Microvascular inflammation: Gene expression changes do not necessarily reflect pathogenesis
Bibliographic record
Abstract
To the Editor: We read with great interest the manuscript by Halloran and the INTERCOMEX investigators recently published in AJT on the molecular phenotype of antibody-mediated kidney transplant rejection (ABMR).1Halloran PF Madill-Thomsen KS Pon S et al.Molecular diagnosis of ABMR with or without donor-specific antibody in kidney transplant biopsies: differences in timing and intensity but similar mechanisms and outcomes.Am J Transplant. 2022; (Online ahead of print.)Abstract Full Text Full Text PDF Scopus (6) Google Scholar We described earlier that the intrarenal molecular signatures of ABMR histology did not differ between patients with and without HLA-DSA.2Callemeyn J Lerut E de Loor H et al.Transcriptional changes in kidney allografts with histology of antibody-mediated rejection without anti-HLA donor-specific antibodies.J Am Soc Nephrol. 2020; 31: 2168-2183Crossref PubMed Scopus (31) Google Scholar The INTERCOMEX data now confirm that also molecularly defined ABMR (mABMR) did not differ between patients with and without HLA-DSA. It is reassuring that the molecular approach by Halloran et al.1Halloran PF Madill-Thomsen KS Pon S et al.Molecular diagnosis of ABMR with or without donor-specific antibody in kidney transplant biopsies: differences in timing and intensity but similar mechanisms and outcomes.Am J Transplant. 2022; (Online ahead of print.)Abstract Full Text Full Text PDF Scopus (6) Google Scholar agrees with our previous results based on light microscopic classification of microvascular inflammation. However, we do not share the conclusion of Halloran et al. that the lack of major differences in gene expression profiles between HLA-DSA-positive and -negative cases suggests that all cases with microvascular inflammation and an increased expression of above transcripts can be diagnosed as ABMR, and hence the management should not differ between HLA-DSA-positive and -negative cases. Neither our study2Callemeyn J Lerut E de Loor H et al.Transcriptional changes in kidney allografts with histology of antibody-mediated rejection without anti-HLA donor-specific antibodies.J Am Soc Nephrol. 2020; 31: 2168-2183Crossref PubMed Scopus (31) Google Scholar nor Halloran’s1Halloran PF Madill-Thomsen KS Pon S et al.Molecular diagnosis of ABMR with or without donor-specific antibody in kidney transplant biopsies: differences in timing and intensity but similar mechanisms and outcomes.Am J Transplant. 2022; (Online ahead of print.)Abstract Full Text Full Text PDF Scopus (6) Google Scholar can make claims on underlying causality. With these analyses, top genes and pathways are essentially uncovered by statistical comparison of samples with versus without inflammation, thus assessing primarily the cellular composition of the kidneys. The mechanism behind this immune cell infiltration and activation, and related tissue/endothelial injury, is not necessarily reflected by their gene expression changes. For instance, the observation that Fc receptors are upregulated in HLA-DSA-negative mABMR is not proof that these receptors contributed to the infiltration and activation of the cells. It is possible that this merely reflects the presence of (Fc receptor-expressing) cells, potentially activated by totally different mechanisms. Hence the transcripts of a molecular phenotype are as specific/non-specific as the lesions of a histologic phenotype. In the absence of data on potential causes or specific risk factors for the HLA-DSA-negative mABMR, it is premature to conclude that this phenotype is explained by antibodies, either missed HLA-DSA (below the levels of current detection methods) or non-HLA antibodies. Recent studies demonstrated the activation of NK cells by antibody- and Fc-receptor independent mechanisms. For instance, the lack of inhibitory signals dependent upon KIR/HLA-I interactions (“missing self”) is sufficient to trigger microvascular inflammation, with a cellular composition like that observed in ABMR.3Koenig A Chen CC Marcais A et al.Missing self triggers NK cell-mediated chronic vascular rejection of solid organ transplants.Nat Commun. 2019; 10: 5350Crossref PubMed Scopus (61) Google Scholar It is also conceivable that other innate allorecognition mechanisms, including myeloid- and monocyte-driven allorecognition,4Dai H Lan P Zhao D et al.PIRs mediate innate myeloid cell memory to nonself MHC molecules.Science. 2020; 368: 1122-1127Crossref PubMed Scopus (59) Google Scholar or even primary T-cell activation by mismatched HLA molecules5Senev A Lerut E Coemans M et al.Association of HLA mismatches and histology suggestive of antibody-mediated injury in absence of donor-specific anti-HLA antibodies.Clin J Am Soc Nephrol. 2022; (Online ahead of print.)Crossref PubMed Scopus (4) Google Scholar could lead to similar histological/molecular pictures. We agree with Halloran et al. that it is time to consider including well-defined HLA-DSA-negative microvascular rejection in clinical trials but urge that this phenotype be kept delineated from its HLA-DSA-positive counterpart. Lumping all mABMR cases together bears the risk of underestimating the heterogeneity of the phenotype and reminds us of earlier discussions on the abandoned chronic allograft nephropathy (CAN) concept, just at the molecular level instead of at the histological level. In the era of precision diagnostics, disease- and pathogenesis-specific approaches are needed. Therefore, an international consensus definition of HLA-DSA-negative microvascular rejection seems necessary to study the mechanisms operating more systematically, and to find better, targeted therapies. 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.019 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".