Decoding the diversity of killer immunoglobulin-like receptors by deep sequencing and a high-resolution imputation method

Comprehensive snapshots of natural killer cells functions, signaling, molecular mechanisms and clinical utilization

Natural killer (NK) cells, initially identified for their rapid virus-infected and leukemia cell killing and tumor destruction, are pivotal in immunity. They exhibit multifaceted roles in cancer, viral infections, autoimmunity, pregnancy, wound healing, and more. Derived from a common lymphoid progenitor, they lack CD3, B-cell, or T-cell receptors but wield high cytotoxicity via perforin and granzymes. NK cells orchestrate immune responses, secreting inflammatory IFNγ or immunosuppressive TGFβ and IL-10. CD56dim and CD56bright NK cells execute cytotoxicity, while CD56bright cells also regulate immunity. However, beyond the CD56 dichotomy, detailed phenotypic diversity reveals many functional subsets that may not be optimal for cancer immunotherapy. In this review, we provide comprehensive and detailed snapshots of NK cells' functions and states of activation and inhibitions in cancer, autoimmunity, angiogenesis, wound healing, pregnancy and fertility, aging, and senescence mediated by complex signaling and ligand-receptor interactions, including the impact of the environment. As the use of engineered NK cells for cancer immunotherapy accelerates, often in the footsteps of T-cell-derived engineering, we examine the interactions of NK cells with other immune effectors and relevant signaling and the limitations in the tumor microenvironment, intending to understand how to enhance their cytolytic activities specifically for cancer immunotherapy.

Disease associations of natural killer (NK) cell KIR gene content variation in 352,783 Finns

Allelic, gene presence/absence, and gene-copy number variations in the KIR genes encoding Natural Killer (NK) cell surface receptors have been reported to be associated in case-control studies with infectious and autoimmune diseases, and relapse after stem cell transplantation. To understand more comprehensively the role of KIR gene presence/absence variation and HLA-KIR interactions in disease susceptibility, we imputed from genome SNP data the presence and absence of 10 KIR genes in the FinnGen cohort. The cohort consists of 352,783 Finns with extensive phenotypes from the national health registries. We tested associations between 762 FinnGen phenotypes and presence/absence variation based on imputation of KIR genes using 5,900 SNPs located in the KIR genomic segment. Our results provide a platform to query HLA-KIR associations in a large population cohort. We found 13 phenotype - KIR gene or KIR - HLA C combination associations with false discovery rate < 0.05. These results differ from the very high number of associations between HLA alleles and diseases reported earlier in the FinnGen cohort. Five of the 13 significant associations included malignant phenotypes, e.g., melanoma, thyroid gland neoplasm, and haematopoietic malignancy, supporting the essential role of NK cells in controlling malignancy.

Statistically and functionally fine-mapped blood eQTLs and pQTLs from 1,405 humans reveal distinct regulation patterns and disease relevance

Studying the genetic regulation of protein expression (through protein quantitative trait loci (pQTLs)) offers a deeper understanding of regulatory variants uncharacterized by mRNA expression regulation (expression QTLs (eQTLs)) studies. Here we report cis-eQTL and cis-pQTL statistical fine-mapping from 1,405 genotyped samples with blood mRNA and 2,932 plasma samples of protein expression, as part of the Japan COVID-19 Task Force (JCTF). Fine-mapped eQTLs (n = 3,464) were enriched for 932 variants validated with a massively parallel reporter assay. Fine-mapped pQTLs (n = 582) were enriched for missense variations on structured and extracellular domains, although the possibility of epitope-binding artifacts remains. Trans-eQTL and trans-pQTL analysis highlighted associations of class I HLA allele variation with KIR genes. We contrast the multi-tissue origin of plasma protein with blood mRNA, contributing to the limited colocalization level, distinct regulatory mechanisms and trait relevance of eQTLs and pQTLs. We report a negative correlation between ABO mRNA and protein expression because of linkage disequilibrium between distinct nearby eQTLs and pQTLs.

Genetic complexity of killer-cell immunoglobulin-like receptor genes in human pangenome assemblies

The killer-cell immunoglobulin-like receptor (KIR) gene complex, a highly polymorphic region of the human genome that encodes proteins involved in immune responses, poses strong challenges in genotyping owing to its remarkable genetic diversity and structural intricacy. Accurate analysis of KIR alleles, including their structural variations, is crucial for understanding their roles in various immune responses. Leveraging the high-quality genome assemblies from the Human Pangenome Reference Consortium (HPRC), we present a novel bioinformatic tool, the structural KIR annoTator (SKIRT), to investigate gene diversity and facilitate precise KIR allele analysis. In 47 HPRC-phased assemblies, SKIRT identifies a recurrent novel KIR2DS4/3DL1 fusion gene in the paternal haplotype of HG02630 and maternal haplotype of NA19240. Additionally, SKIRT accurately identifies eight structural variants and 15 novel nonsynonymous alleles, all of which are independently validated using short-read data or quantitative polymerase chain reaction. Our study has discovered a total of 570 novel alleles, among which eight haplotypes harbor at least one KIR gene duplication, six haplotypes have lost at least one framework gene, and 75 out of 94 haplotypes (79.8%) carry at least five novel alleles, thus confirming KIR genetic diversity. These findings are pivotal in providing insights into KIR gene diversity and serve as a solid foundation for understanding the functional consequences of KIR structural variations. High-resolution genome assemblies offer unprecedented opportunities to explore polymorphic regions that are challenging to investigate using short-read sequencing methods. The SKIRT pipeline emerges as a highly efficient tool, enabling the comprehensive detection of the complete spectrum of KIR alleles within human genome assemblies.

Haplotype reconstruction for genetically complex regions with ambiguous genotype calls: Illustration by the KIR gene region

Advances in DNA sequencing technologies have enabled genotyping of complex genetic regions exhibiting copy number variation and high allelic diversity, yet it is impossible to derive exact genotypes in all cases, often resulting in ambiguous genotype calls, that is, partially missing data. An example of such a gene region is the killer-cell immunoglobulin-like receptor (KIR) genes. These genes are of special interest in the context of allogeneic hematopoietic stem cell transplantation. For such complex gene regions, current haplotype reconstruction methods are not feasible as they cannot cope with the complexity of the data. We present an expectation-maximization (EM)-algorithm to estimate haplotype frequencies (HTFs) which deals with the missing data components, and takes into account linkage disequilibrium (LD) between genes. To cope with the exponential increase in the number of haplotypes as genes are added, we add three components to a standard EM-algorithm implementation. First, reconstruction is performed iteratively, adding one gene at a time. Second, after each step, haplotypes with frequencies below a threshold are collapsed in a rare haplotype group. Third, the HTF of the rare haplotype group is profiled in subsequent iterations to improve estimates. A simulation study evaluates the effect of combining information of multiple genes on the estimates of these frequencies. We show that estimated HTFs are approximately unbiased. Our simulation study shows that the EM-algorithm is able to combine information from multiple genes when LD is high, whereas increased ambiguity levels increase bias. Linear regression models based on this EM, show that a large number of haplotypes can be problematic for unbiased effect size estimation and that models need to be sparse. In a real data analysis of KIR genotypes, we compare HTFs to those obtained in an independent study. Our new EM-algorithm-based method is the first to account for the full genetic architecture of complex gene regions, such as the KIR gene region. This algorithm can handle the numerous observed ambiguities, and allows for the collapsing of haplotypes to perform implicit dimension reduction. Combining information from multiple genes improves haplotype reconstruction.

Untangling immunoglobulin genotype-function associations

Immunoglobulin (IG) genes, encoding B cell receptors (BCRs), are fundamental components of the mammalian immune system, which evolved to recognize the diverse antigenic universe present in nature. To handle these myriad inputs, BCRs are generated through combinatorial recombination of a set of highly polymorphic germline genes, resulting in a vast repertoire of antigen receptors that initiate responses to pathogens and regulate commensals. Following antigen recognition and B cell activation, memory B cells and plasma cells form, allowing for the development of anamnestic antibody (Ab) responses. How inherited variation in IG genes impacts host traits, disease susceptibility, and Ab recall responses is a topic of great interest. Here, we consider approaches to translate emerging knowledge about IG genetic diversity and expressed repertoires to inform our understanding of Ab function in health and disease etiology. As our understanding of IG genetics grows, so will our need for tools to decipher preferences for IG gene or allele usage in different contexts, to better understand antibody responses at the population level.

Meta-analysis fine-mapping is often miscalibrated at single-variant resolution

Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7×; Fisher's exact p = 7.3 × 10-4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.