Background: Donor-derived cell free DNA (ddcf DNA) has been reported as a universal noninvasive biomarker for rejection monitoring in heart, kidney, liver, and lung transplantation. Current approaches based on next-generation sequencing for quantification of ddcf DNA, although promising, may be restricted by the requirement for donor material, as donor samples may not be available. Methods: We proposed a novel next-generation sequencing approach without donor-derived material and compared the non-donor-derived approach and the donor-derived approach using simulation testing and 69 clinical specimens. We also evaluated the performance for acute rejection and infection monitoring in lung transplantation. Results: The non-donor-derived approach reached similar efficacy as the donor-derived approach with a significant linear correlation of R 2 = 0.98. Subsequent validation in clinical specimens demonstrated significant difference between the acute rejection group (4.83% ± 2.11%, mean ± SD) and the non-rejection group (1.61% ± 0.63%, mean ± SD) (P < 0.0001, Welch ’ s t test). With the cut-off value of 2.999, our approach had 90.48% sensitivity (95% CI, 69.62% - 98.83%), 100% specificity (95% CI, 91.59% - 100%), and AUC 0.9266 (95% CI, 0.8277 - 1.026). The test also had the ability to simultaneously detect infectious agents, especially cytomegalovirus, as compared with the clinical test. Conclusion: The proposed approach without donor-derived material could potentially be used to monitor acute rejection and infection in lung transplantation and may be applied to other types of solid organ transplantation.
As the respiratory centre, the lungs require strong abilities for environmental adaptation and immuno-protection against microbial infections. For patients with end-stage lung disease, lung transplantation may constitute the only effective approach and may largely increase life expectancy and substantially improve quality of life [
In 1998, Lo et al. found that there were cell-free donor-derived DNA (ddcfDNA) tags existing in the plasma samples of transplant recipients and that these tags might be used for monitoring graft rejection [
Here, we introduce an non-donor-derived cfDNA transplant dynamics (NDTD) approach that is implemented by genotyping with only genomic DNA from a pre-transplant recipient by targeted capture NGS in a mini-screen SNP array and calculating donor fraction with cell-free DNA from post-transplant recipient samples that contain cfDNA such as plasma and urine by extra-low depth WGS to monitor AR and infection. The scheme of the workflow used to monitor AR by the NDTD approach is shown in
We recruited 16 patients (see online Supplemental
For each time point, 5 ml peripheral blood was collected in an EDTA tube and stored at 4˚C immediately after collection. Plasmas were centrifuged within 4 h following a two-step centrifugation procedure: 1) centrifuge the peripheral blood in a Horizontal centrifuge at 1600 g for 10 minutes at 4˚C, then transfer the supernatant carefully to new 2 ml microcentrifuge tubes, 2) centrifuge the supernatant in a microcentrifuge at 16000 g for 10minutes at 4˚C, then collect the supernatant carefully to new 2ml microcentrifuge tubes and stored at −80˚C. Cell-free DNA was extracted from 0.5 to 1 ml of plasma by using a QIAamp Circulating Nucleic Acid Kit (Qiagen, Hilden, Germany); extracted DNA was then quantified using an Agilent 2100 Bioanalyzer (High Sensitivity DNA Kit). Genomic DNA for genotyping were purified by using a DNeasy blood and tissue kit (Qiagen, Hilden, Germany) and stored at −20˚C.
Purified plasma DNA was prepared into a library following the standard library preparation protocol. For genomic DNA used for genotyping, libraries were captured and enriched according to the manufacturer’s protocol. After library preparation, library size distribution and quantification were confirmed using the Agilent Bioanalyzer 2100 and sequencing was performed with a BGISEQ-100 (Thermo Fisher, Proton) or HiSeq 2000 (Illumina, San Diego, CA) instrument.
SNP saturation analysis in two lung transplant patients with three plasma samples was carried out by the GTD approach to decide how many heterologous SNP locations should be contained in a SNP array. First, genotyping by ALLinONE array (BGI, Shenzhen, China) with a target region size of approximately 180 megabases (Mb) including the whole exome (44 Mb), a population representative tagSNP region (132 Mb), and the major histocompatibility complex region (4.9 Mb) for a specific population was performed to select the complete set of heterologous SNPs. Then, we selected particular random subsets of those SNPs for 100 repetitions each by increasing the ratio from 0.01 to 0.05 with graduations of 0.01, from 0.05 to 0.3 by 0.05, and from 0.3 to 0.9 by 0.1, and re-calculated the average of the donor fraction (
High quality reads were firstly aligned to the human reference genome (UCSC hg19), using BWA or TAMPtools (for BGISEQ-100 sequencing data) with default parameters and then PCR duplications were removed by using SAMtools rmdup or BamDuplicates tools with default parameters. Next, genomic DNA sequencing reads from pre-transplantation recipient samples were genotyped by
SNP calling using SAMtoolsmpileup (-C 50 -E -g -u -I -m 2) or Torrent Variant CallerTM tools with target-seq germline low stringency’s parameters. SNP locations that were not genotyped by the above tools were genotyped autonomously after describing the base-pair information at each chromosomal position by SAMtools mpileup (total depth cut-off 6), based on variant allele frequency (25% - 95% as heterozygote, >95% as homozygote). For sequencing reads of plasma samples, only unique mapping reads were reserved; sequencing information at SNP positions that corresponded to features in genotyping was collected using SAM tools mpileup.
Without the requirement of genotyping the pre-transplant donor genomic DNA, the predicted probability of a population allele such as reference homozygous Pdb(AA) , allele homozygous Pdb(BB), and heterozygous Pdb(AB) genotype frequencies were calculated in the East Asian population from the 1000 Genomes Project database [
Condition | Reference allele | Recipient genotype | Plasma base | Predicted donor Genotype | Predicted probability of Donor |
---|---|---|---|---|---|
1 | A | AA | A | AA; AB; BB | Pdb(AA); Pdb(AB); Pdb(BB) |
2 | A | AA | B | AB; BB | Pdb(AB)/(P(AB) + MP(BB)); Pdb(BB)/(P(AB) + P(BB)) |
3 | A | BB | A | AB; AA | Pdb(BB)/(P(AB) + P(BB)); Pdb(AA)/(P(AB) + P(AA)) |
4 | A | BB | B | BB; AB; AA | Pdb(BB); Pdb(AB); Pdb(AA) |
High quality reads of the sequencing data were primarily aligned using BWA mem tools (-k 32 -M -t 10) to the human reference genome (UCSC hg19). The remaining reads (usually less than 5%) that were unable to map to the human genome were secondarily aligned to the human-related microbe genomics database encompassing viruses, bacteria, fungi, and protozoa, that were mainly collected from National Center for Biotechnology Information (NCBI) genome database autonomously using BWA mem tools (-k 32 -M -t 10). The normalization value of one pathogenic abundance, abu, was calculated according to the formula, [abu = total reads of one pathogenic agent/(millions of mapped reads of all pathogenic agents in the same kingdom × kilobases of pathogenic agent genomic sequence)]. Then, the species taxonomy and gene information identifier was annotated from the NCBI database. Finally, infection event for each recipient was determined with elevated levels of relative abundance, abu, in time-points dynamic monitoring instead of pathogen-specific thresholds to discriminate between colonization, infection, and disease.
Coefficients of determination (R squared) were performed using Excel (Microsoft). Kolmogorov-Smirnov test and Welch’s t test were performed in R 2.15.1. A P value of < 0.05 was considered statistically significant. ROC analyses were performed using GraphPad Prism 5.
To check the availability of the mini-screen target capture array, genomic DNA from two healthy volunteers (to simulate pre-transplant recipient and donor, respectively) was extracted and sequenced with mean depth = 1.7 gigabases (Gb), representing 110-fold coverage per sample (see online Supplemental
Next, the donor fraction was re-calculated from the simulation data abandoning donor genotyping information by the NDTD approach as pre-transplant
donor DNA information is likely particularly lacking in long-term and severely affected patients. This demonstrated a significant linear correlation between % Donor in the library and % Donor DNA (R2 = 0.98,
We performed quantification of ddcfDNA to monitor acute rejection and detection of the infectious agents simultaneously by the NDTD approach and compared the results with clinical examination in a cohort of 69 recipient plasma samples collected from 16 lung transplantation patients. For rejection surveillance, samples collected during the first 14 days post-transplant, which is a period absent of rejection and may exhibit elevated levels of ddcfDNA, were excluded. The ddcfDNA levels (n = 62) were significantly different between the AR group (4.83 ± 2.11 %, mean ± SD) and the non-rejection group (1.61 ± 0.63 %, mean ± SD) (P < 0.0001, Welch’s t test). Findings were validated by biopsies (n = 17) and clinical indications (n = 45) (
For detection of infectious agents, whole genome sequencing reads were used
to evaluate the virus, bacteria, and fungus infection concurrently after removing host reads of human sequence. We found positive infection status that was validated by the clinical tests, including cytomegalovirus (CMV) in 5 recipients (patient No. 03, 04, 06, 13 and 15); bacterial agents, such as Acinetobacterbaumannii, Pseudomonas aeruginosaand Klebsiella pneumoniae in 6 recipients (patient No. 03, 04, 05, 08, 13 and 16); and the fungus Aspergillus fumigatusin1 recipient (patient No. 05). No reads matched the CMV genomic sequence, with zero abundance in 4 CMV-negative recipients (i.e., concentration less than 1000 copies/ml) (patient No. 10, 12, 14 and 16). We also found additional agents including adenovirus in 6 recipient (patient No. 01, 03, 04, 11, 12 and 13) and CMV in 3 recipients (patient No. 01, 05 and 07) (see online Supplemental
The distinction between rejection and infection after solid organ transplantation has always presented a problem for clinical therapy because the clinical symptoms are sometimes similar. There is no reliable marker for AR monitoring, which is limited to detecting restricted pathogen species in the clinic; detection of rejection and infection only using the same data from blood samples thus presents an exciting prospect. Our results demonstrate that the NDTD approach without donor-derived material has the ability to monitor acute rejection by quantification of ddcfDNA and to detect the infectious agents simultaneously. The approach was composed of two processes: genotyping of recipient pre-transplantation and ddcfDNA detection of recipient post-transplantation, which can be carried out on different sequencing platforms with automated data analysis. The genotyping procedure implemented by targeted-capture NGS in a mini-screen SNP array requires less sequencing data and would reduce cost. The whole procedure including genotyping required 3 days, needing only 1.5 days if the genotyping step had been done in advance. Our approach showed high consistency with the previous GTD approach as shown in the validation step, which included both simulation tests and detection of events in lung transplantation. Additionally, the slight variation of the two approaches may imply an individual difference; thus, a more comprehensive and convincing public allele frequency database such as dbSNP may be required in the future. Subsequently, verification of the lung transplant cohort indicated that the differentiation of ddcfDNA between no rejection and rejection groups was obvious, especially in the case of acute rejection.
Notably, the subsequent sequencing data annotation was also indicative of pathogenic agents such as virus, bacteria, and fungus agents. Out of all the screened infectious agents, this approach delivered an advantage in virus testing, especially for CMV infection, which posed the most common threat for infectious complications after lung transplantation. To build up a more reasonable rejection-infection differentiated model, potential modifications include: 1) increasing the sequencing depth of plasma samples to capture more pathogen materials, although the current sequencing depth is sufficient for rejection monitoring; 2) building pathogen-specific thresholds to discriminate between colonization, infection, and disease; and 3) expanding sample types such as sputum, bronchoalveolar lavage fluid, nasopharyngeal swabs, and plasma samples.
In conclusion, these findings suggest that the NDTD approach has the ability of diagnosis and discrimination between rejection and infection post-transplant in lung transplantation and may be applied to other types of solid organ transplantation (such as heart, kidney, and liver) where ddcfDNA may also exist in the recipient’s plasma. It demonstrates a cost-effective and noninvasive sequencing approach without the requirement of donor-derived genotyping, which will better satisfy the needs of clinical situations and show a wider range of clinical application to accelerate the development of precautionary molecular diagnosis in solid organ transplantation.
This project is supported by Guangzhou Key Laboratory of Cancer Trans-Omics Research (GZ2012, NO348), Guangdong Science and Technology Project (2014B020212006, 201400000001-2), and Shenzhen Engineering Laboratory for Clinical Molecular Diagnostic. We would like to acknowledge Shengjian Yuan, Mingjie Huang, Xiyan Xiang, Wei Wei, and Rongqing Deng for their technical assistance in sequencing of DNA.
The authors declare no conflict of interest.
Wei, B., Zeng, L.H., Shao, D., Zheng, C.T., Yang, Q., Zhang, J.B., Xiao, D., Deng, Q.H., Lin, Y.P., Huang, D.X., Liu, L.P., Xu, X., Liang, W.H., Ju, C.R., Wang, J., Kristiansen, K., He, J.X. and Ye, M.Z. (2018) A Novel Next-Generation Sequencing Approach without Donor-Derived Material for Acute Rejection and Infection Monitoring in Solid Organ Transplantation. Journal of Cancer Therapy, 9, 623-638. https://doi.org/10.4236/jct.2018.99054
Characteristic | |
---|---|
Adult recipients, n | 16 |
Age at time of transplant | |
Mean ± SD | 56 ± 15 |
Male sex, no. (%) | 14 (88) |
Type of lung transplanted, no. (%) | |
Both | 12 (75) |
Left | 2 (12.5) |
Right | 2 (12.5) |
Indication for lung transplantation, no. (%) | |
Fibrosis | 7 (43.75) |
Chronic pulmonary obstruction | 4 (25) |
Inflammation | 6 (37.5) |
Silicon lung disease | 1 (6.25) |
Other | 1 (6.25) |
Maintenance immunosuppression, no. (%) | |
Cyclosporine | 1 (6.25) |
Tacrolimus | 16 (100) |
Hospitalization status, no. (%) | |
Inpatient | 11 (68.75) |
Outpatient | 5 (31.25) |
Transbronchial biopsy, no. (%) | |
Yes | 12 (75) |
No | 4 (25) |
Sample | Recipient | Donor |
---|---|---|
Initial bases on target | 7,705,014 | 7,705,014 |
Total effective reads | 12,789,800 | 11,387,756 |
Total effective yield (Mb) | 1705.48 | 1772.44 |
Average read length (bp) | 133.35 | 155.64 |
Effective sequences on target (Mb) | 912.85 | 844.49 |
Fraction of effective bases on target | 53.50% | 47.60% |
Average sequencing depth on target | 118.48 | 109.6 |
Base covered on target | 7,413,741 | 7,621,021 |
Coverage of target region | 96.20% | 98.90% |
Fraction of target covered with at least 20× | 85.00% | 94.00% |
Mapping rate | 98.85% | 99.02% |
Duplicate rate | 19.86% | 20% |
% Donor | 0 | 0.5 | 1 | 2 | 3.5 | 5.5 | 8 | 10 |
---|---|---|---|---|---|---|---|---|
Total reads | 11,602,242 | 9,742,563 | 9,598,491 | 10,676,827 | 10,060,264 | 11,595,993 | 11,862,646 | 16,041,446 |
Aligned | 11,512,905 | 9,663,648 | 9,524,583 | 10,598,886 | 9,992,860 | 11,518,300 | 11,780,794 | 13,655,526 |
Unique | 10,195,569 | 8,520,064 | 8,433,245 | 9,555,528 | 9,025,429 | 10,540,346 | 10,533,601 | 13,655,526 |
Total reads with SNPs | 9111 | 7299 | 7350 | 8071 | 7580 | 8600 | 8826 | 8703 |
Heterozygous SNPs | ||||||||
Total reads | 6951 | 5636 | 5703 | 6201 | 5800 | 6609 | 6819 | 6702 |
Recipient reads | 6914 | 5584 | 5632 | 6111 | 5697 | 6419 | 6567 | 6424 |
Donor reads | 26 | 44 | 64 | 83 | 99 | 180 | 238 | 252 |
Errors | 11 | 8 | 7 | 7 | 4 | 10 | 14 | 26 |
Homozygous SNPs | ||||||||
Total reads | 2160 | 1663 | 1647 | 1870 | 1780 | 1991 | 2007 | 2001 |
Recipient reads | 2143 | 1645 | 1629 | 1832 | 1720 | 1890 | 1873 | 1826 |
Donor reads | 13 | 13 | 17 | 34 | 58 | 95 | 128 | 160 |
Errors | 4 | 5 | 1 | 4 | 2 | 6 | 6 | 15 |
Cutoff | Sensitivity% | 95% CI | Specificity% | 95% CI | Likelihood ratio |
---|---|---|---|---|---|
>0.6213 | 100 | 83.89% to 100.0% | 2.381 | 0.06026% to 12.57% | 1.02 |
>0.6490 | 100 | 83.89% to 100.0% | 4.762 | 0.5820% to 16.16% | 1.05 |
>0.6714 | 100 | 83.89% to 100.0% | 7.143 | 1.498% to 19.48% | 1.08 |
>0.7136 | 100 | 83.89% to 100.0% | 9.524 | 2.656% to 22.62% | 1.11 |
>0.7757 | 95.24 | 76.18% to 99.88% | 9.524 | 2.656% to 22.62% | 1.05 |
>0.8251 | 95.24 | 76.18% to 99.88% | 11.9 | 3.981% to 25.63% | 1.08 |
>0.9344 | 95.24 | 76.18% to 99.88% | 16.67 | 6.974% to 31.36% | 1.14 |
>1.053 | 95.24 | 76.18% to 99.88% | 19.05 | 8.601% to 34.12% | 1.18 |
>1.079 | 95.24 | 76.18% to 99.88% | 21.43 | 10.30% to 36.81% | 1.21 |
>1.086 | 95.24 | 76.18% to 99.88% | 23.81 | 12.05% to 39.45% | 1.25 |
>1.096 | 95.24 | 76.18% to 99.88% | 26.19 | 13.86% to 42.04% | 1.29 |
>1.149 | 95.24 | 76.18% to 99.88% | 28.57 | 15.72% to 44.58% | 1.33 |
>1.210 | 95.24 | 76.18% to 99.88% | 30.95 | 17.62% to 47.09% | 1.38 |
>1.250 | 95.24 | 76.18% to 99.88% | 33.33 | 19.57% to 49.55% | 1.43 |
>1.288 | 90.48 | 69.62% to 98.83% | 33.33 | 19.57% to 49.55% | 1.36 |
>1.333 | 90.48 | 69.62% to 98.83% | 35.71 | 21.55% to 51.97% | 1.41 |
>1.411 | 90.48 | 69.62% to 98.83% | 40.48 | 25.63% to 56.72% | 1.52 |
>1.457 | 90.48 | 69.62% to 98.83% | 42.86 | 27.72% to 59.04% | 1.58 |
>1.491 | 90.48 | 69.62% to 98.83% | 45.24 | 29.85% to 61.33% | 1.65 |
>1.531 | 90.48 | 69.62% to 98.83% | 47.62 | 32.00% to 63.58% | 1.73 |
>1.630 | 90.48 | 69.62% to 98.83% | 50 | 34.19% to 65.81% | 1.81 |
---|---|---|---|---|---|
>1.732 | 90.48 | 69.62% to 98.83% | 52.38 | 36.42% to 68.00% | 1.9 |
>1.744 | 90.48 | 69.62% to 98.83% | 54.76 | 38.67% to 70.15% | 2 |
>1.773 | 90.48 | 69.62% to 98.83% | 57.14 | 40.96% to 72.28% | 2.11 |
>1.820 | 90.48 | 69.62% to 98.83% | 61.9 | 45.64% to 76.43% | 2.38 |
>1.871 | 90.48 | 69.62% to 98.83% | 64.29 | 48.03% to 78.45% | 2.53 |
>1.918 | 90.48 | 69.62% to 98.83% | 66.67 | 50.45% to 80.43% | 2.71 |
>2.021 | 90.48 | 69.62% to 98.83% | 71.43 | 55.42% to 84.28% | 3.17 |
>2.145 | 90.48 | 69.62% to 98.83% | 73.81 | 57.96% to 86.14% | 3.45 |
>2.193 | 90.48 | 69.62% to 98.83% | 76.19 | 60.55% to 87.95% | 3.8 |
>2.256 | 90.48 | 69.62% to 98.83% | 80.95 | 65.88% to 91.40% | 4.75 |
>2.324 | 90.48 | 69.62% to 98.83% | 83.33 | 68.64% to 93.03% | 5.43 |
>2.345 | 90.48 | 69.62% to 98.83% | 85.71 | 71.46% to 94.57% | 6.33 |
>2.355 | 90.48 | 69.62% to 98.83% | 88.1 | 74.37% to 96.02% | 7.6 |
>2.382 | 90.48 | 69.62% to 98.83% | 90.48 | 77.38% to 97.34% | 9.5 |
>2.429 | 90.48 | 69.62% to 98.83% | 92.86 | 80.52% to 98.50% | 12.67 |
>2.576 | 90.48 | 69.62% to 98.83% | 95.24 | 83.84% to 99.42% | 19 |
>2.746 | 90.48 | 69.62% to 98.83% | 97.62 | 87.43% to 99.94% | 38 |
>2.999 | 90.48 | 69.62% to 98.83% | 100 | 91.59% to 100.0% | |
>3.207 | 85.71 | 63.66% to 96.95% | 100 | 91.59% to 100.0% | |
>3.293 | 80.95 | 58.09% to 94.55% | 100 | 91.59% to 100.0% | |
>3.399 | 76.19 | 52.83% to 91.78% | 100 | 91.59% to 100.0% | |
>3.431 | 71.43 | 47.82% to 88.72% | 100 | 91.59% to 100.0% | |
>3.697 | 66.67 | 43.03% to 85.41% | 100 | 91.59% to 100.0% | |
>3.999 | 61.9 | 38.44% to 81.89% | 100 | 91.59% to 100.0% | |
>4.048 | 57.14 | 34.02% to 78.18% | 100 | 91.59% to 100.0% | |
>4.546 | 52.38 | 29.78% to 74.29% | 100 | 91.59% to 100.0% | |
>5.041 | 47.62 | 25.71% to 70.22% | 100 | 91.59% to 100.0% | |
>5.049 | 42.86 | 21.82% to 65.98% | 100 | 91.59% to 100.0% | |
>5.115 | 38.1 | 18.11% to 61.56% | 100 | 91.59% to 100.0% | |
>5.377 | 33.33 | 14.59% to 56.97% | 100 | 91.59% to 100.0% | |
>6.126 | 28.57 | 11.28% to 52.18% | 100 | 91.59% to 100.0% | |
>6.754 | 23.81 | 8.218% to 47.17% | 100 | 91.59% to 100.0% | |
>6.901 | 19.05 | 5.446% to 41.91% | 100 | 91.59% to 100.0% | |
>7.105 | 14.29 | 3.049% to 36.34% | 100 | 91.59% to 100.0% | |
>7.744 | 9.524 | 1.175% to 30.38% | 100 | 91.59% to 100.0% | |
>8.521 | 4.762 | 0.1205% to 23.82% | 100 | 91.59% to 100.0% |