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Record W2887283890 · doi:10.1097/mot.0000000000000570

A view of the future of regulatory immune cell therapy in organ transplantation

2018· editorial· en· W2887283890 on OpenAlexaboutno aff
Angus W. Thomson

Bibliographic record

VenueCurrent Opinion in Organ Transplantation · 2018
Typeeditorial
Languageen
FieldMedicine
TopicMesenchymal stem cell research
Canadian institutionsnot available
Fundersnot available
KeywordsOrgan transplantationTransplantationImmune systemMedicineCell therapyIntensive care medicineImmunologyBiologyStem cellInternal medicineCell biology

Abstract

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On the basis of extensive preclinical research [1–3], there is currently great interest in the potential of specific regulatory immune cell populations for the therapy of organ allograft rejection and the promotion of clinical transplant tolerance. Indeed, clinical trials to assess feasibility, safety and preliminary efficacy of diverse regulatory immune cell populations, that include regulatory T cells (Treg), regulatory myeloid cells (regulatory dendritic cells and macrophages), mesenchymal stromal cells and facilitating cells (that enhance donor hematopoietic cell chimerism) have been instigated in Europe, North America and Asia [4–7]. However, many questions need to be addressed and numerous hurdles need to be overcome if the promise of regulatory immune cell therapy is to be realized [5,8–10]. In this section of Current Opinion in Organ Transplantation, seven articles written by opinion leaders critically review the most recent advances in the field and provide a view of what the future of regulatory immune cell therapy may look like in clinical organ transplantation. Looking towards the future for regulatory T cell (Treg) therapy, Sicard et al. (University of British Columbia, Vancouver, Canada) (pp. 509–515) examine the wave of exciting recent innovations that could be used to overcome current limitations and enhance the antigen (Ag) specificity, stability, function and potential of therapeutic Treg. As they discuss, these approaches include the generation of Ag-specific Treg by genetic modification of polyclonal Treg to express designated T-cell receptors or single-chain chimeric Ag receptors (CAR). Importantly, human Treg expressing allospecific CAR have been shown recently to potently suppress effector T cells in humanized mice. In addition, CRISPR/Cas 9 technology is currently enabling the modification of Treg to make them safer, more stable and long-lived. The authors also discuss the use of third party Treg as a novel approach to develop better-standardized and more accessible therapeutic Treg. In a complementary article written by Vaikunthanathan et al. (King's College London, UK) (pp. 516–523) the authors discuss approaches to optimizing and personalizing the isolation of Treg and their ex-vivo expansion, including the use of mechanistic target of rapamycin (mTOR) inhibitors, all-trans retinoic acid (ATRA), histone deacetylase inhibitors and tumor necrosis factor receptor (TNFR) agonists that increase and stabilize forkhead box p3 (Foxp3) expression. They also address the functional specialization of Treg subsets and the untoward consequences of loss of Foxp3 expression in these subsets, as may occur in a pro-inflammatory environment such as exists in the early posttransplant period. Epigenome editing to promote stable Foxp3 transcription may provide a means to circumvent this limitation. The insertion of suicide genes that act as ‘safety switches’ is also discussed in the context of preventing potential adverse events that may result from the proliferation of genetically modified Treg. Regulatory B cells (Breg) are potent regulators of innate and adaptive immune responses and promote allograft tolerance in rodents. Moreover, patients that develop transplant tolerance have higher frequencies of Breg. However, the absence of a specific phenotype, together with poor understanding of their development and how they exert their regulatory function in vivo has hampered progress in the field. Mohib et al. (University of Pittsburgh, Pittsburgh, USA) (pp. 524–532) discuss the phenotypic markers used to identify murine and human Breg, their induction, maintenance and mechanisms of immune suppression. The authors also highlight recent advances in the in-vitro expansion of Breg, understanding of the influence of immunosuppressive agents on their induction and frequency and the use of Breg defined by their cytokine expression as biomarkers to predict organ allograft rejection. Ex-vivo-generated regulatory innate immune cells (regulatory myeloid cells) are also under evaluation as cellular therapeutic agents for the control of rejection and the promotion of organ transplant tolerance in the clinic. Riquelme and Hutchinson (University of Regensburg, Germany) (pp. 533–537) discuss newly identified mechanisms by which human monocyte-derived regulatory macrophages (Mreg) convert naïve human CD4+ T cells into distinct, IL-10-producing, Foxp3+ Treg that control T-cell-mediated alloimmunity. Their most recent work has focussed on TIGIT+ (T-cell immunoreceptor with Ig and ITIM domains+) induced (i)Treg elicited by human Mreg via mediators that include transforming growth factor β (TGFβ), retinoic acid, indoleamine deoxygenase (IDO), notch and progestogen-associated endometrial protein. Moreover, intravenous infusion of allogeneic Mreg in kidney transplant patients leads to the enrichment of circulating TIGIT+ iTreg. Regulatory dendritic cells (DCreg) have been shown to be highly effective in prolonging organ allograft survival and in promoting transplant tolerance in rodents, whereas their safety and efficacy has been demonstrated in clinically relevant nonhuman primate organ transplantation. Thomson and Ezzelarab (University of Pittsburgh, Pittsburgh, USA) (pp. 538–545) discuss innovative approaches to enhancing dendritic cell tolerogenicity in situ, optimizing the ex-vivo generation of DCreg and the initial testing of DCreg of donor or recipient origin in clinical organ transplantation. Recent ‘omics’ studies are better defining the molecules that enhance the tolerogenic phenotype, stability and longevity of DCreg and their resistance to pro-inflammatory stimuli. It appears that the distinct properties of DCreg result from a specific transcriptional program characterized by the activation of tolerance-enhancing genes. In ‘spontaneous’ liver transplant tolerance in mice, the acquisition of donor MHC gene products via microvesicles (exosomes) by host antigen-presenting cells may be important in expression of immune regulatory function by these cells. Facilitating cells promote hematopoietic stem cell (HSC) engraftment in allogeneic recipients and this function has recently been exploited in the development of chimerism-based approaches to the induction of transplant tolerance in clinical renal transplantation. Chhabra and Ildstad (University of Louisville, Louisville, USA) (pp. 546–551) discuss the heterogeneity of mouse and human facilitating cells, the ability of mouse facilitating cells to enhance clonogenicity, survival and homing of HSC and underlying mechanisms, including the induction of Treg. A gene product that appears to be critical for murine CD8+TCR− graft facilitating cells to home to the hematopoietic niche/enhance HSC engraftment is DOCK2 (dedicator of cytokinesis 2), a protein involved in regulation of lymphocyte migration. Infusion of donor apoptotic cells (dendritic cells or splenocytes) has been shown to exert profound immunomodulatory effects, including the induction of tolerogenic dendritic cells and Treg and to promote donor-specific tolerance in various cell (pancreatic islet and HSC) and organ transplant models. Furthermore, recent clinical trials have demonstrated the safety and potential efficacy of this approach in suppressing acute graft-versus-host disease following HSC transplantation. Dangi et al. (Northwestern University, Chicago, USA) (pp. 552–558) describe how host prior allosensitization and opportunistic infection can antagonize the induction of tolerance by donor apoptotic cells. They also discuss strategies to overcome these potential hurdles and control memory responses and promote tolerance in the context of clinical transplantation. Acknowledgements None. Financial support and sponsorship None. Conflicts of interest There are no conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.537
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.334
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2018
Admission routes1
Has abstractyes

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