General practitioners (GPs) care for a large number of patients with various diseases in very short timeframes under high uncertainty. Thus, systems enabled by artificial intelligence (AI) are promising and time-saving solutions that may increase the quality of care.This study aims to understand GPs’ attitudes toward AI-enabled systems in medical diagnosis.We interviewed 18 GPs from Germany between March 2020 and May 2020 to identify determinants of GPs’ attitudes toward AI-based systems in diagnosis. By analyzing the interview transcripts, we identified 307 open codes, which we then further structured to derive relevant attitude determinants.We merged the open codes into 21 concepts and finally into five categories: concerns, expectations, environmental influences, individual characteristics, and minimum requirements of AI-enabled systems. Concerns included all doubts and fears of the participants regarding AI-enabled systems. Expectations reflected GPs’ thoughts and beliefs about expected benefits and limitations of AI-enabled systems in terms of GP care. Environmental influences included influences resulting from an evolving working environment, key stakeholders’ perspectives and opinions, the available information technology hardware and software resources, and the media environment. Individual characteristics were determinants that describe a physician as a person, including character traits, demographic characteristics, and knowledge. In addition, the interviews also revealed the minimum requirements of AI-enabled systems, which were preconditions that must be met for GPs to contemplate using AI-enabled systems. Moreover, we identified relationships among these categories, which we conflate in our proposed model.This study provides a thorough understanding of the perspective of future users of AI-enabled systems in primary care and lays the foundation for successful market penetration. We contribute to the research stream of analyzing and designing AI-enabled systems and the literature on attitudes toward technology and practice by fostering the understanding of GPs and their attitudes toward such systems. Our findings provide relevant information to technology developers, policymakers, and stakeholder institutions of GP care. As artificial intelligence (AI) enabled systems have surpassed human performance in different aspects of economy and society, the increasing technological maturity and widespread applicability of such systems is leading to skyrocketing expectations . The tecThere are two forms of CDSSs in health care: knowledge-based systems and non–knowledge-based systems. Knowledge-based systems match their knowledge base with individual patient characteristics and make decisions based on preformulated rules ,17. As sAI-enabled systems have already successfully entered various subdisciplines of health care, such as image recognition, diagnosis, and precision medicine . Most AIRegarding these promised benefits, AI-enabled systems have particular potential in the field of primary care. General practitioners (GPs) serve as the first point of medical contact and therefore must diagnose with high levels of uncertainty and under high time pressure. For instance, in Germany, primary care is one of the most frequently used health care services, leading to an average physician–patient contact time of 7.6 minutes . MoreoveTo prevent these risks, the health care system depends on innovative, reliant, and fast approaches to decision-making processes in GP care . ConsideAlthough AI-enabled systems in primary care diagnosis are gradually becoming feasible and useful, their widespread implementation still remains a future scenario ,34. AmonA key driver for successful implementation and uptake of AI-enabled systems is the attitude of physicians. By discussing our findings on GPs’ attitudes toward AI-enabled systems within a facilitating context for practical implementation, we extend the previous work of Blease et al , who recWe understand attitude as a psychological tendency that determines how GPs evaluate their favor or disfavor against AI-enabled systems . FollowiHowever, researchers acknowledge that behavioral intention  does not always correspond to the feelings  and opinions  . This chExtant work describes behavioral intention as the mediator in the relationship between attitude and behavior ,47. ThusDifferent quantitative studies have investigated the relationship between attitude and the intention to use AI-enabled systems, for example, regarding medical students ,52. In tDespite the relevance of AI technologies in the health care sector, a profound understanding of GPs’ attitudes toward AI-enabled systems and their underlying determinants is still lacking. Therefore, the purpose of this study is to investigate which determinants influence GPs’ attitudes toward AI-enabled systems in diagnosis. We see this as an important step in developing user-centered solutions, which will positively affect the intention to use and support the successful introduction of AI-enabled systems in primary care.To identify the determinants of GPs’ attitudes toward AI-enabled systems in diagnosis, in-depth insights are vital. Following the interpretative paradigm, qualitative methods were used to obtain an understanding of individuals’ technological attitudes in the medical context . Thus, wconceptual depth proposed by Nelson  because it will come. Because without [AI technology] [diagnosis] is not possible.Participant 1The participants associated expected time effort with the use of AI-enabled systems in routine diagnoses owing to the necessary AI technology integration into an established and effortless routine process. Therefore, the participants limited the scope of its application to cases of rare diseases and to cases in which the physicians could not reach a diagnosis without additional help (interview 8).concerns and expectations. In addition, we found that the environmental influences and certain individual characteristics influenced their attitudes. Whenever GPs stated that AI-enabled systems must meet certain requirements for them to consider using it, we categorized them as minimum requirements of AI-enabled systems. When grouping the statements, we paid particular attention to the wording and syntactic differentiation that the physicians used in their answers. The interview data revealed 5 main categories that summarize the influencing determinants of GPs’ attitudes toward AI-enabled systems in diagnosis. When we raised questions on potentially using AI-enabled systems in clinical practice, the GPs had various Concerns include all doubts and fears concerning AI-enabled systems. Overall, this category consists of four concepts: (1) existential anxiety, (2) change of the physician–patient relationship, (3) misuse of data, and (4) diagnostic bias.existential anxiety connected with AI-enabled systems as they perceive that this technology can take over some of their tasks. GP 2 said the following:Half  of the participants expressed At one point, the own decision and the own expertise threatens to be pushed into the background or to become redundant.Participant 2GP 14 also perceived the threat of being replaceable by AI-enabled systems and provided an example of an AI-enabled system that has achieved higher diagnostic accuracy than physicians. This concept included the fear of no longer being useful and being replaceable by AI-enabled systems and the worry of losing their unique status as physicians. A participant said the following:Surely, many doctors probably see their unique medical status endangered, that they are under the surveillance of others, that they think there is a bit of an attack on their own vanity.Participant 12The participants mentioned that AI-enabled systems could threaten the physician–patient relationship. Endangerment of this relationship, which fundamentally defines GP care, further compromises appropriate patient care (participant 3). As patients could feel that the AI-enabled system performs the treatment, the physicians assumed that the use of AI-enabled systems might negatively impact the physician–patient relationship (participant 11). In this regard, a participant mentioned the following:Since [the patient] has the feeling [...] that the machine takes care of it and the doctor would only have to put his signature under it.Participant 11Participants mentioned the impairment of the physician–patient conversation through the use of technology as threatening to the physician–patient relationship. The concern is that, by using AI-enabled systems during patient consultations, a GP cannot devote all their attention to the patient sitting in front of them, but instead must also focus on the screen to follow an AI-enabled system’s recommendations. A participant commented the following:[The treatment] may drift off into a standardized interview, and that’s probably not necessary.Participant 12GP 13 was concerned that AI-enabled systems would generally reduce physician–patient contact, which is a core component of GP care and is inevitable for successful treatment and patient care. The potential endangerment of the physician–patient relationship by the use of AI-enabled systems was also often linked to misuse of data.misuse of data is a key concern and impacts GPs’ attitudes toward AI-enabled systems. In this context, GP 3 saw the problem in the connection between AI-enabled systems used in practice and the interconnectedness between these systems and the internet:With the use of AI-enabled systems and the disclosure of both the patients’ and physicians’ data, [AI-enabled systems] are not stand-alone systems but are networked, and [...] actually, work over the internet with such simple things as voice recognition. And in my view, this will change the doctor-patient contact considerably. [...] I consider the fundamental trust in the patient-physician-conversation [...] to be a very important basis for our work. And I also see [the trusting relationship between the patient and the physician] as being in danger due to the increasing use of such procedures. I find this very worrying.Participant 3Internet access makes the data accessible and renders the patient and physician transparent, thus violating data privacy and leading to serious consequences for patients. A participant described it as follows:Patient data are very sensitive data. Disease data are very sensitive data. [There is the risk that] they are passed on somewhere, that some authorities who have nothing to do with it or should have nothing to do with it could intercept the data and use this to the disadvantage of the patients.Participant 11Thus, the physicians are concerned about the data being misused by other stakeholders as supported by GP 4:The problem is that large companies use AI to gain access to lucrative patients and to control them via AI.Participant 4Further, GP 3 warned of the danger of pharmaceutical companies programming AI-enabled systems for their purposes, referring to medication proposals that are not medically indicated but instead deliver a monetary benefit for the producing company. They justified this concern with experiences from working with other technologies (participant 3). This concept also summarizes physicians’ concerns about being monitorable and controllable at work when using AI-enabled systems. Owing to connection to the internet, GP 10 assumed that every step of physicians will be transparent and can be monitored. However, the GPs did not explicitly mention who would have interests in observing and controlling them.diagnostic bias, whereby the technology influences the GP’s decision-making in ways that can negatively affect the course and success of treatment. Once a GP has received suggestions from an AI-enabled system, they may not consider further possible diagnoses (participant 11). In this context, GP 8 spoke of the fear of being put on a completely wrong track and the likelihood that the AI-enabled system indicates a diagnosis that does not fit and therefore leads a GP to mistreat the patient. A frequent concern was that physician might become overreliant on the technology, neglecting their own medical and experience-based knowledge. Furthermore, the participants also mentioned the risk of overexpansion of treatment services as supported by participant 17:According to the participants, AI-enabled systems can cause The AI will recommend examinations that I would personally put last, ie. it will possibly lead to so-called device medicine, involving a lot of safeguard diagnostics, which I consider to be quite questionable.Participant 17concerns, we also found expectations to be determinants of GPs’ attitudes. This category reflects GPs’ thoughts and beliefs about AI-enabled systems’ expected benefits and limitations regarding GP care. Although the expected benefits had a positive connotation in the interview data , the expected limitations depicted a negative perspective (concepts encompassing statements relating to a lack of human competencies and time expenditure).Besides Diagnostic quality represents the expectation that AI-enabled systems can improve the quality of care via more accurate and precise diagnosis. It is GPs’ job to provide patients with the best possible care, which is why the expected benefits of AI-enabled systems positively influenced the GPs’ attitudes. Especially in rare diseases, which GPs do not regularly treat, the expectation from AI-enabled systems is an improvement of diagnostic quality as AI-enabled systems can work with a larger database than the human brain (participant 18). Thus, AI-enabled systems should act as support or a backup for the physician, in parallel or after a medical diagnosis. GP 12 assumed that AI-enabled systems could assist GPs in the decision-making process and thought that this would positively impact the outcome quality:But for rarer diseases, when it comes to making a diagnosis; for example, a red skin spot that I can’t classify at all, then it would be conceivable [...] to reaffirm or reassure oneself [by means of AI].Participant 12Furthermore, the expectation from an AI-enabled system is that it is more enduring than humans. Unlike a physician, an AI-enabled system does not tire and its diagnostic quality does not suffer from human-like, lower-concentration performance during the course of a day. A participant said the following:If AI is well programmed or if there are no failures in it, then AI is more accurate than a person, who is sometimes tired [and thus] makes bad decisions.Participant 2diagnostic efficiency. GP 2 transferred the time advantages of using AI-enabled systems to the area of image recognition and expected AI-enabled systems to be 3 times faster than a physician:Besides the expected diagnostic quality, the participants stated that an AI-enabled system’s ability to make rapid diagnoses is a further expected benefit. We refer to this expectation as While a radiologist might manage 60 diagnostic findings a day, the AI could work day and night and deliver perhaps 180 or 200 findings. And if that happens with similar quality, then [...] you could examine many more patients than a human alone could.Participant 2On the basis of this benefit of AI-enabled systems, GP 14 expected the use of AI-enabled systems to influence disease progression positively. In addition, GP 1 emphasized the necessity of fast-working AI-enabled systems in the detection of health threats:Now a completely new virus has appeared in China or Japan, and to get ahead of it, you need artificial intelligence which can detect [the virus] much faster.Participant 1Diagnostic efficiency included the GPs’ expectations regarding physician support via AI-enabled systems, reducing the daily workload by preselection (participant 7) and patient prioritization (participant 13). This time-saving effort would give GPs some relief and would allow them to concentrate on more serious cases (participant 7).Legal liability included the expectation that AI-enabled systems will give GPs legal backing. All decisions will be documented using AI-enabled systems, allowing the providers to prove the correct decision-making approach in a legal proceeding (participant 12). Furthermore, the participants added the assumption that AI-enabled systems could support the physician’s choice of treatment. In this context, GP 13 mentioned the following:[With] AI, you can then understand how [the physician] came to a decision because AI said the risk was 0.001.Participant 13toward the AI-enabled system (participant 4).This was supported by the expectation of built-in legal protection and shifting responsibility from the GP expectations depicted the perceived limitations of AI-enabled systems. The expected lack of human competencies in AI-enabled systems was mentioned with a high emphasis. It included the GPs’ assumption that AI-enabled systems do not have certain human competencies, which are, in fact, crucial for adequate and appropriate treatment in GP care. The respondents agreed that AI-enabled systems will not—some said never—be able to have certain human competencies. In this context, empathy (participant 5), intuition (participant 1), gestures (participant 13), experience (participant 12), and clinical reasoning ability (participant 3) were mentioned. These competencies are important in GP care to collect all relevant information to be able to provide optimal care. Of the 18 participants, 2 (11%) participants said the following:Besides the above-mentioned positive determinants, the following There is something behind almost every illness that makes [diagnosis] even more challenging. And if this is not considered, it will not be possible to help a patient comprehensively. And I think [AI] can probably not do this.Participant 5Experience can hardly be replaced by AI. Experience and intuition. And empathy. This is just how I treat people, to get something out of them. So, this is something that defines a good physician and cannot be replaced by AI. Empathy.Participant 1Furthermore, describing and verbalizing much of the information collected in GP care (such as mimics or gestures) is not always possible. However, it is an essential data input for the proper operation of any technology (participant 13). Participants expressed that many patients just make an appointment to have some human interaction, for instance, lonely older patients. GP 15 explained this as follows:My experience every day with patients is that they want to be touched, and they want to look you in the eyes.Participant 15For them, AI-enabled systems seemed to be unable to fulfill these needs. In the context of human competencies, GP 13 underlined AI-enabled systems’ limitations:People are certainly beaten by [AI] in many ways. But not in the emotional one.Participant 13Time expenditure included the expectation that in most cases, GPs would need more time for the decision-making process by involving AI-enabled systems, because in routine cases, GPs usually diagnose on their own within seconds. In this context, participant 11 commented the following:[...] in routine cases, [AI] would not be a time saver for me.Participant 11With AI-enabled systems, additional effort is expected by the participants because they fear that data must be entered in the documentation and fed into the AI-enabled system. GP 2 assumed additional time expenditure owing to a person’s need to critically reflect on the results of the AI-enabled system.environmental influences that influence GPs’ attitudes toward AI-enabled systems. The summarized determinants include influences resulting from an evolving working environment (changing working conditions), the perspectives and opinions of key stakeholders (stakeholder influences), the available IT hardware and software resources (IT infrastructure), and the media environment (media).Besides the 2 main categories, we also identified Changing working conditions included GPs’ perspectives on the challenges caused by demographic change (participant 10), a changing spectrum of diseases (participant 1), and the constant increase in medical knowledge (participant 3). Regarding demographic change, GP 1 stated the following:The lack of physicians comes with giant steps, and what is also urgently needed is telemedicine. And this, of course, needs AI with it.Participant 1However, demographic change also included the necessity to modernize a practice’s equipment with new technologies to be interesting for younger physicians (participant 10). AI-enabled systems were also considered necessary to stay updated about the increasing medical knowledge and provide the patients with the best and latest information about their health care (participant 3). Regarding the changing spectrum of germs and viruses and the resulting need for AI-enabled systems, GP 1 referred to the outbreak of the COVID-19 pandemic.stakeholder influences, which indicated how certain groups of people and organizations influence GPs’ opinions. The interviews revealed that patients and institutions are key stakeholders in this context. GP 7 said the following:Another environmental influence was I think we can be influenced [by the patients’ opinions] because, in the end, a medical practice follows the market like a small business. If the patients want [AI technologies] and demand [AI technologies], more and more practices will offer it.Participant 7However, the GPs also stated that they do not expect patients to disapprove of AI-enabled systems (participant 11). In contrast to the patients’ opinions, the GPs agreed that the opinions of institutions such as the German Society of General Medicine (Deutsche Gesellschaft für Allgemeinmedizin und Familienmedizin) or the German General Practitioners Association (Deutscher Hausärzteverband) have key roles in the formation of German GPs’ attitudes. GPs place trust in these institutions and regard them as scientific and validated committees of their profession (participant 11). Supported by the fact that physicians wish to receive more recommendations on which technologies they should use in practice, the influence of these institutions’ attitudes is evident (participant 7).media referred to all informative sources in which physicians had heard or read about AI-enabled systems. As most participants had not yet worked with AI-enabled systems, we assume that the media strongly contributes to AI literacy, which describes what GPs believe AI is and can do. A participant said the following:The concept of Except for what I have read about it in medical journals, [I hardly come in contact with AI].Participant 11GP 14 suggested that physicians should be informed about AI technology via regular journal articles.information technology infrastructure in physicians’ practices. In the event of technical problems, AI-enabled systems cannot be used properly or at all, which can undermine optimal patient care. Physicians are skeptical about AI-enabled systems in this regard and prefer the established ways of performing their routines, as they cannot rely on the overall infrastructure, which needs integration of AI technologies to function properly. In this context, a participant mentioned the following:Another factor that influenced attitudes was the often-inadequate If my system goes down, my AI is on standby, then sorry, I can’t diagnose, my system strikes out. That is why it’s nice to be able to write down with a pen on paper what a patient has and has received.Participant 16Environmental influences are external influences, whereas individual characteristics are determinants that describe a physician as a person and include character traits, demographic specifics, and knowledge. Although there are many individual characteristics, we found that age and affinity with technology are particularly relevant to the GPs.age disagreed on whether it has a role in determining their attitudes. GP 10, an older physician, said the following:The participants who mentioned I am convinced it needs much work because there is certainly much resistance, which clearly depends on age.Participant 10Whereas GP 11, a younger physician, stated the following:I also know young colleagues who are my age, and they also have strong reservations [regarding AI].Participant 11age as a relevant characteristic and leave future research endeavors to challenge its influence on a larger scale.Thus, we included affinity with technology, which indicated whether being open to new technologies supports a positive attitude toward AI-enabled systems. A participant said the following:Another influencing factor was Well, there are also people in my generation who were already technically inclined [...]. So, I think that’s the key to why people [would use AI] or not.Participant 18the minimum requirements of AI-enabled systems, which are preconditions that must be met for GPs to contemplate using AI-enabled systems. Although many of the requirements were thematically related to expectations and concerns, our qualitative data collection allowed us to distinguish between the attitude determinants and the essential and must-have criteria. We will now explain the 6 identified minimum requirements and underline their intensities with statements from the interviews.Besides the above-mentioned categories and concepts, the interviews also revealed time efficiency. GPs need AI-enabled systems to be fast and easy to use, as they have limited time for each patient consultation. A participant mentioned the following:Most participant statements that expressed demands of AI-enabled systems contributed to the minimum requirement of First of all, [AI] should be fast. There is always time pressure.Participant 14Also, participants stated that AI-enabled systems must not take additional time, as this would keep a physician from performing essential tasks (participant 15). Thus, the focus was also on practical relevance and system compatibility with existing practice ISs. The participants demanded a self-explanatory design that can be operated quickly and in a few simple steps. The time component’s importance in the use of AI-enabled systems was shown by GP 15, who had already tested an AI-enabled system and decided against further use stated as follows:[...] [the use of AI] took me far too longParticipant 15diagnostic quality was mentioned as another key requirement of AI-enabled systems. For physicians to consider the use of AI-enabled systems, the AI-enabled system must be validated, must not make mistakes, and must provide accurate diagnoses so that there is no threat to patient care (participant 7). Furthermore, some participants demanded accurate diagnoses and even better results through AI-enabled systems compared with human engagement, because otherwise, AI-enabled systems would be obsolete (participant 2). In addition, AI-enabled systems must be evidence-based and must follow guidelines. In this context, GP 10 said the following:Besides the time components, [AI must be] scientifically grounded and must provide validated results that [the physician] may not be able to produce in their entirety.Participant 10data security as a requirement for using AI-enabled systems. The physicians justified this requirement with concerns about privacy and misuse of data and they do not want patient and physician data to be accessible to anyone. A participant explained as follows:Participants also named guaranteed Of course, it is also important to me that there is corresponding data security. I do not want the patients and us to be completely transparent. That is certainly not in the overall interest.Participant 10Data security issues were the second reason along with time expenditure that made GP 15 decide to refrain from further using that AI-enabled system.Economic viability summarized the statements regarding AI-enabled systems’ affordability and questions about financing them. In this regard, GP 2 mentioned the following:If they are affordable [then I would use AI applications].Participant 2Furthermore, the participants expressed their willingness to use AI-enabled systems based on how the technology is financed and stated that the cost–benefit ratio must be consistent.Transparency and thus the comprehensibility of AI algorithms is another key requirement of AI-enabled systems. To trust AI-enabled systems, it was important to the GPs that the proposals submitted by the AI-enabled system are comprehensible. Thus, a participant said the following:I must know how [AI] obtains information and how [it] works.Participant 11Autonomy represented another requirement, indicating that an AI-enabled system must be self-managed by the providers. Using the technology is feasible only if a physician can continue to work autonomously and the next treatment steps are not decided by an AI-enabled system. However, the participants had a negative attitude toward intervention in a physician’s self-determined work. A participant explained as follows:I would participate only [on a] voluntarily [basis].Participant 15attitude determinants regarding AI-enabled systems in GP care and the relationships among these determinants. We conflate our findings to propose a model  the identified expectations, which picture GPs’ beliefs toward AI-enabled systems and address the cognitive component; and (3) the identified minimum requirements of AI-enabled systems, which are preconditions that must be met for GPs to contemplate using AI-enabled systems and address the behavioral component of attitude.Our attitude determinants rg et al . The datconcerns and expectations form the minimum requirements of AI-enabled systems. This approach is consistent with the 2-component model of attitude, which indicates that the affective and cognitive components explain the behavioral intention [diagnostic quality and time efficiency to be the most important minimum requirements of AI-enabled systems. Obligations to use AI-enabled systems by regulations or by superiors are neglected in this assumption.However, as the relationships among these 3 determinants lack consistency and dependency according to our findings, we could not confirm the 3-component model . Insteadntention . For insntention ,72. Anotntention  and an antention . Thus, Gntention . In partntention . GP consntention . Besidesntention . These intention . Consequntention . On the concerns and expectations to be interrelated and form a construct of attitude. For instance, the participants’ concern of being replaceable was caused by their perception of AI-enabled systems formulating more accurate diagnoses than physicians. However, most of our participants did not fear being replaceable, as AI-enabled systems are unable to have and perform human competencies such as empathy and clinical reasoning. Similar conclusions were made by Oh et al [However, we found not only minimum requirements of AI-enabled systems to be influenced by concerns and expectations but also Oh et al  who condOh et al . FurtherOh et al . EspeciaOh et al . In summOh et al . ConsideOh et al . EnablinOh et al , underliindividual characteristics and environmental influences to determine GP’s attitude toward AI-enabled systems. Regarding individual characteristics, our results for the influence of GP’s age are inconclusive. The GPs in our sample presumed that both old and young physicians would have a negative attitude toward AI-enabled systems. However, both old and young participants in our sample generally had a positive attitude toward AI-enabled systems. As this may be owing to a bias in our sampling, we encourage further examinations of age as an attitude determining individual characteristics. Regarding environmental influences, our respondents indicated that a positive attitude from institutions such as the German General Practitioners Association would positively impact their attitudes toward AI-enabled systems. Moreover, GPs’ individual context such as office size and facilities  might prove themselves in further studies as determinants for GPs’ attitude toward AI-enabled systems. By uncovering individual characteristics and environmental influences as attitude determinants, we found similarities to the factors social influence and age of the UTAUT. Albeit, in the UTAUT, these determinants influence the intention to use [We also found n to use ,53,54. Hn to use  concludeFurthermore, our findings explicate that the consideration of the affective component of attitude is crucial in the medical context despite being neglected often in well-known theories of behavior and acceptance research ,82,83. Ochange of the physician–patient relationship and thus, the diminution of restraints is possible. For this purpose, the distribution of evidence-based information via GP-specific journals and the involvement of advocacy groups are highly recommended, as the GPs value their viewpoints. However, it is also important that potential users are not only informed about the potential of AI technology but also about its limitations and shortcomings on the basis of evidence. In this way, physicians can be empowered to use AI-enabled systems in a reflective manner and thus, for example, prevent automation bias.Besides theoretical contributions, we derived valuable implications for practice by reflecting on GPs’ attitudes before the use of AI-enabled systems and familiarization with the technology. We suggest making the topic of AI more prominent in politics, health-related associations, and stakeholder institutions of GP care. Via these institutions, knowledge and education on AI-enabled systems can be offered, thus improving GPs’ AI literacy. This allows for the mitigation of concerns such as the minimum requirements of AI-enabled systems are of particular interest concerning the practical implications. First, AI-enabled systems must be programmed and designed to make its use as easy and fast as possible as stated by participants and widely spread in the literature on user-centricity [Moreover, the identified ntricity . Second,ntricity  is the pntricity .black-box-medicine, as decisions are less transparent to the patient and to the GP. With this lack of transparency, various types of biases may occur, both for the end users and the AI-enabled system. Such biases may result in patient security, data, and privacy concerns [Furthermore, AI-enabled systems may foster so-called concerns ,90. Therconcerns , physiciFurthermore, AI-enabled systems should be developed to diagnose rare cases because GPs assume that they are faster in routine cases than using AI technology. This information can help developers to narrow the application area and to create better-fitting software solutions. This result also indicates that integrating AI technology is not the solution for every problem. Rather, a critical assessment must be made regarding when using an AI-enabled system makes sense and improves decision-making and when this is not the case. Especially when it comes to human competencies and interpersonal relationships, AI-enabled systems cannot replace GPs. Rather, AI-enabled systems should be designed to free up GPs’ time so that they have more time to nurture relationships with their patients, which is of particular relevance for diagnosis in GP care. Our findings may serve for a better understanding of how to design AI-enabled systems in a conducive manner and how to foster GP’s acceptance in the later adoption of such systems.Although we rigorously followed our designed research approach, our study has limitations, some of which are bound to the choice of a qualitative-explorative approach. By design, qualitative interviews do not focus on drawing conclusions for entire populations, which affects the generalizability of the results. Nevertheless, a qualitative approach is appropriate before a quantitative study when dealing with a new and emotionally charged topic. This approach is reinforced by recent research that puts traditional IS adoption models to the test for AI; thus, calling for in-depth reflections . Blease We further suggest examining the role of the affective attitude component, because we revealed the importance of the identified concerns in our study; whereas, in well-known theories of technology acceptance, this component is often neglected. A closer examination of the affective component will make it possible to determine the extent to which it is relevant in the medical and IS contexts.concerns, expectations, and minimum requirements of AI-enabled systems. Furthermore, we revealed individual characteristics and environmental influences as the 2 conditional determinants of GPs’ attitudes toward AI-enabled systems. The findings emphasize the importance of attitude’s affective component at the interface of medical and AI research. Moreover, the findings show that diagnostic quality and time efficiency are mandatory for GPs to even consider the use of AI-enabled systems. Therefore, integrating user groups’ attitudes and needs is a fundamental prerequisite for user-centered design, which leads to a higher willingness and inclusion of the systems into everyday use. Considering that the GPs in our interview study predominantly corroborated AI-enabled systems’ seminal role in the future of GP care, our findings may serve as a foundation for future research. Besides investigating the attitudes of user groups in other fields in the health care system, research endeavors should also focus on how the attitudes of GPs toward AI-enabled systems can be proactively promoted. In addition, future work should include and conflate findings from related research areas such as human-computer interaction, psychology, sociology, and computer science to account for AI’s interdisciplinary implications for health care.AI-enabled systems are considered as promising solutions to enhance both the effectiveness and quality of health care. Especially in GP care, which is the first point of contact for most medical needs, physicians deal with a shrinking physician–patient time and incomplete or sometimes incorrect information. Here, AI technology promises new solutions to support physicians and decrease diagnostic errors that lead to extensive consequences. Although the application potential of AI-enabled systems in health care has been widely discussed theoretically and conceptually, a widespread application in the professional practice of GPs is still dreams of the future. To tap the undisputed potential of AI-enabled systems in practical use, a fundamental investigation of the technical systems and social actors is required. As academic research, in this respect, is still in its infancy, we investigated the attitudes of GPs toward AI-enabled systems. Thereby, we seek to contribute to a better understanding of GPs’ attitudes, which is crucial for developing and implementing suitable AI-enabled systems. Thus, we used in-depth qualitative-explorative interview data with German GPs and proposed a preliminary research model. We identified three determinants of GPs’ attitudes:


Double-stranded RNA (dsRNA) is produced both by virus and host. Its recognition by the melanoma differentiation–associated gene 5 (MDA5) initiates type I interferon responses. How can a host distinguish self-transcripts from nonself to ensure that responses are targeted correctly? Here, I discuss a role for MDA5 helicase in inducing Z-RNA formation by Alu inverted repeat (AIR) elements. These retroelements have highly conserved sequences that favor Z-formation, creating a site for the dsRNA-specific deaminase enzyme ADAR1 to dock. The subsequent editing destabilizes the dsRNA, ending further interaction with MDA5 and terminating innate immune responses directed against self. By enabling self-recognition, Alu retrotransposons, once invaders, now are genetic elements that keep immune responses in check. I also discuss the possible but less characterized roles of the other helicases in modulating innate immune responses, focusing on DExH-box helicase 9 (DHX9) and Mov10 RISC complex RNA helicase (MOV10). DHX9 and MOV10 function differently from MDA5, but still use nucleic acid structure, rather than nucleotide sequence, to define self. Those genetic elements encoding the alternative conformations involved, referred to as flipons, enable helicases to dynamically shape a cell’s repertoire of responses. In the case of MDA5, Alu flipons switch off the dsRNA-dependent responses against self. I suggest a number of genetic systems in which to study interactions between flipons and helicases further. Ifih1) in modulating the formation of Z-RNA by Alu elements (named for the restriction enzyme that cuts twice within them) to prevent activation of immune responses against host-encoded double-stranded RNAs (dsRNAs). I then describe ways that other helicases, like DExH-box helicase 9 (DHX9) that has a preference for Alu sequences, and Mov10 RISC complex RNA helicase (MOV10) that is also involved in self-recognition, potentially modulate nucleic acid conformation to dynamically revise a cell’s response repertoire as the context changes.Recent findings highlight the central role of Z-RNA in regulating innate immune responses. Here, I describe the discovery of Z-DNA and Z-RNA and of the genetic evidence for their involvement in type I interferon responses. I highlight the important part played by the helicase melanoma differentiation–associated gene 5  [Adarb1), that lacked a Z-binding domain. When an edited allele was hardwired into the mouse genome, Adarb1 knockouts had no phenotype, arguing that recoding of Gria2 mRNA by this enzyme mostly compensated for a deleterious mutation [An important step in understanding the biology of the left-handed Z-conformation was the identification of the Z-DNA specific, Zα binding domain in the dsRNA-specific editing enzyme ADAR1  . This en humans) . The ear humans) . Subsequmutation . The stomutation . They bomutation . The celmutation  or phospmutation .As methods to analyze RNA transcriptomes evolved, it became apparent that transposable elements (TEs) were the major ADAR1 substrates, with few recoding events. The editing substrates most often arose from pairs of TE repeats orientated in opposite directions, enabling transcripts containing both to form dsRNA by base pairing with each other ,21. The Adar knockout mice that lack both p150 and p110, or are homozygous for an editing null Adar variant, died by day 14 of development [Ifih1), a helicase that signals through the mitochondrial antiviral signaling (MAVS) protein (encoded by Mavs) pathway to induce interferon responses  in mice ,45. The The authors examined edited RNAs in the p150 only mouse. Overall, RNA editing is dramatically decreased, especially in the brain, occurring at 2% of wild-type levels. Most edits are in the 3′ UTR with very few in introns. Recoding of the known substrate AZIN1 is observed, most likely due to formation of a dsRNA substrate within the exon sequence . TogetheCollectively, the findings show that p150 is a negative regulator of type I interferon responses and that this outcome is dependent on a functional Zα domain. They demonstrate that one role for Z-RNA is to protect against interferon induction by self-RNAs . Here, M6-methyl adenosine marks restrict MDA5 filament formation [Alu inverted repeats (AIRs) are the most common sequences in the human genome that form dsRNA. They contain a highly conserved sequence block, referred to as a Z-Box, that is prone to Z-formation . That thormation , potentiormation .Z-RNA formation by AIRs provides a way to distinguish self-RNAs from those of pathogens. Their Z-Box is highly conserved, supporting the proposed role in modulating immune responses against self. The data that p150 is key to negatively regulating interferon responses and that loss of function variants the Zα domain are associated with type I interferonopathies provide strong evidence for this mechanism. The model incorporates many elements of previous proposals on how MDA5 discriminates nonself from self that are based on the localization, structure, and availability of dsRNA –58. NoneDoubts have been expressed about whether it is the length of the dsRNA or some other feature that is important. Some higher ordered structure in long dsRNAs appears necessary to activate MDA5 . MDA5 shSo, can viruses and other invaders exploit this mechanism to masquerade as self or to promote their own replication? They can and certainly do. Many viruses produce Z-binding proteins  that canIt is reasonable to ask whether MDA5 is unique in promoting self-recognition or is this a more general property of helicases. There are 117 different helicases annotated in the human genome  that difHelicases can regulate Z-formation in different ways. Like MDA5, one action of helicases is to change the twist of their double-stranded substrate. The term twist measures the rotation of one base relative to an adjacent one. A change in twist affects dsRNA and dsDNA differently. While twisting decreases the length of a dsRNA helix , dsDNA hCurrently, there is insufficient information to say for sure whether other helicases induce Z-formation, but given the diversity of helicases and the available data, it is likely. One characteristic of the Superfamily 2 (SF2) helicases, including MDA5, is that they do not slide along a duplex. Instead, they clamp onto their substrate, initiate local duplex unwinding, hydrolyze ATP as they release, then reattach ,91. One Besides DHX9, other DEAD box RNA helicases like DDX3, DHX29, DHX36, and DDX60 regulate type I interferon production during viral infection . They foMOV10 is a 5′ to 3′ processive helicase, belonging to a different helicase family (SF1) than DHX9 and MDA5 . ExpressMOV10 also inhibits LINE L1 retrotransposition. Knockdown of MOV10 promotes in vivo L1 mobilization, which involves formation of DRH . The DRHJust as phage screens advance the understanding of codon biology, we are now in the position to develop analogous genetic approaches to understand how flipons operate. The MDA5 system represents a minimal flipon system with only 3 major components: MDA5, the Zα domain, and the flipon. Each partner can be manipulated to explore the role of environmental perturbations and base modifications in outcomes. The system is amenable to genetic screens for discovering response modifiers. Mouse models with either gain or loss of functions MDA5 and Zα variants are available for in vivo studies to better understand the role of Alu Z-flipons in disease . The LINThe induction of Z-RNA formation in AIR elements by MDA5 offers a general approach for the recognition of self-transcripts and for the assembly of complexes essential to ADAR1 p150-dependent RNA editing, stability, splicing, and translation of RNAs. Other helicases also have the potential to moderate formation of alternative structures like Z-RNA, Z-DNA, and quadruplexes with different outcomes than those produced by MDA5. The repeat sequences involved and the events leading to the flip may differ . For helThe p150 isoform of the double-stranded RNA (dsRNA) editing enzyme ADAR1 is a negative regulator of the type I interferon response induced by the dsRNA sensor melanoma differentiation–associated gene 5 (MDA5).MDA5 forms a filament on dsRNA, assembling in a patchwise fashion. The helicase twists and shortens the dsRNA segment as it binds. This process induces topological stress in the unbound dsRNA between the MDA5 patches.The stress can be relieved by flipping A-form dsRNA, with a helical length of 24.6 Å, to the longer Z-RNA helix that has a helical length of 45.6 Å. The transition triggers MDA5 release and ATP hydrolysis.The Z-RNA that is formed localizes ADAR1 p150 to the dsRNA through its Zα domain.The dsRNA bound by MDA5 and targeted by ADAR p150 is often formed by base pairing of inverted repeat elements that fold back on themselves.The inverted repeats also contain sequences, called flipons, that form Z-RNA under physiological conditions. By forming Z-RNA, the repeat elements dissociate MDA5 filaments and localize editing by ADAR p150 to host transcripts, thus preventing the activation of interferon responses targeting self dsRNAs. These repeat elements, mostly spread through retrotransposition, once the invader, are now co-opted to protect the host.10.1073/pnas.92.16.7550. PubMed PMID: 7638229; PubMed Central PMCID: PMC41377.Herbert A, Lowenhaupt K, Spitzner J, Rich A. Chicken double-stranded RNA adenosine deaminase has apparent specificity for Z-DNA. Proc Natl Acad Sci U S A. 1995;92(16):7550–4. Epub 1995/08/01. doi: 10.1371/journal.pbio.0020391. PubMed PMID: 15534692; PubMed Central PMCID: PMC526178.Athanasiadis A, Rich A, Maas S. Widespread A-to-I RNA editing of Alu-containing mRNAs in the human transcriptome. PLoS Biol. 2004;2(12):e391. Epub 2004/11/10. doi: 10.1073/pnas.1017241108. PubMed PMID: 21173229; PubMed Central PMCID: PMC3017198.Ward SV, George CX, Welch MJ, Liou LY, Hahm B, Lewicki H, et al. RNA editing enzyme adenosine deaminase is a restriction factor for controlling measles virus replication that also is required for embryogenesis. Proc Natl Acad Sci U S A. 2011;108(1):331–6. Epub 2010/12/22. doi: 10.1126/science.aac7049. PubMed PMID: 26275108; PubMed Central PMCID: PMC5444807.Liddicoat BJ, Piskol R, Chalk AM, Ramaswami G, Higuchi M, Hartner JC, et al. RNA editing by ADAR1 prevents MDA5 sensing of endogenous dsRNA as nonself. Science. 2015;349(6252):1115–20. Epub 2015/08/15. doi: 10.1038/s41431-019-0458-6.Herbert A. Mendelian Disease caused by variants affecting recognition of Z-DNA and Z-RNA by the Zα domain of the double-stranded RNA Editing Enzyme ADAR. European Journal of Human Genetics. 2019. doi:


Selected circRNAs were experimentally validated by qRT-PCR and Sanger sequencing. CircRNAs with abundant and differential expression were identified and their putative function as competing endogenous RNAs (ceRNAs) was analyzed by an integrated analysis of circRNAs, microRNAs, and mRNAs. (3) Results: With a comprehensive search using no information of splicing signals, we systematically identified 179 highly abundant circRNAs in psoriatic skin. Many of these were reported for the first time and many were differentially expressed in involved versus normal or uninvolved skin. Validation based on three additional RNA-seq datasets confirmed most of the identified circRNAs in psoriatic skin. Experimental analyses confirmed the expression of the well-known circRNA CDR1as, a canonical circRNA, and a novel i-circRNA in psoriasis. We also identified many circRNAs that may act as ceRNAs to regulate the expression of mRNA genes in psoriasis-related signaling pathways in psoriasis. (4) Conclusions: The result of the study suggested that circRNAs are abundant in psoriatic skin, have distinct characteristics, and contribute to psoriatic pathogenesis. Psoriasis (PS) is a chronic, inflammatory, and immune-mediated skin disease, characterized by raised, red scaly plaques . Its preCircular RNAs (circRNAs), a new class of non-coding RNAs, have been identified in nearly all eukaryotic species ,24,25,26However, the expression and potential function of circRNAs in PS are understudied and poorly understood. Three previous studies have been reported: A microarray-based analysis of psoriatic lesions identified 4,956 differentially expressed (DE) circRNAs ; a studyWe set forth to identify novel circRNAs and study their potential functions in PS. We studied a cohort of 93 skin tissue samples from which a large collection of circRNA-enriched RNA-seq libraries from 28 psoriatic-involved (PP) lesions, 38 normal (NN) controls, and 27 psoriatic-uninvolved (PN) skin samples were derived. Besides studying circRNAs arising from intron-exon boundaries, we also studied interior circRNAs (i-circRNAs) arising from the interior regions of introns, exons, and intergenic regions . We confA new method for finding circRNAs of all types, termed CAT, was developed to comprehensively identify circRNAs and i-circRNAs from RNA-seq data a. AlthouUsing CAT, we detected and characterized circRNAs in 93 biopsy skin samples for circRNA discovery . We idenWe identified 179 circRNAs expressed in at least 10 samples and supported by at least five reads in one of these samples . Among tMany circRNAs have two features: (1) they have complementary sequences flanking their BF points. Twenty-four circRNAs had paired flanking sequences, among which 17 were i-circRNAs including 15 with no splicing signal see d. Among More than one circRNA may arise from a genomic locus. Among the 161 host genes of the 179 circRNAs, 13 . Using SRPBM, 47 (26.3%) of the 179 circRNAs were expressed at higher levels between PP vs. NN f and 51 A putative function of circRNAs is acting as competing endogenous RNAs (ceRNAs)  for reguFrom these circRNAs, we selected two canonical circRNAs, hsa_skin_194345 (CDR1as) and hsa_skin_088763 (hsa_circ_0109327), and three i-circRNAs, hsa_skin_100269, hsa_skin_143837, and hsa_skin_052271 for experimental validation using psoriatic skin biopsy samples (PP and PN) and HaCaT keratinocyte cells (see Methods). Using sequencing reads across BF points as surrogates to circRNAs, the five circRNAs had 1,440, 405, 41, 24, and 22 reads in the discovery dataset, respectively. Note that these five circRNAs were all detected in the validation datasets, providing the first validation of these circRNAs in psoriasis. For experimental validation, divergent and convergent PCR primers  were appThe three experimentally validated circRNAs may potentially function as ce-circRNAs in psoriatic skin. CDR1as may regulate 149 out of 655 targets of miR-7-5p via 67 binding sites and 242 out of 962 targets of miR-135b-5p via four binding sites . CDR1as CircRNA hsa_skin_088763 from pseudogene RP11-255H23.2 may regulate 109 out of 560 targets of miR-338-3p via four binding sites and 340 out of 1,508 targets of miR-23a/b-3p via 10/11 binding sites . hsa_skihsa_skin_052271, an i-circRNA from the third exon of gene FLG2, may mediate three target genes of miR-135b-5p, two target genes of miR-205-5p, and nine target genes of miR-27a-3p . Hsa_skiWe performed GO and KEGG pathway analyses separately on the 1,558 up-regulated and 2,476 down-regulated circRNA-associated transcripts (see Methods). The results revealed that most of the top 20 KEGG pathways and many of the top 20 GO terms were psoriasis-related . The aboCircRNA has not been well-investigated in psoriasis. The only previous study on this species of RNA in psoriatic lesional skin used a small cohort of six patients but no healthy controls . That stIn the study described here, we leveraged a large collection of RNA-seq data from psoriatic and normal skin biopsies to detect and profile the expression of circRNAs and identify their potential targets contributing to PS. Among the 179 highly abundant circRNAs detected, 64 (35.8%) were identified the first time, and 51 (28.5%) exhibited aberrant expression in PS. Interior circRNAs (i-circRNAs), a newly characterized type of circRNA , were alWe validated most of the identified circRNAs and i-circRNAs with three new validation RNA-seq datasets and experimentally confirmed and studied the expression of three circRNAs  in PS by PCR and Sanger sequencing .The significance of this experimental confirmation is threefold. First, it demonstrated for the first time that the most well-studied circRNA CDR1as can potentially function in psoriasis. CDR1as is known to play a role in cancer ,64, neurThe second significance of this experimental confirmation is that it showed, for the first time, that interior circRNA (i-circRNA) exist and potentially function in human cells . The novThe third is that the result revealed a possible connection between a circRNA and a pseudogene. CircRNA hsa_skin_088763 arose from pseudogene RP11-255H23.2. Many pseudogenes are known to function as ceRNAs as they are prone to become the targets of miRNAs . TherefoThe i-circRNAs reported in the current study were genuine circRNAs, which was supported by the validation datasets. i-circRNAs were previously identified from intergenic transcripts in rice  and oncoThe discovery of i-circRNAs was thought-provoking and shed new light on the biogenesis of circRNAs. While the splicesome-based biogenesis model is well-supported, it cannot explain all canonical circRNAs. In particular, 19,444 (21.05%) of the 92,369 circRNAs in circBase have no splicing signal adjacent to their back splicing points. Interestingly, the back fusion points of most i-circRNAs and some canonical circRNAs were embedded in short homolog sequences SHS, d 46]. W. W46]. WFour paired-end RNA-seq datasets from human skin were used to investigate circRNAs in PS . One wasPsoriatic samples (PP and PN) from skin biopsies of 30 PS patients and HaCaT keratinocyte cells were used for validation. These psoriatic samples were from patients with untreated psoriasis of moderate-to-severe intensity  and they were all collected with ethical approval obtained from The Rockefeller University Institutional Review Board . The RNA was extracted with the miRNeasy kit for purification of total RNA .RNA was digested with RNase R  (a 5′ to 3′ exonuclease) to degrade linear RNA and enrich for circular RNA. RNase R digested RNA was prepared with a reaction containing 2 μg of prepared RNA, 1 μL (40U) RiboLock , 2 μL 10× RNase R reaction buffer, and 1 μL (10U) RNase R; adjusting the volume to 20 μL with nuclease-free water . RNA wasDivergent primers  were designed with the CircBase IDs of circRNAs in the CircInteractome webtool, if available; otherwise, primers were manually designed across the back-fusion (BF) points. The primers used for validation are listed in Divergent primers were used to amplify cDNA in a qRT-PCR reaction with SYBR Green . GAPDH was used as a reference. Both target and reference were amplified in triplicate and the relative level of each circRNA was calculated with the 2−∆∆Ct method.x-mer on the 5′-end and 3′-end of an unmapped read, called the left and right anchors, respectively  using paired-end RNA-seq data without information of splicing signals. CAT first maps sequencing reads to the reference genome with Bowtie2 . The mapectively a. It attectively a. If sucectively a. In thek > 2 in most of our analyses) split-mapped reads in an RNA-seq library. Furthermore, only unambiguous candidate BF points are considered. Specifically, while there certainly exist circRNAs spanning more than one exon, it is difficult to infer such circRNAs with certainty based on RNA-seq data alone, particularly on RNA libraries not enriched for circRNAs. Therefore, circRNAs from single introns, single exons, pairs of adjacent exons and introns, and intergenic non-coding transcripts are considered if RNA-seq libraries are not RNase R treated (https://github.com/xiaoxin8712/find_circ_strand (accessed on 27 July 2019).CAT retains those BF points that are supported by at least k , i.e., the number of circular reads/number of mapped reads (units in billion), to ameliorate the effects of sequencing depth and batch effects. Differentially expressed (DE) circRNAs were identified by the RankSum method [p-value ≤ 0.01 based on SPRBM.Two criteria were introduced to identify abundantly expressed circRNAs. First, the minimal number of reads mapped to a BF point in at least one sample is no less than m method  with a tq < 0.05 and fold change >2.DE genes of PP versus NN and PN were obtained with the Tophat and Cufflinks pipelines , with thp-value cutoff of 0.01, and minimum enrichment of 1.5, with GO biological processes and KEGG pathway for enrichment generated separately.Analyses of GO and KEGG pathway enrichment were carried out with Metascape  with thep-value < 0.01.CircRNA-associated mRNAs were defined as DE mRNAs that were positively regulated by circRNAs via circRNA-miRNA-mRNA regulatory cascades. That is, a DE mRNA was associated with a circRNA if both were bound and regulated by a common miRNA. mRNA and circRNA targets of mature miRNAs  were preOverall, our results reveal and demonstrate the diversity of circRNAs and the functional complexity of circRNAs operating in psoriasis. There exist not only circRNAs originating from exon-intron boundaries, but also i-circRNAs from interior regions of exons, introns, and intergenic regions. Importantly, circRNAs may function as ce-RNAs and contribute to psoriasis pathogenesis. Our results suggest that CDR1as, hsa_skin_088763, and hsa_skin_052271 should be analyzed in future studies as novel biomarkers for PS diagnosis.


Exposure to a single dose of polychlorinated biphenyls (PCBs) and a 12-week high-fat diet (HFD) results in nonalcoholic steatohepatitis (NASH) in mice by altering intracellular signaling and inhibiting epidermal growth factor receptor signaling. Post-transcriptional chemical modification (PTM) of RNA regulates biological processes, but the contribution of epitranscriptomics to PCB-induced steatosis remains unknown. This study tested the hypothesis that PCB and HFD exposure alters the global RNA epitranscriptome in male mouse liver. C57BL/6J male mice were fed a HFD for 12 weeks and exposed to a single dose of Aroclor 1260 (20 mg/kg), PCB 126 (20 µg/kg), both Aroclor 1260 and PCB 126 or vehicle control after 2 weeks on HFD. Chemical RNA modifications were identified at the nucleoside level by liquid chromatography-mass spectrometry. From 22 PTM global RNA modifications, we identified 10 significant changes in RNA modifications in liver with HFD and PCB 126 exposure. Only two modifications were significantly different from HFD control liver in all three PCB exposure groups: 2ʹ-O-methyladenosine (Am) and N(6)-methyladenosine (m6A). Exposure to HFD + PCB 126 + Aroclor 1260 increased the abundance of N(6), O(2)-dimethyladenosine (m6Am), which is associated with the largest number of transcript changes. Increased m6Am and pseudouridine were associated with increased protein expression of the writers of these modifications: Phosphorylated CTD Interacting Factor 1 (PCIF1) and Pseudouridine Synthase 10 (PUS10), respectively, in HFD + PCB 126- + Aroclor 1260-exposed mouse liver. Increased N1-methyladenosine (m1A) and m6A were associated with increased transcript levels of the readers of these modifications: YTH N6-Methyladenosine RNA Binding Protein 2 (YTHDF2), YTH Domain Containing 2 (YTHDC2), and reader FMRP Translational Regulator 1 (FMR1) transcript and protein abundance. The results demonstrate that PCB exposure alters the global epitranscriptome in a mouse model of NASH; however, the mechanism for these changes requires further investigation. Chronic environmental exposures to ubiquitous synthetic chemicals including polychlorinated biphenyls (PCBs) contribute to metabolic diseases including toxicant-associated steatohepatitis (TASH), a form of nonalcoholic fatty liver disease (NAFLD) , 2. FattWe demonstrated that a single exposure of male C57Bl/6J mice to Aroclor 1260 [a non-dioxin-like (NDL) PCB mixture] altered the hepatic proteome, including increased macrophage infiltration detected as increased Cd68 transcript levels, and attenuated the liver’s protective responses against chronic (12 weeks) HFD nutritional stress [Over 160 chemical modifications of transcribed RNAs that regulate transcript fate and function have been identified . The proin vivo, and the authors of this review noted the challenge of measuring RNA modifications represents a significant barrier in the field of environmental toxicology [Few studies have examined the impact of environmental chemicals on the epitranscriptome, the vast majority focused on m6A, and only in a small number of cells or tissues (reviewed in ). Likewixicology . Thus, tin vivo exposure to HFD after a single dose of Aroclor 1260 and PCB 126 or combined Aroclor 1260 + PCB 126 using mass spectrometry (MS) since the resolution of MS allows identification of low-abundance chemical modifications [Here, we tested the hypothesis that a single PCB exposure alters the global RNA epitranscriptome in HFD-fed male mouse liver. We identified 10 significant changes in global post-transcriptional chemical modification (PTM) RNA modifications in C57Bl/6J male mouse liver RNA after a 12-week ications . We examn = 10). All mice were fed ad libitum a HFD  throughout the study. At 10 weeks of age, 10 mice in each group were given either corn oil, Aroclor 1260 (20 mg/kg), PCB 126 (20 μg/kg) or a mixture of Aroclor 1260 (20 mg/kg) plus PCB 126 (20 μg/kg) via a one-time oral gavage. These concentrations were selected based on previous studies showing that Aroclor 1260 and PCB 126 act as ‘second hits’ in HFD-fed mice to induce steatohepatitis in a chronic mouse exposure model [Cyp1a2 transcript expression, as an example of an AhR target gene [Cyp1a2, most likely due to the low levels of DL PCBs in the Aroclor 1260 mixture, which were not sufficient to activate the AhR [The experimental design is modeled in re model . While Are model , it did re model . Becausere model , 18. Becre model . We notere model ; however the AhR . The tot4, pH = 4.5). The mixture was incubated at 37°C for 4 h. After removing nuclease S1 using a Microcon centrifugal filter , the pH of the solution was adjusted to basic using 2.5 µl of a mixture , and 0.5 units of phosphodiesterase I  was added. The solution was then incubated at 37°C overnight. Phosphodiesterase I was removed using a Microcon YM-10 filter. Finally, 3 units of antarctic phosphatase  was added, and the solution was incubated at 37°C for 4 h. Antarctic phosphatase was removed using a Microcon YM-3 filter .RNA was isolated from frozen mouse livers (stored at −80°C) . RNA wasAll samples were analyzed in random order on a Thermo Q Exactive HF Hybrid Quadrupole-Orbitrap Mass Spectrometer coupled with a Thermo DIONEX UltiMate 3000 HPLC system . The UltiMate 3000 HPLC system was equipped with a reversed-phase chromatography column . To obtam/z value, retention time and MS/MS spectra. Chemspider and mzCloud were used for the identification of unknown compounds [m/z variation window were set as  ≤0.15 min and  ≤5 ppm, respectively.XCMS software was used to deconvolute LC–MS spectra . MetSignompounds . The thrP-value and adjusted P-value using the Benjamini–Hochberg (BH or FDR) [SATP was used to analyze the differential expression analysis for each identified chemical RNA modification , 26. The or FDR)  method fq-value cutoff of 0.05. The raw data of the RNA-seq are available at the Gene Expression Omnibus database (accession number GSE173271).RNA was isolated from five mouse livers/experimental exposure using the RNeasy Mini Kit  according to manufacturers’ protocol. RNA-seq was performed as previously described , 29 withq value <0.05. Network analysis for RNA modifications, the DEG in the mRNA transcriptome and the proteome from these liver samples [Data from RNA-seq were analyzed such that transcripts selected had a log2 fold-change (log2FC) >0.34 (or −0.34 for repressed transcripts) and a statistically significant threshold  samples  was evalPCBs, both NDL and DL congeners, are metabolism-disrupting chemicals that result in obesity-related diseases including NAFLD in exposed human and animal populations (reviewed in ). We preExposure of mice to Aroclor 1260 increased the severity of HFD-induced NAFLD  while geThe abundance of Am was increased in Aroclor 1260- and Aroclor 1260- + PCB 126-exposed mouse liver compared to HFD control, whereas exposure to PCB 126 reduced Am abundance . 2ʹO-met6,2ʹ-O-dimethyladenosine (m6Am) [Am is converted to Ne (m6Am) . The enze (m6Am) . m6Am ise (m6Am)  and mitoe (m6Am) . m6Am abe (m6Am) . IncreasThe abundance of m7G, a cap for mRNA , was sig6-threonylcarbamoyladenosine (ms2t6A) is in tRNA and is essential for decoding of Lys codons [2-Methylthio-Ns codons . ms2t6A Ψ is present in the tRNA anticodon loop and is an internal mRNA modification , 10. Ψ aExposure of mice to HFD and either Aroclor 1260 or PCB 126 individually or in combination also reduced the abundance of 5-methyluridine (m5U), a modification found in the T-loop of tRNA  and cataIntegrated network analysis of the 10 significant RNA modifications  in MetaCMetaCore was used to integrate the RNA modification and transcriptome data of the mouse liver in response to HFD and exposure to each PCB . This anNr1i3) and Pregnane X receptor  [PCBs are ‘legacy contaminants’ that act as metabolism-disrupting chemicals and that continue to be present in humans (reviewed in ), includ, Nr1i2) , 17. In , Nr1i2) . The rat, Nr1i2) . In addiUGT2B7 as downregulated by m6A modification [To our knowledge, this is the first study to identify global RNA epitranscriptomic changes by MS in the livers of mice exposed to HFD and PCBs that cause hepatic steatosis and fibrosis. Examination of RNA chemical modification in response to the environmental exposure and the impact of these epitranscriptomic marks on environmental disease is a growing field of interest . To datefication . There afication .Our results support the possible role of m6A modification in HFD and PCB exposure-induced mouse liver steatosis. m6A abundance was reduced in total hepatic RNA from mice on HFD with exposure to Aroclor 1260 or PCB 126; however, when mice were exposed to the combination of Aroclor 1260 and PCB 126, m6A abundance was increased compared to HFD control. This finding may relate to the increase in GO ‘Regulation of Gene Expression Processes’ in the liver proteome of the HFD + co-exposed Aroclor 1260 + PCB 126 mice [Pgc1a1, Fasn and Sirt1  was significantly reduced from birth to 60 days in the livers of male C57Bl/6J mice . BUD23 aSimilarly, mRNA cap m2,2,7G abundance was reduced by PCB 126 exposure. m2,2,7G is also a cap for uridine-rich snRNAs that are recognized by SNUPN (Snurportin 1) . There wiMet) and the association of other ALKBH1-target tRNAs to polysomes, thus affecting protein synthesis [Ψ abundance was significantly reduced by PCB 126 exposure in the HFD mouse livers. Despite the high levels of Ψ in RNA, there is only one study on the impact of environmental exposures on Ψ in mouse liver . That stynthesis .This study identified global changes in the PTM RNA transcriptome in a murine model of PCB and HFD-exposure-induced TASH. Environmental hepatology is an emerging field focused on identifying and treating environmental contributions to liver disease . From 22dvab008_SuppClick here for additional data file.


G-Quadruplex (G4) DNA (G4 DNA) and RNA (G4 RNA) are secondary nucleic acid structures that have multiple roles in vital cellular processes. G4 DNA- and RNA-binding proteins and unwinding helicases associate with and regulate G4s during virtually all processes that involve DNA and RNA. DEAH-Box helicase 36 (DHX36), a member of the large DExD/H box helicase family, enzymatically unwinds both G4 DNA and G4 RNA. By exerting its G4 helicase function, DHX36 regulates transcription, genomic stability, telomere maintenance, translation and RNA metabolism. This review will provide an overview of G4s and DHX36, including DHX36’s potential role in neuronal development and neurodegeneration. We conclude with a discussion of the possible functions of G4s and DHX36 in the aging brain. Guanine-rich nucleic acid structures called G-quadruplexes G4) found in DNA and RNA motifs have become a focal point for the investigation of how secondary structures in nucleic acids affect cellular processes , 2. As n found inDHX36 , a highly conserved member of the DExD/H box helicase family, binds with and unfolds G4s, thereby changing how G4 structures influence DNA- and RNA-dependent processes. DHX36, a major G4 helicase, unwinds both G4 DNA and G4 RNA, and understanding the impact on both structures makes it an interesting research target, considering the role of G4s in a variety of processes . Recent >X>G>X>G>X>G>X, wherein each guanine (>G) is interspaced by a linking nucleotide sequence (>X) . DH. DH19]. Due to the ubiquity of G4 structures in promoters, promoter-enhancement regions, and oncogenes, much of helicase study focuses on how these enzymes unwind DNA and RNA and their impact on cancer tumorigenesis . HelicasDHX36 is a proto-oncogene. The long non-coding RNA, FLJ39051, that contains G4 RNA, binds to and inhibits DHX36, leading to enhanced colon cancer cell migration . Since sNevertheless, DHX36 is implicated as a regulating factor in breast cancer tumor suppression, and is positively associated with increased patient survival. Using a xenograft tumor model in mice, researchers showed that lentiviral knockdown of DHX36 increases tumor growth and cancer cell invasion, with a concurrent reduction of cellular apoptosis . TherefoIn vivo and in vitro, DHX36 binds to the telomerase RNA component, and this binding depends on the presence of the G4 RNA structure in the 5′-region of the telomerase RNA [Telomeres have a central role in aging by protecting the ends of chromosomes from damage during cellular mitosis . In vivorase RNA . DHX36 rrase RNA . Telomerrase RNA . In agrerase RNA . Intrigurase RNA . It wouldhx36 tamoxifen-induced knock-out cellular system, DHX36-deficient cells displayed deficits in IFN production and had higher vulnerability to RNA virus infections [Helicase enzymes, including DHX36, have an active role in innate cellular immune response, acting as cytoplasmic sensors of viral nucleic acids . Among ofections . Thus, DViruses, such as coronaviruses, hijack the host machinery for their replication, and helicases are no exception. Coronaviruses encode their own RNA helicases, but they still hijack the host helicases to regulate virus proliferation. For example, the host helicases, including DHX36, may in fact positively modulate SARS-CoV-2 replication .Many G4 DNA helicases are linked to human diseases with some degree of brain pathology, often severe. For example, an expansion of a non-coding GGGGCC repeat in the gene C9ORF72 is associated with 10% of familial cases of frontotemporal dementia and amyotrophic lateral sclerosis. The repeat forms G4 DNA, G4 RNA, and G4 DNA-RNA hybrids, leading to an accumulation of ribonucleoprotein complexes and increased intranuclear stress . G4s areDHX36 knock-out causes lethality in embryos that show signs of cellular degeneration at the early embryonic stage E7.5 . Is therAs described above, many G4 DNA helicases are linked to human diseases, which are characterized by accelerated aging. Cellular senescent phenotypes arise from genomic instability, telomere dysfunction, inflammation, dysregulated transcription, translation, and mRNA metabolism that are the result of the presence of defective or absent helicases. For example, in knockdown assays, the closely related helicase DHX9 that unwinds G4 DNA, G4 RNA, and R-loops causes premature senescence in human fibroblasts. These cells have significant morphological differences, changing cell shape from a small and spindle shape to an enlarged and irregularly shaped appearance. Positive staining of the widely used senescent marker SA β-gal was also observed when compared to controls .As humans age, declines occur in the systems for controlling protein production quality and mitochondrial function and dynamic processes regulating cellular homeostasis. Stress granule formation, maintenance and clearance processes may become compromised and lead to promotion of aging-related disease and dysfunction. For example, G4s have long been recognized as factors in stress granule assembly . IntriguAtg7expression in neurons and astrocytes, suggesting a mechanism by which G4 DNA downregulates autophagy. Another example is G4-associated transcription of the Brca1 gene [Brca1 downregulation and DNA damage [Brca1 in astrocytes and microglia, indicating differences in DNA damage and repair pathways between brain cell types [As a multifunctional helicase, DHX36 may participate in controlling aging and cellular senescence processes in neurons. Previously, we showed that G4 DNA is important in regulating autophagy, an essential cellular survival and maintenance process [ca1 gene . In neurA damage . IntriguAlthough the interaction of DHX36 and G4 structures has been characterized across a variety of biological processes, much work is still needed to understand the mechanistic roles controlled by this dynamic relationship. As reviewed here, G4 DNA, G4 RNA, and DHX36 together define behavior for many genes at transcriptional and post-transcriptional levels. DHX36 may be an important genome instability hotspot worth deeper investigation in age-associated diseases. Though much attention has been given to the G4 structures and DHX36 interactivity in cancer tumorigenesis  and vira


They owe their evolutionary superiority to the large number of mutations that occur during replication, courtesy of RNA-dependent RNA polymerase. Natural selection rewards successful viral subtypes, whose effective tuning of the ecosystem regulates the interactions between its participants. Thus, ss(+)RNA viruses act as shuttles for the functionally important genes of the participants in symbiotic relationships within the ecosystem, of which the most common ecological triad is “plant–virus–insect”. Due to their short life cycle and large number of offspring, RNA viruses act as skillful tuners of the ecosystem, which benefits both viruses and the system as a whole. A fundamental understanding of this aspect of the role played by viruses in the ecosystem makes it possible to apply this knowledge to the creation of DNA insecticides. In fact, since the genes that viruses are involved in transferring are functionally important for both insects and plants, silencing these genes  can be used to regulate the pest population. RNA viruses are increasingly treated not as micropathogens but as necessary regulators of ecosystem balance. Viruses are highly adaptable to their environments because a huge number of genotypes emerge from the replication process, owing to the peculiarities of RNA-dependent RNA polymerase. During difficult times, the unique viral life cycle allows the survival of sometimes only a few host cells under conditions with access to minimal resources. Mutations play an important role in saving viruses since a large number of viral particles die during the struggle of the fittest genotypes to survive. Plant viruses seek refuge in the host cell, achieving a certain balance between reproduction and the consumption of its resources, and finding evolutionarily advantageous mechanisms that allow symbiotic coexistence. Among plant viruses, the ss(+)RNA group has been rewarded by evolution and has flourished, blessed, on the one hand, with a small genome, and, on the other, with the ability to constantly change its genome and to withstand various environmental factors. In addition, when passing through the “bottleneck”, they give rise to a new subtype or group of subtypes adapted to competitive environmental conditions. Viruses have moved beyond mere participation in the “micropathogen–host” relationship to become an important link for other ecosystem participants . It is nss(+)RNA viruses belong to the most ancient and dominant group of plant viruses, currently numbering more than 430 species from 6 families . The genOurmiavirus: 0.97–2.8 kb) among the RNA-containing viruses. The largest genome among ss(+)RNA viruses belongs to viruses in the Closteroviridae family (13–19.3 kb). A short genome allows the elegant reproduction of viral quasi-individuals that differ from the “parent” by one–two mutations at the lowest cost for the host cell [dsRNA, ss(−)RNA, ssDNA, and dsDNA viruses have various intermediate stages associated with the formation of mRNA that require a substantial amount of time and energy to set up and begin productive reproduction in the host cell. However, ss(+)RNA viruses have the shortest genome RNA viruses, the issue of adaptation to the environment is particularly acute since it is not able to actively move in search of a host. Therefore, when a micropathogen enters a cell, it must present all its best qualities to be able to replicate. To sort out all the best options for interaction with the host cell, these options must first be created. The key role in this is played by RNA-dependent RNA polymerase, which lacks a corrective ability. The error rate of RNA-dependent RNA polymerase is estimated at 100−6–10−7 . Genome 0−6–10−7 .Viruses have an r-development strategy, and only a small number of virions will reach a new target in the form of a new host individual after leaving the old one. They need a lot of offspring, a small genome, a high degree of overlap, and a small number of alternative pathways in the case of mutation, indicating a weak protection of the virus genome . The mosDue to the economic importance of plant viruses, most of the information accumulated about them to date is related to the pathogenic viruses of agricultural plants. Because humans rely on agriculture for survival, this leads to the erroneous opinion that any virus causes damage to plants. However, closer examination of the structure of natural ecosystems shows that viruses, for the most part, do not harm their hosts; in fact, it is safe to say that a significant number of viruses enter into mutually beneficial relationships with their plant hosts. Viruses are very common in wild plants, and they tend to be asymptomatic . To a ceIn our opinion, in modern terms, a virus should not be considered an organism but instead a “program” that facilitates the process of transmitting genetic information. Viruses help increase genetic diversity and are discernible in phenomena such as crossing over, mutation, and independent chromosome divergence. When viruses multiply very intensively, it means that the participants of the ecosystem are not sufficiently adapted to each other. A large number of scientific papers agree that it is viruses, along with microorganisms, that carry out gene transfer between different types of organisms in the ecosystem. For many ss(+)RNA viruses, the possibility of exchanging genome fragments in the form of endogenous viral elements (EVE) in insects  and planThis ability to move back and forth among the members of an ecosystem indicates the possibility that viruses are active participants in the genetic transformation of the genoplast of the community .In a sense, we see nucleic acids as a key regulator of ecosystem life. If we view viruses as “coated nucleic acids”, then they represent the simplest manifestation of life that can affect the ecosystem’s balance. For example, outbreaks of viral diseases in agroecosystems may not just be a consequence of the low biodiversity of the system , but ratLymantria dispar multicapsid nuclear polyhedrosis virus (LdMNPV) to combat the reproduction and further spread of the gypsy moth, thereby confirming this principle in the fight against insect pests. Baculoviruses are ubiquitous in the environment and are known to be an important regulator of insect populations. In the presence of LdMNPV infection, it is better to rely on alterations in the expression of functionally important virus genes  genes) that could have an insecticidal effect on gypsy moths. Phylogenetic analysis of baculoviral IAP genes indicated their host origin [Oberemok’s research group has been developing DNA insecticides since 2008 ,29 and st origin ,33, and t origin . Our rest origin ,36.Insect pest control always involves disseminating a significant portion of the preparation used into the environment. Unmodified oligonucleotides seem to be the safest way to do this since cells contain ubiquitous nucleases that can neutralize them , embodyiIn our opinion, ss(+)RNA plant viruses manifest themselves as great opportunists and ecosystem tuners. This is a consequence of their short life cycle, the peculiarities of the functioning of RNA-dependent RNA polymerase, and the minimum number of substances necessary for the reproduction of viral particles. They adapt easily to various environmental factors, which has allowed them to survive and flourish. At the same time, they act as programmers of the ecosystem by transferring functionally important genes, often using insects as a vehicle, to fine-tune the symbiotic relationships among its participants. For plants, as the main solar energy accumulators and producers, viruses are a necessary component of the ecosystem; by efficiently adjusting how the food network functions, they ensure the sustainable development of its participants in symbiotic relationships. The fundamental knowledge of the role played by RNA viruses in an ecosystem can be used to effectively and safely regulate the number of insect pests using the DNA insecticide approach.