Given this complexity, understanding the cellular actions of endogenous opioids at the DOR and MOR in the amygdala is critical if we hope to utilize opioid related therapy for emotional disorders. However, while endogenous opioids regulate fear and many other behaviours, including: pain, decision making, drug dependence and memory11, their cellular actions in the brain are poorly understood. Previous studies in various brain regions suggest endogenous opioid regulation of synaptic activity requires intense stimulation1213 and often this is shown to regulate long-term plasticity of synapses121415161718, rather than normal synaptic transmission. This has been taken to suggest that endogenously released opioids regulate learning rather than continuous information flow through a dynamic neural circuit19. However, it is an open question whether endogenously released opioids produce this myriad of behavioural responses solely through the regulation of synapses under intense neuronal activity (such as during learning), or whether regulation under basal conditions also contributes.
|
review
| 99.7 |
Opioid receptors and peptides are expressed to varying degrees throughout the amygdala2021. In particular, the intercalated cells (ITCs) are one possible site where enkephalin could regulate fear and anxiety behaviours. ITCs are small clusters of densely packed GABAergic neurons that en-sheath the basolateral amygdala (BLA). Coronal sections give rise to three separate clusters: the smaller lateral (lpc) and medial (mpc) paracapsular ITC clusters are located within the external and intermediate capsules respectively and the larger main island (Im), is located ventromedial to the BLA22 (Fig. 1a). While the lpc provides feedforward inhibition to the BLA23, the mpc acts as an inhibitory interface between the BLA and CeA and thus regulates fear learning24. In particular the mpcs are required for fear extinction25. Less is known about the functional role of the main island although it is possible the Im plays a similar role to the mpc. Indeed, Im neurons also receive sensory information from both the BLA2426 and the thalamus27 along with more complex information from the medial pre-frontal cortex (mPFC), a region highly implicated in fear extinction2829. Like the mpcs, the Im sends inhibitory GABAergic projections to the medial central nucleus (CeM)212630 and thus could gate expression of the conditioned fear response31. The Im may be particularly important during fear extinction as extinction activates Im neurons2232, their ablation (along with the mpc) reduces extinction25 and treatments that reverse the extinction deficit in anxious mice elevate Im neuron activity32.
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Im neurons strongly expresses enkephalin202133 that could act at pre- or postsynaptic sites. For example, the glutamatergic synaptic input from the BLA could be targeted by endogenously released opioids, as BLA pyramidal neurons express both MOR and DOR2134. Further, in other subdivisions of the amygdala, at least 60% of MOR is found in postsynaptic dendritic compartments, rather than in axons or terminals, suggesting potential for postsynaptic regulation3435. To date, endogenously released opioids have not been shown to directly activate a postsynaptic conductance in any brain region. However, given Im neurons express high levels of MOR21 and exogenously applied MOR agonists activate a potassium conductance in Im neurons (Im referred to as medial ventral ITC in this study)16 it is feasible Im neurons may be postsynaptically regulated by endogenously released opioids.
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Using electrophysiology combined with immunohistochemistry, we find that endogenously released opioids directly activate a potassium conductance in Im neurons via MOR. We also find that whilst exogenous Met-enkephalin (ME) and selective MOR, and DOR agonists reduced glutamate release from BLA synaptic inputs, endogenously released opioids only inhibit glutamate release through DOR. These endogenous opioid actions could be potentiated by reducing endogenous opioid breakdown by peptidases or by a positive allosteric modulator (PAM) specific for DOR. These findings indicate that endogenously released opioids tightly regulate the excitability and synaptic activation of Im neurons through two different receptors. Further, this regulation is not limited to high intensity synaptic activity, suggesting a role for endogenously released opioids as regulators of moment-to-moment signalling within a dynamic neuronal network. This would be expected to contribute to the role of endogenous opioids in fear learning and anxiety.
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Consistent with previous reports, we found high immunoreactivity for both MOR and ME in the main ITC cluster (Im; Fig. 1b)2021. Strong ME immunoreactivity also occurred in the central nucleus of the amygdala (CeA) and in a scattering of cells within the BLA (Fig. 1b). Diffuse MOR immunoreactivity was found in both the BLA and CEA (Fig. 1b).
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Given the overlapping distribution of ME and MOR within Im (Fig. 1b, merge) we focused specifically on this cluster of ITCs for this study. To explore possible ME targets, we used immuno-electron microscopy to determine the ultrastructural location of ME within the Im region. We found that ME immunoreactivity was concentrated in dense core vesicles (DCVs) located in axons and axon terminals (Fig. 1c–e). These terminals were directly apposed to (Fig. 1c,d) or convergent with (Fig. 1e) axon terminals that form asymmetrical (assumed glutamatergic) synapses onto dendrites. Therefore, ME is well placed to be released following synaptic activation and in turn, modulate glutamate release within Im.
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One of the main glutamatergic inputs to Im neurons is from pyramidal neurons of the BLA. BLA-Im synapses (Im referred to as medial ventral ITC in ref. 16) in mice have recently been shown to be insensitive to regulation by MOR16, however, it is not known whether this holds across different species. We therefore tested the sensitivity of this synapse to all three opioid receptors. To do this we made whole-cell patch recordings from Im neurons and routinely performed post hoc biocytin staining of the patched cells. As expected, staining revealed small bipolar neurons, with an abundance of dendritic spines, located in a cell dense region ventromedial to the BLA (Fig. 2a)2636. We electrically stimulated the BLA (Fig. 2b) and recorded the resulting evoked excitatory postsynaptic current (eEPSC) in Im neurons. We found BLA-Im eEPSCs were inhibited by selective agonists for DOR (Deltorphin II, 300 nM) or MOR (DAMGO, 1 μM; Fig. 2c,d), which was accompanied with an increase in the paired pulse ratio (PPR, Fig. 2e). Both effects were fully reversed by the selective DOR (ICI174864, 1 μM) and MOR (CTAP, 1 μM) antagonists respectively (Fig. 2c–e). In contrast, neither the κ-opioid receptor (KOR) agonist (U69593, 3 μM) nor antagonist (norBNI, 10 nM) affected the eEPSC amplitude or PPR (Fig. 2c–e). These data indicate the BLA-Im synapse is strongly inhibited through DOR and MOR and the associated increases in PPR indicates the likely site of action is through pre-synaptic reductions in glutamate release. This opioid sensitivity is consistent with the high expression of DOR mRNA in BLA neurons21 and the expression of MOR in BLA pyramidal neurons34 but does differ from the recently reported MOR insensitivity of this synapse described in mice16, suggesting there may be inter-species variability in opioid action within the Im.
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ME is a high-affinity ligand for both MOR and DOR67 and is therefore likely to inhibit the BLA-Im synapse. Exogenous ME (10–30 μM) significantly inhibited the eEPSC amplitude (Fig. 2f,g) and increased the PPR (Fig. 2h). Both DOR and MOR selective antagonists produced a partial reversal of ME inhibition and together they fully reversed both the ME induced inhibition (Fig. 2i) and change in PPR (baseline: 1.3±0.1; ME: 1.8±0.2; ICI+CTAP: 1.2±0.2; n=8, P>0.05 baseline versus ICI+CTAP, paired t-test). Unsurprisingly, given the low affinity of ME for KOR67 we found norBNI had no effect on the ME inhibition (Fig. 2i). Thus, exogenous ME inhibited glutamate release at the BLA-Im synapse through activation of both MOR and DOR, which is consistent with the receptor pharmacology of ME67 and the opioid sensitivity of the BLA-Im synapse, as defined above.
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In other brain regions, detecting the actions of endogenously released opioids requires intense stimulation12131415161718. Consistent with this, in the hypothalamus, where dense core vesicle release has been most thoroughly studied, optimal release is elicited by intense stimulation (at least 5–10 Hz)37. However, less intense stimulation (1 Hz) can elicit small but significant peptide release37. In light of this, we tested whether we could stimulate endogenous opioid peptide release using less intense stimulation. We initially tested whether the standard paired-pulse stimulation (‘low stimulus', Fig. 3a) was sufficient to produce an endogenous opioid effect. To determine the actions of endogenously released opioids, we examined whether the opioid antagonist naloxone (10 μM) increased the eEPSC amplitude, with an increase taken to indicate a reversal of endogenous opioid inhibition (Fig. 3a). However, naloxone did not increase eEPSC amplitude at the BLA-Im synapse in response to the low-stimulus protocol (Fig. 3b,i).
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In the striatum, intense stimulation (five antidromic depolarizations, 100 Hz) results in opioid inhibition of glutamate release that is maximal 500 ms after the stimulus13. Guided by this, we delivered a short train of stimuli (‘moderate' stimulus, 5 stimuli, 150 Hz) followed by a single ‘test' stimulus (500 ms interval) to the BLA (Fig. 3a). Unless otherwise noted, we analysed opioid effects on the test eEPSC. Using this protocol, we found that naloxone significantly increased the amplitude of the test eEPSC (Fig. 3c,i), indicating the moderate stimulus induces endogenous opioid release. We wondered whether opioid release occurs with lower stimulation but due to peptide degradation, we were unable to detect the opioid effect. To test this, we assessed whether inhibition of opioid peptide degradation could reveal an opioid effect during low stimulation. Enkephalin, the most likely opioid to be acting at this synapse, is catabolized by at least three zinc metalloproteases38. We tested whether a cocktail of peptidase inhibitors targeting these enzymes (thiorphan, 10 μM; captopril, 1 μM and bestatin, 10 μM; Fig. 3d), could increase endogenous opioid inhibition. This peptidase inhibitor cocktail significantly enhanced the effects of exogenously applied, submaximal ME (300–500 nM) on eEPSC amplitude (Fig. 3e,f), confirming these peptidases act to break down exogenous ME and thus limit its actions at the BLA-Im synapse.
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It is important to note these targeted peptidases are non-selective metalloproteases. Therefore, blocking their activity has the potential to enhance the activity of other ‘off-target' endogenous signalling peptides (for example, substance P3940, neurokinin4142; neurotensin4243) that may act either in concert or opposition to endogenous opioid signalling. Consistent with this, while inhibiting peptidase activity with the peptidase inhibitor cocktail on average, reduced eEPSC amplitude in response to both low (12.9±5.4% inhibition, n=7) and moderate (32.1±6.2% inhibition, n=8) stimuli (for example, Fig. 3g,h), there was a high degree of variability, with either inhibition (12/18 neurons); no effect (3/18 neurons) or an increase observed (3/18 neurons). Thus, we defined endogenous opioid action as the naloxone-induced increase in eEPSC following peptidase inhibitor treatment (Fig. 3a). In addition, there was variability in the timing of the peptidase inhibitor response, which could reflect the time required to accumulate sufficient peptide or differences in restricting microarchitecture. Despite this variability in peptidase inhibitor response, naloxone significantly increased eEPSC amplitudes in all cells. Due to possible dominance of confounding ‘off-target' signalling peptides in cells where the peptidase inhibitors increased the eEPSC amplitude, these cells were excluded from further analysis of the endogenous opioid effect. In the remaining cells, naloxone induced a significant increase in eEPSC amplitude using both low (Fig. 3g,i) and moderate stimuli (Fig. 3h,i). These data indicate endogenous opioids are released during low-stimulus experiments but eEPSC amplitude remains unchanged due to rapid degradation of the peptide. Further, it shows that the actions of endogenously released opioids, released in response to the moderate stimulus, are limited by peptidase activity. Interestingly, when peptidases are inhibited, the first pulse of the moderate stimulus train was also increased by naloxone (40.2±4.8% increase, n=8, P<0.01 peptidase inhibitor versus naloxone, paired t-test). As this did not occur without peptidase inhibitors (10.7±6.4% increase, n=7, P>0.05 baseline versus naloxone, paired t-test), this suggests endogenously released opioids are broken down by peptidases during the 15 s inter-stimulus interval.
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We tested whether endogenously released opioids acted through DOR or MOR using our strongest paradigm (moderate stimulus and peptidase inhibitor cocktail), and applying selective DOR and MOR antagonists. We found that while CTAP had no effect, ICI174864 increased eEPSC amplitude (Fig. 4a,b) to the same extent as naloxone (P>0.05, unpaired t-test). While ICI174864 does have modest inverse agonist activity at DOR, since the effect of ICI174864 was not different from the neutral antagonist naloxone, inverse agonism is unlikely to explain the current findings44. These data indicate that endogenously released opioids only inhibit the BLA-Im synapse through DOR activation.
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PAMs of DOR could potentiate the inhibition by endogenously released opioids. We explored this possibility using the DOR PAM BMS-986187, which shows 100-fold selectivity for DOR over MOR and increases efficacy, potency and affinity of orthosteric ligands45. BMS-986187 (1 μM) significantly enhanced the inhibition induced by submaximal, exogenous ME (100 nM, Fig. 4c,e), indicating PAM activity at this synapse, which could be reversed to baseline levels by naloxone (6.3±2.6% inhibition; P>0.05 naloxone versus baseline, Tukey's post hoc comparisons, Fig. 4c,e). BMS-986187 is known to have agonist properties at higher concentrations45 and BMS-986187 alone induced a small but significant decrease in eEPSC amplitude (Fig. 4d,e; P<0.05, paired t-test). In these experiments, we used single synaptic stimuli (0.06 Hz) to minimize release of endogenous opioids. However, this BMS-986187 inhibition was reversed by naloxone (Fig. 4d,e) and we found that direct agonism by BMS-986187, resulting in β-arrestin recruitment and inhibition of cAMP production in CHO-OPRD1 cells, was insensitive to naloxone (Supplementary Fig. 1). Therefore, the naloxone sensitive inhibition of the BLA-Im synapse by BMS-986187 alone is not due to intrinsic agonist activity of BMS-986187, rather it is likely due to positive allosteric modulation of endogenously released opioids signalling at DORs. Although we know that PAMs can increase responses to exogenous opioids in cell lines45, this is the first instance in which increases in endogenous opioid signalling have been observed (Fig. 4d,e). To further investigate PAM activity on endogenous opioid signalling, we tested the effects of BMS-986187 on our previous treatment paradigms. When slices were preincubated with BMS-986187 (>45 min) and peptidases were inhibited, the naloxone-induced increase was significantly greater than peptidase inhibitors alone in response to the low stimulus (Fig. 4f,g), but had no further effect when using the moderate stimulus (CTL: 36.0±6.5% increase, n=8; BMS-986187: 36.8±5.9% increase n=5, P>0.05, unpaired t-test). Since naloxone produced comparable increases in eEPSC irrespective of stimulation intensity when in the presence of BMS-986187, this may indicate saturation of endogenously released opioid regulation at this synapse.
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Exogenous opioids activate a potassium conductance in Im neurons16 and in many other brain regions4647. However, it is not known whether endogenously released opioids can activate this potassium conductance. Given the strong endogenous opioid regulation in Im neurons (current findings), their very high MOR expression21 and that 60% of MOR receptors are in postsynaptic dendritic compartments3435, we wondered whether endogenously released opioids could directly regulate Im neurons. We have already shown ME is well placed to regulate synaptic transmission (Fig. 1c–e), but we also found ME immunoreactivity in multiple terminals converging onto large dendrites (Fig. 5a) within Im, suggesting ME directly acts at postsynaptic sites. Indeed, exogenous ME (30 μM) produced an outward current in all neurons (37.5±4.1 pA, n=20) that could be readily reversed either after washout (n=16) or by CTAP (n=4; Fig. 5b). Current–voltage analysis before and during ME indicates activation of a potassium conductance as the reversal potential of the ME-induced current was close to the potassium reversal potential (MErev: −103.2±1.4 mV, n=9; Ek: −104.9 mV, calculated with the Nernst equation; Fig. 5c). In addition, bath application of the KIR 3.1/3.4 antagonist tertiapin Q (300 nM) for 5 min prior to application of ME significantly reduced the ME current (ME+tertiapin 7.4±1 pA, n=5, ME alone 37.5±4.1 pA, n=20; P<0.002, unpaired t-test). This is consistent with findings from mouse Im neurons where the MOR agonist DAMGO activated a potassium conductance and induced a hyperpolarization16. To test whether endogenously released opioids also activate this potassium conductance in Im neurons, we applied the peptidase inhibitor cocktail and observed an outward current in the majority of Im neurons (8/9), which was fully reversed by CTAP (n=4) or naloxone (n=5, Fig. 5d,f). While these peptidase inhibitor-induced currents occurred without any electrical stimulation, the highly variable amplitude (Fig. 5d,f) suggested spontaneous activity of cells in the slice could be influencing the size of the endogenous opioid response. Consistent with this, we found that when we electrically stimulated the BLA (10–20 stimuli at 150 Hz), PIs induced a larger, more consistent outward current, which again was fully reversed by CTAP (n=1) or naloxone (n=3; Fig. 5e,f). These data indicate that in the Im, endogenous opioids are readily released in response to spontaneous or stimulated synaptic activity and act postsynaptically to induce an outward potassium conductance. Given the input resistance of Im neurons (643.9±101.5 MΩ, n=13), the endogenous opioid current with and without stimulation would be expected to hyperpolarize the Im neurons by 24.2±2.7 and 10.1±3.8 mV, respectively.
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ITCs have high intrinsic connectivity within the Im2226 and it is possible endogenously released opioids could also inhibit these GABAergic synapses. To study Im-Im neuron synapses exclusively, excluding GABAergic inputs from other regions such as the CeA, we performed paired recordings between synaptically coupled Im neurons (Fig. 6a). We found that exogenous ME (10 μM) strongly inhibited paired IPSCs (Fig. 6b,c) and in three of the four synaptic pairs, ME also increased synaptic failure rate (CTL: 3.75±0.1% failure, ME: 45.6±0.4% failure, for example, Fig. 6b). This inhibition was reversed by either CTAP (Fig. 6b) or wash of ME and likely resulted from ME acting pre-synaptically to reduce GABA release as ME significantly increased the PPR (Fig. 6d). Unfortunately, the success rate for obtaining viable synaptically coupled pairs of Im neurons was low (2.4%) and as a result, we were unable to test the actions of endogenously released opioids using this approach. Instead, we recorded local Im synaptic activity by electrically stimulating within the Im to evoke eIPSCs (Fig. 6e). Exogenous ME (10–30 μM) robustly inhibited eIPSCs (Fig. 6f,g) and this was reversed by the MOR antagonist CTAP, but not by DOR or KOR antagonists (Fig. 6h). Surprisingly however, we did not find a consistent change in PPR (Fig. 6i), which was in distinct contrast to opioid regulation of glutamatergic inputs (Fig. 2e) and paired Im neuron recordings (Fig. 6a–d). Although unexpected, we reasoned that since ME almost completely eliminated paired Im-Im inputs (Fig. 6b,c), this may allow MOR insensitive GABAergic inputs with different synaptic characteristics to dominate the remaining eIPSC. Indeed, this is supported by the change in eIPSC kinetics before and after ME treatment, in which both the 10–90% rise time (baseline: 1.7±0.2 ms; ME: 1.3±0.1 ms, P<0.01, paired t-test) and the weighted decay time constant (τw; baseline: 34.3±4.8 ms; ME: 17.8±2.8 ms, P<0.01, paired t-test) showed a significant decrease.
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To test whether endogenously released opioids also inhibit local GABA synapses, we used either the low or moderate stimulus protocol together with the peptidase inhibitor cocktail. When we used the moderate stimulus, peptidase inhibitors significantly decreased eIPSC amplitude of the test pulse (13.1±5.2% inhibition, P<0.05 versus baseline, paired t-test, for example, Fig. 6j), which was then significantly increased by naloxone (Fig. 6j,k). However, when the low stimulus was used, neither the peptidase inhibitor cocktail (10.1±6.0% inhibition, n=6, P>0.05 versus baseline, paired t-test) nor naloxone (Fig. 6k) significantly changed eIPSC amplitude. Thus local GABAergic synapses within Im are inhibited by endogenously released opioids but only with a combination of PIs and the moderate stimulus and even then, only to a modest extent. This is somewhat surprising considering the strong regulation of these synapses by exogenous ME. In fact, while endogenously released opioids inhibit the BLA-ITC synapse to almost 50% of the maximal opioid inhibition of this synapse, at local ITC GABAergic synapses, it is <15%. This difference could be due to less effective stimulation of endogenous opioid release when studying GABAergic synapses and may indicate a requirement for intact glutamatergic signalling. Alternatively, this difference may result from other ultrastructural differences such as receptor location relative to dense core vesicle release sites.
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We found that endogenous opioids released by electrical stimulation, activated a post-synaptic potassium conductance in Im neurons and also inhibited their glutamatergic and GABAergic synaptic inputs. Inhibiting peptidase activity potentiated these post-synaptic and pre-synaptic actions of the endogenously released opioid. At the BLA-Im synapse, endogenously released opioids inhibited neurotransmitter release through DOR but not MOR, even though both receptors are functional at the synapse, suggesting other factors control endogenous opioid action. A DOR PAM potentiated this inhibition and to our knowledge, this is the first example of an opioid PAM potentiating the actions of an endogenously released opioid. Up until now, endogenous opioids have been considered neuromodulators that regulate synapses and their plasticity during intense neuronal activity, which suggests an important role in learning121415161718. These current findings indicate a different role of endogenously released opioids, namely as neuromodulators that are engaged by synaptic activity to influence moment-to-moment information flow through dynamic Im-associated circuits.
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It is likely the endogenous opioids regulating Im neuronal activity are met- and leu-enkephalin. The endogenous opioid actions we have described are through MOR and DOR, both of which are potently and efficaciously activated by the enkephalins67. We describe this phenomenon in Im neurons of the amygdala, which express high levels of enkephalins2021 and the effects are terminated by peptidases, known to breakdown enkephalins38. β-Endorphin is the other major opioid peptide that acts at MOR and DOR but is expressed in Im neurons at much lower levels48 and is much less sensitive to peptidase degradation compared to enkephalin49. Although Im neurons have a low firing frequency in vivo (<0.1 Hz)29, subsequent orthodromic spike bursts29 or recurrent firing50 following synaptic stimulation or current injection could effectively release endogenous opioids. Indeed, our present findings support the premise that endogenous opioid release is activity dependent, however the intensity of this activity is much less than originally anticipated. Thus, within our working model (Fig. 7a), enkephalin is released in response to neuronal activity and this is likely from Im neurons, although it is possible that enkephalins are also released from BLA or CeA synaptic projections rather than from Im neurons alone.
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Exogenously applied opioids activate a postsynaptic potassium conductance, in many neurons, including the Im164647. However, while exogenously applied agonists inform us of what to expect when individuals are administered an opioid drug such as morphine, this method provides limited insight into how the endogenous opioid system functions. This study is the first example of endogenously released opioids, or in fact any endogenously released neuropeptide, activating a potassium conductance. The resulting membrane hyperpolarization would be expected to increase the excitatory synaptic input required to produce postsynaptic action potentials. In addition, the lower membrane resistance would likely shunt synaptic inputs from more distal synapses and thus could reduce the ability of all excitatory synaptic inputs to produce postsynaptic action potentials (Fig. 7a). This scenario would be particularly important if enkephalins are acting at the large dendrites we observed to be associated with ME immunereactive terminals (Fig. 4a). These additional roles for endogenous opioids as regulators of postsynaptic excitability and membrane resistance would be expected to have a widespread effect on information flow through the Im. Indeed, it is feasible that endogenously released opioids could limit the impact of all synaptic inputs on the membrane potential of Im neurons and thus alter feed-forward signalling in this neural circuit. This is in contrast to previous studies that describe endogenous opioid actions that are limited to inhibiting pre-synaptic inputs that express opioid receptors1314. This is the first instance in which endogenous opioids have been shown to influence postsynaptic neuronal excitability, which in turn would be expected to alter the neuronal response to synaptic inputs.
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In Im, we found that endogenous opioids are released in response to electrical stimulation, although reducing their breakdown or enhancing DOR sensitivity was required to observe a cellular effect. We found this surprising, as enkephalin is stored in dense core vesicles51 (Fig. 1c–e) and optimal neuropeptide release from dense core vesicles traditionally occurs with more intense, higher frequency stimulation3752. Consistent with this, endogenous opioid actions at other synapses require intense stimulation paradigms121415161718. It is possible that dense core vesicle release is differently regulated in Im neurons, perhaps through differences in release machinery or intracellular calcium handling. Alternatively, we may be able to measure a response to the small amount of peptide released because Im neurons are highly sensitive to opioids and the local microarchitecture between release site and receptor is favourable. Our finding that exogenous ME acts at both MOR and DOR at BLA-Im synapses, while endogenously released opioids only acts through DOR, further suggests local microarchitecture may govern the actions of endogenously released opioids. Both met-enkephalin and leu-enkephalin have similar affinity for MOR and DOR67, so a difference in affinity for the receptors cannot explain our findings. Rather, MOR receptors on BLA-Im synapses may be located farther from the endogenous opioid release site or be more closely associated with membrane bound peptidases53, both of which would reduce the ME concentration at MOR. A similar difference in receptor activation between exogenous and endogenously released agonist, occurs in the striatum13. This, together with our findings, suggests we should be wary of assuming the effects of exogenous agonists are indicative of how endogenous agonists signal. Regardless of the reason for the high sensitivity, this combination of ready releasable endogenous opioids and their action at multiple sites, including direct effects via a potassium conductance and the inhibition of synaptic inputs, would predict that endogenously released opioids are strong regulators of Im activity.
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Activation of MOR and DOR results in opposite behavioural fear responses with activation of DOR being anxiolytic8 and activation of MOR being anxiogenic10. The cellular basis for these opposing actions of opioid receptors is likely complex and occurs at multiple sites. At a cellular level, we have shown there is anatomical specificity of the endogenous opioid action at MOR and DOR in the Im. We suggest this could provide a basis for understanding the opposite actions of MOR and DOR activity on anxiety. In the Im, endogenously released opioids act via DOR to reduce the strength of BLA synaptic inputs. While this reduced excitatory drive from the BLA could decrease Im outputs to target neurons, it is also possible that it could allow Im neurons to be more strongly influenced by other inputs, such as those from the cortex that carry more complex/contextual information3154. Thus, a DOR-mediated shift in strength of synaptic inputs could contribute to DOR-mediated anxiolytic processes such as fear extinction (Fig. 7b). Distinct from this, the endogenous opioid actions at MOR directly inhibit Im neuronal excitability and through this, likely reduce their activation by all synaptic inputs. This in turn, would reduce Im-dependent GABA release onto target neurons such as the CeM16 and disinhibit CeM output to promote fear learning. Thus endogenous opioids acting via postsynaptic MOR to reduce Im excitability, maybe a feature of MOR-mediated anxiogenic processes such as fear learning. This differential pharmacology of endogenously released opioids in the Im could have important consequences. First, the net effect of endogenous opioid actions could change if one receptor sub-type was altered by a physiological55, pathophysiological state or pharmacological treatment56. Second, it raises some interesting therapeutic possibilities. If the desired therapeutic strategy is to enhance endogenous opioid action at both DOR and MOR, this could be achieved by inhibition of peptidases57. Alternatively, if enhancing the activity of only one receptor is desired, a PAM, such as shown in this study, could be utilized45.
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The reasons why anxiety disorders manifest are not fully understood, although it is thought a disruption in the balance between opposing circuits responsible for interpretation of fearful stimuli, a process in which the amygdala is heavily involved, maybe key58. It is possible endogenous opioid signalling is one of the essential components that maintain balance within these ‘interpretation circuits', particularly during times of stress. If so, individual variability in fear and anxiety may result from differential expression of enkephalin. In fact, in a subpopulation of rats where stress strongly reduces ME expression in the amygdala, this was correlated with increased vulnerability to negative stress responses59. While opioid receptors and peptides are expressed and likely act in other subdivisions of the amygdala215660, the strong regulation of Im neurons by endogenous opioids is an appealing mechanism for opioid regulation of fear and anxiety behaviours. Further, since endogenously released opioid regulation is engaged by moderate synaptic activity, this suggests some of their effects on anxiety maybe via regulation of continuous information flow through dynamic Im-associated neural circuits. This is in contrast to the currently perceived role in which endogenous opioids both regulate and are released by a high-intensity trigger such as a synaptic plasticity event or learning processes. In addition, endogenously released opioids may also regulate the expression of previously established plasticity/learning traces. This additional role is important, as treatments for anxiety disorders are rarely able to preempt the initial learning but regulators of prior plasticity/learning could be valuable therapeutic options to reduce expression of fear and anxiety disorders.
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Brain tissue was prepared from male Sprague-Dawley rats (electrophysiology: 4–6 weeks; immunohistochemistry: 5–8 weeks; immuno-EM: adult), housed in standard environmental conditions with open topped cages, adequate enrichment, normal light/dark cycle (12 h/12 h) and ad libitum access to food and water. All experimental procedures were approved by either the Animal Care Ethics Committee of the University of Sydney or the Oregon Health & Science University IACUC and were conducted in accordance with either the Australian code of practice for the care and use of animals for scientific purposes or National Institutes of Health Guide for Care and Use of Laboratory Animals.
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Rats were anaesthetized with isoflurane, decapitated and their brains quickly removed and chilled in ice-cold cutting solution (in mM: NaCl, 125; NaHCO3, 25; D-glucose, 11; KCl, 2.5; NaH2PO4.2H2O, 1.25; MgCl2, 2.5; CaCl2, 0.5) saturated with carbogen (95% O2/5% CO2). Coronal slices (280 μm) containing the amygdala were cut with a vibratome (Leica) and incubated for 1 h at 34 °C then allowed to equilibrate to room temperature prior to recording. For recording, slices were transferred to a recording chamber superfused at 2 ml min−1 with artificial CSF (aCSF) containing (in mM) NaCl, 125; NaHCO3, 25; D-glucose, 11; KCl, 2.5; NaH2PO4.2H2O, 1.25; MgCl2, 1; CaCl2, 2; saturated with carbogen and heated to 32–34 °C. Intercalated cells were visualized using an Olympus BX51 microscope equipped with × 40 water immersion objective and Dodt gradient contrast optics.
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study
| 99.94 |
Main island ITCs (Im) were readily identified by their location, small cell body and dense population. To verify correct targeting of ITCs, slices were routinely fixed after recording and kept for post hoc staining (see below). Whole-cell voltage-clamp recordings were made from ITCs clamped at −70 mV using patch pipettes (3–5 MΩ) containing (in mM): CsCl, 140; EGTA, 10; HEPES, 5; CaCl2, 2; Mg-ATP, 2; Na-GTP, 0.3; QX314-Cl; 3 and 0.1% biocytin, pH 7.3, 280–285 mOsm l−1. Electrically evoked synaptic responses were elicited via a bipolar tungsten stimulating electrode placed in one of two positions depending on the synaptic input studied (rate=0.066 Hz unless otherwise stated, stimuli: 2–20 V, 100 μs). To record evoked excitatory postsynaptic currents (eEPSCs) at BLA-ITC synapses, the electrode was placed close to the basomedial edge of the BLA (Fig. 2b) and the GABAA receptor antagonists picrotoxin (100 μM, Sigma) and SR95531 (10 μM, Abcam Biochemicals) were included in the circulating aCSF to block fast inhibitory transmission. To record locally evoked inhibitory postsynaptic currents (eIPSCs), the electrode was placed within the main ITC cluster (Fig. 6e) and CNQX (10 μM, Tocris) or NBQX (5 μM, Abcam Biochemicals) were used to block fast excitatory synaptic transmission. Recordings of postsynaptic conductance were performed using whole-cell patch recordings in voltage-clamp mode holding at −63.6 mV (adjusted for liquid junction potential measured at 13.6 mV). The pipette internal solution contained (in mM): potassium gluconate, 135; NaCl, 8; Mg-ATP, 2; Na-GTP, 0.3; EGTA, 0.5; HEPES, 10; pH 7.3, 280–285 mOsm l−1. Continuous current recordings were monitored in chart mode and outward currents were calculated as the difference between baseline current and peak current during drug application. During stimulation, electrical stimuli were delivered to the BLA (10–20 pulses, 150 Hz) every 15 s and continuous current was recorded for 1.5 s after the stimulus train. To determine the reversal potential of exogenous ME-induced current, current–voltage analysis to sequential −10 mV voltage steps from −63.6 to −133.6 mV (adjusted for liquid junction potential) was performed before and during ME application.
|
study
| 100.0 |
For paired recordings, designated pre-synaptic cells were loose-cell attached (seal resistance, 10–120 MΩ) and stimulated every 15 s with paired suprathreshold voltage pulses (200 mV, 50 ms interval) delivered via a patch pipette (2–3 MΩ) containing either aCSF or the potassium gluconate-based internal solution. Resulting IPSCs were recorded in post-synaptic cells that were whole-cell voltage clamped at −70 mV with patch pipettes containing the CsCl-based internal solution (as above). Successful pairs were characterized by the generation of short latency (<2 ms) IPSCs in the postsynaptic neuron in response to voltage stimulation of the presynaptic neurons. Synaptic failure was defined as complete failure to respond to both paired pulses and was calculated as a percentage of total episodes between the last 2.5 min of baseline and last 2.5 min drug application.
|
study
| 100.0 |
Recordings were amplified, low-pass filtered (5 kHz), digitized and acquired (sampled at 10 kHz) using Multiclamp 700B amplifier (Molecular Devices) and online/offline analysis was with Axograph Acquisition software (Molecular Devices). In all cases, series resistance was monitored and data were discarded if this fluctuated more than 20% or if series resistance >20 MΩ. All postsynaptic currents were analysed with respect to peak amplitude. PPR was calculated from postsynaptic currents elicited by paired pulses (50 ms interval, second pulse/first pulse). Peak amplitude was quantified across each experiment as the mean peak amplitude of five to six episodes calculated from (1) the last 2–3 min of drug superfusion (that is, once the postsynaptic current had reached a stable plateau) and (2) the last 2–3 min of the baseline (referred to CTL in figures), unless otherwise stated. Representative time plots of postsynaptic currents are shown as an average of two consecutive episodes (that is, every 30 s) with peak amplitude normalized to baseline averages. Numbers on bar charts represent ‘n'=number of cells=number of slices. Effects of exogenously applied opioid agonists/antagonists were represented as percentage inhibition that reflects the difference between mean amplitude during agonist/antagonist superfusion and either baseline (CTL) or the preceding antagonist condition. Reversal (%) of the ME effect by selective antagonists was calculated as the proportion of amplitude recovered by the antagonist over total decrease in amplitude during ME. Endogenous opioid signalling was reflected by an increase in mean peak amplitude following either broad spectrum or selective opioid antagonist superfusion. Antagonist-induced increases (%) were calculated as the difference between amplitudes measured 7.5–10 min after antagonist superfusion and amplitudes at either baseline or the last 2.5 min of peptidase inhibitor superfusion (for example, Fig. 3a). In a subset of experiments, 10–90% rise time and synaptic decay of eIPSCs was analysed. The decay time course was fit to a double exponential function: f(t)=A1e(−t/τ1)+A2e(−t/τ2) where t is time, A1/A2 are peak amplitudes of the fast/slow decay components at t=0 and τ1/τ2 are the fast/slow decay time constants, respectively. From this we calculated the weighted decay time constant (τw) defined by: τw=(A1τ1)/(A1+A2)+(A2τ2)/(A1+A2).
|
study
| 100.0 |
All drugs were diluted to their final concentration in aCSF and applied by superfusion. For experiments requiring the use of selective opioid agonists/antagonists, these drugs were applied cumulatively and sequentially, with the opioid receptor agonist/antagonist order combination varying between experiments to avoid bias. Concentrations of: naloxone (nalox; 10 μM), deltorphin II (delt; 300 nM), ICI174864 (ICI; 1 μM), CTAP (1 μM), naltrindole (nal, 10 nM) (all purchased from Tocris), DAMGO (1 μM), norBNI (10 nM), U69593 (U69; 3 μM) thiorphan (10 μM) (all Abcam Biochemicals), bestatin (10 μM, Cayman Chemicals) and captopril (1 μM, Sigma) remained the same for all experiments, while met-enkephalin (ME, Bachem; 100 nM, 300 nM, 500 nM, 10 μM, 30 μM) concentration was varied where indicated. BMS-986187 (1 μM) was from Bristol-Myer Squibb Co.
|
study
| 99.94 |
Antibodies used were as follows: ME (1:250, Millipore ab5026, RRID: AB_91644), MOR (1:5,000, Aves Labs), Alexa Fluor 488 anti-rabbit (1:800, ThermoFisher Scientific A21206, RRID: AB_10049650), Alexa Fluor 546 anti-chicken (1:500, ThermoFisher Scientific A11040, RRID: AB_2534097), Alexa Fluor 647 streptavidin (1:2,000, ThermoFisher Scientific S32357), DAPI (1:1,000, ThermoFisher Scientific 62248), biotinylated goat anti-rabbit IgG (1:400, Vector Laboratories BA-1000, RRID: AB_2313606), donkey anti-rabbit conjugated to gold particles (1 nm, 1:50, EMS 25700).
|
other
| 99.9 |
Animals (immunohistochemistry: n=6; immuno-EM: n=3) were deeply anaesthetized with isoflurane and/or lethal dose of pentobarbital sodium (120–150 mg kg−1, i.p.). Once reflexes were abolished, animals were perfused through the ascending aorta with differing solutions depending on the imaging required. For confocal imaging animals received 3,000 units per ml heparin in a 0.5% NaNO2/0.9% saline solution followed by 4% paraformaldehyde (PFA) solution in 0.1 M phosphate-buffered saline (PBS, pH 7.4). Brains were removed and post-fixed overnight in 4% PFA (4 °C), then washed three times with PBS and stored in PBS (4 °C) until sectioned. Coronal sections (30 μm) containing the amygdala were cut using a vibratome (Leica), and collected free-floating in PBS. Sections were stored in 40% PBS, 30% glycerol, 30% ethylene glycol cryoprotectant at −30 °C until required for immunohistochemistry. For electron microscopy (EM), animals received the following sequence of solutions: (1) 10 ml of heparinized saline (1,000 units per ml); (2) 50 ml of 3.8% acrolein in 2% paraformaldehyde; and (3) 200 ml of 2% paraformaldehyde (in 0.1 M phosphate buffer (PB), pH 7.4). The block of brain containing the amygdala was placed in 2% paraformaldehyde for 30 min then into 0.1 M PB. Coronal sections (40 μm) were cut on a vibratome (Leica) and collected into 0.1 M PB. Prior to immunohistochemical processing, all sections were incubated in 1% sodium borohydride solution for 30 min to increase the antigenicity and 0.5% bovine serum albumin (BSA) for 30 min to reduce non-specific binding.
|
study
| 99.94 |
Prior to staining, cryoprotectant was removed (3 × 10 min wash, PBS) then incubated 1 h at room temperature in 10% goat serum, 0.5% BSA and 0.3% Triton X-100 in PBS. Primary antibodies for ME and MOR were diluted in a 5% goat serum/0.3% Triton X-100 in PBS and sections were incubated overnight (4 °C). Secondary antibodies were diluted in 5% goat serum/0.3% Triton X-100 in PBS, and incubated 2 h at room temperature (light protected). The nuclear stain was added for the final 30 min of this incubation. Sections were then washed (3 × 10 min) in PBS and mounted onto slides using ProLong Gold Antifade (Life Technologies).
|
other
| 99.8 |
For post hoc staining, slices (280 μm) containing cells filled with 0.1% biocytin (Sigma) during whole-cell recordings were fixed overnight at 4 °C in 4% PFA in 0.1 M PB, then washed three times with PB and stored (<2 weeks) at 4 °C prior to staining. For staining, slices were briefly washed before incubated for 1 h at room temperature in 10% goat serum, 0.5% BSA and 0.3% Triton X-100 in PB. Steptavidin conjugated antibody was diluted in 1% BSA/0.1% Triton X-100 in PB and incubated for 2 h at room temperature (light protected). DAPI was added for the final 30 min of this incubation period. Slices were then washed three to four times (10 min) with PB and mounted onto slides using Fluoromount-G (SouthernBiotech).
|
other
| 67.6 |
Sections were visualized using a Zeiss LSM510 Meta confocal microscope (lasers: 405, 488, 561 and 633 nm; Carl Zeiss) and Zeiss LSM META software. Images were taken sequentially with different lasers using × 10 (numerical aperture (NA) 0.45) and × 20 (NA 0.8) dry objectives, and × 63 (NA 1.4) oil immersion objective. Single images were collected using the × 10 objective and Z-stacks were collected at 4.5 and 2.5 μm for × 20 and × 63 oil objectives, respectively.
|
other
| 99.9 |
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