UNASSIGNED: Antibody-mediated rejection (ABMR) significantly affects transplanted kidney survival, yet the macrophage phenotype, ontogeny, and mechanisms in ABMR remain unclear.
UNASSIGNED: We analyzed post-transplant sequencing and clinical data from GEO and ArrayExpress. Using dimensionality reduction and clustering on scRNA-seq data, we identified macrophage subpopulations and compared their infiltration in ABMR and non-rejection cases. Cibersort quantified these subpopulations in bulk samples. Cellchat, SCENIC, monocle2, and monocle3 helped explore intercellular interactions, predict transcription factors, and simulate differentiation of cell subsets. The Scissor method linked macrophage subgroups with transplant prognosis. Furthermore, hdWGCNA, nichnet, and lasso regression identified key genes associated with core transcription factors in selected macrophages, validated by external datasets.
UNASSIGNED: Six macrophage subgroups were identified in five post-transplant kidney biopsies. M1-like infiltrating macrophages, prevalent in ABMR, correlated with pathological injury severity. MIF acted as a primary intercellular signal in these macrophages. STAT1 regulated monocyte-to-M1-like phenotype transformation, impacting transplant prognosis via the IFNγ pathway. The prognostic models built on the upstream and downstream genes of STAT1 effectively predicted transplant survival. The TLR4-STAT1-PARP9 axis may regulate the pro-inflammatory phenotype of M1-like infiltrating macrophages, identifying PARP9 as a potential target for mitigating ABMR inflammation.
UNASSIGNED: Our study delineates the macrophage landscape in ABMR post-kidney transplantation, underscoring the detrimental impact of M1-like infiltrating macrophages on ABMR pathology and prognosis.

The role of m6A modification in kidney transplant-associated immunity, especially in alloimmunity, still remains unknown. This study aims to explore the potential value of m6A-related immune genes in predicting graft loss and diagnosing T cell mediated rejection (TCMR), as well as the possible role they play in renal graft dysfunction.
Renal transplant-related cohorts and transcript expression data were obtained from the GEO database. First, we conducted correlation analysis in the discovery cohort to identify the m6A-related immune genes. Then, lasso regression and random forest were used respectively to build prediction models in the prognosis and diagnosis cohort, to predict graft loss and discriminate TCMR in dysfunctional renal grafts. Connectivity map (CMap) analysis was applied to identify potential therapeutic compounds for TCMR.
The prognostic prediction model effectively predicts the prognosis and survival of renal grafts with clinical indications (P< 0.001) and applies to both rejection and non-rejection situations. The diagnostic prediction model discriminates TCMR in dysfunctional renal grafts with high accuracy (area under curve = 0.891). Meanwhile, the classifier score of the diagnostic model, as a continuity index, is positively correlated with the severity of main pathological injuries of TCMR. Furthermore, it is found that METTL3, FTO, WATP, and RBM15 are likely to play a pivotal part in the regulation of immune response in TCMR. By CMap analysis, several small molecular compounds are found to be able to reverse TCMR including fenoldopam, dextromethorphan, and so on.
Together, our findings explore the value of m6A-related immune genes in predicting the prognosis of renal grafts and diagnosis of TCMR.