This manuscript describes an experiential learning program for future collaborative biostatisticians (CBs) developed within an academic medical program. The program is a collaborative effort between the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core and the Master of Biostatistics (MB) programme, both housed in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine and supported in partnership with the Duke Clinical and Translational Science Institute. To date, the BERD Core Training and Internship Program (BCTIP) has formally trained over 80 students to work on collaborative teams that are integrated throughout the Duke School of Medicine. This manuscript focuses on the setting for the training program, the experiential learning model on which it is based, the structure of the program, and lessons learned to date. The MB Program is a 2‐year professional degree program that trains future CBs and enrolled its first cohort of students in 2011. Approximately 75% of graduates enter the workforce immediately after graduation, with the remaining graduates entering PhD programs. In addition to statistical coursework, MB students take a year‐long sequence in the practice of Biostatistics. This sequence covers topics such as clinical and domain knowledge , manuscript development, study design, statistical writing, and presentation skills. The program emphasizes flipped classroom approaches and active learning exercises with a strong focus on real‐world case studies. While this exposes students to the essential components of collaboration, it does not inherently provide them with firsthand collaboration experience. For this reason, all students in the MB program are required to participate in an experiential learning opportunity, a practicum, prior to graduation. This requirement ensures all graduates to leave the program with hands‐on experience working with real data in a collaborative team science setting.The Biostatistics, Epidemiology, and Research Design (BERD) Methods Core is a group of over 40 staff and faculty biostatisticians who collaborate on interdisciplinary research teams across the institution. The BERD Core, housed within the Department of Biostatistics and Bioinformatics, is supported in part by a National Institutes of Health (NIH) Clinical and Translational Science Award (CTSA) awarded to the Duke Clinical and Translational Science Institute. The Core's main objectives are to connect investigators with appropriate quantitative and qualitative collaborative methodologists, provide staff and faculty CBs for interdisciplinary research projects, and build additional biostatistics resources at the institution as needed. In 2012, the MB program partnered with the BERD Core to initiate the BERD Core Training and Internship Program (BCTIP). BCTIP created a structure to integrate students into collaborative research teams, helping to meet the MB program's need for experiential learning opportunities for its students while also providing biostatistics support to collaborative research teams throughout the institution. Since then, we have accumulated nearly a decade of experience with BCTIP, enrolling over 80 students in this hybrid  training program. During the program, student interns complete formal coursework aimed at developing key competencies  in their statistical code so that their analyses meet reproducibility standards and can be easily reviewed and validated. Second, under the supervision of staff and faculty CBs, students work through training exercises based on their ongoing research projects to build skills in a real‐world setting. These exercises include meeting with collaborators, scoping out the needs of a project, writing statistical analysis plans, performing data analyses, writing summary reports, and reviewing and revising manuscripts. At the conclusion of the didactic training, interns are expected to solidify the skills and information conveyed during training while they gain new skills through experiential learning.1.3The BCTIP experiential learning model incorporates two related but distinct mentoring roles intended to help students work effectively in a dynamic, team science environment. A program advisor oversees administrative aspects of the learning experience  and a project supervisor oversees the day‐to‐day experiential training experience, is responsible for helping the student develop key collaborative skills, and serves as the biostatistician of record for the intern's assigned collaborative projects. These distinct mentoring roles separate program and project supervision into two complementary roles typically taken on by different biostatisticians.The BCTIP director and assistant director(s) generally serve as program advisors, overseeing the hiring process, training program, and interactions between the interns and their project supervisors. This includes providing interns with a confidential outlet for discussion of concerns and communication challenges experienced within their collaborative teams or with their project supervisors, supporting conflict resolution as needed. These individuals are experienced practicing biostatisticians whose personal expertise and working experience support program design and mentorship. Program advisors also provide career mentorship including resume review and interview coaching. This dual mentorship model trains the student to collaborate effectively with a team of biostatisticians and clinical/translational researchers while ensuring that the project has oversight from an established biostatistician. Our experience has shown that the separation of roles between program advisors and project supervisors is important because it allows the intern to approach the program advisor when uncertain how to navigate a situation with their project supervisor, for example, how to renegotiate deadlines. Advisors hold quarterly meetings that include all interns in a group discussion about ongoing projects, collaborative issues and questions, internship progress, and career development. This provides interns with a sense of belonging to a community of peers and creates opportunities for peer mentoring.BCTIP requires each project supervisor to review and sign a memorandum of expectation (MOE) prior to the beginning of the internship. The MOE clearly outlines supervisor expectations and obtains explicit agreement to fulfil these expectations , depending on collaborative group needs. When classes are in‐session, interns work 10–20 h per week, and 10–40 h during school breaks. Interns may work on a single long‐term project, several projects within a collaboration, or on several projects across multiple collaborations during their internships , and a clinical collaborator who was an oncologist on a research project to study the toxicity attributed to different courses of chemotherapy/radiotherapy in lung cancer patients. It was the first time I ever worked with real patient data and programmed in SAS. I also learned new statistical methods that were not covered in classes at that time such as the proportional odds cumulative logit model, survival analysis, and imputation for missing data. My project supervisor was very supportive in all aspects, including guiding me through the research collaboration process, teaching me statistical knowledge, and reviewing my work. I also had a chance to interact with a staff statistician in DCI throughout the internship. By observing her daily work, I was able to have a deeper understanding of a master‐level biostatistician's job responsibilities. This truly inspired me to apply for a similar position in academia after graduation, because I wanted to practice the statistical knowledge I learned from coursework while focusing on the mission to improve health outcomes through collaboration with different clinical investigators in the Duke Health system. I joined the BERD Core as a staff biostatistician after graduation and found, not surprisingly, that while a full‐time job requires more independence and competency, the role of a BCTIP intern and that of a full‐time biostatistician within the BERD Core were very much alike. I am grateful that I had the internship experience, which prepared me well as a fresh graduate who felt confident to be qualified for this job and adapt quickly to the working environment.This narrative was written by a former MB student and BCTIP intern who graduated from the program in 2018.I began the BCTIP internship during my second semester of the master's program. I worked with my intern supervisor, a faculty member in the School of Nursing, on intensive mobile health data for patients with type II diabetes. I learned how a real biostatistician works and collaborates with medical and nursing researchers in practice. My supervisor patiently guided me through managing real patient data, programming efficiently in both R and SAS, and communicating clearly and effectively with collaborators. It was a great practicum for me to apply statistical concepts learned from coursework as well as build my skillset toward an independent, experienced biostatistician. Moreover, I had a chance to connect with other biostatisticians during my internship and learned a lot from their experiences. Later, we developed the intern project into my master's thesis project after a discussion with my supervisor. These projects exposed me to different phases of research work in biostatistics. I became really interested in exploring new methodology and decided to further pursue a PhD degree in biostatistics. In my current PhD work, I am benefited from the trained research, project management, and programming skills acquired during BCTIP. I also participate in consulting projects as a PhD student, and the BCTIP experience prepared me to work closely with collaborators. I really appreciate the opportunity to work as a BCTIP intern not only for the experience with technical research projects, but also for the mentorship, support, and feedback from my supervisor and other biostatisticians. In BCTIP, I learned what a successful biostatistician and researcher should be like.This narrative was written by a former MB student and BCTIP intern who graduated from the program in 2019:3BCTIP provides students with valuable professional experience that distinguishes them in the eyes of hiring managers and PhD program selection committees. BCTIP alumni demonstrate many of the communication and project management skills that can only be gained through real‐world project experience alongside solid training in statistical methodology. Students who complete BCTIP have been very successful at securing full‐time statistician positions across industry and academia, as well as gaining acceptance to PhD programs. Although a formal study has not been conducted comparing post‐graduation outcomes in students who did and did not participate in BCTIP, anecdotally, feedback from hiring managers who have hired or worked with BCTIP graduates suggests they regularly seek out BCTIP alumni for new positions. Future studies comparing these student groups are warranted.The benefits of BCTIP are apparent not only to program and department leadership but also to MB students. Each fall and spring, when the BCTIP leadership team is preparing to release the call for intern applications, there is overwhelming interest from the MB student body and the program teaching faculty. BCTIP leaders are regularly asked to sit on internship panels for the MB program and serve as resources for faculty and staff statisticians who have questions about internships and student research experiences.One of the primary strengths of BCTIP is how it efficiently integrates interns into collaborative teams across Duke University, an institution with many collaborative teams that include biostatisticians. The strong demand for student support in the BERD Core has resulted in increasing student involvement in these collaborations. Students are strongly interested in these experiential training opportunities, especially because the MB program requires that students complete a practicum and a master's project/thesis. Interns do not receive course credit for participation in the internship, but in many cases, much of the BCTIP intern's project work can be used towards the completion of their required practicum or master's project.Program and departmental leadership have learned valuable lessons throughout the development of BCTIP. The tight integration between programs has required BCTIP and MB program leadership teams to work closely together, deciding which topics are best included as part of formal coursework and which are supplemental and better included as part of BCTIP training sessions. Students in the MB program can select one of three different program tracks , adding additional complexity and considerations to the core content that should be considered base knowledge taught as part of regular coursework.This close collaboration has led to mutually beneficial innovations, thereby contributing to the success of each program. For example, the instructional approaches developed in BCTIP along with the robust continuous assessment components of the program have helped inform the redesign of the MB program's first‐semester coursework in the practice of collaborative biostatistics. Similar continuous assessment approaches used in the MB program have led to reciprocal program improvements in BCTIP.BCTIP is not without challenges and limitations. The internship is only available to students in the MB program at Duke University. The number of students enrolled in the MB program has increased over time, resulting in an increase in the number of applicants to BCTIP; however, supervisory effort and project availability pose barriers to increasing the number of interns in BCTIP at a similar rate. The number and variety of internship projects are reliant on and coordinated by faculty and staff within the department of Biostatistics and Bioinformatics. Securing funding for an intern from a collaborative team often requires substantial time and coordination.Each student and corresponding project supervisor have project‐appropriate effort funded by the clinical/translational research funding corresponding to a specific project. Each funding source needs to be verified and business management support must be available to help manage the back‐end finances of the program. To support sufficient time for program advisors, BCTIP must cover approximately 25%–40% effort for established biostatisticians. The program also utilizes volunteers to teach the didactic training curriculum; if volunteers are not available, additional funding is required to provide salary support for teaching efforts in the program.The whole process of hiring students is also a substantial undertaking for the human resources department. An additional challenge is identifying available funding to support and maintain both the student positions and the dedicated administrative time required to oversee these internship experiences. Increasing effort for program leadership and administrative support would allow for additional student intern collaborative opportunities, so it is essential to consider how funding will be secured for these activities.Implementation of BCTIP in the Department of Biostatistics and Bioinformatics at Duke University School of Medicine has benefited many MB students and ongoing research collaborations across the institution. We have successfully trained and graduated over 80 students during 9 years and have seen impressive growth in both the breadth of the didactic training curriculum and the careful oversight of the experiential internship. We hope that our experience will benefit other institutions seeking to develop and implement internship training program targeted at preparing their students to be effective collaborative biostatisticians. We hope to continue growing and evolving this program to include more collaborative opportunities for students, while maintaining scientific and statistical rigor.SCG reports receiving consulting fees from Gilead Sciences for serving on multiple Data Monitoring Committees. Although this relationship is not perceived to represent a conflict with the present work, it has been included in the spirit of full disclosure. There are no other conflicts of interest to report.Appendix S1. Training CurriculumAppendix S2. Intern ExpectationsAppendix S3. Memorandum of Expectation (MOE)Appendix S4. Monthly Progress ReportAppendix S5. Supervisor Feedback SurveyAppendix S6. Intern Project ExamplesAppendix S7. Call for Intern Support SurveyAppendix S8. Applicant SurveyAppendix S9. MB ProgramClick here for additional data file.


Digital mental health (DMH) interventions incorporating elements that adapt to the evolving needs of consumers have the potential to further our understanding of the optimal intensity of therapist assistance and inform stepped-care models.The primary objective was to compare the efficacy of a transdiagnostic biopsychosocial DMH program, with or without therapist assistance for adults with subthreshold symptoms or a diagnosis of anxiety or depression.d, reliable change index, and mixed-effects linear regression analyses.In a randomized adaptive clinical trial design, all participants had access to the DMH program, with eligibility to have their program augmented with therapist assistance determined by program engagement or symptom severity. Participants who met stepped-care criteria were randomized to have their treatment program augmented with either low-intensity (10 min/week of video chat support for 7 weeks) or high-intensity (50 min/week of video chat support for 7 weeks) therapist assistance. A total of 103 participants  were assessed before (week 0), during (weeks 3 and 6), and after the intervention (week 9) and at the 3-month follow-up (week 21). The effects of 3 treatment conditions  on changes in the 2 primary outcomes of anxiety  and depression  were assessed using the Cohen d values ranging from 0.82 to 1.79 . The mixed-effects models revealed that, in the Life Flex program–only condition at week 3, mean GAD-7 and PHQ-9 scores significantly decreased from baseline by 3.54 and 4.38 , respectively. At weeks 6, 9, and 21, GAD-7 and PHQ-9 scores significantly decreased from baseline by at least 6 and 7 points , respectively. Nonresponders at week 3 who were stepped up to therapist assistance increased program engagement and treatment response. At the postintervention time point and 3-month follow-up, 67% (44/65) and 69% (34/49) of the participants, respectively, no longer met diagnostic criteria for anxiety or depression.There were no substantial differences in the outcome measures among intervention conditions. However, there were significant time effect changes in most outcomes over time. All 3 intervention conditions demonstrated strong and significant treatment effect changes in GAD-7 and PHQ-9 scores, with absolute Cohen The findings highlight that early detection of low engagement and non–treatment response presents an opportunity to effectively intervene by incorporating an adaptive design. Although the study findings indicate that therapist assistance was no more effective than the DMH intervention program alone for reducing symptoms of anxiety or depression, the data highlight the potential influence of participant selection bias and participant preferences within stepped-care treatment models.Australian New Zealand Clinical Trials Registry ACTRN12620000422921; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=378317&isReview=trueRR2-10.2196/45040 Digital health interventions have a well-established evidence base for the effective delivery of mental health care for various psychological disorders, including anxiety and depression -4. DigitDMH intervention programs have been disseminated in a variety of formats, from purely self-help  to guided  delivery methods in which the mode and amount of therapist involvement can vary substantially among studies ,16. The self-management . If or when required , the intensity of treatment can be increased, for example, by adding a therapist to assist while using a web-based DMH intervention program. Adaptive intervention research designs provide the opportunity to evaluate the first 2 stages of a stepped-care model as the type or dosage of treatment can be adapted according to decision criteria [Research on DMH intervention programs incorporating adaptive treatment elements has the potential to further our understanding of the optimal intensity of therapist assistance. Adaptive intervention designs incorporate criteria that influence how and whether treatment is maintained or augmented based on participant responses, such as engagement levels or symptom severity ,25. An acriteria ,25.Therapist assistance within DMH intervention programs has been delivered synchronously and asynchronously. Synchronous interactions include telephone calls, instant messaging, and video chat interactions between a therapist and participant in real time; asynchronous communication can also include messaging and email support . To dateThere is currently a lack of research on the use of video chat as a modality of therapist assistance within a DMH intervention. To our knowledge, there is no identified adaptive randomized clinical trial that has investigated the efficacy of a 2-stage adaptive design whereby a transdiagnostic biopsychosocial DMH intervention program is augmented with either low- or high-intensity video chat–based therapist assistance should a participant not engage with the program or their symptoms not improve. In stepped-care research, little is known about the intensity of therapist assistance required at the next level of care. To further understand the efficacy of stepped-care models and adaptive intervention designs within DMH interventions, this clinical trial sought to evaluate and compare no therapist assistance with participants randomized to receive either 10 minutes (low intensity) or 50 minutes (high intensity) of therapist assistance per week. In this study, both low- and high-intensity therapist assistance were delivered via video chat technology. This further contributes to the body of literature as higher-intensity therapist assistance is typically delivered in person within stepped-care models.The primary aim of this study was to investigate and comparatively evaluate the efficacy of various support intensities of the Life Flex DMH intervention program for adults with subthreshold symptoms or a diagnosis of anxiety or depression. Specifically, the study sought to evaluate whether augmenting a self-help DMH intervention program with low- or high-intensity therapist assistance delivered via video chat technology improved the clinical outcomes of anxiety and depression compared with a DMH intervention program only. Participant secondary outcomes of quality of life, social support, sleep, and physical and mental health ratings were reported, as well as program engagement , acceptability (satisfaction survey), and usability of the Life Flex intervention program. Predictor outcomes of motivation and self-efficacy will be evaluated in another study.Recruitment took place between November 2020 and March 2022 via social media advertisements, health websites, and information flyers sent to medical clinics. All applicants were directed to the Life Flex study website created for the trial, in which they completed a 2-step screening process. All applicants gave their consent to take part in the study by reading a Plain Language Information Statement on the web and clicking a checkbox that said the following: “I have read the Plain Language Information Statement and I agree to the above conditions.” The Plain Language Information Statement informed participants of how long each diagnostic assessment and questionnaire would take, along with the estimated weekly time required to complete the intervention. Participants were also provided with detailed information regarding data protection and storage procedures. The aims of the study were not stated, no formal participant debriefing was undertaken, and the clinical trial adhered to the CONSORT-EHEALTH  checklist  Multime. Upon reParticipants were eligible for the trial if they met the following criteria: aged ≥18 years, Australian residents, access to the internet, ability to register their interest on the web using an email address, ability to read and write in English, and symptoms or diagnosis of anxiety or depression  . ParticiA pre-during-post–follow-up randomized adaptive trial design was used to explore the various support intensities of the Life Flex program. Participants were assessed via a closed survey accessible via their private log-in on the DMH website before (week 0), during (weeks 3 and 6), and after the intervention (week 9) and at the 3-month follow-up (week 21). The web-based questionnaire was created using electronic forms on the DMH website. The web-based questionnaire and associated participant flow were tested for functionality via the creation of dummy participants before the trial commenced. Participants were required to complete the preintervention and week 3 during-the-intervention questionnaires to gain access to the Life Flex program. Participants were not required but were encouraged to complete the week 6 during-the-intervention questionnaire; however, they were incentivized with an Aus $10 (US $6.77) gift card voucher for completion of the postintervention (week 9) and 3-month follow-up (week 21) questionnaires. Duplicate entries from the same participant were not possible as the questionnaire was not displayed a second time. If participants endorsed suicidal ideation on the 9-item Patient Health Questionnaire (PHQ-9) , they reThis study received ethics approval from the Federation University human research ethics committee  and was preregistered with the Australian New Zealand Clinical Trials Registry .Treatment was delivered within a secure and encrypted digitally based treatment platform and comprised the primary intervention for all 3 treatment conditions: a third-wave, transdiagnostic biopsychosocial DMH intervention program called Life Flex. The treatment in step 1 of the trial comprised the Life Flex program only, whereby participants completed the self-help program Life Flex without therapist assistance. All participants received automated emails to inform them of the module content as well as the release and reminders of scheduled assessments. Participants completed the program on a scheduled release design, with a new module being released every week except for module 4 (participants were given 2 weeks to work through and practice module 4 content). Participants who improved or engaged with the Life Flex program within the first 3 weeks maintained their no-therapist-assistance DMH intervention program.The aim of the Life Flex program is to increase psychological flexibility and positive affect in anxiety and depression. The program incorporates CBT, emotion regulation strategies, and neuroplasticity principles. The Life Flex program is suitable for individuals with or without a diagnosis of anxiety or depression and contains 6 core modules along with an introductory and booster module. Each module of the program takes approximately 25 minutes to complete. In addition, to reinforce the module-based information, there are 20 to 30 minutes of offline activities each week.Offline activities include applying the concepts and techniques discussed in the modules , undertaking one of the biological and wellness flexibility intervention strategies, monitoring emotions and thoughts, and undertaking the gradual exposure or behavioral activation activities. Participants also receive automated emails  and are asked several questions at the beginning of each module to help gauge their progress. Module features include text, graphics, audio, video, editable forms, web-based games , and downloads. Modules are accessible via the web on mobile or tablet devices. Further information regarding the modules of the Life Flex program is presented in Participants who were stepped up to the low-intensity therapist-assisted treatment condition completed the same Life Flex program augmented with 10 minutes per week of video chat support for 7 weeks with a therapist for support and progress review of program content. Therapists were encouraged to display warmth and unconditional positive regard and were required to manage any risks  that arose.Participants who were stepped up to the high-intensity therapist-assisted treatment condition also completed the same Life Flex program augmented with 50 minutes per week of video chat support for 7 weeks with a therapist for individualized tailoring of the program, questionnaire feedback, and discussion of module content , as well as skill generalizability beyond the Life Flex program. Similar to the low-intensity condition, therapists were encouraged to be supportive and manage any issues of risk.The therapist assistance in both treatment conditions was manualized via case note templates, which included session agendas used by the therapists. In all 3 treatment conditions, participants had access to the Life Flex program for a further 14 weeks after the last module release.The assessors and therapists (n=22) were registered provisional and generalist psychologists who were undertaking a postgraduate clinical placement within a community mental health service. Each assessor and therapist had undertaken coursework in clinical psychology, including psychological assessment and interventions, as well as completing a purposively developed 14-hour, 5-module web-based training program covering DMH interventions , diagnostic assessments, qualitative interviewing techniques, and transdiagnostic CBT principles that underpin the Life Flex program. The training program comprised a 40-item multiple-choice competency assessment on which therapists were required to obtain a minimum score of 80%.All assessors were required to demonstrate competency in diagnostic assessments before administration. Both assessors and therapists received daily training and supervision throughout the trial from the first author, an experienced clinical psychologist. Assessment and therapist sessions were audio or video recorded (with participant permission) so that random checking  could be completed to ensure fidelity to administration, enable agreement and interrater reliability of diagnoses, and ensure that fidelity to the therapist’s role (low or high intensity) was maintained. At least one treatment session was reviewed for each therapist. An adapted version of the Internet-Delivered Cognitive Behavior Therapy–Therapist Rating Scale  and the Diagnostic assessment interviews were conducted before the intervention (week 0), after the intervention (week 9), and at the 3-month follow-up (week 21), with no more than 3 attempts made to contact participants. Outcomes were assessed before the intervention (week 0), during the intervention (weeks 3 and 6), after the intervention (week 9), and at the 3-month follow-up (week 21). The web-based questionnaire assessment comprised a demographic questionnaire, the 7-item Generalized Anxiety Disorder Scale (GAD-7) , the PHQAll eligible participants were given access to the Life Flex program. Symptoms and program engagement were evaluated at week 3 to assess whether the stepped-care rules were met for participants to have their treatment program augmented with therapist assistance. Stepped-care rules were met if participants’ symptoms deteriorated by >5 points  of the participants failed to complete the introduction module and module 1 of the program by the 3-month follow-up. In terms of scheduled assessment completions, 8.7% (9/103) of the participants failed to complete at least one scheduled assessment following the preintervention assessment. Of the 103 participants who commenced the intervention, 38 (36.9%) did not attend the postassessment interview, and 54 (52.4%) did not attend the 3-month follow-up assessment interview.A block randomization design was used to randomly allocate participants who met the stepped-care rules to either the low- or high-intensity therapist-assisted condition. Both the study participants and therapists were aware of what therapist-assisted condition they had been allocated to; however, they remained unaware of the study’s aims.A total of 240 individuals registered to participate in the adaptive clinical trial during the recruitment period. Of the 240 applicants, 75 (31.3%) were not contactable after registration, 6 (2.5%) were excluded during the prescreening phone interview stage, and a further 46 (19.2%) were excluded during the prediagnostic assessment interview because they met the exclusion criteria  were randomized to low-intensity therapist assistance, and 37 (51%) were randomized to high-intensity therapist assistance. Of the 36 participants allocated to low-intensity therapist assistance, 7 (19%) remained uncontactable, and 7 (19%) scheduled therapist-assisted sessions but did not attend, leaving 22 (61%) who actively attended low-intensity therapist-assisted sessions adjunctive to the Life Flex program. Of the 37 participants allocated to high-intensity therapist assistance, 3 (8%) remained uncontactable, 2 (5%) preferred to stay in the DMH intervention program–only condition, and 5 (14%) scheduled therapist-assisted sessions but did not attend, leaving 27 (73%) who actively attended high-intensity therapist-assisted sessions adjunctive to the Life Flex program.Progress on the Life Flex program of the 16% (12/73) of participants who did not attend the scheduled therapist sessions was also assessed. Of these 12 participants, 1 (8%) engaged with the program for a further 3 days after the offer of therapist assistance, and 1 (8%) engaged for a further week before disengaging from the Life Flex program. A total of 35.4% (40/113) of the participants did not meet the stepped-care criteria at week 3; however, 37.2% (42/113) of the participants continued in the DMH intervention program–only condition as 1.8% (2/113) of the participants, who were initially randomized to high-intensity therapist assistance, preferred to remain in the program-only condition. The primary self-report outcomes were anxiety severity according to the GAD-7  and deprThe CMOTS  is a 24-The wording of the Bipolar Self-Efficacy Scale  was sligThe WAI-S  is an abThe AQol-4D  is a 12-The SUS  is a 10-A purposively designed 10-item satisfaction questionnaire was developed to assess participants’ level of satisfaction with the treatment received. In total, 8 of the 10 items are rated on a 4-point Likert scale. For the other 2 questions, participants are asked to qualitatively share what they believe are the most preferable parts of the Life Flex program, as well as what they believe are the least preferable parts of the Life Flex program.Participants completed a health care resource use questionnaire designed for the trial to evaluate primary and secondary health care consultations and medication use. All assessors and trial therapists also completed a resource questionnaire logging the time spent interacting with participants during the trial.Before participation at baseline, the participants’ treatment preference was assessed by asking which of the 3 treatment options being evaluated in the trial they would prefer if they were given a choice. Participants ranked the 3 options of Life Flex program only, Life Flex program+low-intensity therapist assistance, and Life Flex program+high-intensity therapist assistance from most preferred to least preferred. Of the 103 participants, 38 (36.9%) received their first treatment preference, 34 (33%) received their second treatment preference, and 31 (30.1%) received their third treatment preference. Overwhelmingly, across the total sample (n=103), the first treatment preference was for high-intensity therapist assistance  followed by low-intensity therapist assistance as the second preferred treatment , with the Life Flex program only rated as the least preferred treatment . The preference data were further examined according to participants who did and did not meet the stepped-care criteria. Of the 63 participants who met the stepped-care criteria, 43 (68%) preferred high-intensity therapist assistance, 11 (17%) preferred low-intensity therapist assistance, and 9 (14%) preferred the DMH program only. Of the participants who remained in the program-only condition, 45% (18/40) preferred high-intensity therapist assistance, 35% (14/40) preferred low-intensity therapist assistance, and 20% (8/40) preferred the DMH program only. The remaining treatment preferences are presented in All participants were administered video-based diagnostic interviews using the Mini International Neuropsychiatric Interview 7.0.2  at the pStata   and SPSSt tests or Wilcoxon signed rank tests where applicable  was applied to index and interpret the size of the standardized difference . We illustrated the changes in those outcomes in Adjusted analysis of intervention effects was performed using mixed-effects linear regression models. We estimated both fixed and random effects to assess the intervention effects of time point and condition on the change in the 2 primary outcomes, GAD-7 and PHQ-9 scores. Both measures, GAD-7 and PHQ-9 in long format, were normally distributed, satisfying the assumption of the mixed-effects model. We also tested the interaction effects of time point and condition, but they were not significant; therefore, noninteractive models were assessed for both the GAD-7 and PHQ-9. The appropriateness of the mixed-effects model for each outcome was assessed by comparing each with the standard linear regression model and random-intercept linear model based on likelihood ratio tests with strong evidence P<.0. We illuThe sensitivity analysis was conducted to test whether the model and covariance structures were mis-specified. We began by checking the correlation and covariance structures . Initially, the correlation structure  suggesteP>.05). Further demographic variable information is presented in As can be seen in P<.05) and one of the secondary outcomes , with absolute values of the Cohen d ranging from 0.82 to 1.79.P>.05). No significant changes were observed in the quality-of-life health rating, sleeping time, and physical health ratings across time points in the low-intensity therapist assistance condition and in social support and physical health ratings in the DMH intervention program–only condition . A moderate effect size was also found for social support in the high-intensity therapist-assisted condition.The remaining secondary outcomes achieved only a small to moderate effect size change, although not all of them were significant , respectively. At weeks 6, 9, and 21, GAD-7 scores significantly decreased by at least 6 points, whereas PHQ-9 scores decreased by at least 7 points compared with the baseline . There were no significant differences in GAD-7 and PHQ-9 score changes between the DMH intervention program–only and the low- or high-intensity therapist-assisted conditions .In the fixed-effects component, at week 0 and in the DMH intervention program–only condition, the mean GAD-7 and PHQ-9 scores were 3.29 and 3.22, respectively. At week 3, mean GAD-7 and PHQ-9 scores significantly decreased by 3.54 and 4.38 compared with the baseline  were more likely to decrease compared with lower mean values. Both mixed-effects models of the GAD-7 and PHQ-9 fit the data based on the fit criteria and when compared with conventional linear regression and random-intercept models . The modP<.001). The rapid decrease from the preintervention time point (week 0) to during the intervention (weeks 3 and 6) and the postintervention time point (week 9) was observed for both outcome measures. However, there was no change after the postintervention time point for PHQ-9 scores, whereas GAD-7 scores continued to decrease after week 9, although slowly. There was no interaction effect between time point and intervention condition, suggesting the same treatment effect on the outcome measures.The reliable change index was calculated as 5.06 for pre- and post- and 4.53 for pre- and follow-up treatment, respectively, for anxiety  and as 6.02 for pre- and post- and 4.55 for pre- and follow-up treatment, respectively, for depression . At the preintervention time point, all except 1 (1%) out of 103 participants were assessed as having clinical anxiety or depression. The most common diagnosis was major depressive disorder comorbid with at least one anxiety disorder  followed by major depressive disorder , with 12.6% (13/103) of the participants also meeting the criteria for more than one anxiety disorder. At the postintervention time point, 63.1% (65/103) of the participants attended the diagnostic assessment, with 68% (44/65) of them no longer meeting the criteria for a Diagnostic and Statistical Manual of Mental Disorders  diagnosiF2,100=1.81; P=.17), pages viewed , and the percentage of the program completed . The number of program log-ins, pages viewed, and percentage of program completion for the total sample (n=103) and the 3 treatment conditions are presented in The mean number of log-ins to the Life Flex program for the sample of 103 participants was 24.41 (SD 23.53), and the average total number of pages viewed per participant was 132.54 (SD 100.01). A between-group analysis of variance was completed, with nonsignificant interaction effects found for total program log-ins  and high-intensity  therapist assistance conditions were offered 7 video chat sessions of therapist assistance adjunctive to the Life Flex program. Of the 29 participants randomized to receive low-intensity therapist assistance, 7 (24%) did not engage in or attend any sessions. Among the 22 low-intensity condition participants who did attend the video chat sessions, the mean number of sessions attended was 5.4 (SD 2.36), and the mean total therapist time per participant in the low-intensity therapist-assisted condition was 82 (SD 36.36) minutes. Of the 32 participants randomized to receive high-intensity therapist assistance, 5 (16%) did not engage in or attend any sessions. Among the 27 participants who did attend the video chat sessions, the mean number of sessions attended was 5.4 (SD 2.0), with a mean of 273 (SD 111.14) minutes of therapist assistance provided per participant.At week 3, participants were asked to rate the usability of the Life Flex program by completing the SUS . In totaAt the postintervention time point, participants were asked to rate how satisfied they were with the Life Flex program on a 4-point scale ranging from 1=poor to 4=excellent. In total, 65% (67/103) of the participants provided treatment satisfaction ratings, with scores of ≥75% indicative of treatment acceptability. In total, 85% (57/67) of the participants rated the overall quality of treatment delivered by Life Flex as good to excellent, with 84% (56/67) of the participants reporting that they were very satisfied with the program. Additional participant satisfaction ratings are shown in The primary objective of this adaptive clinical trial was to evaluate the efficacy of various support intensities of a transdiagnostic biopsychosocial DMH intervention program, Life Flex, for anxiety or depression in adults. This study aimed to investigate the efficacy of an adaptive intervention design by testing whether therapist assistance (low and high intensity) in the form of video chat support produced significant clinical outcomes compared with a self-help DMH intervention program only. In the sample of 103 participants, we found no statistically significant difference between the participants who were stepped up to receive therapist assistance and those who remained in the DMH intervention program–only condition for outcomes of anxiety and depression. The participants in all 3 treatment conditions demonstrated significant improvement in anxiety and depression over the course of the intervention, with a large treatment effect size change. Quality of life  improved across all 3 treatment conditions. This section highlights some of the benefits and challenges of adaptive intervention research designs, with implications for the stepped-care models discussed.As this trial used an adaptive intervention design, the results indicate that participants who continued in the Life Flex program–only condition did well. Regarding those participants who met the stepped-care criteria of the study, it could also be argued that they did well after having their treatment program augmented with therapist assistance. It could be speculated that, without the adaptive design, participants who did not engage with the program or those with moderate-to-high symptom severity may have dropped out completely from the Life Flex program–only condition.In step 1 of the adaptive trial, all eligible participants (n=113) were given access to the preintervention questionnaire. At week 3, a total of 64.6% (73/113) of the participants met the criteria to have their treatment program augmented with therapist assistance. For 58% (42/73) of these participants, the stepped-care rule of no program engagement was met, followed by 26% (19/73) who met the no-engagement and noncompletion of scheduled assessment rule, 7% (5/73) who met symptom severity criteria, and 10% (7/73) who met both the symptom severity and no-program-engagement criteria. These results are consistent with previous literature investigating engagement rates of self-help DMH interventions that have demonstrated high attrition and low completion rates in the absence of stepped care ,22,55.The clinical trial presented an opportunity to capture low engagement early on, which meant that most participants allocated to low-intensity  and high-intensity  therapist assistance had the opportunity to access therapist assistance. This is important given the literature demonstrating the relationship between engagement and positive symptom outcomes in DMH intervention research ,57. OtheThe large treatment effect size change found for the DMH intervention program–only condition in this study is consistent with an earlier single-group evaluation demonstrating the preliminary effectiveness of a fully automated self-help version of the Life Flex program . In bothThe finding of no significant difference among the 3 treatment conditions could also be explained by the nature of the clinical trial design. All participants underwent prescreening (semistructured phone interview and video chat diagnostic assessment), followed by postintervention and 3-month follow-up video chat diagnostic assessment interviews with provisional or generally registered psychologists. This contact with research personnel may have been therapeutic for participants. Previous research evaluating the timing and magnitude of symptom improvement found participants to improve following an initial assessment call, with authors concluding that even brief therapeutic contact can influence symptom outcomes in clinical trials . All parIt is also important to consider the influence of participant self-selection bias  and the Closer examination of the available first treatment preferences of the participants who met the stepped-care criteria  indicates that 68% (43/63) preferred high-intensity therapist assistance, 17% (11/63) preferred low-intensity therapist assistance, and only 14% (9/63) preferred the Life Flex program only. The preferences of the participants who did not meet the stepped-care criteria were also examined. Of these participants, 45% (18/40) had a first treatment preference for high-intensity therapist assistance, 35% (14/40) had a preference for low-intensity therapist assistance, and 20% (8/40) had a preference for the Life Flex program only. The preference data across the entire sample indicate a strong preference among participants for therapist assistance . This finding is similar to that of previous research , which fDespite preferences, participants who remained in the Life Flex program–only condition did just as well with regard to symptom reduction of primary outcomes (anxiety and depression) as participants who were stepped up to receive therapist assistance. The health-related quality-of-life utility index scores also indicated significant improvements in quality of life across all 3 treatment conditions. Given that there were no differences among the 3 conditions, the Life Flex program by itself could be the most accessible and cost-effective treatment option. The Life Flex program–only condition also had the highest percentage of program completion of the 3 treatment conditions at 72% compared with 57% for low-intensity therapist assistance and 60% for high-intensity therapist assistance. Therefore, it could also be speculated that participants may have relied on their video chat sessions with their therapist for delivery of program content, reducing engagement with the Life Flex program itself.Participants who were stepped up had their treatment program augmented with therapist assistance delivered via video chat technology. In both the low- and high-intensity therapist assistance conditions, participants received individualized guidance, with a key component of therapist assistance involving reinforcement and motivation enhancement. Therefore, it would be beneficial to further investigate self-efficacy and motivation as predictors of engagement and attrition (as is planned in a subsequent paper by the authors) to better understand who is most suited for self-help and who may benefit from therapist assistance within DMH adaptive intervention designs. Research designed to improve the implementation of stepped care through contextual behavioral science recommends that the “match-mismatch” design be applied to stepped-care research to enhance understanding of treatment intensity and dose based on participant-specific characteristics . Other sThis study did not control for participant contact with the research personnel. This lack of control means that we cannot observe whether the symptom changes were due to the treatment delivered or other extraneous factors such as time and contact with research personnel within a clinical trial. As this study involved 3 assessment time points for the Life Flex program–only participants, future research is required to replicate and evaluate the study findings taking this into consideration. This study also did not have a control condition for the nonresponders, meaning that we are unable to determine whether these participants would have disengaged and whether they improved relative to receiving therapist assistance. Participants who engaged or showed symptom improvement were also only given the option of continuing in the Life Flex program–only condition. Therefore, the possible impact of assigning a therapist to the participants who engaged or showed symptom improvement cannot be determined within this study design.As the trial excluded participants with severe suicidal ideation, this study cannot present the baseline magnitude of depressive severity for individuals who presented with active suicidal ideation. Therefore, trial results are limited to participants experiencing depressive symptoms without active suicidal ideation, which potentially limits the generalizability of the results as it is not representative of conditions in routine clinical practice. Masking of participants and therapists to treatment condition was not possible, introducing a possible risk of bias; however, therapists did not have access to participant preference data, nor were they aware of the study’s objectives. This study also did not ask participants about their previous treatment experiences, which may have affected their preferences and mental health literacy. The study was also conducted during the global COVID-19 pandemic, which may have posed several challenges, including participants’ self-selection into the clinical trial in the hope of accessing therapist assistance and their commitment to the trial being negatively affected, as well as their symptoms of anxiety or depression because of the considerable uncertainty present in the community throughout data collection. However, the diagnostic status results at the postintervention time point and 3-month follow-up are promising of considerable symptom changes.Despite these identified limitations, this study has several important implications. The study shows respect for the evidence base and clinical guidelines of stepped-care models -73 and cGiven the nature of a clinical trial, the study used strict criteria to determine stepped-care eligibility and a randomized approach to participant allocation to low- and high-intensity therapist assistance. This contrasts with stepped-care decision-making typically used outside research contexts, which heavily relies on clinical judgment  and can This study adds to the existing body of literature on stepped-care models and advances the research field of adaptive intervention designs and therapist assistance delivered via video chat technology. Significant improvements in the primary outcomes of anxiety and depression were observed across all 3 treatment conditions, with high levels of treatment acceptability and program completion reported. Adaptive research designs that aim to improve the efficiency and efficacy of DMH interventions while considering participant characteristics of nonengagement and symptom severity present both opportunities and challenges. Although these findings indicate that therapist assistance was no more effective than the DMH intervention program alone for reducing symptoms of anxiety or depression, the data highlight the potential influence of participant selection bias and participant preferences within stepped-care treatment models. Under the caveat that all participants had therapist contact for the diagnostic assessments, the findings present evidence that transdiagnostic DMH interventions can be effective with or without therapist assistance; however, further research is needed to consolidate conclusions.


Arid1a, a subunit of the switching defective/sucrose non-fermenting (SWI/SNF) chromatin remodeling complex, suppresses postnatal cardiomyocyte proliferation while enhancing maturation. Genome-wide transcriptome and epigenome analyses revealed that Arid1a is required for the activation of a cardiomyocyte maturation gene program by promoting DNA access to transcription factors that drive cardiomyocyte maturation. Furthermore, we show that ARID1A directly binds and inhibits the proliferation-promoting transcriptional coactivators YAP and TAZ, indicating ARID1A sequesters YAP/TAZ from their DNA-binding partner TEAD. In ischemic heart disease, Arid1a expression is enhanced in cardiomyocytes of the border zone region. Inactivation of Arid1a after ischemic injury enhanced proliferation of border zone cardiomyocytes. Our study illuminates the pivotal role of Arid1a in cardiomyocyte maturation, and uncovers Arid1a as a crucial suppressor of cardiomyocyte proliferation.The inability of adult human cardiomyocytes to proliferate is an obstacle to efficient cardiac regeneration after injury. Understanding the mechanisms that drive postnatal cardiomyocytes to switch to a non-regenerative state is therefore of great significance. Here we show that  Cardiac regeneration is hindered by the limited division of cardiomyocytes. Here, the authors show that Arid1a drives maturation and limits proliferation through interaction with Yap. Suppression of Arid1a enhances proliferation after injury. Diminished proliferative capacity of adult cardiomyocytes forms a barrier to replenish the massive loss of cardiomyocytes after ischemic injury. To date, no efficient treatment to regenerate cardiomyocytes is available, highlighting the need for mechanistic understanding of the processes regulating the balance between cardiomyocyte proliferation and maturation. Natural cardiomyocyte regeneration occurs through the proliferation of existing cardiomyocytes3. Cardiomyocyte proliferation capacity is quickly lost after birth, concurrent with adaptations characteristic of cardiomyocyte maturation6. Factors that induce cardiomyocyte maturation, such as oxygen and thyroid hormone, have a suppressive effect on cardiomyocyte proliferation9. Adult cardiomyocyte proliferation can be triggered by overexpression of active yes associated protein 1 (YAP), a Hippo pathway effector that controls organ size11. In the neonatal heart, Hippo pathway kinases suppress YAP, thereby limiting cardiomyocyte proliferation13. Forced activation of YAP in postnatal cardiomyocytes induces proliferation, enlarged hearts, and decreased maturation15. In adult cardiomyocytes, ectopic YAP activaty causes cardiac overgrowth, impairing heart function and ultimately causing lethality16.During postnatal development, mammalian cardiomyocytes switch from a pro-regenerative, proliferative state towards mature, non-regenerative cells equipped for a lifetime of forceful contractions18. Incomplete maturation of iPS-CMs and in vitro engineered tissue poses a major limitation in leveraging their potential for in vitro modeling for pathological, pharmacological, or therapeutic purposes. Therefore, insights into the molecular underpinnings of cardiomyocyte maturation are imperative to further enhance in vitro tissue maturation and the advancement of cardiac regenerative medicine.Furthermore, cardiomyocyte proliferation and maturation are important for tissue engineering approaches. Despite recent advances promoting stem cell-derived cardiomyocyte maturation, complete maturation of induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs) has yet to be achieved21. Despite recent advances, our understanding of epigenetic mechanisms involved in cardiac maturation remains incomplete22.Loss of regenerative potential during postnatal cardiac maturation coincides with dramatic changes to the epigenetic landscape. Through chromatin remodeling, DNA methylation, and histone modifications postnatal cardiomyocytes achieve stable silencing of cell cycle genes, and activation of structural, metabolic, and electrophysiological maturation gene programs24. In the adult heart, the SWI/SNF complex is involved in stress-induced re-expression of fetal genes and cardiomyocyte hypertrophic growth25. SWI/SNF complex modulation of chromatin accessibility at target genes is facilitated by AT-rich interactive domain-containing protein 1a (ARID1A)26. Arid1a has essential roles in controlling cell proliferation27, and suppresses tissue regeneration in the context of liver or outer ear injury28. Despite essential functions during cardiac specification and differentiation32, roles of Arid1a in cardiac maturation and adult heart function so far remained unexplored.The SWI/SNF complex is a multiprotein chromatin remodeling complex with essential functions during cardiac lineage specification and cardiomyocyte differentiationArid1a during mammalian postnatal cardiomyocyte maturation. We show that Arid1a is expressed in neonatal mouse cardiomyocytes and that its expression is enhanced in adult cardiomyocytes after ischemic injury. Loss of Arid1a in neonatal mouse cardiomyocytes, as well as human iPS-CMs, leads to increased proliferation and suppression of cardiac maturation gene programs. Furthermore, overexpression of ARID1A enhanced functional maturation of engineered human myocardium (EHM). Combined ChIP-Seq analysis and motif discovery revealed changes in epigenetic marks and induction of YAP dependent gene programs, suggesting ARID1A suppresses YAP activity in neonatal hearts. We show that ARID1A directly interacted with YAP and suppressed its transcriptional activity. Similarly, in adult cardiomyocytes, suppression of ARID1A enhanced proliferation in border zone cardiomyocytes after ischemic injury. Together, our results identify Arid1a as an essential regulator of the cardiomyocyte proliferation to maturation switch, providing novel inroads for development of cardiac regenerative applications.Here, we describe roles of 1. We hypothesized that Arid1a, a known regulator of the balance between proliferation and differentiation, and a subunit of the SWI/SNF chromatin remodeling complex27, may be involved in this process in postnatal cardiomyocytes. To examine this, we first evaluated Arid1a expression in the heart using qPCR and found that Arid1a was expressed abundantly in neonatal hearts and decreases toward adulthood  and Cyclin b1 (Ccnb1) expression  and myosin light chain 2 (Myl2) exemplified contractile protein isoform switch typical of cardiomyocyte maturation 36 to ablate Arid1a  in αMHC-Cre;Arid1a mutants , Ccne1, Ccnb1, Ccna2, cyclin-dependent kinase 1 (Cdk1), and Cdk4 , which is required for cardiomyocyte proliferation, was upregulated in Arid1a cKO hearts37. To evaluate whether increased expression of cell cycle genes translated into increased cardiomyocyte proliferation, we injected the thymidine analog EdU (5-ethynyl-2´-deoxyuridine) subcutaneously in pups at P1, P3, and P5 and assessed EdU incorporation at P7  and multiple homologous ion-transporting ATPases , as well as voltage-dependent calcium channels Cacna2d1, Cacnb2, and Cacng6. Finally, we observed decreased energy metabolism gene expression in mutants, including Cytochrome C Oxidase Subunit 5 A (Cox5a) and Cox6c  and ion homeostasis genes . However, metabolism genes that were downregulated in Arid1a cKO mice were not changed to the same extent in human iPS-CMs . Decreased expression of PLN and TNNI3 translated into a trend towards a similar decrease at the protein level , which offers more advanced and mature cardiomyocytes with functional readouts of contractilityD1A Fig. , and conD1A Fig. . After 4D1A Fig. . MoreoveD1A Fig. . ContracD1A Fig. . Collect29. Furthermore, recent studies have proposed ARID1A as a regulator of chromatin accessibility through histone H3 lysine 27 acetylation (H3K27Ac)41. To investigate the roles of ARID1A in the neonatal cardiac epigenome, we performed ChIP-Seq for H3K27Ac and compared Arid1a cKO mutant and littermate control hearts  , Estrogen related receptor transcription factors , as well as transcription factors involved in epithelial to mesenchymal transition . MEF2 transcription factors have multiple roles during all stages of heart development. MEF2A is the predominant MEF2 family member expressed in the postnatal heart, and it activates markers of terminal differentiation while repressing cell cycle genesArid1a cKO hearts included Fgf7, a cardiomyocyte-derived ligand driving myocardial proliferation during development;48Ankrd1, a YAP/TEAD target in postnatal cardiomyocytes, overexpression of which is associated with impaired response to increased haemodynamic load;50 and Acta2, a contractile protein that is normally downregulated during postnatal cardiac maturation51.Similarly, genes potentially directly activated by enhanced YAP/TEAD activity in Arid1a suppresses YAP/TEAD-regulated cell cycle activity in neonatal cardiomyocytes while stimulating cardiomyocyte maturation via MEF2- and ESRR-dependent pathways.Collectively, our analyses of histone acetylation, gene expression, and motif enrichment indicate that Arid1a cKO hearts display striking similarities to hearts with forced YAP activation15, we decided to further explore the molecular mechanism by which ARID1A interacts with Hippo pathway effectors during heart maturation. First, we assessed the expression of known YAP target genes in cardiomyocytes50 in Arid1a cKO hearts and controls. Our RNA-Seq analysis revealed that the majority of known YAP target genes in postnatal hearts were induced in Arid1a cKO hearts compared to controls , connective tissue growth factor (Ctgf), the Rab effector synaptotagmin like 2 (Sytl2), and a slight increase of insulin-like growth factor I receptor (Igf1r) that did not reach statistical significance (P = 0.0876) by qPCR  facilitates myosin heavy chain isoform switching that occurs during cardiac maturation25. Furthermore, the cardiac enriched SWI/SNF subunit Baf60c regulates a gene expression program that includes genes encoding contractile proteins, modulators of sarcomere function, and cardiac metabolic genes59. These functions are in line with the maturation-promoting functions of Arid1a we uncovered in the present study, indicating Arid1a may act in concert with Baf60c during cardiomyocyte maturation. In contrast, cardiomyocyte specific Baf60c knockout mice show reduced proliferation, indicating that Arid1a regulates cardiomyocyte proliferation via mechanisms independent of Baf60c59.ARID1A is a subunit of the SWI/SNF chromatin remodeling complex. SWI/SNF complexes are made up of multiple subunits that are assembled in a combinatorial manner to tailor their functions, regulating specific (cardiac) developmental eventsArid1a acts as an essential regulator of the balance between proliferation and maturation in postnatal mouse cardiomyocytes, and human iPS-CM. This is in line with its function during embryonic cardiomyocyte differentiation, as well as in other organs such as the liver, where Arid1a promotes cell differentiation at the expense of cell renewal29. Even more, suppression of Arid1a in liver and outer ear injury models significantly enhanced tissue regeneration via enhanced proliferation of parenchymal cells28. In muscle stem cells, SWI/SNF also functions as a context-dependent regulator of this equilibrium, providing an essential brake to cell cycle progression60. In line with its roles in coordinating cell proliferation and differentiation, mutations in SWI/SNF components contribute to an estimated 20% of all cancers, with Arid1a being the most frequently mutated subunit62.This study revealed that Arid1a cKO hearts combined with RNA-Seq and ChIP-Seq revealed a key role for YAP-dependent signaling. Arid1a cKO hearts are overgrown with sustained proliferation, showing striking similarities to postnatal hearts with ectopic YAP activation via knockout of salvador1 (Sav1) or through overexpression of constitutively active YAP12. We observed induction of YAP dependent gene programs in Arid1a cKO hearts, while YAP expression level and phosphorylation status, as assessed by Western Blot, were not changed. Still, YAP appeared more nuclear localized in mutant hearts, which is compatible with enhanced expression of YAP target genes. These findings indicate that ARID1A may suppress YAP through a non-canonical mechanism distinct from the canonical regulation of YAP by the Hippo pathway. Recently, in a screen for nuclear factors that interact with YAP, Chang et al. identified the ARID1A-containing SWI/SNF complex as an inhibitor of YAP and TAZ63. In a series of elegant in vitro and in vivo experiments, they show that ARID1A competes for YAP-binding with TEAD, providing essential tumor-suppressive function of the SWI/SNF complex. Here, we show that ARID1A can bind YAP and inhibit its transcriptional activity in cardiac like cells, using affinity tagged proteins. Immunoprecipitation of endogenous complexes from neonatal hearts would further strengthen this observation.Phenotypic analysis of postnatal 64. Upon liver injury, YAP-dependent gene expression is activated, suggesting that ARID1A-opened chromatin renders hepatocytes competent to respond to YAP signaling. Increased H3K27Ac levels of YAP regulated promoters and enhancers in Arid1a cKO hearts could suggest that Arid1a restricts chromatin access of YAP dependent genes during cardiac maturation. Taken together, these results suggest that ARID1A may have multiple ways to control YAP-dependent gene expression.On the other hand, ARID1A could mediate chromatin accessibility, providing a chromatin status that allows for the expression of cardiomyocyte maturation gene programs, while silencing regions associated with proliferation. In liver cells, ARID1A-mediated chromatin opening facilitates a chromatin environment that is permissive to response to injuryArid1a appeared dispensable for adult heart function under homeostatic conditions. Moreover, we did not observe deregulation of contraction or metabolism genes in infarct tissue after IR, although changes in cardiomyocyte gene expression could be masked by other cell types.Here, we show that ARID1A is essential for the expression of a maturation-promoting gene program in neonatal mouse heart, as well human iPS-CMs. Moreover, we show that overexpression of ARID1A can enhance features of cardiomyocyte maturation in human EHMs. On the other hand, Arid1a cKO hearts indicate roles of Arid1a in this switch, further studies are required to fully understand roles of Arid1a in the energy metabolism switch during cardiac maturation. Together, these data indicate Arid1a plays important roles during the process of maturation.During postnatal maturation, cardiomyocytes switch their energy metabolism from glycolysis towards fatty acid oxidation. While gene expression changes in Arid1a to maintain the expression of these gene programs. Our analysis of H3K27Ac in neonatal hearts indicates that ARID1A is required for the establishment of a maturation-promoting epigenetic landscape in cardiomyocytes. In line with its role during cardiomyocyte development, ARID1A may steer cardiac maturation through chromatin remodeling activity, promoting DNA access of pro-maturation factors MEF2A and ESRRB35. Alternatively, ARID1A could be recruited by MEF2 and ESRR factors, and activate or maintain pro-maturation gene expression through regulating histone H3K27 acetylation levels41.Once cardiomyocytes have matured, they may no longer require Arid1a might contribute to enhanced tissue regeneration after injury. However, due to its critical role as tumor suppressor, such an approach would have to be transient and cardiomyocyte specific. In addition, the modest increase in proliferation in Arid1a ablated adult cardiomyocytes indicates additional factors are required to fully activate proliferation. Suppression of Arid1a in the context of YAP-induced cardiomyocyte proliferation65 might enhance proliferation rates, whereas induction of Arid1a at later stages could be used to limit YAP activity, preventing over-proliferation. Finally, maturation-promoting functions of Arid1a could be utilized for in vitro cardiomyocyte maturation and cardiac tissue engineering efforts.Regulation of cardiomyocyte proliferation and maturation could ultimately be exploited for cardiac regenerative applications. For instance, suppression of Arid1a in the regulation of proliferation and maturation, providing further insights into cardiomyocyte biology. Future studies will be required to discover the full potential of modulating Arid1a levels for cardiac regenerative applications.Together, our studies reveal essential roles of Arid1aloxP 57, Rosa26tdTomato (Rosa26tm14(CAG-tdTomato)Hze; Stock#: 007914)66 and wildtype C57B/6 J (Stock#: 000664) mice were obtained from Jackson Laboratories. αMHC-Cre36, and αMHC-mER-Cre-mER67 transgenes were obtained from M.D. Schneider  and J.D. Molkentin , respectively. Mouse lines were maintained on C57B/6 J background. αMHC-mER-Cre-mER was induced with Tamoxifen  by daily intraperitoneal injection at 30 mg/kg bodyweight for 3 consecutive days. Male and female mice were included in studies performed in pups, studies in adult mice only included males.Animal studies were approved by the animal welfare agency (I.v.D.) of the Royal Dutch Academy of Sciences and Arts (K.N.A.W.) and in compliance with national legislation and institutional guidelines. 68.For ischemia-reperfusion (IR) injury, mice were anaesthetized with a mix of Fentanyl (0.05 mg/kg), Midazolam (5 mg/kg), and Dexmedetomidine (0.125 mg/kg) via intraperitoneal injections. Animals were artificially ventilated during surgery while kept on a heating pad at 38 °C. Buprenorphine was injected subcutaneously as analgesic (0.1 mg/kg) before surgery, after 10 h, and after 24 h. IR was induced by a 1 h ligation of the left anterior descending artery (LAD) by placing a 7–0 silk suture around the LAD together with a 2–3 mm PE 10 tubing. Reperfusion of the myocardium was achieved by cutting the ligature. For sham surgery, a similar procedure was performed with the exclusion of a 1 h ligation of the LAD artery. Permanent LAD occlusion, referred to as myocardial infarction, was performed as described previouslyCardiac function was evaluated by two-dimensional transthoracic echocardiography on sedated, adult mice (2% isoflurane) with a MS400-0257 transducer for adults, or MS700-0112 for pups . Hearts were imaged in parasternal long-axis and short-axis views at the level of the papillary muscles, to record M-mode measurements, determine heart rate, wall thickness, and end-diastolic and end-systolic dimensions. Data was analyzed using Vevo Lab software (version 5.7.1). Fractional shortening  as well as ejection fraction , were used as an index of cardiac contractile function.+, DAPI+) was quantified using Fiji (version 1.53a)69.5-ethynyl-2´-deoxyuridine (EdU) was injected subcutaneous  or intraperitoneal  at 50 mg/kg body weight. EdU was detected in histological sections using Click-iT EdU Cell Proliferation Kit (Thermo Fisher Scientific). EdU incorporation in cardiomyocyte nuclei (Tnnt2Gapdh (mouse) or ARP (human) as housekeeping gene and a minimum of 3 samples per condition as detailed in figure legends.Total RNA was extracted from hearts and iPS-CMs using standard TRIzol extraction (Thermo Fisher) and reverse transcribed using iScript cDNA synthesis kit . Real time quantitative PCR (qPCR) was performed using IQ Sybr-Green supermix , CFX Connect real time PCR detection system (Bio-Rad). Gene expression was quantified using ddCT method with Heart tissue was snap frozen and stored at −80C, then thawed and homogenized using a micropestle, iPS-CMs were collected by cell-scraping in RIPA buffer supplemented with Protease inhibitor cocktail . Samples were denatured in 4x Leammli buffer, supplemented with 2% β-mercaptoethanol for 5 min at 99 °C and subject to SDS-PAGE and western blotting using standard procedures. In case not all samples from one experiment fit one gel, samples of different conditions were distributed equally over multiple gels. Antibodies used are listed in Supplemental Table Hearts were flushed with PBS and fixed with 4% Formalin for 48 h at RT and embedded in paraffin. 4 μm sections were subjected to standard Hematoxylin and Eosin (H&E) or Sirius Red (SR) staining. For immunostaining, sections were subjected to antigen retrieval by boiling 20 min in 10 mM Tris, 1 mM EDTA, pH 9 and slowly cooling down to RT. Sections were blocked with 0,05% bovine serum albumin (BSA) in PBST  for 1 h at RT. Primary antibody incubations were performed o/n at 4 °C, secondary antibody incubations were performed for at least 2 h at RT, using the antibodies and dilutions as listed in Supplemental Table Images were acquired on a Leica SPE or SP8 confocal microscope using LAS X software , or Olympus Slideview VS200. Objectives used for imaging are listed in Supplemental Table 69.Cardiomyocyte cross-sectional area (CSA) was determined using WGA counterstaining in DAPI+ cardiomyocytes by manual tracing of cross-sectioned, nucleated, cardiomyocytes using the ‘measure’ function in Fiji (version 1.53a)Arid1aloxP/loxP controls and 4 αMHC-Cre;Arid1aloxP/loxP mutants using standard TRIzol extraction (Thermo Fisher). Illumina TruSeq stranded mRNA libraries were sequenced single end 75 base pairs on Illumina NextSeq500. Reads were mapped to mouse genome (mm10) and differential expression analysis was performed using DeSeq2 1.24.070. Differentially expressed genes were those with absolute fold change of 1.5 and adjusted Benjamini-Hochberg adjusted P < 0.05. Functional annotation was performed using fGSEA 1.5.2 with molecular signatures database 71.Total RNA was extracted from 4 Arid1aloxP/loxP controls and 2 αMHC-Cre;Arid1aloxP/loxP mutant P7 ventricles as described previously72. Briefly, P7 hearts were minced on ice using scalpels, and taken up in 5 ml 1.5% paraformaldehyde at room temperature (RT). Hearts were homogenized after 5 min using ultraturrax T18 homogenizer (IKA) at setting 4 for 30 sec and incubated for 5 more min at RT. Crosslinking was quenched using Glycine . Nuclei were extracted and chromatin was sheared using an S2 focused ultra-sonicator (Covaris) at 5% duty cycle, intensity 3 and 200 cycles per burst at 4 °C. Immunoprecipitation was performed overnight at 4 °C using anti-H3K27Ac  coated Dynabeads ProtG .Chromatin isolation and H3K27Ac ChIP-Seq was performed on 2 Arid1aloxP/loxP controls and 2 αMHC-Cre;Arid1aloxP/loxP mutant hearts. Reads were mapped to mouse genome (mm10) using Burrows-Wheeler Aligner  and peaks were called using Homer findPeaks (v4.10)73 with setting -style histone, using merged input reads from all samples as input. H3K27Ac signals were detected at the genomic loci of cardiac marker genes, such as Myh6, Tnnt2, Tbx20, and Gata4, at similar levels in replicate samples, validating data reproducibility  gene ontology biological process (GO:BP), using all H3K27Ac regions as background. Heatmaps for Fig. 75 on usegalaxy.org76. Motif discovery was performed using Homer findMotifsGenome package (version 4.11) on gained or lost H3K27Ac regions using all H3K27Ac regions as background.Libraries were generated for input and IP of 2 53 seeded in 10 cm dishes were transfected with 10μg ARID1A expression construct plus 5μg FLAG-YAP, 5μg FLAG-TAZ, or 5μg pcDNA6 empty vector control. 48 h after transfection, cells were lysed on ice in lysis buffer (50 mM HEPES (pH 7.5), 100 mM NaCl, 50 mM KCl, 1% Triton X-100, 5% glycerol, 0.5% NP-40, 2 mM MgCl2, and 1 protease inhibitor tablet) and cleared by centrifugation at 16,000 g for 15 min. Extracts were incubated overnight with anti-FLAG beads  with gentle mixing at 4 °C. Beads were washed three times with lysis buffer on ice, and eluted using 3xFLAG peptide  5 μg/μl in 150 mM NaCl, 50 mM Tris-HCl, pH 7.5. Representative examples of at least 3 independent experiments are shown.Rat heart derived H10 cellsH10 cells were seeded in 24-wells plates and transfected with 0.1 nM siArid1a  or siScr  after 24 h using Lipofectamine RNAiMax (Invitrogen). On day 2, cells were transfected with 100 ng 8xGTIIC-luciferase, 10 ng TK-Renilla, and 6 ng YAP, TAZ or control vector (pcDNA-GFP) per well. On day 4, luciferase activity was measured using Dual-Luciferase® Reporter Assay System  and the Berthold Centro LB 960 luminometer according to manufacturers instructions.KpnI and XbaI sites in pCMV-2xFLAG. psPAX2 and pMD2.G were a gift from Didier Trono . pLVX-eGFP was obtained by cloning PCR amplified eGFP with extended primers containing XhoI (FW) and NotI (RV) sites into XhoI, NotI digested pLVX-IRES-Hygro (Clontech). pLVX-ARID1A was obtained by cloning ARID1A-V5-HIS6 from pcDNA-ARID1A with NheI (compatible with SpeI) and PmeI (blunt) into the 6.9 kb SpeI and MluI (T4 polymerase blunted) backbone of pLVX-IRES-Hygro.pcDNA6-ARID1A was a gift from Ie-Ming Shih (Addgene plasmid # 39311). 8xGTIIC-luciferase construct was a gift from Stefano Piccolo (Addgene plasmids # 34615). To minimize the effect of Hippo pathway-induced cytoplasmic localization of YAP, we used the constitutively nuclear 2xFLAG-YAP-5SA, which was generated by reverting the S94A mutation from Addgene plasmid #33101. 2xFLAG-TAZ was generated by cloning the human WWTR1 coding sequence (NM_015472.6) without ATG into the For directed differentiation towards cardiomyocytes, human induced pluripotent stem cells  were cultured on Geltrex-coated plates in Essential 8 Medium until 80%–90% confluency. Next, cells were cultured in cardio differentiation medium , with 4 μM CHIR99021 .After 48 h, medium was refreshed with cardio differentiation medium with 5 μM IWP2 . After 48 and 96 h, cells were refreshed with plain cardio differentiation medium. From day 8 onward, cells were kept in RPMI-1640-Medium-GlutaMAX-Supplement-HEPES supplemented with B-27 Supplement (50x)-serum free  and spontaneous beating could be observed starting from day 8–10. hiPSC-derived cardiomyocyte (iPS-CM) culture purity was assessed by the percentage of cardiac Troponin T positive cardiomyocytes .HEK293T Lenti-X cells  were transfected with pSPAX2, pMD2.G, and pLVX-eGFP or pLVX-ARID1A to generate virus. Medium was harvested after 48 h, and relative virus titers were determined by qPCR. iPS-CMs were transduced with Lenti-GFP or Lenti-ARID1A by adding the virus to the medium, and incubating for 4 days, when RNA and protein were isolated. Human siRNA against ARID1A (siARID1A) and scrambled siRNA (siScr) were obtained from Origene (Cat# SR305421 / SR30004). iPS-CMs were transfected using Lipofectamine RNAiMax (Invitrogen) at day 0 and again at day 2, cells were collected for analysis at day 4. For EdU analysis, iPS-CMs were seeded onto GelTrex coated coverslips, and subsequently treated with siRNA or Lenti-virus as described. EdU was added to the cells at day 2 and incubated for 48 h. Cells were fixed using 2% paraformaldehyde. EdU incorporation was quantified using positive cell detection in QuPath v0.4.0 .18. In brief, iPSC-derived CMs and human foreskin fibroblasts (HFF)  were mixed at a ratio 70:30, resuspended in Collagen type I  diluted into RPMI 2x  and then cast into wells of an EHM multi-well plate . After 45 min, EHM medium freshly supplemented with TGFβ1  was added, and refreshed daily for the first 3 days. Subsequently, the tissue medium was replaced daily with EHM medium for the entirety of the experimental duration.EHM was generated as described previously38. Data from spontaneously contracting EHM was recorded for at least 2 min at 50 fps at the indicated time-points in a myrImager prototype (Myriamed GmbH). Percent pole bending is reported as a surrogate for force of contraction (F); contraction and relaxation times are recorded from 20 to 80% peak contraction and 20 to 80% relaxation; contraction and relaxation velocities are reported as maximal and minimal dF/dt.Contraction measurements were performed using video-optic analysis of EHM mediated pole bending in a myrPlate-TM5 culture format at 37 °Cn) used in each experiment is indicated in the figure legends. For iPS-CMs, n represents different wells, the number of independent differentiations is indicated with each experiment. For H10 cells, n represents independent experiments, each of which is performed at least in triplicate. Results are presented as the mean ± standard error of the mean (SEM). Statistical analyses were performed using PRISM . For normally distributed data, as assessed by Kolmogorov-Smirnoff test, two-tailed Student’s t-test was used to analyze comparisons between two groups, Mann–Whitney test was used in case normal distribution could not be confirmed. One-way ANOVA with Dunnett’s test for multiple comparisons was used for comparing multiple groups to a control condition. Two-way ANOVA with Sidak’s multiple comparisons test was used to analyze comparisons with two variables. EdU incorporation after IR was analyzed using mean-parametrized Conway-Maxwell Poisson regression analysis as described previously77. EdU labeled cell counts were modeled as a function of location (remote or border zone) and animal genotype (control and Arid1a icKO). The total number of DAPI+ cardiomyocytes was used as an offset value in the regression equation. Analyses revealed that location or genotype alone had no significant effect on counts of EdU labeled cardiomyocytes, whereas the interaction between location and genotype had a significant effect. Factor correction was used to correct for variation between independent differentiations in qPCR, Western blot and EdU experiments in iPS-CMs78. Unless stated otherwise, P < 0.05 was considered significant.The number of biological replicates (Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Reporting SummarySource Data


Phytophthora infestans in nonhost pepper (Capsicum annuum) to identify avirulence effector candidates. Selected effectors were tested against 436 genome‐wide cloned pepper NLRs, and we identified multiple functional NLRs that recognize P. infestans effectors and confer disease resistance in the Nicotiana benthamiana as a surrogate system. The identified NLRs were homologous to known NLRs derived from wild potatoes that recognize P. infestans effectors such as Avr2, Avrblb1, Avrblb2, and Avrvnt1. The identified CaRpi‐blb2 is a homologue of Rpi‐blb2, recognizes Avrblb2 family effectors, exhibits feature of lineage‐specifically evolved gene in microsynteny and phylogenetic analyses, and requires pepper‐specific NRC (NLR required for cell death)‐type helper NLR for proper function. Moreover, CaRpi‐blb2–mediated hypersensitive response and blight resistance were more tolerant to suppression by the PITG_15 278 than those mediated by Rpi‐blb2. Combined results indicate that pepper has stacked multiple NLRs recognizing effectors of non‐adapted P. infestans, and these NLRs could be more tolerant to pathogen‐mediated immune suppression than NLRs derived from the host plants. Our study suggests that NLRs derived from nonhost plants have potential as untapped resources to develop crops with durable resistance against fast‐evolving pathogens by stacking the network of nonhost NLRs into susceptible host plants.Nonhost resistance (NHR) is a robust plant immune response against non‐adapted pathogens. A number of nucleotide‐binding leucine‐rich repeat (NLR) proteins that recognize non‐adapted pathogens have been identified, although the underlying molecular mechanisms driving robustness of NHR are still unknown. Here, we screened 57 effectors of the potato late blight pathogen  This plant immune response against non‐adapted pathogens is known as nonhost resistance (NHR) Heath, . NHR is et al., et al., WRR4 confers resistance against the non‐adapted pathogen Albugo candida when transferred into the susceptible hosts Brassica juncea and B. napus , and several of these effector‐mediated cell death phenotypes were segregated according to Mendelian inheritance –mediated gene expression system  and expressed within 2–3 days after infecting potato  were reportedly overcome by multiple P. infestans isolates  were cloned from pepper genomic DNA based on the pepper genome version 1.55 . Especially, Avr1, Avr2, Avrblb1, Avrblb2, and Avrvnt1 effectors are already known for the corresponding Solanum NLRs. Therefore, pepper NLRs homologous to R1 (CNL‐G3)  were identified from the 1:1 co‐expression screening. However, we did not observe cell death phenotypes using other effectors, even after co‐infiltrating with whole sets of 426 cloned pepper NLRs. One of these effectors was Avrblb2, which consistently induced HR‐like cell death in pepper  was proposed to form receptor networks with sensor NLRs . As described in Wu et al.  , and were previously reported as functional  in qRT‐PCR assays  or CaNRC9a (Ca11g01460) restored the Rpi‐blb2‐mediated HR phenotypes to Avrblb2 and resistance to P. infestans by complementing NbNRC4s, expression of CaNRC4b (Ca11g02410) did not restored HR cell death  and CaRpi‐blb2b (Ca00g87530), induced HR against Avrblb2 only when co‐expressed with CaNRC8 and CaNRC9a, respectively . The CaNRC8/Rpi‐blb2a pair significantly reduced lesion size of Pi T30‐4 and expression level of P. infestans actin compared to the control side of leaf expressing GFP, CaNRC8, or CaRpi‐blb2a  of previously reported NLRs derived from wild  Figures , S3, S12P. infestans effector PITG_15 278 was reported to suppress Rpi‐blb2‐mediated cell death   Figure . Further) Figure . These ret al., et al., CaRpi‐blb1b exhibited a similar level of cell death as Rpi‐blb1 when co‐expressed with IPIO‐4  conferred resistance to adapted Blumeria graminis f. sp. hordei and non‐adapted Puccinia striiformis f. sp. tritici  and Avrpm3 homologues of B. graminis f. sp secalis (Bgs) and dactylidis (Bgd), which are non‐adapted to wheat. In this pathosystem, Svrpm3 of Bgt suppresses Pm3, whereas Svrpm3 homologues derived from Bgs and Bgd cannot suppress Pm3  of P. infestans, have been repeatedly overcome by P. infestans. Therefore, further identification and understanding of NLRs derived from distantly related nonhost plants would provide a promising strategy for developing durable resistant crops against devastating pathogens such as P. infestans. We also expect nonhost plants could be untapped resources for future development of disease resistant crops through biotechnology.In conclusion, our study provide evidence that multiple NLRs mediate recognition of non‐adapted pathogen effectors when the nonhost plant (pepper) is closely related (intrafamily) to the host plant (potato). This result is consistent with previous hypothesis about the relationship between evolutionary distances of nonhost/host plants and the molecular mechanisms of receptor‐mediated nonhost resistance , tobacco , and potato (S. tuberosum cv. Desiree) plants were grown in a controlled chamber at 24–26°C and 40–60% relative humidity with a 16‐h light/8‐h dark cycle.Pepper (Phytophthora infestans isolates (T30‐4 and NL07434) were grown on rye agar plate media in a dark chamber at 17–19°C for 7–9 days. Zoospores were harvested from flooded (with 6–8 mL of TDW) plates after incubating at 4°C for 1 h. Whole plants were sprayed with zoospore solution (5.0 × 104 zoospores/mL) and placed in a growth chamber under the same conditions as described above for plant growth. For the detached leaf assay, the abaxial side of the detached leaves of pepper, potato, and N. benthamiana was inoculated with 11 μL of zoospore solution (1.0 × 105 zoospores/mL) and placed in SPL® rectangular plates with wet tissue paper to maintain 100% relative humidity. Inoculated leaves were incubated at 21–23°C with 16‐h light/8‐h dark cycle until further examination. Detailed information for each experiment is provided in the Supplementary Methods.g), resuspended into Agroinfiltration buffer . Detailed information for each experiment is provided in Supplementary Methods Agrobacterium strain GV3103 containing each construct was cultured in YEP medium for 1 day. Pellets were collected by centrifugation , and transformed into Agrobacterium strain GV3101. Agrobacterium was infiltrated into first and second true leaves of 3‐week‐old N. benthamiana to propagate PVX virions. The uninoculated systemic leaves were harvested, freeze dryed and ground into fine dust at 8–10 days post inoculation (dpi) to prepare virion inoculum. The inoculum powder was suspended in 0.05 m potassium phosphate buffer (pH 7.4), mixed with 400 mesh carborundum, and rubbed on 4‐week‐old CM334 and ECW30R pepper leaves. Inoculated leaves were detached at 7 dpi and destained with 100% ethanol for 2 days to visualize HR phenotypes.Fifty‐seven et al., et al., NLRs containing at least 160 amino acids of the NB‐ARC domain and three major  and minor  motifs were defined as full NLRs. Using these criteria, 426 pepper NLRs Table  were preet al., N. benthamiana version 1.0.1. Known R gene sequences  gathered from the plant resistance gene database (PRGDB) and other published references , NbNRC3 (1–334), and NbNRC4 (1–272) transcripts, with regions covering all functional homologues of NRC2/3/4 .To silence https://github.com/tanghaibao/jcvi/wiki/MCscan) program was used for microsynteny analysis of pepper, potato, and tomato genomes  and Nicotiana benthamiana.Figure S2 Effector screening result via agrobacterium‐mediated transient expression of effectors on CM334 pepper (error bars indicate SEM from 4 replicates for negative cases and at least 18 for positive cases).Figure S3 Detailed (named) phylogenetic tree of screened CaNLRs  and potato NLRs.Figure S4 Expression of pKW:Avrvnt1 (PITG_16294) induced cell death itself in N. benthamiana while p35s:Avrvnt1 was not.Figure S5 Average lesion size data obtained from five independent experiments were presented with 10‐90 percentile box plot.Figure S6 Lesion size of Phytophthora infestans NL07434 is correlated with Pi biomass in transgenic Nicotiana benthamiana with different level of Rpi‐blb2 expression.Figure S7 Decreased lesion size of Phytophthora infestans is correlated with expression level of P. infestans actin in CaNLR‐expressed Nicotiana benthamiana leaves.Figure S8 Maximum‐likelihood (bootstraps = 500) phylogenetic tree of CNL‐G8 NLRs of four Solanaceae species .Figure S9 (A) Amino acid sequences of CaNRC1/2/8/9 and NbNRC2/3/4 were aligned with ClustaW and visualized with BoxShade tool. (B) Reads per kilo base of transcript per million mapped reads (RPKM) of 755 pepper NLRs were obtained from transcriptome data of pepper leaves infected with P. infestans , and presented as a dot plot.Figure S10 Virus‐induced gene silencing (VIGS) of NbNRC2/3/4 significantly compromised known NRC‐dependent sensor (NRC‐S) NLRs ‐mediated HR against each corresponding effector.Figure S11CaNRC4a/b are not expressed as intact from (containing CC‐NB‐ARC‐LRR domains) in CM334 pepper.Figure S12 Phylogeny of Solanaceae CNL‐G1 NLRs. Maximum‐likelihood phylogenetic tree (bootstraps = 500) of CNL‐G1 NLRs of 4 Solanaceae plants .Figure S13 Functional validation of dual‐expression cassette for co‐expression assay (pD35/NOS).Figure S14 Amino acid sequences of CaRpi‐blb2a and CaRpi‐blb2b.Figure S15 Expression of CaNRC8/CaRpi‐blb2a also significantly reduced average lesion size of P. infestans when compared to CaRpi‐blb2 or CaNRC8‐expressed in half of the same leaves of N. benthamiana.Figure S16 Recognition spectrum of Rpi‐blb2, CaRpi‐blb2a, and CaRpi‐blb2b.Figure S17CaRpi‐blb1b‐mediated HR cell death against Avrblb1 is similarly suppressed by IPIO4 compared to Rpi‐blb1.Click here for additional data file.Table S1 Cell death screening results of 57 P. infestans RxLR effectors using PVX‐inoculation method.Table S2 Classification of Cloned 436 pepper NLRs.Table S3 Whole cloned CaNLRs sequences.Table S4 Lists of pepper CNL‐G5, G7, and G11 for co‐expression assay with Avr2, Avrblb1, and Avrvnt1, respectively.Table S5 Lists of known R genes used for phylogenetic analyses.Table S6 Lists of primers used to cloning NRCs.Table S7 Lists of primers used to cloning known R genes and AVR effectors.Table S8 Lists of primers used for qRT‐PCR.Table S9 Converting Id of pepper CNL‐G1 NLRs from 1.55v to 2.0v.Click here for additional data file.Appendix S1 Materials and Methods.Click here for additional data file.