Large Multinucleated Variant Endothelial Cells in Allograft Kidney Microvasculature: A Biopsy Series.

There are few published studies examining cytomorphologic alterations in endothelial cells in human tissue. One fascinating but largely unexplored endothelial morphologic variant is large multinucleated variant endothelial cells (MVECs). To our knowledge, there are no published reports of MVECs identified in the kidney. Here, we present a case series of 4 kidney biopsies from allograft kidneys whose microvasculature contained MVECs. Electron microscopy confirmed the endothelial identity in all cases. A broad immunohistochemical panel used in 1 case was also confirmatory of an endothelial cell origin. All cases occurred in the setting of chronic, active, antibody-mediated rejection, and alternative etiologies, such as viral infections, were excluded. Two patients were positive for concurrent donor-specific antibodies, and 3 of the 4 cases occurred in second kidney allografts. We speculate that MVECs are a rare or often overlooked finding often confused for megakaryocytes and may be associated with chronic endothelial cell injury in the setting of chronic antibody-mediated rejection

Osmotic Tubulopathy and Acute Thrombotic Microangiopathy in a Kidney Transplant Recipient With a Breakthrough SARS-CoV-2 Infection

Acute kidney injury is a known complication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection for which many different pathophysiological processes have been reported. Here, we present a case of a 45-year–old kidney transplant recipient with a breakthrough SARS-CoV-2 infection complicated by an episode of acute kidney injury 26 months after transplant. She had minimal respiratory symptoms, pancytopenia, mild hematuria, and proteinuria. A kidney biopsy revealed acute thrombotic microangiopathy (TMA) as well as an osmotic tubulopathy. The TMA was favored to be secondary to the SARS-CoV-2 infection because other etiologies for TMA, such as acute calcineurin inhibitor toxicity and acute antibody-mediated rejection, were excluded. The osmotic tubulopathy was favored to be secondary to remdesivir therapy, specifically related to the sulfobutylether-β-cyclodextrin solubilizing carrier agent used in its formulation. The patient’s kidney function improved after resolution of the SARS-CoV-2 infection. This case illustrates a unique occurrence of kidney injury secondary to SARS-CoV-2 infection and anti–COVID-19 therapy

Immune Profiles to Predict Response to Desensitization Therapy in Highly HLA-Sensitized Kidney Transplant Candidates

<div><p>Background</p><p>Kidney transplantation is the most effective treatment for end-stage kidney disease. Sensitization, the formation of human leukocyte antigen (HLA) antibodies, remains a major barrier to successful kidney transplantation. Despite the implementation of desensitization strategies, many candidates fail to respond. Current progress is hindered by the lack of biomarkers to predict response and to guide therapy. Our objective was to determine whether differences in immune and gene profiles may help identify which candidates will respond to desensitization therapy.</p><p>Methods and Findings</p><p>Single-cell mass cytometry by time-of-flight (CyTOF) phenotyping, gene arrays, and phosphoepitope flow cytometry were performed in a study of 20 highly sensitized kidney transplant candidates undergoing desensitization therapy. Responders to desensitization therapy were defined as 5% or greater decrease in cumulative calculated panel reactive antibody (cPRA) levels, and non-responders had 0% decrease in cPRA. Using a decision tree analysis, we found that a combination of transitional B cell and regulatory T cell (Treg) frequencies at baseline before initiation of desensitization therapy could distinguish responders from non-responders. Using a support vector machine (SVM) and longitudinal data, <i>TRAF3IP3</i> transcripts and HLA-DR-CD38+CD4+ T cells could also distinguish responders from non-responders. Combining all assays in a multivariate analysis and elastic net regression model with 72 analytes, we identified seven that were highly interrelated and eleven that predicted response to desensitization therapy.</p><p>Conclusions</p><p>Measuring baseline and longitudinal immune and gene profiles could provide a useful strategy to distinguish responders from non-responders to desensitization therapy. This study presents the integration of novel translational studies including CyTOF immunophenotyping in a multivariate analysis model that has potential applications to predict response to desensitization, select candidates, and personalize medicine to ultimately improve overall outcomes in highly sensitized kidney transplant candidates.</p></div

Network of highly connected analytes.

<p>Analytes are grouped by assay and listed alphabetically within assay. Node size indicates the number of connections for a particular analyte. Solid lines indicate negative correlation between the analytes, and dotted lines indicate positive correlation. Colors other than gray indicate the highly connected analytes. Analytes with names that are in gray are associated with one of the highly connected analytes. G = gene expression; Pheno = CyTOF phenotyping; Phospho = phosphoepitope flow cytometry.</p

Levels of transitional B cells and Tregs classify responders and non-responders.

<p>(A) Decision tree algorithm illustrating the classification of responders (R; n = 10) and non-responders (NR; n = 10) based on two phenotypes. Numbers in parentheses are the numbers of patients who reach each leaf node. The leaf nodes are the shaded rectangular boxes. (B) Transitional B cells and Tregs by candidate and response group. Each line indicates one candidate. Horizontal reference lines indicate decision tree threshold values of 0.466 (transitional B cells) and 5.96 (Tregs).</p

Best performing cross-assay pair using longitudinal samples.

<p>(A) Regression model with the solid blue line indicating the estimated relationship between the two analytes for non-responders (NR) and the dotted green line the relationship for responders (R). (B) Support vector machine (SVM) with the dotted line indicating the best linear relationship separating the two groups. Observations that are circled are the “support vectors,” the observations that drive the placement of the line of separation. All observations are correctly classified by the SVM. G = gene expression; Pheno = CyTOF phenotyping.</p

Elastic net logistic regression model.

<p>The model was built using the pairs of highly connected analytes shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0153355#pone.0153355.g005" target="_blank">Fig 5</a>. (A) Values of the coefficients for the 11 analytes included in the model. All data are normalized during the modeling. Thus, the values represent the number of standard deviations from the mean for each particular analyte. (B) Predicted probability of each observation being classified as a responder (R) or as a non-responder (NR). In this limited data set, the predicted probabilities are clearly separated and accurately predict response status. Since the non-responders for whom we had longitudinal multi-assay data were all in the high Tregs group, the large negative coefficient on Tregs is not unexpected. G = gene expression; Pheno = CyTOF phenotyping.</p

Participant demographic, histocompatibility data, immunosuppression and clinical outcomes.

<p>Participant demographic, histocompatibility data, immunosuppression and clinical outcomes.</p