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Epstein–Barr virus (EBV) was first discovered in cells from a patient with Burkitt lymphoma (BL), and is now known to be a contributory factor in 1–2% of all cancers, for which there are as yet, no EBV-targeted therapies available. Like other herpesviruses, EBV adopts a persistent latent infection in vivo and only rarely reactivates into replicative lytic cycle. Although latency is associated with restricted patterns of gene expression, genes are never expressed in isolation; always in groups. Here, we discuss (1) the ways in which the latent genes of EBV are known to modulate cell death, (2) how these mechanisms relate to growth transformation and lymphomagenesis, and (3) how EBV genes cooperate to coordinately regulate key cell death pathways in BL and lymphoblastoid cell lines (LCLs). Since manipulation of the cell death machinery is critical in EBV pathogenesis, understanding the mechanisms that underpin EBV regulation of apoptosis therefore provides opportunities for novel therapeutic interventions.

Managing multiple long-term conditions (MLTC) is complex. Clinical management guidelines are typically focused on individual conditions and lack a robust evidence base for patients with MLTC. MLTC management is largely delivered in primary care, where health care professionals (HCPs) have identified the need for more holistic yet efficient models of care that can address patients' medical, pharmacological, social, and mental health needs. Artificial intelligence (AI) has proven effective in tackling complex, data-driven challenges in various fields, presenting significant opportunities for MLTC care. However, its role in managing patients with multifaceted psychosocial needs remains underexplored. The implementation of AI tools in this context introduces opportunities for innovation and challenges related to clinical appropriateness, trust, and ethical considerations. Understanding HCPs' experiences of MLTC management and the factors influencing their attitudes toward using AI in complex clinical decision-making is crucial for successful implementation. We aimed to explore the perspectives of primary care HCPs on managing MLTC and their attitudes toward using AI tools to support clinical decision-making in MLTC care. In total, 20 HCPs, including general practitioners, geriatricians, nurses, and pharmacists, were interviewed. A patient case study was used to explore how an AI tool might alter the way in which participants approach clinical decision-making with a patient with MLTC. We derived concepts inductively from the interview transcripts and structured them according to the 5 categories of the model by Buck exploring determinants of attitudes toward AI. These included the concerns and expectations that contributed to the minimum requirements for HCPs to consider using an AI decision-making tool, as well as the individual characteristics and environmental influences determining their attitudes. HCPs' perspectives on managing MLTC were grouped into three main themes: (1) balancing multiple competing factors, including accounting for patients' social circumstances; (2) managing polypharmacy; and (3) working beyond single-condition guidelines. HCPs typically expected that AI tools would improve the safety and quality of clinical decision-making. However, they expressed concerns about the impact on the therapeutic clinician-patient relationship that is fundamental to the care of patients with MLTC. The key prerequisites for clinicians adopting AI tools in this context included improving public and patient trust in AI, saving time and integrating with existing systems, and ensuring that the rationale behind a recommendation is apparent to enable a final decision made by an experienced human clinician. This is the first study to examine the attitudes of HCPs toward using AI decision-making tools in the context of managing MLTC. HCPs were optimistic about AI's potential to improve decision-making safety and quality but emphasized that the human touch remains essential for patients with complex needs. We identified critical requirements for AI adoption, including addressing patients' perceptions, time efficiency, and the preservation of clinician and patient autonomy. RR2-10.1136/bmjopen-2023-077156.

BackgroundArtificial intelligence (AI)-based clinical decision support systems (CDSSs) are currently being developed to aid prescribing in primary care. There is a lack of research on how these systems will be perceived and used by healthcare professionals and subsequently on how to optimise the implementation process of AI-based CDSSs (AICDSSs).ObjectivesTo explore healthcare professionals’ perspectives on the use of an AICDSS for prescribing in co-existing multiple long-term conditions (MLTC), and the relevance to shared decision making (SDM).DesignQualitative study using template analysis of semistructured interviews, based on a case vignette and a mock-up of an AICDSS.SettingHealthcare professionals prescribing for patients working in the English National Health Service (NHS) primary care in the West Midlands region.ParticipantsA purposive sample of general practitioners/resident doctors (10), nurse prescribers (3) and prescribing pharmacists (2) working in the English NHS primary care.ResultsThe proposed tool generated interest among the participants. Findings included the perception of the tool as user friendly and as a valuable complement to existing clinical guidelines, particularly in a patient population with multiple long-term conditions and polypharmacy, where existing guidelines may be inadequate. Concerns were raised about integration into existing clinical documentation systems, medicolegal aspects, how to interpret findings that were inconsistent with clinical guidelines, and the impact on patient-prescriber relationships. Views differed on whether the tool would aid SDM.ConclusionAICDSSs such as the OPTIMAL tool hold potential for optimising pharmaceutical treatment in patients with MLTC. However, specific issues related to the tool need to be addressed and careful implementation into the existing clinical practice is necessary to realise the potential benefits.

Evasion of immune T cell responses is crucial for viruses to establish persistence in the infected host. Immune evasion mechanisms of Epstein-Barr virus (EBV) in the context of MHC-I antigen presentation have been well studied. In contrast, viral interference with MHC-II antigen presentation is less well understood, not only for EBV but also for other persistent viruses. Here we show that the EBV encoded BZLF1 can interfere with recognition by immune CD4+ effector T cells. This impaired T cell recognition occurred in the absence of a reduction in the expression of surface MHC-II, but correlated with a marked downregulation of surface CD74 on the target cells. Furthermore, impaired CD4+ T cell recognition was also observed with target cells where CD74 expression was downregulated by shRNA-mediated inhibition. BZLF1 downregulated surface CD74 via a post-transcriptional mechanism distinct from its previously reported effect on the CIITA promoter. In addition to being a chaperone for MHC-II αβ dimers, CD74 also functions as a surface receptor for macrophage Migration Inhibitory Factor and enhances cell survival through transcriptional upregulation of Bcl-2 family members. The immune-evasion function of BZLF1 therefore comes at a cost of induced toxicity. However, during EBV lytic cycle induced by BZLF1 expression, this toxicity can be overcome by expression of the vBcl-2, BHRF1, at an early stage of lytic infection. We conclude that by inhibiting apoptosis, the vBcl-2 not only maintains cell viability to allow sufficient time for synthesis and accumulation of infectious virus progeny, but also enables BZLF1 to effect its immune evasion function.

The reasons behind this problem were discussed during the debate, which was chaired by Alice Roberts (Professor of Public Engagement in Science at the University of Birmingham) and featured a panel comprised of Laura Piddock (Professor of Microbiology and an academic expert in antimicrobial resistance from the University of Birmingham), Matthew O'Shea (an infectious disease specialist at The Jenner Institute, Oxford, UK), journalist and cystic fibrosis sufferer Sharon Brennan, and medical student Ananth Srinivasan.

Multimorbidity, living with 2 or more long-term health conditions, is increasing globally and now affects over one-quarter of adults in England. People with multiple long-term conditions (MLTC) face complex health and treatment challenges, often experiencing fragmented care within systems oriented toward single-disease management. Artificial intelligence (AI) has the potential to support clinicians and patients by analyzing complex health data, optimizing treatment strategies, and predicting disease trajectories. The OPTIMAL (Optimizing Therapies, Disease Trajectories, and AI-Assisted Clinical Management for Patients Living with Complex Multimorbidity) project aims to develop AI-enabled tools to support shared decision-making in primary care. This study explored how patients with MLTC perceive the use of AI to inform joint decision-making in primary care. Semistructured interviews were conducted via telephone or video call with 29 adults living with MLTC between July and November 2023. Participants were recruited through general practitioner practices via the Clinical Practice Research Datalink and community-based organizations across the West Midlands. Interviews were transcribed verbatim and analyzed thematically using an inductive approach. Members of a patient advisory group were involved in developing study materials, refining the interview guide, and reviewing emerging findings to ensure relevance and authenticity. Participants identified potential benefits of AI in enhancing consultation efficiency and accuracy, improving access to information for patients and clinicians, promoting early detection of health changes, and reducing health care inequalities. However, concerns were raised about the loss of human interaction, data privacy and security, transparency of algorithms, and the potential for bias and inequity in AI systems. Trust and acceptance varied by age and familiarity with technology. Some participants expressed uncertainty about what AI entails and how it could be used in primary care. Patients with MLTC viewed AI-assisted decision-making in primary care with cautious optimism. While many recognized potential benefits for coordination and personalization of care, others expressed reservations about privacy, fairness, and the risk of diminished human connection.

Epstein–Barr virus (EBV), which is ubiquitous in the adult population, is causally associated with human malignancies. Like many infectious agents, EBV has evolved strategies to block host cell death, including through expression of viral homologues of cellular BCL-2 pro-survival proteins (vBCL-2s), such as BHRF1. Small molecule inhibitors of the cellular pro-survival BCL-2 family proteins, termed ‘BH3-mimetics’, have entered clinical trials for blood cancers with the BCL-2 inhibitor venetoclax already approved for treatment of therapy refractory chronic lymphocytic leukaemia and acute myeloid leukaemia in the elderly. The generation of BH3-mimetics that could specifically target vBCL-2 proteins may be an attractive therapeutic option for virus-associated cancers, since these drugs would be expected to only kill virally infected cells with only minimal side effects on normal healthy tissues. To achieve this, a better understanding of the contribution of vBCL-2 proteins to tumorigenesis and insights into their biochemical functions is needed. In the context of Burkitt lymphoma (BL), BHRF1 expression conferred strong resistance to diverse apoptotic stimuli. Furthermore, BHRF1 expression in mouse haematopoietic stem and progenitor cells accelerated MYC-induced lymphoma development in a model of BL. BHRF1 interacts with the cellular pro-apoptotic BCL-2 proteins, BIM, BID, PUMA and BAK, but its capability to inhibit apoptosis could not be mapped solely to one of these interactions, suggesting plasticity is a key feature of BHRF1. Site-directed mutagenesis revealed a site in BHRF1 that was critical for its interaction with PUMA and blocking DNA-damage-induced apoptosis, identifying a potentially therapeutically targetable vulnerability in BHRF1.

While the association of Epstein–Barr virus (EBV) with Burkitt lymphoma (BL) has long been recognised, the precise role of the virus in BL pathogenesis is not fully resolved. EBV can be lost spontaneously from some BL cell lines, and these EBV-loss lymphoma cells reportedly have a survival disadvantage. Here we have generated an extensive panel of EBV-loss clones from multiple BL backgrounds and examined their phenotype comparing them to their isogenic EBV-positive counterparts. We report that, while loss of EBV from BL cells is rare, it is consistently associated with an enhanced predisposition to undergo apoptosis and reduced tumorigenicity in vivo . Importantly, reinfection of EBV-loss clones with EBV, but surprisingly not transduction with individual BL-associated latent viral genes, restored protection from apoptosis. Expression profiling and functional analysis of apoptosis-related proteins and transcripts in BL cells revealed that EBV inhibits the upregulation of the proapoptotic BH3-only proteins, BIM and PUMA. We conclude that latent EBV genes cooperatively enhance the survival of BL cells by suppression of the intrinsic apoptosis pathway signalling via inhibition of the potent apoptosis initiators, BIM and PUMA.

Epstein-Barr virus (EBV) has been etiologically associated with Burkitt lymphoma (BL) since its discovery 50 years ago, but despite this long-standing association the precise role of the virus in the pathogenesis of BL remains enigmatic. EBV can be lost spontaneously from EBV-positive BL cell lines, and these EBV-loss clones have been reported to exhibit increased sensitivity to apoptosis. We have confirmed and extended those observations and report that sporadic loss of EBV from BL cells is consistently associated with enhanced sensitivity to apoptosis-inducing agents and conversely, reduced tumorigenicity \\(in\\) \\(vivo\\). Importantly, reinfection of EBV-loss clones with EBV can restore apoptosis protection, although surprisingly, individual Latency I genes cannot. We also used inducible pro-apoptotic BH3 ligands to investigate Bcl-2-family dependence in BL clones as well as profiling gene expression changes in response to apoptosis induction in EBV-positive versus EBV-loss clones. We found that EBV-loss was consistently associated with enhanced sensitivity to BH3-ligand-induced death and increased activation of apoptosis signalling pathways, although no individual apoptosis-related gene was responsible. Instead we find that Latency I EBV genes co-operate to co-ordinately repress the BH3-only proteins Bim, Puma and Noxa to inhibit apoptosis in BL.

Evasion of immune T cell responses is crucial for viruses to establish persistence in the infected host. Immune evasion mechanisms of Epstein-Barr virus (EBV) in the context of MHC-I antigen presentation have been well studied. In contrast, viral interference with MHC-II antigen presentation is less well understood, not only for EBV but also for other persistent viruses. Here we show that the EBV encoded BZLF1 can interfere with recognition by immune [CD4.sup.+] effector T cells. This impaired T cell recognition occurred in the absence of a reduction in the expression of surface MHC-II, but correlated with a marked downregulation of surface CD74 on the target cells. Furthermore, impaired [CD4.sup.+] T cell recognition was also observed with target cells where CD74 expression was downregulated by shRNA-mediated inhibition. BZLF1 downregulated surface CD74 via a post-transcriptional mechanism distinct from its previously reported effect on the CIITA promoter. In addition to being a chaperone for MHC-II αβ dimers, CD74 also functions as a surface receptor for macrophage Migration Inhibitory Factor and enhances cell survival through transcriptional upregulation of Bcl-2 family members. The immune-evasion function of BZLF1 therefore comes at a cost of induced toxicity. However, during EBV lytic cycle induced by BZLF1 expression, this toxicity can be overcome by expression of the vBcl-2, BHRF1, at an early stage of lytic infection. We conclude that by inhibiting apoptosis, the vBcl-2 not only maintains cell viability to allow sufficient time for synthesis and accumulation of infectious virus progeny, but also enables BZLF1 to effect its immune evasion function.