Abstract
Varying HLA allele-specific expression levels are associated with human diseases, such as graft versus host disease (GvHD) in hematopoietic stem cell transplantation (HSCT), cytotoxic T cell response and viral load in HIV infection, and the risk of Crohn’s disease. Only recently, RNA-based next generation sequencing (NGS) methodologies with accompanying bioinformatics tools have emerged to quantify HLA allele-specific expression replacing the quantitative PCR (qPCR) -based methods. These novel NGS approaches enable the systematic analysis of the HLA allele-specific expression changes between individuals and between normal and disease phenotypes. Additionally, analyzing HLA allele-specific expression and allele-specific expression loss provide important information for predicting efficacies of novel immune cell therapies. Here, we review available RNA sequencing-based approaches and computational tools for NGS to quantify HLA allele-specific expression. Moreover, we explore recent studies reporting disease associations with differential HLA expression. Finally, we discuss the role of allele-specific expression in HSCT and how considering the expression quantification in recipient-donor matching could improve the outcome of HSCT.
Introduction
Due to their biological role of presenting peptide antigens to T cells, the highly polymorphic HLA class I and class II molecules are crucial for T cell activation and therefore for effective immune response against various pathogens, autoantigens, alloantigens, and cancer (1, 2). HLA class I molecules, which are constitutively expressed on the surface of nearly all nucleated cells present intracellular peptides to CD8+ T cells and are encoded by genes HLA-A, -B, and -C (3). In contrast, HLA class II molecules, which are expressed on professional antigen presenting cells (APCs) such as B cells, macrophages, and dendritic cells (DCs) display extracellular peptides to CD4+ T cells and are encoded by HLA-DRA, -DRB1-9, -DQA1, -DQB1, -DPA1, and -DPB1 (4). In addition to the classical HLA, the low-polymorphic non-classical HLA molecules such as HLA-E, -F, and -G and HLA-DM and -DO have important roles in immunosuppression (5, 6) and peptide loading (7). Several factors affect HLA expression (Table 1). For example, differential expression levels have been demonstrated between different HLA genes, alleles, tissues, and cell types (8, 9, 12, 13, 15, 30). Part of this variation emerges from structural differences in the promoter motifs involved in transcriptional expression regulation between HLA genes and alleles (31, 32). Besides, multiple transcriptional and translational factors as well as proinflammatory cytokines can affect the mRNA- and surface-level expression of HLA (18, 31, 33, 34). Varying expression may also result from individual-specific factors such as genetic polymorphism, age, environment, and medication (25–28). Since many of these factors affecting HLA expression have been previously described in detail e.g. by Carey et al. (18) and Petersdorf et al. (19), in this mini review, we will focus more on the RNA-based methods in HLA allele-specific expression quantification and known disease associations with HLA expression.
Table 1
| Factor | Effect on expression | References |
|---|---|---|
| Gene | Expression levels can vary between different HLA genes e.g. HLA class I genes are expressed at higher levels than class II genes. | (8–11) |
| Allele | Expression can vary between HLA alleles due to genetic polymorphisms. | (8, 9, 11–14) |
| Tissue and cell | HLA expression and turnover rates of HLA molecules can be tissue- and cell-specific. | (15–17) |
| Promoter polymorphisms | Proximal and distal promoter polymorphisms have been associated with differential HLA expression. | (12, 18, 19) |
| Alternative splicing | Alternative splicing may lead to misfolded HLA proteins and aberrant expression. | (18, 20) |
| Epigenetic regulation | DNA methylation can alter HLA expression e.g. the methylation level in high-expression HLA-A allotypes is higher than in low-expression HLA-A allotypes. | (18) |
| Turnover and stability of mRNA and protein | Post-transcriptional and -translational factors can affect the degradation and internalization rates of HLA molecules. | (18) |
| HLA LOH | In cancer, the decrease of HLA expression can be dependent on the different forms of HLA LOH (total or partial) resulting from genetic mutations and chromosomal aberrations. | (21, 22) |
| Proinflammatory cytokines | Depending on the gene, HLA expression can either be upregulated or downregulated by proinflammatory cytokines. | (23, 24) |
| Age | Ageing can alter class I and class II expression. | (25, 26) |
| Medication and environment | Medication and environmental factors such as diet can alter HLA expression. | (27, 28) |
| Time point | HLA allele-specific expression can vary between different time points in activated memory T cells. | (29) |
| Cell composition of study sample | HLA expression can vary between different cell types and thus when HLA expression is quantified from bulk RNA-seq, cell composition should be taken into account. | (15) |
Factors associated with differential HLA expression levels.
LOH, loss of heterozygosity.
Over the past decade, studies have associated differential HLA expression levels with infectious and autoimmune diseases, neurological disorders, cancer, and drug hypersensitivity (35–39). Additionally, HLA expression variation has been shown to impact the outcomes of flow cytometric crossmatches and HSCT (10, 40, 41). The earlier studies have applied methods such as flow cytometry, qPCR, and microarray in the quantification of HLA expression at the protein- and mRNA-level (12, 42–45). Although these methods have offered valuable information of HLA expression and its associations with human diseases, they are laborious, time-consuming, and do not allow high-resolution and high-throughput expression quantification. Thus, RNA sequencing (RNA-seq) enabling accurate and massively parallel analysis, provides a powerful tool for studying inter-allelic and inter-individual expression differences in healthy tissues and diseases (46). Since determination of HLA gene- and allele-level expression is important in several applications such as biomarker discovery, transplantation medicine, development of cancer vaccines targeting neo-epitopes, and disease susceptibility (41, 47–49), RNA-seq methods together with novel computational software capable of measuring HLA expression at the allele-level (8, 9, 11, 13, 14, 50) hold great promise to meet this need.
Methods in HLA expression quantification
Flow cytometry, qPCR, and microarrays
Different methods exist for HLA expression quantification. At the protein-level, HLA expression at the cell surface can be quantified with fluorolabeled monoclonal antibodies and flow cytometry. Earlier studies have used flow cytometry to compare cell surface expression between different HLA genes as well as to explore the gene-specific changes in constitutive and induced HLA expression (45, 51). Together with a specific antibody flow cytometry has revealed differential HLA-C surface expression between HLA-C allotypes in healthy individuals (42, 44). Moreover, flow cytometry has been used to demonstrate how sequence variation in the coding and non-coding area and the turnover of heavy chain mRNA can modulate HLA-C protein expression (44, 52–54). Before the advent of NGS technologies, the level of HLA transcription was quantified with qPCR and microarrays (12, 43, 54–59). Studies using qPCR have demonstrated the impact of distinct DNA methylation patterns and genomic variants in differential expression between divergent HLA allelic lineages (43, 55). Moreover, at the allele-level, a study with qPCR associated high allele-specific expression variation in HLA-C with HLA-extended haplotypes (12). These more conventional methods, however, have certain limitations. They require careful design and selection of antibodies, PCR primers, and microarray probes to equally capture the high allelic variation of HLA (37). Potential biases introduced at this step may lead to specificity and sensitivity issues and ambiguous results in the downstream analyses.
RNA-based NGS methods for HLA allele-specific expression quantification
Over the past decade, NGS has rapidly replaced the more conventional HLA typing methods providing more accurate high-resolution typing (60, 61). In addition, RNA-based NGS technologies have enabled simultaneous expression analysis and identification of expression quantitative trait loci (eQTLs) (11). Due to this revolution, several RNA-seq methods have emerged allowing accurate HLA allele-specific expression quantification. After a common step of reverse-transcribing RNA into complementary DNA (cDNA), these methods rely on different approaches in the sequencing library preparation such as using whole transcriptome data (9, 10) or enriching HLA genes either with PCR amplification with universal gene-specific primers (9, 13, 62) or capturing them using biotinylated oligonucleotide probes covering the target sequences (8). The first published method was based on cDNA amplicon pyrosequencing using a common universal primer to enrich all class I genes (62). Although, it was initially developed for HLA typing, it was later applied also in a study revealing differential allele-specific expression in human and macaque leukocyte subsets (63). A method capturing and enriching the targeted HLA sequences in a hybridization step prior to sequencing has also been successful in quantifying HLA allele-specific expression (8). The method enabled both HLA genotyping and quantification of HLA allele-specific expression of 12 classical genes from healthy PBMC and umbilical cord bloods samples. By using an in-house method for the Illumina RNA-seq reads, the authors reported differential allele-specific expression in several HLA genes. In our method, we incorporated unique molecular identifiers (UMIs) by using template-switching oligo (TSO) during first-strand synthesis in the library preparation to accurately quantify HLA gene- and allele-specific mRNA expression from PBMC samples of healthy individuals (9). Since UMIs enable the removal of PCR bias in the data analysis step, the expression quantification was based on solely counting the original mRNA transcripts in the sample (64). By quantifying unique UMIs per allele with HLAXPress pipeline, we identified differential expression levels between distinct HLA genes, alleles, and haplotypes. Although, many of the RNA-seq methods in HLA research have used Illumina’s short-read technology, there are also a few studies, which have applied Oxford Nanopore Technology’s (ONT) long-reads in expression quantification (10, 13). A recent study quantified HLA allele-level expression using UMIs from ONT HLA-gene specific PCR amplicons (13). In addition to the high-resolution HLA typing of classical class I and class II genes, the assay provided allele-specific HLA expression quantification using Athlon2 pipeline (65) fast enough to be considered in graft allocation for transplantation from a deceased donor. Moreover, ONT’s whole transcriptome data without any HLA gene enrichment step was demonstrated to be sufficient for accurate HLA typing and measuring of the gene-level expression of HLA class I genes (10). There is also evidence that T cell activation can alter the balance of allele-specific expression (29). Interestingly, by using Illumina’s whole transcriptome data together with an HLA-personalized reference for individuals, the authors showed that the expression balance between two alleles in a heterozygous individual changed over time.
Computational tools for HLA expression quantification from existing RNA-seq datasets
In addition to laboratory protocols, several computational tools have been developed for RNA-seq data, allowing HLA expression quantification from existing public datasets (11, 14, 50, 66, 67). The obvious advantage here is that large, well-documented study materials may be utilized for detailed HLA expression studies.
Seq2HLA, a python- and R-based in silico -method, was the first tool, which was introduced as capable of quantifying HLA expression from RNA-seq data (50). The tool accepts standard RNA-seq reads in fastq format as an input, uses bowtie (68) in aligning reads against exon 2 and 3 sequences of HLA alleles, and outputs HLA types and class-level expression estimates. When applied to a paired-end data with a read length of 37 bp of previously HLA genotyped individuals, seq2HLA achieved 100% specificity and 93.5% sensitivity in 1-field genotyping results. Since seq2HLA was first introduced, two other studies have applied it to quantify HLA expression in human cancer cell lines and non-cancer human tissues and cell types demonstrating HLA expression variation between cancer types and distinct anatomical sites (15, 16).
AltHapAlignR provides estimates of transcript abundance by using alternate reference haplotypes (14). AltHapAlignR first aligns reads against the standard genome reference using read mappers such as Tophat2 (69), HISAT2 (70), or STAR (71) and then extracts reads mapping to HLA region and unmapped reads, which it further re-aligns to the HLA reference haplotypes to obtain the expression estimates for HLA genes and haplotypes. The authors showed that when compared to the standard single reference mapping, AltHapAlignR improved the accuracy of HLA expression quantification. Recently, AlthHapAlignR was applied in a study demonstrating allele-specific expression in HLA-DRB1 in patients with rheumatoid arthritis and in healthy controls (72).
HLApers enables accurate quantification of HLA gene- and allele-specific expression from whole-transcriptome RNA-seq data (11). It provides both HLA genotyping and allele-specific expression quantification and has two pipelines options implemented; one for the STAR mapper combined with Salmon (73) for expression quantification and one for kallisto pseudoaligner (74) providing both the HLA genotyping and expression quantification. To reduce the possibility of multi-mapping reads in the expression quantification step, HLApers uses a personalized index, which comprises only the HLA allele sequences carried by the individual. The personalized pipeline of HLApers demonstrated several advantages such as higher accuracy in read alignment, HLA allele-specific expression quantification, and identification of causal eQTLs. A recent study applied HLApers to measure allele-level expression of HLA class I genes in unstimulated and stimulated PBMCs (30).
ArcasHLA, a python-based pipeline was initially developed for HLA genotyping from RNA-seq reads using kallisto, however, the authors later added the feature for allele-specific expression quantification currently allowing both highly accurate HLA typing and expression analysis (66). By using this novel pipeline, the authors analyzed the clinical significance of HLA class I LOH in multiple tumor types using public RNA-seq data (75).
scHLAcount enables HLA allele-specific expression quantification from single-cell RNA-seq (scRNA-seq) data (67). The pipeline builds a personalized reference based on prior HLA genotyping results and quantifies HLA expression at the allele-level using UMIs. In contrast to the tools for bulk RNA-seq data, scHLAcount provides an option to study HLA allele-specific expression and HLA loss of heterozygosity (LOH) at the single-cell resolution.
Although, these computational tools are advantageous by enabling mining of existing RNA-seq datasets, they face similar challenges as NGS-based HLA typing software with short RNA-seq reads multi-mapping to several alleles or even genes due to the high level of polymorphism and sequence similarity between HLA genes potentially resulting in biased expression estimates. ONT’s long-reads spanning over several exons might reduce ambiguous read alignment in expression quantification. However, currently, there is only one public computational tool, HLAXPress (9), in addition to Athlon2, for ONT long-read RNA-seq data, allowing HLA allele-specific expression quantification. The low throughput of ONT RNA-seq and low number of publicly available ONT RNA-seq datasets have potentially hindered the development of such tools (46). Additionally, although ONT’s whole transcriptome data has been sufficient for HLA gene-level expression analysis (10), accurate allele-level expression quantification may require an additional enrichment step to gain enough reads mapping to HLA. Moreover, in contrast to short-read technologies, ONT data has a higher error rate, which can hamper reliable UMI counting and accurate HLA genotyping. Lower number of reads and higher error rate are challenges that need to be considered in the development of novel computational tools for ONT RNA-seq. Table 2 presents some examples of the criteria and methods in allele-specific expression quantification for different methodological approaches.
Table 2
| Approach | Requirements | Method in expression quantification | References |
|---|---|---|---|
| Illumina bulk RNA-seq and HLApers | Whole-transcriptome RNA-seq data (paired end reads in fastq format) | HLA genotyping is first done by aligning RNA-seq reads against all known HLA allele sequences. HLA expression is estimated by aligning RNA-seq reads against a personalized index containing references for individual-specific HLA alleles based on the genotyping results. HLA allele-specific expression is reported as the number of reads aligning to each allele. | (11) |
| Illumina bulk RNA-seq with UMIs and HLAXpress | 5’end RNA-seq data with UMIs (paired end reads in fastq format) Prior HLA genotyping of samples for the personalized index | Expression levels are determined by first aligning RNA-seq reads against the reference sequences of known HLA alleles carried by an individual and then by counting the unique UMIs per each HLA allele. | (9) |
| ONT bulk RNA-seq and Athlon2 | Amplicon-based RNA-seq data with UMIs tagged in the 5’end (ONT 1D reads in fastq format) | HLA genotyping is performed using the NGSengine bioinformatics pipeline and HLA allele-specific expression is quantified with Athlon2 pipeline by counting the UMI-tagged HLA-specific reads aligning to an allele. | (13) |
| Illumina scRNA-seq with UMIs and scHLAcount | Preferably 5’end RNA-seq data with UMIs (aligned reads in BAM format, cell barcodes) Prior HLA genotyping of samples | Prior knowledge from genotyping is utilized to construct a personalized reference. HLA allele-specific expression is determined by using pseudoalignment resulting in a matrix with allele-specific UMI counts. | (67) |
Examples of different approaches in HLA allele-level expression quantification.
UMI, unique molecular identifier; ONT, Oxford Nanopore Technologies.
HLA expression in human diseases
Despite being crucial for protective immunity against various pathogens, the huge allelic variation of HLA is also responsible for autoimmune reactivity (35). In addition to allele polymorphisms as the susceptibility risk for autoimmune diseases, there is increasing evidence of associations between HLA expression and human diseases. However, in many cases further studies are needed to confirm whether the expression alone is the predisposing factor and how the regulatory variation affects differential expression in diseases. Elevated HLA expression levels have been associated with inflammatory bowel diseases (56, 76), scleroderma patients with interstitial lung disease (77), ankylosing spondylitis (78), Graves’ disease (79), systemic lupus erythematosus (80), rheumatoid arthritis (72), and multiple sclerosis (81). Additionally, in celiac disease and type 1 diabetes, higher expression of DQA1*05 and DQB1*02 alleles was found in patients when compared to healthy controls (82, 83). There is evidence that also low HLA expression predisposes to diseases. Decreased HLA expression levels have been associated with cystic fibrosis (84), immunoglobulin A nephropathy (85), end stage renal disease and acute allograft rejection (86). In addition to multiple autoimmune diseases, differential HLA expression has also been associated with infectious diseases i.e. high HLA-C expression in HIV viral control (42, 44, 56) and high HLA-A expression in higher HIV viremia (87). For class II genes, associations exist between high HLA-DP expression and in hepatitis B virus infection (54) and reduced expression of HLA-DR on monocytes and severity of COVID-19 disease (88). Strong HLA expression is an important factor for anti-tumor immunity, and thus downregulation or loss of HLA expression is a common immune escape mechanism in cancer (21). Indeed, several studies have reported downregulation of HLA expression in lung cancer (89, 90), gastric cancer (91), classic Hodgkin lymphoma (92), and in Merkel cell carcinoma (93). Due to the importance of HLA expression for immune response against tumor cells, high HLA expression has been associated with favorable outcomes and prolonged survival in several cancers (94–97). Opposed to this, high expression of non-classical HLA genes capable of suppressing immune responses are shown to correlate with poor prognosis (98, 99). In many disease studies, HLA expression has been determined from blood, however, to study the role of allele-specific expression in T cell maturation, the expression levels should be measured also from thymus samples. A weaker self-antigen presentation of low expression alleles could lead to impaired negative selection of T cell clones further resulting in elevated risks for the breakdown of tolerance and autoimmune diseases.
Role of HLA allele-specific expression in HSCT
Although, HLA expression analysis is not considered in the current donor-recipient matching, there is evidence for the relevance of HLA allele-specific expression in HSCT. Several studies have associated differential HLA allele-specific expression with detrimental effects such as GvHD after the HSCT (40, 41, 100). Mismatched HLA-C alleles with high cell surface expression levels (mean fluorescence intensity) were identified as the key determinants for the increased risk for acute GvHD (aGvHD) and mortality indicating that high expression of patient’s allotypes enhances the graft-versus-host recognition (40). In the study of Petersdorf et al. (40) particularly the highly expressed C*14 allotype was associated with poor outcome after HSCT and was thus considered as a non-permissive mismatch. Another study also associated patient mismatched C*14:02 with high risk of severe aGvHD, but found no association between the HLA-C expression and the HSCT outcome although the HLA-C*14 allotype was expressed at the highest level (101). High allele expression predisposing to aGvHD is also found in HLA-DPB1. Two studies demonstrated that the risk of aGvHD was greater for patients with highly expressed HLA-DPB1 alleles who received an HLA-DPB1 mismatched transplant from a donor with low expression HLA-DPB1 alleles (41, 100). Therefore, in case no matched donors are available, consideration of HLA expression could enhance the donor selection by helping to avoid mismatching against high-expression allele and thus lower the risks in HSCT. Additionally, identified low expression alleles as tolerated mismatches could broaden the donor pool. Interestingly, cell surface expression of HLA-C allotypes is consistent across populations denoting C*03 as low expression allele and C*14 as high expression allele (42). At the transcript level, there are similar findings for the expression levels of these alleles (8, 11, 30). However, there are also conflicting reports (9, 14) suggesting that allele-level expression is not necessarily universal and possibly population-dependent at least to some extent.
Conclusions and perspectives
The advent of NGS technologies has revolutionized the study of HLA expression. It has led to the rapid development of RNA-seq based laboratory methods as well as computational tools for HLA expression quantification ready to be applied to existing datasets. As it can be challenging to obtain reliable results due to the high polymorphism and high sequence homology in HLA, many of the current methods rely on the use of a patient-specific personalized index in the expression quantification step. With continuous improvements in their sequencing accuracy, long-read NGS technologies such as ONT and PacBio could further increase the accuracy of HLA allele-specific expression quantification. Earlier HLA expression studies have mainly focused on quantifying HLA allele-specific expression from bulk RNA-seq data. However, particularly with class II genes, without information on the proportion of immune cells in the sample, it is hard to tell whether the expression level merely reflects the number of APCs in the sample. ScRNA-seq methods capable of distinguishing HLA expression from single cells enable more accurate comparisons between tissues and samples. Furthermore, spatial RNA-seq methods allow expression quantification between different cells in solid tissues (102). These methods would permit the study of loss of HLA expression in distinct spatial tumor clones. Determination of HLA expression is important in the selection of immune cell therapy. T-cell based therapies are dependent on strong HLA expression (103), whereas natural killer cell -based therapies rely on missing inhibitory ligands of killer-cell immunoglobulin-like receptors on the cell surface (104). Since, the previous studies have already demonstrated HLA allele-specific expression in healthy PBMCs, the focus in the future should be in investigating the role of HLA allele-specific expression in different tissues and diseases and how the expression changes in response to different stimuli and medications. Additionally, more information is needed on the dynamic changes in expression of HLA alleles over time from longitudinal samples from the same individual. Finally, the expression regulation of specific HLA alleles is still poorly understood. Therefore, by studying the methylation levels or using the expression quantitative loci (eQTL) analysis combining HLA expression together with data of non-coding variation obtained from genome-wide association studies might help to find potential factors affecting HLA allele-specific expression. By using scRNA-seq technology, the potential eQTLs behind differential expression could be identified even at the single-cell level.
Funding
This work was financially supported by a grant from the Doctoral Programme in Biomedicine of the Doctoral School in Health Sciences, University of Helsinki. The funding body had no role in the study design or writing the manuscript.
Publisher’s note
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Statements
Author contributions
TJ, JP, and PS conceptualized the article. TJ wrote the first version of the manuscript. All the authors revised the final version of the manuscript and approved the submitted version.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1
LiYYinYMariuzzaRA. Structural and biophysical insights into the role of CD4 and CD8 in T cell activation. Front Immunol (2013) 4:206. doi: 10.3389/fimmu.2013.00206
2
TrowsdaleJKnightJC. Major histocompatibility complex genomics and human disease. Annu Rev Genomics Hum Genet (2013) 14:301–23. doi: 10.1146/annurev-genom-091212-153455
3
RockKLReitsENeefjesJ. Present yourself! by MHC class I and MHC class II molecules. Trends Immunol (2016) 37:724–37. doi: 10.1016/j.it.2016.08.010
4
RochePAFurutaK. The ins and outs of MHC class II-mediated antigen processing and presentation. Nat Rev Immunol (2015) 15:203–16. doi: 10.1038/nri3818
5
WyattRCLanzoniGRussellMAGerlingIRichardsonSJ. What the HLA-I!–classical and non-classical HLA class I and their potential roles in type 1 diabetes. Curr Diabetes Rep (2019) 19: 159. doi: 10.1007/s11892-019-1245-z
6
GoodridgeJPBurianALeeNGeraghtyDE. HLA-f and MHC class I open conformers are ligands for NK cell ig-like receptors. J Immunol (2013) 191:3553–62. doi: 10.4049/jimmunol.1300081
7
Álvaro-BenitoMFreundC. Revisiting nonclassical HLA II functions in antigen presentation: Peptide editing and its modulation. HLA (2020) 96:415–29. doi: 10.1111/tan.14007
8
YamamotoFSuzukiSMizutaniAShigenariAItoSKametaniYet al. Capturing differential allele-level expression and genotypes of all classical HLA loci and haplotypes by a new capture RNA-seq method. Front Immunol (2020) 11:941. doi: 10.3389/fimmu.2020.00941
9
JohanssonTYohannesDAKoskelaSPartanenJSaavalainenP. HLA RNA sequencing with unique molecular identifiers reveals high allele-specific variability in mRNA expression. Front Immunol (2021) 12:629059. doi: 10.3389/fimmu.2021.629059
10
MontgomeryMCLiuCPetraroiaRWeimerET. Using nanopore whole-transcriptome sequencing for human leukocyte antigen genotyping and correlating donor human leukocyte antigen expression with flow cytometric crossmatch results. J Mol Diagn (2020) 22:101–10. doi: 10.1016/j.jmoldx.2019.09.005
11
AguiarVRCCésarJDelaneauODermitzakisETMeyerD. Expression estimation and eQTL mapping for HLA genes with a personalized pipeline. PloS Genet (2019) 15:1–25. doi: 10.1371/journal.pgen.1008091
12
BettensFBrunetLTiercyJ-M. High-allelic variability in HLA-c mRNA expression: association with HLA-extended haplotypes. Genes Immun (2014) 15:176–81. doi: 10.1038/gene.2014.1
13
CornabyCMontgomeryMCLiuCWeimerETParkJ. Unique molecular identifier-based high-resolution HLA typing and transcript quantitation using long-read sequencing. Front. Genet (2022) 13:901377. doi: 10.3389/fgene.2022.901377
14
LeeWPlantKHumburgPKnightJC. AltHapAlignR: improved accuracy of RNA-seq analyses through the use of alternative haplotypes. Bioinformatics (2018) 34:2401–8. doi: 10.1093/bioinformatics/bty125
15
BoegelSLöwerMBukurTSornPCastleJCSahinU. HLA and proteasome expression body map. BMC Med Genomics (2018) 11:1–12. doi: 10.1186/s12920-018-0354-x
16
BoegelSLöwerMBukurTSahinUCastleJC. A catalog of HLA type, HLA expression, and neo-epitope candidates in human cancer cell lines. Oncoimmunology (2014) 3:e954893. doi: 10.4161/21624011.2014.954893
17
PrevostoCUsmaniMFMcDonaldSGumiennyAMKeyTGoodmanRSet al. Allele-independent turnover of human leukocyte antigen (HLA) class ia molecules. PloS One (2016) 11:1–25. doi: 10.1371/journal.pone.0161011
18
CareyBSPoultonKVPolesA. Factors affecting HLA expression: A review. Int J Immunogenet (2019) 46:307–20. doi: 10.1111/iji.12443
19
PetersdorfEWO’hUiginC. The MHC in the era of next-generation sequencing: Implications for bridging structure with function. Hum Immunol (2019) 80:67–78. doi: 10.1016/j.humimm.2018.10.002
20
VoorterCEMGerritsenKEHGroenewegMWietenLTilanusMGJ. The role of gene polymorphism in HLA class I splicing. Int J Immunogenet (2016) 43:65–78. doi: 10.1111/iji.12256
21
GarridoMAPereaFVilchezJRRodríguezTAndersonPGarridoFet al. Copy neutral LOH affecting the entire chromosome 6 is a frequent mechanism of HLA class I alterations in cancer. Cancers (Basel) (2021) 13:5046. doi: 10.3390/cancers13205046
22
LinjamaTImpolaUNiittyvuopioRKuittinenOKaareARimpiläinenJet al. Conflicting HLA assignment by three different typing methods due to the apparent loss of heterozygosity in the MHC region. HLA (2016) 87:350–5. doi: 10.1111/tan.12770
23
JavittABarneaEKramerMPWolf-LevyHLevinYAdmonAet al. Pro-inflammatory cytokines alter the immunopeptidome landscape by modulation of HLA-b expression. Front Immunol (2019) 10:141. doi: 10.3389/fimmu.2019.00141
24
PerssonGBorkJBSIsgaardCLarsenTGBordoyAMBengtssonMSet al. Cytokine stimulation of the choriocarcinoma cell line JEG-3 leads to alterations in the HLA-G expression profile. Cell Immunol (2020) 352:104110. doi: 10.1016/j.cellimm.2020.104110
25
Le MorvanCCognéMDrouetM. HLA-a and HLA-b transcription decrease with ageing in peripheral blood leucocytes. Clin Exp Immunol (2001) 125:245–50. doi: 10.1046/j.1365-2249.2001.01610.x
26
WuJLiuZZhangYWangLFengDLiuLet al. Age-dependent alterations of HLA-DR expression and effect of lipopolysaccharide on cytokine secretion of peripheral blood mononuclear cells in the elderly population. Scand J Immunol (2011) 74:603–8. doi: 10.1111/j.1365-3083.2011.02612.x
27
BeyazSChungCMouHBauer-RoweKEXifarasMEErginIet al. Dietary suppression of MHC class II expression in intestinal epithelial cells enhances intestinal tumorigenesis. Cell Stem Cell (2021) 28:1922–1935.e5. doi: 10.1016/j.stem.2021.08.007
28
Mora-García M deLDuenas-GonzálezAHernández-MontesJde la Cruz-HernándezEPérez-CárdenasEWeiss-SteiderBet al. Up-regulation of HLA class-I antigen expression and antigen-specific CTL response in cervical cancer cells by the demethylating agent hydralazine and the histone deacetylase inhibitor valproic acid. J Transl Med (2006) 4:55. doi: 10.1186/1479-5876-4-55
29
Gutierrez-ArcelusMBaglaenkoYAroraJHannesSLuoYAmariutaTet al. Allele-specific expression changes dynamically during T cell activation in HLA and other autoimmune loci. Nat Genet (2020) 52:247–53. doi: 10.1038/s41588-020-0579-4
30
BettensFOngenHReyGBuhlerSCalderin SolletZDermitzakisEet al. Regulation of HLA class I expression by non-coding gene variations. PloS Genet (2022) 18:e1010212. doi: 10.1371/journal.pgen.1010212
31
van den ElsenPJ. Expression regulation of major histocompatibility complex class I and class II encoding genes. Front Immunol (2011) 2:48. doi: 10.3389/fimmu.2011.00048
32
RenéCLozanoCEliaouJ-FRenéC. Julia Bodmer award review 2015 expression of classical HLA class I molecules: regulation and clinical impacts. HLA (2016) 87:338–49. doi: 10.1111/tan.12787
33
DellgrenCNehlinJOBaringtonT. Cell surface expression level variation between two common human leukocyte antigen alleles, HLA-A2 and HLA-B8 , is dependent on the structure of the c terminal part of the alpha 2 and the alpha 3 domains. PloS One (2015) 10:1–15. doi: 10.1371/journal.pone.0135385
34
KulkarniSSavanRQiYGaoXYukiYBassSEet al. Differential microRNA regulation of HLA-c expression and its association with HIV control. Nature (2011) 472:495–8. doi: 10.1038/nature09914
35
DendrouCAPetersenJRossjohnJFuggerL. HLA variation and disease. Nat Rev Immunol (2018) 18:325–39. doi: 10.1038/nri.2017.143
36
ShiinaTHosomichiKInokoHKulskiJK. The HLA genomic loci map: Expression, interaction, diversity and disease. J Hum Genet (2009) 54:15–39. doi: 10.1038/jhg.2008.5
37
RobinsonJHalliwellJAHayhurstJDFlicekPParhamPMarshSGE. The IPD and IMGT/HLA database: Allele variant databases. Nucleic Acids Res (2015) 43:D423–31. doi: 10.1093/nar/gku1161
38
MatzarakiVKumarVWijmengaCZhernakovaA. The MHC locus and genetic susceptibility to autoimmune and infectious diseases. Genome Biol (2017) 18:76. doi: 10.1186/s13059-017-1207-1
39
MosaadYM. Clinical role of human leukocyte antigen in health and disease. Scand J Immunol (2015) 82:283–306. doi: 10.1111/sji.12329
40
PetersdorfEWGooleyTAMalkkiMBacigalupoAPCesbronADuTEet al. HLA-c expression levels define permissible mismatches in hematopoietic cell transplantation. Blood (2014) 124:3996–4003. doi: 10.1182/blood-2014-09
41
PetersdorfEWMalkkiMO’huiginCCarringtonMGooleyTHaagensonMDet al. High HLA-DP expression and graft-versus-Host disease. N Engl J Med (2015) 373:599–609. doi: 10.1056/NEJMoa1500140
42
AppsRQiYCarlsonJMChenHGaoXThomasRet al. Influence of HLA-c expression level on HIV control. Science (2013) 340:87–91. doi: 10.1126/science.1232685.Influence
43
RamsuranVKulkarniSO’huiginCYukiYAugustoDGGaoXet al. Epigenetic regulation of differential HLA-a allelic expression levels. Hum Mol Genet (2015) 24:4268–75. doi: 10.1093/hmg/ddv158
44
ThomasRAppsRQiYGaoXMaleVO’hUiginCet al. HLA-c cell surface expression and control of HIV/AIDS correlate with a variant upstream of HLA-c. Nat Genet (2009) 41:1290–4. doi: 10.1038/ng.486
45
AppsRMengZDel PreteGQLifsonJDZhouMCarringtonM. Relative expression levels of the HLA class-I proteins in normal and HIV-infected cells. J Immunol (2015) 194:3594–600. doi: 10.4049/jimmunol.1403234
46
StarkRGrzelakMHadfieldJ. RNA Sequencing: the teenage years. Nat Rev Genet (2019) 20:631–56. doi: 10.1038/s41576-019-0150-2
47
SabbatinoFLiguoriLPolcaroGSalvatoICaramoriGSalzanoFAet al. Role of human leukocyte antigen system as a predictive biomarker for checkpoint-based immunotherapy in cancer patients. Int J Mol Sci (2020) 21:1–30. doi: 10.3390/ijms21197295
48
DhatchinamoorthyKColbertJDRockKL. Cancer immune evasion through loss of MHC class I antigen presentation. Front Immunol (2021) 12:636568. doi: 10.3389/fimmu.2021.636568
49
LiuJFuMWangMWanDWeiYWeiX. Cancer vaccines as promising immuno-therapeutics: platforms and current progress. J Hematol Oncol (2022) 15:28. doi: 10.1186/s13045-022-01247-x
50
BoegelSLöwerMSchäferMBukurTde GraafJBoisguérinVet al. HLA typing from RNA-seq sequence reads. Genome Med (2012) 4:102. doi: 10.1186/gm403
51
JohnsonDR. Locus-specific constitutive and cytokine-induced HLA class I gene expression. J Immunol (2003) 170:1894–902. doi: 10.4049/jimmunol.170.4.1894
52
KaurGGrasSMobbsJIVivianJPCortesABarberTet al. Structural and regulatory diversity shape HLA-c protein expression levels. Nat Commun (2017) 8:15924. doi: 10.1038/ncomms15924
53
McCutcheonJAGumperzJSmithKDLutzCTParhamP. Low HLA-c expression at cell surfaces correlates with increased turnover of heavy chain mRNA. J Exp Med (1995) 181:2085–95. doi: 10.1084/jem.181.6.2085
54
ThomasRThioCLAppsRQiYGaoXMartiDet al. A novel variant marking HLA-DP expression levels predicts recovery from hepatitis b virus infection. J Virol (2012) 86:6979–85. doi: 10.1128/JVI.00406-12
55
RamsuranVHernández-SanchezPGO’hUiginCSharmaGSpenceNAugustoDGet al. Sequence and phylogenetic analysis of the untranslated promoter regions for HLA class I genes. J Immunol (2017) 198:2320–9. doi: 10.4049/jimmunol.1601679
56
KulkarniSQiYO’hUiginCPereyraFRamsuranVMcLarenPet al. Genetic interplay between HLA-c and MIR148A in HIV control and crohn disease. Proc Natl Acad Sci (2013) 110:20705–10. doi: 10.1073/pnas.1312237110
57
VandiedonckCTaylorMSLockstoneHEPlantKTaylorJMDurrantCet al. Pervasive haplotypic variation in the spliceo-transcriptome of the human major histocompatibility complex. Genome Res (2011) 21:1042–54. doi: 10.1101/gr.116681.110
58
van EssenTHvan PeltSIBronkhorstIHGVersluisMNématiFLaurentCet al. Upregulation of HLA expression in primary uveal melanoma by infiltrating leukocytes. PloS One (2016) 11:e0164292. doi: 10.1371/journal.pone.0164292
59
SmallHYAkehurstCSharafetdinovaLMcBrideMWMcClureJDRobinsonSWet al. HLA gene expression is altered in whole blood and placenta from women who later developed preeclampsia. Physiol Genomics (2017) 49:193–200. doi: 10.1152/physiolgenomics.00106.2016
60
HosomichiKShiinaTTajimaAInoueI. The impact of next-generation sequencing technologies on HLA research. J Hum Genet (2015) 60:665–73. doi: 10.1038/jhg.2015.102
61
Bravo-EganaVSandersHChitnisN. New challenges, new opportunities: Next generation sequencing and its place in the advancement of HLA typing. Hum Immunol (2021) 82:478–87. doi: 10.1016/j.humimm.2021.01.010
62
LankSMWisemanRWDudleyDMO’ConnorDH. A novel single cDNA amplicon pyrosequencing method for high-throughput, cost-effective sequence-based HLA class I genotyping. Hum Immunol (2010) 71:1011–7. doi: 10.1016/j.humimm.2010.07.012
63
GreeneJMWisemanRWLankSMBimberBNKarlJABurwitzBJet al. Differential MHC class I expression in distinct leukocyte subsets. BMC Immunol (2011) 12:39. doi: 10.1186/1471-2172-12-39
64
KiviojaTVähärautioAKarlssonKBonkeMEngeMLinnarssonSet al. Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods (2011) 9:72–4. doi: 10.1038/nmeth.1778
65
LiuC. A long road/read to rapid high-resolution HLA typing: The nanopore perspective. Hum Immunol (2020) 82:488–95. doi: 10.1016/j.humimm.2020.04.009
66
OrenbuchRFilipIComitoDShamanJPe’ErIRabadanRet al. ArcasHLA: High-resolution HLA typing from RNAseq. Bioinformatics (2020) 36:33–40. doi: 10.1093/bioinformatics/btz474
67
DarbyCAStubbingtonMJTMarksPJMartínez BarrioÁFiddesIT. scHLAcount: Allele-specific HLA expression from single-cell gene expression data. Bioinformatics (2020) 36:3905–6. doi: 10.1093/bioinformatics/btaa264
68
LangmeadBTrapnellCPopMSalzbergSL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol (2009) 10:R25. doi: 10.1186/gb-2009-10-3-r25
69
KimDPerteaGTrapnellCPimentelHKelleyRSalzbergSL. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol (2013) 14:R36. doi: 10.1186/gb-2013-14-4-r36
70
KimDLangmeadBSalzbergSL. HISAT: A fast spliced aligner with low memory requirements. Nat Methods (2015) 12:357–60. doi: 10.1038/nmeth.3317
71
DobinADavisCASchlesingerFDrenkowJZaleskiCJhaSet al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics (2013) 29:15–21. doi: 10.1093/bioinformatics/bts635
72
HoutmanMHesselbergERönnblomLKlareskogLMalmströmVPadyukovL. Haplotype-specific expression analysis of MHC class II genes in healthy individuals and rheumatoid arthritis patients. Front Immunol (2021) 12:707217. doi: 10.3389/fimmu.2021.707217
73
PatroRDuggalGLoveMIIrizarryRAKingsfordC. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods (2017) 14:417–9. doi: 10.1038/nmeth.4197
74
BrayNLPimentelHMelstedPPachterL. Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol (2016) 34:525–7. doi: 10.1038/nbt.3519
75
FilipIOrenbuchRZhaoJManjiGLópez de MaturanaEMalatsNet al. HLA allele-specific expression loss in tumors can shorten survival and hinder immunotherapy. medRxiv (2020) 1–18. doi: 10.1101/2020.09.30.20204875
76
da Costa FerreiraSSadissouIAParraRSFeitosaMRNetoFSLPretti da Cunha TirapelliDet al. Increased HLA-G expression in tissue-infiltrating cells in inflammatory bowel diseases. Dig Dis Sci (2021) 66:2610–8. doi: 10.1007/s10620-020-06561-3
77
OdaniTYasudaSOtaYFujiedaYKonYHoritaTet al. Up-regulated expression of HLA-DRB5 transcripts and high frequency of the HLA-DRB5*01:05 allele in scleroderma patients with interstitial lung disease. Rheumatology (2012) 51:1765–74. doi: 10.1093/rheumatology/kes149
78
CauliADessoleGFiorilloMTVaccaAMameliABittiPet al. Increased level of HLA-B27 expression in ankylosing spondylitis patients compared with healthy HLA-B27-positive subjects: a possible further susceptibility factor for the development of disease. Rheumatol (Oxford) (2002) 41:1375–9. doi: 10.1093/rheumatology/41.12.1375
79
WeiderTRichardsonSJMorganNGPaulsenTHDahl-JørgensenKHammerstadSS. HLA class I upregulation and antiviral immune responses in graves disease. J Clin Endocrinol Metab (2021) 106:e1763–74. doi: 10.1210/clinem/dgaa958
80
ViallardJFBloch-MichelCNeau-CransacMTaupinJLGarrigueSMiossecVet al. HLA-DR expression on lymphocyte subsets as a marker of disease activity in patients with systemic lupus erythematosus. Clin Exp Immunol (2001) 125:485–91. doi: 10.1046/j.1365-2249.2001.01623.x
81
AlcinaAAbad-Grau M delMFedetzMIzquierdoGLucasMFernándezÓet al. Multiple sclerosis risk variant HLA-DRB1*1501 associates with high expression of DRB1 gene in different human populations. PloS One (2012) 7:e29819. doi: 10.1371/journal.pone.0029819
82
PisapiaLPicasciaSFarinaFBarbaPGianfraniCDel PozzoG. Differential expression of predisposing HLA-DQ2.5 alleles in DR5/DR7 celiac disease patients affects the pathological immune response to gluten. Sci Rep (2020) 10:17227. doi: 10.1038/s41598-020-73907-2
83
FarinaFPicasciaSPisapiaLBarbaPVitaleSFranzeseAet al. HLA-DQA1 and HLA-DQB1 alleles, conferring susceptibility to celiac disease and type 1 diabetes, are more expressed than non-predisposing alleles and are coordinately regulated. Cells (2019) 8:751. doi: 10.3390/cells8070751
84
HoferTPFrankenbergerMHeimbeckIBurggrafDWjstMWrightAKAet al. Decreased expression of HLA-DQ and HLA-DR on cells of the monocytic lineage in cystic fibrosis. J Mol Med (Berl) (2014) 92:1293–304. doi: 10.1007/s00109-014-1200-z
85
ZhanXDengFWangAYChenQDuYWangQet al. HLA-DQB1 and HLA-DRB1 expression is associated with disease severity in IgAN. Ann Palliat Med (2021) 10:9453–66. doi: 10.21037/apm-21-2065
86
MisraMKPandeySKKapoorRSharmaRKKapoorRPrakashSet al. HLA-G gene expression influenced at allelic level in association with end stage renal disease and acute allograft rejection. Hum Immunol (2014) 75:833–9. doi: 10.1016/j.humimm.2014.06.005
87
RamsuranVNaranbhaiVHorowitzAQiYMartinMPYukiYet al. Elevated HLA-a expression impairs HIV control through inhibition of NKG2A-expressing cells. Science (2018) 90:86–90. doi: 10.1126/science.aam8825
88
SpinettiTHirzelCFuxMWaltiLNSchoberPStueberFet al. Reduced monocytic human leukocyte antigen-DR expression indicates immunosuppression in critically ill COVID-19 patients. Anesth analg (2020). Available at: https://journals.lww.com/anesthesia-analgesia/Fulltext/2020/10000/Reduced_Monocytic_Human_Leukocyte_Antigen_DR.2.aspx.
89
HirakiAFujiiNMurakamiTKiuraKAoeKYamaneHet al. High frequency of allele-specific down-regulation of HLA class I expression in lung cancer cell lines. anticancer res (2004). Available at: http://ar.iiarjournals.org/content/24/3A/1525.abstract.
90
McGranahanNRosenthalRHileyCTRowanAJWatkinsTBKWilsonGAet al. Allele-specific HLA loss and immune escape in lung cancer evolution. Cell (2017) 171:1259–1271.e11. doi: 10.1016/j.cell.2017.10.001
91
ZhangYLiuYLuNShanNNZhengGXZhaoSMet al. Expression of the genes encoding human leucocyte antigens-a, -b, -DP, -DQ and -G in gastric cancer patients. J Int Med Res (2010) 38:949–56. doi: 10.1177/147323001003800321
92
TanGWJiangPNolteIMKushekharKVeenstraRNHepkemaBGet al. HLA expression in relation to HLA type in classic Hodgkin lymphoma patients. Cancers (Basel) (2021) 13:5833. doi: 10.3390/cancers13225833
93
PaulsonKGTegederAWillmesCIyerJGAfanasievOKSchramaDet al. Downregulation of MHC-I expression is prevalent but reversible in merkel cell carcinoma. Cancer Immunol Res (2014) 2:1071–9. doi: 10.1158/2326-6066.CIR-14-0005
94
MichelakosTKontosFKurokawaTCaiLSadagopanAKrijgsmanDet al. Differential role of HLA-a and HLA-b , c expression levels as prognostic markers in colon and rectal cancer. J Immunother Cancer (2022) 10:1–9. doi: 10.1136/jitc-2021-004115
95
SabbatinoFVillaniVYearleyJHDeshpandeVCaiLKonstantinidisITet al. PD-L1 and HLA class I antigen expression and clinical course of the disease in intrahepatic cholangiocarcinoma. Clin Cancer Res an Off J Am Assoc Cancer Res (2016) 22:470–8. doi: 10.1158/1078-0432.CCR-15-0715
96
Noblejas-LópezMDMNieto-JiménezCMorcillo GarcíaSPérez-PeñaJNuncia-CantareroMAndrés-PretelFet al. Expression of MHC class I, HLA-a and HLA-b identifies immune-activated breast tumors with favorable outcome. Oncoimmunology (2019) 8:e1629780. doi: 10.1080/2162402X.2019.1629780
97
ShehataMMukherjeeADeenSAl-AttarADurrantLGChanS. Human leukocyte antigen class i expression is an independent prognostic factor in advanced ovarian cancer resistant to first-line platinum chemotherapy. Br J Cancer (2009) 101:1321–8. doi: 10.1038/sj.bjc.6605315
98
LevyEMBianchiniMVon EuwEMBarrioMMBravoAIFurmanDet al. Human leukocyte antigen-e protein is overexpressed in primary human colorectal cancer. Int J Oncol (2008) 32:633–41. doi: 10.3892/ijo.32.3.633
99
NückelHRebmannVDürigJDührsenUGrosse-WildeH. HLA-G expression is associated with an unfavorable outcome and immunodeficiency in chronic lymphocytic leukemia. Blood (2005) 105:1694–8. doi: 10.1182/blood-2004-08-3335
100
PetersdorfEWBengtssonMDe SantisDDuboisVFleischhauerKGooleyTet al. Role of HLA-DP expression in graft-Versus-Host disease after unrelated donor transplantation. J Clin Oncol Off J Am Soc Clin Oncol (2020) 38:2712–8. doi: 10.1200/JCO.20.00265
101
MorishimaSKashiwaseKMatsuoKAzumaFYabeTSato-OtsuboAet al. High-risk HLA alleles for severe acute graft-versus-host disease and mortality in unrelated donor bone marrow transplantation. Haematologica (2016) 101:491–8. doi: 10.3324/haematol.2015.136903
102
WilliamsCGLeeHJAsatsumaTVento-TormoRHaqueA. An introduction to spatial transcriptomics for biomedical research. Genome Med (2022) 14:68. doi: 10.1186/s13073-022-01075-1
103
WangCXiongCHsuY-CWangXChenL. Human leukocyte antigen (HLA) and cancer immunotherapy: HLA-dependent and -independent adoptive immunotherapies. Ann Blood (2020) 5:14–4. doi: 10.21037/aob-20-27
104
LiuSGalatVGalat4YLeeYKAWainwrightDWuJ. NK cell-based cancer immunotherapy: From basic biology to clinical development. J Hematol Oncol (2021) 14:7. doi: 10.1186/s13045-020-01014-w
Summary
Keywords
human leucocyte antigen, next generation sequencing, RNA sequencing, allele-specific expression, disease associations
Citation
Johansson T, Partanen J and Saavalainen P (2022) HLA allele-specific expression: Methods, disease associations, and relevance in hematopoietic stem cell transplantation. Front. Immunol. 13:1007425. doi: 10.3389/fimmu.2022.1007425
Received
30 July 2022
Accepted
09 September 2022
Published
28 September 2022
Volume
13 - 2022
Edited by
Effie Wang Petersdorf, Fred Hutchinson Cancer Research Center, United States
Reviewed by
Marcel G. J. Tilanus, Maastricht University, Netherlands
Updates
Copyright
© 2022 Johansson, Partanen and Saavalainen.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Tiira Johansson, tiira.johansson@helsinki.fi
This article was submitted to Alloimmunity and Transplantation, a section of the journal Frontiers in Immunology
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