Extracellular vesicles beyond biomarkers: Effectors of antibody-mediated rejection
Bibliographic record
Abstract
In the past decade, extracellular vesicles (EVs), small lipid bilayer particles of cellular origin, have been identified as mediators of intercellular communication, regulators of homeostatic and disease processes, and as diagnostic biomarkers. EV shows important advantages as biomarkers since their lipid bilayer can protect nucleic acids from degradation and proteins from proteolytic cleavage while transiting into the extracellular environment. Mounting evidence suggests that circulating EV not only reflects the state of inflammation of an organ or tissue but also actively participates in the regulation of immune responses and inflammation. In their publication, Franzin et al.1 explored the different profiles of plasma-derived EVs from patients with biopsy-proven acute or chronic antibody-mediated rejection (AMR) compared to patients with stable graft function and normal controls. Both types of AMR were associated with increased levels of circulating EVs. These findings further highlight the importance of EVs as biomarkers of AMR. Franzin et al. found that one of the prevalent populations of EV in their samples are of platelet origin, an expected finding as the most abundant circulating EVs are known to originate from platelets. Interestingly, they also identified endothelial cells as another prevalent cellular origin from AMR-associated EVs. These results are in line with a predominant role for endothelial injury in the pathophysiology of AMR.2 It is likely, although not specifically tested in this publication, that EVs found in AMR patients are derived from injured or dying endothelial cells from the kidney microvasculature. Indeed, antibody-mediated endothelial injury is one of the main etiologies of microvascular damage in kidney transplant recipients.2 The presence of donor-specific anti-human leukocyte antigen (HLA) antibodies or non-HLA autoantibodies have been implicated in in complement activation with diffuse C4d staining in peritubular capillaries, leading to endothelial stress and death and peritubular capillaritis. These cardinal histological features used clinically to provide both diagnostic and prognostic information are indicative of AMR-induced microvascular damage.2 Various groups, including our own, have shown that endothelial cell stress and death contribute to increasing the release of EVs with molecular signatures that differ from those of EVs released in the normal homeostatic state. In turn, these pro-inflammatory EVs can contribute to vascular inflammation, hypertension, and rejection.3-7 The findings by Franzin et al. reinforce this notion by showing that circulating EVs in patients with AMR are characterized by a specific set of microRNAs. Franzin's work also shed new light on the role of EVs in the pathophysiology of AMR-induced renal dysfunction. The authors demonstrate that EVs recovered from AMR patients induce senescence of tubular epithelial cells, a pathway known to contribute to loss of renal function. In addition, they show that AMR-derived EV trigger endothelial phenotypic changes reminiscent of an endothelial-to-mesenchymal transition (EndoMT). This highlights the capacity of EVs to modulate endothelial phenotypes toward modifications observed in peritubular capillaries in association with AMR and predictive of progressive renal graft dysfunction. Franzin et al. also showed that EVs modulate the expression of key complement proteins such as factor H and complement factor 3. When layered on endothelial cells in vitro, EVs from AMR patients induce activation of the classical and lectin complement pathways, with C4d deposition, another hallmark of AMR. Complement activation is a major contributor to AMR-induced microvascular rarefaction and progressive loss of renal function.2 The results by Franzin et al. suggests that, in addition to donor-specific antibodies (DSA) and non-HLA autoantibodies, EVs per se can contribute to complement activation. Importantly, the authors also demonstrate that exposing AMR-derived EVs to ribonuclease (RNAse) before their exposure to tubular epithelial cells of endothelial cells prevents phenotypic changes in vitro. Collectively, these results provide new evidence suggesting that AMR-associated EVs behave not only as biomarkers but also as effectors of renal injury and inflammation as least in part through transfer of microRNAs. Despite these exciting advances, further studies are needed to extend our understanding of the various pathways potentially triggered by EVs that favor development and progression of AMR. Further insights into the underlying molecular mechanisms controlling biogenesis and function of these EVs as well as mechanisms of uptake and processing are required before therapeutic interventions can be designed and tested. This could pave the way for new targets of interventions aimed at blocking the release of pathological EVs, editing their content, removing them from circulation, or preventing their downstream pro-inflammatory functions. For this to happen, further optimization of EV isolation and manipulation techniques and validation through large scale clinical trials will be necessary. Yet, these joint efforts could promote the translation of the rapidly advancing field of EVs into both novel diagnostics and therapeutic interventions for transplant patients with AMR. The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".