Archetypal Analysis of Injury in Kidney Transplant Biopsies Identifies Two Classes of Early AKI

Epithelial cell states associated with kidney and allograft injury

The kidney epithelium, with its intricate arrangement of highly specialized cell types, constitutes the functional core of the organ. Loss of kidney epithelium is linked to the loss of functional nephrons and a subsequent decline in kidney function. In kidney transplantation, epithelial injury signatures observed during post-transplantation surveillance are strong predictors of adverse kidney allograft outcomes. However, epithelial injury is currently neither monitored clinically nor addressed therapeutically after kidney transplantation. Several factors can contribute to allograft epithelial injury, including allograft rejection, drug toxicity, recurrent infections and postrenal obstruction. The injury mechanisms that underlie allograft injury overlap partially with those associated with acute kidney injury (AKI) and chronic kidney disease (CKD) in the native kidney. Studies using advanced transcriptomic analyses of single cells from kidney or urine have identified a role for kidney injury-induced epithelial cell states in exacerbating and sustaining damage in AKI and CKD. These epithelial cell states and their associated expression signatures are also observed in transplanted kidney allografts, suggesting that the identification and characterization of transcriptomic epithelial cell states in kidney allografts may have potential clinical implications for diagnosis and therapy.

Precision diagnostics in transplanted organs using microarray-assessed gene expression: concepts and technical methods of the Molecular Microscope® Diagnostic System (MMDx)

There is a major unmet need for improved accuracy and precision in the assessment of transplant rejection and tissue injury. Diagnoses relying on histologic and visual assessments demonstrate significant variation between expert observers (as represented by low kappa values) and have limited ability to assess many biological processes that produce little histologic changes, for example, acute injury. Consensus rules and guidelines for histologic diagnosis are useful but may have errors. Risks of over- or under-treatment can be serious: many therapies for transplant rejection or primary diseases are expensive and carry risk for significant adverse effects. Improved diagnostic methods could alleviate healthcare costs by reducing treatment errors, increase treatment efficacy, and serve as useful endpoints for clinical trials of new agents that can improve outcomes. Molecular diagnostic assessments using microarrays combined with machine learning algorithms for interpretation have shown promise for increasing diagnostic precision via probabilistic assessments, recalibrating standard of care diagnostic methods, clarifying ambiguous cases, and identifying potentially missed cases of rejection. This review describes the development and application of the Molecular Microscope® Diagnostic System (MMDx), and discusses the history and reasoning behind many common methods, statistical practices, and computational decisions employed to ensure that MMDx scores are as accurate and precise as possible. MMDx provides insights on disease processes and highly reproducible results from a comparatively small amount of tissue and constitutes a general approach that is useful in many areas of medicine, including kidney, heart, lung, and liver transplants, with the possibility of extrapolating lessons for understanding native organ disease states.

Impact of Deceased-donor Acute Kidney Injury on Kidney Transplantation

Even as record numbers of deceased donors are undergoing organ recovery, the global transplant community continues to struggle with a shortage of donor organs and a high organ discard rate. Acute kidney injury (AKI) occurs in many hospitalized patients, including up to 25% of patients in critical condition. Registry studies have shown a significant increase in nonrecovery or organ discard rates in AKI donors, despite most studies reporting similar clinical outcomes compared with non-AKI donors. This review aims to capture the salient information learned from these studies and to summarize the efforts that have been made to gain a more granular understanding of how kidneys from donors with AKI behave posttransplant. In particular, we reviewed the studies that analyzed the clinical outcomes in different stages of AKI and AKI in marginal donors, such as kidney donor profile index of >85%, older donors, and donation after circulatory death donors. We summarized studies investigating molecular biomarkers, transcriptomics, and possible future therapeutic targets for postdonation AKI.

Distinct Molecular Processes Mediate Donor-derived Cell-free DNA Release From Kidney Transplants in Different Disease States

BACKGROUND

Among all biopsies in the Trifecta-Kidney Study ( ClinicalTrials.gov NCT04239703), elevated plasma donor-derived cell-free DNA (dd-cfDNA) correlated most strongly with molecular antibody-mediated rejection (AMR) but was also elevated in other states: T cell-mediated rejection (TCMR), acute kidney injury (AKI), and some apparently normal biopsies. The present study aimed to define the molecular correlates of plasma dd-cfDNA within specific states.

METHODS

Dd-cfDNA was measured by the Prospera test. Molecular rejection and injury states were defined using the Molecular Microscope system. We studied the correlation between dd-cfDNA and the expression of genes, transcript sets, and classifier scores within specific disease states, and compared AMR, TCMR, and AKI to biopsies classified as normal and no injury (NRNI).

RESULTS

In all 604 biopsies, dd-cfDNA was elevated in AMR, TCMR, and AKI. Within AMR biopsies, dd-cfDNA correlated with AMR activity and stage. Within AKI, the correlations reflected acute parenchymal injury, including cell cycling. Within biopsies classified as MMDx Normal and archetypal No injury (NRNI), dd-cfDNA still correlated significantly with rejection- and injury-related genes. TCMR activity (eg, the TCMR Prob classifier) correlated with dd-cfDNA, but within TCMR biopsies, top gene correlations were complex and not the top TCMR-selective genes.

CONCLUSIONS

In kidney transplants, elevated plasma dd-cfDNA is associated with 3 distinct molecular states in the donor tissue: AMR, recent parenchymal injury (including cell cycling), and TCMR, potentially complicated by parenchymal disruption. Moreover, subtle rejection- and injury-related changes in the donor tissue can contribute to dd-cfDNA elevations in transplants considered to have no rejection or injury.

Allograft tissue under the microscope: only the beginning

PURPOSE OF REVIEW

To review novel modalities for interrogating a kidney allograft biopsy to complement the current Banff schema.

RECENT FINDINGS

Newer approaches of Artificial Intelligence (AI), Machine Learning (ML), digital pathology including Ex Vivo Microscopy, evaluation of the biopsy gene expression using bulk, single cell, and spatial transcriptomics and spatial proteomics are now available for tissue interrogation.

SUMMARY

Banff Schema of classification of allograft histology has standardized reporting of tissue pathology internationally greatly impacting clinical care and research. Inherent sampling error of biopsies, and lack of automated morphometric analysis with ordinal outputs limit its performance in prognostication of allograft health. Over the last decade, there has been an explosion of newer methods of evaluation of allograft tissue under the microscope. Digital pathology along with the application of AI and ML algorithms could revolutionize histopathological analyses. Novel molecular diagnostics such as spatially resolved single cell transcriptomics are identifying newer mechanisms underlying the pathologic diagnosis to delineate pathways of immunological activation, tissue injury, repair, and regeneration in allograft tissues. While these techniques are the future of tissue analysis, costs and complex logistics currently limit their clinical use.

The molecular features of chronic lung allograft dysfunction in lung transplant airway mucosa

BACKGROUND

Many lung transplants fail due to chronic lung allograft dysfunction (CLAD). We recently showed that transbronchial biopsies (TBBs) from CLAD patients manifest severe parenchymal injury and dedifferentiation, distinct from time-dependent changes. The present study explored time-selective and CLAD-selective transcripts in mucosal biopsies from the third bronchial bifurcation (3BMBs), compared to those in TBBs.

METHODS

We used genome-wide microarray measurements in 324 3BMBs to identify CLAD-selective changes as well as time-dependent changes and develop a CLAD classifier. CLAD-selective transcripts were identified with linear models for microarray data (limma) and were used to build an ensemble of 12 classifiers to predict CLAD. Hazard models and random forests were then used to predict the risk of graft loss using the CLAD classifier, transcript sets associated with rejection, injury, and time.

RESULTS

T cell-mediated rejection and donor-specific antibody were increased in CLAD 3BMBs but most had no rejection. Like TBBs, 3BMBs showed a time-dependent increase in transcripts expressed in inflammatory cells that was not associated with CLAD or survival. Also like TBBs, the CLAD-selective transcripts in 3BMBs reflected severe parenchymal injury and dedifferentiation, not inflammation or rejection. While 3BMBs and TBBs did not overlap in their top 20 CLAD-selective transcripts, many CLAD-selective transcripts were significantly increased in both for example LOXL1, an enzyme controlling matrix remodeling. In Cox models for one-year survival, the 3BMB CLAD-selective transcripts and CLAD classifier predicted graft loss and correlated with CLAD stage. Many 3BMB CLAD-selective transcripts were also increased by injury in kidney transplants and correlated with decreased kidney survival, including LOXL1.

CONCLUSIONS

Mucosal and transbronchial biopsies from CLAD patients reveal a diffuse molecular injury and dedifferentiation state that impacts prognosis and correlates with the physiologic disturbances. CLAD state in lung transplants shares features with failing kidney transplants, indicating elements shared by the injury responses of distressed organs.