A Detailed View of KIR Haplotype Structures and Gene Families as Provided by a New Motif-Based Multiple Sequence Alignment

Advancements in HLA Typing Techniques and Their Impact on Transplantation Medicine

HLA typing serves as a standard practice in hematopoietic stem cell transplantation to ensure compatibility between donors and recipients, preventing the occurrence of allograft rejection and graft-versus-host disease. Conventional laboratory methods that have been widely employed in the past few years, including sequence-specific primer PCR and sequencing-based typing (SBT), currently face the risk of becoming obsolete. This risk stems not only from the extensive diversity within HLA genes but also from the rapid advancement of next-generation sequencing and third-generation sequencing technologies. Third-generation sequencing systems like single-molecule real-time (SMRT) sequencing and Oxford Nanopore (ONT) sequencing have the capability to analyze long-read sequences that span entire intronic-exonic regions of HLA genes, effectively addressing challenges related to HLA ambiguity and the phasing of multiple short-read fragments. The growing dominance of these advanced sequencers in HLA typing is expected to solidify further through ongoing refinements, cost reduction, and error rate minimization. This review focuses on hematopoietic stem cell transplantation (HSCT) and explores prospective advancements and application of HLA DNA typing techniques. It explores how the adoption of third-generation sequencing technologies can revolutionize the field by offering improved accuracy, reduced ambiguity, and enhanced assessment of compatibility in HSCT. Embracing these cutting-edge technologies is essential to advancing the success rates and outcomes of hematopoietic stem cell transplantation. This review underscores the importance of staying at the forefront of HLA typing techniques to ensure the best possible outcomes for patients undergoing HSCT.

NK Cells in Protection from HIV Infection

Some people, known as HIV-exposed seronegative (HESN) individuals, remain uninfected despite high levels of exposure to HIV. Understanding the mechanisms underlying their apparent resistance to HIV infection may inform strategies designed to protect against HIV infection. Natural Killer (NK) cells are innate immune cells whose activation state depends on the integration of activating and inhibitory signals arising from cell surface receptors interacting with their ligands on neighboring cells. Inhibitory NK cell receptors use a subset of major histocompatibility (MHC) class I antigens as ligands. This interaction educates NK cells, priming them to respond to cells with reduced MHC class I antigen expression levels as occurs on HIV-infected cells. NK cells can interact with both autologous HIV-infected cells and allogeneic cells bearing MHC antigens seen as non self by educated NK cells. NK cells are rapidly activated upon interacting with HIV-infected or allogenic cells to elicit anti-viral activity that blocks HIV spread to new target cells, suppresses HIV replication, and kills HIV-infected cells before HIV reservoirs can be seeded and infection can be established. In this manuscript, we will review the epidemiological and functional evidence for a role for NK cells in protection from HIV infection.

A survey on the algorithm and development of multiple sequence alignment

Multiple sequence alignment (MSA) is an essential cornerstone in bioinformatics, which can reveal the potential information in biological sequences, such as function, evolution and structure. MSA is widely used in many bioinformatics scenarios, such as phylogenetic analysis, protein analysis and genomic analysis. However, MSA faces new challenges with the gradual increase in sequence scale and the increasing demand for alignment accuracy. Therefore, developing an efficient and accurate strategy for MSA has become one of the research hotspots in bioinformatics. In this work, we mainly summarize the algorithms for MSA and its applications in bioinformatics. To provide a structured and clear perspective, we systematically introduce MSA's knowledge, including background, database, metric and benchmark. Besides, we list the most common applications of MSA in the field of bioinformatics, including database searching, phylogenetic analysis, genomic analysis, metagenomic analysis and protein analysis. Furthermore, we categorize and analyze classical and state-of-the-art algorithms, divided into progressive alignment, iterative algorithm, heuristics, machine learning and divide-and-conquer. Moreover, we also discuss the challenges and opportunities of MSA in bioinformatics. Our work provides a comprehensive survey of MSA applications and their relevant algorithms. It could bring valuable insights for researchers to contribute their knowledge to MSA and relevant studies.

Following Transplantation for Acute Myelogenous Leukemia, Donor KIR Cen B02 Better Protects against Relapse than KIR Cen B01

In the treatment of acute myelogenous leukemia with allogeneic hematopoietic cell transplantation, we previously demonstrated that there is a greater protection from relapse of leukemia when the hematopoietic cell transplantation donor has either the Cen B/B KIR genotype or a genotype having two or more KIR B gene segments. In those earlier analyses, KIR genotyping could only be assessed at the low resolution of gene presence or absence. To give the analysis greater depth, we developed high-resolution KIR sequence-based typing that defines all the KIR alleles and distinguishes the expressed alleles from those that are not expressed. We now describe and analyze high-resolution KIR genotypes for 890 donors of this human transplant cohort. Cen B01 and Cen B02 are the common CenB haplotypes, with Cen B02 having evolved from Cen B01 by deletion of the KIR2DL5, 2DS3/5, 2DP1, and 2DL1 genes. We observed a consistent trend for Cen B02 to provide stronger protection against relapse than Cen B01 This correlation indicates that protection depends on the donor having inhibitory KIR2DL2 and/or activating KIR2DS2, and is enhanced by the donor lacking inhibitory KIR2DL1, 2DL3, and 3DL1. High-resolution KIR typing has allowed us to compare the strength of the interactions between the recipient's HLA class I and the KIR expressed by the donor-derived NK cells and T cells, but no clinically significant interactions were observed. The trend observed between donor Cen B02 and reduced relapse of leukemia points to the value of studying ever larger transplant cohorts.

Accurate and Efficient KIR Gene and Haplotype Inference From Genome Sequencing Reads With Novel K-mer Signatures

The killer-cell immunoglobulin-like receptor (KIR) proteins evolve to fight viruses and mediate the body's reaction to pregnancy. These roles provide selection pressure for variation at both the structural/haplotype and base/allele levels. At the same time, the genes have evolved relatively recently by tandem duplication and therefore exhibit very high sequence similarity over thousands of bases. These variation-homology patterns make it impossible to interpret KIR haplotypes from abundant short-read genome sequencing data at population scale using existing methods. Here, we developed an efficient computational approach for in silico KIR probe interpretation (KPI) to accurately interpret individual's KIR genes and haplotype-pairs from KIR sequencing reads. We designed synthetic 25-base sequence probes by analyzing previously reported haplotype sequences, and we developed a bioinformatics pipeline to interpret the probes in the context of 16 KIR genes and 16 haplotype structures. We demonstrated its accuracy on a synthetic data set as well as a real whole genome sequences from 748 individuals from The Genome of the Netherlands (GoNL). The GoNL predictions were compared with predictions from SNP-based predictions. Our results show 100% accuracy rate for the synthetic tests and a 99.6% family-consistency rate in the GoNL tests. Agreement with the SNP-based calls on KIR genes ranges from 72%-100% with a mean of 92%; most differences occur in genes KIR2DS2, KIR2DL2, KIR2DS3, and KIR2DL5 where KPI predicts presence and the SNP-based interpretation predicts absence. Overall, the evidence suggests that KPI's accuracy is 97% or greater for both KIR gene and haplotype-pair predictions, and the presence/absence genotyping leads to ambiguous haplotype-pair predictions with 16 reference KIR haplotype structures. KPI is free, open, and easily executable as a Nextflow workflow supported by a Docker environment at https://github.com/droeatumn/kpi.

Efficient Sequencing, Assembly, and Annotation of Human KIR Haplotypes

The homology, recombination, variation, and repetitive elements in the natural killer-cell immunoglobulin-like receptor (KIR) region has made full haplotype DNA interpretation impossible in a high-throughput workflow. Here, we present a new approach using long-read sequencing to efficiently capture, sequence, and assemble diploid human KIR haplotypes. Probes were designed to capture KIR fragments efficiently by leveraging the repeating homology of the region. IDT xGen® Lockdown probes were used to capture 2-8 kb of sheared DNA fragments followed by sequencing on a PacBio Sequel. The sequences were error corrected, binned, and then assembled using the Canu assembler. The location of genes and their exon/intron boundaries are included in the workflow. The assembly and annotation was evaluated on 16 individuals (8 African American and 8 Europeans) from whom ground truth was known via long-range sequencing with fosmid library preparation. Using only 18 capture probes, the results show that the assemblies cover 97% of the GenBank reference, are 99.97% concordant, and it takes only 1.8 haplotigs to cover 75% of the reference. We also report the first assembly of diploid KIR haplotypes from long-read WGS. Our targeted hybridization probe capture and sequencing approach is the first of its kind to fully sequence and phase all diploid human KIR haplotypes, and it is efficient enough for population-scale studies and clinical use. The open and free software is available at https://github.com/droeatumn/kass and supported by a environment at https://hub.docker.com/repository/docker/droeatumn/kass.