The cellular landscape of the normal kidney allograft: Main players balancing the alloimmune response

McDaniels, Jennifer; Shetty, Amol; Rousselle, Thomas; Bardhi, Elissa; Maluf, Daniel G.; Mas, Valeria R.

doi:10.3389/frtra.2022.988238

Cited by 2 publications

(1 citation statement)

References 78 publications

(98 reference statements)

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“…The pathway analysis identified 42 significant GO biological processes. The most significant term, cell morphogenesis (GO:0000902), was previously reported specifically significant in the cells from the normal allograft [35], showing its potential impacts on AR development on the basis of normal kidney transplantation.…”

Section: Plos Onementioning

confidence: 99%

Polygenic risk score for acute rejection based on donor-recipient non-HLA genotype mismatch

Cao,

Schladt,

Dorr

et al. 2024

PLoS ONE

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Background Acute rejection (AR) after kidney transplantation is an important allograft complication. To reduce the risk of post-transplant AR, determination of kidney transplant donor-recipient mismatching focuses on blood type and human leukocyte antigens (HLA), while it remains unclear whether non-HLA genetic mismatching is related to post-transplant complications. Methods We carried out a genome-wide scan (HLA and non-HLA regions) on AR with a large kidney transplant cohort of 784 living donor-recipient pairs of European ancestry. An AR polygenic risk score (PRS) was constructed with the non-HLA single nucleotide polymorphisms (SNPs) filtered by independence (r2 < 0.2) and P-value (< 1×10−3) criteria. The PRS was validated in an independent cohort of 352 living donor-recipient pairs. Results By the genome-wide scan, we identified one significant SNP rs6749137 with HR = 2.49 and P-value = 2.15×10−8. 1,307 non-HLA PRS SNPs passed the clumping plus thresholding and the PRS exhibited significant association with the AR in the validation cohort (HR = 1.54, 95% CI = (1.07, 2.22), p = 0.019). Further pathway analysis attributed the PRS genes into 13 categories, and the over-representation test identified 42 significant biological processes, the most significant of which is the cell morphogenesis (GO:0000902), with 4.08 fold of the percentage from homo species reference and FDR-adjusted P-value = 8.6×10−4. Conclusions Our results show the importance of donor-recipient mismatching in non-HLA regions. Additional work will be needed to understand the role of SNPs included in the PRS and to further improve donor-recipient genetic matching algorithms. Trial registry: Deterioration of Kidney Allograft Function Genomics (NCT00270712) and Genomics of Kidney Transplantation (NCT01714440) are registered on ClinicalTrials.gov.

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Section: Plos Onementioning

confidence: 99%

Polygenic risk score for acute rejection based on donor-recipient non-HLA genotype mismatch

Cao,

Schladt,

Dorr

et al. 2024

PLoS ONE

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Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA sequencing and molecular docking

Li,

Zhang,

Qiu

et al. 2024

Renal Failure

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Objectives: Post-transplant allograft fibrosis remains a challenge in prolonging allograft survival. Regulated cell death has been widely implicated in various kidney diseases, including renal fibrosis. However, the role of different regulated cell death (RCD) pathways in post-transplant allograft fibrosis remains unclear. Methods and Results: Microarray transcriptome profiling and single-nuclei sequencing data of post-transplant fibrotic and normal grafts were obtained and used to identify RCD-related differentially expressed genes. The enrichment activity of nine RCD modalities in tissue and cells was examined using single-sample gene set enrichment analysis, and their relations with immune infiltration in renal allograft samples were also assessed. Parenchymal and non-parenchymal cells displayed heterogeneity in RCD activation. Additionally, cell–cell communication analysis was also conducted in fibrotic samples. Subsequently, weighted gene co-expression network analysis and seven machine learning algorithms were employed to identify RCD-related hub genes for renal fibrosis. A 9-gene signature, termed RCD risk score (RCDI), was constructed using the least absolute shrinkage and selection operator and multivariate Cox regression algorithms. This signature showed robust accuracy in predicting 1-, 2-, and 3-year allograft survival status (area under the curve for 1-, 2-, and 3-year were 0.900, 0.877, 0.858, respectively). Immune infiltration analysis showed a strong correlation with RCDI and the nine model genes. Finally, molecular docking simulation suggested rapamycin, tacrolimus and mycophenolate mofetil exhibit strong interactions with core RCD-related receptors. Conclusions: In summary, this study explored the activation of nine RCD pathways and their relationships with immune traits, identified potential RCD-related hub genes associated with renal fibrosis, and highlighted potential therapeutic targets for renal allograft fibrosis.

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The cellular landscape of the normal kidney allograft: Main players balancing the alloimmune response

Cited by 2 publications

References 78 publications

Polygenic risk score for acute rejection based on donor-recipient non-HLA genotype mismatch

Polygenic risk score for acute rejection based on donor-recipient non-HLA genotype mismatch

Diverse regulated cell death patterns and immune traits in kidney allograft with fibrosis: a prediction of renal allograft failure based on machine learning, single-nucleus RNA sequencing and molecular docking

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