Applications of donor-derived cell-free DNA in kidney transplantation healthcare: view from a prospective single-center study

Applications of donor-derived cell-free DNA in kidney transplantation healthcare: view from a prospective single-center study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Applications of donor-derived cell-free DNA in kidney transplantation healthcare: view from a prospective single-center study Phuong Thanh Nguyen, Hirofumi Nakaoka, Shigeki Mitsunaga, Hiromichi Aoyama, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6592444/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Non-invasive biomarkers enable effective assessment of allograft status without compromising patient’s quality of life. Donor-derived cell-free DNA (dd-cfDNA) has recently emerged as a promising biomarker in solid organ transplants. In this study, we developed a cost-effective, scalable targeted sequencing approach to assess dd-cfDNA performance in monitoring kidney transplant (KTx) integrity. We quantified dd-cfDNA with a customized probe set of 1,000 SNPs specifically designed for the East Asian population. Longitudinal analyses were conducted on 322 blood samples collected from a single-center cohort of 39 KTx patients over an observational period of up to five years. We showed that elevated dd-cfDNA levels are associated with various forms of acute rejection and graft injury in KTx patients. In particular, dd-cfDNA levels effectively distinguished between acute rejection and non-rejection in KTx patients (p = 1.9×10⁻⁴), aligning with serum markers and histological evidences. The surges of dd-cfDNA were evident in antibody-mediated rejection and diverse types of kidney injuries. Additionally, donor bleeding volume was found to influence the stabilization of dd-cfDNA levels during the early post-transplant period. We also reported a unique correlation between dd-cfDNA and Tacrolimus trough levels (Spearman’s rho = -0.25, p = 0.008), which was not observed with other serum markers, underscoring its potential in guiding immunosuppressive therapy. Our study demonstrates the robust role of dd-cfDNA as a responsive biomarker for detecting acute rejection and monitoring kidney integrity. Furthermore, the proposed methodology offers proof-of-concept for scalable diagnostic applications in transplant healthcare. Medical Genetics Urology & Nephrology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Kidney transplantation (KTx) is often the last resort for patients with end-stage kidney diseases, offering benefits that can significantly enhance a patient's quality of life 1 . However, renal acute rejection (AR) poses a substantial risk for graft failure 2 . AR can manifest in various forms, including microvascular damage, ischemia, or inflammation, affecting 5.4–8.8% of adult KTx recipients within the first-year post-transplant (post-Tx) 3 , 4 . Advancements in pre- and post-transplant care for KTx, such as improved HLA matching, preemptive interventions, and effective immunosuppressant therapies, have greatly reduced the incidence of AR in KTx 4 – 6 . Assessment of allograft kidney typically involves monitoring serum markers such as creatinine levels, cystatin C, blood urea nitrogen and serum procalcitonin, as well as estimated glomerular filtration rate (eGFR) or proteinuria 5 , 7 . Kidney biopsy, despite being the gold standard for diagnosing AR 8 , 9 , is an invasive procedure which may associate with risk of secondary complications and interpretational variability due to issues like patchy sampling or inadequate specimens 7 , 10 . Hence, balancing the risk-benefit tradeoff, the use of biopsy is limited to cases with specific concerns (i.e., on a ‘for-cause’ basis); otherwise, its use in protocol surveillance may be deemed unnecessary 11 , 12 . Alternatively, molecular diagnostics recently have gained attention as a valuable tool to support clinical decisions in transplant healthcare 9 . Donor-derived cell-free DNA (dd-cfDNA), which is released from disrupted cells of transplanted organ, can be detected in the recipient's blood plasma or urine, where its amount and fluctuation can inform the status of graft integrity 13 , 14 . Based on this concept, a growing number of studies have focused on quantifying dd-cfDNA to enhance the diagnosis of AR and other forms of allograft injury 15 – 24 . However, long-term observation using a scalable methodology for monitoring dd-cfDNA is still lacking. Here, utilizing a SNP panel designed specifically for the East-Asian population, we examined longitudinal dd-cfDNA levels in KTx patients from a single-center cohort for up to five years post-Tx. Elevated dd-cfDNA levels provided early indications of AR episodes and diverse forms of graft injury. The relation of dd-cfDNA was observed not only with clinical parameters and biopsy-derived histological assessments but also with immunosuppressant dosages. Our study validated the potential of dd-cfDNA as a non-invasive biomarker for evaluating graft integrity in KTx. Furthermore, we established a proof of concept using a cost-effective targeted sequencing protocol to quantify dd-cfDNA, highlighting its potential applicability in clinical settings. RESULTS Cohort description and sample collection A single-center cohort consisting of 39 KTx donor-recipient pairs was recruited into this study with written consent. After determining the genotypes of both donors and recipients, we collected a total of 322 blood samples from patients (i.e., KTx recipients) for the extraction of cell-free DNA, resulting in 321 samples eligible for dd-cfDNA analysis. A detailed demographic summary of all patients is shown in Table 1 . Patients underwent sampling at fixed timepoints (i.e., protocol sampling, n = 241), including within the first several weeks, at one month, and at one year post-Tx. A portion of samples were collected on unprompted occasions with clinical concerns (i.e., for-cause sampling, n = 37), especially where AR episodes are suspected. Needle biopsies were also performed to corroborate the diagnosis. Additionally, we also assessed dd-cfDNA levels of samples collected 3 to 5 years post-Tx (n = 21), regarded as long-term sampling. Following the biopsy-based Banff classification guidelines 8 , 9 , six patients were detected exhibiting AR episodes, including two cross-sectional cases with KTx performed a long time ago, enrolled in this study upon hospitalization. Specifically, of the nine reported AR episodes, two were ABMR, and seven were acute T cell-mediated rejection (TCMR). All clinical characteristics are summarized in Table 2 (see Supplemental Data 1 for detail). Table 1 Demography of kidney transplant cases Number of Tx recipients 39 Recipient characteristics Gender (male/female), n (%) 25 / 14 (64.1%/35.9%) Median age, years (range) 48 (24–69) Post-Tx follow-up cases (longitudinal), n 37 Enrolled mid-term cases (cross-sectional), n 2 Donor-recipient relationship Parent-to-child, n 9 Sibling, n 7 Non-relative, n 23 Total number of plasma sample collected 321 Collected within 10 days post-transplantation, n (%) 143 (44.5%) Collected after 10 days, n (%) 178 (55.5%) Total number of samples collected with biopsy, n (%) 66 (20.6%) Longterm longitudinal samples (> 3 years), n (%) 21 (6.5%) Longterm cross-sectional samples (> 3 years), n (%) 10 (3.1%) Sample collection day in regard to biopsy day 0 (Day of biopsy) 36 within 1 day 11 within 2 days 3 within 3–6 days 15 Table 2 Summary of clinical characteristics from KTx patients Transplant prescreening Preemptive transplant, n (%) 15 (38.5%) ABO Incompatible, n (%) 12 (30.8%) IgA nephropathy, n (%) 11 (28.2%) Transplantation characteristics Donor operation time (min), mean (range) 215.1 (153–419) Donor bleeding amount (ml), mean (range) 45.3 (0-700) Recipient Operation time (min), mean (range) 370.2 (245–600) Recipient bleeding amount (ml), mean (range) 226.0 (60–794) Warm ischemic time (sec), mean (range) 324.7 (122–775) Total ischemic time (min), mean (range) 108.2 (51–353) Clinical characteristics Serum creatinine (mg/dL), median (range) 1.95 (0.8-11.58) Cystatin C (mg/L), median (range) 1.91 (0.98–7.18) Blood urea nitrogen (mg/dL), median (range) 25.8 (9-111.5) Immunosuppression treatments Tacrolimus (mg/day) (n), median (range) n = 35, 6 (0–15) Mycophenolate (mg/day) (n), median (range) n = 39, 1500 (500–1500) Cyclosporin (mg/day) (n), median (range) n = 4, 300 (140–400) Everolimus (mg/day) (n), median (range) n = 1, 1.5 (0.5–1.5) Samples with biopsy confirmed rejection T cell-mediated rejection (TCMR), n 7 Antibody-mediated rejection (ABMR), n 2 Validation of pooled capture methods and evaluation of informative SNPs First, we validated the robustness and reliability of our pooled capture method by deliberately mixing cfDNA from an anonymous donor and recipient with known genotypes in sequential ratios. The measured dd-cfDNA levels showed very high concordance (adjusted r² = 0.9922, p = 2.35×10 − 16 ) (Figure S1). Besides, the total amount of cfDNA yield (ng/mL) did not correlate with the quantified dd-cfDNA levels (Figure S2). This suggests the measurement was not influenced by the total amount of cfDNA, which is expected to fluctuate largely depending on physiological condition 25 . An informative bi-allelic SNP is defined by the absence of one allele from the donor genotype in the recipient genotype, resulting in a genotypic difference in either the homozygous or heterozygous state. Consequently, among pairs of donor-recipient, we identified 165 to nearly 400 informative SNPs out of a total of 1000 candidate SNPs. Parent-child and sibling pairs are expected to share common allele, hence, typically have fewer informative SNPs (Figure S3). To ensure accurate dd-cfDNA quantification, we applied a sequencing depth cutoff, allowing only sample with a minimum of 50 informative SNPs and each SNP locus must have at least 5 reads covered (Figure S4). General dynamics of dd-cfDNA levels post-transplant The dynamics of dd-cfDNA levels in all 37 KTx patients having follow-up records from the first day to 1-year post-Tx are presented in Fig. 1 . Significantly elevated dd-cfDNA levels were observed immediately after KTx, with initial levels varying widely among patients and reaching up to 30% on the first day. Consistent with most studies, we observed a rapid exponential decline in dd-cfDNA levels during the first week post-Tx, with the median value approaching the 1% baseline within 5 days. By day 14 post-Tx, dd-cfDNA levels stabilized below 1% in 15 out of 35 patients. At the one-year mark, most patients exhibited well-controlled status, with 14 out of 19 maintaining dd-cfDNA levels below 1%. We additionally evaluated dd-cfDNA levels in 21 long-term follow-up cases (3 to 5 years post-Tx). Generally, dd-cfDNA remained detectable but stayed below the 1% baseline (Fig. 1 , right panel), indicating the ongoing turnover of donor cells even in long-term stabilized allograft kidneys. Interestingly, only two long-term samples showed elevated dd-cfDNA levels: 3.61% in patient #8 at 5 years post-Tx and 2.43% in patient #31 at 3 years post-Tx, likely due to the significantly reduced tacrolimus (FK506) administration. Examining individual dd-cfDNA profiles from day 10 post-Tx, when fluctuations begin to stabilize, we found that patients without suspected AR episodes showed a ‘quiescent’ dd-cfDNA dynamic, remaining consistently low throughout the first month and year post-Tx (Fig. 2 A and 2 B). In contrast, in the group of patients experiencing at least one event of suspected rejection (n = 12), the levels of dd-cfDNA were reported to elevate erratically during the entire period (Fig. 2 C and 2 D). Intriguingly, these peaks aligned with AR episodes confirmed by same-day histological assessments, validating the well-established link between dd-cfDNA surges and rejection events 20 , 26 , 27 . Donor-derived cell-free DNA as a sensitive indicator of rejection and other forms of kidney injuries Examining case-by-case, we observed that dd-cfDNA levels in 8 out of 9 samples from six patients with biopsy-confirmed AR episodes stayed above the 1% baseline (Figure S5). In longitudinal patients, a 'spiking' pattern in dd-cfDNA levels of varying magnitudes was observed at the time of biopsy-confirmed rejection, highlighting the robustness of dd-cfDNA in indicating AR episodes (Fig. 3A). Notably, in two reported episodes of ABMR, a sharp surge in dd-cfDNA level were observed, reaching over 7% as seen in patient #26 (Fig. 3A). Similarly, ABMR episode on patient #2 also represented a relatively high dd-cfDNA level, reaching up to approximately 4% (Figure S5A). Meanwhile, TCMR was also associated with an increase in dd-cfDNA levels, albeit to a lesser extent and potentially with a delayed peak, as exemplified by patient #36 (Figure S5A). The two cross-sectional cases with ABMR confirmed also exhibited extremely high levels of dd-cfDNA upon hospitalization (Figure S5B). For example, dd-cfDNA levels of patient #24 exceeded 20% on the first day (Fig. 3B). Besides, we reported elevated levels of other kidney function markers, which displayed only marginal variations. Focusing on samples with histological assessments, we found that dd-cfDNA levels were significantly higher in the group of samples diagnosed with AR episodes compared to the group without rejection (p = 1.9×10 − 4 , Wilcoxon rank-sum test) (Fig. 3C). Other serum markers indicating kidney function also exhibited significant differences between these groups (Figure S6). Interestingly, we also observed particularly high levels of dd-cfDNA in KTx patients with kidney injury not related to rejection, as in patient #15, diagnosed with severe calcineurin inhibitors toxicity (Figure S5C), and in patient #38, diagnosed with cortical necrosis due to post-surgery complications (Fig. 3D). Strikingly, in the latter case, dd-cfDNA levels rose exceptionally high (over 7% at day 28 post-Tx), despite the lowered levels of other serum markers. This result emphasizes the indiscriminate sensitivity of dd-cfDNA to various forms of kidney injuries. Notably, fluctuations in plasma dd-cfDNA might be linked to diverse sources of organ damage or invasive manipulation. However, our preliminary analysis confirmed that procedure like needle biopsy did not affect dd-cfDNA levels (Figure S7). The relationship between dd-cfDNA and conventional assessments of kidney integrity Since high levels of dd-cfDNA and other serum markers help in identifying groups with AR, we anticipated strong correlations between them. In fact, we observed varying degrees of correlation. All three serum markers derived from kidney function (i.e., creatinine, cystatin C, and blood urea nitrogen) exhibited strong pairwise correlations (Spearman’s rho > 0.82). In contrast, the correlation coefficients between dd-cfDNA and any of these markers were relatively low, ranging from 0.23 to 0.39 (p < 0.001) (Fig. 4 A). However, we observed that elevated dd-cfDNA levels accurately correspond with moderate to extremely high values in other serum markers (Fig. 4 B). Hence, due to its distinct origin and wide fluctuation range, dd-cfDNA may offer a unique perspective on allograft integrity compared to other markers. Indeed, we found that pairwise correlations between dd-cfDNA and other serum markers remained significant in the ‘eventless’ group (patients without suspected rejection episodes) (Figure S8A), but became non-significant in the group with suspected rejection (the 'event' group) (Figure S8B). This suggests that dd-cfDNA dynamics become increasingly erratic in the presence of kidney injuries. Regarding Banff classification assessments on biopsy samples, we observed that elevated levels of dd-cfDNA generally associate with severe scores (i.e., higher grades) of diverse Banff Lesion Score categories (Fig. 4 C). Moreover, all reported AR episodes in our studies consistently showed very high dd-cfDNA levels, regardless of their wide-ranging Banff Lesion Scores (Figure S9A). We also found that certain indicators of chronic kidney injuries, such as interstitial fibrosis and tubular atrophy (IF/TA), and arteriosclerosis, do not consistently relate with dd-cfDNA levels. In other words, AR-positive high levels of dd-cfDNA may coexist with mild to moderate grades of chronic assessments, hinting potential limitation in relying solely on histological findings (Figure S9B). Detecting elevated dd-cfDNA levels in intermediate chronic stages may indicate subclinical injuries and provide an advantage for early intervention. Therefore, a comprehensive evaluation that combines various sources, including dd-cfDNA, may be desired for an accurate diagnosis. We hypothesized that various surgical factors during KTx procedure might affect dd-cfDNA stabilization in recipients. By categorized KTx patients into two groups based on the patterns of dd-cfDNA dynamics within the first week post-Tx, we found that the amount of donor bleeding significantly differed between these two groups (p = 0.0063, Wilcoxon rank-sum test) (Fig. 5 ). While it is possible that bleeding could contribute to an increase in initial dd-cfDNA levels, our finding suggests a potential link to a delayed stabilization status in the recipient. Donor-derived cell-free DNA sensitively indicates immunosuppression responses in KTx patients In all 39 KTx patients in our study, nearly 25% required consecutive increases in their immunosuppressant dosage, i.e., tacrolimus (FK506), based on comprehensive assessments of allograft integrity. We aimed to systematically evaluate how these dosage changes reflect in dd-cfDNA levels as well as other conventional serum markers. Using the day 5 post-Tx (D5) as a baseline, we quantified the longitudinal changes of a clinical measurement by calculating the tangent of the slope formed by the measured values on each subsequent day relative to the baseline day and the temporal distance between measurements (Fig. 6 A). Henceforth, tangent values were individually calculated for dd-cfDNA level, three conventional serum markers, and FK506 trough levels (C 0 ) (Supplemental Data 1). We examined the pairwise correlations and found that longitudinal changes in dd-cfDNA levels were marginally and negatively correlated with that of FK506 C 0 levels (Spearman’s rho = −0.25, p = 0.008) (Fig. 6 A), indicating that higher immunosuppressant concentrations were significantly associated with lower dd-cfDNA levels, and vice versa. Conversely, longitudinal changes in creatinine, cystatin C, and urea nitrogen levels did not correlate with that of FK506 C 0 levels (Fig. 6 A). Interestingly, when decomposing those relationships by the temporal facets, we observed that significant negative correlations for dd-cfDNA longitudinal changes were only present on day 14 and day 28 post-Tx (Figure S10). Supposedly, a more stable condition in KTx recipients may improve the responsiveness of dd-cfDNA in immunosuppressive monitoring. Consistently, in representative cases with consecutive increases of FK506 dosage (patients #8, #16 and #18), we witnessed an apparent inverse trend between dd-cfDNA levels and both FK506 dosage and FK506 C 0 levels (Fig. 6 B). This pattern was not observed on other serum markers. Conceivably, the wide dynamic range of dd-cfDNA may enable the rapid assessment of patients' responses to any change in treatment. This result demonstrated the advantage of dd-cfDNA over other serum markers in monitoring immunosuppressant response in KTx patients. DISCUSSION In this study, employing a population-specific SNP panel combined with a customized target sequencing protocol, we comprehensively examined longitudinal dd-cfDNA dynamics among 321 samples of 39 kidney transplant patients from a single-center cohort. We showed that elevated dd-cfDNA levels accurately indicated kidney injuries, particularly biopsy-confirmed AR, and matched conventional markers in distinguishing AR episodes. We also proposed that stabilization of dd-cfDNA levels in the early days post-Tx may be influenced by surgical factors, particularly the amount of donor bleeding. Additionally, dd-cfDNA levels aligned with histological grading and elevated before severe stages, suggesting their ability in informing subclinical injuries. Importantly, we demonstrated the value of dd-cfDNA in assessment of immunosuppressant responses in KTx patients, aiding Tx healthcare decisions. This study has several constraints, including the limited number of recruited KTx patients. Additionally, a small number of biopsy-confirmed AR cases restrains the estimation of the area under the curve (AUC) of an optimal dd-cfDNA threshold. In addition, our quantification of dd-cfDNA relies on knowing both donor and recipient’s genotypes, limiting its use case in cadaveric KTx. Alternative methods using only the recipient's genotype were described in our previous study and others 17 , 28 , 29 . Recent studies on dd-cfDNA for kidney transplantation have progressed significantly in both technological and analytical aspects. The quantification of dd-cfDNA have been achieved using high-throughput sequencing 17 , 19 , 22 , 23 , 30 and quantitative digital droplet PCR 30 , 31 , 31 – 33 ; the latter is suitable for clinical use due to its superior sensitivity and rapid turnaround, despite having lower throughput. In our study, we employed a versatile protocol for quantifying dd-cfDNA using pooled capture-hybridization steps followed by high-throughput sequencing, as described previously 34 . Our method is a cost-effective, high-throughput and scalable approach which has been successfully applied in monitoring dd-cfDNA in liver transplant 28 and various research purposes 34 – 37 . Furthermore, by designing probes targeting population-specific SNPs, we can optimize SNP panel to accommodate diverse ethnicities, thereby improving quantification potential. On the analytical aspect, some established statistical models help to infer informative SNPs and their related allele frequencies without the need to know the donor's genotype 29 , or even both the donor's and recipient's genotypes 38 . However, these approaches may not be preferable when dd-cfDNA fractions reach extremely high level 18 . In our study, all cases utilized living donors with accessible genotypes, which greatly improving dd-cfDNA quantification accuracy. In future studies, we aim to employ absolute quantification of dd-cfDNA (copies per mL), as its combination with the dd-cfDNA fraction has been shown to enhance diagnostic performance 18 , 39 . Moreover, the promising results from the integration of dd-cfDNA and gene expression signatures also deserve more attention 40 – 42 . While dd-cfDNA is known for lacking specificity in distinguishing AR from other kidney injuries 43 , 44 , its high sensitivity makes it valuable for assessing overall allograft integrity. A growing number of studies has documented the potential of dd-cfDNA as a diagnostic biomarker of AR as well as other types of graft injury in KTx, such as acute tubular necrosis, nephrotoxicity or infection 14 , 26 . Recently, a meta-analysis scrutinized 11 studies and obtained a summary receiver operating characteristics (SROC) curve with AUC of 0.83 (pooled sensitivity and specificity of 0.75 and 0.78, respectively) for the dd-cfDNA accuracy in rejection diagnosis, with favorable performance in ABMR diagnosis (SROC AUC of 0.85) 44 . Indeed, large multi-center cohorts enabled extensive cross-sectional analyses to estimate dd-cfDNA cut-off level for detection of AR in KTx recipients 19 , 23 , 45 . A general consensus is that a dd-cfDNA level above 1% should be used to diagnose rejection in KTx patients, as it effectively distinguishes ABMR from non-rejection 15 , 21 , 23 , 26 , 27 , 44 , 45 . Yet in a number studies, the chosen dd-cfDNA cut-off level could vary widely, ranging from 0.74–2.7% 19,22,23,45,46 . The robust capability of dd-cfDNA in detecting both acute and chronic ABMR, but not TCMR, was also reported 23 , 45 . Concordantly, we observed substantial surges in dd-cfDNA levels during ABMR episodes, far more noticeable when compared with those of TCMR. The key advantage of dd-cfDNA monitoring over other serum markers is the wide-ranged and responsive fluctuation, owing to its intrinsic origin of cellular damages. Therefore, the application of dd-cfDNA in monitoring immunosuppressant responses is crucial, not only for preventing AR and lessening subclinical abnormalities that can lead to chronic injury, but also for adjusting treatment appropriately to avoid toxicity. Recently, one study demonstrated that dd-cfDNA was successfully used to stratify rejection risk, leading to a reduction of mycophenolic mofetil in low-risk patients 47 . Also, Benning et al. reported the decreases of dd-cfDNA levels following anti-rejection treatments, though observed cases were rather sporadic 16 . Our study is the first to statistically demonstrate the response of KTx recipients to immunosuppressant dosing through longitudinal changes in dd-cfDNA levels, showing a significant negative correlation superior to conventional serum markers. We successfully provided solid evidence indicating that dd-cfDNA monitoring could be exploited for fine-tuning personalized immunosuppression and guiding treatment strategies, potentially decreasing the risk of AR and graft loss. In conclusion, our study reported the elevated level of dd-cfDNA occur during diverse forms of acute rejection and graft injury in KTx patients. Our findings collectively underscored the utility of dd-cfDNA as a molecular biomarker for monitoring the integrity of kidney allograft, as well as other solid organ transplants. The current study also served as a proof-of-concept for our established rapid and cost-effective protocol in dd-cfDNA assessment, which can be readily scaled up to accommodate a large number of patients and implemented as a valuable diagnostic marker in transplant healthcare. METHODS Designing of SNPs targeting probes We selected 1000 SNPs across all autosomes of which MAF is between 0.4 and 0.5 based on the reported allele frequencies in Japanese population 48 (Supplemental Data 2). Assuming Hardy–Weinberg equilibrium, this MAF value is expected to be of 23 to 25% homozygous in both donor and recipient, and the theoretical probability of both donor and recipient having a different allele is 11.5 to 12.5% 31 . Probes for capture-hybridization of cfDNA targeting each of 1000 SNPs were purchased from Roche as SeqCap EZ Library (Nippon Genetics, Japan). In new batch of long-term samples, we used a refined version of probes (shortlisted 300 SNPs) based on 3.5KJPNv2 database 49 , provided by SeqCap EZ Prime Choice (Roche, Switzerland). Cell-free DNA extraction, probe capture protocol and sequencing procedure Blood samples were collected in 10ml PAXgene® Blood ccfDNA tube (QIAGEN, Germany) by peripheral venesection and plasma was separated according to manufacturer’s instruction. Plasma was stored at -80 o C until cfDNA extraction. Cell-free DNA was isolated from 1 mL of the recipient’s plasma using the QIAamp MinElute ccfDNA Mini Kit (QIAGEN, Germany). The cfDNA was eluted with 33 µL of ultra-clean water and 24 µL of it was used for DNA repair with NEBNext FFPE DNA Repair Mix (New England Biolabs, USA). Libraries were prepared using the repaired cfDNA and NEBNext Ultra II DNA Library Prep Kit. After minimal amplification by PCR, target SNPs were enriched by capture-hybridization using KAPA HyperCap Target Enrichment Probes and KAPA HyperCapture Reagent Kit (Roche, Switzerland). Capture-hybridization was performed using our designed probe sets, following our single-reaction protocol 34 . The enriched library was amplified by 18 cycles of PCR and sequenced using the MiSeq or NovaSeq 6000 platform (Illumina, USA). Where kits were used, all manufacturer's instructions were followed. Genotyping of target SNPs from donors and recipients Raw fastq reads obtained from sequencing facility were trimmed of sequencing adaptors and performed quality control using Trimmomatic-0.38 and the following parameters: leading:15 / trailing:15 / slidingwindow:4:20 / crop:220 / minlen:36. Trimmed sequences were mapped to reference human genome hg19 using bwa-mem followed by quality controls using picard/MarkDuplicates and samtools v1.18. Variant call was conducted using HaplotypeCaller tool of GATK v4.2.5.0 package 50 using options: –output-mode EMIT_ALL_ACTIVE_SITES and –emit-ref-confidence BP_RESOLUTION. Then filtering steps on resulted vcf files were performed using GATK/VariantFiltration tool with parameters: FS 2 / MQ > 40 / DP ≥ 30. Heterozygous sites in recipients were excluded before selection of informative SNPs. Quantification of donor-derived cell-free DNA After genotyping of 1000 SNPs, we selected informative SNPs personalized for each donor-recipient pair that satisfy either one in two conditions: (1) homozygous genotype is different between donor and recipient, or (2) genotype of recipient is homozygous and that of donor is heterozygous. The counting the reads mapped on each informative SNP was performed by GATK/DepthOfCoverage. For dd-cfDNA quantification, informative SNP locus must satisfy coverage depth ≥ 5, minimum mapping quality ≥ 30 and base quality ≥ 26. Subsequently, summarized ratios of donor-derived read counts on the total mapped reads were calculated by in-house scripts, and resulted dd-cfDNA fraction was represented in percentage. Statistical analyses Statistical analyses were performed using Hmisc package in R-software. The Wilcoxon rank-sum test and Type III repeated measures ANOVA were used for comparisons between independent groups and for continuous dependent variables, respectively. Spearman’s correlation was used to evaluate relationship between residues. A p-value less than 0.05 is considered to be statistically significant. Visualization of the data was done using the ggplot2 package in R. Histopathological findings Renal biopsy was performed with ultrasound-guided needle biopsy and histopathological examination was assessed and classified according to Banff classification by renal pathologist. Intra-graft C4d stain was performed to evaluate acute antibody–mediated rejection (ABMR). Ethical conduct of research This study was approved by the institutional ethical boards of National Hospital Organization Chiba-East Hospital (Approved ID: 41) and the National Institute of Genetics (Approved ID: 28 − 7). All subjects provided written informed consent for the collection of samples and subsequent analyses. Declarations COMPETING INTERESTS Ituro Inoue and Shigeki Mitsunaga are cofounders of iSan Bio Inc., a company whose focus lies outside the scope of this study and has no influence on its results or conclusions. The other authors declare no conflict of interest. AUTHOR CONTRIBUTIONS Ituro Inoue, Hirofumi Nakaoka and Kenichi Saigo conceived and designed the study. Phuong Thanh Nguyen, Shigeki Mitsunaga, Hiromichi Aoyama and Hiroshi Kitamura collected samples, conducted experiments and jointly performed analyses. Phuong Thanh Nguyen and Shigeki Mitsunaga wrote the manuscript. Hirofumi Nakaoka, Kenichi Saigo and Ituro Inoue revised the manuscript. All authors have read and approved of all the contents of this manuscript. ACKNOWLEDGEMENTS We would like to thank Yumiko Sato, Junko Kajiwara and Junko Kitayama for their technical assistance. This study was partly supported by JSPS KAKENHI Grant Number JP16K10445 to Kenichi Saigo and by AMED under Grant Number 24ek0510040h0002 to Ituro Inoue. DATA AVAILABILITY All calculated dd-cfDNA levels, clinical assessments, and the measurements of longitudinal changes are provided in Supplemental Data 1 . A list of 1000 SNPs employed in our target-sequencing protocol is disclosed in Supplemental Data 2 . The raw sequencing data generated in the current study are available from the corresponding author upon reasonable request. References Tonelli M et al (2011) Systematic review: Kidney transplantation compared with dialysis in clinically relevant outcomes. 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Am J Transpl 19:3087–3099 Grskovic M et al (2016) Validation of a Clinical-Grade Assay to Measure Donor-Derived Cell-Free DNA in Solid Organ Transplant Recipients. J Mol Diagn. 10.1016/j.jmoldx.2016.07.003 Nie W et al (2022) Dynamics of Donor-Derived Cell-Free DNA at the Early Phase After Pediatric Kidney Transplantation: A Prospective Cohort Study. Front Med (Lausanne) 8:814517 Jordan SC et al (2018) Donor-derived cell-free DNA identifies antibody-mediated rejection in donor specific antibody positive kidney transplant recipients. Transpl Direct. 10.1097/TXD.0000000000000821 Gielis EM et al (2020) The use of plasma donor-derived, cell-free DNA to monitor acute rejection after kidney transplantation. Nephrol Dialysis Transplantation. 10.1093/ndt/gfz091 Bloom RD et al (2017) Cell-Free DNA and Active Rejection in Kidney Allografts. J Am Soc Nephrol 28:2221–2232 Gielis EM et al (2015) Cell-Free DNA: An Upcoming Biomarker in Transplantation. Am J Transplant. 10.1111/ajt.13387 Grabuschnig S et al (2020) Putative Origins of Cell-Free DNA in Humans: A Review of Active and Passive Nucleic Acid Release Mechanisms. Int J Mol Sci 21:1–24 Knight SR, Thorne A, Lo Faro ML (2019) Donor-specific Cell-free DNA as a Biomarker in Solid Organ Transplantation. A Systematic Review. https://doi.org/10.1097/TP.0000000000002482 . Transplantation Preprint at Martuszewski A, Paluszkiewicz P, Król M, Banasik M, Kepinska M (2021) Donor-Derived Cell-Free DNA in Kidney Transplantation as a Potential Rejection Biomarker: A Systematic Literature Review. J Clin Med 2021 10(10):193 Kanamori H et al (2024) Noninvasive graft monitoring using donor-derived cell-free DNA in Japanese liver transplantation. Hepatol Res 54:300–314 Sharon E et al (2017) Quantification of transplant-derived circulating cell-free DNA in absence of a donor genotype. PLoS Comput Biol. 10.1371/journal.pcbi.1005629 Sigdel T et al (2018) Optimizing Detection of Kidney Transplant Injury by Assessment of Donor-Derived Cell-Free DNA via Massively Multiplex PCR. J Clin Med 8:19 Beck J et al (2013) Digital droplet PCR for rapid quantification of donor DNA in the circulation of transplant recipients as a potential universal biomarker of graft injury. Clin Chem 59:1732–1741 Sigdel TK et al (2013) A rapid noninvasive assay for the detection of renal transplant injury. Transplantation. 10.1097/TP.0b013e318295ee5a Clausen FB, Jørgensen KMCL, Wardil LW, Nielsen LK, Krog GR (2023) Droplet digital PCR-based testing for donor-derived cell-free DNA in transplanted patients as noninvasive marker of allograft health: Methodological aspects. PLoS ONE 18:e0282332 Ahmadloo S et al (2017) Rapid and cost-effective high-throughput sequencing for identification of germline mutations of BRCA1 and BRCA2. J Hum Genet 62:561–567 Nakaoka H et al (2016) Allelic Imbalance in Regulation of ANRIL through Chromatin Interaction at 9p21 Endometriosis Risk Locus. PLoS Genet 12 Yamaguchi M et al (2022) Spatiotemporal dynamics of clonal selection and diversification in normal endometrial epithelium. Nat Commun 2022 13(1 13):1–18 Jinam TA et al (2022) Allelic and haplotypic HLA diversity in indigenous Malaysian populations explored using Next Generation Sequencing. Hum Immunol 83:17–26 Gordon PMK et al (2016) An Algorithm Measuring Donor Cell-Free DNA in Plasma of Cellular and Solid Organ Transplant Recipients That Does Not Require Donor or Recipient Genotyping. Front Cardiovasc Med. 10.3389/fcvm.2016.00033 Halloran PF et al (2022) Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation. Transplantation 106:2435–2442 Halloran PF et al (2022) Combining Donor-derived Cell-free DNA Fraction and Quantity to Detect Kidney Transplant Rejection Using Molecular Diagnoses and Histology as Confirmation. Transplantation 106:2435–2442 Obrișcă B et al (2022) Combining donor-derived cell-free DNA and donor specific antibody testing as non-invasive biomarkers for rejection in kidney transplantation. Sci Rep 12 Rizvi A et al (2023) Kidney Allograft Monitoring by Combining Donor-Derived Cell-Free DNA and Molecular Gene Expression: A Clinical Management Perspective. J Pers Med 13:1205 Pagliazzi A, Bestard O, Naesens M (2023) Donor-Derived Cell-Free DNA: Attractive Biomarker Seeks a Context of Use. Transpl Int 36 Yang H et al (2024) Diagnostic performance of GcfDNA in kidney allograft rejection: a meta-analysis. Front Physiol 14 Huang E et al (2019) Early clinical experience using donor-derived cell-free DNA to detect rejection in kidney transplant recipients. Am J Transplant 19:1663–1670 Dauber EM et al (2020) Quantitative PCR of INDELs to measure donor-derived cell-free DNA—a potential method to detect acute rejection in kidney transplantation: a pilot study. Transpl Int. 10.1111/tri.13554 Osuchukwu G et al (2024) Use of Donor-derived Cell-free DNA to Inform Tapering of Immunosuppression Therapy in Kidney Transplant Recipients: An Observational Study. Transpl Direct 10:E1610 Higasa K et al (2016) Human genetic variation database, a reference database of genetic variations in the Japanese population. J Hum Genet 2016 61(6 61):547–553 Tadaka S et al (2019) 3.5KJPNv2: an allele frequency panel of 3552 Japanese individuals including the X chromosome. Human Genome Variation. 6:1 6, 1–9 (2019) Poplin R et al (2018) Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv 201178. 10.1101/201178 Additional Declarations The authors declare no competing interests. Supplementary Files FigureS1.tif Figure S1. Calibration of known volume ratios of donor-recipient samples validating the consistency of dd-cfDNA quantification. FigureS2.tif Figure S2. Correlation between total cfDNA yield (ng/mL) and measured dd-cfDNA (%) among all collected samples. FigureS3.tif Figure S3. Number of informative SNPs detected between each pair of donor and recipient, via genotyping of 1000 SNP loci (donor-recipient pair #14 opted out from current study). Informative SNPs were stratified based on the donor's genotype: termed as donor heterozygote SNP if the genotype of donor is heterozygous and that of recipient is homozygous, and donor homozygote SNP if homozygous genotype is different between donor and recipient. FigureS4.tif Figure S4. The number of informative SNP count per sample and the corresponding total read coverage on those informative SNPs. Dashed red line indicates the minimum 50 informative SNPs detected on each sample. FigureS5.tif Figure S5. Detail demonstrations of measurements from all patients with biopsy-confirmed rejections (n=6), including (A) four longitudinal cases and (B) two cross-sectional cases; (C) two cases with severe kidney injuries not related to rejection are also presented. Abbreviations: aT.BIA = acute T cell–mediated rejection Banff IA; aT.BIIA = acute T cell–mediated rejection Banff IIA; aT.BIB = acute T cell–mediated rejection Banff IB; aT.BIIB = acute T cell–mediated rejection Banff IIB; aA. = acute antibody–mediated rejection. FigureS6.tif Figure S6. Comparison of levels of dd-cfDNA (%), creatinine (mg/dL), cystatin C (mg/dL), and blood urea nitrogen (mg/dL) between the group of samples with biopsy-confirmed rejection episodes and the group of samples without histologically detectable rejection. FigureS7.tif Figure S7. Examining the effect of biopsy procedure on the fluctuation of dd-cfDNA levels. Red lines connect dd-cfDNA levels of the same patient at the day of biopsy (Bx), 1 day after biopsy (Bx+1) and 2 days after biopsy (Bx+2). Type III Repeated measures ANOVA test was performed. FigureS8.tif Figure S8. Pairwise correlations between each of the three serum markers (i.e., creatinine, cystatin C and urea nitrogen) and dd-cfDNA level among different groups of KTx patient. (A) Correlations in the group without suspected rejection (‘eventless’ group) (Right panel derived from Figure 2A and 2B); (B) Correlations in the group with suspected rejection episodes (‘event’ group) (Right panel derived from Figure 2C and 2D). Spearman correlation tests were performed. FigureS9.tif Figure S9. Distribution of dd-cfDNA levels across various histological assessment of kidney integrity. (A) Distribution of dd-cfDNA levels across various Banff Lesion Score categories, with the severity of the condition increases as the score moves to the right. Abbreviation of Banff Lesion Score categories: i = Interstitial inflammation, t = Tubulitis, g = Glomerulitis, v = Intimal arteritis, ci = Interstitial fibrosis, ct = Tubular atrophy, ptc = Peritubular capillaritis, C4d = C4d staining scores, ptcbm = peritubular capillary basement membrane, ah = Arteriolar hyalinosis, aah = Hyaline arteriolar thickening; (B) Distribution of dd-cfDNA levels across different grades of chronic renal injury assessments, including IF/TA and Arteriosclerosis. Grading annotations: MILD=mild; SEMIMOD=semi-moderate; MOD=moderate; SEMISEVERE=semi-severe; SEVERE=severe. FigureS10.tif Figure S10. Scatter plots show individual correlations between longitudinal change values from each serum-based measurement (including dd-cfDNA) and the corresponding values calculated from FK506 C 0 . These correlations were facetted by timepoints compared to the baseline day (Day 5), represented by three columns corresponding to Day 7, Day 14 and Day 28 post-Tx. SupplementalData1.xlsx Supplemental Data 1 SupplementalData2.xlsx Supplemental Data 2 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6592444","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451947311,"identity":"3ad11f24-24f3-4620-8557-49fb353de4f1","order_by":0,"name":"Phuong Thanh Nguyen","email":"","orcid":"","institution":"Vietnam Academy of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Phuong","middleName":"Thanh","lastName":"Nguyen","suffix":""},{"id":451947312,"identity":"a77b212f-98d3-4d41-8304-71f5f33444f3","order_by":1,"name":"Hirofumi Nakaoka","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-8454-1159","institution":"Kagoshima University","correspondingAuthor":true,"prefix":"","firstName":"Hirofumi","middleName":"","lastName":"Nakaoka","suffix":""},{"id":451947313,"identity":"3db3a54d-a769-4d06-874f-d5064d35eaa9","order_by":2,"name":"Shigeki Mitsunaga","email":"","orcid":"","institution":"National Institute of Genetics","correspondingAuthor":false,"prefix":"","firstName":"Shigeki","middleName":"","lastName":"Mitsunaga","suffix":""},{"id":451947314,"identity":"f4106403-175b-4117-8328-52178481ee24","order_by":3,"name":"Hiromichi Aoyama","email":"","orcid":"","institution":"National Hospital Organization Chiba-East Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hiromichi","middleName":"","lastName":"Aoyama","suffix":""},{"id":451947315,"identity":"d72586a8-991b-4959-b2a7-ae3482f268af","order_by":4,"name":"Hiroshi Kitamura","email":"","orcid":"","institution":"National Hospital Organization Chiba-East Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hiroshi","middleName":"","lastName":"Kitamura","suffix":""},{"id":451947316,"identity":"305ad7f2-6356-41c1-bcc4-967022778bb2","order_by":5,"name":"Kenichi Saigo","email":"","orcid":"","institution":"National Hospital Organization Chiba-East Hospital","correspondingAuthor":false,"prefix":"","firstName":"Kenichi","middleName":"","lastName":"Saigo","suffix":""},{"id":451947317,"identity":"bbea598c-7ce4-4b77-9987-b07a93d566ce","order_by":6,"name":"Ituro Inoue","email":"","orcid":"","institution":"National Institute of Genetics","correspondingAuthor":false,"prefix":"","firstName":"Ituro","middleName":"","lastName":"Inoue","suffix":""}],"badges":[],"createdAt":"2025-05-05 08:14:49","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6592444/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6592444/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82070558,"identity":"fe890cac-ac4c-40ef-9333-8e3f973e17d6","added_by":"auto","created_at":"2025-05-06 13:11:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":113491,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of dd-cfDNA dynamic by protocol sampling within 1 year and after long period post-Tx (long-term samples); n = number of collected samples.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/14a6e527c04659d80da9346f.png"},{"id":82071054,"identity":"200918a8-e6ec-4886-ab81-6ba49c060a55","added_by":"auto","created_at":"2025-05-06 13:19:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":294736,"visible":true,"origin":"","legend":"\u003cp\u003eThe dynamic of dd-cfDNA level among different groups of KTx patients. (a) The dynamic of dd-cfDNA level in the group of patients without any suspected episode of rejection (i.e., ‘eventless’ patients), within the first month post-Tx; (b) Follow-up on ‘eventless’ patients from day 10\u003csup\u003e \u003c/sup\u003eto 1-year post-Tx; (c) The dynamic of dd-cfDNA level in the group of patients with suspected episodes of rejection (i.e., ‘event’ patient), within the first month post-Tx; (d) Follow-up on ‘event’ patients from day 10\u003csup\u003e \u003c/sup\u003eto 1-year post-Tx. Biopsy-confirmed rejection episodes are annotated by star shape; p = number of KTx patients.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/deb13d2abd37675a7e219254.png"},{"id":82070571,"identity":"efd47009-ac2f-420e-86d3-06f342aec402","added_by":"auto","created_at":"2025-05-06 13:11:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":691187,"visible":true,"origin":"","legend":"\u003cp\u003eLongitudinal clinical measurements in representative cases of biopsy-confirmed rejection and other types of KTx injury. (a) Representative longitudinal cases with biopsy-confirmed rejections; (b) Representative cross-sectional case with biopsy-confirmed rejection upon hospitalization. Abbreviations: aT.BIA = acute T cell–mediated rejection Banff IA; aT.BIIA = acute T cell–mediated rejection Banff IIA; aT.BIB = acute T cell–mediated rejection Banff IB; aA. = acute antibody–mediated rejection. (c) Comparison of dd-cfDNA levels between groups of biopsy-verified samples with and without rejection, Wilcoxon rank-sum test was performed; (d) Representative case of elevated dd-cfDNA level due to severe kidney injury not related to rejection.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/71e1afd8b98163fd31c7fd74.png"},{"id":82071060,"identity":"ba189dc7-e74a-4dbf-afaf-8f2a145142c3","added_by":"auto","created_at":"2025-05-06 13:19:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":576913,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between dd-cfDNA and conventional assessments of kidney integrity. (a) Pairwise correlations between dd-cfDNA level and other serum markers. Spearman correlation was performed, n = number of samples, p = number of KTx patients; (b) Visualization of dd-cfDNA levels in relate to the distribution of serum markers for KTx integrity. Only samples taken at least 10 days post-Tx were plotted; dd-cfDNA levels are represented by both color intensity and dot size. (c) Integrative visualization of median dd-cfDNA level corresponding to various Banff Lesion Scores of histological assessments from biopsies (for simplification, some sub-scores are merged into the main corresponding scores, e.g., 1b ® 1; 2b ® 2; 3b ® 3; 2.5 ® 2); Abbreviation of Banff Lesion Score categories: \u003cem\u003ei\u003c/em\u003e = Interstitial inflammation, \u003cem\u003et\u003c/em\u003e = Tubulitis, \u003cem\u003eg\u003c/em\u003e= Glomerulitis, \u003cem\u003ev\u003c/em\u003e = Intimal arteritis, \u003cem\u003eci\u003c/em\u003e = Interstitial fibrosis, \u003cem\u003ect\u003c/em\u003e = Tubular atrophy, \u003cem\u003eptc\u003c/em\u003e = Peritubular capillaritis, \u003cem\u003eC4d\u003c/em\u003e= C4d staining scores, \u003cem\u003eptcbm\u003c/em\u003e = peritubular capillary basement membrane, \u003cem\u003eah\u003c/em\u003e= Arteriolar hyalinosis, \u003cem\u003eaah\u003c/em\u003e = Hyaline arteriolar thickening.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/0544b718cc9e04b53e9ef5d1.png"},{"id":82073261,"identity":"266d65e0-73b4-4dd2-93bf-56f03c698977","added_by":"auto","created_at":"2025-05-06 13:27:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":234076,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of surgical factors between groups of patients with typical (normal) stabilization curve of dd-cfDNA (dd-cfDNA reaches \u0026lt; 1% within 7 days post-Tx) versus those showing slow stabilization pattern (dd-cfDNA stays above 1% until 7 days post-Tx); Wilcoxon rank-sum tests were performed.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/406f2e6600fb25514b743333.png"},{"id":82071055,"identity":"8f1000ad-4f45-4cfb-b4c6-c56c037ded99","added_by":"auto","created_at":"2025-05-06 13:19:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":433510,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between clinical serum-based measurements and immunosuppression levels in KTx patients. (a) Demonstration of estimating longitudinal changes by calculating the tangent of the slope formed by the measured values on each subsequent day relative to the baseline day (defined here as day 5 post-Tx) and the temporal distance between measurements. Scatter plots show individual correlations between longitudinal changes of each type of serum-based measurements (including dd-cfDNA) and those of FK506 trough level (FK506 C\u003csub\u003e0\u003c/sub\u003e); (b) Representative cases demonstrating sharp decreases in dd-cfDNA levels, which negatively correspond to both consecutive increases in FK506 dosage and succeeding increases in FK506 C\u003csub\u003e0\u003c/sub\u003e (highlighted in dashed yellow boxes).\u003c/p\u003e","description":"","filename":"FIgure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/9749d8a218e0c68372707aa3.png"},{"id":82075551,"identity":"d0d8f748-ed5a-4959-87c5-45a28d7c3c63","added_by":"auto","created_at":"2025-05-06 13:43:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3328805,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/c222af24-0043-4575-a8de-5fde061cf0e0.pdf"},{"id":82070559,"identity":"dd43baef-fafe-46cf-acea-92bff3faf77a","added_by":"auto","created_at":"2025-05-06 13:11:51","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20446,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1. Calibration of known volume ratios of donor-recipient samples validating the consistency of dd-cfDNA quantification.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/d02e034b6ee7b2e904ba786a.tif"},{"id":82070560,"identity":"ce4f1832-26ef-46a8-8e86-1a3f80716e44","added_by":"auto","created_at":"2025-05-06 13:11:51","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":52224,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2. Correlation between total cfDNA yield (ng/mL) and measured dd-cfDNA (%) among all collected samples.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/ab5e74244dd516b0dd75a393.tif"},{"id":82070564,"identity":"9a048703-674a-42bc-be75-47d50322a4fc","added_by":"auto","created_at":"2025-05-06 13:11:51","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":67868,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3. Number of informative SNPs detected between each pair of donor and recipient, via genotyping of 1000 SNP loci (donor-recipient pair #14 opted out from current study). Informative SNPs were stratified based on the donor's genotype: termed as donor heterozygote SNP if the genotype of donor is heterozygous and that of recipient is homozygous, and donor homozygote SNP if homozygous genotype is different between donor and recipient.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/696889f2ef0ce36580f2654f.tif"},{"id":82070563,"identity":"7fd03914-4d76-4e3f-901a-1af947edbd64","added_by":"auto","created_at":"2025-05-06 13:11:51","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":88584,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S4. The number of informative SNP count per sample and the corresponding total read coverage on those informative SNPs. Dashed red line indicates the minimum 50 informative SNPs detected on each sample.\u003c/p\u003e","description":"","filename":"FigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/505560d06e6bce76aff4ace2.tif"},{"id":82073932,"identity":"b4972b86-74b7-41ba-9534-f4db51277830","added_by":"auto","created_at":"2025-05-06 13:35:51","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":218906,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S5. Detail demonstrations of measurements from all patients with biopsy-confirmed rejections (n=6), including (A) four longitudinal cases and (B) two cross-sectional cases; (C) two cases with severe kidney injuries not related to rejection are also presented. Abbreviations: aT.BIA = acute T cell–mediated rejection Banff IA; aT.BIIA = acute T cell–mediated rejection Banff IIA; aT.BIB = acute T cell–mediated rejection Banff IB; aT.BIIB = acute T cell–mediated rejection Banff IIB; aA. = acute antibody–mediated rejection.\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/43ea2ea3bf6c7aad9d7a404c.tif"},{"id":82070573,"identity":"beaa1dcb-cd84-476a-9335-d388312283a0","added_by":"auto","created_at":"2025-05-06 13:11:51","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":74440,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S6. Comparison of levels of dd-cfDNA (%), creatinine (mg/dL), cystatin C (mg/dL), and blood urea nitrogen (mg/dL) between the group of samples with biopsy-confirmed rejection episodes and the group of samples without histologically detectable rejection.\u003c/p\u003e","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/e7a22e07f5084efb749b66b9.tif"},{"id":82071059,"identity":"b5805c36-ee80-4f3b-9539-1324139fa80c","added_by":"auto","created_at":"2025-05-06 13:19:51","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":22898,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S7. Examining the effect of biopsy procedure on the fluctuation of dd-cfDNA levels. Red lines connect dd-cfDNA levels of the same patient at the day of biopsy (Bx), 1 day after biopsy (Bx+1) and 2 days after biopsy (Bx+2). Type III Repeated measures ANOVA test was performed.\u003c/p\u003e","description":"","filename":"FigureS7.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/fd1d51e2416398409ba67850.tif"},{"id":82073263,"identity":"79d3f883-6741-4300-9759-2c1a7d877aed","added_by":"auto","created_at":"2025-05-06 13:27:51","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":108036,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S8. Pairwise correlations between each of the three serum markers (i.e., creatinine, cystatin C and urea nitrogen) and dd-cfDNA level among different groups of KTx patient. (A) Correlations in the group without suspected rejection (‘eventless’ group) (Right panel derived from Figure 2A and 2B); (B) Correlations in the group with suspected rejection episodes (‘event’ group) (Right panel derived from Figure 2C and 2D). Spearman correlation tests were performed.\u003c/p\u003e","description":"","filename":"FigureS8.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/97eee1363200b3da9720fa96.tif"},{"id":82073933,"identity":"f024eccc-dcd2-4e54-8cbd-fe81b51c6b3e","added_by":"auto","created_at":"2025-05-06 13:35:51","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":90722,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S9. Distribution of dd-cfDNA levels across various histological assessment of kidney integrity. (A) Distribution of dd-cfDNA levels across various Banff Lesion Score categories, with the severity of the condition increases as the score moves to the right. Abbreviation of Banff Lesion Score categories: \u003cem\u003ei\u003c/em\u003e= Interstitial inflammation, \u003cem\u003et\u003c/em\u003e = Tubulitis, \u003cem\u003eg\u003c/em\u003e = Glomerulitis, \u003cem\u003ev\u003c/em\u003e= Intimal arteritis, \u003cem\u003eci\u003c/em\u003e = Interstitial fibrosis, \u003cem\u003ect\u003c/em\u003e = Tubular atrophy, \u003cem\u003eptc\u003c/em\u003e = Peritubular capillaritis, \u003cem\u003eC4d\u003c/em\u003e = C4d staining scores, \u003cem\u003eptcbm\u003c/em\u003e = peritubular capillary basement membrane, \u003cem\u003eah\u003c/em\u003e = Arteriolar hyalinosis, \u003cem\u003eaah\u003c/em\u003e = Hyaline arteriolar thickening; (B) Distribution of dd-cfDNA levels across different grades of chronic renal injury assessments, including IF/TA and Arteriosclerosis. Grading annotations: MILD=mild; SEMIMOD=semi-moderate; MOD=moderate; SEMISEVERE=semi-severe; SEVERE=severe.\u003c/p\u003e","description":"","filename":"FigureS9.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/d8fb8586f769e58ad0086414.tif"},{"id":82070579,"identity":"22ea366a-4430-4bbe-82ff-5f015cb8820e","added_by":"auto","created_at":"2025-05-06 13:11:51","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":136674,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S10. Scatter plots show individual correlations between longitudinal change values from each serum-based measurement (including dd-cfDNA) and the corresponding values calculated from FK506 C\u003csub\u003e0\u003c/sub\u003e. These correlations were facetted by timepoints compared to the baseline day (Day 5), represented by three columns corresponding to Day 7, Day 14 and Day 28 post-Tx.\u003c/p\u003e","description":"","filename":"FigureS10.tif","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/73e52192a897e8aa18e483fb.tif"},{"id":82070575,"identity":"9f988e4c-625e-4096-b562-4feac617b2d6","added_by":"auto","created_at":"2025-05-06 13:11:51","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":63255,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Data 1\u003c/p\u003e","description":"","filename":"SupplementalData1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/3d0dbfdb1c1025a694c0f08f.xlsx"},{"id":82071064,"identity":"5e10b236-1bff-483d-ad2b-3acc35dba17e","added_by":"auto","created_at":"2025-05-06 13:19:51","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":45160,"visible":true,"origin":"","legend":"\u003cp\u003eSupplemental Data 2\u003c/p\u003e","description":"","filename":"SupplementalData2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6592444/v1/feb70d7ea3ee1d1eb9c02293.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eApplications of donor-derived cell-free DNA in kidney transplantation healthcare: view from a prospective single-center study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eKidney transplantation (KTx) is often the last resort for patients with end-stage kidney diseases, offering benefits that can significantly enhance a patient's quality of life \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. However, renal acute rejection (AR) poses a substantial risk for graft failure \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. AR can manifest in various forms, including microvascular damage, ischemia, or inflammation, affecting 5.4\u0026ndash;8.8% of adult KTx recipients within the first-year post-transplant (post-Tx) \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAdvancements in pre- and post-transplant care for KTx, such as improved HLA matching, preemptive interventions, and effective immunosuppressant therapies, have greatly reduced the incidence of AR in KTx \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Assessment of allograft kidney typically involves monitoring serum markers such as creatinine levels, cystatin C, blood urea nitrogen and serum procalcitonin, as well as estimated glomerular filtration rate (eGFR) or proteinuria \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Kidney biopsy, despite being the gold standard for diagnosing AR \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, is an invasive procedure which may associate with risk of secondary complications and interpretational variability due to issues like patchy sampling or inadequate specimens \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Hence, balancing the risk-benefit tradeoff, the use of biopsy is limited to cases with specific concerns (i.e., on a \u0026lsquo;for-cause\u0026rsquo; basis); otherwise, its use in protocol surveillance may be deemed unnecessary \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlternatively, molecular diagnostics recently have gained attention as a valuable tool to support clinical decisions in transplant healthcare \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Donor-derived cell-free DNA (dd-cfDNA), which is released from disrupted cells of transplanted organ, can be detected in the recipient's blood plasma or urine, where its amount and fluctuation can inform the status of graft integrity \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Based on this concept, a growing number of studies have focused on quantifying dd-cfDNA to enhance the diagnosis of AR and other forms of allograft injury \u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17 CR18 CR19 CR20 CR21 CR22 CR23\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, long-term observation using a scalable methodology for monitoring dd-cfDNA is still lacking.\u003c/p\u003e \u003cp\u003eHere, utilizing a SNP panel designed specifically for the East-Asian population, we examined longitudinal dd-cfDNA levels in KTx patients from a single-center cohort for up to five years post-Tx. Elevated dd-cfDNA levels provided early indications of AR episodes and diverse forms of graft injury. The relation of dd-cfDNA was observed not only with clinical parameters and biopsy-derived histological assessments but also with immunosuppressant dosages. Our study validated the potential of dd-cfDNA as a non-invasive biomarker for evaluating graft integrity in KTx. Furthermore, we established a proof of concept using a cost-effective targeted sequencing protocol to quantify dd-cfDNA, highlighting its potential applicability in clinical settings.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCohort description and sample collection\u003c/h2\u003e \u003cp\u003eA single-center cohort consisting of 39 KTx donor-recipient pairs was recruited into this study with written consent. After determining the genotypes of both donors and recipients, we collected a total of 322 blood samples from patients (i.e., KTx recipients) for the extraction of cell-free DNA, resulting in 321 samples eligible for dd-cfDNA analysis. A detailed demographic summary of all patients is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Patients underwent sampling at fixed timepoints (i.e., protocol sampling, n\u0026thinsp;=\u0026thinsp;241), including within the first several weeks, at one month, and at one year post-Tx. A portion of samples were collected on unprompted occasions with clinical concerns (i.e., for-cause sampling, n\u0026thinsp;=\u0026thinsp;37), especially where AR episodes are suspected. Needle biopsies were also performed to corroborate the diagnosis. Additionally, we also assessed dd-cfDNA levels of samples collected 3 to 5 years post-Tx (n\u0026thinsp;=\u0026thinsp;21), regarded as long-term sampling. Following the biopsy-based Banff classification guidelines \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, six patients were detected exhibiting AR episodes, including two cross-sectional cases with KTx performed a long time ago, enrolled in this study upon hospitalization. Specifically, of the nine reported AR episodes, two were ABMR, and seven were acute T cell-mediated rejection (TCMR). All clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (see Supplemental Data 1 for detail).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemography of kidney transplant cases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of Tx recipients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecipient characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (male/female), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 / 14 (64.1%/35.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age, years (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48 (24\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePost-Tx follow-up cases (longitudinal), n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnrolled mid-term cases (cross-sectional), n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDonor-recipient relationship\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent-to-child, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSibling, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-relative, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal number of plasma sample collected\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollected within 10 days post-transplantation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e143 (44.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCollected after 10 days, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178 (55.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of samples collected with biopsy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongterm longitudinal samples (\u0026gt;\u0026thinsp;3 years), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (6.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLongterm cross-sectional samples (\u0026gt;\u0026thinsp;3 years), n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (3.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSample collection day in regard to biopsy day\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0 (Day of biopsy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewithin 1 day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewithin 2 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewithin 3\u0026ndash;6 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of clinical characteristics from KTx patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransplant prescreening\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePreemptive transplant, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (38.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eABO Incompatible, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIgA nephropathy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (28.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTransplantation characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonor operation time (min), mean (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e215.1 (153\u0026ndash;419)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonor bleeding amount (ml), mean (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45.3 (0-700)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecipient Operation time (min), mean (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e370.2 (245\u0026ndash;600)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecipient bleeding amount (ml), mean (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226.0 (60\u0026ndash;794)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWarm ischemic time (sec), mean (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e324.7 (122\u0026ndash;775)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal ischemic time (min), mean (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e108.2 (51\u0026ndash;353)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinical characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerum creatinine (mg/dL), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.95 (0.8-11.58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCystatin C (mg/L), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.91 (0.98\u0026ndash;7.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen (mg/dL), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.8 (9-111.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eImmunosuppression treatments\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTacrolimus (mg/day) (n), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;35, 6 (0\u0026ndash;15)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMycophenolate (mg/day) (n), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;39, 1500 (500\u0026ndash;1500)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclosporin (mg/day) (n), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;4, 300 (140\u0026ndash;400)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEverolimus (mg/day) (n), median (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;1, 1.5 (0.5\u0026ndash;1.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSamples with biopsy confirmed rejection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT cell-mediated rejection (TCMR), n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibody-mediated rejection (ABMR), n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eValidation of pooled capture methods and evaluation of informative SNPs\u003c/h3\u003e\n\u003cp\u003eFirst, we validated the robustness and reliability of our pooled capture method by deliberately mixing cfDNA from an anonymous donor and recipient with known genotypes in sequential ratios. The measured dd-cfDNA levels showed very high concordance (adjusted r\u0026sup2; = 0.9922, p\u0026thinsp;=\u0026thinsp;2.35\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;16\u003c/sup\u003e) (Figure S1). Besides, the total amount of cfDNA yield (ng/mL) did not correlate with the quantified dd-cfDNA levels (Figure S2). This suggests the measurement was not influenced by the total amount of cfDNA, which is expected to fluctuate largely depending on physiological condition \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn informative bi-allelic SNP is defined by the absence of one allele from the donor genotype in the recipient genotype, resulting in a genotypic difference in either the homozygous or heterozygous state. Consequently, among pairs of donor-recipient, we identified 165 to nearly 400 informative SNPs out of a total of 1000 candidate SNPs. Parent-child and sibling pairs are expected to share common allele, hence, typically have fewer informative SNPs (Figure S3). To ensure accurate dd-cfDNA quantification, we applied a sequencing depth cutoff, allowing only sample with a minimum of 50 informative SNPs and each SNP locus must have at least 5 reads covered (Figure S4).\u003c/p\u003e\n\u003ch3\u003eGeneral dynamics of dd-cfDNA levels post-transplant\u003c/h3\u003e\n\u003cp\u003eThe dynamics of dd-cfDNA levels in all 37 KTx patients having follow-up records from the first day to 1-year post-Tx are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Significantly elevated dd-cfDNA levels were observed immediately after KTx, with initial levels varying widely among patients and reaching up to 30% on the first day. Consistent with most studies, we observed a rapid exponential decline in dd-cfDNA levels during the first week post-Tx, with the median value approaching the 1% baseline within 5 days. By day 14 post-Tx, dd-cfDNA levels stabilized below 1% in 15 out of 35 patients. At the one-year mark, most patients exhibited well-controlled status, with 14 out of 19 maintaining dd-cfDNA levels below 1%. We additionally evaluated dd-cfDNA levels in 21 long-term follow-up cases (3 to 5 years post-Tx). Generally, dd-cfDNA remained detectable but stayed below the 1% baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, right panel), indicating the ongoing turnover of donor cells even in long-term stabilized allograft kidneys. Interestingly, only two long-term samples showed elevated dd-cfDNA levels: 3.61% in patient #8 at 5 years post-Tx and 2.43% in patient #31 at 3 years post-Tx, likely due to the significantly reduced tacrolimus (FK506) administration.\u003c/p\u003e \u003cp\u003eExamining individual dd-cfDNA profiles from day 10 post-Tx, when fluctuations begin to stabilize, we found that patients without suspected AR episodes showed a \u0026lsquo;quiescent\u0026rsquo; dd-cfDNA dynamic, remaining consistently low throughout the first month and year post-Tx (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In contrast, in the group of patients experiencing at least one event of suspected rejection (n\u0026thinsp;=\u0026thinsp;12), the levels of dd-cfDNA were reported to elevate erratically during the entire period (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Intriguingly, these peaks aligned with AR episodes confirmed by same-day histological assessments, validating the well-established link between dd-cfDNA surges and rejection events \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eDonor-derived cell-free DNA as a sensitive indicator of rejection and other forms of kidney injuries\u003c/h3\u003e\n\u003cp\u003eExamining case-by-case, we observed that dd-cfDNA levels in 8 out of 9 samples from six patients with biopsy-confirmed AR episodes stayed above the 1% baseline (Figure S5). In longitudinal patients, a 'spiking' pattern in dd-cfDNA levels of varying magnitudes was observed at the time of biopsy-confirmed rejection, highlighting the robustness of dd-cfDNA in indicating AR episodes (Fig.\u0026nbsp;3A). Notably, in two reported episodes of ABMR, a sharp surge in dd-cfDNA level were observed, reaching over 7% as seen in patient #26 (Fig.\u0026nbsp;3A). Similarly, ABMR episode on patient #2 also represented a relatively high dd-cfDNA level, reaching up to approximately 4% (Figure S5A). Meanwhile, TCMR was also associated with an increase in dd-cfDNA levels, albeit to a lesser extent and potentially with a delayed peak, as exemplified by patient #36 (Figure S5A). The two cross-sectional cases with ABMR confirmed also exhibited extremely high levels of dd-cfDNA upon hospitalization (Figure S5B). For example, dd-cfDNA levels of patient #24 exceeded 20% on the first day (Fig.\u0026nbsp;3B). Besides, we reported elevated levels of other kidney function markers, which displayed only marginal variations. Focusing on samples with histological assessments, we found that dd-cfDNA levels were significantly higher in the group of samples diagnosed with AR episodes compared to the group without rejection (p\u0026thinsp;=\u0026thinsp;1.9\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, Wilcoxon rank-sum test) (Fig.\u0026nbsp;3C). Other serum markers indicating kidney function also exhibited significant differences between these groups (Figure S6).\u003c/p\u003e \u003cp\u003eInterestingly, we also observed particularly high levels of dd-cfDNA in KTx patients with kidney injury not related to rejection, as in patient #15, diagnosed with severe calcineurin inhibitors toxicity (Figure S5C), and in patient #38, diagnosed with cortical necrosis due to post-surgery complications (Fig.\u0026nbsp;3D). Strikingly, in the latter case, dd-cfDNA levels rose exceptionally high (over 7% at day 28 post-Tx), despite the lowered levels of other serum markers. This result emphasizes the indiscriminate sensitivity of dd-cfDNA to various forms of kidney injuries. Notably, fluctuations in plasma dd-cfDNA might be linked to diverse sources of organ damage or invasive manipulation. However, our preliminary analysis confirmed that procedure like needle biopsy did not affect dd-cfDNA levels (Figure S7).\u003c/p\u003e\n\u003ch3\u003eThe relationship between dd-cfDNA and conventional assessments of kidney integrity\u003c/h3\u003e\n\u003cp\u003eSince high levels of dd-cfDNA and other serum markers help in identifying groups with AR, we anticipated strong correlations between them. In fact, we observed varying degrees of correlation. All three serum markers derived from kidney function (i.e., creatinine, cystatin C, and blood urea nitrogen) exhibited strong pairwise correlations (Spearman\u0026rsquo;s rho\u0026thinsp;\u0026gt;\u0026thinsp;0.82). In contrast, the correlation coefficients between dd-cfDNA and any of these markers were relatively low, ranging from 0.23 to 0.39 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). However, we observed that elevated dd-cfDNA levels accurately correspond with moderate to extremely high values in other serum markers (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Hence, due to its distinct origin and wide fluctuation range, dd-cfDNA may offer a unique perspective on allograft integrity compared to other markers. Indeed, we found that pairwise correlations between dd-cfDNA and other serum markers remained significant in the \u0026lsquo;eventless\u0026rsquo; group (patients without suspected rejection episodes) (Figure S8A), but became non-significant in the group with suspected rejection (the 'event' group) (Figure S8B). This suggests that dd-cfDNA dynamics become increasingly erratic in the presence of kidney injuries.\u003c/p\u003e \u003cp\u003eRegarding Banff classification assessments on biopsy samples, we observed that elevated levels of dd-cfDNA generally associate with severe scores (i.e., higher grades) of diverse Banff Lesion Score categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Moreover, all reported AR episodes in our studies consistently showed very high dd-cfDNA levels, regardless of their wide-ranging Banff Lesion Scores (Figure S9A). We also found that certain indicators of chronic kidney injuries, such as interstitial fibrosis and tubular atrophy (IF/TA), and arteriosclerosis, do not consistently relate with dd-cfDNA levels. In other words, AR-positive high levels of dd-cfDNA may coexist with mild to moderate grades of chronic assessments, hinting potential limitation in relying solely on histological findings (Figure S9B). Detecting elevated dd-cfDNA levels in intermediate chronic stages may indicate subclinical injuries and provide an advantage for early intervention. Therefore, a comprehensive evaluation that combines various sources, including dd-cfDNA, may be desired for an accurate diagnosis.\u003c/p\u003e \u003cp\u003eWe hypothesized that various surgical factors during KTx procedure might affect dd-cfDNA stabilization in recipients. By categorized KTx patients into two groups based on the patterns of dd-cfDNA dynamics within the first week post-Tx, we found that the amount of donor bleeding significantly differed between these two groups (p\u0026thinsp;=\u0026thinsp;0.0063, Wilcoxon rank-sum test) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e). While it is possible that bleeding could contribute to an increase in initial dd-cfDNA levels, our finding suggests a potential link to a delayed stabilization status in the recipient.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eDonor-derived cell-free DNA sensitively indicates immunosuppression responses in KTx patients\u003c/h2\u003e \u003cp\u003eIn all 39 KTx patients in our study, nearly 25% required consecutive increases in their immunosuppressant dosage, i.e., tacrolimus (FK506), based on comprehensive assessments of allograft integrity. We aimed to systematically evaluate how these dosage changes reflect in dd-cfDNA levels as well as other conventional serum markers. Using the day 5 post-Tx (D5) as a baseline, we quantified the longitudinal changes of a clinical measurement by calculating the tangent of the slope formed by the measured values on each subsequent day relative to the baseline day and the temporal distance between measurements (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Henceforth, tangent values were individually calculated for dd-cfDNA level, three conventional serum markers, and FK506 trough levels (C\u003csub\u003e0\u003c/sub\u003e) (Supplemental Data 1). We examined the pairwise correlations and found that longitudinal changes in dd-cfDNA levels were marginally and negatively correlated with that of FK506 C\u003csub\u003e0\u003c/sub\u003e levels (Spearman\u0026rsquo;s rho\u0026thinsp;=\u0026thinsp;\u0026minus;0.25, p\u0026thinsp;=\u0026thinsp;0.008) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), indicating that higher immunosuppressant concentrations were significantly associated with lower dd-cfDNA levels, and vice versa. Conversely, longitudinal changes in creatinine, cystatin C, and urea nitrogen levels did not correlate with that of FK506 C\u003csub\u003e0\u003c/sub\u003e levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Interestingly, when decomposing those relationships by the temporal facets, we observed that significant negative correlations for dd-cfDNA longitudinal changes were only present on day 14 and day 28 post-Tx (Figure S10). Supposedly, a more stable condition in KTx recipients may improve the responsiveness of dd-cfDNA in immunosuppressive monitoring.\u003c/p\u003e \u003cp\u003eConsistently, in representative cases with consecutive increases of FK506 dosage (patients #8, #16 and #18), we witnessed an apparent inverse trend between dd-cfDNA levels and both FK506 dosage and FK506 C\u003csub\u003e0\u003c/sub\u003e levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). This pattern was not observed on other serum markers. Conceivably, the wide dynamic range of dd-cfDNA may enable the rapid assessment of patients' responses to any change in treatment. This result demonstrated the advantage of dd-cfDNA over other serum markers in monitoring immunosuppressant response in KTx patients.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, employing a population-specific SNP panel combined with a customized target sequencing protocol, we comprehensively examined longitudinal dd-cfDNA dynamics among 321 samples of 39 kidney transplant patients from a single-center cohort. We showed that elevated dd-cfDNA levels accurately indicated kidney injuries, particularly biopsy-confirmed AR, and matched conventional markers in distinguishing AR episodes. We also proposed that stabilization of dd-cfDNA levels in the early days post-Tx may be influenced by surgical factors, particularly the amount of donor bleeding. Additionally, dd-cfDNA levels aligned with histological grading and elevated before severe stages, suggesting their ability in informing subclinical injuries. Importantly, we demonstrated the value of dd-cfDNA in assessment of immunosuppressant responses in KTx patients, aiding Tx healthcare decisions. This study has several constraints, including the limited number of recruited KTx patients. Additionally, a small number of biopsy-confirmed AR cases restrains the estimation of the area under the curve (AUC) of an optimal dd-cfDNA threshold. In addition, our quantification of dd-cfDNA relies on knowing both donor and recipient\u0026rsquo;s genotypes, limiting its use case in cadaveric KTx. Alternative methods using only the recipient's genotype were described in our previous study and others \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies on dd-cfDNA for kidney transplantation have progressed significantly in both technological and analytical aspects. The quantification of dd-cfDNA have been achieved using high-throughput sequencing \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e and quantitative digital droplet PCR \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e; the latter is suitable for clinical use due to its superior sensitivity and rapid turnaround, despite having lower throughput. In our study, we employed a versatile protocol for quantifying dd-cfDNA using pooled capture-hybridization steps followed by high-throughput sequencing, as described previously \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Our method is a cost-effective, high-throughput and scalable approach which has been successfully applied in monitoring dd-cfDNA in liver transplant \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and various research purposes \u003csup\u003e\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Furthermore, by designing probes targeting population-specific SNPs, we can optimize SNP panel to accommodate diverse ethnicities, thereby improving quantification potential. On the analytical aspect, some established statistical models help to infer informative SNPs and their related allele frequencies without the need to know the donor's genotype \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, or even both the donor's and recipient's genotypes \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. However, these approaches may not be preferable when dd-cfDNA fractions reach extremely high level \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In our study, all cases utilized living donors with accessible genotypes, which greatly improving dd-cfDNA quantification accuracy. In future studies, we aim to employ absolute quantification of dd-cfDNA (copies per mL), as its combination with the dd-cfDNA fraction has been shown to enhance diagnostic performance \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Moreover, the promising results from the integration of dd-cfDNA and gene expression signatures also deserve more attention \u003csup\u003e\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile dd-cfDNA is known for lacking specificity in distinguishing AR from other kidney injuries \u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, its high sensitivity makes it valuable for assessing overall allograft integrity. A growing number of studies has documented the potential of dd-cfDNA as a diagnostic biomarker of AR as well as other types of graft injury in KTx, such as acute tubular necrosis, nephrotoxicity or infection \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Recently, a meta-analysis scrutinized 11 studies and obtained a summary receiver operating characteristics (SROC) curve with AUC of 0.83 (pooled sensitivity and specificity of 0.75 and 0.78, respectively) for the dd-cfDNA accuracy in rejection diagnosis, with favorable performance in ABMR diagnosis (SROC AUC of 0.85) \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Indeed, large multi-center cohorts enabled extensive cross-sectional analyses to estimate dd-cfDNA cut-off level for detection of AR in KTx recipients \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. A general consensus is that a dd-cfDNA level above 1% should be used to diagnose rejection in KTx patients, as it effectively distinguishes ABMR from non-rejection \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Yet in a number studies, the chosen dd-cfDNA cut-off level could vary widely, ranging from 0.74\u0026ndash;2.7% \u003csup\u003e19,22,23,45,46\u003c/sup\u003e. The robust capability of dd-cfDNA in detecting both acute and chronic ABMR, but not TCMR, was also reported \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Concordantly, we observed substantial surges in dd-cfDNA levels during ABMR episodes, far more noticeable when compared with those of TCMR.\u003c/p\u003e \u003cp\u003eThe key advantage of dd-cfDNA monitoring over other serum markers is the wide-ranged and responsive fluctuation, owing to its intrinsic origin of cellular damages. Therefore, the application of dd-cfDNA in monitoring immunosuppressant responses is crucial, not only for preventing AR and lessening subclinical abnormalities that can lead to chronic injury, but also for adjusting treatment appropriately to avoid toxicity. Recently, one study demonstrated that dd-cfDNA was successfully used to stratify rejection risk, leading to a reduction of mycophenolic mofetil in low-risk patients \u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Also, Benning \u003cem\u003eet al.\u003c/em\u003e reported the decreases of dd-cfDNA levels following anti-rejection treatments, though observed cases were rather sporadic \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Our study is the first to statistically demonstrate the response of KTx recipients to immunosuppressant dosing through longitudinal changes in dd-cfDNA levels, showing a significant negative correlation superior to conventional serum markers. We successfully provided solid evidence indicating that dd-cfDNA monitoring could be exploited for fine-tuning personalized immunosuppression and guiding treatment strategies, potentially decreasing the risk of AR and graft loss.\u003c/p\u003e \u003cp\u003eIn conclusion, our study reported the elevated level of dd-cfDNA occur during diverse forms of acute rejection and graft injury in KTx patients. Our findings collectively underscored the utility of dd-cfDNA as a molecular biomarker for monitoring the integrity of kidney allograft, as well as other solid organ transplants. The current study also served as a proof-of-concept for our established rapid and cost-effective protocol in dd-cfDNA assessment, which can be readily scaled up to accommodate a large number of patients and implemented as a valuable diagnostic marker in transplant healthcare.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDesigning of SNPs targeting probes\u003c/h2\u003e \u003cp\u003eWe selected 1000 SNPs across all autosomes of which MAF is between 0.4 and 0.5 based on the reported allele frequencies in Japanese population \u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e (Supplemental Data 2). Assuming Hardy\u0026ndash;Weinberg equilibrium, this MAF value is expected to be of 23 to 25% homozygous in both donor and recipient, and the theoretical probability of both donor and recipient having a different allele is 11.5 to 12.5% \u003csup\u003e31\u003c/sup\u003e. Probes for capture-hybridization of cfDNA targeting each of 1000 SNPs were purchased from Roche as SeqCap EZ Library (Nippon Genetics, Japan). In new batch of long-term samples, we used a refined version of probes (shortlisted 300 SNPs) based on 3.5KJPNv2 database \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, provided by SeqCap EZ Prime Choice (Roche, Switzerland).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell-free DNA extraction, probe capture protocol and sequencing procedure\u003c/h2\u003e \u003cp\u003eBlood samples were collected in 10ml PAXgene\u0026reg; Blood ccfDNA tube (QIAGEN, Germany) by peripheral venesection and plasma was separated according to manufacturer\u0026rsquo;s instruction. Plasma was stored at -80\u003csup\u003eo\u003c/sup\u003eC until cfDNA extraction. Cell-free DNA was isolated from 1 mL of the recipient\u0026rsquo;s plasma using the QIAamp MinElute ccfDNA Mini Kit (QIAGEN, Germany). The cfDNA was eluted with 33 \u0026micro;L of ultra-clean water and 24 \u0026micro;L of it was used for DNA repair with NEBNext FFPE DNA Repair Mix (New England Biolabs, USA). Libraries were prepared using the repaired cfDNA and NEBNext Ultra II DNA Library Prep Kit. After minimal amplification by PCR, target SNPs were enriched by capture-hybridization using KAPA HyperCap Target Enrichment Probes and KAPA HyperCapture Reagent Kit (Roche, Switzerland). Capture-hybridization was performed using our designed probe sets, following our single-reaction protocol \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The enriched library was amplified by 18 cycles of PCR and sequenced using the MiSeq or NovaSeq 6000 platform (Illumina, USA). Where kits were used, all manufacturer's instructions were followed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eGenotyping of target SNPs from donors and recipients\u003c/h2\u003e \u003cp\u003eRaw \u003cem\u003efastq\u003c/em\u003e reads obtained from sequencing facility were trimmed of sequencing adaptors and performed quality control using Trimmomatic-0.38 and the following parameters: leading:15 / trailing:15 / slidingwindow:4:20 / crop:220 / minlen:36. Trimmed sequences were mapped to reference human genome hg19 using bwa-mem followed by quality controls using picard/MarkDuplicates and samtools v1.18. Variant call was conducted using HaplotypeCaller tool of GATK v4.2.5.0 package\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e using options: \u0026ndash;output-mode EMIT_ALL_ACTIVE_SITES and \u0026ndash;emit-ref-confidence BP_RESOLUTION. Then filtering steps on resulted vcf files were performed using GATK/VariantFiltration tool with parameters: FS\u0026thinsp;\u0026lt;\u0026thinsp;60.00 / QD\u0026thinsp;\u0026gt;\u0026thinsp;2 / MQ\u0026thinsp;\u0026gt;\u0026thinsp;40 / DP \u0026ge; 30. Heterozygous sites in recipients were excluded before selection of informative SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eQuantification of donor-derived cell-free DNA\u003c/h2\u003e \u003cp\u003eAfter genotyping of 1000 SNPs, we selected informative SNPs personalized for each donor-recipient pair that satisfy either one in two conditions: (1) homozygous genotype is different between donor and recipient, or (2) genotype of recipient is homozygous and that of donor is heterozygous. The counting the reads mapped on each informative SNP was performed by GATK/DepthOfCoverage. For dd-cfDNA quantification, informative SNP locus must satisfy coverage depth \u0026ge; 5, minimum mapping quality \u0026ge; 30 and base quality \u0026ge; 26. Subsequently, summarized ratios of donor-derived read counts on the total mapped reads were calculated by in-house scripts, and resulted dd-cfDNA fraction was represented in percentage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analyses\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using \u003cem\u003eHmisc\u003c/em\u003e package in R-software. The Wilcoxon rank-sum test and Type III repeated measures ANOVA were used for comparisons between independent groups and for continuous dependent variables, respectively. Spearman\u0026rsquo;s correlation was used to evaluate relationship between residues. A p-value less than 0.05 is considered to be statistically significant. Visualization of the data was done using the \u003cem\u003eggplot2\u003c/em\u003e package in R.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eHistopathological findings\u003c/h2\u003e \u003cp\u003eRenal biopsy was performed with ultrasound-guided needle biopsy and histopathological examination was assessed and classified according to Banff classification by renal pathologist. Intra-graft C4d stain was performed to evaluate acute antibody\u0026ndash;mediated rejection (ABMR).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eEthical conduct of research\u003c/h2\u003e \u003cp\u003e This study was approved by the institutional ethical boards of National Hospital Organization Chiba-East Hospital (Approved ID: 41) and the National Institute of Genetics (Approved ID: 28\u0026thinsp;\u0026minus;\u0026thinsp;7). All subjects provided written informed consent for the collection of samples and subsequent analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCOMPETING INTERESTS\u003c/h2\u003e \u003cp\u003eIturo Inoue and Shigeki Mitsunaga are cofounders of iSan Bio Inc., a company whose focus lies outside the scope of this study and has no influence on its results or conclusions. The other authors declare no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAUTHOR CONTRIBUTIONS\u003c/h2\u003e \u003cp\u003eIturo Inoue, Hirofumi Nakaoka and Kenichi Saigo conceived and designed the study. Phuong Thanh Nguyen, Shigeki Mitsunaga, Hiromichi Aoyama and Hiroshi Kitamura collected samples, conducted experiments and jointly performed analyses. Phuong Thanh Nguyen and Shigeki Mitsunaga wrote the manuscript. Hirofumi Nakaoka, Kenichi Saigo and Ituro Inoue revised the manuscript. All authors have read and approved of all the contents of this manuscript.\u003c/p\u003e\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eWe would like to thank Yumiko Sato, Junko Kajiwara and Junko Kitayama for their technical assistance. This study was partly supported by JSPS KAKENHI Grant Number JP16K10445 to Kenichi Saigo and by AMED under Grant Number 24ek0510040h0002 to Ituro Inoue.\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e \u003cp\u003eAll calculated dd-cfDNA levels, clinical assessments, and the measurements of longitudinal changes are provided in \u003cb\u003eSupplemental Data 1\u003c/b\u003e. A list of 1000 SNPs employed in our target-sequencing protocol is disclosed in \u003cb\u003eSupplemental Data 2\u003c/b\u003e. The raw sequencing data generated in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTonelli M et al (2011) Systematic review: Kidney transplantation compared with dialysis in clinically relevant outcomes. Am J Transplant. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1600-6143.2011.03686.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1600-6143.2011.03686.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoratyńska M, Szepietowski T, Szewczyk Z (1996) Acute rejection and delayed graft function\u0026ndash;risk factors of graft loss. 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Human Genome Variation. 6:1 6, 1\u0026ndash;9 (2019)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePoplin R et al (2018) Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv 201178. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/201178\u003c/span\u003e\u003cspan address=\"10.1101/201178\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"National Institute of Genetics","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6592444/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6592444/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNon-invasive biomarkers enable effective assessment of allograft status without compromising patient\u0026rsquo;s quality of life. Donor-derived cell-free DNA (dd-cfDNA) has recently emerged as a promising biomarker in solid organ transplants. In this study, we developed a cost-effective, scalable targeted sequencing approach to assess dd-cfDNA performance in monitoring kidney transplant (KTx) integrity. We quantified dd-cfDNA with a customized probe set of 1,000 SNPs specifically designed for the East Asian population. Longitudinal analyses were conducted on 322 blood samples collected from a single-center cohort of 39 KTx patients over an observational period of up to five years. We showed that elevated dd-cfDNA levels are associated with various forms of acute rejection and graft injury in KTx patients. In particular, dd-cfDNA levels effectively distinguished between acute rejection and non-rejection in KTx patients (p\u0026thinsp;=\u0026thinsp;1.9\u0026times;10⁻⁴), aligning with serum markers and histological evidences. The surges of dd-cfDNA were evident in antibody-mediated rejection and diverse types of kidney injuries. Additionally, donor bleeding volume was found to influence the stabilization of dd-cfDNA levels during the early post-transplant period. We also reported a unique correlation between dd-cfDNA and Tacrolimus trough levels (Spearman\u0026rsquo;s rho = -0.25, p\u0026thinsp;=\u0026thinsp;0.008), which was not observed with other serum markers, underscoring its potential in guiding immunosuppressive therapy. Our study demonstrates the robust role of dd-cfDNA as a responsive biomarker for detecting acute rejection and monitoring kidney integrity. Furthermore, the proposed methodology offers proof-of-concept for scalable diagnostic applications in transplant healthcare.\u003c/p\u003e","manuscriptTitle":"Applications of donor-derived cell-free DNA in kidney transplantation healthcare: view from a prospective single-center study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-06 13:11:46","doi":"10.21203/rs.3.rs-6592444/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"21d7a0e8-948e-4eae-b95e-6aa2eaff5313","owner":[],"postedDate":"May 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48069135,"name":"Medical Genetics"},{"id":48069136,"name":"Urology \u0026 Nephrology"}],"tags":[],"updatedAt":"2025-05-06T13:11:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-06 13:11:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6592444","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6592444","identity":"rs-6592444","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}