Involving collaborators and partners in research may increase relevance and uptake, while reducing health and social inequities. Collaborators and partners include people and groups interested in health research: health care providers, patients and caregivers, payers of health research, payers of health services, publishers, policymakers, researchers, product makers, program managers, and the public. Evidence syntheses inform decisions about health care services, treatments, and practice, which ultimately affect health outcomes.Our objectives are to:A. Identify, map, and synthesize qualitative and quantitative findings related to engagement in evidence synthesesB. Explore how engagement in evidence synthesis promotes health equityC. Develop equity-oriented guidance on methods for conducting, evaluating, and reporting engagement in evidence synthesesOur diverse, international team will develop guidance for engagement with collaborators and partners throughout multiple sequential steps using an integrated knowledge translation approach:1. Reviews. We will co-produce 1 scoping review, 3 systematic reviews and 1 evidence map focusing on (a) methods, (b) barriers and facilitators, (c) conflict of interest considerations, (d) impacts, and (e) equity considerations of engagement in evidence synthesis.2. Methods study, interviews, and survey. We will contextualise the findings of step 1 by assessing a sample of evidence syntheses reporting on engagement with collaborators and partners and through conducting interviews with collaborators and partners who have been involved in producing evidence syntheses. We will use these findings to develop draft guidance checklists and will assess agreement with each item through an international survey.3. Consensus. The guidance checklists will be co-produced and finalised at a consensus meeting with collaborators and partners.4. Dissemination. We will develop a dissemination plan with our collaborators and partners and work collaboratively to improve adoption of our guidance by key organizations.Our international team will develop guidance for collaborator and partner engagement in health care evidence syntheses. Incorporating partnership values and expectations may result in better uptake, potentially reducing health inequities.The online version contains supplementary material available at 10.1186/s13643-023-02279-1. An evidence synthesis, sometimes called a systematic review, is a method which identifies, brings together, and analyses all the research studies which address a specific question about healthcare. They are often used to provide evidence for healthcare decisions, such as the therapies that will be recommended by a physician, or for public health. Including people and groups who have an interest in these decisions, such as, patients, healthcare providers, those who pay for health services, those who manage health programs, and others, can help make sure that the right questions are asked, and the right information is assessed. The best way to engage with these all these groups has not been identified. The goal of this project is to develop guidance for engaging with multiple groups or ‘collaborators and partners’ in each step of the evidence synthesis process. To prepare this guidance, we will identify and synthesize the available information on collaborator and partner engagement in evidence synthesis, conduct interviews and a survey and hold international meetings to develop and finalise draft checklists for collaborator and partner engagement in evidence synthesis.Evidence syntheses are used to inform the development of clinical practice, health systems, and public health guidelines . While tThere is international recognition that the engagement of collaborators and partners in health care research is important for improving its impact. Meaningful engagement benefits the usefulness, relevance, quality, buy-in, uptake, and impact of research –9. For eThere has been an increase in research teams undertaking collaborator and partner engagement in evidence synthesis as well as greater expectations from funders, but reporting of engagement is poor and there are evaluations or guidance about how to effectively engage different groups –18. In pHowever, the most effective methods for engaging different collaborators and partners in evidence synthesis, identifying the barriers or facilitators for engagement, or reporting how collaborators and partners were engaged have not been identified. The Multi-Stakeholder Engagement (MuSE) Consortium has recently addressed these issues in relation to health guidelines . LessonsA)Identify, map, and synthesize qualitative and quantitative findings related to collaborator and partner engagement in evidence synthesesB)Explore perspectives on how collaborator and partner engagement in evidence syntheses promotes health equityC)Develop equity-oriented guidance on methods for collaborator and patient engagement in evidence synthesisD)Develop guidance on methods for evaluating collaborator and partner engagement in evidence synthesesE)Develop a guideline for reporting collaborator and partner engagement in evidence syntheses  extension).Our overarching goals are to synthesize evidence relating to collaborator and partner engagement in health care evidence syntheses and to explore perspectives on how engagement in evidence syntheses can promote health equity. Our specific objectives are to:Definitions of terms in this field often lack consistency and differ internationally . For thiSynthesize research evidence to address a health care question using rigorous, explicit, and transparent methods. These include scoping reviews, rapid reviews, and quantitative or qualitative systematic reviews .Patients/consumers, caregivers, and patient groupsPayers/funders of researchPayers and purchasers of health services Publishers Policy-makers Principal investigators (e.g. researchers conducting studies that may or may not be relevant to the review)Product makers Producers and commissioners of guidelines  .Program managers . These individuals may be health care providers but are not on the point of care delivering health care related to the program of interest (e.g. overseeing an immunisation program but not delivering vaccinations)Providers Public Any interested person or group who is responsible for or affected by health- and healthcare-related decisions . We prev“A conflict of interest exists when a past, current or expected interest creates a significant risk of inappropriately influencing an individual’s judgment, decision, or action when carrying out a specific duty”  and may Refers to the approach to gather input or contribution from collaborators and partners and is multi-directional, resulting in “informed decision-making about the selection, conduct, and use of the research” . Languag. We use the acronym PROGRESS-Plus to identify characteristics which may contribute to health inequities (place of residence (e.g. country), race/ethnicity/culture, occupation, gender or sex and other identities, religion, education, socioeconomic status, social capital, and other characteristics including age or career stage) and to identify groups who may be underrepresented in evidence synthesis . We. We34]. Collaborator and partner engagement methods (scoping review and evidence map)Barriers and facilitators to collaborator and partner engagement in conduct of evidence synthesis (systematic review)Impact of collaborator and partner engagement in evidence synthesis (systematic review)Conflicts of interest in collaborator and partner engagement in evidence synthesis (systematic review)Equity considerations in collaborator and partner engagement in evidence synthesis (evidence map)We will complete four reviews and an evidence map to identify and synthesize qualitative and quantitative findings related to collaborator and partner engagement. The topics of these reviews are:We will follow established methods for high quality evidence syntheses and evidence mapping and publish separate, detailed protocols review will using the Open Science Framework , 35–37. https://www.covidence.org/) and de-duplicated. Pairs of independent reviewers will conduct title and abstract screening, tagging potentially relevant studies for inclusion into any of our reviews. Full text “tagged” papers will be retrieved for each of the individual reviews and will be considered alongside specific eligibility criteria. Data from studies judged as relevant will be extracted by two independent reviewers and the methodological quality of the evidence assessed, as appropriate, for each review.Records will be imported into Covidence  and across gender, sex, and other intersecting identity factors. We will utilize the factors for identification and invitation of individual representatives previously published by members of our team . Our advhttps://www.qsrinternational.com/).Two researchers will analyze transcribed interviews using a six-phase process of thematic analysis . The proWe will use the results of the above systematic reviews and key informant interviews to draft a list of candidate items for guidance on collaborator and partner engagement. We will survey international collaborators and partners using this preliminary list of guidance items to gather opinions about each draft item. We will disseminate the survey broadly though the members of the MuSE Consortium and their networks as well as through relevant organizations, such as Campbell, Cochrane, and JBI (formerly the Joanna Briggs Institute). We will aim for diversity in the identification of survey recipients by inviting responses from LMICs and by targeted outreach, using methods our group has used previously, such as sharing the survey invitation through listservs, inviting key individuals or organizations as recommended by the MuSE members, and social media .We will ask respondents to indicate their agreement with each draft guidance item and we will invite open-ended responses, as well as suggestions for additional items. We will tabulate the frequency of agreement for each proposed item. We will code the open-ended responses into themes using the methods described above. This survey will be submitted to the Bruyère Research Ethics Board.We will use the results of the systematic reviews, key informant interviews, and survey to develop and refine the draft guidance and finalise the guidance based on a consensus approach at an in-person (if possible) or virtual meeting. We will use nominal group technique to gain agreement , which aThe guidance finalised at the consensus meeting will be included in a series of guidance documents addressing equity-oriented guidance on methods for collaborator and partner engagement in evidence synthesis, evaluating engagement in evidence synthesis, and reporting engagement in evidence synthesis. The core management team, collaborator and partner co-leads, and advisory group will co-produce a dissemination plan and work collaboratively to improve adoption of our guidance by key organizations. We will utilize the international MuSE Consortium network to disseminate guidance documents and share these via social media, including Twitter and blogs, as well as through the multiple listservs maintained by the members of the MuSE Consortium.This work is intended to produce guidance for engaging collaborators and partners in conducting evidence syntheses, transparently reporting this engagement, and evaluating engagement. The guidance for collaborator and partner engagement in evidence syntheses will be tested through an iterative, consensus building process, via in-person meetings, teleconferences, and email correspondence with collaborators and partners of our 11 categories. We will ensure synergy with our related guidance for engagement in guideline development. Developing guidance for collaborator and partner engagement, guidance for evaluating, and reporting engagement may assist with the uptake of these resources by relevant organizations.Additional file 1.


Modified vaccinia Ankara (MVA) virus does not replicate in human cells and is the vaccine deployed to curb the current outbreak of mpox. Here, we conduct a multiplexed proteomic analysis to quantify >9000 cellular and ~80% of viral proteins throughout MVA infection of human fibroblasts and macrophages. >690 human proteins are down-regulated >2-fold by MVA, revealing a substantial remodelling of the host proteome. >25% of these MVA targets are not shared with replication-competent vaccinia. Viral intermediate/late gene expression is necessary for MVA antagonism of innate immunity, and suppression of interferon effectors such as ISG20 potentiates virus gene expression. Proteomic changes specific to infection of macrophages indicate modulation of the inflammatory response, including inflammasome activation. Our approach thus provides a global view of the impact of MVA on the human proteome and identifies mechanisms that may underpin its abortive infection. These discoveries will prove vital to design future generations of vaccines. Modified vaccinia Ankara (MVA) virus is the vaccine deployed to curb mpox. Here the authors conduct a multiplexed proteomic analysis to quantify cellular and viral proteins throughout MVA virus infection of human fibroblasts and macrophages and see substantial remodelling of the host proteome. Since May 2022, thousands of cases of mpox have been reported in >90 non-endemic countries worldwide, with sustained human-to-human transmission3. MPXV causes a smallpox-like illness, with severe disease seen in immunocompromised individuals, children and pregnant women. The ongoing outbreak has been caused by MPXV clade 2b (within the formerly designated “West African” clade)4. Although mortality is commoner with MPXV clade 1 , 0.1% of infections with clade 2 MPXV have resulted in death, with increased risk in the context of HIV co-infection5.Monkeypox virus (MPXV), the cause of the mpox disease, is a zoonotic orthopoxvirus endemic in Central and West Africa3. Highly effective protection can be provided by live vaccines originally developed against smallpox, which was itself eradicated through vaccination with a related orthopoxvirus, vaccinia virus (VACV)6. The most recent generation of smallpox vaccines derive from modified vaccinia Ankara (MVA), itself derived by >570 passages of the VACV strain chorioallantois vaccinia Ankara (CVA) in chicken embryo fibroblasts9. Serial passage resulted in loss of ~30 kb of the genome, and loss of replicative capacity in human cells10. Because MVA has been shown to be safe and immunogenic in both healthy and immunocompromised individuals12, it has also been investigated extensively as a vaccine vector for viruses including Ebola, respiratory syncytial virus, HIV, and SARS-CoV-213. Vaccination with MVA has also been shown to induce MPXV neutralising antibodies in humans14 and is protective in a non-human primate model15.Although the World Health Organisation (WHO) has not advised mass vaccination, post-exposure prophylaxis is recommended for higher-risk mpox contacts, and pre-exposure prophylaxis for at-risk healthcare workers16. More recently, a single-cell transcriptomic (scRNAseq) analysis of dendritic cells infected with MVA revealed two possible outcomes: sensing of infection via cGAS-STING leading to production of inflammatory cytokines and activation of uninfected bystanders, or induction of caspase activity and cell death via apoptosis17. Previously, we conducted a systematic quantitative temporal proteomic analysis of VACV-WR-infected human fibroblasts to elucidate the functional consequences of VACV-induced dynamic changes in the host proteome18. However, little information is available about the consequence to the host proteome of non-productive, abortive infections.The biological effects of MVA at the cellular level are poorly understood with no prior systematic proteomic investigation. Previous transcriptomic analyses of MVA and VACV-WR infection of the transformed HeLa cell line indicated that MVA induced a number of changes in the host transcriptome, including changes in the immune regulation and NF-κB signallingIn this study, we therefore conduct a comprehensive proteomic analysis of MVA and host throughout the whole course of infection, including inactivated controls to understand the contribution of the viral particle with no-, or limited viral gene expression. We show extensive remodelling of the human proteome in both fibroblasts and macrophages following MVA infection. Despite attenuation, MVA exerts antagonism of the innate immune response, which might contribute to its potent capacity to express high levels of heterologous antigens. We also show that MVA activates the inflammasome and induces pyroptosis in human macrophages, despite retaining proteins that inhibit both processes.21, and MOI 5 infected >96% of cells -immortalised primary human foetal foreskin fibroblasts (HFFF-TERTs) with MVA at a multiplicity of infection (MOI) of 5 in biological duplicate. HFFF-TERTs have been well established as a model for a variety of different viral infectionsion Fig. . In the ion Fig. .Fig. 1Qu7545 human and 144 viral proteins were quantified, providing a global view of changes in protein expression during infection. The greatest magnitude of protein fold change occurred late during infection, and heat inactivation ablated the majority of host protein downregulation Fig. . All dat24 revealed that diverse groups of cell surface proteins were downregulated early during MVA infection including multiple NK and T-cell ligands suggesting that a key focus of the first phase of infection may be evasion of cellular immunity  and transforming growth factor β−1 (TGFB1)  softwareity Fig. . Upregul29. Graph assortativity was then calculated, measuring the tendency of proteins in a particular group to interact preferentially with each other compared to proteins that are not part of the selected group   and the proto-oncogene FER whereas the latter included multiple interferon-stimulated genes and MAP4K2, an upstream activator of mitogen-activated protein kinases, key regulators of immune signalling downstream of pathogen sensing34 , we next characterised similarities and differences in host regulation. We compared results from the present study with our prior proteomic analysis of VACV-WRK) Fig. 5. MVA, bu) Fig. 55. IκBs se35.Therefore, to analyse MVA-induced changes in the proteome of antigen-presenting cells and to test the generality of our findings in human fibroblasts, we repeated the proteomic analysis in phorbol 12-myristate 13-acetate (PMA)-differentiated THP-1 macrophages  release assay. Infection of THP-1s with MVA induced a sharp increase in cell death, which was reversed by treatment with a caspase-1 inhibitor (VX-765) and, to a lesser extent, the pan-caspase inhibitor (zVAD-fmk)  and downregulation of anti-apoptotic MCL1 mk) Fig. . Convers Fig. 888. Lastly, Fig. 888.Multiplebitor VX-65 and, tbitor VX-65 and, tIn general, there was good correspondence between protein changes in HFFF-TERTs and THP-1s, albeit with subtle differences in the magnitude of change in each cell type. 70% of proteins downregulated >2-fold in either HFFF-TERTs or THP-1s were also downregulated at least 1.25-fold in the other cell type, whereas 18% were only downregulated in HFFFs and 12% only in THP-1s Fig. . To aid 43 , which is a host-dependency factor for VACV downstream of mitogen-activated protein kinases43 Fig. . Also, w43 Fig. . MVA-inf43 Fig. . Blockad18. We quantified 77% and 74% of all predicted MVA proteins in HFFF-TERTs and THP-1s, respectively. To define different classes of protein expression, we employed k-means clustering and included data from HFFF-TERT samples treated with AraC. This suggested that there are at least four distinct temporal protein profiles of viral protein expression in both cell lines tested   and Tp2 classes of viral protein exhibited increased expression in the presence of AraC  Fig. . Previou48. The comparison between transcript and protein data was conspicuous: 22/23 Tp1 proteins were encoded by E1.1 or E1.2 class transcripts, and 22/24 Tp4 proteins by I or L transcripts . The Tp2 proteins were mostly encoded by E1.2 transcripts , but MVA-BN was further passaged in chicken embryo fibroblasts55. MVA-induced regulation of the interferon response and NF-κB signalling, indicated both by limited upregulation of interferon-stimulated gene (ISG) products and direct regulation of NF-κB, will therefore be likely to impact the immunological memory elicited by vaccination. Interferon regulation is likely to occur at all stages of infection, indicated by the increased response seen upon blockage of post-replicative viral gene expression and in cells exposed to heat-inactivated virions in comparison to cells infected with unmodified MVA. This is in contrast with the antagonism of the interferon response by VACV-WR. For instance, VACV early protein C9 targets ISG products IFIT1, IFIT2, and IFIT3 for proteosomal degradation56. Through serial passage in chicken embryo fibroblasts, the MVA genome has accumulated six major deletions and many small insertion and deletions (indels), which, in combination, result in the loss of 25 open reading frames (ORFs) when compared to wildtype VACV strains57. In addition, 12 ORFs have been split due to mutations, including the ORF encoding a C9 orthologue, explaining the absence of IFIT degradation during MVA infection57. Analysis both of viral protein temporal profiles and inactivating mutations are thus likely to yield candidates antagonists for host factors downregulated during VACV-WR, but not MVA infection including Ephrins and MAP4K2.Our proteomic analysis of MVA infection in human fibroblasts and macrophages showed ~2,500 proteins were only quantified in one cell type, underscoring the importance of analysing multiple cell types in order to obtain a high-definition proteomic landscape of human cellular infection. Interferons and other cytokines induced upon viral sensing are key regulatory molecules in innate immunity and inflammation and play important roles in fine-tuning antigen-specific adaptive immunity during viral infection50. Of three ISGs included in our gain-of-function screen, we could confidently conclude that ectopic ISG20 can limit MVA gene expression whereas ectopic OASL cannot. A 3’−5’ exonuclease with strong preference for RNA, ISG20 can also degrade deaminated viral DNA (reviewed in ref. 58). Genomic analyses support that APOBEC3-mediated deamination of the viral genome is a key mutational driver in the MPXV strain that emerged globally in 20224. A plausible hypothesis is that the degradation APOBEC3-deaminated viral DNA by ISG20 might constitute another host defence against poxviruses. In light of the recent proposal that subset of “dominant” ISGs may confer most of the inhibition of a given virus by IFNs59, it is not surprising that, out of three ISGs, only ISG20 exerted significant antiviral activity against MVA.Suppression of the IFN response might represent one of the mechanisms responsible for the capacity of MVA vectors to attain high levels of heterologous gene expression under the control of viral promoters28. Proteasomal degradation represents the main mechanism responsible for downregulation of host protein during VACV-WR infection18 and therefore, most host proteins downregulated during MVA infection are likely also to be degraded by the proteasome. Alternatively, common modulation of protein expression might reflect common cellular responses to infection, such as the downregulation of multiple collagens in fibroblasts.Host proteins with concordant modulation by MVA and VACV-WR might indicate that viral mechanisms of host modulation are still functional in MVA. Examples include common downregulation of the antiviral restriction factors HDAC5 and TRIM5 by VACV-WR and MVA, mediated by VACV protein C6 (MVA019)60. The innate immune response to virus infection requires an intact NPC to mediate nucleocytoplasmic transport of transcription factors and mRNAs and therefore, downregulation/degradation of NPC proteins may be a viral immune evasion strategy. Although some viral proteases inactivate nucleoporins (NUPs) by cleavage61, NUP abundance did not vary substantially during infection by multiple human pathogenic viruses, such as human cytomegalovirus19, herpes simplex virus type 120, influenza A virus62, Epstein-Barr virus63, SARS-CoV-264 and HIV65. Three of the NUPs down-regulated by MVA  were identified as host factors necessary for VACV-WR morphogenesis26, raising the possibility that NUP downregulation contributes to arrested virion maturation during MVA infection of human cells. It remains to be determined whether this MVA-induced modulation is shared with the parental strain (CVA) or was acquired during the serial passage in chicken embryo fibroblasts. The molecular mechanisms underpinning restriction of MVA in human cells are not fully understood but are largely attributed to the loss or disruption of genes encoding host-range factors67. Nucleoporins have been implicated in antiviral defences, including as co-factors in the MX2-mediated restriction of HIV-169 or as substrates for proteolytic cleavage by the antiviral restriction factor FAM111A, which restricts SV40 by disrupting the nuclear pore complex70. In line with our observations, a recent report confirmed that FAM111A cleaves NUP62 and disrupts nuclear barrier during MVA infection, in addition to inducing the degradation of the viral single-stranded DNA-binding (SSB) protein I327. The antiviral functions of FAM111A are antagonised by the poxvirus host-range factor SPI-1, which is missing in MVA71. Restoration of SPI-1 in MVA rescues virus replication in human cells66. In our gain-of-function screen, ectopic expression of NUP54, NUP62 or NUP88 did not significantly affect MVA gene expression, possibly because the rescue of individual nucleoporins is insufficient to restore the functional consequence of the simultaneous downregulation of several nuclear pore complex components.A total of 101 proteins were downregulated during MVA, but not VACV-WR infection. These included multiple components of the nuclear pore complex, whose downregulation is likely to disrupt nucleocytoplasmic transport35. We therefore compared our findings in THP-1 cells with the recent single-cell transcriptomic analysis of human dendritic cells infected with MVA17. Döring et al. 17. made key observations that were corroborated by our proteomic analysis. First, MVA infection triggers type I interferons and inflammatory cytokines downstream of virus DNA sensing by the cGAS-STING-TBK1-IRF3 pathway. Second, bystander, but not infected, dendritic cells express activation markers stimulated by inflammatory cytokines derived from infected cells. Third, MVA infection induces apoptosis in dendritic cells via upregulation of Bcl-2 protein NOXA17.Studies of immune sensing of MVA infection have focused on antigen-presenting cells. These include dendritic cells and macrophages, which express high levels of MVA proteins at the site of inoculation and present MVA-derived antigens in lymph nodes, underpinning the immunogenicity of MVA-based vaccines41 following MVA genome detection. We did not observe upregulation of the activation marker SIGLEC1 in MVA-infected THP-1s, but this agrees with the single-cell transcriptomic analysis, which showed that only bystander, non-infected dendritic cells express activation markers17 whereas our data represents the proteomic changes in homogeneously infected THP-1s. We did observe SIGLEC1 upregulation in THP-1s stimulated with heat-inactivated MVA, showing that this experimental model captures macrophage activation upon antigen stimulation. Last, we also observed upregulation of NOXA (PMAIP1) during MVA infection of THP-1s72. NOXA has been shown to contribute to the downregulation of the anti-apoptotic protein MCL1, whilst the anti-apoptotic protein BCL2L1 (Bcl-XL) contributed to antagonise apoptosis in MVA-infected cells and did not change in abundance73. Our proteomic analysis of infected THP-1s also captured known abundance behaviour of both these anti-apoptotic proteins.Our proteomic analysis provided evidence of secretion of inflammatory cytokines and chemokines and induction of an interferon response in THP-1s infected with MVA. These macrophage responses were largely unaffected by the DNA replication blocker AraC, suggesting that incoming viral DNA genomes, rather than replication products, are sensed by innate immune sensors. Furthermore, we observed downregulation of STING following MVA infection, which strongly indicates the autophagic degradation of this sensor75. Therefore, we predict that F1 and A47 are largely non-functional during MVA infection because we observed inflammasome assembly, GSDMD processing, IL-1β secretion and pyroptotic cell death in MVA-infected macrophages. MVA also lacks C1, a viral protein recently shown to enhance inflammasome activation74. It is likely that viral DNA triggers inflammasome activation and pyroptosis via the absent in melanoma 2 (AIM2) inflammasome76 and/or NLRP3 inflammasome downstream of STING activation78. Inflammasome-dependent release of cytokines and antigens derived from pyroptotic cells shape adaptive antigen-specific immunity79. It remains to be determined how activation of the inflammasome and pyroptosis contribute to the immunogenicity and protection of MVA-based vaccines in vivo.Other than corroborating previous observations, our proteomic analysis also provided multiple insights into the interactions between MVA and antigen-presenting myeloid cells. The downregulation of IL-1β and the modulation of the abundance of proteins involved in inflammasome activation implicates this immune sensing pathway and inflammatory, pyroptotic cell death in the response to MVA infection in human macrophages. Our data supports that caspase-1-dependent pyroptosis is the dominant mechanism of cell death of MVA-infected macrophages. Notably, MVA lacks a functional orthologue of B13, a viral antagonist of caspase-1 but still encodes F1, an apoptosis and inflammasome inhibitor, and A47, a homologue of gasdermins that counteracts IL-1β secretion and pyroptosis80. ZAP is an antiviral factor that restricts MVA infection in human cells67. VACV-WR induces the proteasomal degradation of ZAP and encodes at least one protein that interacts with and overcomes its antiviral function67. Blockade of MVA DNA replication and post-replicative gene expression upregulated ZAP, whilst PARPs 9, 10, 12 and 14 were only upregulated by heat-inactivated virus. This suggests that these latter PARPs might be targeted by early gene product(s), may indicate that they serve as-yet uncharacterised antiviral function, and highlights the usefulness of our multi-level approach, that included MVA infection with or without a replication inhibitor (AraC), and heat-inactivated virions, to reveal regulation that would otherwise not be observed.Proteins upregulated by heat-inactivated MVA included five members of the poly-ADP-ribosyl polymerase (PARP) family, some of which have well-characterised roles in antiviral responses, such as ZC3HAV1 In sum, our quantitative temporal analysis of the infection of human cells by the MVA vaccine has revealed an extensive remodelling of the host proteome. This highlighted that, at different stages of infection, MVA retains a limited capacity to antagonise innate immune sensing and exhibits features not present in the VACV-WR strain related to the first-generation vaccine. Our data offer an opportunity to dissect how multiple, seemingly redundant MVA and VACV immunomodulatory effectors function, to discover viral strategies to escape antiviral immunity and to explore how sensing of MVA infection shape adaptive immune responses to vaccination and immunological memory. A comprehensive understanding of all these issues may be essential to generate future, fourth-generation smallpox/mpox vaccines in addition to providing the basis for future genomic editing of MVA to better antagonise the host immune response and provide a more effective vaccine vector.2. HFFF-TERTs have been tested at regular intervals since isolation to confirm both that the HLA and MICA genotypes, and the morphology and antibiotic resistances are consistent with the original description81. In addition, human fibroblasts  are the only permissive cells for human cytomegalovirus (HCMV) in cell culture, and HFFF-TERTs are routinely infected with the HCMV Merlin strain, further limiting the chances that the cells have been contaminated with another cell type21. Fresh CEFs were obtained from the Pirbright Institute  and directly seeded for virus production without further passages. THP-1 monocytes  and derivative cell line expressing caspase-1 CARD fused to EGFP (C1C-EGFP)40 were grown in RPMI 1640 medium supplemented with 10% FBS, GlutaMAX (Thermo Fisher Scientific), and penicillin/streptomycin at 37 °C in 5% CO2; additionally, 200 μg/ml hygromycin was added to the growth medium of THP-1C1C-EGFP cells. All cell lines used regularly tested negative for mycoplasma.Primary human foetal foreskin fibroblast cells immortalised with human telomerase  and primary chicken embryo fibroblasts (CEFs) were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with foetal bovine serum (FBS: 10% v/v), and penicillin/streptomycin at 37 °C in 5% CO82. A MVA strain expressing GFP under the control of a synthetic vaccinia early/late promoter (MVA-GFP) was described elsewhere30. MVA and MVA-GFP were propagated in CEFs, purified by ultracentrifugation through two 36% (w/v) sucrose cushions and suspended in 10 mM Tris-HCl pH 9.0. MVA infectivity was determined by immunocytochemistry on CEFs and HFFF-TERTs cells by using a polyclonal rabbit anti-VACV antibody83. For heat inactivation, MVA stock was diluted ten-fold in DMEM or RPMI 1640 supplemented with 2% FBS and heated at 56 °C for 1 h in a water bath85.Modified vaccinia Ankara (MVA) was obtained as a seed stock prepared from an original MVA stock at passage 575GLE1 (NM_001003722.2:88-2184 [https://www.ncbi.nlm.nih.gov/nuccore/NM_001003722.2/]), ISG20 (NM_002201.6:86-631 [https://www.ncbi.nlm.nih.gov/nuccore/NM_002201.6]), NSA2 (NM_014886.6:113-895 [https://www.ncbi.nlm.nih.gov/nuccore/NM_014886.6]), NUP54 (NM_017426.4:24-1547 [https://www.ncbi.nlm.nih.gov/nuccore/NM_017426.4]), NUP62 (NM_153719.4:412-1980 [https://www.ncbi.nlm.nih.gov/nuccore/NM_153719.4]), NUP88 (NM_001320653.2:13-2286 [https://www.ncbi.nlm.nih.gov/nuccore/NM_001320653.2]), OASL (NM_003733.4:276-1820 [https://www.ncbi.nlm.nih.gov/nuccore/NM_003733.4]), RBM28 (NM_018077.3:116-2395 [https://www.ncbi.nlm.nih.gov/nuccore/NM_018077.3]), and ZNFX1 (NM_021035.3:86-5842 [https://www.ncbi.nlm.nih.gov/nuccore/NM_021035.3]), and firefly luciferase (Luc) open reading frames (ORFs) were synthesised by GenScript with flanking 5’ MluI and 3’ NotI restriction and lacking the stop codon. These were subcloned into the lentivector pJAGE1-C-FLAG cut with MluI and NotI, in frame with the sequence encoding a C-terminal FLAG epitope. The plasmid pJAGE1-C-FLAG is a derivative of pHAGE-SFFV86. The cloned lentivectors were used to generate lentiviral particles for transduction of HFFF-TERTs.The human HEK 293T (ATCC CRL-11268) seeded in 12-well plates were transfected with 500 ng lentivector, and 200 ng and 400 ng of helper plasmids pVSV-G and pCMV-dR8.91, respectively, with TransIT LT1 transfection reagent (Mirus). Supernatants containing lentiviral particles were harvested 48 h post-transfection, filtered with a 0.45-μm syringe filter to remove cellular debris, and serial dilutions of the filtered supernatants were used to transduce HFFF-TERTs seeded in 12-well plates. After 48 h, transduced cells with stable, constitutive expression of the transgene were selected with 1 μg/ml puromycin for at least three passages.5 HFFF-TERTs were seeded in a 25-cm2 flask two days prior to infection. Alternatively, 2.25 × 106 THP-1 cells were seeded in a 25-cm2 flask in presence of 20 ng/ml PMA  for 48 h for macrophage differentiation prior to infection. Cells were infected with MVA, or the equivalent amount of heat-inactivated MVA, at MOI 5 for 2, 4, 8, 12, or 18 h for HFFF-TERTs, or 4 and 12 h for PMA-differentiated macrophages, with time zero defined as the time the virus was added to the cells. Mock-infected controls were harvested at 12 h post-infection for HFFF-TERTs or at 4 and 12 h post-infection for THP-1 macrophages. Where indicated, cells were incubated with cytosine arabinoside (AraC) at 40 μg/ml from the time zero. The time course experiments were performed in biological duplicate, using the same virus and cell stocks but performed on different days. The 12-h or the 4-h time points of mock-infected cells are shown as time zero in the graphs for HFFF-TERTs or THP-1 data, respectively.For proteomic experiments, 7.5 × 105 cells per well one day prior to infection, or 7.0 × 105 cells per well two days prior to infection. Cells were infected with MVA-GFP at MOI 0.01 for two days.For the gain-of-function screen, overexpression cell lines were seeded in duplicate in 24-well plates at 1.35 × 105 overexpression cell lines were seeded in 6-well dishes for one day, or 1.0 × 106 THP-1 cells were seeded 6-well plates in presence of 20 ng/ml PMA for two days. Then, THP-1 macrophages were treated with 40 μM VX-765 , 25 μM z-VAD-fmk (BD Pharmingen) or the equivalent amount of DMSO, for 30 min and infected with MVA at MOI 5 for 8 h in the continued presence of VX-765 or DMSO.For immunoblotting, 4.5 × 104 THP-1s were seeded in triplicate in 96-well plates in presence of 20 ng/ml PMA and, two days later, infected with MVA at MOI 5 for 8 h. The infection was carried out in presence of the following inhibitors, which were added 30 min before infection: 0.5 μM necrosulfonamide , 30 μM necrostatin-1 , 1.5 μM GSK’872 , 40 μM VX-765 , 25 μM z-VAD-fmk (BD Pharmingen) or the equivalent volume of DMSO. As control for the activity of necroptosis inhibitors, HFFF-TERTs were seeded in 96-well plate (1.5 × 104 cell/well) and next day, necroptosis was stimulated with 30 ng/ml TNFα (R&D systems), 5 μM BV-6 (Selleckchem), and 25 μM z-VAD-fmk in presence of DMSO, NSA, necrostatin-1, or GSK’872 for 24 h.For the LDH release assay, 3.0 × 105 THP-1 or THP-1C1C-EGFP cells seeded on 13-mm coverslips in 12-well plates were treated with 40 μM VX-765  for 30 min and then, infected with MVA at MOI 5 for 4 and 8 h in the continued presence of VX-765.For fluorescence microscopy, 4.5 × 1083 diluted 1:500 in Perm/Wash buffer, followed by AlexaFluor 568-conjugated goat anti-rabbit IgG (Invitrogen) diluted 1:100. Alternatively, transduced HFFF-TERTs were harvested as described above and stained with rabbit anti-FLAG  diluted 1:200 and AlexaFluor 488-conjugated goat anti-rabbit IgG (Abcam) diluted 1:100. Stained cells were suspended in PBS before data acquisition with an Attune NxT flow cytometer. Data were analysed with FlowJo software.MVA-infected HFFF-TERTs or PMA-differentiated THP-1 macrophages were detached with trypsin-EDTA (Gibco) 24 h post-infection, passed through a 70-μm cell strainer, washed in PBS and stained with Zombie Violet viability dye (BioLegend) for 30 min at 4 °C in the dark. Cells were collected by centrifugation, washed once with PBS and fixed in Cytofix/Cytoperm fixation and permeabilization solution (BD Biosciences) for 30 min at 4 °C in the dark. Cells were washed twice with Perm/Wash buffer (BD Biosciences) and stained with a polyclonal rabbit anti-VACV antibodyGFP fluorescence and phase contrast images of MVA-GFP-infected cells were acquired every 3 h on Incucyte S3 live cell imaging instrument (Sartorius) starting at 3 h p.i. A total of 16 images were acquired per well at 10x magnification. The following parameters were applied to calculate the total GFP area per well on Incucyte Basic Analysis Software (Sartorius): background subtraction by Top-Hat segmentation with radius of 100 μm, threshold of 1.0 green calibrated unit (GCU) and edge sensitivity of −15.Cells were washed twice with PBS and lysed on ice with cell lysis buffer  glycerol, 1% (v/v) Triton X-100 and 0.05% (v/v) Nonidet P-40 (NP-40)), supplemented with protease inhibitors , for 20 min. Lysates were transferred to fresh tubes and spun at 17,000 g for 10 min at 4 °C. Protein concentration was determined using a bicinchoninic acid protein assay kit (Pierce). After mixing with 5× SDS-gel loading buffer and boiling at 100 °C for 5 min, equivalent amounts of protein samples (15–20 μg per well) were loaded onto 4–15% Mini-PROTEAN TGX precast protein gels (Bio-Rad), separated by electrophoresis and transferred onto nitrocellulose membranes . Membranes were blocked at room temperature with 5% (w/v) non-fat milk in Tris-buffered saline (TBS) containing 0.1% (v/v) Tween-20. The membranes were incubated with specific primary antibodies diluted in blocking buffer or 5% (w/v) BSA (Fisher Scientific) at 4 °C overnight. After washing, membranes were probed with fluorophore-conjugated secondary antibodies (LI-COR Biosciences) diluted in 5% (w/v) non-fat milk at room temperature for 1 h. After washing, membranes were imaged using the Odyssey CLx imaging system (LI-COR Biosciences). The band intensities on the immunoblots were quantified using the Image Studio Lite software (LI-COR Biosciences). The antibodies used for immunoblotting are listed: rabbit anti-FLAG , rabbit anti-CANX , mouse monoclonal anti-GAPDH 6C5 , rabbit monoclonal anti-GSDMD E504N , IRDye 800CW-conjugated goat anti-rabbit IgG , and IRDye 680LT-conjugated goat anti-mouse IgG .Lactate dehydrogenase (LDH) release was measured using the CytoTox 96 Non-Radioactive Cytotoxicity Assay (Promega) according to the manufacturer’s instructions. To obtain the maximum LDH release, extra wells with DMSO-treated, mock-infected cells were lysed with lysis buffer (Promega) for 45 min at 37 °C prior to assay. Average absorbance values derived from culture medium alone were subtracted from each absorbance value from experimental wells, and the percentage of LDH release  was calculated relative to the LDH released from lysed cells.Cells were fixed with fixation solution  for 15 min at room temperature. After fixation, cells were washed three times with PBS and permeabilized with 0.5% Triton X-100 for 10 minutes. After permeabilization, cells was stained with rabbit plyclonal anti-ASC  followed by AlexaFluor 568 goat anti-rabbit IgG . Cells was mounted with ProLong Gold Antifade with DAPI (Invitrogen). Imaging was performed using an AxioObserverZ2 (Carl Zeiss).C1C-EGFP) or red channel (THP-1) and a particle diameter of about 1.5 μm.Inflammasome speck quantification was performed using the Imaris software (Oxford Instruments). Images were taken at 20x magnification with 3 × 3 tiles stitched together to achieve 1.9 mm × 1.9 mm area containing an average of ~3000 cells. The cell number was quantified using the DAPI channel and a particle diameter of about 6.5 μm. The inflammasome assembly was quantified using either eGFP channel . Cell lifters (Corning) were used to scrape cells in lysis buffer, which was removed to an eppendorf tube, vortexed extensively then sonicated. Cell debris was removed by centrifugation at 21,000 g for 10 min twice. For half of each sample (estimated from our previous experience with HFFF-TERT cultures as ~500 μg), dithiothreitol (DTT) was added to a final concentration of 5 mM and samples were incubated for 20 min. Cysteines were alkylated with 14 mM iodoacetamide and incubated 20 min at room temperature in the dark. Excess iodoacetamide was quenched with DTT for 15 min. Samples were diluted with 200 mM HEPES pH 8.5 to 1.5 M Guanidine followed by digestion at room temperature for 3 h with LysC protease at a 1:100 protease-to-protein ratio. Samples were further diluted with 200 mM HEPES pH 8.5 to 0.5 M Guanidine. Trypsin was then added at a 1:100 protease-to-protein ratio followed by overnight incubation at 37 °C. The reaction was quenched with 5% formic acid, then centrifuged at 21,000 g for 10 min to remove undigested protein. Peptides were subjected to C18 solid-phase extraction  and vacuum-centrifuged to near-dryness.In preparation for TMT labelling, desalted peptides were dissolved in 200 mM HEPES pH 8.5. Peptide concentration was measured by microBCA (Pierce), and 25 μg of peptide labelled with TMT reagent. TMT reagents (0.5 mg) were dissolved in 43 μl anhydrous acetonitrile and 5 μl added to peptide at a final acetonitrile concentration of 30% (v/v). HFFF-TERTs sample labelling was: 126, MVA (2 h infection); 127 N, MVA+AraC (2 h); 127 C, MVA-HI (2 h); 128 N, MVA (4 h); 128 C, MVA+AraC (4 h); 129 N, MVA-HI (4 h); 129 C, MVA (8 h); 130 N, MVA+AraC (8 h); 130 C, MVA-HI (8 h); 131 N, MVA (12 h); 131 C, MVA+AraC (12 h); 132 N, MVA-HI (12 h); 132 C, MVA (18 h); 133 N, MVA+AraC (18 h); 133 C, MVA-HI (18 h); 134 N, mock infection. THP-1 sample labelling was: 126b, Mock (4 h) WCL1; 127 N, MVA (4 h) WCL1; 127 C, MVA+AraC (4 h) WCL1; 128 N, MVA-HI (4 h) WCL1; 128 C, Mock (12 h) WCL1; 129 N, MVA (12 h) WCL1; 129 C, MVA+AraC (12 h) WCL1; 130 N, MVA-HI (12 h) WCL1; 130 C, Mock (4 h) WCL2; 131 N, MVA (4 h) WCL2; 131 C, MVA+AraC (4 h) WCL2; 132 N, MVA-HI (4 h) WCL2; 132 C, Mock (12 h) WCL2; 133 N, MVA (12 h) WCL2; 133 C, MVA+AraC (12 h) WCL2; 134 N, MVA-HI (12 h) WCL2. TMT impurities from both experiments are detailed in Supplementary Data 87(described below).TMT-labelled tryptic peptides were subjected to HpRP fractionation using an Ultimate 3000 RSLC UHPLC system (Thermo Fisher Scientific) equipped with a 2.1 mm internal diameter (ID) x 25 cm long, 1.7 µm particle Kinetix Evo C18 column (Phenomenex). Mobile phase consisted of A: 3% acetonitrile (MeCN), B: MeCN and C: 200 mM ammonium formate pH 10. Isocratic conditions were 90% A / 10% C, and C was maintained at 10% throughout the gradient elution. Separations were conducted at 45 °C. Samples were loaded at 200 µl/min for 5 min. The flow rate was then increased to 400 µl/min over 5 min, after which the gradient elution proceed as follows: 0–19% B over 10 min, 19–34% B over 14.25 min, 34–50% B over 8.75 min, followed by a 10 min wash at 90% B. UV absorbance was monitored at 280 nm and 15 s fractions were collected into 96-well microplates using the integrated fraction collector. Fractions were recombined orthogonally in a checkerboard fashion, combining alternate wells from each column of the plate into a single fraction, and commencing combination of adjacent fractions in alternating rows. Wells were excluded prior to the start or after the cessation of elution of peptide-rich fractions, as identified from the UV trace. This yielded two sets of 12 combined fractions, A and B, which were dried in a vacuum centrifuge and resuspended in 10 µl MS solvent (4% MeCN / 5% formic acid) prior to LC-MS3. 12 set ‘A’ fractions were used for MS3 analysis of all experiments, and half of each sample was subjected to Synchronous Precursor Selection MS3 (SPS-MS3) analysis, with the other half subjected to real-time search (RTS) MS3 analysisFor ‘singleshot’ analysis of unfractionated peptides from each experiment, mass spectrometry data was acquired using an Orbitrap Lumos. Subsequently, fractions were acquired using an Orbitrap Eclipse.88. The following settings were used: MS1: 380–1500 Th, 120,000 resolution, 2 ×105 automatic gain control (AGC) target, 50 ms maximum injection time. MS2: Quadrupole isolation at an isolation width of m/z 0.7, collision-induced dissociation (CID) fragmentation  34) with ion trap scanning in turbo mode, with 1.5 ×104 AGC target and 120 ms maximum injection time. MS3: In SPS mode the top 10 MS2 ions were selected for higher-energy collisional dissociation (HCD) fragmentation (NCE 45) and scanned in the Orbitrap at 60,000 resolution with an AGC target of 1 × 105 and a maximum accumulation time of 150 ms. Ions were not accumulated for all parallelisable time. The entire MS/MS/MS cycle had a target time of 3 s. Data analysis is discussed below.For Orbitrap Lumos analyses, an Ultimate 3000 RSLC nano UHPLC equipped with a 300 µm ID x5 mm Acclaim PepMap µ-Precolumn (Thermo Fisher Scientific) and a 75 µm ID x50 cm 2.1 µm particle Acclaim PepMap RSLC analytical column was used. Loading solvent was 0.1% FA, analytical solvent A: 0.1% FA and B: 80% MeCN +0.1% FA. All separations were carried out at 40 °C. Samples were loaded at 5 µL/min for 5 min in loading solvent before beginning the analytical gradient. The following gradient was used: 3–7% B over 2 min, 7–37% B over 173 min, followed by a 4 min wash at 95% B and equilibration at 3% B for 15 min. Each analysis used a MultiNotch MS3-based TMT method89. MS2 analysis consisted of collision-induced dissociation (CID) with quadrupole ion trap analysis, using the following parameters: scan speed, turbo; AGC target, 100%; NCE, 35; q-value, 0.25; maximum injection time, 35 ms; and isolation window, 0.5 Th. MS3 precursors were fragmented by HCD and analysed using the Orbitrap with the following parameters: resolution, 50,000; NCE, 55; AGC, 250%; maximum injection time, 86 ms; maximum synchronous precursor selection (SPS) ions, 10; and isolation window, 1.2 Th. RTS-MS3 data were collected with the following parameters: “close-out” 2; peptide length, 7; maximum number of SPS Ions, 10; minimum SPS m/z, 378 Th; AGC target, 250,000; source RF, 30%; MS2 isolation width, 1.2 Th; MS3 isolation width, 3 Th; MS2 collision energy, 35%; MS3 collision energy, 55%; resolution, 50,000; maximum injection time, 200 s:; search tolerance ±10ppm; minimum XCorr, 0.5; minimal delta Cn 0.05. Dynamic exclusion was set at 120 s with ±10 ppm error tolerance.For Orbitrap Eclipse analyses, the FAIMS Pro interface (ThermoFisher Scientific) was coupled to a Proxeon EASY-nLC 1200 liquid chromatograph (LC) (ThermoFisher Scientific). Peptides were separated on a 100 μm inner diameter microcapillary column packed with ~35 cm of Accucore150 resin . For each analysis, 1–2 μg of peptide was loaded onto the column and fractionated over a 90 min gradient of 7 to 27% acetonitrile in 0.125% formic acid at a flow rate of ~600 nL/min. Mass spectrometric data for each fraction of each sample were collected using two distinct data acquisition modes (SPS-MS3 and RTS-MS3), all with FAIMS. SPS-MS3 data were collected with a FAIMS compensation voltage (CV) set of −40 V, −60 V, and −80 V, while RTS-MS3 data were collected with a FAIMS CV set of −30 V, −50 V, and −70 V, with each segment as a 1 s TopSpeed method. For both acquisition methods, the scan sequence began with an MS1 spectrum  target 100%; maximum injection time, auto). Precursors  were then selected for MS2/MS3 analysisIn the following description, the first report in the literature for each relevant algorithm is listed. Mass spectra were processed using a Sequest-based software pipeline for quantitative proteomics developed by Professor Steven Gygi’s laboratory at Harvard Medical School. MS spectra were converted to mzXML using an extractor built upon Thermo Fisher’s RAW File Reader library (version 4.0.26).90 and then searched against a database of protein sequences. A combined database was constructed from (a) the human Uniprot database , (b) the MVA proteome , (c) common contaminants such as porcine trypsin and endoproteinase LysC. The combined database was concatenated with a reverse database composed of all protein sequences in reversed order. Sequest searches were performed using a 20 ppm precursor ion tolerance92. Product ion tolerance was set to 1 Da. Oxidation of methionine residues (15.99492 Da) was set as a variable modification. TMT tags on lysine residues and peptide N termini (304.207145 Da) and carbamidomethylation of cysteine residues (57.02146 Da) were included as static modifications. Peptides were assumed to be fully tryptic with up to two missed cleavages.After converting RAW files to mzxml format, MS data were analysed to correct errors in monoisotopic peak assignment using a published algorithm93. Peptide spectral matches (PSMs) were filtered to an initial peptide-level false discovery rate (FDR) of 1% with subsequent filtering to attain a final protein-level FDR of 1%. PSM filtering was performed using a linear discriminant analysis, as described previously93. This distinguishes correct from incorrect peptide IDs in a manner analogous to the widely used Percolator algorithm (https://noble.gs.washington.edu/proj/percolator/), though employing a distinct machine learning algorithm. The following parameters were considered: XCorr, ΔCn, missed cleavages, peptide length, charge state, and precursor mass accuracy. Peptides shorter than seven amino acids in length or with XCorr less than 1.0 were excluded prior to LDA filtering. Protein assembly was guided by principles of parsimony to produce the smallest set of proteins necessary to account for all observed peptides .To control the fraction of erroneous protein identifications, a target-decoy strategy was employed88. Briefly, a 0.003 Th window around the theoretical m/z of each reporter ion  was scanned for ions, and the maximum intensity nearest to the theoretical m/z was used. An isolation specificity filter with a cutoff of 50% was employed to minimise peptide co-isolation88. Peptide-spectral matches with poor quality MS3 spectra  ratio of less than 250 across all TMT reporter ions) or no MS3 spectra at all were excluded from quantitation. Peptides meeting the stated criteria for reliable quantitation were then summed by parent protein, in effect weighting the contributions of individual peptides to the total protein signal based on their individual TMT reporter ion yields. In order to maximise coverage, the dataset was not filtered to eliminate proteins with single peptide quantitations. Protein quantitation values were exported for further analysis in Excel.Proteins were quantified by summing TMT reporter ion counts across all matching peptide-spectral matches, as described previouslyFor protein quantitation, reverse and contaminant proteins were removed, then each reporter ion channel was summed across all quantified proteins and normalised assuming equal protein loading across all channels. For further analysis and display, fractional TMT signals were used . In three instances (for MVA proteins MVA045 and MVA131 and human protein DZIP1L), the S:N in the mock sample was zero. For the purposes of calculating fold change of each infected sample compared to mock, these mock values were set to 2% of the maximum S:N for any sample in the same WCL experiment. Normalised spectral abundance factors were calculated for a given protein from the sum of peptides quantified divided by the sequence length, then corrected to the sum of all abundance factors. Separate abundance factors were calculated for each cell type.UP000172909) was matched with the VACV-WR proteome (UP000000344) using the PathoSystems Resource Integration Center (PATRIC)’s proteome comparison tool with default settings94.For analysis of the viral proteome, the MVA proteome (http://jtreeview.sourceforge.net) unless otherwise noted.Hierarchical centroid clustering based on uncentered Pearson correlation, and k-means clustering were performed using Cluster 3.0 (Stanford University) and visualised using Java Treeview  was used to determine pathway enrichment29, whereby networks from 293 T and HCT116 cells were merged to act as a comparator to our present data. All analyses were performed in Mathematica 13.1 (Wolfram Research).Interaction analysis was performed on groups of proteins defined by changes in abundance during MVA, MVA+AraC, MVA-HI and VACV infection of HFFF-TERTs indicated in Figs. To assess the tendency for sets of differentially expressed proteins to interact with each other, each was mapped onto the combined BioPlex network after converting all protein identifiers to Entrez GeneIDs. Graph assortativity was then calculated and the process repeated using 1000 randomised protein sets of equal size. Scores from randomised protein sets were then fit to a Gaussian distribution and Z-scores and p-values were estimated.98, with the restart probability set to 0.5. After 40 iterations, proteins were sorted by descending weights. In Fig. To produce interaction networks shown in Fig. Further information on research design is available in the Peer Review FileDescription of Additional Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Reporting SummarySupplementary FigureSource data


Currently, there are no guidelines for the treatment of rheumatoid arthritis (RA) tailored to the context of the Kingdom of Saudi Arabia (KSA). Adaptation of guidelines accounts for contextual factors and becomes more efficient than de novo guideline development when relevant, good quality, and up-to-date guidelines are available. The objective of this study is to describe the methodology used for the adolopment of the 2021 American College of Rheumatology (ACR) guidelines for the treatment of RA in the KSA.We followed the ‘Grading of Recommendations Assessment, Development and Evaluation’ (GRADE)-ADOLOPMENT methodology. The adolopment KSA panel included relevant stakeholders and leading contributors to the original guidelines. We developed a list of five adaptation-relevant prioritization criteria that the panelists applied to the original recommendations. We updated the original evidence profiles with newly published studies identified by the panelists. We constructed Evidence to Decision (EtD) tables including contextual information from the KSA setting. We used the PanelVoice function of GRADEPro Guideline Development Tool (GDT) to obtain the panel’s judgments on the EtD criteria ahead of the panel meeting. Following the meeting, we used the PANELVIEW instrument to obtain the panel’s evaluation of the process.The KSA panel prioritized five recommendations, for which one evidence profile required updating. Out of five adoloped recommendations, two were modified in terms of direction, and one was modified in terms of certainty of the evidence. Criteria driving the modifications in direction were valuation of outcomes, balance of effects, cost, and acceptability. The mean score on the 7-point scale items of the PANELVIEW instrument had an average of 6.47 (SD = 0.18) across all items.The GRADE-ADOLOPMENT methodology proved to be efficient. The panel assessed the process and outcome positively. Engagement of stakeholders proved to be important for the success of this project.The online version contains supplementary material available at 10.1186/s12874-023-02031-2. The development of new guidelines is time- and resource-intensive. Alternatively, guideline developers can adopt existing recommendations, or adapt them to their own context . When reThe ‘Grading of Recommendations Assessment, Development and Evaluation’ (GRADE)-ADOLOPMENT approach is increasingly being used to build on existing guidelines . AdolopmCurrently, there are no guidelines for the treatment of rheumatoid arthritis (RA) tailored to the context of the Kingdom of Saudi Arabia (KSA). Management follows recommendations of both the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) , 11. YetRecently, the ACR published the 2021 update of guidelines for the treatment of RA . The objWe followed the GRADE-ADOLOPMENT methodology . This meThe 2021 American College of Rheumatology (ACR) guidelines for the treatment of RA includes 43 recommendations addressing questions on treatment with disease-modifying antirheumatic drugs (DMARDs) (24 recommendations), use of glucocorticoids (4 recommendations), and use of DMARDs in certain high-risk populations (15 recommendations). The development of the guidelines included the use of GRADE evidence tables and graded recommendations while accounting for the balance of benefits and harms, the certainty of the evidence, patient values and preferences, and resource use. While the methodology builds on the key principles of GRADE and includes many of the components of the GRADE EtD framework in a modified decision support voting mechanism, it does not explicitly include the production of an EtD table for each recommendation .The project involved a coordination group and a guideline panel. The coordination group included the two content experts leading the project  and three methodologists . That group facilitated the handling of both logistical and methodological aspects of the work. The panel included representatives from different stakeholder groups, including rheumatologists, a pharmacist, a patient representative, and policymakers. The rheumatologists included two international experts (the chair of the source guideline and the chair of the 2015 ACR guideline ). The paGiven the limited time and resources, the coordination group chose to prioritize five recommendations to be adapted from the source guideline. First, we developed a list of five adaptation-relevant prioritization criteria Table . The selAs for the prioritization of outcomes, we reviewed the priority list set by the ACR and the systematic review on the topic , i.e., dEvidence on health effects: we judged that a formal update of the evidence reports published by the ACR guidelines was not needed given the short timeframe between the publication of the source guideline and the adaptation project. Instead, we asked panelists to suggest any new studies published since the source guideline. If that was the case, we followed standard systematic review methodology to assess eligibility, abstract data and update the analyses, as described by the Cochrane handbook  . Then, tValues and preferences: as mentioned above, the panel adopted the source guideline's ratings after consideration of the relevant systematic review on the topic  and the Cost data: we collected relevant cost data using two sources: the Saudi Food Drug Authority (SFDA) for the private sector  and the Other contextual factors : as we did not conduct a formal assessment of these factors, we relied on the panel’s personal knowledge and experience.Figure https://www.gradepro.org/). During the online panel meetings, the panelists decided on a judgment for each criterion of the EtD after reviewing the preliminary feedback and judgments, and discussion. When consensus could not be reached, we went with the majority vote and noted the vote results in the EtD.The methodologists created EtD tables using the sources of data mentioned above. Before each recommendation meeting, we electronically shared the EtDs with the panelists and obtained their preliminary feedback and judgments using the PanelVoice function of GRADEPro Guideline Development Tool (GDT)  were less relevant to the KSA setting . This tilted the balance of desirable and undesirable effects in favor of MTX. Also, the panel took into consideration that the cost of a 12-week treatment course of SSZ is substantially higher than that of MTX. In addition, the panel judged that a generally low rate of adherence to medications in the KSA favored MTX (administered weekly) over SSZ (administered twice daily) (acceptability criterion).For recommendation #3, the panel modified the direction in relation to the valuation of outcomes and the balance of effects. Specifically, the KSA panel highly valued rapid alleviation of symptoms at the time of diagnosis and during flares. Accordingly, the KSA panel judged that the balance of effects “probably favors” short-term (< 3 months) glucocorticoids when initiating a csDMARD. Still, the KSA panel acknowledged a few challenges with glucocorticoid treatment and detailed them in the rationale as a note of caution to users.For recommendation #5, we updated the evidence to incorporate a newly published study. As a result, the certainty of evidence increased from very low to moderate, but did not lead to a modification in the strength or direction of the recommendation.Out of 19 panelists, 13 filled the PANELVIEW survey. Additional file In this paper, we described the methodology used for the adolopment of the 2021 ACR guidelines for the treatment of RA in the KSA. The process followed the GRADE-ADOLOPMENT methodology, and involved prioritization of recommendations and outcomes, and construction of EtDs for each prioritized recommendation. We populated the EtDs with data from the source guideline and with contextual data from the KSA.We engaged relevant stakeholder groups as panel members and they contributed to the different steps of the process, including prioritization of recommendations and outcomes, evidence gathering, and adolopment of recommendations. Representation from the different Saudi regions and type of practice  enriched the discussions and contributed to enhancing the applicability of the recommendations. In addition, the inclusion of two international experts from the source guideline provided the rest of the panel with unique insight into the context of the source guideline and the source panel’s judgments.The use of the GRADE-ADOLOPMENT methodology allowed the contextualization of guidelines in a relatively short period of time. A major facilitator was the use of the same methodology  and tools  used in the source guideline. In addition, the use of the EtD framework facilitated structured discussions, and ensured that the panel considered both evidence on health effects and contextual data. While we collected primary cost data from the KSA context, we did not do so for the remaining contextual factors and had to rely on expert evidence provided by the panelists. This could have been achieved through either quantitative surveys , 23 or mIn this project, none of the five adoloped recommendations were modified from the source recommendations in terms of strength. Two out of the five adoloped recommendations were modified in terms of direction. In a previous adolopment effort of the 2015 ACR treatment guideline for RA, five out eight adoloped recommendations were modified, specifically in terms of their strengths .n = 2), valuation of outcomes (n = 1), cost (n = 1), and acceptability (n = 1). These factors, in addition to impact on health equity, were also driving factors for modification of recommendations in two other guideline adolopment efforts in the region [The factors driving modification in this project were balance of effects n = , valuatie region .The evaluation scores on the PANELVIEW instrument were high when compared with scores for eight other panels . When coWith the increasing move towards living guidelines, groups adapting recommendations must deal with the challenge of frequently updated recommendations , 26. TheThe GRADE-ADOLOPMENT methodology proved to be efficient. The panel positively assessed the process and outcome of this adolopment effort. The engagement of stakeholders with wide representation in terms of groups, regions and type of practice proved to be important for the success of this project.Additional file 1. Detailed timeline for the adolopment project.Additional file 2. Survey data Additional file 3. PANELVIEW instrument scores.


In Canada, virtual health care rapidly expanded during the COVID-19 pandemic. There is substantial variability between older adults in terms of digital literacy skills, which precludes equitable participation of some older adults in virtual care. Little is known about how to measure older adults’ electronic health  literacy, which could help healthcare providers to support older adults in accessing virtual care. Our study objective was to examine the diagnostic accuracy of eHealth literacy tools in older adults.We completed a systematic review examining the validity of eHealth literacy tools compared to a reference standard or another tool. We searched MEDLINE, EMBASE, CENTRAL/CDSR, PsycINFO and grey literature for articles published from inception until January 13, 2021. We included studies where the mean population age was at least 60 years old. Two reviewers independently completed article screening, data abstraction, and risk of bias assessment using the Quality Assessment for Diagnostic Accuracy Studies-2 tool. We implemented the PROGRESS-Plus framework to describe the reporting of social determinants of health.r = 0.34) and TMeHL correlated moderately to highly with eHEALS (r = 0.47–0.66). Using the PROGRESS-Plus framework, we identified shortcomings in the reporting of study participants’ social determinants of health, including social capital and time-dependent relationships.We identified 14,940 citations and included two studies. Included studies described three methods for assessing eHealth literacy: computer simulation, eHealth Literacy Scale (eHEALS), and Transactional Model of eHealth Literacy (TMeHL). eHEALS correlated moderately with participants’ computer simulation performance .The online version contains supplementary material available at 10.1186/s12877-023-03899-x. Virtual care is used 4.6 times more often by older adults than before the COVID-19 pandemic . DespiteTo tackle the digital divide, we must be able to assess eHealth literacy. eHealth involves health information and services provided via the Internet and other technologies, including virtual care, forums, electronic health records, and smartphone applications to facilitate healthcare decision-making –12. eHeaIncreased uptake of virtual care, specifically the need to use videoconference-based assessments due to our greater certainty in their diagnostic accuracy compared to telephone-based assessments, indicates an urgent need for evaluation of eHealth literacy skills . CliniciWe reported our systematic review as per the Preferred Reporting Items for Systematic reviews and Meta-Analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) and Synthesis without meta-analysis (SWiM) guidance , 27. Thith, 2021. We used controlled vocabulary and keywords related to clusters of terms for eHealth Literacy and Older Adults  was too restrictive, and we revised our criteria to include all studies where the mean population age was 60 years of age or older . We chosTwo reviewers independently  abstracted data from each included full-text article and appraised the risk of bias using the Quality Assessment for Diagnostic Accuracy Studies-2 (QUADAS-2) tool . DiscrepWe could not complete a meta-analysis of diagnostic accuracy outcomes because there were too few included studies.n = 45) . Th. Th37]. There was high risk of bias for patient selection as study recruitment was completed by telephone and 90% of surveyed participants withdrew from the study before participating in the computer simulation component; additional participants were recruited by snowball sampling Table  37]. Th. Th37]. This study included 283 community-dwelling older adults from a university-based research registry in the United States Table  39]. Th. Th39]. r = 0.47; p < 0.01), communicative , critical , and translational  scales . It. It39]. We found two tools that will support clinicians in measuring older adults’ eHealth literacy , 39. HowNot being able to assess older adults’ eHealth literacy represents a critical knowledge gap and barrier to the sustainability of digital health solutions, especially as virtual care is integrated into routine healthcare delivery . FurtherOur systematic review has limitations. First, we could be missing relevant articles; however, we were inclusive in our database search and grey literature search. Second, there were too few studies to complete a meta-analysis of diagnostic accuracy estimates. Third, included studies had small sample sizes with limited recruitment strategies. Recruited participants were predominantly Israeli and White. As illustrated by our equity analyses following the PROGRESS-Plus framework, the applicability of these two eHealth literacy tools is limited as there wasn’t diverse representation within the small sample size . MoreoveIn conclusion, we completed the first systematic review on the diagnostic accuracy of eHealth literacy tools in older adults. We identified two eHealth literacy tools that were compared to a reference standard or another tool ; however, study limitations such as incomplete reporting of diagnostic accuracy measures  and unclear to high risk of bias across multiple components of each study’s risk of bias assessment preclude us from recommending one tool over another. Future research describing the sensitivity and specificity of tools for measuring eHealth literacy in older adults and how social determinants of health impact the diagnostic accuracy of eHealth literacy tools would strengthen tool implementation in clinical practice.Additional file 1: Supplementary file 1. Search strategy.Additional file 2: Supplementary file 2. Grey literature databases searched.


Cholangiocarcinoma (CCA), a highly heterogeneous cancer, is the second most common type of primary liver cancer. It is characterized by resistance to therapy and poor prognosis, with a 5-year survival rate lower than 20%. The pathogenesis of CCA is complex and multifactorial, and in recent years, bile acids (BAs) have been implicated in CCA development and prognosis. BAs belong to a category of amphipathic compounds that hold significant importance as signaling molecules and inflammatory agents. They possess the ability to activate transcriptional factors and cellular signaling pathways, thereby governing the regulation of lipid, glucose, and energy metabolism in diverse human disorders. These disorders encompass chronic liver diseases among other conditions. In this review, we provided an update on the current knowledge on the molecular mechanisms involving BAs in cholangiocarcinogenesis. Additionally, we analyzed the role of gut and biliary microbiota in CCA pathogenesis. Future research is required to better understand how to modulate BA activity and, possibly, identify new therapeutic strategies. Cholangiocarcinoma (CCA) is the second most common primary liver cancer after hepatocellular carcinoma (HCC), comprising approximately 15% of all primary hepatic malignancies and 3% of gastrointestinal tumors . CCA incA causal relationship between bile acids (BAs) and tumors was firstly described in 1940 , but onlBA synthesis mainly occurs in the liver through different pathways and several reactions, starting with the enzymatic conversion of cholesterol molecules. This phenomenon proceeds through two distinct pathways, namely the classical  and the alternative (or acidic) routes, involving a minimum of 17 hepatic enzymes. The classic pathway is the major pathway and is responsible for about 90% of total BA production in the liver. The first biochemical reaction is catalyzed by 7α-hydroxylase (CYP7A1), converting cholesterol to 7α-hydroxycholesterol. This is a crucial enzyme because the BA synthesis rate is mainly controlled by transcriptional regulation of its gene . The altThe primary BAs are cholic acid (CA) and chenodeoxycholic acid (CDCA). Both are mainly produced in the classic way, otherwise the alternative way only produces CDCA. Subsequently, the BAs are conjugated with either glycine (the major part) or taurine ,11.Once synthesized and conjugated, BAs are transported into bile, mainly by ATP-dependent cassette transporter (BSEP), and they are stored in gallbladder. Food intake stimulates the release of BAs into the intestine. Along the small intestinal tract, micellar BAs work as effective detergents to help the solubilization of fatty acids and monoacylglycerols, digestion, and the assimilation of dietary lipids and fat-soluble vitamins . SubsequIn the small and mainly in the large intestine, a small percentage (about 5%) of BAs are deconjugated and dehydroxylated by bacteria via bile salt hydrolases, losing their ability to act as detergents. This process leads to the conversion of primary BAs into secondary BAs, more hydrophobic and toxic to humans. Specifically, CA and CDCA are converted to deoxycholic acid (DCA) and lithocholic acid (LCA). DCA undergoes passive reabsorption within the large intestine, while a significant portion of LCA is sulfated within the intestine and subsequently excreted in fecal matter. Furthermore, within the human intestine, LCA has the potential to undergo conversion into more soluble and less toxic compounds, such as hyocholic acid (HCA) and ursodeoxycholic acid (UDCA) ,15,16.BAs, together with cholesterol and phosphatidylcholine, form the lipid phase of bile. They are amphipathic detergent molecules formed by a polar group and an apolar part. The conjugation process makes them negatively charged molecules with increased solubility, and when their concentration increases, conjugated BAs form micelles able to solubilize cholesterol. This function concurs with cholesterol homeostasis and favors fat and liposoluble vitamin absorption in the intestine. They also sustain canalicular bile flow through specific BA export pumps, regulating the extrusion of BAs linked with water in biliary canaliculi, determining the “BA-dependent bile flow” and facilitating biliary secretion of xenobiotics and endogenous metabolites ,17.In addition to their digestive role, BAs act as signaling molecules and as ligands of many receptors that can modulate fundamental metabolic and cellular function. BAs directly activate nuclear receptors (NR), such as pregnane X receptor (PXR), farnesoid X receptor (FXR), and vitamin D receptor (VDR), which act by limiting BA synthesis . FXR, maAnother metabolic role of BAs is their contribution to the control of glucose and lipid homeostasis. One method of metabolism regulation is signaling through G protein-coupled receptors (GPCRs), in particular Takeda G protein-coupled bile acid receptor (TGR5) and sphingosine-1-phosphate 2 receptor (S1PR2). TGR5 can be activated not only by conjugated and unconjugated primary BAs but also by secondary BAs, such as LCA, which is one of the most potent activators of this receptor. Ligand binding to TGR5 triggers activation leading to an increase in intracellular adenosine monophosphate (cAMP) concentration to modulate glucose homeostasis through glucagon-like peptide 1 (GLP1) secretion and to promote energy metabolism; furthermore, TGR5 couples with stimulatory G proteins (Gs) to modulate cell proliferation. In the literature, substantial evidence has been presented regarding the significant role of TGR5 in supporting the proliferation and growth of bile duct epithelial cells in a bile acid (BA)-dependent manner ,23. ConjIn summary, BAs have many key roles in humans . Their sThe pathogenesis of CCA is a multistep process involving oxidative stress and inflammation, mutagenic processes with genetic and epigenetic factors, as well as environmental proliferative stimuli  with an intricate network of oncogenic mechanisms, creating heterogeneous tumors . CholestThe imbalance of lipids and “toxic BAs” in bile seems to be created by the activation of peroxisome proliferator-activated receptor-a (PPARa). As a matter of fact, PPARa agonists, used for treating metabolic syndrome, are associated, in animal studies, with increased synthesis of BAs, hepatic damage, cholangiocyte proliferation, and liver carcinogenesis .BAs have both hydrophilic and hydrophobic surfaces, with a protective and less cytotoxic role played by the hydrophilic surface, while the hydrophobic part of BAs causes cell damage, inducing oxidative stress and genomic instability, modulating the expression of tumor-suppressor/-promoting genes. BAs exhibit an additional role as signaling molecules, effectively inducing the proliferation of liver cancer cells. BAs engage in binding interactions with both nuclear and membrane receptors, with varying affinities, notably involving FXR, S1PR2, and TGR5  34,35,3,335,36.Farnesoid X receptor (FXR) is a nuclear hormone receptor for BAs, which is so important in their homeostasis that it is considered the intracellular sensor of BAs. The FXR expression is elevated in cells exposed to high BA concentrations, such as cholangiocytes and enterocytes, and above all, FXR maintains BA concentrations within a physiological range to prevent their accumulation and the associated cellular damage . MoreoveIn in vitro studies, FXR activation could strongly suppress the activity of the nuclear factor-kappa B (NF-kB) . AccordiMoreover, Sirtuin 1, another regulator of BA homeostasis, appears to play a crucial role in modulating the regenerative response of the liver through post-transcriptional modifications, including acetylation of FXR and neighboring histones, which potentially contribute to CCA onset by disrupting the homeostasis of BAs via persistent FXE deacetylation. This suggests that the dysregulation of BA homeostasis and subsequent CCA development may be influenced by the aberrant deacetylation of FXR facilitated by Sirtuin 1 . SimilarFinally, alcohol is able to downregulate FXR, with a subsequent increase in BA synthesis and the liver BA pool. So, chronic alcohol intake is also able to deeply affect the entero-hepatic circulation of BAs through effects on BA transporters, leading to increased serum levels of BAs ,48.The sphingosine 1-phosphate (S1P) pathway is involved in the inhibition of the apoptosis of biliary cancer cells  and in CTGR5 (G-protein-coupled BA receptor 1) is a membrane-bound receptor responsive to BAs, expressed in several non-parenchymal cells of the liver, including sinusoidal endothelial cells, Kupffer cells, gallbladder epithelial cells, and cholangiocytes, even if its expression level is much lower than that of S1PR2 . TGR5 prIn conclusion, the BAs-FXR-S1PR2-TGR5 axis needs to be considered within the overall framework of cholangiocarcinogenesis, taking into account that BAs are involved in the regulation of apoptosis resistance, proliferation, and preservation of ductal bile secretion during damage . ConversFirmicutes and Bacteroidetes makes up more than 90% of the human gut microbiota [The human gut is colonized by a complex and dynamic range of symbiotic microorganisms, collectively termed the “gut microbiome”, which is one of the most complex and densely populated ecosystems known. The gut microbiota has the role of defending the human host from pathogens and maintaining immune balance and metabolic homeostasis . The grecrobiota . Even thcrobiota ,60.Firmicutes, including bacteria that dehydroxylate primary BAs into secondary toxic BAs [Due to its close connection to the gut via the hepatic portal vein, the liver is the first recipient of gut microbiome molecules and metabolites, including BAs, choline, indole derivatives, lipopolysaccharide (LPS), and short-chain fatty acids (SCFAs) . In partoxic BAs . On the oxic BAs .Clostridium species, reduce the accumulation of NKT cells, with subsequent tumor growth induction [Clostridium species induced by various factors cause an increased production of secondary BAs. This increases cyclooxygenase-2 (COX-2) activity in hepatic stellate cells and prostaglandine-mediated suppression of antitumor immunity [A direct relationship between the gut microbiota, secondary BAs, and liver cancer has been suggested by recent studies, and two pathways involved in the gut microbiota regulation of liver cancer via secondary BAs have been described . Ma C. enduction . Moreoveimmunity . HoweverGammaproteobacteria, such as Enterobacteriaceae, and a lower presence of Clostridia compared to the other groups. Some butyrate-producing bacteria were, instead, more abundant in healthy subjects. These different compositions of fecal microbiota suggest that the development of BTC can lead to changes in microbiota at different levels.Instead, several studies have investigated the gut microbiota composition in patients with CCA. Ito Z. et al.  evaluateFirmicutes was the most abundant in all the groups. Regarding the alpha- and beta-diversity, patients with iCCA had the highest biodiversity among all four groups. At genus level, patients with iCCA showed higher prevalence rates of Lactobacillus, Actinomyces, Peptostreptococcaceae, and Alloscardovia than the other groups. These results showed that gut microbiota composition is more different in CCA compared to HCC and healthy controls, probably due to the strict connection between the gut and cholangiocytes. Moreover, the authors analyzed the plasma–stool ratio of BAs and showed that the glycoursodeoxycholic acid and tauroursodeoxycholic acid plasma–stool ratios were increased in patients with ICC and were positively correlated with the genera Lactobacillus and Alloscardovia. Furthermore, patients with iCCA and vascular invasion have different gut composition compared to patients without vascular invasion, suggesting that the microbiota could be related to vascular invasion.Jia X. et al.  analyzedIn brief, gut microbiota composition is significantly different in patients with CCA compared to patients with other liver diseases or controls. BAs are among the main factors responsible for this difference and could be involved in liver carcinogenesis through gut microbiota modification.Enterobacteriaceae compared to patients with BBD. Moreover, they observed that bile samples had significantly lower sequencing depths than stool samples, reflecting their lower bacterial content. Another important study conducted by Miyabe K. et al. [Firmicutes, Fusobacteria, and Actinobacteria in comparison to control bile. It is remarkable that both Fusobacteria and Firmicutes were also increased in PSC patients in relation to the duration of the disease. Similar results on bile microbiota composition were obtained by Okuda S. et al. [Akkermansia was detected in the majority of bile samples of patients with CCA but not in patients with pancreatic cancer, and its presence was related to a better outcome after biliary drainage.Bile has traditionally been considered sterile in subjects without prior biliary intervention or biliary disease. The chemical and physical properties of bile and its antimicrobial capacity were able to create a hostile environment for bacteria. Furthermore, the challenge of collecting bile samples joined with the lack of awareness of culture techniques in detecting microbes in low-charge samples supported this hypothesis for a long time ,72. Inst. et al.  analyzed. et al. . MoreoveHowever, the results of these studies are contrasting and limited by several factors. First of all, the standardization of the sampling methods should be evaluated. Different methods have been applied to perform bile sampling; with a high risk of contamination, sampling standards are currently lacking. Considering that studies on healthy human biliary microbiota are not feasible for ethical issues, comparative studies on the biliary microbiota of subjects with different biliary diseases could be able to identify the microbial fingerprint of each illness. Furthermore, samples are obtained from patients with CCAs in different stages, and comparison between the bile microbiota of these patients is not possible.In conclusion, CCA pathogenesis is complex and multifactorial. This review provides a comprehensive overview of the role of BAs in liver carcinogenesis. These molecules not only play a pivotal role in bile formation and secretion but are also crucial in protective and harmful processes involving the biliary tract. In particular, in a CCA setting, BAs seem to promote cholangiocarcinogenesis through several mechanisms, such as the activation of proliferative pathways, the inhibition of apoptosis, the promotion of cell proliferation, and the modulation of gut and biliary microbiota. Currently, the pathways involved still need to be completely elucidated. In the future, the modulation of these mechanisms may warrant a better comprehension of genetic, acute, and chronic cholestatic human liver diseases and possibly suggest therapeutic strategies for these conditions.


Caricature is an exaggerated pictorial representation of a person, which is widely used in entertainment and political media. Recently, GAN-based methods achieved automatic caricature generation through transferring caricature style and performing shape exaggeration simultaneously. However, the caricature synthesized by these methods cannot perfectly reflect the characteristics of the subject, whose shape exaggeration are not reasonable and requires facial landmarks of caricature. In addition, the existing methods always produce the bad cases in caricature style due to the simpleness of their style transfer method.In this paper, we propose a Style Attention based Global-local Aware GAN to apply the characteristics of a subject to generate personalized caricature. To integrate the facial characteristics of a subject, we introduce a landmark-based warp controller for personalized shape exaggeration, which employs the facial landmarks as control points to warp image according to its facial features, without requirement of the facial landmarks of caricature. To fuse the facial feature with caricature style appropriately, we introduce a style-attention module, which adopts an attention mechanism, instead of the simple Adaptive Instance Normalization (AdaIN) for style transfer. To reduce the bad cases and increase the quality of generated caricatures, we propose a multi-scale discriminator to both globally and locally discriminate the synthesized and real caricature, which improves the whole structure and realistic details of the synthesized caricature.Experimental results on two publicly available datasets, the WebCaricature and the CaVINet datasets, validate the effectiveness of our proposed method and suggest that our proposed method achieves better performance than the existing methods.The caricatures generated by the proposed method can not only preserve the identity of input photo but also the characteristic shape exaggeration for each person, which are highly close to the real caricatures drawn by real artists. It indicates that our method can be adopted in the real application. Caricature represents a person or subject in an art form, which exaggerates individual characteristics to create a comic and grotesque effect Redman, . The chaThe features of caricature can be summarized into three main elements.Likeness: Caricature should share the same identity as its subject, which needs to represent who it is.Exaggeration: Caricature needs to exaggerate the facial features of its subject in a reasonable way, which highlights the unique characteristics of its subject.Style: Caricature is a rendered image, which is non-photo realistic. It can preserve a variety of expressive styles like sketching, pencil strokes, oil painting, and other artistic styles.In the past two decades, many researchers have been working on caricature generation. These methods can be divided into two groups, i.e., conventional methods and GAN-based methods. In conventional methods, most of them mainly focused on the style-specific caricature generation, such as facial sketch caricature     and content feature Fc = Ec(x), respectively. Here, H, W, and C are height, width and number of channels respectively. Then, style-attention module fuses Fc and a random latent style code Fcs. Afterwards, the decoder takes Fcs to generate a caricature texture R(Fcs). These processes can stylize the photo image with caricature style. To further exaggerate the caricature texture, we utilize a landmark-based warp controller for personalized shape exaggeration to predict the control points and the corresponding displacements to warp the generated texture.Given a 2D photo image To provide more details about our method, the architecture of style encoder, content encoder, decoder, local-aware discriminator and global-aware discriminator are shown in When transferring the caricature style to a photo image, most deep learning based methods applied the Adaptive Instance Normalization (AdaIN) . Then, both the style and content feature are normalized and weighted by two separate 1 × 1 convolutional layers . Finally, the stylized content feature are computed as follows:Inspired by the method proposed by Park and Lee , we propF. To simplify the representation, the style-attention module can be defined as:where This module can locally embed the latent style feature in each position of the content feature by mapping a relationship between the style and content feature maps, which is the output of softmax in this module.R takes the stylized content feature Fcs and outputs a generated texture R(Fcs). Moreover, we introduce two reconstruction losses to supervise the encoders to correctly and effectively extract style feature and content feature. The reconstruction losses are formulated as:To further generate a caricature texture, a decoder y denotes a real caricature image.where To automatically perform shape exaggeration, WarpGAN  denotes the center crop operation.where The overall objective functions for generator and discriminator are as follows:1, λ2, λ3, and λ4 denote the hyper-parameters to balance different losses.where λWe conduct our experiments on two publicly available datasets, i.e., the WebCaricature dataset    matching number of the generated caricatures. To construct a pair of gallery set and prob set for WebCaricature datset, we randomly pick 126 identities from testset and employ the corresponding 126 photos as gallery set. Then, we generate the caricatures from the rest of 2, 832 = 2, 958 − 126 photos as the prob set. Finally, we randomly construct 10 pairs of gallery set and prob set and compute the average for the results.For n et al.  to extraFréchet Inception Distance (FID) is introduced to measure the distribution of the fake caricatures generated by our proposed model and the real caricatures. The lower FID means that the distribution of synthesized caricatures are closer to that of real caricatures. As for the WebCaricature dataset, we randomly pick 2,930 photos of testset and generate the corresponding caricatures by our proposed model. Afterwards, we measure the FID between the generated caricatures and all the real caricatures from testset. Similarly, to evaluate on the CaVINet dataset, we generate 2,817 caricatures from the photos of testset and compute the FID between the fake and real caricatures.The We conduct ablation study on the WebCaricature dataset to evaluate the effectiveness of each module, i.e., style-attention module (SAM), landmark-based warp controller for personalized shape exaggeration (LWC), and multi-scale discriminator (MD). We adopt WarpGAN as our baseline. When ablating style-attention module, we replace the instance normalization of decoder to Adaptive Instance Normalization (AdaIN) and require style encoder to predict the corresponding β and γ. As for the landmark-based warp controller, we applied the warp controller of WarpGAN for ablation study. When performing ablation study on multi-scale discriminator, we simply remove the local-aware discriminator. The ablation study results are shown in In this section, we qualitatively and quantitatively evaluate our proposed method on both the WebCaricature and CaVINet datasets. We mainly compare with the GAN-based image translation methods, including CycleGAN (Zhu et al., As for the WebCaricature dataset, As for the CaVINet dataset, To evaluate different methods quantitatively, we first employ FID as our evaluation metrics. Then, we perform a user study on the visual quality of the caricatures generated by our proposed method, WarpGAN (Shi et al., As for user study, we apply the four approaches to generate caricatures from 50 photos on WebCaricature and CaVINet dataset, respectively. The 400 = 50 × 4 × 2 generated caricatures are shown to the volunteers and they are asked to select the best caricature, in terms of overall visual quality. In addition, they are also asked to select their favorite caricatures in terms of either style transferring or shape exaggeration. As a number of 22 volunteers participated the questionnaire, a maximum of 6, 600 = 100 × 3 × 22 votes can be received for each approach. As for identity preservation, we employ CMC and the rank one-to-ten matching number of the generated caricatures to quantify the performance of face identification. 6 parameters, ranking the second place after WarpGAN (Shi et al., As shown in In this paper, we propose a Style Attention based Global-local Aware GAN for Personalized Facial Caricature Generation, which applies the characteristics of a subject to generate personalized caricature. To integrate the facial characteristics of a subject, we introduce a landmark-based warp controller for personalized shape exaggeration, which employs the facial landmarks as control points to warp image according to its facial features. To fuse the facial feature with caricature style appropriately, we introduce a style-attention module, which adopts an attention mechanism, instead of the simple Adaptive Instance Normalization, to perform style transfer. To reduce the bad cases and increase the quality of generated caricatures, we propose a multi-scale discriminator to both globally and locally discriminate the synthesized and real caricature, which improves the whole structure and realistic details of the synthesized caricature. Furthermore, we qualitatively and quantitatively evaluate our proposed method on both WebCaricature and CaVINet dataset and empirically prove that our proposed method achieves the best performance among the compared methods. Since the multi-scale discriminator classifies the identities of the photo, the generated caricature and the real caricature, our method requires the caricature dataset with multiple photo-caricature pairs from the same identity. In future work, we will redesign the identity classification part. Specifically, we will extract the identity features of the given images and adopt a classifier to classify the identity without considering whether it is photo, the generated caricature or the real caricature.https://cs.nju.edu.cn/rl/WebCaricature.htm; https://github.com/lsaiml/CaVINet.Publicly available datasets were analyzed in this study. This data can be found here: XZ was responsible to study conception and design. WC took in charge of the data collection. WX was in charge of the analysis and interpretation of results. LS drafted the manuscript. All authors reviewed the results and approved the final version of the manuscript.