Current and emerging tools for simultaneous assessment of infection and rejection risk in transplantation

Infection and rejection are major complications that impact transplant longevity and recipient survival. Balancing their risks is a significant challenge for clinicians. Current strategies aimed at interrogating the degree of immune deficiency or activation and their attendant risks of infection and rejection are imprecise. These include immune (cell counts, function and subsets, immunoglobulin levels) and non-immune (drug levels, viral loads) markers. The shared risk factors between infection and rejection and the bidirectional and intricate relationship between both entities further complicate transplant recipient care and decision-making. Understanding the dynamic changes in the underlying net state of immunity and the overall risk of both complications in parallel is key to optimizing outcomes. The allograft biopsy is the current gold standard for the diagnosis of rejection but is associated with inherent risks that warrant careful consideration. Several biomarkers, in particular, donor derived cell-free-DNA and urinary chemokines (CXCL9 and CXCL10), show significant promise in improving subclinical and clinical rejection risk prediction, which may reduce the need for allograft biopsies in some situations. Integrating conventional and emerging risk assessment tools can help stratify the individual’s short- and longer-term infection and rejection risks in parallel. Individuals identified as having a low risk of rejection may tolerate immunosuppression wean to reduce medication-related toxicity. Serial monitoring following immunosuppression reduction or escalation with minimally invasive tools can help mitigate infection and rejection risks and allow for timely diagnosis and treatment of these complications, ultimately improving allograft and patient outcomes.

1 Introduction

With the increase in the global burden of chronic disease and the continued shortfall of transplantable organs, optimizing transplant recipient and allograft outcomes becomes paramount. Achieving and maintaining transplant tolerance, the ultimate goal of transplantation, requires an understanding of the individuals’ net state of immunity and achieving the optimal net-immune balance. Whilst over-immunosuppression is thought to lead to infective complications and underimmunosuppression to allograft rejection, the relationship between the two entities is likely more complicated and interlinked. Despite steady improvements in early allograft and patient survival, rejection, and infection continue to pose significant long-term risks (1). Balancing these two important complications remains a significant challenge for transplant clinicians.

An individual’s net state of immunity is a composite of their net state of immunosuppression and its resultant risk of infection and the net state of immune activation and its attendant risk of infection. The net state of immunity varies over time and is modulated by several factors, chiefly the degree of immunosuppression. Clinical risk assessments performed by Infectious Diseases and Transplant physicians may focus on infection and rejection risks in isolation, whereby optimal transplant recipient care and outcomes require the understanding of these risks in parallel. The clinical risk assessments are often further complicated by shared risk factors for both complications.

Strides to prevent and treat allograft rejection through potent immunosuppression increase susceptibility to infections. Conversely, infections can promote rejection by triggering immune mechanisms (e.g., heterologous immunity/alloreactive virus-specific T-cell activation, upregulation of surface markers and altered MHC class II signaling, de novo donor-specific antibody (DSA) formation and direct inflammation) or following the intentional reduction of immunosuppression to facilitate recovery from severe infection (2, 3). Furthermore, severe infections and the use of antimicrobial agents may affect immunosuppression medication levels through altered pharmacokinetic and pharmacodynamic profiles (4). Viruses pose a specific challenge, given their immune-evasive capabilities allowing for viral persistence and latency. Additionally, viral control relies on robust T-cell immune surveillance and responses, which are dampened by efforts to curb the risk of rejection.

Similarities in immune responses and clinical and histological features of infection and rejection, as is the case with polyomavirus (BK) nephropathy, can complicate therapeutic decision-making (5). Additionally, both rejection and infection episodes (particularly cytomegalovirus (CMV)) in the early post-transplant period predispose the recipient to both rejection and infective complications later on (2, 6–9). Immunosuppression reduction in response to leukopenia, a complication of both cytomegalovirus (CMV) infection and its’ treatments, has been associated with allograft rejection (10, 11). Moreover, lymphopenia/neutropenia, a well-described risk factor for primary and recurrent CMV disease, is a side effect of both immunosuppression (e.g., mycophenolate mofetil (MMF)) used to prevent rejection, and chemoprophylaxis used to prevent and treat infections such as CMV (e.g., valganciclovir, ganciclovir) and Pneumocystis jirovecii (PJP) (trimethoprim/sulfamethoxazole) (10, 11). Finally, rejection and infection can occur concurrently, rendering treatment options particularly difficult.

Current long-term monitoring of transplant health includes non-invasive and invasive measures. The conventional blood and urine parameters, serum creatinine, estimated glomerular filtration rate (eGFR), and proteinuria are not sensitive nor specific for rejection and often lag behind the onset of histological changes that may potentially be irreversible. Allograft biopsy, the current gold standard diagnostic test for rejection, is invasive, making it impractical for the regular surveillance of allograft health. It is also subject to large variabilities in sample adequacy and pathologists’ scoring (12, 13).

Emerging biomarkers show great promise in complementing conventional measures to improve their predictive power and pave the way for personalized transplant care and improved graft and patient survival. Detection of graft injury prior to changes in conventional markers may allow for early definitive histological diagnosis of rejection and timely therapy initiation prior to irreversible histological damage. Biomarkers may also provide valuable insight into the degree of immune activation and guide immunosuppression weaning strategies to reduce infection and toxicities. This review aims to describe the currently available and evolving tools relevant to simultaneously assessing the risk of infection and rejection. Informed, parallel infection and rejection risk assessments can allow clinicians to appropriately counsel recipients on their risks and personalize decisions around immunosuppression optimization and follow-up care.

2 Net state of immunity

A transplant recipient’s net state of immunity is dynamic and influenced by several host, donor, graft, surgical, immunosuppression, immunological, epidemiological, and environmental factors (14).

A shift towards an overall state of immune deficiency or activation increases the overall infection and rejection risks, respectively. Infection risk is primarily modulated by the degree of immune deficiency, epidemiological exposures, and preventative measures. Rejection risk increases with a state of immune activation, predisposed by an immunological mismatch/sensitization and influenced by inadequate immunosuppression levels.

Several shared risk factors for infection and rejection further complicate risk assessment strategies (Figure 1). These include a high degree of immunological mismatch (HLA mismatch and degree of sensitization), deceased donor transplantation, extended criteria donors, older donor age, prolonged cold ischemic time, delayed graft function, and prior history of rejection and infection (15–18).

Figure 1

Figure 1. Factors impacting net state of immunosuppression (infection risk) and net state of immune activation (rejection risk). Shared risk factors are highlight in bold. Immunosup, immunosuppression; DGF, delayed graft function; HLA, human leukocyte antigen; D+/R-, Donor positive/Recipient negative; CMV, Cytomegalovirus; UTI, urinary tract infection; HIV, Human immunodeficiency virus.

An infection-rejection risk stratification model proposed by Cippa et al. (2015) revealed that older recipient age, deceased donor transplants, a higher number of HLA mismatches, and CMV donor +ve/recipient -ve status were highly associated with infection, while rejection was associated with deceased donor transplants, a higher number of HLA mismatches, and cyclosporin based immunosuppression (compared to tacrolimus) (19).

3 Cause-and-effect relationship between infection and rejection

Significant temporal and geographical variations exist in infection and rejection rates post-transplantation. Infection and rejection episodes can occur in isolation, sequentially, with a period of overlap, or concurrently (Figure 2).

Figure 2

Figure 2. The possible temporal relationships between infection and rejection.

3.1 Infection causing rejection

Several pathogen-induced innate and adaptive immune responses can foster allosensitization and trigger an immune cascade that may lead to allograft rejection (2, 9). Direct graft inflammation, ischemia-reperfusion injury (IRI) following septic shock, priming of the adaptive immune response, T-cell phenotype switching from regulatory to inflammatory T-cells, heterologous immunity/cross-reactivity of virus-specific memory T-cells, and modulation of surface proteins (ICAM, VCAM) to facilitate immune cell infiltration or altering antigen (MHC-Class II) expression are postulated pathogen-induced injurious mechanisms (Table 1) (2, 9, 20). While the relationship between CMV infection and allograft rejection has been well described, chemoprophylaxis against CMV was demonstrated to reduce rejection risk (21–24). Heterologous immunity describes the cross-reactivity of virus-specific memory T-cells to alloantigens and is a potential pathogen-driven mechanism of allosensitization and rejection (9, 25). Recurrent CMV infections and accumulation of cross-reactive T-cells through heterologous immunity may be a potential barrier to achieving graft tolerance (26–28).

Table 1

Table 1. Possible mechanisms by which infection trigger rejection.

Direct graft inflammation and immune cell recruitment following infections with viral (CMV, BK) and bacterial pathogens (pyelonephritis) have also been linked to allograft rejection. However, the mechanisms by which rejection occurs remains poorly understood (2, 3, 9, 29). Lymphopenia is a common complication of CMV disease and/or its treatment. Immunosuppression modulation in response to lymphopenia, particularly mycophenolate dose reduction, may increase the risk of immune activation and rejection (10, 11).

Several studies demonstrate that immunosuppression modulation/interruption in the context of infection, particularly following BK viremia, increases the risk of rejection (4, 14). However, most studies on rejection risk following immunosuppression reduction or interruption are mixed (30–33). There may be an increased risk of denovo DSA formation and/or rejection in transplant recipients who experience more severe infection and require intensive and/or prolonged immunosuppression reduction and in those who are at high immunological risk (31, 34).

Table 1 outlines the various mechanisms by which infection may elicit immunological changes that lead to rejection.

3.2 Rejection causing infection

Treatment of allograft rejection with lymphocyte-depleting therapies increases the risk of opportunistic infections that carry significant morbidity and mortality risk (14, 16). The infection risk is often prolonged with therapies such as anti-thymocyte globulin, where T-cell depletion can persist beyond one year. Immunosuppression-induced leukopenia/lymphopenia increases the risk of infections, particularly CMV (57–59). Furthermore, CMV and PJP chemoprophylaxis with agents such as valganciclovir and trimethoprim/sulfamethoxazole can also cause lymphopenia/neutropenia, and interruption of these agents to reverse the leukopenia can increase the susceptibility to these opportunistic infections (10, 11).

Transplant recipients with neutropenia experienced more bacterial infections and the degree of neutropenia correlated with infection risk (60). Several international guidelines recommend initiating CMV prophylaxis for at least three months following anti-rejection therapy with lymphocyte-depleting agents (61, 62). Intentional or accidental omission of chemoprophylaxis following potent anti-rejection immunosuppression may pave the way for viral reactivation and disease.

Chronic kidney disease, particularly late-stage kidney dysfunction, is associated with adaptive and innate immune system dysregulation and accelerated immune aging (63, 64). Shift towards exhausted and immunosenescent lymphocyte phenotype, CD4⁺ T-cell lymphopenia, reduced CD4⁺/CD8⁺ ratio, increased terminally differentiated T-cells, and chronic systemic inflammation are all described to be associated with uremia and progressive CKD (63, 64). Profound allograft dysfunction in the context of rejection, coupled with anti-rejection therapy, can compound the state of immunocompromise and hinder infection and vaccine-induced immune responses. Transplant recipients with lower eGFR (progressive dysfunction beyond <30mL/min/1.73m²) were less likely to achieve positive vaccine sero-response and require extra booster doses to achieve seroprotection (65).

3.3 Immunosuppression modulation during infection

The decision for immunosuppression reduction should consider infection severity, immunological risk of rejection, availability of targeted anti-microbial therapies, and the need for immune reconstitution for infection clearance (Figure 3) (14). Invasive fungal infections (e.g., Cryptococcus spp) and other severe opportunistic infections (e.g., norcadia spp) have considerable 1-year mortality rates and often require immune reconstitution for pathogen control.

Figure 3

Figure 3. Factors to consider prior to immunosuppression reduction following infection. IS, immunosuppression.

While immunosuppression reduction following severe infection was associated with improved patient survival, most infections, particularly mild infections, did not require immunosuppression reduction for infection resolution (14). Additionally, there are no clear guidelines on the immunosuppression agents to reduce. Higher immunosuppression levels are required in the early post-transplantation period. Therefore, immunosuppression reduction in the early phase may be associated with a higher risk of rejection (4).

Most clinicians reduce anti-metabolites first, particularly in the setting of leukopenia, followed by calcineurin inhibitor (CNI) reduction. However, there are no evidence-based protocols on the best immunosuppression reduction strategy (66, 67). Individual immunosuppression agents have different impacts on the innate and adaptive immune systems and, as such, may have different risk profiles for infections with specific microbial organisms (14, 68). Understanding this may assist with clinical decision-making around immunosuppression modulation (14). Adopting a mammalian target of rapamycin (mTOR) inhibitor-based immunosuppression regimen is a common strategy, particularly following viremia. The mTOR inhibitors suppress viral replication and are associated with a reduced risk of viral respiratory infections, increased viral clearance following BK and CMV infections, and milder SARs-CoV-2 infections (69–72).

4 Current and emerging tools to measure infection and rejection risks

Given the lack of validated or standardized tests, clinicians rely on surrogate markers of immunity to determine the individual risk of rejection versus infection. Conventional organ-specific allograft dysfunction measurements are neither specific nor sensitive for rejection diagnosis and often lag behind intragraft immunological injury. Biopsy, the definite diagnostic tool for rejection, is invasive and imprecise and is subject to variations in sampling, processing, and pathologists’ reporting.

Immune markers provide insights into the innate and/or adaptive arms of the immune system (73–75). Some immune cells, such as NK cells, play a dual role in immune activation and graft tolerance (76). Supplementary Table S1 provides an overview of the immunological markers of infection and rejection, including absolute lymphocyte counts and subsets (57, 58, 77–89), NK cell count and function (76, 90–96), immunoglobulin levels (37, 97–101), complements (102, 103), mannose-binding lectin levels (104), soluble CD30 (a transmembrane glycoprotein of the tumor necrosis factor family, cleaved from activated effector and memory T-cells) levels (74, 105–110), and CD4+ intracellular adenosine triphosphate (iATP) concentrations (75, 111–115). Lymphocyte number and subsets, immunoglobulin levels, CD4+ iATP concentrations, sCD30 levels, and cell-mediated immunity assays (IFN-γ release) reflect the adaptive immune response (73–75), whereas NK-cell number, complement components 3,4 (C3, C4) levels, and mannose-binding lectin (MBL) levels are markers related to innate immunity (73).

Viral load quantification describes the coordinated efforts of the innate and adaptive arms of the immune system (14). Current and historical drug levels are common non-immune-based surrogate markers of the immune state.

Several emerging biomarkers have been evaluated for use in the transplant population, however, their integration into routine clinical care requires standardization of assays and test thresholds, favorable test characteristics, clarification of clinical contexts of use, and optimization of costs and availability. A rapid turnaround time is also essential for the dynamic analysis of the immune state.

Despite the mounting evidence in favor of the use of certain biomarkers, to date widespread uptake has been restricted, primarily by the lack of external validation and standardization of commercial assays and diagnostic thresholds, excess costs and limited access. We will explore several emerging biomarkers of infection and rejection, emphasizing those with the greatest promise for clinical implementation.

4.1 Immune composite scoring systems

Several immune scoring systems have been evaluated to improve the infection-risk prediction. Dendle et al. (2018) describe a 4-point composite scoring system predicting severe infection risk in kidney transplant recipients (KTRs). Lower NK cell or CD4⁺ T-cell count, mycophenolate use, and lower eGFR positively correlated with the risk of severe infection (116). Crepin et al. (2016) examined an “immune risk profile (IRP)” defined by positive CMV status, CD4/CD8 ratio <1 and/or CD8 T-cell count >90^th percentile, which was predictive of opportunistic and spontaneous bacterial infections (117). The incidence of acute allograft rejection was lower in the IRP-positive group.

In kidney transplant recipients, the “simplicity score” calculated one month post-transplant was able to predict future risk of infections with good discrimination capacity (118). The score incorporates immune (C3 level, IgG level, CD4+ T-cell count, CD8+ T-cell count), and clinical (recipient age, glomerular filtration rate, recipient age, and infection within the first month) variables (118).

Sarmiento et al. (2014) described a composite “immunological score” using immunoglobulins (IgG, IgM, IgA), complements (C3, C4), and lymphocyte subsets (CD3+, CD4+, CD8+ T cells, NK cells, and B-cells) correlating with severe infections in a cohort of heart transplant recipients (119). IgG <600 mg/dL, C3 <80 mg/dL, C4 <18 mg/dL, NK count <30 cells/µL, and CD4 count <350 cells/µL were all associated with a significantly higher risk of infection, and these five parameters were used to derive the immunological score. Assigned points for each parameter totaled a maximum score of 16. An immune score ≥13 was associated with the highest risk of infection.

4.2 Quantification of viral loads

Several viruses, including CMV, EBV, and BK, are highly seroprevalent and remain latent until waning immune surveillance and clearance allows for viral replication and reactivation. Higher quantitative viral loads positively correlate with the intensity of immunosuppression (14, 120). Identifying and quantifying viral replication through nucleic acid amplification helps guide prophylactic, pre-emptive, and therapeutic treatment strategies. The emergence of viremia and/or rising viral loads suggests overimmunosuppression and waning viral surveillance (14). Viral infections lacking targeted therapies rely on robust immune responses. T-cell function is particularly important for viral suppression and clearance (14).

BK viral loads over 10,000 copies/mL increase the risk of BK nephropathy. Clinicians commonly use this threshold for immunosuppression reduction (121). Calcineurin level reduction >20%, mycophenolate discontinuation, and high peak BK viral loads correlate with an increased risk of allograft rejection (5). Higher EBV load is linked to PTLD development and the need for immunosuppression reduction (56).

Torque tenovirus (TTV) is a ubiquitous human virus of unclear pathogenic significance. It is unaffected by currently available antiviral therapies and, as such, is a valuable marker of immune competence (14, 122, 123). The kinetics of TTV can provide a measure of the integrated innate and adaptive immune responses. High TTV titres correlate with a reduced risk of allograft rejection and an increased risk of infection (124, 125). A prospective observational study by Doberer et al. (2020) demonstrated a 22% reduced risk of kidney allograft rejection and an 11% increased risk of infection with each log increase in TTV copies/mL (122). TTV counts were significantly higher in KTRs with bacterial, viral, and fungal infections, and elevated counts were detected up to 3 months before the infection (125, 126). A TTV viral load of >3.45 log DNA copies/ml within the first ten days post-transplantation positively predicted the risk of CMV reactivation (127). A TTV threshold level >1x10⁶ copies/ml could exclude rejection with a sensitivity of 94% (124).

KTRs with histopathological lesions of active rejection had lower TTV loads (128). Furthermore, the risk of developing histological features of chronic rejection was associated with the number of days with a TTV viral load <1x 10⁶ copies/ml between 3 to 12 months post-transplant, suggesting suboptimal immunosuppression (128).

4.3 Virus-specific cell mediated immunity

Measurement of virus-specific cell-mediated immunity (CMI) can provide insights into cellular immunocompetence and the ability to suppress viral replication. Understanding the strength of virus-specific responses can risk stratify transplant recipients and personalize chemoprophylaxis duration and monitoring (129–132). Immune assays commonly stimulate and measure T-cell functions, including activation, cytokine expression/production (IFN-γ), proliferation, and cytotoxicity (133).

Enzyme-linked immunospot, enzyme-linked immunosorbent assay, flow cytometry and intracellular cytokine staining (ICS), and MHC multimer staining (CMV CD8⁺ Immune Competence) are currently available techniques to assess viral-CMI (133). The flow cytometry ICS also allows for the co-staining of other cytokines and T-cell surface markers to characterize immune cell phenotypes.

CMV-CMI (QuantiFERON-CMV, ELISpot and ICS assays) correlated with functional T-cell responses and viral control (129–132). A positive QuantiFERON-CMV assay at the end of chemoprophylaxis in high-risk recipients (donor +ve/recipient -ve) yielded a positive predictive value of 90% for immune protection (132). Whereas indeterminant or negative results yielded the highest risk of CMV disease, likely correlating with blunted cellular responses (129–132). Quantiferon-CMV status was helpful for risk stratification of individuals with asymptomatic, low-level viremia following prophylaxis cessation (134). Those who were Quantiferon-CMV positive had a higher likelihood of spontaneous clearance, whilst those who were negative had a higher risk of CMV disease (134). Quantiferon-CMV guided pre-emptive therapy was more cost-effective than pre-emptive therapy alone (135). CMV-CMI responses were suppressed for up to 3 months post-ATG therapy. High dose prednisolone and elevated tacrolimus levels particularly impaired CMV-specific functional T-cell responses (129).

T-cell alloreactive CMI assays may be helpful in predicting immune activation and rejection risk, but studies to date are heterogeneous and quote different performance characteristics. Positive pre-transplant donor-reactive IFN-γ responses (ELISpot), particularly at higher levels, were associated with a greater risk of post-transplant rejection (136, 137). The meta-analysis by Montero et al. (2019) found that KTRs with positive pre-transplant donor-specific IFN-γ ELISPOT results had a 3.3-fold higher risk of acute rejection (138). A high number of donor-reactive memory T-cells, as measured by IFN-γ and interleukin-21 ELISPOT assays, was significantly associated with the risk of kidney allograft rejection (139).

CMI is an evolving additive tool for infection risk quantification, particularly for CMV infection. However, further research is needed to establish clear test thresholds relevant to different risk groups (recipient CMV positive vs. negative).

4.4 Medication/immunosuppression levels

Drug levels are often used to assess the overall immune state. However, the impact of the individual immunosuppressants on the overall immune state is difficult to quantify due to heterogeneity in individual drug pharmacokinetics and pharmacodynamics coupled with concomitant drug dosage adjustments. Therapeutic drug monitoring and target concentration intervention aims to optimize immunosuppression while minimizing toxicity. Calcineurin inhibitor and mTOR inhibitor levels are routinely measured for dose titration.

The proposed optimal target trough tacrolimus level for kidney allografts is 5-8ng/ml beyond the first few months post-transplant[128]. Lower levels are associated with rejection, and higher levels with infection and toxicity (140). CNI level variability relating to non-adherence or underdosing increased the risk of intragraft interstitial fibrosis/tubular atrophy, allograft rejection, and failure (121). Time in therapeutic range (TTR) >78% in the first year post-transplantation was associated with reduced rates of rejection and infection (141). Tacrolimus trough levels >10ng/ml were associated with an increased risk of BKVN, while lower levels were associated with rejection (142, 143). Every 1ng/mL increase in Tacrolimus trough levels beyond 5.35ng/mL at one month post-transplant was associated with an 11% higher rate of infections (144). Genetic polymorphisms related to drug clearance can play a critical role in rejection. Friebus-Kardash et al. (2022) demonstrated that CYP3A5 expressers achieved lower tacrolimus trough levels and were at greater risk of de novo DSA formation (145).

In contrast to CNI and mTOR level-based dose titration, mycophenolate dosing based on regular area under the concentration-time curve (MMF.AUC) measurements or Cmin (minimum concentration) levels have yet to be universally implemented. A fixed dosage mycophenolate regimen is standard practice in many transplant centers worldwide (146). Mycophenolate AUC measurements (targeting 30-60mg/L.h) are often used on a case-by-case basis, such as in the context of infection or malignancy, to guide dose adjustments. Mycophenolate AUC-based dose adjustment reduced infection rates in the 12 months post-kidney transplantation relative to a fixed-dose regimen (147). Individuals with high mycophenolate exposure (AUC 60-100mg/L.h) may safely and cautiously have their mycophenolate dose reduced (e.g. following leukopenia) while maintaining a level required for rejection prophylaxis. A mycophenolate AUC >50mg/L.h at three months post-transplant was associated with sustained BK viremia and BKVN in the subsequent two years (142). Conversely, mycophenolate AUC levels <30mg/L.h have been strongly correlated with rejection, particularly with other risk factors such as high immunological mismatch, delayed graft function, and low levels of concomitant immunosuppressants (148, 149). Despite this, most studies suggest an increased risk of rejection below a threshold of 40mg/L.h, particularly beyond six months post-transplantation (146). Reduction or discontinuation of mycophenolate, irrespective of tacrolimus levels, was associated with adverse graft outcomes, including rejection (146, 150). Hence, the impact of individual drug levels on the overall immune state warrants consideration.

4.5 Donor specific antibody detection (HLA and non-HLA)

Anti-HLA donor-specific antibodies may be pre-formed or de novo (develop post-transplantation). The detection of donor-specific antibodies (against HLA antigens) has long been the hallmark of the diagnosis of antibody-mediated rejection (AMR) according to the BANFF criteria (151). Recipients with pre-formed antibodies at the time of transplant are at a higher risk of AMR. Within five years post-transplant, 15-25% of transplant recipients develop de novo DSA, with an incidence of 2% per year in immunosuppression adherent transplant recipients (152, 153). The DSA titer, as measured by the mean fluorescence intensity (MFI), correlated strongly with the risk of antibody-mediated rejection and graft failure (154).

This correlation is especially true of complement activating (C1q-positive) and anti-HLA class II DSA (155, 156). There is emerging evidence on the significance of several non-HLA antibodies, including those against major histocompatibility complex class 1-related chain A (MICA), type I angiotensin II receptor, endothelin A receptor, and collagen, in the development of AMR (157).

Under-immunosuppression, owing to medication dosing, genetic variability in drug metabolism, or non-adherence strongly favors DSA formation (152, 158, 159). A high degree of HLA mismatches (especially DQ) and events that cause graft inflammation and increased immunogenicity, including ischemic injury, infections, and cellular rejection, can also trigger de novo DSA formation (154, 160–162).

During the first-year post-transplantation, de novo DSA formation was more common in transplant recipients with subtherapeutic tacrolimus levels (<5ng/ml), particularly in the preceding 6 months (163, 164). Additionally, achieving less than 60% of the time within the therapeutic range (5-10ng/mL) in the first year post-transplant was associated with an increased risk of de novo DSA formation, rejection at 12 months, and graft loss by five years (143).

With the advent of molecular diagnostics, DSA-negative AMR was revealed to be a more common AMR phenotype than previously recognized (165). Several minimally invasive biomarkers may detect graft injury and assist in diagnosing AMR without clear indicators, such as DSA.

4.6 Donor-derived cell-free deoxyribonucleic acid

Donor-derived cell-free DNA (dd-cfDNA) is one of the most promising and extensively studied biomarkers. Also termed the “liquid biopsy,” dd-cfDNA is a useful screening tool to identify individuals at risk of rejection, who would benefit from a definitive histological diagnosis. A threshold of 1% dd-cfDNA distinguished allograft injury and rejection (166–169). Higher levels of dd-cfDNA correlated with a greater degree of histological injury (170).

Following cell apoptosis and necrosis, a small proportion of DNA, termed cell-free DNA, enters the circulation. Graft tissue necrosis was associated with larger fragment cfDNA (10,000 base pairs), while apoptosis with smaller fragments (60-500bp) (168). In transplant recipients, a small fraction of dd-cfDNA may enter the recipient’s circulation following graft injury (171). Urinary dd-cfDNA can arise from glomerular filtration of circulating dd-cfDNA or donor DNA released from the donor urinary tract (169, 172). Dd-cfDNA can be quantified as relative (proportion (%) of total cf-DNA) or absolute quantitative dd-cfDNA (cp/ml). The diagnostic performance of dd-cfDNA varies depending on the rejection phenotypes, clinical context, immunological risk, and assay type.

Several local and systemic causes affecting graft integrity can elevate circulating dd-cfDNA levels, including infections, calcineurin inhibitor-induced nephropathy, disease recurrence, and ischemia/acute tubular necrosis (167). As such, all potential causes of allograft injury must be considered when assessing dd-cfDNA elevation.

4.6.1 Donor-derived cell-free DNA and infection

KTRs with infections that compromised graft integrity, including BK nephropathy and urinary tract infections, had elevated plasma dd-cfDNA levels (173). A dd-cfDNA rise of >1% was noted within seven days of a respiratory viral infection (RVI) in lung transplant recipients (174). In lung transplant recipients with RVI, a greater plasma %dd-cfDNA rise correlated with poorer lung function recovery post-RVI (174). In a prospective cohort study of 44 heart transplant recipients, those who tested positive for CMV infection had significantly higher plasma dd-cfDNA levels as compared to those who were CMV-negative (175). Dd-cfDNA elevations have also been described in case studies of heart transplant recipients with myocardial injury following COVID-19 infection (176).

4.6.2 Donor-derived cell-free DNA and rejection

4.6.2.1 Liver transplant

In a multicenter cohort study of 219 liver transplant recipients, serial plasma dd-cfDNA elevations accurately predicted rejection in recipients with normal liver function tests (177). Additionally, dd-cfDNA levels decreased following successful rejection treatment. Day 7 dd-cfDNA level >10.2% accurately predicted the risk of early rejection within the first three months post-transplant (sensitivity-93%, specificity-94%, positive predictive value (PPV)- 88%, negative predictive value (NPV)-97%) (178).

4.6.2.2 Lung transplants

The systematic review by Li et al.(2023) demonstrated that elevated plasma dd-cfDNA in lung transplant recipients distinguished rejection versus no-rejection with high pooled sensitivity and specificity of 87% (95% CI: 80-92%) and 82% (95% CI: 76-86%) respectively (179). Dd-cfDNA was elevated in lung transplant recipients with subclinical rejection (AMR & TCMR) and infection (180, 181). A multicenter retrospective cohort study by Keller et al. (2022) assessed the performance characteristics of plasma dd-cfDNA (≥ 1%) in detecting acute lung allograft dysfunction, a composite marker of infection and rejection, in 175 asymptomatic lung transplant recipients. Sensitivity, specificity, PPV, and NPV of dd-cfDNA ≥1% were 74%, 88%, 43%, and 97%, respectively (181). Given the very high NPV, a normal dd-cfDNA may be helpful in excluding underlying rejection or infection in stable lung transplant recipients.

4.6.2.3 Heart transplant

In a prospective cohort study of 223 heart transplant recipients, dd-cfDNA of ≥0.15% accurately predicted rejection with sensitivity, specificity, PPV, and NPV of 79%, 77%, 25%, and 97%, respectively (182). The specificity for rejection was slightly higher (82%) with a ≥0.2% threshold, however the PPV remained poor (30%). In a multicenter-prospective cohort study of 740 heart transplant recipients, those who had any-cause rejection had higher plasma %dd-cfDNA than those who did not (median: 0.17% vs. 0.07%) (183). Despite a poor sensitivity (44%), a threshold of >0.2% demonstrated a very high NPV (97%) for allograft rejection.

4.6.2.4 Kidney transplants

The addition of dd-cfDNA to standard diagnostic algorithms greatly enhanced their discriminatory power for AMR, TCMR, and mixed rejection phenotypes (170). However, dd-cfDNA can better predict AMR than TCMR. Additionally, the magnitude of dd-cfDNA correlated with the degree of graft injury and rejection severity (166–168, 170, 173).

Owing to significant heterogeneity amongst studies, a wide range of performance metrics for dd-cfDNA in diagnosing kidney allograft rejection has been reported. A systematic review and meta-analysis by Xing et al. (2024) included nine studies assessing the accuracy of plasma dd-cfDNA in diagnosing any rejection and 12 studies specific to AMR (184). The diagnostic accuracy of dd-cfDNA was much greater for AMR as compared to any-rejection phenotypes. The pooled sensitivity was only 59% (95% CI, 48–69%) for any-rejection diagnosis; however, the specificity and the area under the receiver operating characteristics curve (AUROC) were more favorable at 83% (95% CI,76–0.88%) and 80% (95% CI,76–83%) respectively. Comparatively, the pooled sensitivity, specificity, and AUROC for AMR diagnosis were 81% (95% CI, 72–88%), 80% (95% CI, 73–86%), and 87 (95% CI, 84–90%), respectively. “The European Society of Organ Transplantation’s (ESOT) Consensus Statement on Non-invasive Diagnosis of Kidney Allograft Rejection” favored the use of dd-cfDNA in transplant recipients with allograft dysfunction to exclude rejection, particularly AMR (185). The magnitude of plasma dd-cfDNA coupled with histological features may help re-classify rejection severity and predict clinical outcomes such as eGFR decline and risk of de novo DSA formation (186, 187). The prospective, multicenter Trifecta study by Halloran et al. (2023) assessed the relationship between plasma dd-cfDNA, DSA, and molecular signatures in 280 kidney transplant biopsies (188). DSA-negative AMR was more prevalent than previously described, as 56% of the molecular and 51% of the histological AMR diagnoses were DSA-negative. DSA-negative and positive AMR had similar degrees of dd-cfDNA elevation, making dd-cfDNA a useful predictive tool for identifying AMR in the absence of DSA and prompting a confirmatory allograft biopsy (188). Screening with dd-cfDNA may not be reliable for detecting subclinical or low-grade T-cell mediated rejection (type 1 A) (166, 185). Compared to AMR, the fractional plasma dd-cfDNA threshold of 1% was less sensitive for TCMR, which required a higher diagnostic threshold (189). However, combining dd-cfDNA with other biomarkers such as molecular markers may improve the overall test accuracy for predicting TCMR. The dynamics of dd-cfDNA in the early postoperative period is unclear, and several confounding factors that cause graft injury can elevate the levels during this time. As such, dd-cfDNA is not particularly useful for rejection-infection diagnosis in the immediate peri-operative period. Shen et al. (2019) assessed the dd-cfDNA fluctuations in the first two weeks post- kidney transplant (190). Deceased donor grafts had higher plasma %dd-cfDNA immediately post-transplant as compared to living donor grafts (45% vs. 10%), and those with delayed graft function had a slower decline in dd-cfDNA. A sudden rebound of dd-cfDNA levels may point to rejection as a possible cause.

With respect to diagnosing rejection in different SOT populations, most studies report higher sensitivity than specificity and a much higher negative predictive value (>90%) than a positive predictive value (191). As such, dd-cfDNA is particularly useful for ruling out allograft rejection (168, 169). Additionally, given the short half-life of circulating cfDNA (30-120 minutes), the return of dd-cfDNA levels to baseline can indicate successful treatment of allograft injury (rejection or infection), allowing for real-time assessment of allograft injury and recovery (192, 193).

Studies to date have not examined the use of dd-cfDNA in place of biopsies to diagnose rejection or assess longer-term clinical outcomes following rejection. Additionally, dd-cfDNA cannot discriminate between different rejection phenotypes. Dd-cfDNA can be useful for identifying transplant recipients with allograft injury who require a histological diagnosis whilst considering alternative causes, including local and systemic infections. Integration of dd-cfDNA into routine clinical care is gaining momentum, with commercial assays currently available in several countries in Europe and the United States. In the United States, dd-cfDNA has been Medicare reimbursable since 2017 (194). Despite its’ commercial availability, concerns regarding its poor specificity, assay validation, and significant associated costs have limited its uptake elsewhere including in Europe and Australasia. While the cost and availability currently prohibit widespread adoption of dd-cfDNA, there may be long-term cost savings if its integration as a surveillance tool leads to earlier histological rejection diagnosis and therapeutic intervention before chronic and permanent allograft damage ensues (168).

4.7 Urinary chemokines

Urinary chemokines are gaining significant ground as a valuable biomarker in assessing kidney allograft health. CXC-motif chemokine ligands 9 and 10 (CXCL9 and CXCL10) are interferon-γ induced chemokines that promote leukocyte migration and infiltration during allograft rejection (195, 196).

4.7.1 Urinary chemokines and infection

Urinary chemokines are not specific for rejection, but rather, renal inflammation, and may also be elevated in KTRs with local infections, including BK nephropathy and urinary tract infections (197, 198). In a longitudinal study of 60 KTRs with BK viremia, urinary CXCL10/cr levels were identified as a prognostic marker of graft dysfunction and predicted the degree of infection related renal inflammation (199). A threshold value of 12.86ng/mmol was associated with a greater degree of inflammatory burden and eGFR decline. Urinary CXCL10 levels were not elevated with CMV viremia alone, highlighting that renal-specific inflammation was required to trigger urinary chemokine elevation (200).

Given the association between urinary chemokines and renal inflammation, all potential causes, including infection, must be considered and excluded before attributing the rise to rejection.

4.7.2 Urinary chemokines and kidney allograft rejection

The reported sensitivity and specificity of both urinary chemokines in diagnosing rejection vary widely owing to different test thresholds and variations in the prevalence of rejection phenotypes in study populations. Reported CXCL9 sensitivity and specificity for detecting kidney allograft rejection ranged between 58%-86% and 64%-80%, respectively, and CXCL10 sensitivity and specificity from 59-80% and 76%-90%, respectively (195–197, 200). The high negative predictive value of CXCL9 and CXCL10 make them useful for ruling out acute kidney allograft rejection at low levels (195). Combination of both CXCL9 and CXCL10 did not provide superior discriminatory power compared to measuring individual levels (197).

Urinary CXCL9 and CXCL10 could accurately discriminate between allograft dysfunction due to rejection and non-rejection causes but were not able to distinguish between AMR and TCMR (195, 196). They may serve as an early indicator of allograft dysfunction with elevated CXCL9 levels detected up to 30 days before clinical changes and biopsy-confirmed acute rejection (195). Whereas lower urinary chemokine levels at 1- and 3-months post-transplant were associated with immunological quiescence and a lower risk of allograft rejection (201). Post-rejection monitoring with urinary chemokines identified individuals at risk of rejection recurrence. Low CXCL9 levels six months following a rejection episode correlated with a reduced risk of rejection in the subsequent 18 months (NPV 99.3%). Conversely, a rising CXCL10, a potential sign of persistent inflammation, was linked to eGFR decline (202). Successful treatment of allograft rejection was associated with reduced CXCL10 levels (197). CXCL10 elevations may reflect renal compartment-specific histological injury, as elevations accompanied tubulointerstitial inflammation and peritubular capillaritis but not isolated glomerulitis or vascular inflammation (200).

4.7.3 Chemokines in other solid organ transplants

Plasma and tissue chemokines may serve as potential indicators of graft injury in other solid organ transplants, however their value in allograft rejection diagnosis has not been established. Plasma and bronchoalveolar lavage CXCL9 and CXCL10 levels were shown to positively correlate with chronic lung allograft damage and acute rejection in a prospective multicenter study of 184 lung transplant recipients (203). Liver transplants with early allograft dysfunction had elevated plasma levels of T-lymphocyte-associated chemokines and cytokines including CXCL9/CXCL10, in the early postoperative period (204). Inhibition of plasma CXCL9/CXCL10 levels delayed cardiac allograft rejection in murine models (205).

The ESOT consensus statement on non-invasive diagnostic tests recommended the use of urinary CXCL9 and CXCL10 to exclude or consider kidney allograft rejection in transplant recipients with acute allograft dysfunction (185). Whilst urinary chemokines show great promise as a serial surveillance tool, given that they are easy to access and non-invasive, they are currently not yet recommended for use in subclinical rejection (185). The randomized controlled trial by Hirt-Minkowski et al. assessed the utility of a serial urinary CXCL10 monitoring-based care in reducing poor graft outcomes at 1-year post-kidney transplant (206). CXCL10 monitoring did not reduce the primary endpoints of allograft rejection, death-censored graft loss, denovo DSA formation, or eGFR decline to <25 ml/min. Several prospective studies are currently underway that may shed light on the utility of serial CXCL9 and CXCL10 monitoring in predicting clinical and subclinical rejection.

4.8 Molecular markers/transcriptomics

Molecular diagnostics is a rapidly evolving field that could change the landscape of precision medicine by providing mechanistic insights into immune cell phenotypes and molecular pathways involved in allograft disease states. Gene expression changes in blood, peripheral blood mononuclear cells (PBMC), urine and tissue have been described in relation to various microbial infections and rejection phenotypes.

4.8.1 Molecular markers of infection

Measurement of characteristic gene signatures may help clinicians tailor chemoprophylaxis and guide testing for viral reactivation. Ahn et al (2021) describe changes in whole-blood gene expression in CMV-positive KTRs across multiple time points within the first year post-transplantation (47). Peak gene expression differences occurred between baseline and 1-week timepoints involving the innate and adaptive arms of the immune system (e.g., interferon signaling and cytotoxic T-cells). While many pathways normalized post-infection, several genes remained differentially expressed at one year, suggesting long-term adaptations to the immune system (47).

Given similarities in histological features, molecular markers may prove useful in overcoming the diagnostic dilemma of rejection versus BK Nephropathy of the kidney allograft. Adam et al. (2020) described a five-gene set (Agnoprotein, LTAg, VP1, VP2, VP3) that reliably distinguished BK virus nephropathy from TCMR in biopsy specimens (207). Gene expression biomarkers may help differentiate between rejection and viral or bacterial infections in lung transplant recipients with acute respiratory symptoms (208, 209).

4.8.2 Molecular markers of rejection

Molecular markers of subclinical rejection would help facilitate early diagnosis and treatment, ultimately reducing the risk of chronic damage and allograft loss.

The Molecular Microscope Diagnostic System (MMDx) project collates genome-wide microarray data to describe different molecular phenotypes (e.g. AMR, TCMR, parenchymal injury, irreversible atrophy-fibrosis) associated with allografts. Several molecular AMR phenotypes have been identified, including subclinical, DSA-negative, and C4d-negative subtypes (210). Molecular disease classifiers were able to discriminate between the rejection phenotypes (AMR, TCMR, mixed) in cases where histology was ambiguous (211). Halloran et al. (2024) identified gene transcripts that were TCMR and AMR selective and those shared by both rejection phenotypes. IFN-γ inducible (CXCL11, WARS, IDO1, and GBP4), effector T cells, and NK cell-related transcripts (KLRD1 and CCL4) were common to both AMR and TCMR phenotypes (210). The top TCMR-selective transcripts were predominantly associated with activated effector T-cells (IFNG, LAG3, SIRPG), macrophages, and dendritic cells (ADAMDEC1, CXCL13, CD86, and SLAMF8) (210). Some IFN-γ-inducible transcripts (ANKRD22 and AIM2) were highly selective for TCMR. NK cell (CD160, GNLY, KLRD1, SHD2D1B, CX3CR1) transcripts strongly correlated with AMR, suggesting a prominent role of NK-induced cell-mediated cytotoxicity in its pathogenesis (210). Endothelial cell (CCL4, DARC/ACKR1, CDH5, CDH13, COL13A1, etc.) and IFN-γ inducible gene transcripts (CXCL11, CXCL10, PLA1A) are also commonly described to be associated with AMR (165, 212, 213). AMR molecular classifiers closely correlated with microcirculation lesions, histological damage, and DSA (210).

The BANFF Molecular Diagnostics Working Group (MDWG) compiled a validated 770-gene BANFF-Human Organ Transplant (B-HOT) panel, which incorporated genes involved in immune responses, rejection, tolerance, and viral infections (214). The panel uses the NanoString nCounter platform, which allows relatively rapid quantification of transcripts from fresh or formalin-fixed paraffin-embedded samples.

Blood gene expression profiling (GEP) (commercial assay-AlloMap^®) has been extensively studied, primarily in heart transplant recipients, to reduce the need for frequent endomyocardial biopsies. The 11 rejection-associated gene set was discovered and validated in the CARGO study, a prospective observational study of heart transplant recipients (215). A score beyond a determined threshold was associated with a higher likelihood of acute cellular rejection in cardiac allografts (215). The IMAGE and EIMAGE trials revealed non-inferiority of GEP compared to a biopsy-driven protocol for detecting rejection (216, 217). The Outcome AlloMap registry, a multicenter prospective study of 1504 heart transplant recipients, showed that GEP surveillance was associated with improved survival, reduced rates of allograft dysfunction, and acute rejection (218). Low 2-6 months and >6-month post-transplant GEP scores had NPVs for rejection of 98.4% and 98.5% respectively. Kidney-specific GEP (5-gene classifier: DCAF12, MARCH8, FLT3, IL1R2, and PDCD1) discriminated between immune quiescence and rejection (219).

The Kidney Solid Organ Response test (kSORT) assay is a peripheral blood 17 gene-set panel associated with rejection (220). Whilst the original study, the Acute Rejection in Renal Transplantation (AART) study of 436 KTRs, reported a high PPV (81.3%–95.5%) and NPV (91.6%–98.0%) for detecting rejection, this finding was not validated in a subsequent retrospective multicenter study of 1763 KTRs (220, 221). Several studies since have yielded conflicting results.

TruGraf^®v1 is another commercially available blood 200-probe micro-array gene-expression signature assay that is useful in stable allografts to identify subclinical rejection (222). A positive test was associated with poorer 24-month allograft outcomes and the development of DSA (222). The application of an 11-gene set signature, termed the “common rejection module” on urine and tissue (kidney allograft biopsies) was shown to discriminate between stable and rejection biopsies (223–225).

Given the comparative ease of collection relative to biopsies, blood and urine gene expression profiling may be a helpful adjunct in diagnostic algorithms. Despite the promising studies, ESOT guidelines currently do not recommend the clinical use of gene expression signatures to diagnose allograft rejection in those with acute allograft dysfunction (185). The issue lies in the absence of clearly defined, validated, and reproducible gene set signatures that can distinguish between rejection and no rejection, and differentiate between rejection phenotypes. Furthermore, the gene sets need to be specific to the biological specimen type. Identifying specific gene sets in more extensive, prospective studies, improving costs and availability, and standardizing testing may push molecular diagnostics to the forefront of rejection-infection diagnosis. The MMDx project and the global collaboration of gene transcript research using the Nanostring B-HOT panel may help with this endeavor (214). Molecular profiling may allow for the re-classification of rejection phenotypes and severity, particularly where histology is ambiguous. Additionally, gene signatures can provide insights into intragraft changes associated with rejection therapies.

4.8.3 MicroRNAs

MicroRNAs (miRNA) are short (22 nucleotides), non-coding RNA segments that regulate gene expression and play an important role in modulating homeostatic and disease processes, including allograft rejection (226, 227). Up to 60% of transcribed genes of the human genome are targeted by miRNA, and different cell types express specific subsets of miRNA (227). MiRNAs may also provide organ and disease-specific signatures relevant to allograft rejection and infection. MiRNAs may be up- or down-regulated and detected in the systemic circulation (serum or PBMC) or urine using real-time PCR, microarray and next-generation sequencing (228). They are annotated by a “miR” prefix and a unique number identifier (229).

4.8.3.1 miRNA in infection

Whilst miRNAs have predominantly been assessed with respect to rejection, miRNA profiling may also assist with infection prediction and diagnosis. Viral miRNAs regulate viral genes relating to replication, immune evasion and viral persistence and can be detected in biological samples.

Two BK-virus miRNAs, bkv-miR-3p and bkv-miR-5p, regulate viral replication. Serum levels of these miRNAs were higher in KTRs with BK Nephropathy (230). Demey et al. (2021) demonstrated that in KTRs with BK viremia, urinary bkv-miR-B1-3p and bkv-miR-B1-5p levels reduced in concert with reductions in serum viral loads (231). Additionally, increased urine bkv-miR-B1-5p levels suggested active viral replication (232). BK-virus encoded miRNA bkv-miR-5p and bkv-miR-3p downregulate viral large T-antigen (LTag) expression, reducing viral recognition and allowing immune evasion and persistence (230). Downregulation of ULBP3 by BK-virus encoded miRNA dampened NK-cell mediated killing of viral-infected cells (233).

Several miRNAs are described to be associated with CMV viremia and infection. Afshari et al. (2022) found that KTRs with CMV viremia had significantly higher plasma expression of the CMV-encoded miRNAs, miR-UL112-3p/5p, miR-UL22A-3p/5p, miR-US25-1-5p, miR-US25-2-3p/5p, miR-UL36-3p/5p and miR-UL70-3p, relative to those with latent CMV (234). Reduced expression of miR-125a-5p in CMV seropositive KTRs with positive CMV-specific cell-mediated immunity predicted those who were at higher risk of CMV infection despite the positive CMI result (235).

Other microbial pathogens also induce characteristic miRNA signatures. In bronchoalveolar lavage (BAL) samples of lung transplant recipients, increased levels of miR-23b-3p expression were associated with pneumonia (236). Dysregulation of five miRNAs (miR-145-5p, miR-424-5p, miR-99b-5p, miR-4488, and miR-4454/miR-7975) in BAL specimen was specific for invasive aspergillosis in lung transplant recipients (237). A 25-set intragraft miRNA signature differentiated acute pyelonephritis from allograft rejection in KTRs (238).

4.8.3.2 miRNA in rejection

Studies have associated miRNA expression with immune cell pathways implicated in rejection, notably T-cell activation and regulation. FOXO1 is a key regulator of several cellular and immune cell processes and plays a critical role in the development of FOXP3 regulatory T-cells (226). Increased miR-182-5p expression following IL-2 and STAT5 activation, has been shown to suppress FOXO1 and reduce regulatory T-cell production (226). Elevated miR-182-5p levels were also noted in mice with rejecting cardiac allografts (239). Another miRNA, miR-146a was significantly upregulated in activated T-cells, particularly memory T-cells (226).

4.8.3.3 Liver transplants

Several hepatocyte-derived miRNAs have been investigated for their potential to diagnose clinical and subclinical liver transplant rejection. In liver transplant recipients (LTRs), plasma miRNAs, miR-122, miR-148a, miR-194, were linked to liver injury and acute rejection (240). Plasma miR-483-3p and miR-885-5p levels could predict rejection in those undergoing immunosuppression withdrawal (241). Pre-transplant plasma miR-155-5p and post-transplant miR-155-5p and miR-181a-5p expression correlated with the risk of developing rejection in LTRs (242). These miRNAs may be helpful in stratifying immunological risk pre-transplant and rejection risk post-transplant (242).

4.8.3.4 Heart transplant

Myocardium-specific miRNAs have also been linked to cardiac allograft rejection. The multicenter prospective cohort study, the Genomic Research Alliance for Transplantation (GRAfT), identified 12 plasma miRNAs that predicted acute cellular rejection (ACR) (AUROC-0.92, 95% CI:0.86-0.98) and 17 that predicted antibody-mediated rejection (AUROC-0.82, 95% CI:0.74-0.90) (243). Other circulating miRNAs including miR-486-5p and miR-181a-5p have also been shown to predict ACR (244, 245). Circulating miR-10a, miR-92s, and miR-155 had previously been linked to cellular rejection, however the prospective multicenter study by Coutance et al. (2023) showed no associations between these three miRNAs and allograft rejection (246).

4.8.3.5 Lung transplant

Epithelial-to-mesenchymal transition (EMT) is important in the pathophysiology of chronic lung allograft dysfunction (CLAD). Increased expression of circulating miR-21, a regulator of EMT, was seen in lung transplants with CLAD (247). TGF-β, is a potent inducer of EMT and a critical mediator of fibrosis. TGF-β associated miRNAs, miR-369-5p and miR-144, were dysregulated in lung transplant recipients with DSA and bronchiolitis obliterans syndrome (BOS) (248, 249). The miRNA signature of mononuclear cells (miR-369-5p, miR-144, miR-134, miR-10a, miR-195 miR-142-5p, miR-133b, and miR-155) was able to predict the development of DSA and BOS (248, 249). In bronchoalveolar lavage samples, low miR-148b-5p and high miR-744-3p expression distinguished rejection from no-rejection and was significantly associated with shorter time to the acute rejection episode (236).

4.8.3.6 Kidney transplant

Urinary and blood miR-155-5p expression has been evaluated as a diagnostic and prognostic marker of allograft rejection in KTRs. Urinary miR-155-5p levels correlated with eGFR levels and normalized after successful treatment and resolution of allograft injury (250). Elevation in plasma miR-155, miR-223, and miR-21 expression was associated with AMR (251). MiR-223 was also raised in TCMR, alongside miR-142-3p, miR-10a, and miR-100a, whilst miR-99a declined (252). Changes in miRNA expression could precede clinical and histological features of TCMR, with elevation in miR-155-5p and miR-142 expression and reduction in miR-210-3p expression noted before and during TCMR (250).

Whilst several studies have linked specific miRNAs with allograft injury and damage, consistent associations are yet to be validated. Available studies vary substantially with respect to study populations, sample type and size, detection assays, and thresholds used and demonstrate conflicting results. When tissue-specific, unique miRNA signatures are established, miRNA profiling may be a valuable additive tool for allograft surveillance and rejection prediction.

4.8.4 Exomes

Exomes are nanometer-sized (50-200nm) extracellular vesicles that carry proteins, lipids, metabolites, and/or nucleic acids (mRNA or miRNA) and play a key role in inter-cell communication (253). They are present in almost all biofluids, and their composition and function are specific to the originating cell and are modulated by physiological conditions and stressors. The role of exomes in SOT has gained considerable interest. Exosomes that transport donor and self-antigens may play a key role in allorecognition and rejection (254). Urinary and plasma exome contents, particularly proteomic and nucleic acid profiles, may provide valuable insights into the underlying pathological processes such as rejection.

4.8.4.1 Kidney transplant

In a prospective study of 175 KTRs, a urinary mRNA exome signature (CXCL11, CD74, IL32, STAT1, CXCL14, SERPINA1, B2M, C3, PYCARD, BMP7, TBP, NAMPT, IFNGR1, IRAK2, and IL18BP) distinguished any-cause rejection with no rejection (255).

The sensitivity, specificity, AUROC, and NPV were 85% (95% CI, 74 -92%), 94% (95%CI 88 – 97%), 93% (95% CI 87 – 98%) and 93% (95% CI 88 – 96%) respectively. Additionally, another specific gene signature (CD74, C3, CXCL11, CD44, and IFNAR2) distinguished TCMR and AMR phenotypes, with a AUROC of 0.87 (95% CI, 0.76 to 0.97). Eleven urinary exome proteins were enriched in KTRs with rejection (256). Tower et al. revealed an increase in the plasma concentrations of C4d⁺/CD144⁺ microvesicles, an endothelial marker, in KTRs with AMR (257). Additionally, C4d⁺/CD144⁺ microvesicle levels decreased following anti-rejection therapy.

4.8.4.2 Heart transplant

A serum proteomic profile of 15 exomal proteins distinguish cardiac allograft rejection with no-rejection. These related to complement activation (C1QA, C1R), coagulation (FIBA, FIBB, FIBG, FINC, F13A, and TSP1), IgG subfraction (KV302, HV304, HV315) and APOL1 (258). In a prospective study of 10 heart transplant recipients, serum exosomal miRNAs, miR-142-3p, miR-92a-3p, miR-339-3p, and miR-21-5p were enriched in individuals with allograft rejection (259).

4.8.4.3 Lung transplant

Circulating exomes containing self-antigens may identify lung transplant recipients at risk of allograft rejection and CALD (260). In an observational study of 30 lung transplant recipients, donor-HLA and self-antigens were detected in bronchioalveolar lavage and serum exosomes of recipients with rejection and BOS but not in stable patients. Exomes containing Col-V and immunomodulatory miRNA were also isolated in lung transplant recipients with rejection (261).

Further large-scale research is required to validate and reproduce relevant organ specific exomes that may serve as biomarkers of organ health.

4.9 Integrating biomarkers

Integrating several markers, including molecular, immune, serological, and clinical parameters, may strengthen the overall diagnostic and predictive accuracy for rejection compared to a single method used in isolation. The combination of dd-cfDNA with gene expression profiling increased the predictive power for detecting allograft rejection (262). Additionally, in the study by Park et al. (2021) GEP detected more TCMR whilst dd-cfDNA detected more AMR (262). In a heart transplant cohort, compared to GEP alone for rejection surveillance, GEP with dd-cfDNA produced similar one year rejection-free survival but did so with significantly fewer biopsies (263). Integrating a pre-transplant functional immune assay (donor-alloreactive IFN-γ release assay/ELIspot) with the six-month post-transplant blood gene-expression kSORT assay was shown to predict subclinical rejection (264).

An integrated model combining urinary chemokines, CXCL9, and CXCL10 with six other clinical parameters (recipient age, gender, eGFR, DSA, signs of urinary tract infection, blood BK viral load) showed moderate diagnostic accuracy (AUROC, 0.85) for the detection of kidney allograft rejection (198). Similarly, an integrated model combining CXCL9, CXCL10 with clinical markers (eGFR, DSA, and BK viremia) also showed similar diagnostic performance for detecting acute rejection (AUROC-0.81) (265). Moreover, using the latter composite score, when the predicted rejection risk was <10%, 59 out of 100 protocol biopsies were avoided (265).

The combination of chemokines and miRNA profiling has also been assessed. Millan et al. (2023) described an integrated plasmatic model including three miRNAs (miR-155, miR-181a-5p, miR-122-5p) and CXCL10, which predicted TCMR in liver transplant recipients with good diagnostic performance, AUROC 0.98 and high PPV-97.7% and NPV-97.1% (266). In another study, an integrated model of miR-155-5p, miR-615-3p, and CXCL-9 had strong diagnostic accuracy for predicting kidney transplant allograft rejection (AUROC- 0.92) (267).

5 Simultaneous infection and rejection risk assessments and clinical application of biomarkers

Several tools support the assessment of the transplant recipients’ net immune state and help refine infection-rejection risk stratification. Considering the complex interplay between these two complications, their risks should be assessed concurrently (Table 2). Furthermore, the dynamic nature of the immune state necessitates regular assessments tailored to the clinical contexts. Several biomarkers, including dd-cfDNA, urinary chemokines, and miRNAs, lack the specificity for the prediction and diagnosis of rejection and may be deranged in the context of other confounding contributors that cause graft inflammation, including local and systemic infections (BK, CMV, UTIs, pneumonia). Consequently, the distinction between these entities requires clinical evaluation and assessment to identify the most probable clinical scenario. Additionally, both rejection and infection may co-exist. Table 2 summarizes the currently available and emerging immune and non-immune tools for simultaneous infection and rejection risk assessments.

Table 2

Table 2. Tests to assess the net-state-of immunity, and the results infection (immunosuppression) and rejection (immune activation) risk.

If concerns regarding accessibility and affordability are circumvented, validated biomarkers should ideally be performed at regular intervals during high-risk periods for rejection and infection, particularly in transplant recipients with high immunologic risk. The first-year post-transplant carries significant risks for both infection and rejection. Monitoring during this period with 1-3 monthly biomarkers may allow for early detection of graft injury. Identifying the appropriate clinical cause of graft injury requires integrating the biomarkers with the clinical picture, infection-rejection risk assessments (e.g., immunological risk, donor/recipient serological match, chemoprophylaxis, previous rejection), and relevant investigations (viral serology, CMV-CMI, specimen cultures, inflammatory markers, biopsy).

Biomarkers could enhance pre-transplant risk and immunocompatibility assessments and allow for personalization of induction and maintenance immunosuppression and post-transplant monitoring. A positive ELISPOT (IFN-γ) identified individuals with donor-reactive memory and/or effector B and T-cells who were at a higher risk of rejection and may require a more potent induction regimen (e.g., Anti-thymocyte globulin) (268).

Other clinically useful time points include following anti-rejection therapy, completion of antimicrobial prophylaxis or infection therapies, and immunosuppression adjustments (e.g., escalation following rejection or modulation following infections such as BK/CMV).

6 Future direction and conclusion

Understanding the transplant recipient’s net state of immunity and balancing the infection-rejection risk axis is a constant challenge for clinicians. The cause-and-effect relationship between infection and rejection is complicated and intricate, with several shared risk factors. Heightening immunosuppression reduces the risk of rejection, albeit with increased risk of rejection and short and long-term immunosuppression-related toxicities. Accurately identifying transplant recipients with allograft tolerance remains elusive. Developing effective measures to identify graft tolerance will enable the safe reduction of immunosuppression and its attendant complications.

Conventional markers of allograft dysfunction, such as eGFR and proteinuria, are not sufficiently sensitive nor specific for timely diagnosis of allograft rejection, with their perturbations often trailing behind immunological injury. Biomarkers, particularly when integrated with clinical, immunological, and serological parameters, could help bridge this gap. Several commercially available tests include but are not limited to: Luminex DSA detection assays (anti-HLA, and non-HLA antibodies); dd-cfDNA quantification (Allosure^®); urinary chemokine measurement; molecular diagnostics (Allomap^®, TruGraf, kSORT assay) and immune profiling (ELIspot, ImmuKNOW).

Despite considerable focus and research efforts devoted to identifying non-invasive biomarkers of graft health, most currently fall short of clinical implementation. The complex and evolving immunological changes associated with rejection may not be accurately reflected by a single biomarker alone. Many of the described biomarkers do not consistently demonstrate both high sensitivity, specificity, and favorable predictive values. Additionally, the lack of standardization and validation of commercial assays and diagnostic thresholds with respect to different biological tissue types, clinical phenotypes and patient populations, significantly limits the universal adoption of several emerging biomarkers into routine clinical practice. The significant costs and lack of commercial availability further hinder the transition of some biomarkers from bench to bedside.

Dd-cfDNA and urinary chemokines are the most promising biomarkers that have garnered significant interest with increasing commercial availability. They are now incorporated into several international clinical guidelines for the assessment of allograft dysfunction (185). Dd-cfDNA is particularly advantageous as it is applicable to all SOT and its clinical use has gathered considerable momentum, particularly in the United States (191, 194). Several large clinical trials have been conducted to validate the predictive and diagnostic test characteristics of both dd-cfDNA and urinary chemokines, particularly for the detection or exclusion of AMR (170, 191, 275). Their high NPV makes them especially effective for excluding allograft injury and minimizing unnecessary biopsies (275). However, their poor specificity for rejection diagnosis and inability to distinguish rejection phenotypes may dampen the enthusiasm for widespread clinical uptake. In addition to their role in rejection diagnosis, both dd-cfDNA and urinary chemokines have shown potential for use in detecting and monitoring graft infections such as CMV, BK nephropathy, UTI and pneumonia (197, 199).

Emerging evidence suggests that dd-cfDNA and urinary chemokines may be beneficial in excluding subclinical rejection in those with stable graft function and for assessing immunological injury resolution following anti-rejection therapy (170, 191). Despite promising evidence, the utility of biomarkers in detecting subclinical allograft injury has yet to be verified. Prospective clinical trials are currently underway to clarify their role in this context. Temporal variations of these biomarkers, pre-test risk modifiers, and confounding factors need to be carefully considered when interpreting the results. The incorporation of dd-cfDNA and urinary chemokines in the proper clinical context in conjunction with other predictive and diagnostic immune and non-immune markers could further strengthen the infection-rejection risk stratification algorithm.

Molecular profiling (gene expression, miRNA, exomes) methods, such as the Molecular Microscope Diagnostics system, is also making significant ground and could drastically advance transplant diagnostics. Rejection is a complex process involving the graft, circulating immune cells, and secondary/tertiary lymphoid tissue/organs. Molecular changes at all of these levels must be clearly understood to better appreciate the underlying immunological changes underpinning rejection. Furthermore, identified rejection-specific gene signatures need to be reproduced and validated in large, prospective trials. If this is achieved, molecular diagnostics may eventually enable clinicians to refine the diagnosis of rejection phenotypes and provide insights into the intragraft mechanistic impacts of rejection and anti-rejection therapies.

Biomarkers would be particularly beneficial in clinical scenarios associated with high infection and/or rejection risk, and where currently available risk assessment strategies are imprecise. While allograft biopsies are integral for definitive rejection diagnosis, they are invasive and carry organ-specific risks. Incorporating immunological, serological, and clinical parameters with minimally invasive biomarkers could revolutionize transplant recipient care by allowing serial surveillance of allograft health, enhanced risk stratification of infection and rejection, and personalization of therapeutic and diagnostic decisions. The overarching goal is to detect immunological injury at its earliest stage when therapeutic interventions are most likely to be effective. Developing biomarkers that accurately identify subclinical rejection could substantially advance this goal and significantly enhance both allograft and patient survival.

Author contributions

DT: Conceptualization, Visualization, Writing – original draft, Writing – review & editing. WM: Conceptualization, Supervision, Writing – review & editing. CD: Conceptualization, Supervision, Visualization, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The author(s) declare that financial support was received from Monash Health Nephrology department to publish this article.

Acknowledgments

The authors thank the Monash Health Nephrology department for the financial support received to publish this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2024.1490472/full#supplementary-material

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