, Introduction
Various attempts have been made to improve the success of organ transplant surgery and a number of indicators of likely organ rejection have been developed. However, these have not yet been used to precisely determine the long-term health and status of transplanted organs. Studies involving kidney transplants have aimed to establish effective methods to sustain stable transplants over time. Biopsies are recognized as the gold standard and have been used to monitor the status of allografts, and therefore serve as a baseline against which to measure novel techniques [1].
Serum creatinine levels have been shown to indicate possible kidney transplant failure. While different studies have shown that increased serum creatinine levels can predict deficiencies in kidney function, they do not specifically signal transplant injury or rejection, so it remains necessary to closely monitor serum creatinine levels after a kidney transplant for early detection of any potential complications. However, because the kidney is usually stable during subclinical rejection, biopsies remain necessary. Currently, various biomarkers, including donor-derived cell-free DNA (dd-cfDNA) are used to monitor the development of kidney rejection. Its effectiveness as a rejection indicator has provided significant and reliable outcomes [2–4]. It is important to note that allograft biopsy is both expensive and invasive, while dd-cfDNA provides a quicker and harmless alternative [5]. Although both methods are effective in identifying potential organ rejection or injury, dd-cfDNA is preferred as it allows for real-time status updates [6]. The use of dd-cfDNA as a monitoring system may improve the control of kidney transplants compared with invasive biopsies due to the potential damage that biopsies can cause to patients. Moreover, as well as being noninvasive, dd-cfDNA provides precise results, thereby making it an important tool in the development of medical analysis in kidney transplantation [7].
This review aims to describe the reliability of dd-cfDNA in determining the status of a kidney transplant. Various studies have used immunological indicators such as T cell-mediated rejection (TCMR), antibody-mediated rejection (ABMR), and estimated glomerular filtration rate (eGFR) to estimate dd-cfDNA levels in the bloodstream. Here, we discuss the role of dd-cfDNA as a biomarker, its release mechanism, and its advantages and limitations.
Dynamics of cell-free DNA
The human body is a complex organism that fulfills multiple functions, each physiological process having distinct pathways that maintain homeostasis. However, an unhealthy lifestyle and genetic issues can result in imbalances and physiological failures. Furthermore, aging, obesity, and other physiological conditions may impair bodily functions.
In 1948, Mandela and Metais discovered the presence of cell-free DNA (cfDNA) in the bloodstream while investigating lupus erythematosus and the differences between cfDNA levels in healthy and sick people [8]. Epigenetic alterations can affect the concentrations of DNA, messenger RNA, and microRNA (miRNA) which may activate increased cellular apoptosis. These cellular components arising from apoptosis are transported in the blood and may be observed in various bodily components, including urine, stools, airways, cerebrospinal fluid, blood serum, and plasma [9].
cfDNA is usually fragmented into small base pairs with sizes ranging from 70 to 200 bases, wrapped around histones to form nucleosomes [10]. Pedini et al. [11] reported that 80% of the cfDNA in blood samples had 120 bases, but that its concentration and number of base pairs varied depending on the sample source examined. For example, in urine samples, 77% of the cfDNA fragment sizes were between 80 and 81 bp, while the remaining 23% were variable in size. On the other hand, in blood samples, cfDNA size was observed to be stable. Markus et al. [12] found that cfDNA in urine had multiple peaks between 40 and 120 bp due to the digestion of nucleosomes.
The release of cfDNA commences following cellular breakdown, after which it circulates through the bloodstream. As shown in Fig. 1 [13–17], cfDNA is released as a result of transplant injury, after which the resulting DNA molecules are transported to the liver, which is responsible for clearing about 80% of nucleosomes, with ~3% in the spleen and ~4% in the kidneys [13]. Approximately 1.5 L of blood flows every minute inside the liver, and 5 L of blood flows through the liver every 3 to 4 minutes. This means that the liver could remove the body’s cfDNA 350 times daily [18]. cfDNA is fragmented by DNA fragmentation factor subunit beta and DNases, such as deoxyribonuclease 1 and deoxyribonuclease 1 like 3 [14]. Because their nucleosome fragments are toxic, they may harm other cells [19]. The kidneys filter the blood and subsequently send the filtrate, including cfDNA, as urine to be temporarily stored in the bladder [20]. cfDNA can also be removed from the plasma through other pathways in different parts of the body. Apoptosis occurs in the first elimination process and is followed by necrosis with active secretion. Approximately 20% of the cfDNA is expelled through the urine [15].
Current reports indicate that cfDNA is not solely derived from tumors but is also generated from various other sources. Different types of cfDNA exist, including circulating cell-free mitochondrial DNA, circulating tumor DNA, cell-free fetal DNA, and dd-cfDNA. Fig. 2 [21–24] shows the concentration of each type of cfDNA released into the bloodstream [8].
A potential complication arises when the concentration of cfDNA reaches 3.5 to 100 ng/mL. Determining a reliable cfDNA concentration for kidney injuries may take some time. Its levels were found to be 24% after 10 to 15 minutes of ischemia-reperfusion in experiments on male mice. cfDNA has a half-life ranging from 30 minutes to 2 hours [25,26], so it is crucial to take its decay rate into account to take the necessary precautions when measuring it.
Analysis of dd-cfDNA levels during the early stages of renal transplantation has shown that they may rise above 5% immediately following surgery and then decline to less than 0.5% after a week. It should be noted that the scope of these findings is limited since they only cover the period from the beginning of the transplantation process to 3 days afterward [27]. Shen et al. [28] noted a difference between deceased and living donors. While the dd-cfDNA percentage in the deceased donors was 23.98%, declining to 19%, 47.74% dd-cfDNA was observed in living donors. On the first day after transplant, while the dd-cfDNA levels in deceased individuals were high (19.34%), they were only 4.46% in living individuals. The levels in recipients were monitored for possible rejection between the third and fourth day after transplant, on the basis that previous observations have noted increased dd-cfDNA amounts from 2.47% on the third day, to 7.23% on the seventh day [28]. Nie et al. [29] reported that dd-cfDNA was stable on the eighth day, with a median level of 5.4% on the first day, decreasing to 1.49% by the fourth day, and 1.08% by the 14th day, the notable decline in dd-cfDNA levels within 3 to 4 days indicating a favorable transplantation outcome.
Infection and kidney rejection
Infections associated with transplantation are a common occurrence and may cause additional complications in transplant patients. Among them, BK virus (BKV) and cytomegalovirus (CMV) infections are frequently clinically evaluated for kidney rejection and their occurrence is often associated with an increase in dd-cfDNA [30]. While the administration of immunosuppressants may mitigate antibody rejection during kidney transplantation, this also presents a potential risk to the allograft, since a variety of infections may infiltrate the transplant and could result in a future rejection [31].
BKV belongs to the Polyomaviridae family, first isolated by Ludwig Gross in 1953 and was observed to cause adenocarcinoma in the parotid gland in newborn mice. In 1970, BKV infections were reported in the urine of kidney transplant patients. However, it is imperative to acknowledge that this virus can infect various parts of the body [32] and can cause cell necrosis and tissue inflammation, because polyomavirus BKV (BKPyV) infections typically occur in the initial stage of kidney transplantation, with a seroprevalence of approximately 80% to 90% in the adult population and 91% among children [33,34]. CMV belongs to the Herpesviridae family and is commonly identified in the first 3 months after a transplant with a seroprevalence of 70% to 90% in the adult population [35] Fig. 3 illustrates the different characteristics of BKV and CMV [36,37]. Additionally, one study has indicated that there is a risk of CMV infection and identification among patients on immunosuppressants during the first month after transplant [38]
The administration of immunosuppressants during kidney transplantation has the potential to activate CMV in other parts of the body [39], since the CMV virus remains latent in the body after initial infection and may be reactivated by conditions such as immunodeficiency syndrome and then invade the graft. Table 1 [34,40–43] shows the various factors related to infection and reactivation with both BKV and CMV [44].
A biomarker is crucial for identifying infection-related rejection since it can accurately measure the level of illness at any given time. Currently, several researchers are investigating biomarkers to indicate the risks of rejection due to infections. However, determining the percentage levels of dd-cfDNA in ABMR remains difficult. Whitlam et al. [45] have reported only 1.5% of dd-cfDNA with BKPyV in the plasma and Kant et al. [46] found a dd-cfDNA level of 2.84% in their study, while Zhou et al. [30] reported a dd-cfDNA level of 1.87% in cases of acute rejection involving several types of infection, including with BK and CMV.
The viral infections caused by BKV and CMV have different effects on dd-cfDNA, which are contingent on the specific circumstances and the presence of other bacterial or fungal infections, and Afzal et al. [47] demonstrated that the dd-cfDNA released in CMV infections was above the threshold. Given the limited information available on dd-cfDNA load among transplant recipients, it is imperative to improve our understanding of its role in infections and the incidence of rejection. Some researchers have studied how these viruses discharge dd-cfDNA during a patient’s immunosuppression treatment, in order to prevent rejection. However, more investigations are needed to better understand the dynamics of dd-cfDNA in viremia.
Why are kidney transplants sometimes rejected?
There have been many studies into the potential remedies for various kidney diseases since kidney failure may lead to systemic bodily dysfunction. Kidney transplantation is currently the most reliable management strategy to resolve chronic kidney disease and was pioneered by Sir Frank Macfarlane Burnet in 1949. His research showed that different viruses and bacteria have varying effects on the kidney [48]. Currently, researchers are evaluating the benefits of alternative treatments, such as hemodialysis, antibody therapies, and chemotherapy to cure kidney-related carcinoma. However, organ transplantation remains the best way to address kidney dysfunction since it provides favorable outcomes in most cases despite complications such as poor surgical practices, infections, and patient lifestyle choices which may aggravate organ injury [49,50].
Currently, the major challenge in kidney transplantation is finding a suitable organ to replace the affected kidney. Despite the invasiveness of transplantation compared to chemotherapy or immunotherapy, organ transplantation remains the most effective solution for end-stage kidney disease. Additionally, it outperforms other alternative available methods [51]. Although transplantation is a viable solution, it is crucial to note that acute or chronic rejection is an ever-present possibility and must be prevented through monitoring. It is important to understand that acute rejection is the most common diagnosis during monitoring and is aggressive and swift. Acute rejection occurs when the recipient identifies the transplant as a foreign body, prompting it to create an immunological response that leads to pathophysiological complications. TCMR is the most frequent type of acute rejection, with 40% of cases being unresponsive to therapy [52]. Fig. 4 illustrates the kidney transplant and rejection pathway [53–56].
The immunological response starts with T lymphocyte activation and the production of macrophage or dendritic cells, which cause the antigen to produce inflammation and tissue destruction, because TCMR generates interstitial inflammation, arteritis, and tubulitis through the immune cells [16]. Furthermore, when T cells are present, the antigen uses the complement system to destroy and activate immune cells for self-defense. The T cell system comprises more than 40 proteins in the cell membrane, which may significantly damage the allograft [57]. Another form of rejection is ABMR, which is commonly observed in organs that are genetically incompatible. It induces the immunoglobulin G donor-specific antibody (DSA) response in the allogenic endothelium, thereby triggering the C1q-dependant complement activation response. This is noteworthy since human leukocyte antigen (HLA), ABMR, and anti-HLA DSA levels are strongly correlated [58].
Donor-derived cell-free DNA as a biomarker
Biopsy studies are the primary method used in the diagnosis of kidney cancer and are widely considered to be the gold standard. However, they have significant drawbacks, being invasive and requiring surgical intervention to collect tissue samples, with the added risk of infection [59]
Over time, researchers have sought alternatives to biopsies and discovered that creatinine levels may be used to assess the condition of a kidney. This technique helps to determine whether a kidney transplant shows any signs of complications. However, it is important to note that serum creatinine levels may not always indicate kidney transplant rejection. Nonetheless, this technique is still used to diagnose kidney rejection, although it does not provide a definite result. Moreover, creatinine levels are not directly correlated with dd-cfDNA level [60]. Another method to assess kidney damage is by measuring the proteinuria level which is also a risk factor in kidney injury. A protein/creatinine ratio greater than 2 mg/mL is associated with a 79% to 94% chance of chronic kidney disease [61]. However, these biomarkers are of limited use during the early diagnosis of acute and chronic kidney disease or kidney rejection when intervening to save the allograft.
Thus, when a clinician seeks to investigate early rejection, it is crucial to prioritize accuracy and specificity. Therefore, the most efficient approach for identifying rejection is through the sequencing of dd-cfDNA using next-generation sequencing (NGS) technology or droplet-based digital polymerase chain reaction (ddPCR) [53]. Fig. 5 illustrates the necessary protocol for examining dd-cfDNA, from obtaining a sample to sequencing. dd-cfDNA analysis is crucial to predict rejection 48 hours after biopsy, since dd-cfDNA levels can be a biomarker for early diagnosis [53]. It allows medical staff to intervene with immunosuppressants, thus preventing immunological activity and saving the allograft. Liquid biopsy does not interfere with dd-cfDNA concentrations and can indicate changes that could be due to the transplant’s progress [62].
Currently, various studies have indicated a correlation between the percentage of total dd-cfDNA and kidney transplant injury. According to Halloran et al. [63] dd-cfDNA levels inside the bloodstream may determine the likelihood of rejection. These findings were compared with biopsies and the dd-cfDNA level could be linked to the progression of a kidney transplant. Specifically, low levels of dd-cfDNA suggest a reduced likelihood of rejection [63]. Bu et al. [64] highlighted that elevated concentrations of dd-cfDNA are associated with a reduced eGFR, which is a significant determinant of kidney function since it reflects the rate of fluid filtration in the kidneys. Consequently, high levels of dd-cfDNA may lead to kidney rejection [63–65]. While research has shown that dd-cfDNA levels below 1% indicate a low risk of kidney rejection, levels exceeding 1% suggest a high likelihood of rejection. This is significant because it informs the development of a reliable method for detecting kidney rejection at an early stage [66]. Other cutoffs may also be valid, as evidenced by the 0.5% limit noted in the data set analyzed by Oellerich et al. [67].
Donor-derived cell-free DNA quantification methods
Detection of allograft rejection was originally primarily based on chromosomal evidence and the Y-chromosome, which is absent in female recipients, was used as a reference in female patients receiving male organ transplants [68]. Y-chromosomes were found circulating in the serum in a 1998 study and were the first form of detected dd-cfDNA used to assess a kidney transplant [69]. Since then, novel, highly accurate techniques with improved detection capabilities have been developed, including ddPCR, quantitative polymerase chain reaction (qPCR), massively parallel shot-gun sequencing (MPSS), and NGS.
Methods employed to quantify donor-derived cell-free DNA
The NGS method can analyze a large number of sequences, due to its incorporation of adapters to identify more than 50 genes and is effective in exploratory experiments [70]. It was developed using kidney and heart samples, and dd-cfDNA is currently usually diagnosed using AlloSure technology (CareDx, Inc.). Prospera (Natera, Inc.) can also be used to measure the levels of dd-cfDNA [71,72]. These tests are employed in different situations and are versatile in determining single-nucleotide polymorphisms (SNPs) in the genome. NGS has a low possibility of error since it employs adapters to identify samples in the investigation and is therefore a favorite for processing large numbers of samples [73,74].
ddPCR is a unique method and a favorite for measuring dd-cfDNA, even at low concentrations, due to its high target sensibility and ability to quantify ddPCR concentrations of less than 0.1% DNA, even if the DNA is degraded [75]. It uses the drop emulsification technique to create partitions, with each drop having a unique DNA molecule for amplification [76]. Therefore, ddPCR and NGS are the most commonly used methods for dd-cfDNA quantification, due to their versatility and ability to produce high-resolution amplification results [77].
MPSS is used to amplify the whole genome before DNA fragment quantification and therefore provides a measure of cfDNA and may identify SNPs in the genome [78]. Currently, MPSS is most commonly used to analyze the copies of viral CMV cfDNA, and thus to determine the viral load, but also to analyze fetal cfDNA and to detect dd-cfDNA [69].
qPCR is used in clinical laboratories to measure dd-cfDNA using short tandem repeats (STRs) and SNPs [77]. dd-cfDNA can be found 1 to 2 weeks before the diagnosis of acute rejection and therefore may be useful as a reliable assay to improve the diagnosis of acute kidney rejection in laboratories which lack sufficient resources for new generation sequencing [77].
Nowadays, NGS and ddPCR are the most used techniques due to their superior precision and primary quantitative readings compared with conventional qPCR [53]. The approaches used depend on the research direction, since there are two specific methods to determine dd-cfDNA: 1) MPSS, which is employed to investigate different sections of dd-cfDNA to identify the donor and recipient DNA and 2) the TRAC method, which is a commercial method that can identify SNPs in the whole genome over their entire dynamic range and can perform complex bioinformatical analyses, although its turnaround time is prolonged [53,79]. CareDx provides another method to evaluate dd-cfDNA levels, using targets to study and identify specific sections of dd-cfDNA, and the company has also provided primers with the versatility for use in both NGS and ddPCR. Therasure (Oncocyte Corp.) determines dd-cfDNA levels using the recipient, while AlloSure does not require recipient identification [53,69]. These methodologies can quantify dd-cfDNA by utilizing diverse strategies to identify the dd-cfDNA in the allograft and the recipient and can quantify the levels of dd-cfDNA and determine the likelihood of rejection by utilizing the differences between genomes.
Strategies to identify donor-derived cell-free DNA
SNPs can be used to determine the provenance of dd-cfDNA and identify variations among the individuals involved. Because the protocol involves the creation of a DNA library, SNP use can reduce the error probability using long fragments. The number of SNPs and the predictor model are important, particularly in reducing the amount of information needed to identify the dd-cfDNA [80].
STRs are also used to determine the provenance of dd-cfDNA. The analysis of characteristic STRs of 2 to 6 nucleotides, and their chimerism, may determinate cfDNA from kidney allografts [81]. The determination of STRs can be performed using qPCR in laboratories with limited resources [82]; however, NGS and ddPCR are more sensitive than qPCR [83].
Insertion/deletion (INDEL) is another form of genome marker, which has been recently used for quantitative assessment of dd-cfDNA by NGS and ddPCR. INDELs exhibit a minimum of two consecutive variable bases and show greater specificity than classical SNPs because they are restricted to a single variable base only. Furthermore, INDELs have demonstrated superior performance compared to conventional PCR of STRs [68]. NGS provides excellent yields and user-friendliness in monitoring dd-cfDNA in routine clinical practice. INDELs are known to be less prone to errors [84]. Fig. 6 illustrates the different analytical methods used to identify the provenance of dd-cfDNA using SNPs, STRs, and INDELs [80,81,84].
Advantages and limitations of donor-derived cell-free DNA
Donor-derived cell-free DNA as a reliable biomarker
Nowadays, dd-cfDNA is a popular biomarker to monitor the success and proper functioning of an allograft. Prompt determination of stable or high dd-cfDNA levels is crucial in reducing the risk of transplant failure. Biopsies are the gold standard in determining allograft rejection. However, the process of obtaining a kidney sample is complex. Therefore, dd-cfDNA is useful for kidney monitoring, since the methods for obtaining samples of blood, serum, or urine for liquid biopsy are noninvasive, harmless to the patient, and easily performed [85].
Currently, the most notable advantage of dd-cfDNA as a biomarker is that it can be used to detect potential cases of kidney rejection using highly sensitive technologies such as NGS, even at low dd-cfDNA concentrations. This approach facilitates prompt detection, provides a quantitative pathway in patients undergoing monotherapy, and is useful in patients receiving immunosuppressive treatment [86]. Table 2 shows the advantages and limitations of dd-cfDNA monitoring.
dd-cfDNA levels in the donor may be difficult to calculate, since it is necessary to compare the level of cfDNA by receptor. Taking a cfDNA concentration of 1% as an indicator of a stable patient, bioinformatics is important in determining dd-cfDNA provenance [87]. Furthermore, it is imperative to acknowledge that a significant proportion of cfDNA is released by the recipient when they exhibit various ailments, such as sepsis and inflammatory diseases, or engage in strenuous exercise, so that comparisons may result in a false positive [86,87].
Other nucleic acids which can be used to determine donor-derived cell-free DNA
It is important to note that the use of miRNA has proved applicable in transplant monitoring. Sequencing of miRNA may describe T lymphocyte activity. Numerous sequences have been discovered which provide valuable insights into the state of the kidneys, such as miR-210 which is indicative of cellular aging and has been associated with biopsy rejection. Regrettably, this gene exhibits low specificity and sensitivity [88] and although miRNA monitoring is a noninvasive method with the potential to diagnose kidney rejection, it does not allow the underlying cause of rejection to be determined. Furthermore, although it may detect rejection before symptoms manifest, this is often due to direct correlations. Further studies on diagnostic kidney rejection using miRNA are therefore required to produce a specific standardized method [89].
Immunological biomarkers to monitor kidney rejection
The presence of DSAs has been identified as one of the key biomarkers for diagnosing ABMR and is also associated with the long-term survival of allografts due to its correlation with ABMR. The detection performance of the Luminex system (Luminex) has recently been improved, although its current effectiveness is uncertain since allograft rejection activity does not always trigger DSA activity [90]. Studies have indicated that other factors may also be associated with this issue, since false negatives may occur when attempting to identify these factors through sampling because the B lymphocytes producing these antibodies may be located in the lymphoid organs. The absence of antibodies therefore does not always indicate that rejection is not occurring [91].
Metabolomics in kidney rejection
The application of metabolomics in the diagnosis of kidney rejection is a novel technique using liquid biopsies. This technique involves the analysis of various components in urine biopsies. Although the study of these components requires further extensive research to validate the biomarker [92], it is worth noting that the assay is currently being validated. Kostidis et al. [93] demonstrated that the prediction of functional delayed graft function may be detected by analyzing lactate/fumarate and branched-chain amino acids/pyroglutamate. Furthermore, they discovered that the concentrations of lactate, glucose, and leucine glutamate in perfusate change during the posttransplant period and that the concentration of analyte decreases with increasing time after the transplant [93]. Zhao et al. [94] designed a study to determine the various metabolites in the serum. The presence of dehydroepiandrosterone sulfate, dihydrotestosterone sulfate, androsterone sulfate, and etiocholanolone sulfate were found to be possible indicators of kidney rejection. These metabolites were compared between cases of nonacute and acute rejection, and although the results showed significant differences, the reasons for this are currently unclear and the results were not consistent. It would therefore be valuable to further investigate the possible interventions in their immunological system and the prednisolone dosages used [94].
Proteinuria is measured to assess the functionality of a transplant, particularly when the transplanted kidney suffers complications. However, proteinuria levels are not always elevated during organ rejection. Moreover, higher levels can also be caused by a graft malfunction, resulting in false positives on certain occasions. Given the relatively poor outcomes of kidney transplants in children, this study found a low sensitivity of >1 g per 24 hours. Additionally, proteinuria measures have a negative predictive value and are not correlated with TCMR and ABMR levels in children [15,95], suggesting that they cannot be used as a standard to diagnose organ rejection across all age groups.
Chemokines in monitoring studies
Another alternative to allograft diagnostics is the measurement of urinary chemokine levels in which elevated CXCL-9, CXCL-10, and CCL2 levels indicate an immunological response. It has been shown that chemokine levels are correlated with eGFR, as evidenced by the fact that over a period of 6 months to 2 years, the eGFR level decreased by ≥30% as the CXCL-9 level rose to >200 pg/mL [96]. It is imperative to understand that chemokines may be expressed by a variety of diverse organs in response to infections or tumors [97]. Gielis et al. [98] examined the chemokine profile in urine in combination with miRNA to assess kidney rejection and found that CXCL-9 levels have high sensitivity and low specificity (90.7% and 58.1%, respectively) among patients both with and without rejection. Moreover, the use of chemokine levels typically exhibits delayed differentiation and may or may not be useful for diagnostic purposes. Furthermore, although Handschin et al. [99] reported that CXCL-10 may be present in allograft rejection patients, the levels detected did not meet the Banff criteria due to the presence of an unidentified inflammation. In a study using CXCL-10, Hirt-Minkowski et al. [100] described the relationship between CXCL-10 and the biopsy using the Banff 2019 criteria in several rejection situations, making it possible to predict kidney rejection within 1 year. However, their data had different interference criteria for BKPyV presence, since in the 29 samples in which rejection occurred its sensitivity was high, but its specificity was low. Therefore, the observed concentration required to determine whether the transplant would be rejected could not be confirmed [100]. The use of dd-cfDNA does not present this problem due to the possibility of identifying its provenance.
Proteomics in kidney rejection monitoring
Transplantomics is a relatively young field that has been introduced following the dynamic application of liquid biopsy alongside the study of transcriptomics, epigenomics, proteomics, and metabolomics [101]. Fang et al. [102] reported the correlation between kidney damage and 106 proteins expressed during TCMR, with more than 60% of them being enzymes. Furthermore, the protein number varies depending on the level of infection, changing in the presence of BKV infection and opening the possibility that cystatin C, vimentin, lymphocyte cytosolic protein 1, and ferritin light chain might be potential biomarkers for clinical diagnosis [102]. Han et al. [103] found four other significant proteins for diagnosing kidney rejection, including beta-2-microtubulin, complement factor B, immunoglobulin heavy constant alpha 1, and immunoglobulin κ constant and concluded that their diagnostic accuracies were high. However, it is important to understand that the provenance of the samples is important, since the proteins used by Fang et al. [102] were from the tissue, while those used by Han et al. [103] were from urine and blood, probably accounting for the differences in their results.
Currently, chemokines, proteomics, and metabolomics require further investigation since the number of proteins in the samples could change in different situations; their diagnostic specificities were low [104]; and metabolites produced as subproducts from biological systems may be susceptible to environmental or microbiome effects [105].
Table 2 compares the advantages and limitations of dd-cfDNA as a biomarker for allograft rejection. The benefits and drawbacks can help to choose the best method to use in various scenarios and enable a clinician to determine the most convenient assay in any given situation. They can also highlight the specific limitations that require improvement. Table 3 shows the various transplant monitoring tests with their different characteristics, levels of effectiveness, and objectives, compared with the benefits of the dd-cfDNA method.
Donor-derived cell-free DNA: perspectives and comments on recent studies
Monitoring patient progress is of crucial importance when examining an allograft, whether looking for cancers or ascertaining the patient’s acceptance of the transplant. Presently, investigations are focused on exploring and assessing various monitoring options available to assess the condition of a transplanted organ. Biopsy is commonly used to examine the status of an allograft and is widely regarded as the gold standard for diagnosing organ functionality. However, its capabilities are limited. Noninvasive techniques, such as liquid biopsies based on urine, blood serum, or fecal samples have recently been developed as effective alternatives in transplant monitoring. Identifying the molecules that trigger transplant rejection is crucial for accurate prediction and each newly developed test must be compared to the gold standard, or its immunological indicator derivatives, such as TCMR and ABMR, which are helpful for interpretating and predicting new biomarkers.
Donor-derived cell-free DNA as a reliable biomarker to monitor kidney rejection
dd-cfDNA is a novel biomarker that has shown promise in monitoring transplant conditions. Variations in its concentration in the body allow the status of a renal transplant to be monitored [106]. Rizvi et al. [107] found a correlation between dd-cfDNA levels and ABMR status, with dd-cfDNA levels of 2.45% in organ recipients compared with 0.44% in healthy patients. Moreover, their study showed a significant difference in the occurrence of combined ABMR and TCMR at dd-cfDNA levels of 1.3%, compared with 0.44% in patients with no transplant rejection [107].
Ranch et al. [108] observed dd-cfDNA levels of 3.42% in patients with ABMR compared with 0.21% in patients without transplant rejection, based on histological biopsies. They also showed that dd-cfDNA is a good biomarker for monitoring possible rejection, as confirmed by parallel histological studies. Boonpheng et al. [109] reported dd-cfDNA levels of 2.24% for patients with ABMR indicative of chronic organ rejection and showed that their dd-cfDNA levels decreased with time. These results point to new directions for further studies of immunological function and the role that immunosuppressive treatment may have on monitoring dd-cfDNA levels.
Table 4 shows varying results of receiver-operating characteristic (ROC) curves in different studies using dd-cfDNA as a biomarker [60,64,67,107,110], confirming its high accuracy in determining kidney rejection and how dd-cfDNA levels can improve our understanding of immunosuppressive treatment to control possible organ rejection. These findings indicate the importance, sensitivity, and specificity of dd-cfDNA level monitoring.
Therapy and donor-derived cell-free DNA; one more step to design the best treatment
dd-cfDNA could be used to monitor the progression of therapy in cases of acute kidney rejection, especially when the situation cannot be remedied using corticosteroid pulse therapy, since it is usually then necessary to perform closer monitoring using a new biopsy which risks causing greater damage to the transplant. dd-cfDNA might be able to determine borderline changes and could be a convenient biomarker to more closely monitor acute rejection [111,112].
Currently, various immunosuppressants are under test to maintain stable transplant status. Guo et al. [17] identified a connection between dd-cfDNA and inflammatory processes. The infiltration of inflammatory cells, such as CD3–, CD8–, CD20–, and CD68– were significantly correlated with high levels of dd-cfDNA. Therefore, increases in dd-cfDNA levels could be related to the infiltration of inflammatory cells because macrophages contribute to the release of dd-cfDNA. Apoptosis may be caused by macrophages in the immune system when they identify the transplant as a foreign body [17]. dd-cfDNA may be used to analyze the progress of immunosuppressive agents in kidney transplants. Considering the importance of monitoring the progress of patients with recent transplants, different methods have been used to determine the status of the transplant. Currently, dd-cfDNA has been considered a reliable factor when monitoring the allograft using different immunosuppressives. Oellerich et al. [67] described the use of tacrolimus to monitor immunosuppression and showed that administration of low concentrations of tacrolimus (<8 μg/L) may correlate with high concentrations of dd-cfDNA (≥50 cp/mL), while higher concentrations of tacrolimus (≥8 μg/L) could help to decrease the levels of dd-cfDNA. The use of belatacept as an immunosuppressive has also been described. While Osmanodja et al. [113] claimed that dd-cfDNA is not suitable for the detection of subtle changes in kidney injury, further research is required, since their study inferred a correlation between late conversion to belatacept therapy to combat chronic calcineurin-inhibitor–induced toxicity and high dd-cfDNA levels.
Currently, dd-cfDNA can be used to analyze the progress of kidney transplants in treatments involving monoclonal antibody therapy with anti-interleukin-6. dd-cfDNA cannot yet be confirmed as a definitive biomarker to determine the progress of therapy in kidney transplants. Mayer et al. [114] analyzed dd-cfDNA levels to verify the progress of clazakizumab treatment and found that dd-cfDNA levels remained above 1% for 9 to 12 months after treatment. They found that dd-cfDNA was not a suitable biomarker for the diagnosis of late ABMR. However, this conclusion may have been a result of different adverse events during the experiment, such as the timing of treatment, or that some patients did not receive the treatment on every occasion.
Callahan et al. [115] showed that the use of nivolumab in kidney transplantation could generate injuries, noting that nivolumab is used as an immunosuppressant and anti-tumoral treatment. A case report described a patient with a kidney received from a deceased donor who developed melanoma. Analysis revealed an increased dd-cfDNA level at the moment of treatment, with the level increasing to a maximum value of 23.1% during the first 12 days [116]. Therefore, dd-cfDNA may be a good biomarker to determine the effect of immunosuppressive treatment on the health status of an allograft. Lakhani et al. [117] showed that dd-cfDNA may be used to program biopsies. However, it is important to understand that while the absolute level of dd-cfDNA can be predictive, its percentage value is of more use in monitoring [67].
dd-cfDNA may also indicate the eGFR status. Bu et al. [64] found a negative correlation between eGFR progression and high levels of dd-cfDNA (≥0.5%), leading to a 25% decline in eGFR over the course of 3 years. However, it is important to note that multiple factors, such as infections or toxicity resulting from inhibitors and calcineurin, may contribute to organ injury [64]. Sawinski et al. [118] found a high likelihood of kidney rejection when the dd-cfDNA concentration exceeds 2.5%, with a corresponding decrease of more than 25% in eGFR between 1 to 2 years. It is however worth noting that even small variations in dd-cfDNA levels can significantly impact kidney function and potentially lead to rejection [119].
Identifying the specific dd-cfDNA concentration that indicates the presence of ABMR, TCMR, and potential renal rejection is crucial. In 2013, Beck et al. [120] suggested that dd-cfDNA is a viable biomarker since their study found a dangerous risk of rejection only when its concentration exceeded 2.5%. These verifications of the effectiveness of dd-cfDNA as a viable biomarker should be utilized to examine its potential in monitoring kidney rejection.
Economic perspectives on donor-derived cell-free DNA compared with biopsies
dd-cfDNA has the potential to save costs in organ transplant surgery. In Germany, transplants cost 19,365 US dollar (USD) annually. Biopsy analyses and services cost 3,931 USD annually, while ddPCR analyses amount to 4,012 USD in the first year and 1,604 USD each year thereafter [121]. In China, a biopsy analysis costs about 500 USD, a dd-cfDNA concentration measurement using NGS costs around 800 USD, and a ddPCR analysis costs about 500 USD. Considering that biopsy analyses in China are performed using serum creatinine analyses as well as immunological indicators such as DSA analysis as references, dd-cfDNA monitoring would be less costly in nearly all situations [122]. This is especially true since its use in real-time monitoring may help in early detection and treatment of an allograft rejection, therefore, avoiding the need for a re-transplantation at a cost of 72,150 to 111,891 USD [121].
Donor-derived cell-free DNA and kidney rejection in atypical conditions
Inflammation significantly impacts kidney filtration capacity, so the use of dd-cfDNA monitoring as a biomarker for the progression of eGFR and the prediction of TCMR and ABMR status would be useful in renal transplant patients. Notably, eGFR measures are essential in measuring filtration rate and indicating renal activity and kidney function to predict the risk of rejection. Today, doctors employ eGFR to assess kidney function and determine the best treatment approach.
The results of dd-cfDNA studies in re-transplantations remain unclear, although dd-cfDNA levels have been shown to decrease in posttransplant settings, as well as in some re-transplantation patients. However, while INDELs may avoid interference with dd-cfDNA during the first transplant, in many cases the nonfunctional graft is not removed first during the second transplant [84,123].
dd-cfDNA concentration levels in patients with interstitial fibrosis and tubular atrophy (IFTA) appear to be comparable with those of normal transplants. Using biopsies, Paul et al. [124] found that dd-cfDNA levels were high, but not statistically different from the normal, and further histological analysis showed negative ABMR. Other studies have described insignificant levels of dd-cfDNA in biopsies showing IFTA. Xie et al. [125] proposed that while normal levels of dd-cfDNA may indicate minimal or no cellular injury, epigenetic or microbiological changes may be precursors of long-term injuries and dd-cfDNA level alterations. Nevertheless, there is no information on dd-cfDNA specifically for IFTA patients [124].
dd-cfDNA studies could be useful with reference to virus infection, since BK analyses have been reported that may cause rejection at dd-cfDNA levels of 1.2% in ABMR patients and 0.54% in TCMR patients [126]. However, further studies are necessary to determine the basis for predicting kidney rejection using dd-cfDNA in the context of BKV infection. Immunological conditions are the most critical factors for assessing transplant rejection. Studies of dd-cfDNA concentrations in other organs may enable its wider utilization as a biomarker, especially given the ease of obtaining-cfDNA samples. It is worth noting that research is ongoing to explore new directions in dd-cfDNA–based investigations of methylome to enable the identification of a specific methylated sequence to ascertain the origin of any cfDNA sample [127] due to its high reliability. Table 4 shows the results of various investigations into ROC analyses, illustrating the efficacy of dd-cfDNA as a biomarker in determining kidney rejection.
Conclusion
This paper presents up-to-date data from a number of studies on the use of dd-cfDNA levels as an indicator for predicting and understanding potential kidney rejection. This information is vital for assessing the future monitoring of kidney transplant dynamics. The studies have provided important information across a range of outcomes and show that dd-cfDNA, ABMR, and TCMR are the most commonly used predictors of allograft rejection. Immunological factors may detect the allograft and consider it a foreign body, thereby inducing apoptosis and necrosis resulting in the rejection of the allograft and the release of dd-cfDNA, making high dd-cfDNA levels an ideal biomarker for detecting allograft rejection.
Furthermore, high levels of dd-cfDNA can reduce eGFR, a crucial factor in understanding kidney functionality. Our report provides important information on studies into dd-cfDNA and should encourage further study into its use as a kidney rejection biomarker. Several unknowns remain to be investigated, such as the situation in re-transplant patients and kidney transplants in patients suffering from IFTA. Furthermore, the use of methylation alongside cfDNA monitoring could facilitate identification of the cfDNA provenance, and the further study of transplant-related issues.
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