Satu Koskela

Variant Bernard-Soulier syndrome due to homozygous Asn45Ser mutation in the platelet glycoprotein (GP) IX in seven patients of five unrelated Finnish families.

Abstract:  Bernard‐Soulier syndrome (BSS), a rare bleeding disorder with macrothrombocytopenia, is caused by a defect of the platelet glycoprotein (GP) Ib/IX/V complex. Here we report a variant form of BSS in eleven patients of five unrelated families who originate from a particular area of Finland. The differential diagnosis from idiopathic thrombocytopenic purpura was difficult. Bleeding symptoms were epistaxis and haematomas debuting in childhood, but no spontaneous, severe bleeding episodes were reported. The platelet count varied from 43 to 81 × 109/l. Screening the entire GP Ibα, GP Ibβ, GP IX and GP V genes revealed a recurrent homozygous Asn45Ser mutation in GP IX in all probands. Flow cytometry showed markedly reduced expression of GP Ib (<10%), and only moderately reduced expression of GP IX (24–36%) and GP V (38–49%). The expression of subunits seemed to vary independently from the normal polymorphisms. Heterozygotes did not differ significantly from controls by their GP Ib/IX/V expression. Since the Asn45Ser mutation has also been reported in three other kindreds of northern and central European origin, this study reveals that instead of being a mutation hot spot, it may be ancient and scattered in Europe. Moderate, chronic thrombocytopenia should be carefully studied to diagnose variant BSS correctly from treatment resistant idiopathic thrombocytopenia.

Donor haplotype B of NK KIR receptor reduces the relapse risk in HLA-identical sibling hematopoietic stem cell transplantation of AML patients

Successful allogeneic hematopoietic stem cell transplantation (HSCT) depends not only on good HLA match but also on T-cell mediated graft-versus-leukemia (GvL) effect. Natural killer (NK) cells are able to kill malignant cells by receiving activation signal from the killer-cell immunoglobulin-like receptors (KIR) recognizing HLA molecules on a cancer cell. It has been recently reported that the risk of relapse in allogeneic hematopoietic stem cell transplantation (HSCT) is reduced in acute myeloid leukemia (AML) patients whose donors have several activating KIR genes or KIR B-motifs in unrelated donor setting, obviously due to enhanced GvL effect by NK cells. We studied the effect on relapse rate of donor KIR haplotypes in the HLA-identical adult sibling HSCT, done in a single center, in Helsinki University Central Hospital, Helsinki, Finland. Altogether, 134 patients with 6 different diagnoses were identified. Their donors were KIR genotyped using the Luminex and the SSP techniques. The clinical endpoint, that is, occurrence of relapse, was compared with the presence or absence of single KIR genes. Also, time from transplantation to relapse was analyzed. The patients with AML whose donors have KIR2DL2 or KIR2DS2 had statistically significantly longer relapse-free survival (P = 0.015). Our data support previous reports that donors with KIR B-haplotype defining genes have a lower occurrence of relapse in HSCT of AML patients. Determination of donor KIR haplotypes could be a useful addition for a risk assessment of HSCT especially in AML patients.

HLA antigen, allele and haplotype frequencies and their use in virtual panel reactive antigen calculations in the Finnish population

The human leukocyte antigen (HLA) antigen, allele and haplotype frequencies of the Finnish population are quite unique because of a rather restricted and homogeneous gene pool. This has a strong influence on finding suitable donors for transplant patients; hence knowledge about the HLA frequencies of the patient population is essential. Here we report the HLA antigen frequencies for a large population sample and show high resolution HLA allele frequencies for 11 loci, including the rarely typed DPA1 and DQA1 loci. Furthermore, the most common Finnish high resolution haplotypes are presented for five HLA loci. The study shows that there are fewer HLA haplotypes in the Finnish population compared with mixed populations, and the common Finnish HLA haplotypes are more frequent. Using HLA antibody identification and panel reactive antibody calculations we show that a virtual population-specific panel, combined with single antigen testing, gives a more accurate and reliable estimate of the reactivity of the recipient serum against potential solid organ donors within the Finnish population. The results can be directly used to improve donor search for patients waiting for stem cell transplantation and to allocate highly immunised patients accurately to acceptable mismatch programs.

Description of four new HLA alleles in the Finnish population: A*03:283N, A*68:167, C*03:327, C*03:361

New HLA alleles found in the Finnish population: A*03:283N, A*68:167, C*03:327 and C*03:361.

HLA RNAseq reveals high allele-specific variability in mRNA expression

The HLA gene complex is the most important, single genetic factor in susceptibility to most diseases with autoimmune or autoinflammatory origin and in transplantation matching. The majority of the studies have focused on the huge allelic variation in these genes; only a few studies have explored differences in expression levels of HLA alleles. To study the expression levels of HLA alleles more systematically we utilised two different RNA sequencing methods. Illumina RNAseq has a high sequencing accuracy and depth but is limited by the short read length, whereas Oxford Nanopore’s technology can sequence long templates, but has a poor accuracy. We studied allelic mRNA levels of HLA class I and II alleles from peripheral blood samples of 50 healthy individuals. The results demonstrate large differences in mRNA expression levels between HLA alleles. The method can be applied to quantitate the expression differences of HLA alleles in various tissues and to evaluate the role of this type of variation in transplantation matching and susceptibility to autoimmune diseases. Author Summary Even though HLA is widely studied less is known of its allele-specific expression. Due to the pivotal role of HLA in infection response, autoimmunity, and transplantation biology its expression surely must play a part as well. In hematopoietic stem cell transplantation the challenge often is to find a suitable HLA-matched donor due to the high allelic variation. Classical HLA typing methods do not take into account HLA allele-specific expression. However, differential allelic expression levels could be crucial in finding permissive mismatches in order to save a patient’s life. Additionally, differential HLA expression levels can lead into beneficial impact in viral clearance but also undesirable effects in autoimmune diseases. To study HLA expression we developed a novel RNAseq-based method to systematically characterize allele-specific expression levels of classical HLA genes. We tested our method in a set of 50 healthy individuals and found differential expression levels between HLA alleles as well as interindividual variability at the gene level. Since NGS is already well adopted in HLA research the next step could be to determine HLA allele-specific expression in addition to HLA allelic variation and HLA-disease association studies in various cells, tissues, and diseases.

Hidden genomic MHC disparity between HLA-matched sibling pairs in hematopoietic stem cell transplantation

Matching classical HLA alleles between donor and recipient is an important factor in avoiding adverse immunological effects in HSCT. Siblings with no differences in HLA alleles, either due to identical-by-state or identical-by-descent status, are considered to be optimal donors. We carried out a retrospective genomic sequence and SNP analysis of 336 fully HLA-A, -B, -DRB1 matched and 14 partially HLA-matched sibling HSCT pairs to determine the level of undetected mismatching within the MHC segment as well as to map their recombination sites. The genomic sequence of 34 genes locating in the MHC region revealed allelic mismatching at 1 to 8 additional genes in partially HLA-matched pairs. Also, fully matched pairs were found to have mismatching either at HLA-DPB1 or at non-HLA region within the MHC segment. Altogether, 3.9% of fully HLA-matched HSCT pairs had large genomic mismatching in the MHC segment. Recombination sites mapped to certain restricted locations. The number of mismatched nucleotides correlated with the risk of GvHD supporting the central role of full HLA matching in HSCT. High-density genome analysis revealed that fully HLA-matched siblings may not have identical MHC segments and even single allelic mismatching at any classical HLA gene often implies larger genomic differences along MHC.

Genomic prediction of relapse in recipients of allogeneic haematopoietic stem cell transplantation

Allogeneic haematopoietic stem cell transplantation currently represents the primary potentially curative treatment for cancers of the blood and bone marrow. While relapse occurs in approximately 30% of patients, few risk-modifying genetic variants have been identified. The present study evaluates the predictive potential of patient genetics on relapse risk in a genome-wide manner. We studied 151 graft recipients with HLA-matched sibling donors by sequencing the whole-exome, active immunoregulatory regions, and the full MHC region. To assess the predictive capability and contributions of SNPs and INDELs, we employed machine learning and a feature selection approach in a cross-validation framework to discover the most informative variants while controlling against overfitting. Our results show that germline genetic polymorphisms in patients entail a significant contribution to relapse risk, as judged by the predictive performance of the model (AUC = 0.72 [95% CI: 0.63–0.81]). Furthermore, the top contributing variants were predictive in two independent replication cohorts (n = 258 and n = 125) from the same population. The results can help elucidate relapse mechanisms and suggest novel therapeutic targets. A computational genomic model could provide a step toward individualized prognostic risk assessment, particularly when accompanied by other data modalities.

Accuracy of programs for the determination of HLA alleles from NGS data

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing on the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as many HLA alleles are major genetic factors that confer susceptibility to autoimmune diseases and is important for the matching of HLA alleles in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next generation sequencing (NGS) data. As genome screens are becoming increasingly routine in research, we wanted to test how well HLA alleles can be deduced from genome screens not designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using the ImmunoSEQ platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from the 1000 Genomes collection. When used with the default settings none of the methods gave perfect results for all the genes and samples. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. Most of the algorithms did not perform very well for the exome-only data. The results indicate that the use of these algorithms for accurate HLA allele determination based on NGS data is not straightforward.

Accuracy of Programs for the Determination of Human Leukocyte Antigen Alleles from Next-Generation Sequencing Data

The human leukocyte antigen (HLA) genes code for proteins that play a central role in the function of the immune system by presenting peptide antigens to T cells. As HLA genes show extremely high genetic polymorphism, HLA typing at the allele level is demanding and is based on DNA sequencing. Determination of HLA alleles is warranted as HLA alleles are major genetic risk factors in autoimmune diseases and are matched in transplantation. Here, we compared the accuracy of several published HLA-typing algorithms that are based on next-generation sequencing (NGS) data. As genome sequencing is becoming increasingly common in research, we wanted to test how well HLA alleles can be deduced from genome data produced in studies with objectives other than HLA typing and in platforms not especially designed for HLA typing. The accuracies were assessed using datasets consisting of NGS data produced using an in-house sequencing platform, including the full 4 Mbp HLA segment, from 94 stem cell transplantation patients and exome sequences from 63 samples of the 1000 Genomes collection. In the patient dataset, none of the software gave perfect results for all the samples and genes when programs were used with the default settings. However, we found that ensemble prediction of the results or modifications of the settings could be used to improve accuracy. For the exome-only data, most of the algorithms did not perform very well. The results indicate that the use of these algorithms for accurate HLA allele determination is not straightforward when based on NGS data not especially targeted to the HLA typing and their accurate use requires HLA expertise.