Using next-generation sequencing technology, we analysed 17-locus typing data from 10,059 healthy Chinese individuals to establish common and well-documented categories for HLA-E, -F, -G, -H, MICA and MICB alleles. In total, we identified 8 HLA-E, 5 HLA-F, 8 HLA-G, 15 HLA-H, 39 MICA and 27 MICB alleles. Alleles with frequencies greater than 10% included E*01:03, E*01:01, F*01:01, G*01:01, G*01:04, H*01:01, H*02:07, MICA*010:01, MICA*002:01, MICA*008:04, MICA*008:01, MICB*005:02 and MICB*002:01. Among the globally common alleles E*01:03 and E*01:01, E*01:03 was more prevalent than E*01:01 in Chinese and East Asian populations, whereas the opposite pattern was observed in African, European, Admixed American and South Asian populations. Subsequently, we identified 24 types of 17-locus homozygous haplotypes. Although these homozygotes were present in only 0.53% of individuals, their cumulative haplotype frequency reached 12.57%. As the number of loci increased from 3 to 17, the homozygosity carrier rate declined from 1.46% to 0.53%, with a pronounced decrease observed after inclusion of the DPB1 locus. Notably, most of the homozygotes identified in this study had not been reported in the 18th International HLA & Immunogenetics Workshop. For example, 26 of 27 types of 11-locus homozygotes were previously unrecorded. The establishment of a database for HLA-E, -F, -G, -H, MICA and MICB alleles, as well as the distribution of homozygotes in the Chinese population, will provide valuable resources for genetic research, disease susceptibility, transplantation and cell therapy.

[Objective] To compare next generation sequencing (NGS) library construction technology between probe hybridization capture and amplicon methods, and analyze the influencing factors of HLA genotyping resolution level and its prospects in clinical applications. [Methods] A total of 207 clinical samples with known typing results and samples from the proficiency testing plan were selected. The conformity rate of HLA genotyping results, allele coverage and typing data analysis indicators were confirmed, and the effects of two library construction methods on the level of HLA genotyping discrimination were compared. [Results] The concordance rate of 207 samples with the feedback results of PT or prior well-characterized HLA genotypes was 100%. Among them, 91 samples were captured using hybridization probe capture method. Compared with the original amplicon method, the hybridization probe capture method can distinguish the alleles of DRB1 and DPB1 that cannot be determined in 13 samples. The allelic imbalance of DRB1, DPA1, and DQB1 loci in 6 samples was resolved. Three samples were found to have missed detection of alleles at the DQA1 and DQB1 loci. [Conclusion] The performance indicators of hybridization probe capture and amplicon performance confirmation meet the requirements of clinical detection of HLA genotyping, which provides an experimental method and basis for clinical application.

To analyse the effect of HLA‐DPA1 and HLA‐DPB1 allelic mismatches on the outcomes of unrelated donor haematopoietic stem cell transplantation (URD‐HSCT), we collected 258 recipients with haematological disease who underwent HLA‐10/10 matched URD‐HSCT. HLA‐A, ‐B, ‐C, ‐DRB1, ‐DQB1, ‐DRB3/4/5, ‐DQA1, ‐DPA1 and ‐DPB1 typing was performed for the donors and recipients using next‐generation sequencing (NGS) technology. After excluding 8 cases with DQA1 or DRB3/4/5 mismatches, we included 250 cases with HLA‐14/14 matching for further analysis. Our results showed that the proportion of matched DPA1 and DPB1 alleles was only 10.4% (26/250). The remaining 89.6% of donors and recipients demonstrated DPA1 or DPB1 mismatch. In the DPA1 matched and DPB1 mismatched group, accounting for 18.8% (47/250) of the cohort, DPB1*02:01/DPB1*03:01 allelic mismatches were associated with decreased 2‐year OS and increased NRM. DPB1*02:02/DPB1*05:01 and DPB1*02:01/DPB1*05:01 mismatches showed no impact on outcomes. Moreover, the specific allelic mismatches observed were consistent with the DPB1 T‐cell epitope (TCE) classification as permissive and non‐permissive. We innovatively established an analysis method for DPA1 ~ DPB1 linkage mismatch for cases with both DPA1 and DPB1 mismatched, accounting for 70% (175/250) of the total. DPA1*02:02 ~ DPB1*05:01/DPA1*02:01 ~ DPB1*17:01 linkage mismatches were associated with lower 2‐year OS, especially among AML/MDS recipients. DPA1*02:02 ~ DPB1*05:01/DPA1*01:03 ~ DPB1*02:01 linkage mismatches showed no impact on outcomes. In conclusion, applying the DPA1 ~ DPB1 linkage mismatch analysis approach can identify different types of mismatches affecting transplant outcomes and provide valuable insight for selecting optimal donors for AML/MDS and ALL recipients.

Objective To explore the risk factors for the production of anti-HLA antibodies in patients with hematologi-cal diseases before hematopoietic stemcell transplantation.Methods The results and clinical data of 1 008 patients with he-matological diseases in our hospital who underwent anti-HLA antibody testing were collected by using Luminex technology platform before transplantation from 2016 to 2018 for statistical analysis.Results The total positive rate of anti-HLA anti-bodies in 1 008 patients was 24.08％.Multivariate analysis showed that independent risk factors associated with the produc-tion of anti-HLA antibodies included age≥ 30 years old(P=0.046,OR1.467,95％CI1.007-2.136),time from disease diag-nosis to antibody testing≥41 days(P=0.000,OR1.830,95％ CI 1.306-2.565),initial platelet count＜20×109/L(P=0.020,OR1.543,95％ CI 1.072-2.220),prior pregnancy(P=0.000,OR5.187,95％CI3.689-7.293),transfusions before admission(P=0.001,OR1.762,95％ CI 1.257-2.470)and total platelet transfusion volumes after admission≥30 U(P=0.000,OR 2.352,95％ CI 1.638-3.376).Age ≥30 years old(P=0.023,OR=1.839,95％ CI 1.088-3.108)and prior pregnancy(P=0.042,OR=5.258,95％ CI 1.062-26.038)are associated with the production of anti-HLA class Ⅰ and class Ⅱ antibodies,respectively.The time from disease diagnosis to antibody testing≥41 days(P=0.000,OR=2.873,95％ CI 1.612-5.119),in-itial platelet count＜20×109/L(P=0.008,OR=2.164,95％ CI 1.225-3.822),prior pregnancy(P=0.002,OR=6.734,95％ CI 1.993-22.751),transfusions before admission(P=0.001,OR=2.746,95％ CI 1.531-4.925)and total platelet transfusion volumes after admission＞30 U(P=0.006,OR=3.459,95％ CI 1.416-8.451)are associated with the production of anti-HLA class Ⅰ and Ⅱ antibodies.Conclusion Older age,longer course of disease,lower PLT count,history of pregnancy and blood transfusion,and higher total amount of PLT transfusion are risk factors which affect the production of anti-HLA anti-bodies.Therefore,it is advisable to test for anti-HLA antibodies according to the situation before transplantation,which is of great value in guiding donor selection,monitoring antibody changes and improving transplant prognosis.

To examine the production time, type, and MFI of post-transplantation de novo HLA antibodies, and their effects on haplo-HSCT outcomes, we retrospectively included 116 patients who were negative for pre-existing HLA antibodies. In total, 322 serum samples from pre-transplantation to post-transplantation were dynamically tested by Luminex and single-antigen bead reagents. Patients were divided into: HLA antibody persistently negative group (group 1), the de novo HLA antibody transiently positive group (group 2), the de novo HLA antibody non-persistently positive group (group 3), and the de novo HLA antibody persistently positive group (group 4). Group 4 included DSA+non-DSA (NDSA) (group 4a) and NDSA (group 4b) groups. The detection rate of de novo HLA antibodies was 75.9% (88/116). The median MFI for de novo HLA antibodies was 2439 (1033-20162). The incidence of II–IV aGvHD was higher in group 2 than in group 1 (52.6% vs 17.9%, P < 0.01); in group 4a than in group 1 (87.5% vs 17.9%, P < 0.001); and in group 4a than in group 4b (87.5% vs 40.0%, P = 0.001). The DFS (37.5% vs 85.7%, P < 0.01) and OS (37.5% vs 85.7%, P < 0.01) of group 4a were lower than those of group 1. The DFS (48.0% vs 85.7%, P < 0.01) and OS (56.0% vs 85.7%, P = 0.03) of group 4b were lower than those of group 1. Multivariate analysis showed that de novo HLA antibody being transiently positive (HR: 5.30; 95% CI: 1.71–16.42, P = 0.01) and persistently positive (HR: 5.67; 95% CI: 2.00–16.08, P < 0.01) were both associated with a higher incidence of II–IV aGvHD. Persistently positive de novo HLA antibodies were a risk factor for reduced DFS (HR: 6.57; 95% CI: 2.08–20.70, P < 0.01) and OS (HR: 5.51; 95% CI: 1.73–17.53, P < 0.01). DSA and NDSA can be detected since 15 days after haplo-HSCT in patients without pre-existing HLA antibodies, and affect aGvHD, DFS, and OS. Haplo-HSCT patients must be monitored for HLA antibodies changes for appropriate preventive clinical management, and we recommend that 1-month post-transplantation is the best test time point.

The focus of this study was to analyze polymorphisms in the HLA gene at 11 loci in 4845 Chinese Han populations using next‐generation sequencing methods, and to compare common and well‐documented (CWD) allelic differences between China and other CWD lists. A total of 44 DPB1 alleles, 13 DPA1 alleles, 20 DQA1 alleles and 19 DRB3/4/5 alleles were detected in this study. About 20%–50% of the CWD alleles in China differ from the American Society for Histocompatibility and Immunogenetics and European Federation for Immunogenetics (EFI) data. The revised list of HLA‐CWD alleles in the Han population will provide additional data for the update of the IMGT/HLA database and contribute to a better understanding of hematopoietic stem cell transplantation and organ transplantation.

We collected HLA typing data from 653 families in the Eastern Han Chinese population. HLA‐A, B, C, DRB1, DRB3, DRB4, DRB5, DQA1, DQB1, DPA1, and DPB1 (HLA‐11 loci) typing of 1781 subjects was performed using a commercial next‐generation sequencing (NGS) method in our laboratory. The phasing of haplotypes in each family was determined by Mendelian segregation. Haplotype analysis revealed 1634 different haplotypes among a total of 2230 haplotypes. The predominant haplotype was A*30:01‐C*06:02‐B*13:02‐DRB1*07:01‐DRB4*01:03‐DQA1*02:01‐DQB1*02:02‐DPA1*02:01‐DPB1*17:01 (HF = 4.04%), followed by A*02:07‐C*01:02‐B*46:01‐DRB1*09:01‐DRB4*01:03‐DQA1*03:02‐DQB1*03:03‐DPA1*02:02‐DPB1*05:01 (HF = 1.84%) and A*33:03‐C*03:02‐B*58:01‐DRB1*03:01‐DRB3*02:02‐DQA1*05:01‐DQB1*02:01‐DPA1*01:03‐DPB1*04:01 (HF = 1.48%), accounting for 7.35% of the total. Meanwhile 76.41% of all haplotypes were observed only once or twice (HF < 0.1%). Different from HLA‐DRB3/4/5 and DQA1 loci, DP linkage markedly increased haplotype variation by 34.82% based on the 5‐locus haplotype. The much weaker linkage disequilibrium (LD) of DQB1‐DPB1 indicated the reason. We observed 10 analyzable recombination events, most of which occurred at DP loci. Even with the same common 5‐locus haplotype, HLA‐DP linkage alters the haplotype diversity and frequency. Analysis of related haplotype assignment and unrelated recipient‐donor pairs matching at the 9‐locus haplotype revealed that HLA‐DP affects the donor selection strategy. Haplotype study of a large sample size using NGS identified linkage haplotypes beyond the 5 loci. LD, recombination events, and haplotype variation caused by DP loci emphasized that HLA 9‐locus haplotype matching should be considered in donor selection, particularly the effect of DP loci. The finding lays the foundation for further studies on the effect of HLA‐DP mismatch on transplantation.

Objective:Establish the decision threshold value of mean fluorescence intensity of anti-human leukocyte antigen(HLA)antibody through statistical analyzing the results of international proficiency testing(PT)organized by American Society for Histocompatibility and Immunogenetics(ASHI).Methods:Single antigen reagent and liquid chip(Luminex)technique were used to detect anti-HLA antibody. A retrospective analysis of the HLA antibody PT results of 55 quality control samples from 11 times organized by ASHI from 2012 to 2019 was reviewed.Results:Among 79 kinds of HLA-I antibodies, 21, 43 and 15 types of HLA-A, B and Cw antibodies were detected respectively, while among 44 kinds of HLA-Ⅱ antibodies, 18, 7 and 19 types of HLA-DRB1, DQB1 and DPB1 antibodies were detected respectively. After analyzing the MFI detection value of different specific antibodies in each PT samples at our laboratory and the coincidence rate of the negative / positive results judged by ASHI through summarizing the results of multicenter participating in the same period, MFI values of HLA antibody were arranged from high to low into the intervals of possible saturation value, positive decision value, positive judgment threshold value, suspicious positive reference value and suspicious negative reference value , according to the coincidence rate of 95%, 90%, 80%, 79%~50% and <50%.Thus, the decision limit value table of HLA specific antibody at our laboratory was established. And 42 kinds of HLA antibody types were detected with complete data.When the MFI values of various HLA-I or HLA-Ⅱ antibodies are found to be 80% or more in the table, it can be used to judge the detection of HLA antibodies. When HLA antibody MFI value reaches the positive decision value, it may have a certain guiding significance for clinical diagnosis and treatment. And when antibody MFI value reaches the saturation value and lies in the suspicious positive or suspicious negative reference threshold, it just suggests that the clinical need for dynamic follow-up of anti-HLA antibody detection.Conclusions:The decision limit value of MFI of laboratory HLA antibody is established based on the international PT experimental results, which is of reference value for the interpretation of experimental results and clinical diagnosis and treatment. A transplant ation center should pay attention to the quality control of comparison test between laboratories in the detection of HLA antibodies.

Human leukocyte antigen (HLA) is the major histocompatibility complex for humans. Previous studies have shown that high-resolution HLA matching can reduce graft vs. host disease and improve the outcome of hematopoietic stem cell transplantation (HSCT). Unrelated donor HSCT is very important when patients have no HLA-identical sibling donors. However, the chance of finding an available unrelated donor is not equal for every patient. Previous studies in Western countries have found that the HLA haplotype frequency (HF) could help to predict the probability of identifying HLA allele-matched unrelated donors.[1] However, the HLA system shows ethnic diversity and regional disparity. Therefore, if we want to use the HLA haplotype tool in China, a reliable HLA haplotype database for the Chinese population needs to be established. A previous study in a Japanese population showed that unrelated individual data were similar to family data.[2] However, family studies are a reliable way to identify rare haplotypes.[3] This study was a family study of five-locus HLA A-C-B-DRB1-DQB1 high-resolution haplotypes, with a very large sample size in China to date. In addition, we verified the accordance between expected HFs calculated by the expectation-maximization (EM) algorithm from unrelated individuals and observed HFs by segregation analysis (direct counting) in families to find a suitable method for haplotype database setup. A total of 2152 families, all four haplotypes (a, b, c, and d) presenting and confirmed by descent, were included in this study. They were divided into three groups: (1) Families with parents (n=1531); (2) Families with one parent and one or more siblings (n=175); and (3) Families without parents, but the haplotypes (a, b, c, and d) were checked by two or more siblings (n=446). Among these families, 1907, 173, and 72 were from the East China, Central China, and South China areas, respectively. According to doctors’ typing applications and laboratory standard operation procedure, sequence-based typing plus sequence-specific oligonucleotide probe methods were performed for high-resolution HLA-A, B, C, DRB1, and DQB1 typing for all patients. Additional tests were performed to resolve ambiguities. Genomic DNA was extracted from peripheral blood. The study was reviewed by the ethics committee of our hospital (No. 2020-322). Informed consent was obtained from all the participants when they submitted a sample for HLA typing. First, observed HFs for the 2152 families were calculated by segregation analysis by using Arlequin software version 3.5.2.2 (http://www.cmpg.unibe.ch/software/arlequin35/Arl35Downloads.html). Only four haplotypes (a, b, c, and d) were counted for each family to avoid repetition. In Supplementary Table 1, https://links.lww.com/CM9/A520, a total of 3274 five-locus A-C-B-DRB1-DQB1 haplotypes were observed. Only 285 haplotypes were common, and most were less common or even rare. The thresholds of common HLA haplotypes were not agreed upon in different studies,[4] so we defined HF ≥0.1% as a common HLA haplotype, referring to the acknowledged threshold of the common HLA allele.[5] In Table 1, the top 20 segregation analysis results in our study were compared to unrelated individual results (EM algorithm) reported,[6] and no statistically significant differences were found (P values were all >0.5). Our data were very similar to the results of East China reported[6] because a majority of the families in our study were living in this area. The seven most common haplotypes showed the best accordance. Table 1 - Comparison of the top 20 haplotypes between our family study and an unrelated individual study reported in China. Rank and Haplotype frequency Our family study The unrelated study reported[6] HLA A-C-B-DRB1-DQB1 haplotype (4n = 8608) China East China A∗ C∗ B∗ DRB1∗ DQB1∗ Rank† HF (%) Rank HF (%) Rank HF (%) 30:01 06:02 13:02 07:01 02:02 1 4.98 1 3.70 1 4.50 02:07 01:02 46:01 09:01 03:03 2 3.18 2 2.46 3 2.56 33:03 03:02 58:01 03:01 02:01 3 2.83 3 2.40 2 2.89 33:03 03:02 58:01 13:02 06:09 4 1.45 5 1.06 4 1.43 11:01 08:01 15:02 12:02 03:01 5 1.30 4 1.13 5 1.01 02:07 01:02 46:01 08:03 06:01 6 0.94 6 0.93 7 0.92 33:03 14:03 44:03 13:02 06:04 6 0.94 7 0.74 6 0.95 11:01 04:01 15:01 04:06 03:02 8 0.86 12 0.56 8 0.60 02:01 03:04 13:01 12:02 03:01 9 0.60 10 0.58 9 0.58 02:01 03:03 15:11 09:01 03:03 9 0.60 22 0.36 18 0.40 11:01 03:04 13:01 15:01 06:01 11 0.58 9 0.64 11 0.56 01:01 06:02 57:01 07:01 03:03 12 0.53 14 0.45 16 0.46 24:02 01:02 54:01 04:05 04:01 13 0.51 16 0.44 14 0.47 11:01 07:02 40:01 08:03 06:01 14 0.48 17 0.42 13 0.49 24:02 14:02 51:01 09:01 03:03 14 0.48 26 0.31 19 0.35 11:01 01:02 46:01 09:01 03:03 16 0.46 11 0.58 10 0.56 11:01 07:02 40:01 09:01 03:03 17 0.45 21 0.36 15 0.46 33:03 07:06 44:03 07:01 02:02 18 0.43 13 0.47 17 0.45 01:01 06:02 37:01 10:01 05:01 19 0.39 8 0.66 12 0.56 11:01 14:02 51:01 09:01 03:03 19 0.39 25 0.31 20 0.34 24:02 03:04 13:01 12:02 03:01 19 0.39 >20‡ – >20‡ – †The same ranks were assigned to different haplotypes if their HFs were equal.‡>20 means that the ranks and frequencies of some haplotypes outside of the top 20 list of all the areas showed in study reported.[6]HF: Haplotype frequency; –: No data. Second, the same data of the first part were used for allele frequency (AF) estimation and pairwise linkage disequilibrium (LD) by using Arlequin. HFs were compared to AFs, and they were not always completely positively associated. Supplementary Table 2, https://links.lww.com/CM9/A520 shows the top 20 haplotypes and the comparison with alleles. For example, A∗30:01-C∗06:02-B∗13:02-DRB1∗07:01-DQB1∗02:02 was the most frequent haplotype, but the ranks of the A, C, B, DRB1, and DQB1 alleles were 6, 3, 3, 3, and 4, respectively. AFs were not as frequent as HF because of the strong positive association between each allele, and the D′ values of the LD test were 0.88, 0.87, 0.71, 0.71, 0.98, 0.83, 0.82, 0.70, 0.73, and 1.00 for A-C, A-B, A-DRB1, A-DQB1, B-C, B-DRB1, B-DQB1, C-DRB1, C-DQB1, and DR-DQB1, respectively. In another example, the ranks of A∗11:01-C∗01:02-B∗46:01-DRB1∗09:01-DQB1∗03:03 and A∗11:01-C∗07:02-B∗40:01-DRB1∗09:01-DQB1∗03:03 were 16 and 17, respectively. However, the ranks were 1 or 2 for each allele. This is because the positive association was not very strong, with even a negative association for some two-locus haplotypes. In Supplementary Table 3, https://links.lww.com/CM9/A520, 11 A-B, 5 A-C, 27 B-C, 23 DRB1-DQB1, 3 A-DRB1, 5 B-DRB1, 4 C-DRB1, 2 A-DQB1, and 4 C-DQB1 two-locus haplotypes show strong positive associations (HF ≥ 0.1%, D′ > 0.5, r2 > 0.1). A∗30:01-C∗06:02-B∗13:02, A∗02:07-C∗01:02-B∗46:01, A∗33:03-C∗03:02-B∗58:01, A∗29:01-C∗15:05-B∗07:05, and A∗69:01-C∗12:02-B∗52:01 three-locus haplotypes show very strong linkages, and they accounted for proportions of 6.02%, 5.63%, 5.30%, 0.49%, and 0.34%, respectively. Third, the patients’ typing results were used as phase-known and phase-unknown data to obtain observed and expected HFs, respectively, by the direct counting EM algorithm (only HFs >1 × 10–5 were outputted by Arlequin). In Supplementary Table 4, https://links.lww.com/CM9/A520, a total of 2050 observed and 1852 expected haplotypes were obtained, and 1228 haplotypes overlapped. The remaining 822 observed and 624 expected haplotypes did not overlap, and their HFs were all less than 0.1%. Among the 1228 overlapping haplotypes, less-common haplotypes were more common, but the numbers of common and less-common haplotypes were not equal between the observed and expected groups. Because 17 commonly observed haplotypes were less common in the expected group, 41 less commonly observed haplotypes were common in the expected group. Therefore, observed HF was the common and less-common threshold for the chi-square test. The Chi-square test for trend was carried out for the overlapping haplotypes by using GraphPad Prism 6 software. The P values of the Chi-square test showed that there were no statistically significant differences between observed and expected haplotypes, not only in total overlapping data (P = 0.2424) and common data (HF ≥ 0.1%, P = 0.3698) but also in less-common data (HF < 0.1%, P = 0.1582). Therefore, the tendencies of observed and expected haplotypes are coincident, and the tendency concordance of common data is the best; then, the total overlapping data last the less common data. However, the family segregation analysis found 822 haplotypes that were missed by EM, and more importantly, 624 haplotypes were incorrectly built using EM. These 822 haplotypes found from the family segregation analysis are real because these are observed haplotypes from the segregation. AFs and pairwise LDs are two important factors for these phenomenon, which can be proved by Supplementary Tables 2 and 5, https://links.lww.com/CM9/A520. For example, A∗02:07-C∗03:04-B∗40:01-DRB1∗10:01-DQB1∗05:01 was the incorrect haplotype built by EM. All the constituent alleles were common and should be observed easily. However, it had eight negative pairwise LDs, so it would be hard to present. Unrelated data are easier to obtain than family data, and good consistency of tendency between expected and observed haplotypes was the basis using unrelated related study (EM) for HLA haplotype database setup. However, EM could miss less-common real haplotypes but built incorrectly less-common haplotypes. Therefore, identifying less common haplotypes from segregation analysis must be used for checking and as supplements to cover the shortage of EM. The HLA haplotype tool will be very useful in both unrelated-HSCT and haplo-HSCT fields. In unrelated HSCT, it can be helpful to predict the possibility of finding an HLA allele-matching unrelated donor and the possible mismatching alleles. Patients with common haplotypes can find unrelated HLA matching donors more easily. The chance for patients will decline along with a decline in HFs. Some patients with common HLA alleles also have difficulty finding HLA allele-matching unrelated donors because of the less common haplotypes caused by strong negative LD. In addition, if patients or donors only have A, B, and DRB1 typing results, it can help to predict C and DQB1 results. The prediction can help clinicians choose several suitable donors at the HLA confirmatory typing stage. In haplo-HSCT, for some families with patients and only one sibling, the donor may be 5/10 allele match, but he/she may not be real 1-haplo-match; for some families with patients and one parent/child, the donor is 10/10 allele match, but he/she is still 1-haplo-match; for some families with patients and one sibling, the donor is 10/10 allele match, and he/she may not be a sib-match but is still 1-haplo-match. Before complete family data are obtained, these data can help clinicians to predict whether the 10/10 or 5/10 allele match sibling is truly 2-haplo-match or 1-haplo-match and take appropriate treatment. Usually, the more common the haplotype is, the more reliable it is. Funding This work was supported by grants from the National Natural Science Foundation of China (No. 82070180), the Jiangsu Province Medical Innovation Team (No. CXTDB2017009), and the Jiangsu Provincial Key Research and Development Program (No. BE2019656). Conflicts of interest None.

抗人类白细胞抗原(HLA)抗体检测作为医学实验室服务临床的重要检测项目,可帮助临床医生做出正确决策、协助临床治疗.但应用Luminex技术检测抗HLA抗体受检测试剂、检测方法的局限性以及检测对象的疾病状态和治疗的干扰,给临床判断造成困扰.实验室如何规范地检测抗HLA抗体,保证批内、批间结果准确,是每一位技术人员长期摸索和学习的过程.该文根据美国组织相容性与免疫遗传学协会(ASHI)关于抗体检测的要求,结合该院十余年对抗HLA抗体检测的认识,提出用Luminex技术检测抗HLA抗体的规范化操作和结果解读的建议,供国内实验室参考.

With the goal of improving the population-specific criteria to distinguish KIR genotypes and haplotypes in the region, we examined the KIR gene and haplotype data of Eastern Han based on a large population and analyzed the component genes of centromeric (Cen) and telomeric (Tel) KIR haplotype segments, which may differ in the protection they provide in hematopoietic stem cell transplantation. Samples from 598 families and 2845 unrelated individuals of Eastern Han origin were collected between 2010 and 2017. Genotyping of 17 KIR genes was performed by PCR-SSP and SSO methods. The results showed we obtained the KIR gene distribution of the Eastern Han population. The KIR gene frequencies (GF) in the present study are similar to those observed in other studies on Han but different from other populations. We observed a total of 56 different genotypes, including 1 AA and 55 group B genotypes. The high-frequency KIR genotype profiles found in the present population were consistent with other studies on Han populations but different from those conducted on other populations. In the family panel, a total of 28 KIR haplotypes were identified in the segregation study. The majority of Eastern Han carried group A KIR gene motifs. Comparison of the frequencies of Cen and Tel KIR gene motifs shows that they differ from other populations. The study on the distribution of KIR genes in the population may aid in the development of a complementary population-specific criterion to distinguish between KIR haplotypes and offer a research direction for further gene functional studies.

Objective:To investigate the relationships of C4d,HLA antibody,and MⅠCA antibody with renal pathology.Methods:Totally 52 patients with complete pathological data were included and followed up for one year.The C4d was stained with indirect immunofluorescence,the levels of HLA antibody and MⅠCA antibody in peripheral blood were detected by Luminex method.The relationships of C4d with antibodies in peripheral blood and pathology of renal graft were analyzed,and their influences on renal function were exposed.Results:The positive rate of HLA antibody was 88.9 ％ in cases with positive C4d and 40％ in the cases with negative C4d.The positive rate of HLA antibody was higher in patients with positive C4d than that in patients with negative C4d(P=0.003).The proportion of glomerulitis was higher in cases with positive C4d (P=0.023),and vasculitis and arterial intimal thickening of transplanted renal were more common in cases with positive HLA antibody than those with negative HLA antibody (P＜0.05),while no correlations between histological features and MⅠCA antibody were found.There was no difference in glomerular filtration rate (GFR) between cases with positive and negative C4d,but in patients of positive C4d with positive HLA antibody or MⅠCA antibody,the GFR decreased more remarkably compared with those of single positive C4d (P＜0.05).Conclusions:For patients of acute rejection,positive C4d combined with positive antibodies of HLA and MⅠCA in peripheral blood has better predictive value for graft survival compared with single positive C4d.

Objective To investigate the clinical features of the pre-formed and de novo human leucocyte antigen (HLA) antibodies post-kidney transplantation,and the clues to clinical strategy.Methods The Luminex assays were used to detect the HLA antibodies in the pre-and post-operative serum samples of the kidney transplant recipients.The variation trends of the pre-formed and de novo HLA antibodies post-transplantation were analyzed based on the HLA antibody assessment,gene typing and clinical data.The potential factors that may affect the outcome,persistent or not,of the de novo anti-donor specific HLA antibody (DSA) were also determined.Finally,the effect of the HLA mismatch on the risk of the development of the de novo DSA was investigated.Results Pre-formed HLA antibodies were detected in 9 recipients (8.0％,9/113),of which 88.9％ (8/9) developed HLA-Ⅰ antibodies,including one with DSA.All of the pre-formed antibodies were cleared in 6 (1-36) months post-transplant.The serum creatinine (SCr) of these recipients in 1,6,12,36,60 months were 125.0 (82-591),102.0 (84-105),98.0 (76-131),111.5 (75-137) and 89.0 (68-125) mmol/L,respectively.20 recipients (19.2％,20/104) developed de novo HLA antibodies,of which 6 were with transient HLA antibodies (Class Ⅰ,n=3;Class Ⅱ,n=3) and 14 were persistent (Class Ⅰ,n=2;Class Ⅱ,n=10;Class Ⅰ + Ⅱ,n =2).One of the six recipients with transient antibodies developed DSA,while 12/14 recipients with persistent antibodies had DSA (P =0.003).The cumulative rate of the recipients with HLA DSA in 1,2 and 3-6 years were around 1/3,respectively.Totally 25 DSA were detected in 13 de novo HLA DSA positive recipients.The majority of the DSA was HLA-Ⅱ (80.0％,20/25),and most of them were DQ DSA (80.0％,16/20).The initial and the peak titers were higher in the persistent DSA versus that in transient DSA (P =0.059,P =0.027),presenting an increasing manner (P =0.017).Comparing to HLA-Ⅰ,HLA-Ⅱ DSA were more likely to be persistent (P =0.016).However,there was no difference in the first-time-appearance between persistent and transient DSA (P =0.501).Comparing to HLA-A,B,C and DR loci,HLA-DQ mismatch showed a higher risk of developing DSA (P =0.019,0.000,0.000 and 0.015,respectively),which was observed in a dose-dependent manner (P =0.084).Conclusion With an intensive two-year post-transplant follow-up,the recipient candidates with low to moderate pre-formed HLA NDSA can be acceptable for the kidney transplantation.Class Ⅱ,high titer,especially with an increasing trend were risk factors for the persistence of the de novo HLA DSA.DQ matching is an important way to prevent the development of the de novo HLA DSA.

OBJECTIVE:To analyze allele mismatches of HLA- A, - B, - C, - DRB1, - DQB1 and haplotype mismatch of donor- recipient pairs on the outcome of haploidentical transplantation combined with a third part cord blood unit. METHODS:230 pairs of donor-recipient were performed HLA-A, B, C, DRB1, DQB1 typing using SBT and SSOP methods from January 2012 to December 2014. RESULTS:Pairs were divided into HLA- 5/10、6/10、7/10 and ≥8/10 groups according to HLA- A, B, C and DRB1 highresolution typing and matched degrees, the 3-year probability of overall survival (OS) for each group were 48.7%, 59.3%, 71.1%, 38.3% (P=0.068) respectively. HLA-6/10 matched group associated with significant favorable effect on OS compared with HLA- 5/10 matched one (P=0.041).When the HLA class I antigen matched on the recipient and donor, improved OS and event free survival (EFS) in HLA- 6/10 matched group than in HLA-5/10 matched one (P=0.017,P=0.088), especially in single HLA-A loci allele matched one (P=0.013,P=0.013), were observed. As to the third part cord blood unit, sharing the same haplotype with the recipient-donor pairs produced better platelet recovery than the misfit one (95.3%vs 86.2%,P= 0.007), similar result was found in terms of neutrophil recovery (98.8%vs 96.1% ,P=0.022). CONCLUSIONS:HLA locus mismatch and haplotype mismatch of the donor and recipient should be useful for selection of the most optimum donor. Co- infused of an unrelated cord blood unit sharing the same haplotype with the recipient-donor pairs could improve hematopoietic recovery.

Background. C1q-binding donor-specific antibody (DSA) is detrimental to transplanted kidney function. However, the factors that affect C1q binding status are unclear. Methods. A total of 519 samples from 129 consecutive kidney transplantation patients during 8 years of dynamic follow-up were collected for HLA antibody (Ab) screening and C1q detection. Results. Among the detected HLA Abs, the majority were class II, and the DQ subtypes composed the highest proportion. The C1q-binding Abs were all HLA-II, and the DQ subtypes had the highest rate of C1q positivity. With a cutoff mean fluorescence intensity (MFI) value of 7349, the sensitivity and specificity of detecting C1q-binding Abs from all HLA-II Abs were 84.48% and 83.56%, respectively. Additionally, C1q is more likely to be bound by DSA than non-donor-specific antibody (NDSA). Compared with free DSA/NDSA, the MFI values of C1q-binding DSA/NDSA are more closely correlated with serum creatinine levels and reflect the effect of anti-antibody-mediated rejection treatment more sensitively. Conclusions. HLA-II Abs (particularly DQ subtypes), high titers of Abs, and DSA are important relevant factors of C1q positivity. The MFI value of C1q-binding DSA may be a useful clinical indicator of HLA antibody-mediated graft injury before the appearance of histologically typical humoral rejection.

Donor killer immunoglobulin-like receptor (KIR) group B profiles (Bx) and homozygous of centromeric motif B (Cen-B/B) are the most preferable KIR gene content motifs for hematopoietic stem cell transplantation (HSCT). The risk of transplant from Bx1 donors and the benefit of the presence of Cen-B (regardless of number) were observed for standard-risk acute myeloid leukemia/myelodysplastic syndrome (AML/MDS) patients in this 4-year retrospective study. A total of 210 Chinese patients who underwent unrelated donor HSCT were investigated. Donor KIR profile Bx was associated with significantly improved overall survival (OS; P = .026) and relapse-free survival (RFS; P = .021) and reduced nonrelapse mortality (NRM; P = .017) in AML/MDS patients. A significantly lower survival rate was observed for transplants from Bx1 donors compared with Bx2, Bx3, and Bx4 donors for patients in first complete remission (n = 82; OS: P = .024; RFS: P = .021). Transplant from donors with Cen-B resulted in improved OS (HR = .256; 95% CI, .084 to .774; P = .016) and RFS (HR = .252; 95% CI, .084 to .758; P = .014) in AML/MDS patients at standard risk. However, this particular effect did not increase with a higher number of Cen-B motifs (cB/B versus cA/B; OS: P = .755; RFS: P = .768). No effect was observed on high-risk AML/MDS, acute lymphoblastic leukemia/non-Hodgkin lymphoma, and chronic myelogenous leukemia patients. Avoiding the selection of HSCT donors of KIR profile Bx1 is strongly advisable for standard-risk AML/MDS patients. The presence of the Cen-B motif rather than its number was more important in donor selection for the Chinese population.